diff --git a/CHANGELOG.md b/CHANGELOG.md index 7bb54c3b..c57d1217 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # @vladmandic/human - Version: **3.1.2** + Version: **3.2.0** Description: **Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition** Author: **Vladimir Mandic ** @@ -9,7 +9,14 @@ ## Changelog -### **HEAD -> main** 2023/09/18 mandic00@live.com +### **3.2.0** 2023/12/06 mandic00@live.com + + +### **origin/main** 2023/12/06 aug@iterative.day + +- https://github.com/vladmandic/human/issues/402 + +### **release: 3.1.2** 2023/09/18 mandic00@live.com - full rebuild diff --git a/demo/README.md b/demo/README.md index 8e551852..c40e74c8 100644 --- a/demo/README.md +++ b/demo/README.md @@ -8,6 +8,7 @@ For details on other demos see Wiki: [**Demos**](https://github.com/vladmandic/h `index.html`: Full demo using `Human` ESM module running in Browsers, Includes: + - Selectable inputs: - Sample images - Image via drag & drop diff --git a/demo/facedetect/facedetect.js b/demo/facedetect/facedetect.js index 432ea8de..7b80a17b 100644 --- a/demo/facedetect/facedetect.js +++ b/demo/facedetect/facedetect.js @@ -93,7 +93,7 @@ function addFace(face, source) { e.preventDefault(); document.getElementById('description').innerHTML = canvas.title; }; - human.tf.browser.toPixels(face.tensor, canvas); + human.tf.browser.draw(face.tensor, canvas); human.tf.dispose(face.tensor); return canvas; } diff --git a/demo/faceid/index.js b/demo/faceid/index.js index 37f2e0ac..a05fb4f5 100644 --- a/demo/faceid/index.js +++ b/demo/faceid/index.js @@ -5,5 +5,5 @@ */ import*as S from"../../dist/human.esm.js";var l,L="human",f="person",v=(...a)=>console.log("indexdb",...a);async function h(){return l?!0:new Promise(a=>{let n=indexedDB.open(L,1);n.onerror=o=>v("error:",o),n.onupgradeneeded=o=>{v("create:",o.target),l=o.target.result,l.createObjectStore(f,{keyPath:"id",autoIncrement:!0})},n.onsuccess=o=>{l=o.target.result,v("open:",l),a(!0)}})}async function C(){let a=[];return l||await h(),new Promise(n=>{let o=l.transaction([f],"readwrite").objectStore(f).openCursor(null,"next");o.onerror=i=>v("load error:",i),o.onsuccess=i=>{i.target.result?(a.push(i.target.result.value),i.target.result.continue()):n(a)}})}async function b(){return l||await h(),new Promise(a=>{let n=l.transaction([f],"readwrite").objectStore(f).count();n.onerror=o=>v("count error:",o),n.onsuccess=()=>a(n.result)})}async function x(a){l||await h();let n={name:a.name,descriptor:a.descriptor,image:a.image};l.transaction([f],"readwrite").objectStore(f).put(n),v("save:",n)}async function D(a){l||await h(),l.transaction([f],"readwrite").objectStore(f).delete(a.id),v("delete:",a)}var g={cacheSensitivity:0,modelBasePath:"../../models",filter:{enabled:!0,equalization:!0},debug:!0,face:{enabled:!0,detector:{rotation:!0,return:!0,mask:!1},description:{enabled:!0},iris:{enabled:!0},emotion:{enabled:!1},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!1},hand:{enabled:!1},object:{enabled:!1},gesture:{enabled:!0}},B={order:2,multiplier:25,min:.2,max:.8},r={minConfidence:.6,minSize:224,maxTime:3e4,blinkMin:10,blinkMax:800,threshold:.5,distanceMin:.4,distanceMax:1,mask:g.face.detector.mask,rotation:g.face.detector.rotation,...B},e={faceCount:{status:!1,val:0},faceConfidence:{status:!1,val:0},facingCenter:{status:!1,val:0},lookingCenter:{status:!1,val:0},blinkDetected:{status:!1,val:0},faceSize:{status:!1,val:0},antispoofCheck:{status:!1,val:0},livenessCheck:{status:!1,val:0},distance:{status:!1,val:0},age:{status:!1,val:0},gender:{status:!1,val:0},timeout:{status:!0,val:0},descriptor:{status:!1,val:0},elapsedMs:{status:void 0,val:0},detectFPS:{status:void 0,val:0},drawFPS:{status:void 0,val:0}},E=()=>e.faceCount.status&&e.faceSize.status&&e.blinkDetected.status&&e.facingCenter.status&&e.lookingCenter.status&&e.faceConfidence.status&&e.antispoofCheck.status&&e.livenessCheck.status&&e.distance.status&&e.descriptor.status&&e.age.status&&e.gender.status,c={face:null,record:null},u={start:0,end:0,time:0},s=new S.Human(g);s.env.perfadd=!1;s.draw.options.font='small-caps 18px "Lato"';s.draw.options.lineHeight=20;var t={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("fps"),match:document.getElementById("match"),name:document.getElementById("name"),save:document.getElementById("save"),delete:document.getElementById("delete"),retry:document.getElementById("retry"),source:document.getElementById("source"),ok:document.getElementById("ok")},y={detect:0,draw:0},I=0,d=(...a)=>{t.log.innerText+=a.join(" ")+` -`,console.log(...a)};async function H(){let a={audio:!1,video:{facingMode:"user",resizeMode:"none",width:{ideal:document.body.clientWidth}}},n=await navigator.mediaDevices.getUserMedia(a),o=new Promise(i=>{t.video.onloadeddata=()=>i(!0)});t.video.srcObject=n,t.video.play(),await o,t.canvas.width=t.video.videoWidth,t.canvas.height=t.video.videoHeight,t.canvas.style.width="50%",t.canvas.style.height="50%",s.env.initial&&d("video:",t.video.videoWidth,t.video.videoHeight,"|",n.getVideoTracks()[0].label),t.canvas.onclick=()=>{t.video.paused?t.video.play():t.video.pause()}}async function T(){var a;if(!t.video.paused){(a=c.face)!=null&&a.tensor&&s.tf.dispose(c.face.tensor),await s.detect(t.video);let n=s.now();e.detectFPS.val=Math.round(1e4/(n-y.detect))/10,y.detect=n,requestAnimationFrame(T)}}function z(){let a=32;for(let[n,o]of Object.entries(e)){let i=document.getElementById(`ok-${n}`);i||(i=document.createElement("div"),i.id=`ok-${n}`,i.innerText=n,i.className="ok",i.style.top=`${a}px`,t.ok.appendChild(i)),typeof o.status=="boolean"&&(i.style.backgroundColor=o.status?"lightgreen":"lightcoral");let m=o.status?"ok":"fail";i.innerText=`${n}: ${o.val===0?m:o.val}`,a+=28}}async function R(){var o;let a=s.next(s.result);s.draw.canvas(t.video,t.canvas),await s.draw.all(t.canvas,a);let n=s.now();if(e.drawFPS.val=Math.round(1e4/(n-y.draw))/10,y.draw=n,e.faceCount.val=s.result.face.length,e.faceCount.status=e.faceCount.val===1,e.faceCount.status){let i=Object.values(s.result.gesture).map(m=>m.gesture);(i.includes("blink left eye")||i.includes("blink right eye"))&&(u.start=s.now()),u.start>0&&!i.includes("blink left eye")&&!i.includes("blink right eye")&&(u.end=s.now()),e.blinkDetected.status=e.blinkDetected.status||Math.abs(u.end-u.start)>r.blinkMin&&Math.abs(u.end-u.start)=r.minConfidence,e.antispoofCheck.val=s.result.face[0].real||0,e.antispoofCheck.status=e.antispoofCheck.val>=r.minConfidence,e.livenessCheck.val=s.result.face[0].live||0,e.livenessCheck.status=e.livenessCheck.val>=r.minConfidence,e.faceSize.val=Math.min(s.result.face[0].box[2],s.result.face[0].box[3]),e.faceSize.status=e.faceSize.val>=r.minSize,e.distance.val=s.result.face[0].distance||0,e.distance.status=e.distance.val>=r.distanceMin&&e.distance.val<=r.distanceMax,e.descriptor.val=((o=s.result.face[0].embedding)==null?void 0:o.length)||0,e.descriptor.status=e.descriptor.val>0,e.age.val=s.result.face[0].age||0,e.age.status=e.age.val>0,e.gender.val=s.result.face[0].genderScore||0,e.gender.status=e.gender.val>=r.minConfidence}return e.timeout.status=e.elapsedMs.val<=r.maxTime,z(),E()||!e.timeout.status?(t.video.pause(),s.result.face[0]):(e.elapsedMs.val=Math.trunc(s.now()-I),new Promise(i=>{setTimeout(async()=>{await R(),i(s.result.face[0])},30)}))}async function F(){var a,n,o,i;if(t.name.value.length>0){let m=(a=t.canvas.getContext("2d"))==null?void 0:a.getImageData(0,0,t.canvas.width,t.canvas.height),p={id:0,name:t.name.value,descriptor:(n=c.face)==null?void 0:n.embedding,image:m};await x(p),d("saved face record:",p.name,"descriptor length:",(i=(o=c.face)==null?void 0:o.embedding)==null?void 0:i.length),d("known face records:",await b())}else d("invalid name")}async function j(){c.record&&c.record.id>0&&await D(c.record)}async function $(){var i,m,p,k;if(t.canvas.style.height="",(i=t.canvas.getContext("2d"))==null||i.clearRect(0,0,r.minSize,r.minSize),!((m=c==null?void 0:c.face)!=null&&m.tensor)||!((p=c==null?void 0:c.face)!=null&&p.embedding))return!1;if(console.log("face record:",c.face),d(`detected face: ${c.face.gender} ${c.face.age||0}y distance ${100*(c.face.distance||0)}cm/${Math.round(100*(c.face.distance||0)/2.54)}in`),await s.tf.browser.toPixels(c.face.tensor,t.canvas),await b()===0)return d("face database is empty: nothing to compare face with"),document.body.style.background="black",t.delete.style.display="none",!1;let a=await C(),n=a.map(w=>w.descriptor).filter(w=>w.length>0),o=s.match.find(c.face.embedding,n,B);return c.record=a[o.index]||null,c.record&&(d(`best match: ${c.record.name} | id: ${c.record.id} | similarity: ${Math.round(1e3*o.similarity)/10}%`),t.name.value=c.record.name,t.source.style.display="",(k=t.source.getContext("2d"))==null||k.putImageData(c.record.image,0,0)),document.body.style.background=o.similarity>r.threshold?"darkgreen":"maroon",o.similarity>r.threshold}async function M(){var a,n,o,i;return e.faceCount.status=!1,e.faceConfidence.status=!1,e.facingCenter.status=!1,e.blinkDetected.status=!1,e.faceSize.status=!1,e.antispoofCheck.status=!1,e.livenessCheck.status=!1,e.age.status=!1,e.gender.status=!1,e.elapsedMs.val=0,t.match.style.display="none",t.retry.style.display="none",t.source.style.display="none",t.canvas.style.height="50%",document.body.style.background="black",await H(),await T(),I=s.now(),c.face=await R(),t.canvas.width=((n=(a=c.face)==null?void 0:a.tensor)==null?void 0:n.shape[1])||r.minSize,t.canvas.height=((i=(o=c.face)==null?void 0:o.tensor)==null?void 0:i.shape[0])||r.minSize,t.source.width=t.canvas.width,t.source.height=t.canvas.height,t.canvas.style.width="",t.match.style.display="flex",t.save.style.display="flex",t.delete.style.display="flex",t.retry.style.display="block",E()?$():(d("did not find valid face"),!1)}async function q(){var a,n;d("human version:",s.version,"| tfjs version:",s.tf.version["tfjs-core"]),d("options:",JSON.stringify(r).replace(/{|}|"|\[|\]/g,"").replace(/,/g," ")),d("initializing webcam..."),await H(),d("loading human models..."),await s.load(),d("initializing human..."),d("face embedding model:",g.face.description.enabled?"faceres":"",(a=g.face.mobilefacenet)!=null&&a.enabled?"mobilefacenet":"",(n=g.face.insightface)!=null&&n.enabled?"insightface":""),d("loading face database..."),d("known face records:",await b()),t.retry.addEventListener("click",M),t.save.addEventListener("click",F),t.delete.addEventListener("click",j),await s.warmup(),await M()}window.onload=q; +`,console.log(...a)};async function H(){let a={audio:!1,video:{facingMode:"user",resizeMode:"none",width:{ideal:document.body.clientWidth}}},n=await navigator.mediaDevices.getUserMedia(a),o=new Promise(i=>{t.video.onloadeddata=()=>i(!0)});t.video.srcObject=n,t.video.play(),await o,t.canvas.width=t.video.videoWidth,t.canvas.height=t.video.videoHeight,t.canvas.style.width="50%",t.canvas.style.height="50%",s.env.initial&&d("video:",t.video.videoWidth,t.video.videoHeight,"|",n.getVideoTracks()[0].label),t.canvas.onclick=()=>{t.video.paused?t.video.play():t.video.pause()}}async function T(){var a;if(!t.video.paused){(a=c.face)!=null&&a.tensor&&s.tf.dispose(c.face.tensor),await s.detect(t.video);let n=s.now();e.detectFPS.val=Math.round(1e4/(n-y.detect))/10,y.detect=n,requestAnimationFrame(T)}}function P(){let a=32;for(let[n,o]of Object.entries(e)){let i=document.getElementById(`ok-${n}`);i||(i=document.createElement("div"),i.id=`ok-${n}`,i.innerText=n,i.className="ok",i.style.top=`${a}px`,t.ok.appendChild(i)),typeof o.status=="boolean"&&(i.style.backgroundColor=o.status?"lightgreen":"lightcoral");let m=o.status?"ok":"fail";i.innerText=`${n}: ${o.val===0?m:o.val}`,a+=28}}async function R(){var o;let a=s.next(s.result);s.draw.canvas(t.video,t.canvas),await s.draw.all(t.canvas,a);let n=s.now();if(e.drawFPS.val=Math.round(1e4/(n-y.draw))/10,y.draw=n,e.faceCount.val=s.result.face.length,e.faceCount.status=e.faceCount.val===1,e.faceCount.status){let i=Object.values(s.result.gesture).map(m=>m.gesture);(i.includes("blink left eye")||i.includes("blink right eye"))&&(u.start=s.now()),u.start>0&&!i.includes("blink left eye")&&!i.includes("blink right eye")&&(u.end=s.now()),e.blinkDetected.status=e.blinkDetected.status||Math.abs(u.end-u.start)>r.blinkMin&&Math.abs(u.end-u.start)=r.minConfidence,e.antispoofCheck.val=s.result.face[0].real||0,e.antispoofCheck.status=e.antispoofCheck.val>=r.minConfidence,e.livenessCheck.val=s.result.face[0].live||0,e.livenessCheck.status=e.livenessCheck.val>=r.minConfidence,e.faceSize.val=Math.min(s.result.face[0].box[2],s.result.face[0].box[3]),e.faceSize.status=e.faceSize.val>=r.minSize,e.distance.val=s.result.face[0].distance||0,e.distance.status=e.distance.val>=r.distanceMin&&e.distance.val<=r.distanceMax,e.descriptor.val=((o=s.result.face[0].embedding)==null?void 0:o.length)||0,e.descriptor.status=e.descriptor.val>0,e.age.val=s.result.face[0].age||0,e.age.status=e.age.val>0,e.gender.val=s.result.face[0].genderScore||0,e.gender.status=e.gender.val>=r.minConfidence}return e.timeout.status=e.elapsedMs.val<=r.maxTime,P(),E()||!e.timeout.status?(t.video.pause(),s.result.face[0]):(e.elapsedMs.val=Math.trunc(s.now()-I),new Promise(i=>{setTimeout(async()=>{await R(),i(s.result.face[0])},30)}))}async function F(){var a,n,o,i;if(t.name.value.length>0){let m=(a=t.canvas.getContext("2d"))==null?void 0:a.getImageData(0,0,t.canvas.width,t.canvas.height),p={id:0,name:t.name.value,descriptor:(n=c.face)==null?void 0:n.embedding,image:m};await x(p),d("saved face record:",p.name,"descriptor length:",(i=(o=c.face)==null?void 0:o.embedding)==null?void 0:i.length),d("known face records:",await b())}else d("invalid name")}async function j(){c.record&&c.record.id>0&&await D(c.record)}async function $(){var i,m,p,k;if(t.canvas.style.height="",(i=t.canvas.getContext("2d"))==null||i.clearRect(0,0,r.minSize,r.minSize),!((m=c==null?void 0:c.face)!=null&&m.tensor)||!((p=c==null?void 0:c.face)!=null&&p.embedding))return!1;if(console.log("face record:",c.face),d(`detected face: ${c.face.gender} ${c.face.age||0}y distance ${100*(c.face.distance||0)}cm/${Math.round(100*(c.face.distance||0)/2.54)}in`),await s.tf.browser.draw(c.face.tensor,t.canvas),await b()===0)return d("face database is empty: nothing to compare face with"),document.body.style.background="black",t.delete.style.display="none",!1;let a=await C(),n=a.map(w=>w.descriptor).filter(w=>w.length>0),o=s.match.find(c.face.embedding,n,B);return c.record=a[o.index]||null,c.record&&(d(`best match: ${c.record.name} | id: ${c.record.id} | similarity: ${Math.round(1e3*o.similarity)/10}%`),t.name.value=c.record.name,t.source.style.display="",(k=t.source.getContext("2d"))==null||k.putImageData(c.record.image,0,0)),document.body.style.background=o.similarity>r.threshold?"darkgreen":"maroon",o.similarity>r.threshold}async function M(){var a,n,o,i;return e.faceCount.status=!1,e.faceConfidence.status=!1,e.facingCenter.status=!1,e.blinkDetected.status=!1,e.faceSize.status=!1,e.antispoofCheck.status=!1,e.livenessCheck.status=!1,e.age.status=!1,e.gender.status=!1,e.elapsedMs.val=0,t.match.style.display="none",t.retry.style.display="none",t.source.style.display="none",t.canvas.style.height="50%",document.body.style.background="black",await H(),await T(),I=s.now(),c.face=await R(),t.canvas.width=((n=(a=c.face)==null?void 0:a.tensor)==null?void 0:n.shape[1])||r.minSize,t.canvas.height=((i=(o=c.face)==null?void 0:o.tensor)==null?void 0:i.shape[0])||r.minSize,t.source.width=t.canvas.width,t.source.height=t.canvas.height,t.canvas.style.width="",t.match.style.display="flex",t.save.style.display="flex",t.delete.style.display="flex",t.retry.style.display="block",E()?$():(d("did not find valid face"),!1)}async function q(){var a,n;d("human version:",s.version,"| tfjs version:",s.tf.version["tfjs-core"]),d("options:",JSON.stringify(r).replace(/{|}|"|\[|\]/g,"").replace(/,/g," ")),d("initializing webcam..."),await H(),d("loading human models..."),await s.load(),d("initializing human..."),d("face embedding model:",g.face.description.enabled?"faceres":"",(a=g.face.mobilefacenet)!=null&&a.enabled?"mobilefacenet":"",(n=g.face.insightface)!=null&&n.enabled?"insightface":""),d("loading face database..."),d("known face records:",await b()),t.retry.addEventListener("click",M),t.save.addEventListener("click",F),t.delete.addEventListener("click",j),await s.warmup(),await M()}window.onload=q; //# sourceMappingURL=index.js.map diff --git a/demo/faceid/index.js.map b/demo/faceid/index.js.map index 28a18c93..cb1d7b8c 100644 --- a/demo/faceid/index.js.map +++ b/demo/faceid/index.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["index.ts", "indexdb.ts"], - "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\nimport * as indexDb from './indexdb'; // methods to deal with indexdb\n\nconst humanConfig = { // user configuration for human, used to fine-tune behavior\n cacheSensitivity: 0,\n modelBasePath: '../../models',\n filter: { enabled: true, equalization: true }, // lets run with histogram equilizer\n debug: true,\n face: {\n enabled: true,\n detector: { rotation: true, return: true, mask: false }, // return tensor is used to get detected face image\n description: { enabled: true }, // default model for face descriptor extraction is faceres\n // mobilefacenet: { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model\n // insightface: { enabled: true, modelPath: 'https://vladmandic.github.io/insightface/models/insightface-mobilenet-swish.json' }, // alternative model\n iris: { enabled: true }, // needed to determine gaze direction\n emotion: { enabled: false }, // not needed\n antispoof: { enabled: true }, // enable optional antispoof module\n liveness: { enabled: true }, // enable optional liveness module\n },\n body: { enabled: false },\n hand: { enabled: false },\n object: { enabled: false },\n gesture: { enabled: true }, // parses face and iris gestures\n};\n\n// const matchOptions = { order: 2, multiplier: 1000, min: 0.0, max: 1.0 }; // for embedding model\nconst matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; // for faceres model\n\nconst options = {\n minConfidence: 0.6, // overal face confidence for box, face, gender, real, live\n minSize: 224, // min input to face descriptor model before degradation\n maxTime: 30000, // max time before giving up\n blinkMin: 10, // minimum duration of a valid blink\n blinkMax: 800, // maximum duration of a valid blink\n threshold: 0.5, // minimum similarity\n distanceMin: 0.4, // closest that face is allowed to be to the cammera in cm\n distanceMax: 1.0, // farthest that face is allowed to be to the cammera in cm\n mask: humanConfig.face.detector.mask,\n rotation: humanConfig.face.detector.rotation,\n ...matchOptions,\n};\n\nconst ok: Record = { // must meet all rules\n faceCount: { status: false, val: 0 },\n faceConfidence: { status: false, val: 0 },\n facingCenter: { status: false, val: 0 },\n lookingCenter: { status: false, val: 0 },\n blinkDetected: { status: false, val: 0 },\n faceSize: { status: false, val: 0 },\n antispoofCheck: { status: false, val: 0 },\n livenessCheck: { status: false, val: 0 },\n distance: { status: false, val: 0 },\n age: { status: false, val: 0 },\n gender: { status: false, val: 0 },\n timeout: { status: true, val: 0 },\n descriptor: { status: false, val: 0 },\n elapsedMs: { status: undefined, val: 0 }, // total time while waiting for valid face\n detectFPS: { status: undefined, val: 0 }, // mark detection fps performance\n drawFPS: { status: undefined, val: 0 }, // mark redraw fps performance\n};\n\nconst allOk = () => ok.faceCount.status\n && ok.faceSize.status\n && ok.blinkDetected.status\n && ok.facingCenter.status\n && ok.lookingCenter.status\n && ok.faceConfidence.status\n && ok.antispoofCheck.status\n && ok.livenessCheck.status\n && ok.distance.status\n && ok.descriptor.status\n && ok.age.status\n && ok.gender.status;\n\nconst current: { face: H.FaceResult | null, record: indexDb.FaceRecord | null } = { face: null, record: null }; // current face record and matched database record\n\nconst blink = { // internal timers for blink start/end/duration\n start: 0,\n end: 0,\n time: 0,\n};\n\n// let db: Array<{ name: string, source: string, embedding: number[] }> = []; // holds loaded face descriptor database\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('fps') as HTMLPreElement,\n match: document.getElementById('match') as HTMLDivElement,\n name: document.getElementById('name') as HTMLInputElement,\n save: document.getElementById('save') as HTMLSpanElement,\n delete: document.getElementById('delete') as HTMLSpanElement,\n retry: document.getElementById('retry') as HTMLDivElement,\n source: document.getElementById('source') as HTMLCanvasElement,\n ok: document.getElementById('ok') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0 }; // holds information used to calculate performance and possible memory leaks\nlet startTime = 0;\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\n\nasync function webCam() { // initialize webcam\n // @ts-ignore resizeMode is not yet defined in tslib\n const cameraOptions: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth } } };\n const stream: MediaStream = await navigator.mediaDevices.getUserMedia(cameraOptions);\n const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });\n dom.video.srcObject = stream;\n void dom.video.play();\n await ready;\n dom.canvas.width = dom.video.videoWidth;\n dom.canvas.height = dom.video.videoHeight;\n dom.canvas.style.width = '50%';\n dom.canvas.style.height = '50%';\n if (human.env.initial) log('video:', dom.video.videoWidth, dom.video.videoHeight, '|', stream.getVideoTracks()[0].label);\n dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click\n if (dom.video.paused) void dom.video.play();\n else dom.video.pause();\n };\n}\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused) {\n if (current.face?.tensor) human.tf.dispose(current.face.tensor); // dispose previous tensor\n await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const now = human.now();\n ok.detectFPS.val = Math.round(10000 / (now - timestamp.detect)) / 10;\n timestamp.detect = now;\n requestAnimationFrame(detectionLoop); // start new frame immediately\n }\n}\n\nfunction drawValidationTests() {\n let y = 32;\n for (const [key, val] of Object.entries(ok)) {\n let el = document.getElementById(`ok-${key}`);\n if (!el) {\n el = document.createElement('div');\n el.id = `ok-${key}`;\n el.innerText = key;\n el.className = 'ok';\n el.style.top = `${y}px`;\n dom.ok.appendChild(el);\n }\n if (typeof val.status === 'boolean') el.style.backgroundColor = val.status ? 'lightgreen' : 'lightcoral';\n const status = val.status ? 'ok' : 'fail';\n el.innerText = `${key}: ${val.val === 0 ? status : val.val}`;\n y += 28;\n }\n}\n\nasync function validationLoop(): Promise { // main screen refresh loop\n const interpolated = human.next(human.result); // smoothen result using last-known results\n human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen\n await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.\n const now = human.now();\n ok.drawFPS.val = Math.round(10000 / (now - timestamp.draw)) / 10;\n timestamp.draw = now;\n ok.faceCount.val = human.result.face.length;\n ok.faceCount.status = ok.faceCount.val === 1; // must be exactly detected face\n if (ok.faceCount.status) { // skip the rest if no face\n const gestures: string[] = Object.values(human.result.gesture).map((gesture: H.GestureResult) => gesture.gesture); // flatten all gestures\n if (gestures.includes('blink left eye') || gestures.includes('blink right eye')) blink.start = human.now(); // blink starts when eyes get closed\n if (blink.start > 0 && !gestures.includes('blink left eye') && !gestures.includes('blink right eye')) blink.end = human.now(); // if blink started how long until eyes are back open\n ok.blinkDetected.status = ok.blinkDetected.status || (Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax);\n if (ok.blinkDetected.status && blink.time === 0) blink.time = Math.trunc(blink.end - blink.start);\n ok.facingCenter.status = gestures.includes('facing center');\n ok.lookingCenter.status = gestures.includes('looking center'); // must face camera and look at camera\n ok.faceConfidence.val = human.result.face[0].faceScore || human.result.face[0].boxScore || 0;\n ok.faceConfidence.status = ok.faceConfidence.val >= options.minConfidence;\n ok.antispoofCheck.val = human.result.face[0].real || 0;\n ok.antispoofCheck.status = ok.antispoofCheck.val >= options.minConfidence;\n ok.livenessCheck.val = human.result.face[0].live || 0;\n ok.livenessCheck.status = ok.livenessCheck.val >= options.minConfidence;\n ok.faceSize.val = Math.min(human.result.face[0].box[2], human.result.face[0].box[3]);\n ok.faceSize.status = ok.faceSize.val >= options.minSize;\n ok.distance.val = human.result.face[0].distance || 0;\n ok.distance.status = (ok.distance.val >= options.distanceMin) && (ok.distance.val <= options.distanceMax);\n ok.descriptor.val = human.result.face[0].embedding?.length || 0;\n ok.descriptor.status = ok.descriptor.val > 0;\n ok.age.val = human.result.face[0].age || 0;\n ok.age.status = ok.age.val > 0;\n ok.gender.val = human.result.face[0].genderScore || 0;\n ok.gender.status = ok.gender.val >= options.minConfidence;\n }\n // run again\n ok.timeout.status = ok.elapsedMs.val <= options.maxTime;\n drawValidationTests();\n if (allOk() || !ok.timeout.status) { // all criteria met\n dom.video.pause();\n return human.result.face[0];\n }\n ok.elapsedMs.val = Math.trunc(human.now() - startTime);\n return new Promise((resolve) => {\n setTimeout(async () => {\n await validationLoop(); // run validation loop until conditions are met\n resolve(human.result.face[0]); // recursive promise resolve\n }, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n });\n}\n\nasync function saveRecords() {\n if (dom.name.value.length > 0) {\n const image = dom.canvas.getContext('2d')?.getImageData(0, 0, dom.canvas.width, dom.canvas.height) as ImageData;\n const rec = { id: 0, name: dom.name.value, descriptor: current.face?.embedding as number[], image };\n await indexDb.save(rec);\n log('saved face record:', rec.name, 'descriptor length:', current.face?.embedding?.length);\n log('known face records:', await indexDb.count());\n } else {\n log('invalid name');\n }\n}\n\nasync function deleteRecord() {\n if (current.record && current.record.id > 0) {\n await indexDb.remove(current.record);\n }\n}\n\nasync function detectFace() {\n dom.canvas.style.height = '';\n dom.canvas.getContext('2d')?.clearRect(0, 0, options.minSize, options.minSize);\n if (!current?.face?.tensor || !current?.face?.embedding) return false;\n console.log('face record:', current.face); // eslint-disable-line no-console\n log(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${100 * (current.face.distance || 0)}cm/${Math.round(100 * (current.face.distance || 0) / 2.54)}in`);\n await human.tf.browser.toPixels(current.face.tensor, dom.canvas);\n if (await indexDb.count() === 0) {\n log('face database is empty: nothing to compare face with');\n document.body.style.background = 'black';\n dom.delete.style.display = 'none';\n return false;\n }\n const db = await indexDb.load();\n const descriptors = db.map((rec) => rec.descriptor).filter((desc) => desc.length > 0);\n const res = human.match.find(current.face.embedding, descriptors, matchOptions);\n current.record = db[res.index] || null;\n if (current.record) {\n log(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1000 * res.similarity) / 10}%`);\n dom.name.value = current.record.name;\n dom.source.style.display = '';\n dom.source.getContext('2d')?.putImageData(current.record.image, 0, 0);\n }\n document.body.style.background = res.similarity > options.threshold ? 'darkgreen' : 'maroon';\n return res.similarity > options.threshold;\n}\n\nasync function main() { // main entry point\n ok.faceCount.status = false;\n ok.faceConfidence.status = false;\n ok.facingCenter.status = false;\n ok.blinkDetected.status = false;\n ok.faceSize.status = false;\n ok.antispoofCheck.status = false;\n ok.livenessCheck.status = false;\n ok.age.status = false;\n ok.gender.status = false;\n ok.elapsedMs.val = 0;\n dom.match.style.display = 'none';\n dom.retry.style.display = 'none';\n dom.source.style.display = 'none';\n dom.canvas.style.height = '50%';\n document.body.style.background = 'black';\n await webCam();\n await detectionLoop(); // start detection loop\n startTime = human.now();\n current.face = await validationLoop(); // start validation loop\n dom.canvas.width = current.face?.tensor?.shape[1] || options.minSize;\n dom.canvas.height = current.face?.tensor?.shape[0] || options.minSize;\n dom.source.width = dom.canvas.width;\n dom.source.height = dom.canvas.height;\n dom.canvas.style.width = '';\n dom.match.style.display = 'flex';\n dom.save.style.display = 'flex';\n dom.delete.style.display = 'flex';\n dom.retry.style.display = 'block';\n if (!allOk()) { // is all criteria met?\n log('did not find valid face');\n return false;\n }\n return detectFace();\n}\n\nasync function init() {\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('options:', JSON.stringify(options).replace(/{|}|\"|\\[|\\]/g, '').replace(/,/g, ' '));\n log('initializing webcam...');\n await webCam(); // start webcam\n log('loading human models...');\n await human.load(); // preload all models\n log('initializing human...');\n log('face embedding model:', humanConfig.face.description.enabled ? 'faceres' : '', humanConfig.face['mobilefacenet']?.enabled ? 'mobilefacenet' : '', humanConfig.face['insightface']?.enabled ? 'insightface' : '');\n log('loading face database...');\n log('known face records:', await indexDb.count());\n dom.retry.addEventListener('click', main);\n dom.save.addEventListener('click', saveRecords);\n dom.delete.addEventListener('click', deleteRecord);\n await human.warmup(); // warmup function to initialize backend for future faster detection\n await main();\n}\n\nwindow.onload = init;\n", "let db: IDBDatabase; // instance of indexdb\n\nconst database = 'human';\nconst table = 'person';\n\nexport interface FaceRecord { id: number, name: string, descriptor: number[], image: ImageData }\n\nconst log = (...msg) => console.log('indexdb', ...msg); // eslint-disable-line no-console\n\nexport async function open() {\n if (db) return true;\n return new Promise((resolve) => {\n const request: IDBOpenDBRequest = indexedDB.open(database, 1);\n request.onerror = (evt) => log('error:', evt);\n request.onupgradeneeded = (evt: IDBVersionChangeEvent) => { // create if doesnt exist\n log('create:', evt.target);\n db = (evt.target as IDBOpenDBRequest).result;\n db.createObjectStore(table, { keyPath: 'id', autoIncrement: true });\n };\n request.onsuccess = (evt) => { // open\n db = (evt.target as IDBOpenDBRequest).result;\n log('open:', db);\n resolve(true);\n };\n });\n}\n\nexport async function load(): Promise {\n const faceDB: FaceRecord[] = [];\n if (!db) await open(); // open or create if not already done\n return new Promise((resolve) => {\n const cursor: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).openCursor(null, 'next');\n cursor.onerror = (evt) => log('load error:', evt);\n cursor.onsuccess = (evt) => {\n if ((evt.target as IDBRequest).result) {\n faceDB.push((evt.target as IDBRequest).result.value);\n (evt.target as IDBRequest).result.continue();\n } else {\n resolve(faceDB);\n }\n };\n });\n}\n\nexport async function count(): Promise {\n if (!db) await open(); // open or create if not already done\n return new Promise((resolve) => {\n const store: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).count();\n store.onerror = (evt) => log('count error:', evt);\n store.onsuccess = () => resolve(store.result);\n });\n}\n\nexport async function save(faceRecord: FaceRecord) {\n if (!db) await open(); // open or create if not already done\n const newRecord = { name: faceRecord.name, descriptor: faceRecord.descriptor, image: faceRecord.image }; // omit id as its autoincrement\n db.transaction([table], 'readwrite').objectStore(table).put(newRecord);\n log('save:', newRecord);\n}\n\nexport async function remove(faceRecord: FaceRecord) {\n if (!db) await open(); // open or create if not already done\n db.transaction([table], 'readwrite').objectStore(table).delete(faceRecord.id); // delete based on id\n log('delete:', faceRecord);\n}\n"], - "mappings": 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+ "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\nimport * as indexDb from './indexdb'; // methods to deal with indexdb\n\nconst humanConfig = { // user configuration for human, used to fine-tune behavior\n cacheSensitivity: 0,\n modelBasePath: '../../models',\n filter: { enabled: true, equalization: true }, // lets run with histogram equilizer\n debug: true,\n face: {\n enabled: true,\n detector: { rotation: true, return: true, mask: false }, // return tensor is used to get detected face image\n description: { enabled: true }, // default model for face descriptor extraction is faceres\n // mobilefacenet: { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model\n // insightface: { enabled: true, modelPath: 'https://vladmandic.github.io/insightface/models/insightface-mobilenet-swish.json' }, // alternative model\n iris: { enabled: true }, // needed to determine gaze direction\n emotion: { enabled: false }, // not needed\n antispoof: { enabled: true }, // enable optional antispoof module\n liveness: { enabled: true }, // enable optional liveness module\n },\n body: { enabled: false },\n hand: { enabled: false },\n object: { enabled: false },\n gesture: { enabled: true }, // parses face and iris gestures\n};\n\n// const matchOptions = { order: 2, multiplier: 1000, min: 0.0, max: 1.0 }; // for embedding model\nconst matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; // for faceres model\n\nconst options = {\n minConfidence: 0.6, // overal face confidence for box, face, gender, real, live\n minSize: 224, // min input to face descriptor model before degradation\n maxTime: 30000, // max time before giving up\n blinkMin: 10, // minimum duration of a valid blink\n blinkMax: 800, // maximum duration of a valid blink\n threshold: 0.5, // minimum similarity\n distanceMin: 0.4, // closest that face is allowed to be to the cammera in cm\n distanceMax: 1.0, // farthest that face is allowed to be to the cammera in cm\n mask: humanConfig.face.detector.mask,\n rotation: humanConfig.face.detector.rotation,\n ...matchOptions,\n};\n\nconst ok: Record = { // must meet all rules\n faceCount: { status: false, val: 0 },\n faceConfidence: { status: false, val: 0 },\n facingCenter: { status: false, val: 0 },\n lookingCenter: { status: false, val: 0 },\n blinkDetected: { status: false, val: 0 },\n faceSize: { status: false, val: 0 },\n antispoofCheck: { status: false, val: 0 },\n livenessCheck: { status: false, val: 0 },\n distance: { status: false, val: 0 },\n age: { status: false, val: 0 },\n gender: { status: false, val: 0 },\n timeout: { status: true, val: 0 },\n descriptor: { status: false, val: 0 },\n elapsedMs: { status: undefined, val: 0 }, // total time while waiting for valid face\n detectFPS: { status: undefined, val: 0 }, // mark detection fps performance\n drawFPS: { status: undefined, val: 0 }, // mark redraw fps performance\n};\n\nconst allOk = () => ok.faceCount.status\n && ok.faceSize.status\n && ok.blinkDetected.status\n && ok.facingCenter.status\n && ok.lookingCenter.status\n && ok.faceConfidence.status\n && ok.antispoofCheck.status\n && ok.livenessCheck.status\n && ok.distance.status\n && ok.descriptor.status\n && ok.age.status\n && ok.gender.status;\n\nconst current: { face: H.FaceResult | null, record: indexDb.FaceRecord | null } = { face: null, record: null }; // current face record and matched database record\n\nconst blink = { // internal timers for blink start/end/duration\n start: 0,\n end: 0,\n time: 0,\n};\n\n// let db: Array<{ name: string, source: string, embedding: number[] }> = []; // holds loaded face descriptor database\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('fps') as HTMLPreElement,\n match: document.getElementById('match') as HTMLDivElement,\n name: document.getElementById('name') as HTMLInputElement,\n save: document.getElementById('save') as HTMLSpanElement,\n delete: document.getElementById('delete') as HTMLSpanElement,\n retry: document.getElementById('retry') as HTMLDivElement,\n source: document.getElementById('source') as HTMLCanvasElement,\n ok: document.getElementById('ok') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0 }; // holds information used to calculate performance and possible memory leaks\nlet startTime = 0;\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\n\nasync function webCam() { // initialize webcam\n // @ts-ignore resizeMode is not yet defined in tslib\n const cameraOptions: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth } } };\n const stream: MediaStream = await navigator.mediaDevices.getUserMedia(cameraOptions);\n const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });\n dom.video.srcObject = stream;\n void dom.video.play();\n await ready;\n dom.canvas.width = dom.video.videoWidth;\n dom.canvas.height = dom.video.videoHeight;\n dom.canvas.style.width = '50%';\n dom.canvas.style.height = '50%';\n if (human.env.initial) log('video:', dom.video.videoWidth, dom.video.videoHeight, '|', stream.getVideoTracks()[0].label);\n dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click\n if (dom.video.paused) void dom.video.play();\n else dom.video.pause();\n };\n}\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused) {\n if (current.face?.tensor) human.tf.dispose(current.face.tensor); // dispose previous tensor\n await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const now = human.now();\n ok.detectFPS.val = Math.round(10000 / (now - timestamp.detect)) / 10;\n timestamp.detect = now;\n requestAnimationFrame(detectionLoop); // start new frame immediately\n }\n}\n\nfunction drawValidationTests() {\n let y = 32;\n for (const [key, val] of Object.entries(ok)) {\n let el = document.getElementById(`ok-${key}`);\n if (!el) {\n el = document.createElement('div');\n el.id = `ok-${key}`;\n el.innerText = key;\n el.className = 'ok';\n el.style.top = `${y}px`;\n dom.ok.appendChild(el);\n }\n if (typeof val.status === 'boolean') el.style.backgroundColor = val.status ? 'lightgreen' : 'lightcoral';\n const status = val.status ? 'ok' : 'fail';\n el.innerText = `${key}: ${val.val === 0 ? status : val.val}`;\n y += 28;\n }\n}\n\nasync function validationLoop(): Promise { // main screen refresh loop\n const interpolated = human.next(human.result); // smoothen result using last-known results\n human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen\n await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.\n const now = human.now();\n ok.drawFPS.val = Math.round(10000 / (now - timestamp.draw)) / 10;\n timestamp.draw = now;\n ok.faceCount.val = human.result.face.length;\n ok.faceCount.status = ok.faceCount.val === 1; // must be exactly detected face\n if (ok.faceCount.status) { // skip the rest if no face\n const gestures: string[] = Object.values(human.result.gesture).map((gesture: H.GestureResult) => gesture.gesture); // flatten all gestures\n if (gestures.includes('blink left eye') || gestures.includes('blink right eye')) blink.start = human.now(); // blink starts when eyes get closed\n if (blink.start > 0 && !gestures.includes('blink left eye') && !gestures.includes('blink right eye')) blink.end = human.now(); // if blink started how long until eyes are back open\n ok.blinkDetected.status = ok.blinkDetected.status || (Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax);\n if (ok.blinkDetected.status && blink.time === 0) blink.time = Math.trunc(blink.end - blink.start);\n ok.facingCenter.status = gestures.includes('facing center');\n ok.lookingCenter.status = gestures.includes('looking center'); // must face camera and look at camera\n ok.faceConfidence.val = human.result.face[0].faceScore || human.result.face[0].boxScore || 0;\n ok.faceConfidence.status = ok.faceConfidence.val >= options.minConfidence;\n ok.antispoofCheck.val = human.result.face[0].real || 0;\n ok.antispoofCheck.status = ok.antispoofCheck.val >= options.minConfidence;\n ok.livenessCheck.val = human.result.face[0].live || 0;\n ok.livenessCheck.status = ok.livenessCheck.val >= options.minConfidence;\n ok.faceSize.val = Math.min(human.result.face[0].box[2], human.result.face[0].box[3]);\n ok.faceSize.status = ok.faceSize.val >= options.minSize;\n ok.distance.val = human.result.face[0].distance || 0;\n ok.distance.status = (ok.distance.val >= options.distanceMin) && (ok.distance.val <= options.distanceMax);\n ok.descriptor.val = human.result.face[0].embedding?.length || 0;\n ok.descriptor.status = ok.descriptor.val > 0;\n ok.age.val = human.result.face[0].age || 0;\n ok.age.status = ok.age.val > 0;\n ok.gender.val = human.result.face[0].genderScore || 0;\n ok.gender.status = ok.gender.val >= options.minConfidence;\n }\n // run again\n ok.timeout.status = ok.elapsedMs.val <= options.maxTime;\n drawValidationTests();\n if (allOk() || !ok.timeout.status) { // all criteria met\n dom.video.pause();\n return human.result.face[0];\n }\n ok.elapsedMs.val = Math.trunc(human.now() - startTime);\n return new Promise((resolve) => {\n setTimeout(async () => {\n await validationLoop(); // run validation loop until conditions are met\n resolve(human.result.face[0]); // recursive promise resolve\n }, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n });\n}\n\nasync function saveRecords() {\n if (dom.name.value.length > 0) {\n const image = dom.canvas.getContext('2d')?.getImageData(0, 0, dom.canvas.width, dom.canvas.height) as ImageData;\n const rec = { id: 0, name: dom.name.value, descriptor: current.face?.embedding as number[], image };\n await indexDb.save(rec);\n log('saved face record:', rec.name, 'descriptor length:', current.face?.embedding?.length);\n log('known face records:', await indexDb.count());\n } else {\n log('invalid name');\n }\n}\n\nasync function deleteRecord() {\n if (current.record && current.record.id > 0) {\n await indexDb.remove(current.record);\n }\n}\n\nasync function detectFace() {\n dom.canvas.style.height = '';\n dom.canvas.getContext('2d')?.clearRect(0, 0, options.minSize, options.minSize);\n if (!current?.face?.tensor || !current?.face?.embedding) return false;\n console.log('face record:', current.face); // eslint-disable-line no-console\n log(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${100 * (current.face.distance || 0)}cm/${Math.round(100 * (current.face.distance || 0) / 2.54)}in`);\n await human.tf.browser.draw(current.face.tensor, dom.canvas);\n if (await indexDb.count() === 0) {\n log('face database is empty: nothing to compare face with');\n document.body.style.background = 'black';\n dom.delete.style.display = 'none';\n return false;\n }\n const db = await indexDb.load();\n const descriptors = db.map((rec) => rec.descriptor).filter((desc) => desc.length > 0);\n const res = human.match.find(current.face.embedding, descriptors, matchOptions);\n current.record = db[res.index] || null;\n if (current.record) {\n log(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1000 * res.similarity) / 10}%`);\n dom.name.value = current.record.name;\n dom.source.style.display = '';\n dom.source.getContext('2d')?.putImageData(current.record.image, 0, 0);\n }\n document.body.style.background = res.similarity > options.threshold ? 'darkgreen' : 'maroon';\n return res.similarity > options.threshold;\n}\n\nasync function main() { // main entry point\n ok.faceCount.status = false;\n ok.faceConfidence.status = false;\n ok.facingCenter.status = false;\n ok.blinkDetected.status = false;\n ok.faceSize.status = false;\n ok.antispoofCheck.status = false;\n ok.livenessCheck.status = false;\n ok.age.status = false;\n ok.gender.status = false;\n ok.elapsedMs.val = 0;\n dom.match.style.display = 'none';\n dom.retry.style.display = 'none';\n dom.source.style.display = 'none';\n dom.canvas.style.height = '50%';\n document.body.style.background = 'black';\n await webCam();\n await detectionLoop(); // start detection loop\n startTime = human.now();\n current.face = await validationLoop(); // start validation loop\n dom.canvas.width = current.face?.tensor?.shape[1] || options.minSize;\n dom.canvas.height = current.face?.tensor?.shape[0] || options.minSize;\n dom.source.width = dom.canvas.width;\n dom.source.height = dom.canvas.height;\n dom.canvas.style.width = '';\n dom.match.style.display = 'flex';\n dom.save.style.display = 'flex';\n dom.delete.style.display = 'flex';\n dom.retry.style.display = 'block';\n if (!allOk()) { // is all criteria met?\n log('did not find valid face');\n return false;\n }\n return detectFace();\n}\n\nasync function init() {\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('options:', JSON.stringify(options).replace(/{|}|\"|\\[|\\]/g, '').replace(/,/g, ' '));\n log('initializing webcam...');\n await webCam(); // start webcam\n log('loading human models...');\n await human.load(); // preload all models\n log('initializing human...');\n log('face embedding model:', humanConfig.face.description.enabled ? 'faceres' : '', humanConfig.face['mobilefacenet']?.enabled ? 'mobilefacenet' : '', humanConfig.face['insightface']?.enabled ? 'insightface' : '');\n log('loading face database...');\n log('known face records:', await indexDb.count());\n dom.retry.addEventListener('click', main);\n dom.save.addEventListener('click', saveRecords);\n dom.delete.addEventListener('click', deleteRecord);\n await human.warmup(); // warmup function to initialize backend for future faster detection\n await main();\n}\n\nwindow.onload = init;\n", "let db: IDBDatabase; // instance of indexdb\n\nconst database = 'human';\nconst table = 'person';\n\nexport interface FaceRecord { id: number, name: string, descriptor: number[], image: ImageData }\n\nconst log = (...msg) => console.log('indexdb', ...msg); // eslint-disable-line no-console\n\nexport async function open() {\n if (db) return true;\n return new Promise((resolve) => {\n const request: IDBOpenDBRequest = indexedDB.open(database, 1);\n request.onerror = (evt) => log('error:', evt);\n request.onupgradeneeded = (evt: IDBVersionChangeEvent) => { // create if doesnt exist\n log('create:', evt.target);\n db = (evt.target as IDBOpenDBRequest).result;\n db.createObjectStore(table, { keyPath: 'id', autoIncrement: true });\n };\n request.onsuccess = (evt) => { // open\n db = (evt.target as IDBOpenDBRequest).result;\n log('open:', db);\n resolve(true);\n };\n });\n}\n\nexport async function load(): Promise {\n const faceDB: FaceRecord[] = [];\n if (!db) await open(); // open or create if not already done\n return new Promise((resolve) => {\n const cursor: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).openCursor(null, 'next');\n cursor.onerror = (evt) => log('load error:', evt);\n cursor.onsuccess = (evt) => {\n if ((evt.target as IDBRequest).result) {\n faceDB.push((evt.target as IDBRequest).result.value);\n (evt.target as IDBRequest).result.continue();\n } else {\n resolve(faceDB);\n }\n };\n });\n}\n\nexport async function count(): Promise {\n if (!db) await open(); // open or create if not already done\n return new Promise((resolve) => {\n const store: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).count();\n store.onerror = (evt) => log('count error:', evt);\n store.onsuccess = () => resolve(store.result);\n });\n}\n\nexport async function save(faceRecord: FaceRecord) {\n if (!db) await open(); // open or create if not already done\n const newRecord = { name: faceRecord.name, descriptor: faceRecord.descriptor, image: faceRecord.image }; // omit id as its autoincrement\n db.transaction([table], 'readwrite').objectStore(table).put(newRecord);\n log('save:', newRecord);\n}\n\nexport async function remove(faceRecord: FaceRecord) {\n if (!db) await open(); // open or create if not already done\n db.transaction([table], 'readwrite').objectStore(table).delete(faceRecord.id); // delete based on id\n log('delete:', faceRecord);\n}\n"], + "mappings": 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"names": ["H", "db", "database", "table", "log", "msg", "open", "resolve", "request", "evt", "load", "faceDB", "cursor", "count", "store", "save", "faceRecord", "newRecord", "remove", "humanConfig", "matchOptions", "options", "ok", "allOk", "current", "blink", "human", "dom", "timestamp", "startTime", "log", "msg", "webCam", "cameraOptions", "stream", "ready", "resolve", "detectionLoop", "_a", "now", "drawValidationTests", "y", "key", "val", "el", "status", "validationLoop", "interpolated", "gestures", "gesture", "saveRecords", "_b", "_c", "_d", "image", "rec", "save", "count", "deleteRecord", "remove", "detectFace", "db", "load", "descriptors", "desc", "res", "main", "init"] } diff --git a/demo/faceid/index.ts b/demo/faceid/index.ts index d460ecf2..77b3efd0 100644 --- a/demo/faceid/index.ts +++ b/demo/faceid/index.ts @@ -240,7 +240,7 @@ async function detectFace() { if (!current?.face?.tensor || !current?.face?.embedding) return false; console.log('face record:', current.face); // eslint-disable-line no-console log(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${100 * (current.face.distance || 0)}cm/${Math.round(100 * (current.face.distance || 0) / 2.54)}in`); - await human.tf.browser.toPixels(current.face.tensor, dom.canvas); + await human.tf.browser.draw(current.face.tensor, dom.canvas); if (await indexDb.count() === 0) { log('face database is empty: nothing to compare face with'); document.body.style.background = 'black'; diff --git a/demo/facematch/facematch.js b/demo/facematch/facematch.js index 4d340ff0..5a444aa8 100644 --- a/demo/facematch/facematch.js +++ b/demo/facematch/facematch.js @@ -70,7 +70,7 @@ async function selectFaceCanvas(face) { if (face.tensor) { title('Sorting Faces by Similarity'); const c = document.getElementById('orig'); - await human.tf.browser.toPixels(face.tensor, c); + await human.tf.browser.draw(face.tensor, c); const arr = db.map((rec) => rec.embedding); const res = await human.match.find(face.embedding, arr); log('Match:', db[res.index].name); @@ -97,7 +97,7 @@ async function selectFaceCanvas(face) { canvas.tag.similarity = similarity; // get best match // draw the canvas - await human.tf.browser.toPixels(current.tensor, canvas); + await human.tf.browser.draw(current.tensor, canvas); const ctx = canvas.getContext('2d'); ctx.font = 'small-caps 1rem "Lato"'; ctx.fillStyle = 'rgba(0, 0, 0, 1)'; @@ -144,7 +144,7 @@ async function addFaceCanvas(index, res, fileName) { gender: ${Math.round(100 * res.face[i].genderScore)}% ${res.face[i].gender} emotion: ${emotion} `.replace(/ /g, ' '); - await human.tf.browser.toPixels(res.face[i].tensor, canvas); + await human.tf.browser.draw(res.face[i].tensor, canvas); const ctx = canvas.getContext('2d'); if (!ctx) return; ctx.font = 'small-caps 0.8rem "Lato"'; diff --git a/demo/index.js b/demo/index.js index 934d159b..b25436f0 100644 --- a/demo/index.js +++ b/demo/index.js @@ -223,7 +223,7 @@ async function calcSimmilarity(result) { log('setting face compare baseline:', result.face[0]); if (result.face[0].tensor) { const c = document.getElementById('orig'); - human.tf.browser.toPixels(result.face[0].tensor, c); + human.tf.browser.draw(result.face[0].tensor, c); } else { document.getElementById('compare-canvas').getContext('2d').drawImage(compare.original.canvas, 0, 0, 200, 200); } diff --git a/demo/segmentation/index.js b/demo/segmentation/index.js index 1040e70e..aa4912a9 100644 --- a/demo/segmentation/index.js +++ b/demo/segmentation/index.js @@ -85,7 +85,7 @@ async function main() { return; } dom.fps.innerText = `fps: ${Math.round(10000 / (t1 - t0)) / 10}`; // mark performance - human.tf.browser.toPixels(rgba, dom.output); // draw raw output + human.tf.browser.draw(rgba, dom.output); // draw raw output human.tf.dispose(rgba); // dispose tensors ctxMerge.globalCompositeOperation = 'source-over'; ctxMerge.drawImage(dom.background, 0, 0); // draw original video to first stacked canvas diff --git a/demo/tracker/index.js.map b/demo/tracker/index.js.map index f154bcd2..d23e1158 100644 --- a/demo/tracker/index.js.map +++ b/demo/tracker/index.js.map @@ -2,6 +2,6 @@ "version": 3, "sources": ["index.ts", "tracker.js"], "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\nimport tracker from './tracker.js';\n\nconst humanConfig: Partial = { // user configuration for human, used to fine-tune behavior\n debug: true,\n backend: 'webgl',\n // cacheSensitivity: 0,\n // cacheModels: false,\n // warmup: 'none',\n modelBasePath: 'https://vladmandic.github.io/human-models/models',\n filter: { enabled: true, equalization: false, flip: false },\n face: {\n enabled: true,\n detector: { rotation: false, maxDetected: 10, minConfidence: 0.3 },\n mesh: { enabled: true },\n attention: { enabled: false },\n iris: { enabled: false },\n description: { enabled: false },\n emotion: { enabled: false },\n antispoof: { enabled: false },\n liveness: { enabled: false },\n },\n body: { enabled: false, maxDetected: 6, modelPath: 'movenet-multipose.json' },\n hand: { enabled: false },\n object: { enabled: false, maxDetected: 10 },\n segmentation: { enabled: false },\n gesture: { enabled: false },\n};\n\ninterface TrackerConfig {\n unMatchedFramesTolerance: number, // number of frame when an object is not matched before considering it gone; ignored if fastDelete is set\n iouLimit: number, // exclude things from beeing matched if their IOU less than; 1 means total overlap; 0 means no overlap\n fastDelete: boolean, // remove new objects immediately if they could not be matched in the next frames; if set, ignores unMatchedFramesTolerance\n distanceLimit: number, // distance limit for matching; if values need to be excluded from matching set their distance to something greater than the distance limit\n matchingAlgorithm: 'kdTree' | 'munkres', // algorithm used to match tracks with new detections\n}\n\ninterface TrackerResult {\n id: number,\n confidence: number,\n bearing: number,\n isZombie: boolean,\n name: string,\n x: number,\n y: number,\n w: number,\n h: number,\n}\n\nconst trackerConfig: TrackerConfig = {\n unMatchedFramesTolerance: 100,\n iouLimit: 0.05,\n fastDelete: false,\n distanceLimit: 1e4,\n matchingAlgorithm: 'kdTree',\n};\n\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('status') as HTMLPreElement,\n tracker: document.getElementById('tracker') as HTMLInputElement,\n interpolation: document.getElementById('interpolation') as HTMLInputElement,\n config: document.getElementById('config') as HTMLFormElement,\n ctx: (document.getElementById('canvas') as HTMLCanvasElement).getContext('2d') as CanvasRenderingContext2D,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused && dom.video.readyState >= 2) {\n if (timestamp.start === 0) timestamp.start = human.now();\n // log('profiling data:', await human.profile(dom.video));\n await human.detect(dom.video, humanConfig); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n timestamp.tensors = tensors;\n fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n fps.frames++;\n fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n }\n timestamp.detect = human.now();\n requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nfunction drawLoop() { // main screen refresh loop\n if (!dom.video.paused && dom.video.readyState >= 2) {\n const res: H.Result = dom.interpolation.checked ? human.next(human.result) : human.result; // interpolate results if enabled\n let tracking: H.FaceResult[] | H.BodyResult[] | H.ObjectResult[] = [];\n if (human.config.face.enabled) tracking = res.face;\n else if (human.config.body.enabled) tracking = res.body;\n else if (human.config.object.enabled) tracking = res.object;\n else log('unknown object type');\n let data: TrackerResult[] = [];\n if (dom.tracker.checked) {\n const items = tracking.map((obj) => ({\n x: obj.box[0] + obj.box[2] / 2,\n y: obj.box[1] + obj.box[3] / 2,\n w: obj.box[2],\n h: obj.box[3],\n name: obj.label || (human.config.face.enabled ? 'face' : 'body'),\n confidence: obj.score,\n }));\n tracker.updateTrackedItemsWithNewFrame(items, fps.frames);\n data = tracker.getJSONOfTrackedItems(true) as TrackerResult[];\n }\n human.draw.canvas(dom.video, dom.canvas); // copy input video frame to output canvas\n for (let i = 0; i < tracking.length; i++) {\n // @ts-ignore\n const name = tracking[i].label || (human.config.face.enabled ? 'face' : 'body');\n dom.ctx.strokeRect(tracking[i].box[0], tracking[i].box[1], tracking[i].box[1], tracking[i].box[2]);\n dom.ctx.fillText(`id: ${tracking[i].id} ${Math.round(100 * tracking[i].score)}% ${name}`, tracking[i].box[0] + 4, tracking[i].box[1] + 16);\n if (data[i]) {\n dom.ctx.fillText(`t: ${data[i].id} ${Math.round(100 * data[i].confidence)}% ${data[i].name} ${data[i].isZombie ? 'zombie' : ''}`, tracking[i].box[0] + 4, tracking[i].box[1] + 34);\n }\n }\n }\n const now = human.now();\n fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n timestamp.draw = now;\n status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status\n setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function handleVideo(file: File) {\n const url = URL.createObjectURL(file);\n dom.video.src = url;\n await dom.video.play();\n log('loaded video:', file.name, 'resolution:', [dom.video.videoWidth, dom.video.videoHeight], 'duration:', dom.video.duration);\n dom.canvas.width = dom.video.videoWidth;\n dom.canvas.height = dom.video.videoHeight;\n dom.ctx.strokeStyle = 'white';\n dom.ctx.fillStyle = 'white';\n dom.ctx.font = '16px Segoe UI';\n dom.video.playbackRate = 0.25;\n}\n\nfunction initInput() {\n document.body.addEventListener('dragenter', (evt) => evt.preventDefault());\n document.body.addEventListener('dragleave', (evt) => evt.preventDefault());\n document.body.addEventListener('dragover', (evt) => evt.preventDefault());\n document.body.addEventListener('drop', async (evt) => {\n evt.preventDefault();\n if (evt.dataTransfer) evt.dataTransfer.dropEffect = 'copy';\n const file = evt.dataTransfer?.files?.[0];\n if (file) await handleVideo(file);\n log(dom.video.readyState);\n });\n (document.getElementById('inputvideo') as HTMLInputElement).onchange = async (evt) => {\n evt.preventDefault();\n const file = evt.target?.['files']?.[0];\n if (file) await handleVideo(file);\n };\n dom.config.onchange = () => {\n trackerConfig.distanceLimit = (document.getElementById('distanceLimit') as HTMLInputElement).valueAsNumber;\n trackerConfig.iouLimit = (document.getElementById('iouLimit') as HTMLInputElement).valueAsNumber;\n trackerConfig.unMatchedFramesTolerance = (document.getElementById('unMatchedFramesTolerance') as HTMLInputElement).valueAsNumber;\n trackerConfig.unMatchedFramesTolerance = (document.getElementById('unMatchedFramesTolerance') as HTMLInputElement).valueAsNumber;\n trackerConfig.matchingAlgorithm = (document.getElementById('matchingAlgorithm-kdTree') as HTMLInputElement).checked ? 'kdTree' : 'munkres';\n tracker.setParams(trackerConfig);\n if ((document.getElementById('keepInMemory') as HTMLInputElement).checked) tracker.enableKeepInMemory();\n else tracker.disableKeepInMemory();\n tracker.reset();\n log('tracker config change', JSON.stringify(trackerConfig));\n humanConfig.face!.enabled = (document.getElementById('box-face') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion\n humanConfig.body!.enabled = (document.getElementById('box-body') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion\n humanConfig.object!.enabled = (document.getElementById('box-object') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion\n };\n dom.tracker.onchange = (evt) => {\n log('tracker', (evt.target as HTMLInputElement).checked ? 'enabled' : 'disabled');\n tracker.setParams(trackerConfig);\n tracker.reset();\n };\n}\n\nasync function main() { // main entry point\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('platform:', human.env.platform, '| agent:', human.env.agent);\n status('loading...');\n await human.load(); // preload all models\n log('backend:', human.tf.getBackend(), '| available:', human.env.backends);\n log('models loaded:', human.models.loaded());\n status('initializing...');\n await human.warmup(); // warmup function to initialize backend for future faster detection\n initInput(); // initialize input\n await detectionLoop(); // start detection loop\n drawLoop(); // start draw loop\n}\n\nwindow.onload = main;\n", "/* eslint-disable */\nvar __defProp = Object.defineProperty;\nvar __getOwnPropNames = Object.getOwnPropertyNames;\nvar __name = (target, value) => __defProp(target, \"name\", { value, configurable: true });\nvar __commonJS = (cb, mod) => function __require() {\n return mod || (0, cb[__getOwnPropNames(cb)[0]])((mod = { exports: {} }).exports, mod), mod.exports;\n};\n\n// node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/lib/rng-browser.js\nvar require_rng_browser = __commonJS({\n \"node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/lib/rng-browser.js\"(exports, module) {\n var getRandomValues = typeof crypto != \"undefined\" && crypto.getRandomValues.bind(crypto) || typeof msCrypto != \"undefined\" && msCrypto.getRandomValues.bind(msCrypto);\n if (getRandomValues) {\n rnds8 = new Uint8Array(16);\n module.exports = /* @__PURE__ */ __name(function whatwgRNG() {\n getRandomValues(rnds8);\n return rnds8;\n }, \"whatwgRNG\");\n } else {\n rnds = new Array(16);\n module.exports = /* @__PURE__ */ __name(function mathRNG() {\n for (var i = 0, r; i < 16; i++) {\n if ((i & 3) === 0)\n r = Math.random() * 4294967296;\n rnds[i] = r >>> ((i & 3) << 3) & 255;\n }\n return rnds;\n }, \"mathRNG\");\n }\n var rnds8;\n var rnds;\n }\n});\n\n// node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/lib/bytesToUuid.js\nvar require_bytesToUuid = __commonJS({\n \"node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/lib/bytesToUuid.js\"(exports, module) {\n var byteToHex = [];\n for (i = 0; i < 256; ++i) {\n byteToHex[i] = (i + 256).toString(16).substr(1);\n }\n function bytesToUuid(buf, offset) {\n var i2 = offset || 0;\n var bth = byteToHex;\n return bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + \"-\" + bth[buf[i2++]] + bth[buf[i2++]] + \"-\" + bth[buf[i2++]] + bth[buf[i2++]] + \"-\" + bth[buf[i2++]] + bth[buf[i2++]] + \"-\" + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]];\n }\n __name(bytesToUuid, \"bytesToUuid\");\n module.exports = bytesToUuid;\n var i;\n }\n});\n\n// node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/v4.js\nvar require_v4 = __commonJS({\n \"node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/v4.js\"(exports, module) {\n var rng = require_rng_browser();\n var bytesToUuid = require_bytesToUuid();\n function v4(options, buf, offset) {\n var i = buf && offset || 0;\n if (typeof options == \"string\") {\n buf = options === \"binary\" ? new Array(16) : null;\n options = null;\n }\n options = options || {};\n var rnds = options.random || (options.rng || rng)();\n rnds[6] = rnds[6] & 15 | 64;\n rnds[8] = rnds[8] & 63 | 128;\n if (buf) {\n for (var ii = 0; ii < 16; ++ii) {\n buf[i + ii] = rnds[ii];\n }\n }\n return buf || bytesToUuid(rnds);\n }\n __name(v4, \"v4\");\n module.exports = v4;\n }\n});\n\n// utils.js\nvar require_utils = __commonJS({\n \"utils.js\"(exports) {\n exports.isDetectionTooLarge = (detections, largestAllowed) => {\n if (detections.w >= largestAllowed) {\n return true;\n } else {\n return false;\n }\n };\n var isInsideArea = /* @__PURE__ */ __name((area, point) => {\n const xMin = area.x - area.w / 2;\n const xMax = area.x + area.w / 2;\n const yMin = area.y - area.h / 2;\n const yMax = area.y + area.h / 2;\n if (point.x >= xMin && point.x <= xMax && point.y >= yMin && point.y <= yMax) {\n return true;\n } else {\n return false;\n }\n }, \"isInsideArea\");\n exports.isInsideArea = isInsideArea;\n exports.isInsideSomeAreas = (areas, point) => {\n const isInside = areas.some((area) => isInsideArea(area, point));\n return isInside;\n };\n exports.ignoreObjectsNotToDetect = (detections, objectsToDetect) => {\n return detections.filter((detection) => objectsToDetect.indexOf(detection.name) > -1);\n };\n var getRectangleEdges = /* @__PURE__ */ __name((item) => {\n return {\n x0: item.x - item.w / 2,\n y0: item.y - item.h / 2,\n x1: item.x + item.w / 2,\n y1: item.y + item.h / 2\n };\n }, \"getRectangleEdges\");\n exports.getRectangleEdges = getRectangleEdges;\n exports.iouAreas = (item1, item2) => {\n var rect1 = getRectangleEdges(item1);\n var rect2 = getRectangleEdges(item2);\n var overlap_x0 = Math.max(rect1.x0, rect2.x0);\n var overlap_y0 = Math.max(rect1.y0, rect2.y0);\n var overlap_x1 = Math.min(rect1.x1, rect2.x1);\n var overlap_y1 = Math.min(rect1.y1, rect2.y1);\n if (overlap_x1 - overlap_x0 <= 0 || overlap_y1 - overlap_y0 <= 0) {\n return 0;\n } else {\n const area_rect1 = item1.w * item1.h;\n const area_rect2 = item2.w * item2.h;\n const area_intersection = (overlap_x1 - overlap_x0) * (overlap_y1 - overlap_y0);\n const area_union = area_rect1 + area_rect2 - area_intersection;\n return area_intersection / area_union;\n }\n };\n exports.computeVelocityVector = (item1, item2, nbFrame) => {\n return {\n dx: (item2.x - item1.x) / nbFrame,\n dy: (item2.y - item1.y) / nbFrame\n };\n };\n exports.computeBearingIn360 = function(dx, dy) {\n var angle = Math.atan(dx / dy) / (Math.PI / 180);\n if (angle > 0) {\n if (dy > 0)\n return angle;\n else\n return 180 + angle;\n } else {\n if (dx > 0)\n return 180 + angle;\n else\n return 360 + angle;\n }\n };\n }\n});\n\n// ItemTracked.js\nvar require_ItemTracked = __commonJS({\n \"ItemTracked.js\"(exports) {\n var uuidv4 = require_v4();\n var computeBearingIn360 = require_utils().computeBearingIn360;\n var computeVelocityVector = require_utils().computeVelocityVector;\n exports.ITEM_HISTORY_MAX_LENGTH = 15;\n var idDisplay = 0;\n exports.ItemTracked = function(properties, frameNb, unMatchedFramesTolerance, fastDelete) {\n var DEFAULT_UNMATCHEDFRAMES_TOLERANCE = unMatchedFramesTolerance;\n var itemTracked = {};\n itemTracked.available = true;\n itemTracked.delete = false;\n itemTracked.fastDelete = fastDelete;\n itemTracked.frameUnmatchedLeftBeforeDying = unMatchedFramesTolerance;\n itemTracked.isZombie = false;\n itemTracked.appearFrame = frameNb;\n itemTracked.disappearFrame = null;\n itemTracked.disappearArea = {};\n itemTracked.nameCount = {};\n itemTracked.nameCount[properties.name] = 1;\n itemTracked.x = properties.x;\n itemTracked.y = properties.y;\n itemTracked.w = properties.w;\n itemTracked.h = properties.h;\n itemTracked.name = properties.name;\n itemTracked.confidence = properties.confidence;\n itemTracked.itemHistory = [];\n itemTracked.itemHistory.push({\n x: properties.x,\n y: properties.y,\n w: properties.w,\n h: properties.h,\n confidence: properties.confidence\n });\n if (itemTracked.itemHistory.length >= exports.ITEM_HISTORY_MAX_LENGTH) {\n itemTracked.itemHistory.shift();\n }\n itemTracked.velocity = {\n dx: 0,\n dy: 0\n };\n itemTracked.nbTimeMatched = 1;\n itemTracked.id = uuidv4();\n itemTracked.idDisplay = idDisplay;\n idDisplay++;\n itemTracked.update = function(properties2, frameNb2) {\n if (this.disappearFrame) {\n this.disappearFrame = null;\n this.disappearArea = {};\n }\n this.isZombie = false;\n this.nbTimeMatched += 1;\n this.x = properties2.x;\n this.y = properties2.y;\n this.w = properties2.w;\n this.h = properties2.h;\n this.confidence = properties2.confidence;\n this.itemHistory.push({\n x: this.x,\n y: this.y,\n w: this.w,\n h: this.h,\n confidence: this.confidence\n });\n if (itemTracked.itemHistory.length >= exports.ITEM_HISTORY_MAX_LENGTH) {\n itemTracked.itemHistory.shift();\n }\n this.name = properties2.name;\n if (this.nameCount[properties2.name]) {\n this.nameCount[properties2.name]++;\n } else {\n this.nameCount[properties2.name] = 1;\n }\n this.frameUnmatchedLeftBeforeDying = DEFAULT_UNMATCHEDFRAMES_TOLERANCE;\n this.velocity = this.updateVelocityVector();\n };\n itemTracked.makeAvailable = function() {\n this.available = true;\n return this;\n };\n itemTracked.makeUnavailable = function() {\n this.available = false;\n return this;\n };\n itemTracked.countDown = function(frameNb2) {\n if (this.disappearFrame === null) {\n this.disappearFrame = frameNb2;\n this.disappearArea = {\n x: this.x,\n y: this.y,\n w: this.w,\n h: this.h\n };\n }\n this.frameUnmatchedLeftBeforeDying--;\n this.isZombie = true;\n if (this.fastDelete && this.nbTimeMatched <= 1) {\n this.frameUnmatchedLeftBeforeDying = -1;\n }\n };\n itemTracked.updateTheoricalPositionAndSize = function() {\n this.itemHistory.push({\n x: this.x,\n y: this.y,\n w: this.w,\n h: this.h,\n confidence: this.confidence\n });\n if (itemTracked.itemHistory.length >= exports.ITEM_HISTORY_MAX_LENGTH) {\n itemTracked.itemHistory.shift();\n }\n this.x = this.x + this.velocity.dx;\n this.y = this.y + this.velocity.dy;\n };\n itemTracked.predictNextPosition = function() {\n return {\n x: this.x + this.velocity.dx,\n y: this.y + this.velocity.dy,\n w: this.w,\n h: this.h\n };\n };\n itemTracked.isDead = function() {\n return this.frameUnmatchedLeftBeforeDying < 0;\n };\n itemTracked.updateVelocityVector = function() {\n if (exports.ITEM_HISTORY_MAX_LENGTH <= 2) {\n return { dx: void 0, dy: void 0 };\n }\n if (this.itemHistory.length <= exports.ITEM_HISTORY_MAX_LENGTH) {\n const start = this.itemHistory[0];\n const end = this.itemHistory[this.itemHistory.length - 1];\n return computeVelocityVector(start, end, this.itemHistory.length);\n } else {\n const start = this.itemHistory[this.itemHistory.length - exports.ITEM_HISTORY_MAX_LENGTH];\n const end = this.itemHistory[this.itemHistory.length - 1];\n return computeVelocityVector(start, end, exports.ITEM_HISTORY_MAX_LENGTH);\n }\n };\n itemTracked.getMostlyMatchedName = function() {\n var nameMostlyMatchedOccurences = 0;\n var nameMostlyMatched = \"\";\n Object.keys(this.nameCount).map((name) => {\n if (this.nameCount[name] > nameMostlyMatchedOccurences) {\n nameMostlyMatched = name;\n nameMostlyMatchedOccurences = this.nameCount[name];\n }\n });\n return nameMostlyMatched;\n };\n itemTracked.toJSONDebug = function(roundInt = true) {\n return {\n id: this.id,\n idDisplay: this.idDisplay,\n x: roundInt ? parseInt(this.x, 10) : this.x,\n y: roundInt ? parseInt(this.y, 10) : this.y,\n w: roundInt ? parseInt(this.w, 10) : this.w,\n h: roundInt ? parseInt(this.h, 10) : this.h,\n confidence: Math.round(this.confidence * 100) / 100,\n // Here we negate dy to be in \"normal\" carthesian coordinates\n bearing: parseInt(computeBearingIn360(this.velocity.dx, -this.velocity.dy)),\n name: this.getMostlyMatchedName(),\n isZombie: this.isZombie,\n appearFrame: this.appearFrame,\n disappearFrame: this.disappearFrame\n };\n };\n itemTracked.toJSON = function(roundInt = true) {\n return {\n id: this.idDisplay,\n x: roundInt ? parseInt(this.x, 10) : this.x,\n y: roundInt ? parseInt(this.y, 10) : this.y,\n w: roundInt ? parseInt(this.w, 10) : this.w,\n h: roundInt ? parseInt(this.h, 10) : this.h,\n confidence: Math.round(this.confidence * 100) / 100,\n // Here we negate dy to be in \"normal\" carthesian coordinates\n bearing: parseInt(computeBearingIn360(this.velocity.dx, -this.velocity.dy), 10),\n name: this.getMostlyMatchedName(),\n isZombie: this.isZombie\n };\n };\n itemTracked.toMOT = function(frameIndex) {\n return `${frameIndex},${this.idDisplay},${this.x - this.w / 2},${this.y - this.h / 2},${this.w},${this.h},${this.confidence / 100},-1,-1,-1`;\n };\n itemTracked.toJSONGenericInfo = function() {\n return {\n id: this.id,\n idDisplay: this.idDisplay,\n appearFrame: this.appearFrame,\n disappearFrame: this.disappearFrame,\n disappearArea: this.disappearArea,\n nbActiveFrame: this.disappearFrame - this.appearFrame,\n name: this.getMostlyMatchedName()\n };\n };\n return itemTracked;\n };\n exports.reset = function() {\n idDisplay = 0;\n };\n }\n});\n\n// lib/kdTree-min.js\nvar require_kdTree_min = __commonJS({\n \"lib/kdTree-min.js\"(exports) {\n (function(root, factory) {\n if (typeof define === \"function\" && define.amd) {\n define([\"exports\"], factory);\n } else if (typeof exports === \"object\") {\n factory(exports);\n } else {\n factory(root);\n }\n })(exports, function(exports2) {\n function Node(obj, dimension, parent) {\n this.obj = obj;\n this.left = null;\n this.right = null;\n this.parent = parent;\n this.dimension = dimension;\n }\n __name(Node, \"Node\");\n function kdTree(points, metric, dimensions) {\n var self = this;\n function buildTree(points2, depth, parent) {\n var dim = depth % dimensions.length, median, node;\n if (points2.length === 0) {\n return null;\n }\n if (points2.length === 1) {\n return new Node(points2[0], dim, parent);\n }\n points2.sort(function(a, b) {\n return a[dimensions[dim]] - b[dimensions[dim]];\n });\n median = Math.floor(points2.length / 2);\n node = new Node(points2[median], dim, parent);\n node.left = buildTree(points2.slice(0, median), depth + 1, node);\n node.right = buildTree(points2.slice(median + 1), depth + 1, node);\n return node;\n }\n __name(buildTree, \"buildTree\");\n function loadTree(data) {\n self.root = data;\n function restoreParent(root) {\n if (root.left) {\n root.left.parent = root;\n restoreParent(root.left);\n }\n if (root.right) {\n root.right.parent = root;\n restoreParent(root.right);\n }\n }\n __name(restoreParent, \"restoreParent\");\n restoreParent(self.root);\n }\n __name(loadTree, \"loadTree\");\n if (!Array.isArray(points))\n loadTree(points, metric, dimensions);\n else\n this.root = buildTree(points, 0, null);\n this.toJSON = function(src) {\n if (!src)\n src = this.root;\n var dest = new Node(src.obj, src.dimension, null);\n if (src.left)\n dest.left = self.toJSON(src.left);\n if (src.right)\n dest.right = self.toJSON(src.right);\n return dest;\n };\n this.insert = function(point) {\n function innerSearch(node, parent) {\n if (node === null) {\n return parent;\n }\n var dimension2 = dimensions[node.dimension];\n if (point[dimension2] < node.obj[dimension2]) {\n return innerSearch(node.left, node);\n } else {\n return innerSearch(node.right, node);\n }\n }\n __name(innerSearch, \"innerSearch\");\n var insertPosition = innerSearch(this.root, null), newNode, dimension;\n if (insertPosition === null) {\n this.root = new Node(point, 0, null);\n return;\n }\n newNode = new Node(point, (insertPosition.dimension + 1) % dimensions.length, insertPosition);\n dimension = dimensions[insertPosition.dimension];\n if (point[dimension] < insertPosition.obj[dimension]) {\n insertPosition.left = newNode;\n } else {\n insertPosition.right = newNode;\n }\n };\n this.remove = function(point) {\n var node;\n function nodeSearch(node2) {\n if (node2 === null) {\n return null;\n }\n if (node2.obj === point) {\n return node2;\n }\n var dimension = dimensions[node2.dimension];\n if (point[dimension] < node2.obj[dimension]) {\n return nodeSearch(node2.left, node2);\n } else {\n return nodeSearch(node2.right, node2);\n }\n }\n __name(nodeSearch, \"nodeSearch\");\n function removeNode(node2) {\n var nextNode, nextObj, pDimension;\n function findMin(node3, dim) {\n var dimension, own, left, right, min;\n if (node3 === null) {\n return null;\n }\n dimension = dimensions[dim];\n if (node3.dimension === dim) {\n if (node3.left !== null) {\n return findMin(node3.left, dim);\n }\n return node3;\n }\n own = node3.obj[dimension];\n left = findMin(node3.left, dim);\n right = findMin(node3.right, dim);\n min = node3;\n if (left !== null && left.obj[dimension] < own) {\n min = left;\n }\n if (right !== null && right.obj[dimension] < min.obj[dimension]) {\n min = right;\n }\n return min;\n }\n __name(findMin, \"findMin\");\n if (node2.left === null && node2.right === null) {\n if (node2.parent === null) {\n self.root = null;\n return;\n }\n pDimension = dimensions[node2.parent.dimension];\n if (node2.obj[pDimension] < node2.parent.obj[pDimension]) {\n node2.parent.left = null;\n } else {\n node2.parent.right = null;\n }\n return;\n }\n if (node2.right !== null) {\n nextNode = findMin(node2.right, node2.dimension);\n nextObj = nextNode.obj;\n removeNode(nextNode);\n node2.obj = nextObj;\n } else {\n nextNode = findMin(node2.left, node2.dimension);\n nextObj = nextNode.obj;\n removeNode(nextNode);\n node2.right = node2.left;\n node2.left = null;\n node2.obj = nextObj;\n }\n }\n __name(removeNode, \"removeNode\");\n node = nodeSearch(self.root);\n if (node === null) {\n return;\n }\n removeNode(node);\n };\n this.nearest = function(point, maxNodes, maxDistance) {\n var i, result, bestNodes;\n bestNodes = new BinaryHeap(\n function(e) {\n return -e[1];\n }\n );\n function nearestSearch(node) {\n var bestChild, dimension = dimensions[node.dimension], ownDistance = metric(point, node.obj), linearPoint = {}, linearDistance, otherChild, i2;\n function saveNode(node2, distance) {\n bestNodes.push([node2, distance]);\n if (bestNodes.size() > maxNodes) {\n bestNodes.pop();\n }\n }\n __name(saveNode, \"saveNode\");\n for (i2 = 0; i2 < dimensions.length; i2 += 1) {\n if (i2 === node.dimension) {\n linearPoint[dimensions[i2]] = point[dimensions[i2]];\n } else {\n linearPoint[dimensions[i2]] = node.obj[dimensions[i2]];\n }\n }\n linearDistance = metric(linearPoint, node.obj);\n if (node.right === null && node.left === null) {\n if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {\n saveNode(node, ownDistance);\n }\n return;\n }\n if (node.right === null) {\n bestChild = node.left;\n } else if (node.left === null) {\n bestChild = node.right;\n } else {\n if (point[dimension] < node.obj[dimension]) {\n bestChild = node.left;\n } else {\n bestChild = node.right;\n }\n }\n nearestSearch(bestChild);\n if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {\n saveNode(node, ownDistance);\n }\n if (bestNodes.size() < maxNodes || Math.abs(linearDistance) < bestNodes.peek()[1]) {\n if (bestChild === node.left) {\n otherChild = node.right;\n } else {\n otherChild = node.left;\n }\n if (otherChild !== null) {\n nearestSearch(otherChild);\n }\n }\n }\n __name(nearestSearch, \"nearestSearch\");\n if (maxDistance) {\n for (i = 0; i < maxNodes; i += 1) {\n bestNodes.push([null, maxDistance]);\n }\n }\n if (self.root)\n nearestSearch(self.root);\n result = [];\n for (i = 0; i < Math.min(maxNodes, bestNodes.content.length); i += 1) {\n if (bestNodes.content[i][0]) {\n result.push([bestNodes.content[i][0].obj, bestNodes.content[i][1]]);\n }\n }\n return result;\n };\n this.balanceFactor = function() {\n function height(node) {\n if (node === null) {\n return 0;\n }\n return Math.max(height(node.left), height(node.right)) + 1;\n }\n __name(height, \"height\");\n function count(node) {\n if (node === null) {\n return 0;\n }\n return count(node.left) + count(node.right) + 1;\n }\n __name(count, \"count\");\n return height(self.root) / (Math.log(count(self.root)) / Math.log(2));\n };\n }\n __name(kdTree, \"kdTree\");\n function BinaryHeap(scoreFunction) {\n this.content = [];\n this.scoreFunction = scoreFunction;\n }\n __name(BinaryHeap, \"BinaryHeap\");\n BinaryHeap.prototype = {\n push: function(element) {\n this.content.push(element);\n this.bubbleUp(this.content.length - 1);\n },\n pop: function() {\n var result = this.content[0];\n var end = this.content.pop();\n if (this.content.length > 0) {\n this.content[0] = end;\n this.sinkDown(0);\n }\n return result;\n },\n peek: function() {\n return this.content[0];\n },\n remove: function(node) {\n var len = this.content.length;\n for (var i = 0; i < len; i++) {\n if (this.content[i] == node) {\n var end = this.content.pop();\n if (i != len - 1) {\n this.content[i] = end;\n if (this.scoreFunction(end) < this.scoreFunction(node))\n this.bubbleUp(i);\n else\n this.sinkDown(i);\n }\n return;\n }\n }\n throw new Error(\"Node not found.\");\n },\n size: function() {\n return this.content.length;\n },\n bubbleUp: function(n) {\n var element = this.content[n];\n while (n > 0) {\n var parentN = Math.floor((n + 1) / 2) - 1, parent = this.content[parentN];\n if (this.scoreFunction(element) < this.scoreFunction(parent)) {\n this.content[parentN] = element;\n this.content[n] = parent;\n n = parentN;\n } else {\n break;\n }\n }\n },\n sinkDown: function(n) {\n var length = this.content.length, element = this.content[n], elemScore = this.scoreFunction(element);\n while (true) {\n var child2N = (n + 1) * 2, child1N = child2N - 1;\n var swap = null;\n if (child1N < length) {\n var child1 = this.content[child1N], child1Score = this.scoreFunction(child1);\n if (child1Score < elemScore)\n swap = child1N;\n }\n if (child2N < length) {\n var child2 = this.content[child2N], child2Score = this.scoreFunction(child2);\n if (child2Score < (swap == null ? elemScore : child1Score)) {\n swap = child2N;\n }\n }\n if (swap != null) {\n this.content[n] = this.content[swap];\n this.content[swap] = element;\n n = swap;\n } else {\n break;\n }\n }\n }\n };\n exports2.kdTree = kdTree;\n exports2.BinaryHeap = BinaryHeap;\n });\n }\n});\n\n// node_modules/.pnpm/munkres-js@1.2.2/node_modules/munkres-js/munkres.js\nvar require_munkres = __commonJS({\n \"node_modules/.pnpm/munkres-js@1.2.2/node_modules/munkres-js/munkres.js\"(exports, module) {\n var MAX_SIZE = parseInt(Number.MAX_SAFE_INTEGER / 2) || (1 << 26) * (1 << 26);\n var DEFAULT_PAD_VALUE = 0;\n function Munkres() {\n this.C = null;\n this.row_covered = [];\n this.col_covered = [];\n this.n = 0;\n this.Z0_r = 0;\n this.Z0_c = 0;\n this.marked = null;\n this.path = null;\n }\n __name(Munkres, \"Munkres\");\n Munkres.prototype.pad_matrix = function(matrix, pad_value) {\n pad_value = pad_value || DEFAULT_PAD_VALUE;\n var max_columns = 0;\n var total_rows = matrix.length;\n var i;\n for (i = 0; i < total_rows; ++i)\n if (matrix[i].length > max_columns)\n max_columns = matrix[i].length;\n total_rows = max_columns > total_rows ? max_columns : total_rows;\n var new_matrix = [];\n for (i = 0; i < total_rows; ++i) {\n var row = matrix[i] || [];\n var new_row = row.slice();\n while (total_rows > new_row.length)\n new_row.push(pad_value);\n new_matrix.push(new_row);\n }\n return new_matrix;\n };\n Munkres.prototype.compute = function(cost_matrix, options) {\n options = options || {};\n options.padValue = options.padValue || DEFAULT_PAD_VALUE;\n this.C = this.pad_matrix(cost_matrix, options.padValue);\n this.n = this.C.length;\n this.original_length = cost_matrix.length;\n this.original_width = cost_matrix[0].length;\n var nfalseArray = [];\n while (nfalseArray.length < this.n)\n nfalseArray.push(false);\n this.row_covered = nfalseArray.slice();\n this.col_covered = nfalseArray.slice();\n this.Z0_r = 0;\n this.Z0_c = 0;\n this.path = this.__make_matrix(this.n * 2, 0);\n this.marked = this.__make_matrix(this.n, 0);\n var step = 1;\n var steps = {\n 1: this.__step1,\n 2: this.__step2,\n 3: this.__step3,\n 4: this.__step4,\n 5: this.__step5,\n 6: this.__step6\n };\n while (true) {\n var func = steps[step];\n if (!func)\n break;\n step = func.apply(this);\n }\n var results = [];\n for (var i = 0; i < this.original_length; ++i)\n for (var j = 0; j < this.original_width; ++j)\n if (this.marked[i][j] == 1)\n results.push([i, j]);\n return results;\n };\n Munkres.prototype.__make_matrix = function(n, val) {\n var matrix = [];\n for (var i = 0; i < n; ++i) {\n matrix[i] = [];\n for (var j = 0; j < n; ++j)\n matrix[i][j] = val;\n }\n return matrix;\n };\n Munkres.prototype.__step1 = function() {\n for (var i = 0; i < this.n; ++i) {\n var minval = Math.min.apply(Math, this.C[i]);\n for (var j = 0; j < this.n; ++j)\n this.C[i][j] -= minval;\n }\n return 2;\n };\n Munkres.prototype.__step2 = function() {\n for (var i = 0; i < this.n; ++i) {\n for (var j = 0; j < this.n; ++j) {\n if (this.C[i][j] === 0 && !this.col_covered[j] && !this.row_covered[i]) {\n this.marked[i][j] = 1;\n this.col_covered[j] = true;\n this.row_covered[i] = true;\n break;\n }\n }\n }\n this.__clear_covers();\n return 3;\n };\n Munkres.prototype.__step3 = function() {\n var count = 0;\n for (var i = 0; i < this.n; ++i) {\n for (var j = 0; j < this.n; ++j) {\n if (this.marked[i][j] == 1 && this.col_covered[j] == false) {\n this.col_covered[j] = true;\n ++count;\n }\n }\n }\n return count >= this.n ? 7 : 4;\n };\n Munkres.prototype.__step4 = function() {\n var done = false;\n var row = -1, col = -1, star_col = -1;\n while (!done) {\n var z = this.__find_a_zero();\n row = z[0];\n col = z[1];\n if (row < 0)\n return 6;\n this.marked[row][col] = 2;\n star_col = this.__find_star_in_row(row);\n if (star_col >= 0) {\n col = star_col;\n this.row_covered[row] = true;\n this.col_covered[col] = false;\n } else {\n this.Z0_r = row;\n this.Z0_c = col;\n return 5;\n }\n }\n };\n Munkres.prototype.__step5 = function() {\n var count = 0;\n this.path[count][0] = this.Z0_r;\n this.path[count][1] = this.Z0_c;\n var done = false;\n while (!done) {\n var row = this.__find_star_in_col(this.path[count][1]);\n if (row >= 0) {\n count++;\n this.path[count][0] = row;\n this.path[count][1] = this.path[count - 1][1];\n } else {\n done = true;\n }\n if (!done) {\n var col = this.__find_prime_in_row(this.path[count][0]);\n count++;\n this.path[count][0] = this.path[count - 1][0];\n this.path[count][1] = col;\n }\n }\n this.__convert_path(this.path, count);\n this.__clear_covers();\n this.__erase_primes();\n return 3;\n };\n Munkres.prototype.__step6 = function() {\n var minval = this.__find_smallest();\n for (var i = 0; i < this.n; ++i) {\n for (var j = 0; j < this.n; ++j) {\n if (this.row_covered[i])\n this.C[i][j] += minval;\n if (!this.col_covered[j])\n this.C[i][j] -= minval;\n }\n }\n return 4;\n };\n Munkres.prototype.__find_smallest = function() {\n var minval = MAX_SIZE;\n for (var i = 0; i < this.n; ++i)\n for (var j = 0; j < this.n; ++j)\n if (!this.row_covered[i] && !this.col_covered[j]) {\n if (minval > this.C[i][j])\n minval = this.C[i][j];\n }\n return minval;\n };\n Munkres.prototype.__find_a_zero = function() {\n for (var i = 0; i < this.n; ++i)\n for (var j = 0; j < this.n; ++j)\n if (this.C[i][j] === 0 && !this.row_covered[i] && !this.col_covered[j])\n return [i, j];\n return [-1, -1];\n };\n Munkres.prototype.__find_star_in_row = function(row) {\n for (var j = 0; j < this.n; ++j)\n if (this.marked[row][j] == 1)\n return j;\n return -1;\n };\n Munkres.prototype.__find_star_in_col = function(col) {\n for (var i = 0; i < this.n; ++i)\n if (this.marked[i][col] == 1)\n return i;\n return -1;\n };\n Munkres.prototype.__find_prime_in_row = function(row) {\n for (var j = 0; j < this.n; ++j)\n if (this.marked[row][j] == 2)\n return j;\n return -1;\n };\n Munkres.prototype.__convert_path = function(path, count) {\n for (var i = 0; i <= count; ++i)\n this.marked[path[i][0]][path[i][1]] = this.marked[path[i][0]][path[i][1]] == 1 ? 0 : 1;\n };\n Munkres.prototype.__clear_covers = function() {\n for (var i = 0; i < this.n; ++i) {\n this.row_covered[i] = false;\n this.col_covered[i] = false;\n }\n };\n Munkres.prototype.__erase_primes = function() {\n for (var i = 0; i < this.n; ++i)\n for (var j = 0; j < this.n; ++j)\n if (this.marked[i][j] == 2)\n this.marked[i][j] = 0;\n };\n function make_cost_matrix(profit_matrix, inversion_function) {\n var i, j;\n if (!inversion_function) {\n var maximum = -1 / 0;\n for (i = 0; i < profit_matrix.length; ++i)\n for (j = 0; j < profit_matrix[i].length; ++j)\n if (profit_matrix[i][j] > maximum)\n maximum = profit_matrix[i][j];\n inversion_function = /* @__PURE__ */ __name(function(x) {\n return maximum - x;\n }, \"inversion_function\");\n }\n var cost_matrix = [];\n for (i = 0; i < profit_matrix.length; ++i) {\n var row = profit_matrix[i];\n cost_matrix[i] = [];\n for (j = 0; j < row.length; ++j)\n cost_matrix[i][j] = inversion_function(profit_matrix[i][j]);\n }\n return cost_matrix;\n }\n __name(make_cost_matrix, \"make_cost_matrix\");\n function format_matrix(matrix) {\n var columnWidths = [];\n var i, j;\n for (i = 0; i < matrix.length; ++i) {\n for (j = 0; j < matrix[i].length; ++j) {\n var entryWidth = String(matrix[i][j]).length;\n if (!columnWidths[j] || entryWidth >= columnWidths[j])\n columnWidths[j] = entryWidth;\n }\n }\n var formatted = \"\";\n for (i = 0; i < matrix.length; ++i) {\n for (j = 0; j < matrix[i].length; ++j) {\n var s = String(matrix[i][j]);\n while (s.length < columnWidths[j])\n s = \" \" + s;\n formatted += s;\n if (j != matrix[i].length - 1)\n formatted += \" \";\n }\n if (i != matrix[i].length - 1)\n formatted += \"\\n\";\n }\n return formatted;\n }\n __name(format_matrix, \"format_matrix\");\n function computeMunkres(cost_matrix, options) {\n var m = new Munkres();\n return m.compute(cost_matrix, options);\n }\n __name(computeMunkres, \"computeMunkres\");\n computeMunkres.version = \"1.2.2\";\n computeMunkres.format_matrix = format_matrix;\n computeMunkres.make_cost_matrix = make_cost_matrix;\n computeMunkres.Munkres = Munkres;\n if (typeof module !== \"undefined\" && module.exports) {\n module.exports = computeMunkres;\n }\n }\n});\n\n// tracker.js\nvar require_tracker = __commonJS({\n \"tracker.js\"(exports) {\n var itemTrackedModule = require_ItemTracked();\n var ItemTracked = itemTrackedModule.ItemTracked;\n var kdTree = require_kdTree_min().kdTree;\n var iouAreas = require_utils().iouAreas;\n var munkres = require_munkres();\n var DEBUG_MODE = false;\n var iouDistance = /* @__PURE__ */ __name(function(item1, item2) {\n var iou = iouAreas(item1, item2);\n var distance = 1 - iou;\n if (distance > 1 - params.iouLimit) {\n distance = params.distanceLimit + 1;\n }\n return distance;\n }, \"iouDistance\");\n var params = {\n // DEFAULT_UNMATCHEDFRAMES_TOLERANCE\n // This the number of frame we wait when an object isn't matched before considering it gone\n unMatchedFramesTolerance: 5,\n // DEFAULT_IOU_LIMIT, exclude things from beeing matched if their IOU is lower than this\n // 1 means total overlap whereas 0 means no overlap\n iouLimit: 0.05,\n // Remove new objects fast if they could not be matched in the next frames.\n // Setting this to false ensures the object will stick around at least\n // unMatchedFramesTolerance frames, even if they could neven be matched in\n // subsequent frames.\n fastDelete: true,\n // The function to use to determine the distance between to detected objects\n distanceFunc: iouDistance,\n // The distance limit for matching. If values need to be excluded from\n // matching set their distance to something greater than the distance limit\n distanceLimit: 1e4,\n // The algorithm used to match tracks with new detections. Can be either\n // 'kdTree' or 'munkres'.\n matchingAlgorithm: \"kdTree\"\n };\n var mapOfItemsTracked = /* @__PURE__ */ new Map();\n var mapOfAllItemsTracked = /* @__PURE__ */ new Map();\n var keepAllHistoryInMemory = false;\n exports.computeDistance = iouDistance;\n exports.updateTrackedItemsWithNewFrame = function(detectionsOfThisFrame, frameNb) {\n var treeItemsTracked = new kdTree(Array.from(mapOfItemsTracked.values()), params.distanceFunc, [\"x\", \"y\", \"w\", \"h\"]);\n var treeDetectionsOfThisFrame = new kdTree(detectionsOfThisFrame, params.distanceFunc, [\"x\", \"y\", \"w\", \"h\"]);\n if (mapOfItemsTracked.size === 0) {\n detectionsOfThisFrame.forEach(function(itemDetected) {\n var newItemTracked = new ItemTracked(itemDetected, frameNb, params.unMatchedFramesTolerance, params.fastDelete);\n mapOfItemsTracked.set(newItemTracked.id, newItemTracked);\n treeItemsTracked.insert(newItemTracked);\n });\n } else {\n var matchedList = new Array(detectionsOfThisFrame.length);\n matchedList.fill(false);\n if (detectionsOfThisFrame.length > 0) {\n if (params.matchingAlgorithm === \"munkres\") {\n var trackedItemIds = Array.from(mapOfItemsTracked.keys());\n var costMatrix = Array.from(mapOfItemsTracked.values()).map((itemTracked) => {\n var predictedPosition = itemTracked.predictNextPosition();\n return detectionsOfThisFrame.map(\n (detection) => params.distanceFunc(predictedPosition, detection)\n );\n });\n mapOfItemsTracked.forEach(function(itemTracked) {\n itemTracked.makeAvailable();\n });\n munkres(costMatrix).filter((m) => costMatrix[m[0]][m[1]] <= params.distanceLimit).forEach((m) => {\n var itemTracked = mapOfItemsTracked.get(trackedItemIds[m[0]]);\n var updatedTrackedItemProperties = detectionsOfThisFrame[m[1]];\n matchedList[m[1]] = { idDisplay: itemTracked.idDisplay };\n itemTracked.makeUnavailable().update(updatedTrackedItemProperties, frameNb);\n });\n matchedList.forEach(function(matched, index) {\n if (!matched) {\n if (Math.min(...costMatrix.map((m) => m[index])) > params.distanceLimit) {\n var newItemTracked = ItemTracked(detectionsOfThisFrame[index], frameNb, params.unMatchedFramesTolerance, params.fastDelete);\n mapOfItemsTracked.set(newItemTracked.id, newItemTracked);\n newItemTracked.makeUnavailable();\n costMatrix.push(detectionsOfThisFrame.map(\n (detection) => params.distanceFunc(newItemTracked, detection)\n ));\n }\n }\n });\n } else if (params.matchingAlgorithm === \"kdTree\") {\n mapOfItemsTracked.forEach(function(itemTracked) {\n var predictedPosition = itemTracked.predictNextPosition();\n itemTracked.makeAvailable();\n var treeSearchResult = treeDetectionsOfThisFrame.nearest(predictedPosition, 1, params.distanceLimit)[0];\n var treeSearchResultWithoutPrediction = treeDetectionsOfThisFrame.nearest(itemTracked, 1, params.distanceLimit)[0];\n var treeSearchMultipleResults = treeDetectionsOfThisFrame.nearest(predictedPosition, 2, params.distanceLimit);\n if (treeSearchResult) {\n var indexClosestNewDetectedItem = detectionsOfThisFrame.indexOf(treeSearchResult[0]);\n if (!matchedList[indexClosestNewDetectedItem]) {\n matchedList[indexClosestNewDetectedItem] = {\n idDisplay: itemTracked.idDisplay\n };\n var updatedTrackedItemProperties = detectionsOfThisFrame[indexClosestNewDetectedItem];\n mapOfItemsTracked.get(itemTracked.id).makeUnavailable().update(updatedTrackedItemProperties, frameNb);\n } else {\n }\n }\n });\n } else {\n throw `Unknown matching algorithm \"${params.matchingAlgorithm}\"`;\n }\n } else {\n if (DEBUG_MODE) {\n console.log(\"[Tracker] Nothing detected for frame n\\xBA\" + frameNb);\n }\n mapOfItemsTracked.forEach(function(itemTracked) {\n itemTracked.makeAvailable();\n });\n }\n if (params.matchingAlgorithm === \"kdTree\") {\n if (mapOfItemsTracked.size > 0) {\n treeItemsTracked = new kdTree(Array.from(mapOfItemsTracked.values()), params.distanceFunc, [\"x\", \"y\", \"w\", \"h\"]);\n matchedList.forEach(function(matched, index) {\n if (!matched) {\n var treeSearchResult = treeItemsTracked.nearest(detectionsOfThisFrame[index], 1, params.distanceLimit)[0];\n if (!treeSearchResult) {\n var newItemTracked = ItemTracked(detectionsOfThisFrame[index], frameNb, params.unMatchedFramesTolerance, params.fastDelete);\n mapOfItemsTracked.set(newItemTracked.id, newItemTracked);\n treeItemsTracked.insert(newItemTracked);\n newItemTracked.makeUnavailable();\n } else {\n }\n }\n });\n }\n }\n mapOfItemsTracked.forEach(function(itemTracked) {\n if (itemTracked.available) {\n itemTracked.countDown(frameNb);\n itemTracked.updateTheoricalPositionAndSize();\n if (itemTracked.isDead()) {\n mapOfItemsTracked.delete(itemTracked.id);\n treeItemsTracked.remove(itemTracked);\n if (keepAllHistoryInMemory) {\n mapOfAllItemsTracked.set(itemTracked.id, itemTracked);\n }\n }\n }\n });\n }\n };\n exports.reset = function() {\n mapOfItemsTracked = /* @__PURE__ */ new Map();\n mapOfAllItemsTracked = /* @__PURE__ */ new Map();\n itemTrackedModule.reset();\n };\n exports.setParams = function(newParams) {\n Object.keys(newParams).forEach((key) => {\n params[key] = newParams[key];\n });\n };\n exports.enableKeepInMemory = function() {\n keepAllHistoryInMemory = true;\n };\n exports.disableKeepInMemory = function() {\n keepAllHistoryInMemory = false;\n };\n exports.getJSONOfTrackedItems = function(roundInt = true) {\n return Array.from(mapOfItemsTracked.values()).map(function(itemTracked) {\n return itemTracked.toJSON(roundInt);\n });\n };\n exports.getJSONDebugOfTrackedItems = function(roundInt = true) {\n return Array.from(mapOfItemsTracked.values()).map(function(itemTracked) {\n return itemTracked.toJSONDebug(roundInt);\n });\n };\n exports.getTrackedItemsInMOTFormat = function(frameNb) {\n return Array.from(mapOfItemsTracked.values()).map(function(itemTracked) {\n return itemTracked.toMOT(frameNb);\n });\n };\n exports.getAllTrackedItems = function() {\n return mapOfAllItemsTracked;\n };\n exports.getJSONOfAllTrackedItems = function() {\n return Array.from(mapOfAllItemsTracked.values()).map(function(itemTracked) {\n return itemTracked.toJSONGenericInfo();\n });\n };\n }\n});\nexport default require_tracker();\n/**\n * k-d Tree JavaScript - V 1.01\n *\n * https://github.com/ubilabs/kd-tree-javascript\n *\n * @author Mircea Pricop , 2012\n * @author Martin Kleppe , 2012\n * @author Ubilabs http://ubilabs.net, 2012\n * @license MIT License \n */\n"], - "mappings": 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"nfalseArray", "step", "steps", "func", "results", "j", "val", "minval", "done", "col", "star_col", "z", "path", "make_cost_matrix", "profit_matrix", "inversion_function", "maximum", "x", "format_matrix", "columnWidths", "entryWidth", "formatted", "s", "computeMunkres", "m", "require_tracker", "itemTrackedModule", "ItemTracked", "iouAreas", "munkres", "DEBUG_MODE", "iouDistance", "iou", "params", "mapOfItemsTracked", "mapOfAllItemsTracked", "keepAllHistoryInMemory", "detectionsOfThisFrame", "treeItemsTracked", "treeDetectionsOfThisFrame", "itemDetected", "newItemTracked", "matchedList", "trackedItemIds", "costMatrix", "predictedPosition", "updatedTrackedItemProperties", "matched", "index", "treeSearchResult", "treeSearchResultWithoutPrediction", "treeSearchMultipleResults", "indexClosestNewDetectedItem", "newParams", "key", "tracker_default", "humanConfig", "trackerConfig", "human", "dom", "timestamp", "fps", "log", "msg", "status", "detectionLoop", "tensors", "drawLoop", "res", "tracking", "data", "items", "obj", "tracker_default", "i", "name", "now", "handleVideo", "file", "url", "initInput", "evt", "_a", "_b", "main"] } diff --git a/demo/typescript/index.js.map b/demo/typescript/index.js.map index 66c7c38d..0d0679e0 100644 --- a/demo/typescript/index.js.map +++ b/demo/typescript/index.js.map @@ -2,6 +2,6 @@ "version": 3, "sources": ["index.ts"], "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst width = 1920; // used by webcam config as well as human maximum resultion // can be anything, but resolutions higher than 4k will disable internal optimizations\n\nconst humanConfig: Partial = { // user configuration for human, used to fine-tune behavior\n debug: true,\n backend: 'webgl',\n // cacheSensitivity: 0,\n // cacheModels: false,\n // warmup: 'none',\n // modelBasePath: '../../models',\n modelBasePath: 'https://vladmandic.github.io/human-models/models/',\n filter: { enabled: true, equalization: false, flip: false },\n face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },\n body: { enabled: false },\n hand: { enabled: false },\n object: { enabled: false },\n segmentation: { enabled: false },\n gesture: { enabled: true },\n};\n\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\nhuman.draw.options.drawPoints = true; // draw points on face mesh\n// human.draw.options.fillPolygons = true;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('status') as HTMLPreElement,\n perf: document.getElementById('performance') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors.toString() + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused) {\n if (timestamp.start === 0) timestamp.start = human.now();\n // log('profiling data:', await human.profile(dom.video));\n await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n timestamp.tensors = tensors;\n fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n fps.frames++;\n fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n if (fps.frames % 100 === 0 && !dom.video.paused) log('performance', { ...fps, tensors: timestamp.tensors });\n }\n timestamp.detect = human.now();\n requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nasync function drawLoop() { // main screen refresh loop\n if (!dom.video.paused) {\n const interpolated = human.next(human.result); // smoothen result using last-known results\n const processed = await human.image(dom.video); // get current video frame, but enhanced with human.filters\n human.draw.canvas(processed.canvas as HTMLCanvasElement, dom.canvas);\n\n const opt: Partial = { bodyLabels: `person confidence [score] and ${human.result?.body?.[0]?.keypoints.length} keypoints` };\n await human.draw.all(dom.canvas, interpolated, opt); // draw labels, boxes, lines, etc.\n perf(interpolated.performance); // write performance data\n }\n const now = human.now();\n fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n timestamp.draw = now;\n status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status\n setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function webCam() {\n const devices = await human.webcam.enumerate();\n const id = devices[0].deviceId; // use first available video source\n const webcamStatus = await human.webcam.start({ element: dom.video, crop: false, width, id }); // use human webcam helper methods and associate webcam stream with a dom element\n log(webcamStatus);\n dom.canvas.width = human.webcam.width;\n dom.canvas.height = human.webcam.height;\n dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click\n if (human.webcam.paused) await human.webcam.play();\n else human.webcam.pause();\n };\n}\n\nasync function main() { // main entry point\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('platform:', human.env.platform, '| agent:', human.env.agent);\n status('loading...');\n await human.load(); // preload all models\n log('backend:', human.tf.getBackend(), '| available:', human.env.backends);\n log('models stats:', human.models.stats());\n log('models loaded:', human.models.loaded());\n log('environment', human.env);\n status('initializing...');\n await human.warmup(); // warmup function to initialize backend for future faster detection\n await webCam(); // start webcam\n await detectionLoop(); // start detection loop\n await drawLoop(); // start draw loop\n}\n\nwindow.onload = main;\n"], - "mappings": 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Lr=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],Or=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],Cr=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],Wr=[[474,475],[475,476],[476,477],[477,474]],Dr=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],Fr=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],Br=[[469,470],[470,471],[471,472],[472,469]],Hr=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function se(e){let t=e.map(n=>n[0]);return t.push(e[e.length-1][1]),t}var Gr={lips:se(Lr),leftEye:se(Or),leftEyebrow:se(Cr),leftIris:se(Wr),rightEye:se(Dr),rightEyebrow:se(Fr),rightIris:se(Br),faceOval:se(Hr)},Vr=Object.entries(Gr).map(([e,t])=>t.map(n=>[n,e])).flat(),ea=new Map(Vr),n2=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],Me=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],Pe=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];var B;function Zr(e,t){var r,s,A,a,i,c,d,y,l;if(!B.drawLabels||((r=B.faceLabels)==null?void 0:r.length)===0)return;let n=B.faceLabels.slice();if(n=H(n,"[id]",e.id.toFixed(0)),e.score&&(n=H(n,"[score]",100*e.score)),e.gender&&(n=H(n,"[gender]",e.gender)),e.genderScore&&(n=H(n,"[genderScore]",100*e.genderScore)),e.age&&(n=H(n,"[age]",e.age)),e.distance&&(n=H(n,"[distance]",100*e.distance)),e.real&&(n=H(n,"[real]",100*e.real)),e.live&&(n=H(n,"[live]",100*e.live)),e.emotion&&e.emotion.length>0){let m=e.emotion.map(x=>`${Math.trunc(100*x.score)}% ${x.emotion}`);m.length>3&&(m.length=3),n=H(n,"[emotions]",m.join(" "))}(A=(s=e.rotation)==null?void 0:s.angle)!=null&&A.roll&&(n=H(n,"[roll]",ge(e.rotation.angle.roll))),(i=(a=e.rotation)==null?void 0:a.angle)!=null&&i.yaw&&(n=H(n,"[yaw]",ge(e.rotation.angle.yaw))),(d=(c=e.rotation)==null?void 0:c.angle)!=null&&d.pitch&&(n=H(n,"[pitch]",ge(e.rotation.angle.pitch))),(l=(y=e.rotation)==null?void 0:y.gaze)!=null&&l.bearing&&(n=H(n,"[gaze]",ge(e.rotation.gaze.bearing))),C0(t,n,e.box[0],e.box[1],B)}function Xr(e,t){var n,r,s,A;if((n=e.annotations)!=null&&n.leftEyeIris&&((r=e.annotations)!=null&&r.leftEyeIris[0])){t.strokeStyle=B.useDepth?"rgba(255, 200, 255, 0.3)":B.color,t.beginPath();let a=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,i=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],a,i,0,0,2*Math.PI),t.stroke(),B.fillPolygons&&(t.fillStyle=B.useDepth?"rgba(255, 255, 200, 0.3)":B.color,t.fill())}if((s=e.annotations)!=null&&s.rightEyeIris&&((A=e.annotations)!=null&&A.rightEyeIris[0])){t.strokeStyle=B.useDepth?"rgba(255, 200, 255, 0.3)":B.color,t.beginPath();let a=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,i=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],a,i,0,0,2*Math.PI),t.stroke(),B.fillPolygons&&(t.fillStyle=B.useDepth?"rgba(255, 255, 200, 0.3)":B.color,t.fill())}}function qr(e,t){var n;if(B.drawGaze&&((n=e.rotation)!=null&&n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let r=e.box[0]+e.box[2]/2-e.box[3]*ge(e.rotation.angle.yaw)/90,s=e.box[1]+e.box[3]/2+e.box[2]*ge(e.rotation.angle.pitch)/90,A=new Path2D(` M ${e.box[0]+e.box[2]/2} ${e.box[1]} C - ${o} ${e.box[1]}, - ${o} ${e.box[1]+e.box[3]}, + ${r} ${e.box[1]}, + ${r} ${e.box[1]+e.box[3]}, ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]} `),a=new Path2D(` M ${e.box[0]} ${e.box[1]+e.box[3]/2} @@ -109,7 +109,7 @@ var at=Object.defineProperty;var Jn=Object.getOwnPropertyDescriptor;var Qn=Objec ${e.box[0]} ${s}, ${e.box[0]+e.box[2]} ${s}, ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} - `);t.stroke(a),t.stroke(A)}}function qr(e,t){var n;if(B.drawGaze&&((n=e.rotation)!=null&&n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];mt(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let s=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];mt(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[s[0],s[1]],4)}}function Ur(e,t){if(B.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;ne.mesh[s]);ft(t,o,B)}Zr(e,t)}}function Yr(e,t){if(B.drawPoints)if((e==null?void 0:e.mesh.length)>=468)for(let n=0;n0&&(Yr(s,o),Ur(s,o),Xr(s,o),qr(s,o))}}function g2(e,t,n){var A,a;let o=Q(n0,n);if(!t||!e)return;let s=O0(e);if(s){s.lineJoin="round";for(let i=0;i0)){let c=o.bodyLabels.slice();c=H(c,"[id]",t[i].id.toFixed(0)),c=H(c,"[score]",100*t[i].score),C0(s,c,t[i].box[0],t[i].box[1],o)}if(o.drawPoints&&t[i].keypoints)for(let c=0;c0&&t[i].keypoints){s.font=o.font;for(let c of t[i].keypoints){if(!c.score||c.score===0)continue;let d=o.bodyPartLabels.slice();d=H(d,"[label]",c.part),d=H(d,"[score]",100*c.score),C0(s,d,c.position[0],c.position[1],o)}}if(o.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let c of Object.values(t[i].annotations))for(let d of c)i1(s,d,o)}}}function T2(e,t,n){var A,a;let o=Q(n0,n);if(!t||!e)return;let s=O0(e);if(s){s.lineJoin="round",s.font=o.font;for(let i of t){if(o.drawBoxes){if(s.strokeStyle=o.color,s.fillStyle=o.color,Y0(s,i.box[0],i.box[1],i.box[2],i.box[3],o),o.drawLabels&&((A=o.handLabels)==null?void 0:A.length)>0){let c=o.handLabels.slice();c=H(c,"[id]",i.id.toFixed(0)),c=H(c,"[label]",i.label),c=H(c,"[score]",100*i.score),C0(s,c,i.box[0],i.box[1],o)}s.stroke()}if(o.drawPoints&&i.keypoints&&i.keypoints.length>0)for(let c of i.keypoints)s.fillStyle=Te(c[2],o),U0(s,c[0],c[1],0,o);if(o.drawLabels&&i.annotations&&((a=o.fingerLabels)==null?void 0:a.length)>0)for(let[c,d]of Object.entries(i.annotations)){let y=o.fingerLabels.slice();y=H(y,"[label]",c),C0(s,y,d[d.length-1][0],d[d.length-1][1],o)}if(o.drawPolygons&&i.annotations){let c=d=>{if(!(!d||d.length===0||!d[0]))for(let y=0;y0?y-1:0][0],d[y>0?y-1:0][1]),s.lineTo(d[y][0],d[y][1]),s.stroke()}};s.lineWidth=o.lineWidth,c(i.annotations.index),c(i.annotations.middle),c(i.annotations.ring),c(i.annotations.pinky),c(i.annotations.thumb)}}}}function v2(e,t,n){var A;let o=Q(n0,n);if(!t||!e)return;let s=O0(e);if(s){s.lineJoin="round",s.font=o.font;for(let a of t)if(o.drawBoxes){if(s.strokeStyle=o.color,s.fillStyle=o.color,Y0(s,a.box[0],a.box[1],a.box[2],a.box[3],o),o.drawLabels&&((A=o.objectLabels)==null?void 0:A.length)>0){let i=o.objectLabels.slice();i=H(i,"[id]",a.id.toFixed(0)),i=H(i,"[label]",a.label),i=H(i,"[score]",100*a.score),C0(s,i,a.box[0],a.box[1],o)}s.stroke()}}}function R2(e,t,n){var s;let o=Q(n0,n);if(!(!t||!e)&&o.drawGestures&&((s=o.gestureLabels)==null?void 0:s.length)>0){let A=O0(e);if(!A)return;A.font=o.font,A.fillStyle=o.color;let a=1;for(let i=0;i1&&d[1].length>0){let y=c[1]>0?`#${c[1]}`:"",l=o.gestureLabels.slice();l=H(l,"[where]",c[0]),l=H(l,"[who]",y),l=H(l,"[what]",d[1]),C0(A,l,8,2+a*o.lineHeight,o),a+=1}}}}var Ae={face:`face + `);t.stroke(a),t.stroke(A)}}function Ur(e,t){var n;if(B.drawGaze&&((n=e.rotation)!=null&&n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let r=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];pt(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[r[0],r[1]],4);let s=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];pt(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[s[0],s[1]],4)}}function Yr(e,t){if(B.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;ne.mesh[s]);mt(t,r,B)}Xr(e,t)}}function Kr(e,t){if(B.drawPoints)if((e==null?void 0:e.mesh.length)>=468)for(let n=0;n0&&(Kr(s,r),Yr(s,r),qr(s,r),Ur(s,r))}}function g2(e,t,n){var A,a;let r=Q(n0,n);if(!t||!e)return;let s=O0(e);if(s){s.lineJoin="round";for(let i=0;i0)){let c=r.bodyLabels.slice();c=H(c,"[id]",t[i].id.toFixed(0)),c=H(c,"[score]",100*t[i].score),C0(s,c,t[i].box[0],t[i].box[1],r)}if(r.drawPoints&&t[i].keypoints)for(let c=0;c0&&t[i].keypoints){s.font=r.font;for(let c of t[i].keypoints){if(!c.score||c.score===0)continue;let d=r.bodyPartLabels.slice();d=H(d,"[label]",c.part),d=H(d,"[score]",100*c.score),C0(s,d,c.position[0],c.position[1],r)}}if(r.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let c of Object.values(t[i].annotations))for(let d of c)l1(s,d,r)}}}function T2(e,t,n){var A,a;let r=Q(n0,n);if(!t||!e)return;let s=O0(e);if(s){s.lineJoin="round",s.font=r.font;for(let i of t){if(r.drawBoxes){if(s.strokeStyle=r.color,s.fillStyle=r.color,Y0(s,i.box[0],i.box[1],i.box[2],i.box[3],r),r.drawLabels&&((A=r.handLabels)==null?void 0:A.length)>0){let c=r.handLabels.slice();c=H(c,"[id]",i.id.toFixed(0)),c=H(c,"[label]",i.label),c=H(c,"[score]",100*i.score),C0(s,c,i.box[0],i.box[1],r)}s.stroke()}if(r.drawPoints&&i.keypoints&&i.keypoints.length>0)for(let c of i.keypoints)s.fillStyle=Te(c[2],r),U0(s,c[0],c[1],0,r);if(r.drawLabels&&i.annotations&&((a=r.fingerLabels)==null?void 0:a.length)>0)for(let[c,d]of Object.entries(i.annotations)){let y=r.fingerLabels.slice();y=H(y,"[label]",c),C0(s,y,d[d.length-1][0],d[d.length-1][1],r)}if(r.drawPolygons&&i.annotations){let c=d=>{if(!(!d||d.length===0||!d[0]))for(let y=0;y0?y-1:0][0],d[y>0?y-1:0][1]),s.lineTo(d[y][0],d[y][1]),s.stroke()}};s.lineWidth=r.lineWidth,c(i.annotations.index),c(i.annotations.middle),c(i.annotations.ring),c(i.annotations.pinky),c(i.annotations.thumb)}}}}function v2(e,t,n){var A;let r=Q(n0,n);if(!t||!e)return;let s=O0(e);if(s){s.lineJoin="round",s.font=r.font;for(let a of t)if(r.drawBoxes){if(s.strokeStyle=r.color,s.fillStyle=r.color,Y0(s,a.box[0],a.box[1],a.box[2],a.box[3],r),r.drawLabels&&((A=r.objectLabels)==null?void 0:A.length)>0){let i=r.objectLabels.slice();i=H(i,"[id]",a.id.toFixed(0)),i=H(i,"[label]",a.label),i=H(i,"[score]",100*a.score),C0(s,i,a.box[0],a.box[1],r)}s.stroke()}}}function R2(e,t,n){var s;let r=Q(n0,n);if(!(!t||!e)&&r.drawGestures&&((s=r.gestureLabels)==null?void 0:s.length)>0){let A=O0(e);if(!A)return;A.font=r.font,A.fillStyle=r.color;let a=1;for(let i=0;i1&&d[1].length>0){let y=c[1]>0?`#${c[1]}`:"",l=r.gestureLabels.slice();l=H(l,"[where]",c[0]),l=H(l,"[who]",y),l=H(l,"[what]",d[1]),C0(A,l,8,2+a*r.lineHeight,r),a+=1}}}}var Ae={face:`face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -118,7 +118,7 @@ var at=Object.defineProperty;var Jn=Object.getOwnPropertyDescriptor;var Qn=Objec live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] 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vt=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],Rt={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var W0,ke=224,d1,$r=5,P2=[8,16,32,32,32];function es(){let e=[],t=0;for(;t<$r;){let n=0,o=t;for(;on.x)),y:r.tensor1d(e.map(n=>n.y))}}async function x1(e){if(M.initial&&(W0=null),!W0&&e.body.detector&&e.body.detector.modelPath){W0=await L(e.body.detector.modelPath);let t=W0!=null&&W0.executor?Object.values(W0.modelSignature.inputs):void 0;ke=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&W0&&b("cached model:",W0.modelUrl);return es(),W0}var c1=[5,5];function ts(e,t){return r.tidy(()=>{let n=r.split(e,12,1),o=r.squeeze(n[0]),s=r.squeeze(n[1]),A=r.squeeze(n[2]),a=r.squeeze(n[3]);o=r.add(r.div(o,ke),t.x),s=r.add(r.div(s,ke),t.y),A=r.mul(r.div(A,ke),c1[0]),a=r.mul(r.div(a,ke),c1[1]);let i=r.sub(o,r.div(A,2)),c=r.sub(s,r.div(a,2)),d=r.add(i,A),y=r.add(c,a);return r.stack([i,c,d,y],1)})}async function ns(e,t,n,o){var d,y;let s=[],A={};A.boxes=ts(e,d1),A.scores=r.sigmoid(t),A.nms=await r.image.nonMaxSuppressionAsync(A.boxes,A.scores,1,((d=n.body.detector)==null?void 0:d.minConfidence)||.1,((y=n.body.detector)==null?void 0:y.iouThreshold)||.1);let a=await A.nms.data(),i=await A.scores.data(),c=await A.boxes.array();for(let l of Array.from(a)){let m=i[l],x=c[l],p=[Math.round(x[0]*o[0]),Math.round(x[1]*o[1]),Math.round(x[2]*o[0]),Math.round(x[3]*o[1])],f={score:m,boxRaw:x,box:p};s.push(f)}return Object.keys(A).forEach(l=>r.dispose(A[l])),s}async function y1(e,t,n){let o={};o.res=W0==null?void 0:W0.execute(e,["Identity"]),o.logitsRaw=r.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=r.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=r.squeeze(o.logitsRaw),o.boxes=r.squeeze(o.boxesRaw);let s=await ns(o.boxes,o.logits,t,n);return Object.keys(o).forEach(A=>r.dispose(o[A])),s}function ae(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],o=[Math.min(...n[0]),Math.min(...n[1])],s=[Math.max(...n[0]),Math.max(...n[1])],A=[o[0],o[1],s[0]-o[0],s[1]-o[1]],a=[A[0]/t[0],A[1]/t[1],A[2]/t[0],A[3]/t[1]];return{box:A,boxRaw:a}}function f1(e,t=[1,1]){let n=[e.map(d=>d[0]),e.map(d=>d[1])],o=[Math.min(...n[0]),Math.min(...n[1])],s=[Math.max(...n[0]),Math.max(...n[1])],A=[(o[0]+s[0])/2,(o[1]+s[1])/2],a=Math.max(A[0]-o[0],A[1]-o[1],-A[0]+s[0],-A[1]+s[1]),i=[Math.trunc(A[0]-a),Math.trunc(A[1]-a),Math.trunc(2*a),Math.trunc(2*a)],c=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:c}}function k2(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var P0,Pt=256,Mt=Number.MAX_SAFE_INTEGER,os={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},E2=[],ie=[[0,0],[0,0],[0,0],[0,0]],m1=0,p1=e=>1-1/(1+Math.exp(e)),h1=e=>x1(e);async function b1(e){if(M.initial&&(P0=null),P0)e.debug&&b("cached model:",P0.modelUrl);else{P0=await L(e.body.modelPath);let t=P0!=null&&P0.executor?Object.values(P0.modelSignature.inputs):void 0;Pt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return P0}function u1(e,t,n){var A,a;let o={};if(!((A=e==null?void 0:e.shape)!=null&&A[1])||!((a=e==null?void 0:e.shape)!=null&&a[2]))return e;let s;if(n&&(o.cropped=r.image.cropAndResize(e,[n],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let i=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],c=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];ie=[[0,0],i,c,[0,0]],o.pad=r.pad(o.cropped||e,ie),o.resize=r.image.resizeBilinear(o.pad,[t,t]),s=r.div(o.resize,O.tf255)}else e.shape[1]!==t?(o.resize=r.image.resizeBilinear(o.cropped||e,[t,t]),s=r.div(o.resize,O.tf255)):s=r.div(o.cropped||e,O.tf255);return Object.keys(o).forEach(i=>r.dispose(o[i])),s}function rs(e,t,n){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+ie[2][0]+ie[2][1])/t[0]-ie[2][0]),Math.trunc(o.position[1]*(t[1]+ie[1][0]+ie[1][1])/t[1]-ie[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(n){let o=n[2]-n[0],s=n[3]-n[1];for(let A of e)A.positionRaw=[A.positionRaw[0]/s+n[1],A.positionRaw[1]/o+n[0],A.positionRaw[2]],A.position=[Math.trunc(A.positionRaw[0]*t[0]),Math.trunc(A.positionRaw[1]*t[1]),A.positionRaw[2]]}return e}function ss(e){let t=e.find(i=>i.part==="leftPalm"),n=e.find(i=>i.part==="leftWrist"),o=e.find(i=>i.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let s=e.find(i=>i.part==="rightPalm"),A=e.find(i=>i.part==="rightWrist"),a=e.find(i=>i.part==="rightIndex");s.position[2]=((A.position[2]||0)+(a.position[2]||0))/2}async function As(e,t,n){if(!(P0!=null&&P0.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=P0==null?void 0:P0.execute(e,os.landmarks);let s=(await 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A=[e.shape[2]||0,e.shape[1]||0],a=r.image.resizeBilinear(e,[we,we]),i=t.object.enabled?k0==null?void 0:k0.execute(a,["tower_0/detections"]):null;T1=R(),r.dispose(a);let c=await as(i,A,t);wt=c,s(c)}))}var z2={};oe(z2,{connected:()=>jt,kpt:()=>St});var St=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],jt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var _,M1=0,v0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},It=Number.MAX_SAFE_INTEGER;async function P1(e){return M.initial&&(_=null),_?e.debug&&b("cached model:",_.modelUrl):_=await L(e.body.modelPath),_}async function is(e,t){let[n,o]=e.shape,s=r.reshape(e,[o*n]),A=r.max(s,0),a=(await A.data())[0];if(a>t){let i=r.argMax(s,0),c=r.mod(i,n),d=(await c.data())[0],y=r.div(i,n),l=(await y.data())[0];return r.dispose([s,A,i,c,y]),[d,l,a]}return r.dispose([s,A]),[0,0,a]}async function Nt(e,t){if(!(_!=null&&_.executor)||!(_!=null&&_.inputs[0].shape))return[];let n=(t.body.skipTime||0)>R()-M1,o=It<(t.body.skipFrames||0);return t.skipAllowed&&n&&o&&Object.keys(v0.keypoints).length>0?(It++,[v0]):(It=0,new Promise(async s=>{let A=r.tidy(()=>{var p,f;let l=r.image.resizeBilinear(e,[((p=_==null?void 0:_.inputs[0].shape)==null?void 0:p[2])||0,((f=_==null?void 0:_.inputs[0].shape)==null?void 0:f[1])||0],!1),m=r.mul(l,O.tf2);return r.sub(m,O.tf1)}),a;if(t.body.enabled&&(a=_==null?void 0:_.execute(A)),M1=R(),r.dispose(A),a){v0.keypoints.length=0;let l=r.squeeze(a);r.dispose(a);let m=r.unstack(l,2);r.dispose(l);for(let x=0;x(t.body.minConfidence||0)&&v0.keypoints.push({score:Math.round(100*h)/100,part:St[x],positionRaw:[p/_.inputs[0].shape[2],f/_.inputs[0].shape[1]],position:[Math.round(e.shape[2]*p/_.inputs[0].shape[2]),Math.round(e.shape[1]*f/_.inputs[0].shape[1])]})}m.forEach(x=>r.dispose(x))}v0.score=v0.keypoints.reduce((l,m)=>m.score>l?m.score:l,0);let i=v0.keypoints.map(l=>l.position[0]),c=v0.keypoints.map(l=>l.position[1]);v0.box=[Math.min(...i),Math.min(...c),Math.max(...i)-Math.min(...i),Math.max(...c)-Math.min(...c)];let d=v0.keypoints.map(l=>l.positionRaw[0]),y=v0.keypoints.map(l=>l.positionRaw[1]);v0.boxRaw=[Math.min(...d),Math.min(...y),Math.max(...d)-Math.min(...d),Math.max(...y)-Math.min(...y)];for(let[l,m]of Object.entries(jt)){let x=[];for(let p=0;pv.part===m[p]),h=v0.keypoints.find(v=>v.part===m[p+1]);f&&h&&f.score>(t.body.minConfidence||0)&&h.score>(t.body.minConfidence||0)&&x.push([f.position,h.position])}v0.annotations[l]=x}s([v0])}))}var Fe=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],S2=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],j2=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],I2=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],z1=(e,t,n)=>{let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],A=e.landmarks.map(a=>[(a[0]+n[0])*t[0],(a[1]+n[1])*t[1]]);return{startPoint:o,endPoint:s,landmarks:A,confidence:e.confidence}},Lt=(e,t,n)=>{let 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d=S2(t),y=[d[0]/n.shape[2],d[1]/n.shape[1]],l=r.image.rotateWithOffset(n,A,0,[y[0],y[1]]);a=j1(-A,d),i=Lt(t,l,[o,o]),r.dispose(l)}else i=Lt(t,n,[o,o]);else i=Lt(t,n,[o,o]);return[A,a,i]}var fs=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},O1=(e,t)=>{let n=fs(e),o=Fe(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var C1=6,ms=1.4,G0,O2=null,le=0,Be=null,W1=()=>le;async function D1(e){var t;return M.initial&&(G0=null),G0?e.debug&&b("cached model:",G0.modelUrl):G0=await L((t=e.face.detector)==null?void 0:t.modelPath),le=G0.executor&&G0.inputs[0].shape?G0.inputs[0].shape[2]:256,Be=r.scalar(le,"int32"),O2=r.tensor2d(I1(le)),G0}function ps(e){if(!O2||!Be)return r.zeros([0,0]);let t={};t.boxStarts=r.slice(e,[0,1],[-1,2]),t.centers=r.add(t.boxStarts,O2),t.boxSizes=r.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=r.div(t.boxSizes,Be),t.centersNormalized=r.div(t.centers,Be),t.halfBoxSize=r.div(t.boxSizesNormalized,O.tf2),t.starts=r.sub(t.centersNormalized,t.halfBoxSize),t.ends=r.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=r.mul(t.starts,Be),t.endNormalized=r.mul(t.ends,Be);let n=r.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>r.dispose(t[o])),n}async function F1(e,t){var i,c,d,y,l,m;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=r.image.resizeBilinear(e,[le,le]),n.div=r.div(n.resized,O.tf127),n.normalized=r.sub(n.div,O.tf05);let o=G0==null?void 0:G0.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let x=o.sort((p,f)=>p.size-f.size);n.concat384=r.concat([x[0],x[2]],2),n.concat512=r.concat([x[1],x[3]],2),n.concat=r.concat([n.concat512,n.concat384],1),n.batch=r.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=r.squeeze(o[0]):n.batch=r.squeeze(o);r.dispose(o),n.boxes=ps(n.batch),n.logits=r.slice(n.batch,[0,0],[-1,1]),n.sigmoid=r.sigmoid(n.logits),n.scores=r.squeeze(n.sigmoid),n.nms=await r.image.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((c=t.face.detector)==null?void 0:c.iouThreshold)||0,((d=t.face.detector)==null?void 0:d.minConfidence)||0);let s=await n.nms.array(),A=[],a=await n.scores.data();for(let x=0;x(((y=t.face.detector)==null?void 0:y.minConfidence)||0)){let f={};f.bbox=r.slice(n.boxes,[s[x],0],[1,-1]),f.slice=r.slice(n.batch,[s[x],C1-1],[1,-1]),f.squeeze=r.squeeze(f.slice),f.landmarks=r.reshape(f.squeeze,[C1,-1]);let h=await f.bbox.data(),v={startPoint:[h[0],h[1]],endPoint:[h[2],h[3]],landmarks:await f.landmarks.array(),confidence:p};f.anchor=r.slice(O2,[s[x],0],[1,2]);let T=await f.anchor.data(),u=z1(v,[(e.shape[2]||0)/le,(e.shape[1]||0)/le],T),g=N2(u,t.face.scale||ms),w=L2(g);w.size[0]>(((l=t.face.detector)==null?void 0:l.minSize)||0)&&w.size[1]>(((m=t.face.detector)==null?void 0:m.minSize)||0)&&A.push(w),Object.keys(f).forEach(k=>r.dispose(f[k]))}}return Object.keys(n).forEach(x=>r.dispose(n[x])),A}var z0,ce=0,us=2.3,Wt=F0.leftEyeLower0,Dt=F0.rightEyeLower0,He={leftBounds:[Wt[0],Wt[Wt.length-1]],rightBounds:[Dt[0],Dt[Dt.length-1]]},Ge={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function Z1(e){var t,n;return M.initial&&(z0=null),z0?e.debug&&b("cached model:",z0.modelUrl):z0=await L((t=e.face.iris)==null?void 0:t.modelPath),ce=z0!=null&&z0.executor&&((n=z0.inputs)!=null&&n[0].shape)?z0.inputs[0].shape[2]:0,ce===-1&&(ce=64),z0}function C2(e,t,n,o){for(let s=0;s{let t=e[He.leftBounds[0]][2],n=e[He.rightBounds[0]][2];return t-n},H1=(e,t,n,o,s,A=!1)=>{let a=L2(N2(S1([e[n],e[o]]),us)),i=Fe(a),c=r.image.cropAndResize(t,[[a.startPoint[1]/s,a.startPoint[0]/s,a.endPoint[1]/s,a.endPoint[0]/s]],[0],[ce,ce]);if(A&&M.kernels.includes("flipleftright")){let d=r.image.flipLeftRight(c);r.dispose(c),c=d}return{box:a,boxSize:i,crop:c}},G1=(e,t,n,o=!1)=>{let s=[];for(let A=0;A{let o=e[F0[`${n}EyeUpper0`][Ge.upperCenter]][2],s=e[F0[`${n}EyeLower0`][Ge.lowerCenter]][2],A=(o+s)/2;return t.map((a,i)=>{let c=A;return i===2?c=o:i===4&&(c=s),[a[0],a[1],c]})};async function X1(e,t,n){if(!(z0!=null&&z0.executor))return e;let{box:o,boxSize:s,crop:A}=H1(e,t,He.leftBounds[0],He.leftBounds[1],n,!0),{box:a,boxSize:i,crop:c}=H1(e,t,He.rightBounds[0],He.rightBounds[1],n,!0),d=r.concat([A,c]);r.dispose(A),r.dispose(c);let y=z0.execute(d);r.dispose(d);let l=await y.data();r.dispose(y);let m=l.slice(0,Ge.numCoordinates*3),{rawCoords:x,iris:p}=G1(m,o,s,!0),f=l.slice(Ge.numCoordinates*3),{rawCoords:h,iris:v}=G1(f,a,i,!1),T=hs(e);Math.abs(T)<30?(C2(e,x,"left",null),C2(e,h,"right",null)):T<1?C2(e,x,"left",["EyeUpper0","EyeLower0"]):C2(e,h,"right",["EyeUpper0","EyeLower0"]);let u=V1(e,p,"left"),g=V1(e,v,"right");return e.concat(u).concat(g)}async function U1(e,t){var A,a,i,c,d,y,l,m,x,p;let n={lips:await((a=(A=t.filter(f=>f.size===160))==null?void 0:A[0])==null?void 0:a.data()),irisL:await((c=(i=t.filter(f=>f.size===10))==null?void 0:i[0])==null?void 0:c.data()),eyeL:await((y=(d=t.filter(f=>f.size===142))==null?void 0:d[0])==null?void 0:y.data()),irisR:await((m=(l=t.filter(f=>f.size===10))==null?void 0:l[1])==null?void 0:m.data()),eyeR:await((p=(x=t.filter(f=>f.size===142))==null?void 0:x[1])==null?void 0:p.data())};for(let f of Object.values(n))if(!f)return e;let o=Me.reduce((f,h)=>f+=e[h][2],0)/Me.length;for(let f=0;ff+=e[h][2],0)/Pe.length;for(let f=0;fR()-K0.timestamp,o=K0.skipped<(((d=t.face.detector)==null?void 0:d.skipFrames)||0);!t.skipAllowed||!n||!o||K0.boxes.length===0?(K0.boxes=await F1(e,t),K0.timestamp=R(),K0.skipped=0):K0.skipped++;let s=[],A=[],a=0,i=o2;for(let T=0;T[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/i]);for(let N of Object.keys(ve))k.annotations[N]=[k.mesh[ve[N]]]}else if(!K)t.debug&&b("face mesh detection requested, but model is not loaded");else{if((x=t.face.attention)!=null&&x.enabled&&!M.kernels.includes("atan2"))return t.face.attention.enabled=!1,r.dispose(k.tensor),s;let N=K.execute(k.tensor),V=await N.find(D=>D.shape[D.shape.length-1]===1).data();if(k.faceScore=Math.round(100*V[0])/100,k.faceScore<(((p=t.face.detector)==null?void 0:p.minConfidence)||1)){if(u.confidence=k.faceScore,t.face.mesh.keepInvalid){k.box=j2(u,e),k.boxRaw=I2(u,e),k.size=u.size,k.score=k.boxScore,k.mesh=u.landmarks,k.meshRaw=k.mesh.map(D=>[D[0]/(e.shape[2]||1),D[1]/(e.shape[1]||1),(D[2]||0)/i]);for(let D of Object.keys(ve))k.annotations[D]=[k.mesh[ve[D]]]}}else{let D=N.find(U=>U.shape[U.shape.length-1]===1404),Z=r.reshape(D,[-1,3]),J=await Z.array();r.dispose(Z),(f=t.face.attention)!=null&&f.enabled?J=await U1(J,N):(h=t.face.iris)!=null&&h.enabled&&(J=await X1(J,k.tensor,o2)),k.mesh=N1(J,u,g,w,o2),k.meshRaw=k.mesh.map(U=>[U[0]/(e.shape[2]||0),U[1]/(e.shape[1]||0),(U[2]||0)/i]);for(let U of Object.keys(F0))k.annotations[U]=F0[U].map(m0=>k.mesh[m0]);k.score=k.faceScore;let q={...O1(k.mesh,u),confidence:u.confidence,landmarks:u.landmarks,size:u.size};k.box=j2(q,e),k.boxRaw=I2(q,e),k.size=q.size,A.push(q)}r.dispose(N)}k.score>(((v=t.face.detector)==null?void 0:v.minConfidence)||1)?s.push(k):r.dispose(k.tensor)}return K0.boxes=A,s}async function K1(e){var t,n,o,s,A,a;return M.initial&&(K=null),(t=e.face.attention)!=null&&t.enabled&&(K!=null&&K.signature)&&Object.keys(((n=K==null?void 0:K.signature)==null?void 0:n.outputs)||{}).length<6&&(K=null),K?e.debug&&b("cached model:",K.modelUrl):(o=e.face.attention)!=null&&o.enabled?K=await L(e.face.attention.modelPath):K=await L((s=e.face.mesh)==null?void 0:s.modelPath),o2=K.executor&&((A=K==null?void 0:K.inputs)!=null&&A[0].shape)?(a=K==null?void 0:K.inputs)==null?void 0:a[0].shape[2]:256,K}var J1=Re,Q1=t2;var Ht=[],d0,W2=[],_1=0,$1=0,Bt=Number.MAX_SAFE_INTEGER,Gt=!1;async function e3(e){var t,n,o;return M.initial&&(d0=null),d0?e.debug&&b("cached model:",d0.modelUrl):(d0=await L((t=e.face.emotion)==null?void 0:t.modelPath),Gt=((o=(n=d0==null?void 0:d0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,Gt?Ht=["angry","disgust","fear","happy","neutral","sad","surprise"]:Ht=["angry","disgust","fear","happy","sad","surprise","neutral"]),d0}async function Vt(e,t,n,o){var a,i;if(!d0)return[];let s=Bt<(((a=t.face.emotion)==null?void 0:a.skipFrames)||0),A=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>R()-$1;return t.skipAllowed&&A&&s&&_1===o&&W2[n]&&W2[n].length>0?(Bt++,W2[n]):(Bt=0,new Promise(async c=>{var y,l,m;let d=[];if((y=t.face.emotion)!=null&&y.enabled){let x={},p=d0!=null&&d0.inputs[0].shape?d0.inputs[0].shape[2]:0;if(((l=t.face.emotion)==null?void 0:l.crop)>0){let h=(m=t.face.emotion)==null?void 0:m.crop,v=[[h,h,1-h,1-h]];x.resize=r.image.cropAndResize(e,v,[0],[p,p])}else x.resize=r.image.resizeBilinear(e,[p,p],!1);Gt?(x.mul=r.mul(x.resize,255),x.normalize=r.sub(x.mul,[103.939,116.779,123.68]),x.emotion=d0==null?void 0:d0.execute(x.normalize)):(x.channels=r.mul(x.resize,O.rgb),x.grayscale=r.sum(x.channels,3,!0),x.grayscaleSub=r.sub(x.grayscale,O.tf05),x.grayscaleMul=r.mul(x.grayscaleSub,O.tf2),x.emotion=d0==null?void 0:d0.execute(x.grayscaleMul)),$1=R();let f=await x.emotion.data();for(let h=0;h(t.face.emotion.minConfidence||0)&&d.push({score:Math.min(.99,Math.trunc(100*f[h])/100),emotion:Ht[h]});d.sort((h,v)=>v.score-h.score),Object.keys(x).forEach(h=>r.dispose(x[h]))}W2[n]=d,_1=o,c(d)}))}var x0,de=[],n3=0,o3=0,Zt=Number.MAX_SAFE_INTEGER;async function r3(e){var t;return M.initial&&(x0=null),x0?e.debug&&b("cached model:",x0.modelUrl):x0=await L((t=e.face.description)==null?void 0:t.modelPath),x0}function gs(e,t){var A,a;let n=e.image||e.tensor||e;if(!(x0!=null&&x0.inputs[0].shape))return n;let o;if(((A=t.face.description)==null?void 0:A.crop)>0){let i=(a=t.face.description)==null?void 0:a.crop,c=[[i,i,1-i,1-i]];o=r.image.cropAndResize(n,c,[0],[x0.inputs[0].shape[2],x0.inputs[0].shape[1]])}else o=r.image.resizeBilinear(n,[x0.inputs[0].shape[2],x0.inputs[0].shape[1]],!1);let s=r.mul(o,O.tf255);return r.dispose(o),s}async function Xt(e,t,n,o){var i,c,d,y;let s={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(x0!=null&&x0.executor))return s;let A=Zt<(((i=t.face.description)==null?void 0:i.skipFrames)||0),a=(((c=t.face.description)==null?void 0:c.skipTime)||0)>R()-n3;return t.skipAllowed&&A&&a&&o3===o&&((d=de==null?void 0:de[n])==null?void 0:d.age)>0&&((y=de==null?void 0:de[n])==null?void 0:y.genderScore)>0?(Zt++,de[n]):(Zt=0,new Promise(async l=>{var m;if((m=t.face.description)!=null&&m.enabled){let x=gs(e,t),p=x0==null?void 0:x0.execute(x);n3=R(),r.dispose(x);let h=await p.find(W=>W.shape[1]===1).data(),v=Math.trunc(200*Math.abs(h[0]-.5))/100;v>(t.face.description.minConfidence||0)&&(s.gender=h[0]<=.5?"female":"male",s.genderScore=Math.min(.99,v));let T=r.argMax(p.find(W=>W.shape[1]===100),1),u=(await T.data())[0];r.dispose(T);let w=await p.find(W=>W.shape[1]===100).data();s.age=Math.round(w[u-1]>w[u+1]?10*u-100*w[u-1]:10*u+100*w[u+1])/10,(Number.isNaN(h[0])||Number.isNaN(w[0]))&&b("faceres error:",{model:x0,result:p});let k=p.find(W=>W.shape[1]===1024),N=k?await k.data():[];s.descriptor=Array.from(N),p.forEach(W=>r.dispose(W))}de[n]=s,o3=o,l(s)}))}var Ve=.1,qt=.5;function Ts(e,t,n){let o=!1,s=n.length-1;for(let A=0;At!=n[s].y>t&&e<(n[s].x-n[A].x)*(t-n[A].y)/(n[s].y-n[A].y)+n[A].x&&(o=!o);return o}async function A3(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,o=await e.tensor.buffer(),s=[];for(let a of F0.silhouette)s.push({x:(e.mesh[a][0]-e.box[0])/e.box[2],y:(e.mesh[a][1]-e.box[1])/e.box[3]});Ve&&Ve>0&&(s=s.map(a=>({x:a.x>.5?a.x+Ve:a.x-Ve,y:a.y>.5?a.y+Ve:a.y-Ve})));for(let a=0;aR()-i3,A=Ut<(((i=t.face.antispoof)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&A&&a3===o&&D2[n]?(Ut++,D2[n]):(Ut=0,new Promise(async c=>{let d=r.image.resizeBilinear(e,[y0!=null&&y0.inputs[0].shape?y0.inputs[0].shape[2]:0,y0!=null&&y0.inputs[0].shape?y0.inputs[0].shape[1]:0],!1),y=y0==null?void 0:y0.execute(d),l=(await y.data())[0];D2[n]=Math.round(100*l)/100,a3=o,i3=R(),r.dispose([d,y]),c(D2[n])}))}var f0,F2=[],Kt=Number.MAX_SAFE_INTEGER,d3=0,x3=0;async function y3(e){var t;return M.initial&&(f0=null),f0?e.debug&&b("cached model:",f0.modelUrl):f0=await L((t=e.face.liveness)==null?void 0:t.modelPath),f0}async function Jt(e,t,n,o){var a,i;if(!(f0!=null&&f0.executor))return 0;let s=(((a=t.face.liveness)==null?void 0:a.skipTime)||0)>R()-x3,A=Kt<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&A&&d3===o&&F2[n]?(Kt++,F2[n]):(Kt=0,new Promise(async c=>{let d=r.image.resizeBilinear(e,[f0!=null&&f0.inputs[0].shape?f0.inputs[0].shape[2]:0,f0!=null&&f0.inputs[0].shape?f0.inputs[0].shape[1]:0],!1),y=f0==null?void 0:f0.execute(d),l=(await y.data())[0];F2[n]=Math.round(100*l)/100,d3=o,x3=R(),r.dispose([d,y]),c(F2[n])}))}var B0,Qt=[],Rs=["white","black","asian","indian","other"],Ms=[15,23,28,35.5,45.5,55.5,65],m3=0,p3=0,_t=Number.MAX_SAFE_INTEGER;async function u3(e){var t;return M.initial&&(B0=null),B0?e.debug&&b("cached model:",B0.modelUrl):B0=await L((t=e.face.gear)==null?void 0:t.modelPath),B0}async function $t(e,t,n,o){var a,i;if(!B0)return{age:0,gender:"unknown",genderScore:0,race:[]};let s=_t<(((a=t.face.gear)==null?void 0:a.skipFrames)||0),A=(((i=t.face.gear)==null?void 0:i.skipTime)||0)>R()-p3;return t.skipAllowed&&A&&s&&m3===o&&Qt[n]?(_t++,Qt[n]):(_t=0,new Promise(async c=>{var v,T,u,g;if(!(B0!=null&&B0.inputs[0].shape))return;let d={},y=[[0,.1,.9,.9]];if(((v=t.face.gear)==null?void 0:v.crop)>0){let w=(T=t.face.gear)==null?void 0:T.crop;y=[[w,w,1-w,1-w]]}d.resize=r.image.cropAndResize(e,y,[0],[B0.inputs[0].shape[2],B0.inputs[0].shape[1]]);let l={age:0,gender:"unknown",genderScore:0,race:[]};(u=t.face.gear)!=null&&u.enabled&&([d.age,d.gender,d.race]=B0.execute(d.resize,["age_output","gender_output","race_output"]));let m=await d.gender.data();l.gender=m[0]>m[1]?"male":"female",l.genderScore=Math.round(100*(m[0]>m[1]?m[0]:m[1]))/100;let x=await d.race.data();for(let w=0;w(((g=t.face.gear)==null?void 0:g.minConfidence)||.2)&&l.race.push({score:Math.round(100*x[w])/100,race:Rs[w]});l.race.sort((w,k)=>k.score-w.score);let f=Array.from(await d.age.data()).map((w,k)=>[Ms[k],w]).sort((w,k)=>k[1]-w[1]),h=f[0][0];for(let w=1;wr.dispose(d[w])),Qt[n]=l,m3=o,p3=R(),c(l)}))}var 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h=Math.sqrt(s*s+i*i),v=Math.sqrt(A*A+c*c),T=Math.sqrt(a*a+d*d),u=Math.max(h,v,T),g=e[0],w=e[1],k=n[0],N=n[1];u===h?(k=n[0],N=n[1]):u===T&&(g=t[0],w=t[1]);let D=G3([g,w],[k,N]),Z=H3(D,je.TOTAL_ANGLE_VOTE_POWER);m+=Z[0],x+=Z[1],p+=Z[2];for(let q of o){let U=H3(q,je.SINGLE_ANGLE_VOTE_POWER);m+=U[0],x+=U[1],p+=U[2]}let J;return m===Math.max(m,x,p)?J=Z3(c,i,d,l):p===Math.max(x,p)?J=V3(A,s,a,y):J=Is(c,i,d,l,A,s,a,y),J}function X3(e){let t=[],n=[],o=[],s=[];if(!e)return{curls:o,directions:s};for(let A of M0.all){let a=M0.getPoints(A),i=[],c=[];for(let d of a){let y=e[d[0]],l=e[d[1]],m=G3(y,l),x=m[0],p=m[1];i.push(x),c.push(p)}t.push(i),n.push(c)}for(let A of M0.all){let a=A===M0.thumb?1:0,i=M0.getPoints(A),c=e[i[a][0]],d=e[i[a+1][1]],y=e[i[3][1]],l=js(c,d,y),m=Ns(c,d,y,t[A].slice(a));o[A]=l,s[A]=m}return{curls:o,directions:s}}function G2(e){if(!e||e.length===0)return null;let t=X3(e),n={};for(let o of M0.all)n[M0.getName(o)]={curl:ye.getName(t.curls[o]),direction:$.getName(t.directions[o])};return n}function q3(e){let t=[];if(!e||e.length===0)return t;let n=X3(e);for(let o of F3){let s=o.matchAgainst(n.curls,n.directions);s>=Ss&&t.push({name:o.name,confidence:s})}return t}var U3=e=>{if(!e)return[];let t=[];for(let n=0;nc.part==="leftWrist"),s=e[n].keypoints.find(c=>c.part==="rightWrist"),A=e[n].keypoints.find(c=>c.part==="nose");A&&o&&s&&o.position[1]c.part==="leftShoulder"),i=e[n].keypoints.find(c=>c.part==="rightShoulder");a&&i&&Math.abs(a.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${a.position[1]>i.position[1]?"left":"right"}`})}return t},Y3=e=>{if(!e)return[];let t=[];for(let n=0;n450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),s=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/s)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let c=e[n].mesh[152][2]||0;Math.abs(c)>10&&t.push({face:n,gesture:`head ${c<0?"up":"down"}`})}return t},K3=e=>{var n,o,s,A;if(!e)return[];let t=[];for(let a=0;a.06||h>.06)&&(x=!1),f>h?h>.04&&t.push({iris:a,gesture:"looking right"}):f>.04&&t.push({iris:a,gesture:"looking left"});let v=Math.abs(e[a].mesh[145][1]-e[a].annotations.rightEyeIris[0][1])/e[a].box[3],T=Math.abs(e[a].mesh[374][1]-e[a].annotations.leftEyeIris[0][1])/e[a].box[3];(T<.01||v<.01||T>.022||v>.022)&&(x=!1),(T<.01||v<.01)&&t.push({iris:a,gesture:"looking down"}),(T>.022||v>.022)&&t.push({iris:a,gesture:"looking up"}),x&&t.push({iris:a,gesture:"looking center"})}return t},J3=e=>{if(!e)return[];let t=[];for(let n=0;n0){let s=o.reduce((a,i)=>(a.position[2]||0)<(i.position[2]||0)?a:i);t.push({hand:n,gesture:`${s.name} forward`});let A=o.reduce((a,i)=>a.position[1][A[0]*t[0],A[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:s,confidence:e.confidence}}function Z2(e,t=1.5){let n=r2(e),o=V2(e),s=[t*o[0]/2,t*o[1]/2],A=[n[0]-s[0],n[1]-s[1]],a=[n[0]+s[0],n[1]+s[1]];return{startPoint:A,endPoint:a,palmLandmarks:e.palmLandmarks}}function X2(e){let t=r2(e),n=V2(e),s=Math.max(...n)/2,A=[t[0]-s,t[1]-s],a=[t[0]+s,t[1]+s];return{startPoint:A,endPoint:a,palmLandmarks:e.palmLandmarks}}function Os(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function tn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Os(n)}var Q3=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ue(e,t){let n=0;for(let o=0;o[a.x,a.y]),this.anchorsTensor=r.tensor2d(this.anchors),this.inputSize=((A=(s=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:s.shape)==null?void 0:A[2])||0,this.inputSizeTensor=r.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=r.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=r.slice(t,[0,0],[-1,2]),n.boxSizes=r.slice(t,[0,2],[-1,2]),n.div=r.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=r.add(n.div,this.anchorsTensor),n.halfBoxSizes=r.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=r.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=r.mul(n.sub,this.inputSizeTensor),n.add=r.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=r.mul(n.add,this.inputSizeTensor);let o=r.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(s=>r.dispose(n[s])),o}normalizeLandmarks(t,n){let o={};o.reshape=r.reshape(t,[-1,7,2]),o.div=r.div(o.reshape,this.inputSizeTensor),o.landmarks=r.add(o.div,this.anchors[n]?this.anchors[n]:0);let s=r.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(A=>r.dispose(o[A])),s}async predict(t,n){var i;let o={};o.resize=r.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=r.div(o.resize,O.tf127),o.image=r.sub(o.div,O.tf1),o.batched=this.model.execute(o.image),o.predictions=r.squeeze(o.batched),o.slice=r.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=r.sigmoid(o.slice),o.scores=r.squeeze(o.sigmoid);let s=await o.scores.data();o.boxes=r.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await r.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((i=n.hand)==null?void 0:i.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let A=await o.nms.array(),a=[];for(let c of A){let d={};d.box=r.slice(o.norm,[c,0],[1,-1]),d.slice=r.slice(o.predictions,[c,5],[1,14]),d.norm=this.normalizeLandmarks(d.slice,c),d.palmLandmarks=r.reshape(d.norm,[-1,2]);let y=await d.box.data(),l=y.slice(0,2),m=y.slice(2,4),x=await d.palmLandmarks.array(),p={startPoint:l,endPoint:m,palmLandmarks:x,confidence:s[c]},f=en(p,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);a.push(f),Object.keys(d).forEach(h=>r.dispose(d[h]))}return Object.keys(o).forEach(c=>r.dispose(o[c])),a}};var Fs=5,sn=1.65,An=[0,5,9,13,17,1,2],Bs=0,Hs=2,an=0,U2=class{constructor(t,n){z(this,"handDetector");z(this,"handPoseModel");z(this,"inputSize");z(this,"storedBoxes");z(this,"skipped");z(this,"detectedHands");var o,s,A;this.handDetector=t,this.handPoseModel=n,this.inputSize=((A=(s=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:s[0].shape)==null?void 0:A[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(a=>a[0]),o=t.map(a=>a[1]),s=[Math.min(...n),Math.min(...o)],A=[Math.max(...n),Math.max(...o)];return{startPoint:s,endPoint:A}}getBoxForPalmLandmarks(t,n){let o=t.map(A=>y5([...A,1],n)),s=this.calculateLandmarksBoundingBox(o);return Z2(X2(s),Fs)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Z2(X2(n),sn);o.palmLandmarks=[];for(let s=0;s[a[0]*(x[0]-this.inputSize/2),a[1]*(x[1]-this.inputSize/2),a[2]*x[2]]),c=x5(o,[0,0]),d=i.map(x=>[...y5(x,c),x[2]]),y=nn(s),l=[...r2(n),1],m=[ue(l,y[0]),ue(l,y[1])];return d.map(x=>[Math.trunc(x[0]+m[0]),Math.trunc(x[1]+m[1]),Math.trunc(x[2])])}async estimateHands(t,n){let o=!1,s,A=(n.hand.skipTime||0)>R()-an,a=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&A&&a?this.skipped++:(s=await this.handDetector.predict(t,n),this.skipped=0),s&&s.length>0&&(s.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...s],this.storedBoxes.length>0&&(o=!0));let i=[];for(let c=0;c=n.hand.minConfidence/4){let w=r.reshape(u,[-1,3]),k=await w.array();r.dispose(u),r.dispose(w);let N=this.transformRawCoords(k,f,y,p),W=this.getBoxForHandLandmarks(N);this.storedBoxes[c]={...W,confidence:g};let V={landmarks:N,confidence:g,boxConfidence:d.confidence,fingerConfidence:g,box:{topLeft:W.startPoint,bottomRight:W.endPoint}};i.push(V)}else this.storedBoxes[c]=null;r.dispose(u)}else{let y=Z2(X2(d),sn),l={confidence:d.confidence,boxConfidence:d.confidence,fingerConfidence:0,box:{topLeft:y.startPoint,bottomRight:y.endPoint},landmarks:[]};i.push(l)}}return this.storedBoxes=this.storedBoxes.filter(c=>c!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var ln={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},Ie,Ne,f5;function Vs(){let e=Ie?new q2(Ie):void 0;e&&Ne&&(f5=new U2(e,Ne))}async function m5(e,t){f5||Vs();let n=await f5.estimateHands(e,t);if(!n)return[];let o=[];for(let s=0;sn[s].landmarks[l]);let a=n[s].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],c=[0,0,0,0];if(a&&a.length>0){for(let y of a)y[0]i[2]&&(i[2]=y[0]),y[1]>i[3]&&(i[3]=y[1]);i[2]-=i[0],i[3]-=i[1],c=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[s].box?[Math.trunc(Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.max(0,n[s].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[s].box.bottomRight[0])-Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[s].box.bottomRight[1])-Math.max(0,n[s].box.topLeft[1]))]:[0,0,0,0],c=[n[s].box.topLeft[0]/(e.shape[2]||0),n[s].box.topLeft[1]/(e.shape[1]||0),(n[s].box.bottomRight[0]-n[s].box.topLeft[0])/(e.shape[2]||0),(n[s].box.bottomRight[1]-n[s].box.topLeft[1])/(e.shape[1]||0)];let d=G2(a);o.push({id:s,score:Math.round(100*n[s].confidence)/100,boxScore:Math.round(100*n[s].boxConfidence)/100,fingerScore:Math.round(100*n[s].fingerConfidence)/100,label:"hand",box:i,boxRaw:c,keypoints:a,annotations:A,landmarks:d})}return o}async function cn(e){var t;return M.initial&&(Ie=null),Ie?e.debug&&b("cached model:",Ie.modelUrl):Ie=await L((t=e.hand.detector)==null?void 0:t.modelPath),Ie}async function dn(e){var t;return M.initial&&(Ne=null),Ne?e.debug&&b("cached model:",Ne.modelUrl):Ne=await L((t=e.hand.skeleton)==null?void 0:t.modelPath),Ne}var r0=[null,null],Zs=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],he=[[0,0],[0,0]],Xs=["hand","fist","pinch","point","face","tip","pinchtip"],yn=4,fn=1.6,qs=512,Us=1.4,Y2=Number.MAX_SAFE_INTEGER,p5=0,ee=[0,0],o0={boxes:[],hands:[]},mn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function pn(e){var t;if(M.initial&&(r0[0]=null),r0[0])e.debug&&b("cached model:",r0[0].modelUrl);else{h2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),r0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=r0[0].executor?Object.values(r0[0].modelSignature.inputs):void 0;he[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,he[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return r0[0]}async function un(e){var t;if(M.initial&&(r0[1]=null),r0[1])e.debug&&b("cached model:",r0[1].modelUrl);else{r0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=r0[1].executor?Object.values(r0[1].modelSignature.inputs):void 0;he[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,he[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return r0[1]}async function Ys(e,t){let n=[];if(!e||!r0[0])return n;let o={},s=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,qs),a=Math.round(A*s/8)*8;o.resize=r.image.resizeBilinear(e,[A,a]),o.cast=r.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await r0[0].executeAsync(o.cast,Zs),o.boxes=r.squeeze(o.rawBoxes,[0,2]),o.scores=r.squeeze(o.rawScores,[0]);let i=r.unstack(o.scores,1);r.dispose(i[yn]),i.splice(yn,1),o.filtered=r.stack(i,1),r.dispose(i),o.max=r.max(o.filtered,1),o.argmax=r.argMax(o.filtered,1);let c=0;o.nms=await r.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let d=await o.nms.data(),y=await o.max.data(),l=await o.argmax.data();for(let m of Array.from(d)){let x=r.slice(o.boxes,m,1),p=await x.data();r.dispose(x);let f=[p[1],p[0],p[3]-p[1],p[2]-p[0]],h=k2(f,Us),v=[Math.trunc(f[0]*ee[0]),Math.trunc(f[1]*ee[1]),Math.trunc(f[2]*ee[0]),Math.trunc(f[3]*ee[1])],T=y[m],u=Xs[l[m]],g={id:c++,score:T,box:v,boxRaw:h,label:u};n.push(g)}return Object.keys(o).forEach(m=>r.dispose(o[m])),n.sort((m,x)=>x.score-m.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function u5(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&r0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let s={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];s.crop=r.image.cropAndResize(e,[A],[0],[he[1][0],he[1][1]],"bilinear"),s.div=r.div(s.crop,O.tf255),[s.score,s.keypoints]=r0[1].execute(s.div,["Identity_1","Identity"]);let a=(await s.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(n.hand.minConfidence||0)){o.fingerScore=i,s.reshaped=r.reshape(s.keypoints,[-1,3]);let y=(await s.reshaped.array()).map(l=>[l[0]/he[1][1],l[1]/he[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);o.keypoints=y.map(l=>[ee[0]*(l[0]+t.boxRaw[0]),ee[1]*(l[1]+t.boxRaw[1]),l[2]||0]),o.landmarks=G2(o.keypoints);for(let l of Object.keys(mn))o.annotations[l]=mn[l].map(m=>o.landmarks&&o.keypoints[m]?o.keypoints[m]:null)}Object.keys(s).forEach(c=>r.dispose(s[c]))}return o}async function h5(e,t){var s,A;if(!((s=r0[0])!=null&&s.executor)||!((A=r0[1])!=null&&A.executor)||!r0[0].inputs[0].shape||!r0[1].inputs[0].shape)return[];ee=[e.shape[2]||0,e.shape[1]||0],Y2++;let n=(t.hand.skipTime||0)>R()-p5,o=Y2<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?o0.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>R()-p5,c=Y2<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(y=>u5(e,y,t))):t.skipAllowed&&i&&c&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(y=>u5(e,y,t))):(o0.boxes=await Ys(e,t),p5=R(),o0.hands=await Promise.all(o0.boxes.map(y=>u5(e,y,t))),Y2=0);let d=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let 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K2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],b5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],g5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],T5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],J2={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var j=te(),v5=0;function bn(e,t){var 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n={};n.resize=r.image.resizeBilinear(e,[h0.inputs[0].shape?h0.inputs[0].shape[1]:0,h0.inputs[0].shape?h0.inputs[0].shape[2]:0],!1),n.norm=r.div(n.resize,O.tf255),n.res=h0.execute(n.norm),n.squeeze=r.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=r.unstack(n.squeeze,2),n.fg=r.softmax(n.fgRaw),n.mul=r.mul(n.fg,O.tf255),n.expand=r.expandDims(n.mul,2),n.output=r.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=r.squeeze(e),n.concat=r.concat([n.input,n.output],-1),o=r.cast(n.concat,"int32");break;case"alpha":o=r.cast(n.output,"int32");break;default:o=r.tensor(0)}return Object.keys(n).forEach(A=>r.dispose(n[A])),o}var P5={};oe(P5,{distance:()=>M5,find:()=>Qs,similarity:()=>Js});function M5(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let s=0;s{if(e===0)return 1;let A=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return Math.max(Math.min(A,1),0)};function 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c=[A[51+1],A[51+0],A[51+3]-A[51+1],A[51+2]-A[51+0]],d=[Math.trunc(c[0]*(n.shape[2]||0)),Math.trunc(c[1]*(n.shape[1]||0)),Math.trunc(c[2]*(n.shape[2]||0)),Math.trunc(c[3]*(n.shape[1]||0))],y={};for(let[m,x]of Object.entries(J2)){let p=[];for(let f=0;fT.part===x[f]),v=i.find(T=>T.part===x[f+1]);h&&v&&h.score>(t.body.minConfidence||0)&&v.score>(t.body.minConfidence||0)&&p.push([h.position,v.position])}y[m]=p}let l={id:s,score:a,box:d,boxRaw:c,keypoints:[...i],annotations:y};k5(l),o.push(l)}}return o.sort((s,A)=>A.score-s.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function E5(e,t){var s;if(!(A0!=null&&A0.executor)||!((s=A0==null?void 0:A0.inputs)!=null&&s[0].shape))return[];t.skipAllowed||(Le.boxes.length=0),w5++;let n=(t.body.skipTime||0)>R()-Le.last,o=w5<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?Le.bodies:new Promise(async A=>{let a={};w5=0,a.input=Pn(e,Q2),a.res=A0==null?void 0:A0.execute(a.input),Le.last=R();let i=await 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Rt=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],Mt={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var W0,ke=224,x1,es=5,P2=[8,16,32,32,32];function ts(){let e=[],t=0;for(;tn.x)),y:o.tensor1d(e.map(n=>n.y))}}async function y1(e){if(M.initial&&(W0=null),!W0&&e.body.detector&&e.body.detector.modelPath){W0=await L(e.body.detector.modelPath);let t=W0!=null&&W0.executor?Object.values(W0.modelSignature.inputs):void 0;ke=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&W0&&b("cached model:",W0.modelUrl);return ts(),W0}var d1=[5,5];function ns(e,t){return o.tidy(()=>{let n=o.split(e,12,1),r=o.squeeze(n[0]),s=o.squeeze(n[1]),A=o.squeeze(n[2]),a=o.squeeze(n[3]);r=o.add(o.div(r,ke),t.x),s=o.add(o.div(s,ke),t.y),A=o.mul(o.div(A,ke),d1[0]),a=o.mul(o.div(a,ke),d1[1]);let i=o.sub(r,o.div(A,2)),c=o.sub(s,o.div(a,2)),d=o.add(i,A),y=o.add(c,a);return o.stack([i,c,d,y],1)})}async function os(e,t,n,r){var d,y;let s=[],A={};A.boxes=ns(e,x1),A.scores=o.sigmoid(t),A.nms=await o.image.nonMaxSuppressionAsync(A.boxes,A.scores,1,((d=n.body.detector)==null?void 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n=[e.map(d=>d[0]),e.map(d=>d[1])],r=[Math.min(...n[0]),Math.min(...n[1])],s=[Math.max(...n[0]),Math.max(...n[1])],A=[(r[0]+s[0])/2,(r[1]+s[1])/2],a=Math.max(A[0]-r[0],A[1]-r[1],-A[0]+s[0],-A[1]+s[1]),i=[Math.trunc(A[0]-a),Math.trunc(A[1]-a),Math.trunc(2*a),Math.trunc(2*a)],c=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:c}}function k2(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var P0,kt=256,Pt=Number.MAX_SAFE_INTEGER,rs={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},E2=[],ie=[[0,0],[0,0],[0,0],[0,0]],p1=0,u1=e=>1-1/(1+Math.exp(e)),b1=e=>y1(e);async function g1(e){if(M.initial&&(P0=null),P0)e.debug&&b("cached model:",P0.modelUrl);else{P0=await L(e.body.modelPath);let t=P0!=null&&P0.executor?Object.values(P0.modelSignature.inputs):void 0;kt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return P0}function h1(e,t,n){var A,a;let r={};if(!((A=e==null?void 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e)r.position=[Math.trunc(r.position[0]*(t[0]+ie[2][0]+ie[2][1])/t[0]-ie[2][0]),Math.trunc(r.position[1]*(t[1]+ie[1][0]+ie[1][1])/t[1]-ie[1][0]),r.position[2]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1],2*r.position[2]/(t[0]+t[1])];if(n){let r=n[2]-n[0],s=n[3]-n[1];for(let A of e)A.positionRaw=[A.positionRaw[0]/s+n[1],A.positionRaw[1]/r+n[0],A.positionRaw[2]],A.position=[Math.trunc(A.positionRaw[0]*t[0]),Math.trunc(A.positionRaw[1]*t[1]),A.positionRaw[2]]}return e}function As(e){let t=e.find(i=>i.part==="leftPalm"),n=e.find(i=>i.part==="leftWrist"),r=e.find(i=>i.part==="leftIndex");t.position[2]=((n.position[2]||0)+(r.position[2]||0))/2;let s=e.find(i=>i.part==="rightPalm"),A=e.find(i=>i.part==="rightWrist"),a=e.find(i=>i.part==="rightIndex");s.position[2]=((A.position[2]||0)+(a.position[2]||0))/2}async function as(e,t,n){if(!(P0!=null&&P0.executor))return null;let r={};[r.ld,r.segmentation,r.heatmap,r.world,r.poseflag]=P0==null?void 0:P0.execute(e,rs.landmarks);let s=(await 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A=[e.shape[2]||0,e.shape[1]||0],a=o.image.resizeBilinear(e,[we,we]),i=t.object.enabled?k0==null?void 0:k0.execute(a,["tower_0/detections"]):null;v1=R(),o.dispose(a);let c=await is(i,A,t);Et=c,s(c)}))}var z2={};oe(z2,{connected:()=>It,kpt:()=>jt});var jt=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],It={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var _,P1=0,v0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Nt=Number.MAX_SAFE_INTEGER;async function k1(e){return M.initial&&(_=null),_?e.debug&&b("cached model:",_.modelUrl):_=await L(e.body.modelPath),_}async function ls(e,t){let[n,r]=e.shape,s=o.reshape(e,[r*n]),A=o.max(s,0),a=(await A.data())[0];if(a>t){let i=o.argMax(s,0),c=o.mod(i,n),d=(await c.data())[0],y=o.div(i,n),l=(await y.data())[0];return o.dispose([s,A,i,c,y]),[d,l,a]}return o.dispose([s,A]),[0,0,a]}async function Lt(e,t){if(!(_!=null&&_.executor)||!(_!=null&&_.inputs[0].shape))return[];let n=(t.body.skipTime||0)>R()-P1,r=Nt<(t.body.skipFrames||0);return t.skipAllowed&&n&&r&&Object.keys(v0.keypoints).length>0?(Nt++,[v0]):(Nt=0,new Promise(async s=>{let A=o.tidy(()=>{var p,f;let l=o.image.resizeBilinear(e,[((p=_==null?void 0:_.inputs[0].shape)==null?void 0:p[2])||0,((f=_==null?void 0:_.inputs[0].shape)==null?void 0:f[1])||0],!1),m=o.mul(l,O.tf2);return o.sub(m,O.tf1)}),a;if(t.body.enabled&&(a=_==null?void 0:_.execute(A)),P1=R(),o.dispose(A),a){v0.keypoints.length=0;let l=o.squeeze(a);o.dispose(a);let m=o.unstack(l,2);o.dispose(l);for(let 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d=S2(t),y=[d[0]/n.shape[2],d[1]/n.shape[1]],l=o.image.rotateWithOffset(n,A,0,[y[0],y[1]]);a=I1(-A,d),i=Ot(t,l,[r,r]),o.dispose(l)}else i=Ot(t,n,[r,r]);else i=Ot(t,n,[r,r]);return[A,a,i]}var ms=e=>{let t=e.map(r=>r[0]),n=e.map(r=>r[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},C1=(e,t)=>{let n=ms(e),r=Fe(t);return{startPoint:[n[0]-r[0]/2,n[1]-r[1]/2],endPoint:[n[0]+r[0]/2,n[1]+r[1]/2]}};var W1=6,ps=1.4,G0,O2=null,le=0,Be=null,D1=()=>le;async function F1(e){var t;return M.initial&&(G0=null),G0?e.debug&&b("cached model:",G0.modelUrl):G0=await L((t=e.face.detector)==null?void 0:t.modelPath),le=G0.executor&&G0.inputs[0].shape?G0.inputs[0].shape[2]:256,Be=o.scalar(le,"int32"),O2=o.tensor2d(N1(le)),G0}function us(e){if(!O2||!Be)return o.zeros([0,0]);let t={};t.boxStarts=o.slice(e,[0,1],[-1,2]),t.centers=o.add(t.boxStarts,O2),t.boxSizes=o.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=o.div(t.boxSizes,Be),t.centersNormalized=o.div(t.centers,Be),t.halfBoxSize=o.div(t.boxSizesNormalized,O.tf2),t.starts=o.sub(t.centersNormalized,t.halfBoxSize),t.ends=o.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=o.mul(t.starts,Be),t.endNormalized=o.mul(t.ends,Be);let n=o.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(r=>o.dispose(t[r])),n}async function B1(e,t){var i,c,d,y,l,m;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=o.image.resizeBilinear(e,[le,le]),n.div=o.div(n.resized,O.tf127),n.normalized=o.sub(n.div,O.tf05);let r=G0==null?void 0:G0.execute(n.normalized);if(Array.isArray(r)&&r.length>2){let x=r.sort((p,f)=>p.size-f.size);n.concat384=o.concat([x[0],x[2]],2),n.concat512=o.concat([x[1],x[3]],2),n.concat=o.concat([n.concat512,n.concat384],1),n.batch=o.squeeze(n.concat,[0])}else Array.isArray(r)?n.batch=o.squeeze(r[0]):n.batch=o.squeeze(r);o.dispose(r),n.boxes=us(n.batch),n.logits=o.slice(n.batch,[0,0],[-1,1]),n.sigmoid=o.sigmoid(n.logits),n.scores=o.squeeze(n.sigmoid),n.nms=await o.image.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((c=t.face.detector)==null?void 0:c.iouThreshold)||0,((d=t.face.detector)==null?void 0:d.minConfidence)||0);let s=await n.nms.array(),A=[],a=await n.scores.data();for(let x=0;x(((y=t.face.detector)==null?void 0:y.minConfidence)||0)){let f={};f.bbox=o.slice(n.boxes,[s[x],0],[1,-1]),f.slice=o.slice(n.batch,[s[x],W1-1],[1,-1]),f.squeeze=o.squeeze(f.slice),f.landmarks=o.reshape(f.squeeze,[W1,-1]);let h=await f.bbox.data(),v={startPoint:[h[0],h[1]],endPoint:[h[2],h[3]],landmarks:await f.landmarks.array(),confidence:p};f.anchor=o.slice(O2,[s[x],0],[1,2]);let T=await f.anchor.data(),u=S1(v,[(e.shape[2]||0)/le,(e.shape[1]||0)/le],T),g=N2(u,t.face.scale||ps),E=L2(g);E.size[0]>(((l=t.face.detector)==null?void 0:l.minSize)||0)&&E.size[1]>(((m=t.face.detector)==null?void 0:m.minSize)||0)&&A.push(E),Object.keys(f).forEach(k=>o.dispose(f[k]))}}return Object.keys(n).forEach(x=>o.dispose(n[x])),A}var z0,ce=0,hs=2.3,Dt=F0.leftEyeLower0,Ft=F0.rightEyeLower0,He={leftBounds:[Dt[0],Dt[Dt.length-1]],rightBounds:[Ft[0],Ft[Ft.length-1]]},Ge={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function X1(e){var t,n;return M.initial&&(z0=null),z0?e.debug&&b("cached model:",z0.modelUrl):z0=await L((t=e.face.iris)==null?void 0:t.modelPath),ce=z0!=null&&z0.executor&&((n=z0.inputs)!=null&&n[0].shape)?z0.inputs[0].shape[2]:0,ce===-1&&(ce=64),z0}function C2(e,t,n,r){for(let s=0;s{let t=e[He.leftBounds[0]][2],n=e[He.rightBounds[0]][2];return t-n},G1=(e,t,n,r,s,A=!1)=>{let a=L2(N2(j1([e[n],e[r]]),hs)),i=Fe(a),c=o.image.cropAndResize(t,[[a.startPoint[1]/s,a.startPoint[0]/s,a.endPoint[1]/s,a.endPoint[0]/s]],[0],[ce,ce]);if(A&&M.kernels.includes("flipleftright")){let d=o.image.flipLeftRight(c);o.dispose(c),c=d}return{box:a,boxSize:i,crop:c}},V1=(e,t,n,r=!1)=>{let s=[];for(let A=0;A{let r=e[F0[`${n}EyeUpper0`][Ge.upperCenter]][2],s=e[F0[`${n}EyeLower0`][Ge.lowerCenter]][2],A=(r+s)/2;return t.map((a,i)=>{let c=A;return i===2?c=r:i===4&&(c=s),[a[0],a[1],c]})};async function q1(e,t,n){if(!(z0!=null&&z0.executor))return e;let{box:r,boxSize:s,crop:A}=G1(e,t,He.leftBounds[0],He.leftBounds[1],n,!0),{box:a,boxSize:i,crop:c}=G1(e,t,He.rightBounds[0],He.rightBounds[1],n,!0),d=o.concat([A,c]);o.dispose(A),o.dispose(c);let y=z0.execute(d);o.dispose(d);let l=await y.data();o.dispose(y);let m=l.slice(0,Ge.numCoordinates*3),{rawCoords:x,iris:p}=V1(m,r,s,!0),f=l.slice(Ge.numCoordinates*3),{rawCoords:h,iris:v}=V1(f,a,i,!1),T=bs(e);Math.abs(T)<30?(C2(e,x,"left",null),C2(e,h,"right",null)):T<1?C2(e,x,"left",["EyeUpper0","EyeLower0"]):C2(e,h,"right",["EyeUpper0","EyeLower0"]);let u=Z1(e,p,"left"),g=Z1(e,v,"right");return e.concat(u).concat(g)}async function Y1(e,t){var A,a,i,c,d,y,l,m,x,p;let n={lips:await((a=(A=t.filter(f=>f.size===160))==null?void 0:A[0])==null?void 0:a.data()),irisL:await((c=(i=t.filter(f=>f.size===10))==null?void 0:i[0])==null?void 0:c.data()),eyeL:await((y=(d=t.filter(f=>f.size===142))==null?void 0:d[0])==null?void 0:y.data()),irisR:await((m=(l=t.filter(f=>f.size===10))==null?void 0:l[1])==null?void 0:m.data()),eyeR:await((p=(x=t.filter(f=>f.size===142))==null?void 0:x[1])==null?void 0:p.data())};for(let f of Object.values(n))if(!f)return e;let r=Me.reduce((f,h)=>f+=e[h][2],0)/Me.length;for(let f=0;ff+=e[h][2],0)/Pe.length;for(let f=0;fR()-K0.timestamp,r=K0.skipped<(((d=t.face.detector)==null?void 0:d.skipFrames)||0);!t.skipAllowed||!n||!r||K0.boxes.length===0?(K0.boxes=await B1(e,t),K0.timestamp=R(),K0.skipped=0):K0.skipped++;let s=[],A=[],a=0,i=o2;for(let T=0;T[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/i]);for(let N of Object.keys(ve))k.annotations[N]=[k.mesh[ve[N]]]}else if(!K)t.debug&&b("face mesh detection requested, but model is not loaded");else{if((x=t.face.attention)!=null&&x.enabled&&!M.kernels.includes("atan2"))return t.face.attention.enabled=!1,o.dispose(k.tensor),s;let N=K.execute(k.tensor),V=await N.find(D=>D.shape[D.shape.length-1]===1).data();if(k.faceScore=Math.round(100*V[0])/100,k.faceScore<(((p=t.face.detector)==null?void 0:p.minConfidence)||1)){if(u.confidence=k.faceScore,t.face.mesh.keepInvalid){k.box=j2(u,e),k.boxRaw=I2(u,e),k.size=u.size,k.score=k.boxScore,k.mesh=u.landmarks,k.meshRaw=k.mesh.map(D=>[D[0]/(e.shape[2]||1),D[1]/(e.shape[1]||1),(D[2]||0)/i]);for(let D of Object.keys(ve))k.annotations[D]=[k.mesh[ve[D]]]}}else{let D=N.find(U=>U.shape[U.shape.length-1]===1404),Z=o.reshape(D,[-1,3]),J=await Z.array();o.dispose(Z),(f=t.face.attention)!=null&&f.enabled?J=await Y1(J,N):(h=t.face.iris)!=null&&h.enabled&&(J=await q1(J,k.tensor,o2)),k.mesh=L1(J,u,g,E,o2),k.meshRaw=k.mesh.map(U=>[U[0]/(e.shape[2]||0),U[1]/(e.shape[1]||0),(U[2]||0)/i]);for(let U of Object.keys(F0))k.annotations[U]=F0[U].map(m0=>k.mesh[m0]);k.score=k.faceScore;let q={...C1(k.mesh,u),confidence:u.confidence,landmarks:u.landmarks,size:u.size};k.box=j2(q,e),k.boxRaw=I2(q,e),k.size=q.size,A.push(q)}o.dispose(N)}k.score>(((v=t.face.detector)==null?void 0:v.minConfidence)||1)?s.push(k):o.dispose(k.tensor)}return K0.boxes=A,s}async function J1(e){var t,n,r,s,A,a;return M.initial&&(K=null),(t=e.face.attention)!=null&&t.enabled&&(K!=null&&K.signature)&&Object.keys(((n=K==null?void 0:K.signature)==null?void 0:n.outputs)||{}).length<6&&(K=null),K?e.debug&&b("cached model:",K.modelUrl):(r=e.face.attention)!=null&&r.enabled?K=await L(e.face.attention.modelPath):K=await L((s=e.face.mesh)==null?void 0:s.modelPath),o2=K.executor&&((A=K==null?void 0:K.inputs)!=null&&A[0].shape)?(a=K==null?void 0:K.inputs)==null?void 0:a[0].shape[2]:256,K}var Q1=Re,_1=t2;var Gt=[],d0,W2=[],$1=0,e3=0,Ht=Number.MAX_SAFE_INTEGER,Vt=!1;async function t3(e){var t,n,r;return M.initial&&(d0=null),d0?e.debug&&b("cached model:",d0.modelUrl):(d0=await L((t=e.face.emotion)==null?void 0:t.modelPath),Vt=((r=(n=d0==null?void 0:d0.inputs)==null?void 0:n[0].shape)==null?void 0:r[3])===3,Vt?Gt=["angry","disgust","fear","happy","neutral","sad","surprise"]:Gt=["angry","disgust","fear","happy","sad","surprise","neutral"]),d0}async function Zt(e,t,n,r){var a,i;if(!d0)return[];let s=Ht<(((a=t.face.emotion)==null?void 0:a.skipFrames)||0),A=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>R()-e3;return t.skipAllowed&&A&&s&&$1===r&&W2[n]&&W2[n].length>0?(Ht++,W2[n]):(Ht=0,new Promise(async c=>{var y,l,m;let d=[];if((y=t.face.emotion)!=null&&y.enabled){let x={},p=d0!=null&&d0.inputs[0].shape?d0.inputs[0].shape[2]:0;if(((l=t.face.emotion)==null?void 0:l.crop)>0){let h=(m=t.face.emotion)==null?void 0:m.crop,v=[[h,h,1-h,1-h]];x.resize=o.image.cropAndResize(e,v,[0],[p,p])}else x.resize=o.image.resizeBilinear(e,[p,p],!1);Vt?(x.mul=o.mul(x.resize,255),x.normalize=o.sub(x.mul,[103.939,116.779,123.68]),x.emotion=d0==null?void 0:d0.execute(x.normalize)):(x.channels=o.mul(x.resize,O.rgb),x.grayscale=o.sum(x.channels,3,!0),x.grayscaleSub=o.sub(x.grayscale,O.tf05),x.grayscaleMul=o.mul(x.grayscaleSub,O.tf2),x.emotion=d0==null?void 0:d0.execute(x.grayscaleMul)),e3=R();let f=await x.emotion.data();for(let h=0;h(t.face.emotion.minConfidence||0)&&d.push({score:Math.min(.99,Math.trunc(100*f[h])/100),emotion:Gt[h]});d.sort((h,v)=>v.score-h.score),Object.keys(x).forEach(h=>o.dispose(x[h]))}W2[n]=d,$1=r,c(d)}))}var x0,de=[],o3=0,r3=0,Xt=Number.MAX_SAFE_INTEGER;async function s3(e){var t;return M.initial&&(x0=null),x0?e.debug&&b("cached model:",x0.modelUrl):x0=await L((t=e.face.description)==null?void 0:t.modelPath),x0}function Ts(e,t){var A,a;let n=e.image||e.tensor||e;if(!(x0!=null&&x0.inputs[0].shape))return n;let r;if(((A=t.face.description)==null?void 0:A.crop)>0){let i=(a=t.face.description)==null?void 0:a.crop,c=[[i,i,1-i,1-i]];r=o.image.cropAndResize(n,c,[0],[x0.inputs[0].shape[2],x0.inputs[0].shape[1]])}else r=o.image.resizeBilinear(n,[x0.inputs[0].shape[2],x0.inputs[0].shape[1]],!1);let s=o.mul(r,O.tf255);return o.dispose(r),s}async function qt(e,t,n,r){var i,c,d,y;let s={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(x0!=null&&x0.executor))return s;let A=Xt<(((i=t.face.description)==null?void 0:i.skipFrames)||0),a=(((c=t.face.description)==null?void 0:c.skipTime)||0)>R()-o3;return t.skipAllowed&&A&&a&&r3===r&&((d=de==null?void 0:de[n])==null?void 0:d.age)>0&&((y=de==null?void 0:de[n])==null?void 0:y.genderScore)>0?(Xt++,de[n]):(Xt=0,new Promise(async l=>{var m;if((m=t.face.description)!=null&&m.enabled){let x=Ts(e,t),p=x0==null?void 0:x0.execute(x);o3=R(),o.dispose(x);let h=await p.find(W=>W.shape[1]===1).data(),v=Math.trunc(200*Math.abs(h[0]-.5))/100;v>(t.face.description.minConfidence||0)&&(s.gender=h[0]<=.5?"female":"male",s.genderScore=Math.min(.99,v));let T=o.argMax(p.find(W=>W.shape[1]===100),1),u=(await T.data())[0];o.dispose(T);let E=await p.find(W=>W.shape[1]===100).data();s.age=Math.round(E[u-1]>E[u+1]?10*u-100*E[u-1]:10*u+100*E[u+1])/10,(Number.isNaN(h[0])||Number.isNaN(E[0]))&&b("faceres error:",{model:x0,result:p});let k=p.find(W=>W.shape[1]===1024),N=k?await k.data():[];s.descriptor=Array.from(N),p.forEach(W=>o.dispose(W))}de[n]=s,r3=r,l(s)}))}var Ve=.1,Ut=.5;function vs(e,t,n){let r=!1,s=n.length-1;for(let A=0;At!=n[s].y>t&&e<(n[s].x-n[A].x)*(t-n[A].y)/(n[s].y-n[A].y)+n[A].x&&(r=!r);return r}async function a3(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,r=await e.tensor.buffer(),s=[];for(let a of F0.silhouette)s.push({x:(e.mesh[a][0]-e.box[0])/e.box[2],y:(e.mesh[a][1]-e.box[1])/e.box[3]});Ve&&Ve>0&&(s=s.map(a=>({x:a.x>.5?a.x+Ve:a.x-Ve,y:a.y>.5?a.y+Ve:a.y-Ve})));for(let a=0;aR()-l3,A=Yt<(((i=t.face.antispoof)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&A&&i3===r&&D2[n]?(Yt++,D2[n]):(Yt=0,new Promise(async c=>{let d=o.image.resizeBilinear(e,[y0!=null&&y0.inputs[0].shape?y0.inputs[0].shape[2]:0,y0!=null&&y0.inputs[0].shape?y0.inputs[0].shape[1]:0],!1),y=y0==null?void 0:y0.execute(d),l=(await y.data())[0];D2[n]=Math.round(100*l)/100,i3=r,l3=R(),o.dispose([d,y]),c(D2[n])}))}var f0,F2=[],Jt=Number.MAX_SAFE_INTEGER,x3=0,y3=0;async function f3(e){var t;return M.initial&&(f0=null),f0?e.debug&&b("cached model:",f0.modelUrl):f0=await L((t=e.face.liveness)==null?void 0:t.modelPath),f0}async function Qt(e,t,n,r){var a,i;if(!(f0!=null&&f0.executor))return 0;let s=(((a=t.face.liveness)==null?void 0:a.skipTime)||0)>R()-y3,A=Jt<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&A&&x3===r&&F2[n]?(Jt++,F2[n]):(Jt=0,new Promise(async c=>{let d=o.image.resizeBilinear(e,[f0!=null&&f0.inputs[0].shape?f0.inputs[0].shape[2]:0,f0!=null&&f0.inputs[0].shape?f0.inputs[0].shape[1]:0],!1),y=f0==null?void 0:f0.execute(d),l=(await y.data())[0];F2[n]=Math.round(100*l)/100,x3=r,y3=R(),o.dispose([d,y]),c(F2[n])}))}var B0,_t=[],Ms=["white","black","asian","indian","other"],Ps=[15,23,28,35.5,45.5,55.5,65],p3=0,u3=0,$t=Number.MAX_SAFE_INTEGER;async function h3(e){var t;return M.initial&&(B0=null),B0?e.debug&&b("cached model:",B0.modelUrl):B0=await L((t=e.face.gear)==null?void 0:t.modelPath),B0}async function e5(e,t,n,r){var a,i;if(!B0)return{age:0,gender:"unknown",genderScore:0,race:[]};let s=$t<(((a=t.face.gear)==null?void 0:a.skipFrames)||0),A=(((i=t.face.gear)==null?void 0:i.skipTime)||0)>R()-u3;return t.skipAllowed&&A&&s&&p3===r&&_t[n]?($t++,_t[n]):($t=0,new Promise(async c=>{var v,T,u,g;if(!(B0!=null&&B0.inputs[0].shape))return;let d={},y=[[0,.1,.9,.9]];if(((v=t.face.gear)==null?void 0:v.crop)>0){let E=(T=t.face.gear)==null?void 0:T.crop;y=[[E,E,1-E,1-E]]}d.resize=o.image.cropAndResize(e,y,[0],[B0.inputs[0].shape[2],B0.inputs[0].shape[1]]);let l={age:0,gender:"unknown",genderScore:0,race:[]};(u=t.face.gear)!=null&&u.enabled&&([d.age,d.gender,d.race]=B0.execute(d.resize,["age_output","gender_output","race_output"]));let m=await d.gender.data();l.gender=m[0]>m[1]?"male":"female",l.genderScore=Math.round(100*(m[0]>m[1]?m[0]:m[1]))/100;let x=await d.race.data();for(let E=0;E(((g=t.face.gear)==null?void 0:g.minConfidence)||.2)&&l.race.push({score:Math.round(100*x[E])/100,race:Ms[E]});l.race.sort((E,k)=>k.score-E.score);let f=Array.from(await d.age.data()).map((E,k)=>[Ps[k],E]).sort((E,k)=>k[1]-E[1]),h=f[0][0];for(let E=1;Eo.dispose(d[E])),_t[n]=l,p3=r,u3=R(),c(l)}))}var 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h=Math.sqrt(s*s+i*i),v=Math.sqrt(A*A+c*c),T=Math.sqrt(a*a+d*d),u=Math.max(h,v,T),g=e[0],E=e[1],k=n[0],N=n[1];u===h?(k=n[0],N=n[1]):u===T&&(g=t[0],E=t[1]);let D=V3([g,E],[k,N]),Z=G3(D,je.TOTAL_ANGLE_VOTE_POWER);m+=Z[0],x+=Z[1],p+=Z[2];for(let q of r){let U=G3(q,je.SINGLE_ANGLE_VOTE_POWER);m+=U[0],x+=U[1],p+=U[2]}let J;return m===Math.max(m,x,p)?J=X3(c,i,d,l):p===Math.max(x,p)?J=Z3(A,s,a,y):J=Ns(c,i,d,l,A,s,a,y),J}function q3(e){let t=[],n=[],r=[],s=[];if(!e)return{curls:r,directions:s};for(let A of M0.all){let a=M0.getPoints(A),i=[],c=[];for(let d of a){let y=e[d[0]],l=e[d[1]],m=V3(y,l),x=m[0],p=m[1];i.push(x),c.push(p)}t.push(i),n.push(c)}for(let A of M0.all){let a=A===M0.thumb?1:0,i=M0.getPoints(A),c=e[i[a][0]],d=e[i[a+1][1]],y=e[i[3][1]],l=Is(c,d,y),m=Ls(c,d,y,t[A].slice(a));r[A]=l,s[A]=m}return{curls:r,directions:s}}function G2(e){if(!e||e.length===0)return null;let t=q3(e),n={};for(let r of M0.all)n[M0.getName(r)]={curl:ye.getName(t.curls[r]),direction:$.getName(t.directions[r])};return n}function U3(e){let t=[];if(!e||e.length===0)return t;let n=q3(e);for(let r of B3){let s=r.matchAgainst(n.curls,n.directions);s>=js&&t.push({name:r.name,confidence:s})}return t}var Y3=e=>{if(!e)return[];let t=[];for(let n=0;nc.part==="leftWrist"),s=e[n].keypoints.find(c=>c.part==="rightWrist"),A=e[n].keypoints.find(c=>c.part==="nose");A&&r&&s&&r.position[1]c.part==="leftShoulder"),i=e[n].keypoints.find(c=>c.part==="rightShoulder");a&&i&&Math.abs(a.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${a.position[1]>i.position[1]?"left":"right"}`})}return t},K3=e=>{if(!e)return[];let t=[];for(let n=0;n450){let r=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),s=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(r/s)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${r<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let c=e[n].mesh[152][2]||0;Math.abs(c)>10&&t.push({face:n,gesture:`head ${c<0?"up":"down"}`})}return t},J3=e=>{var n,r,s,A;if(!e)return[];let t=[];for(let a=0;a.06||h>.06)&&(x=!1),f>h?h>.04&&t.push({iris:a,gesture:"looking right"}):f>.04&&t.push({iris:a,gesture:"looking left"});let v=Math.abs(e[a].mesh[145][1]-e[a].annotations.rightEyeIris[0][1])/e[a].box[3],T=Math.abs(e[a].mesh[374][1]-e[a].annotations.leftEyeIris[0][1])/e[a].box[3];(T<.01||v<.01||T>.022||v>.022)&&(x=!1),(T<.01||v<.01)&&t.push({iris:a,gesture:"looking down"}),(T>.022||v>.022)&&t.push({iris:a,gesture:"looking up"}),x&&t.push({iris:a,gesture:"looking center"})}return t},Q3=e=>{if(!e)return[];let t=[];for(let n=0;n0){let s=r.reduce((a,i)=>(a.position[2]||0)<(i.position[2]||0)?a:i);t.push({hand:n,gesture:`${s.name} forward`});let A=r.reduce((a,i)=>a.position[1][A[0]*t[0],A[1]*t[1]]);return{startPoint:n,endPoint:r,palmLandmarks:s,confidence:e.confidence}}function Z2(e,t=1.5){let n=r2(e),r=V2(e),s=[t*r[0]/2,t*r[1]/2],A=[n[0]-s[0],n[1]-s[1]],a=[n[0]+s[0],n[1]+s[1]];return{startPoint:A,endPoint:a,palmLandmarks:e.palmLandmarks}}function X2(e){let t=r2(e),n=V2(e),s=Math.max(...n)/2,A=[t[0]-s,t[1]-s],a=[t[0]+s,t[1]+s];return{startPoint:A,endPoint:a,palmLandmarks:e.palmLandmarks}}function Cs(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function nn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Cs(n)}var _3=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ue(e,t){let n=0;for(let r=0;r[a.x,a.y]),this.anchorsTensor=o.tensor2d(this.anchors),this.inputSize=((A=(s=(r=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:r[0])==null?void 0:s.shape)==null?void 0:A[2])||0,this.inputSizeTensor=o.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=o.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=o.slice(t,[0,0],[-1,2]),n.boxSizes=o.slice(t,[0,2],[-1,2]),n.div=o.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=o.add(n.div,this.anchorsTensor),n.halfBoxSizes=o.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=o.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=o.mul(n.sub,this.inputSizeTensor),n.add=o.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=o.mul(n.add,this.inputSizeTensor);let r=o.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(s=>o.dispose(n[s])),r}normalizeLandmarks(t,n){let r={};r.reshape=o.reshape(t,[-1,7,2]),r.div=o.div(r.reshape,this.inputSizeTensor),r.landmarks=o.add(r.div,this.anchors[n]?this.anchors[n]:0);let s=o.mul(r.landmarks,this.inputSizeTensor);return Object.keys(r).forEach(A=>o.dispose(r[A])),s}async predict(t,n){var i;let r={};r.resize=o.image.resizeBilinear(t,[this.inputSize,this.inputSize]),r.div=o.div(r.resize,O.tf127),r.image=o.sub(r.div,O.tf1),r.batched=this.model.execute(r.image),r.predictions=o.squeeze(r.batched),r.slice=o.slice(r.predictions,[0,0],[-1,1]),r.sigmoid=o.sigmoid(r.slice),r.scores=o.squeeze(r.sigmoid);let s=await r.scores.data();r.boxes=o.slice(r.predictions,[0,1],[-1,4]),r.norm=this.normalizeBoxes(r.boxes),r.nms=await o.image.nonMaxSuppressionAsync(r.norm,r.scores,3*(((i=n.hand)==null?void 0:i.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let A=await r.nms.array(),a=[];for(let c of A){let d={};d.box=o.slice(r.norm,[c,0],[1,-1]),d.slice=o.slice(r.predictions,[c,5],[1,14]),d.norm=this.normalizeLandmarks(d.slice,c),d.palmLandmarks=o.reshape(d.norm,[-1,2]);let y=await d.box.data(),l=y.slice(0,2),m=y.slice(2,4),x=await d.palmLandmarks.array(),p={startPoint:l,endPoint:m,palmLandmarks:x,confidence:s[c]},f=tn(p,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);a.push(f),Object.keys(d).forEach(h=>o.dispose(d[h]))}return Object.keys(r).forEach(c=>o.dispose(r[c])),a}};var Bs=5,An=1.65,an=[0,5,9,13,17,1,2],Hs=0,Gs=2,ln=0,U2=class{constructor(t,n){z(this,"handDetector");z(this,"handPoseModel");z(this,"inputSize");z(this,"storedBoxes");z(this,"skipped");z(this,"detectedHands");var r,s,A;this.handDetector=t,this.handPoseModel=n,this.inputSize=((A=(s=(r=this.handPoseModel)==null?void 0:r.inputs)==null?void 0:s[0].shape)==null?void 0:A[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(a=>a[0]),r=t.map(a=>a[1]),s=[Math.min(...n),Math.min(...r)],A=[Math.max(...n),Math.max(...r)];return{startPoint:s,endPoint:A}}getBoxForPalmLandmarks(t,n){let r=t.map(A=>f5([...A,1],n)),s=this.calculateLandmarksBoundingBox(r);return Z2(X2(s),Bs)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),r=Z2(X2(n),An);r.palmLandmarks=[];for(let s=0;s[a[0]*(x[0]-this.inputSize/2),a[1]*(x[1]-this.inputSize/2),a[2]*x[2]]),c=y5(r,[0,0]),d=i.map(x=>[...f5(x,c),x[2]]),y=on(s),l=[...r2(n),1],m=[ue(l,y[0]),ue(l,y[1])];return d.map(x=>[Math.trunc(x[0]+m[0]),Math.trunc(x[1]+m[1]),Math.trunc(x[2])])}async estimateHands(t,n){let r=!1,s,A=(n.hand.skipTime||0)>R()-ln,a=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&A&&a?this.skipped++:(s=await this.handDetector.predict(t,n),this.skipped=0),s&&s.length>0&&(s.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...s],this.storedBoxes.length>0&&(r=!0));let i=[];for(let c=0;c=n.hand.minConfidence/4){let E=o.reshape(u,[-1,3]),k=await E.array();o.dispose(u),o.dispose(E);let N=this.transformRawCoords(k,f,y,p),W=this.getBoxForHandLandmarks(N);this.storedBoxes[c]={...W,confidence:g};let V={landmarks:N,confidence:g,boxConfidence:d.confidence,fingerConfidence:g,box:{topLeft:W.startPoint,bottomRight:W.endPoint}};i.push(V)}else this.storedBoxes[c]=null;o.dispose(u)}else{let y=Z2(X2(d),An),l={confidence:d.confidence,boxConfidence:d.confidence,fingerConfidence:0,box:{topLeft:y.startPoint,bottomRight:y.endPoint},landmarks:[]};i.push(l)}}return this.storedBoxes=this.storedBoxes.filter(c=>c!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var cn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},Ie,Ne,m5;function Zs(){let e=Ie?new q2(Ie):void 0;e&&Ne&&(m5=new U2(e,Ne))}async function p5(e,t){m5||Zs();let n=await m5.estimateHands(e,t);if(!n)return[];let r=[];for(let s=0;sn[s].landmarks[l]);let a=n[s].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],c=[0,0,0,0];if(a&&a.length>0){for(let y of a)y[0]i[2]&&(i[2]=y[0]),y[1]>i[3]&&(i[3]=y[1]);i[2]-=i[0],i[3]-=i[1],c=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[s].box?[Math.trunc(Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.max(0,n[s].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[s].box.bottomRight[0])-Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[s].box.bottomRight[1])-Math.max(0,n[s].box.topLeft[1]))]:[0,0,0,0],c=[n[s].box.topLeft[0]/(e.shape[2]||0),n[s].box.topLeft[1]/(e.shape[1]||0),(n[s].box.bottomRight[0]-n[s].box.topLeft[0])/(e.shape[2]||0),(n[s].box.bottomRight[1]-n[s].box.topLeft[1])/(e.shape[1]||0)];let d=G2(a);r.push({id:s,score:Math.round(100*n[s].confidence)/100,boxScore:Math.round(100*n[s].boxConfidence)/100,fingerScore:Math.round(100*n[s].fingerConfidence)/100,label:"hand",box:i,boxRaw:c,keypoints:a,annotations:A,landmarks:d})}return r}async function dn(e){var t;return M.initial&&(Ie=null),Ie?e.debug&&b("cached model:",Ie.modelUrl):Ie=await L((t=e.hand.detector)==null?void 0:t.modelPath),Ie}async function xn(e){var t;return M.initial&&(Ne=null),Ne?e.debug&&b("cached model:",Ne.modelUrl):Ne=await L((t=e.hand.skeleton)==null?void 0:t.modelPath),Ne}var r0=[null,null],Xs=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],he=[[0,0],[0,0]],qs=["hand","fist","pinch","point","face","tip","pinchtip"],fn=4,mn=1.6,Us=512,Ys=1.4,Y2=Number.MAX_SAFE_INTEGER,u5=0,ee=[0,0],o0={boxes:[],hands:[]},pn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function un(e){var t;if(M.initial&&(r0[0]=null),r0[0])e.debug&&b("cached model:",r0[0].modelUrl);else{h2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),r0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=r0[0].executor?Object.values(r0[0].modelSignature.inputs):void 0;he[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,he[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return r0[0]}async function hn(e){var t;if(M.initial&&(r0[1]=null),r0[1])e.debug&&b("cached model:",r0[1].modelUrl);else{r0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=r0[1].executor?Object.values(r0[1].modelSignature.inputs):void 0;he[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,he[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return r0[1]}async function Ks(e,t){let n=[];if(!e||!r0[0])return n;let r={},s=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,Us),a=Math.round(A*s/8)*8;r.resize=o.image.resizeBilinear(e,[A,a]),r.cast=o.cast(r.resize,"int32"),[r.rawScores,r.rawBoxes]=await r0[0].executeAsync(r.cast,Xs),r.boxes=o.squeeze(r.rawBoxes,[0,2]),r.scores=o.squeeze(r.rawScores,[0]);let i=o.unstack(r.scores,1);o.dispose(i[fn]),i.splice(fn,1),r.filtered=o.stack(i,1),o.dispose(i),r.max=o.max(r.filtered,1),r.argmax=o.argMax(r.filtered,1);let c=0;r.nms=await o.image.nonMaxSuppressionAsync(r.boxes,r.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let d=await r.nms.data(),y=await r.max.data(),l=await r.argmax.data();for(let m of Array.from(d)){let x=o.slice(r.boxes,m,1),p=await x.data();o.dispose(x);let f=[p[1],p[0],p[3]-p[1],p[2]-p[0]],h=k2(f,Ys),v=[Math.trunc(f[0]*ee[0]),Math.trunc(f[1]*ee[1]),Math.trunc(f[2]*ee[0]),Math.trunc(f[3]*ee[1])],T=y[m],u=qs[l[m]],g={id:c++,score:T,box:v,boxRaw:h,label:u};n.push(g)}return Object.keys(r).forEach(m=>o.dispose(r[m])),n.sort((m,x)=>x.score-m.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function h5(e,t,n){let r={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&r0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let s={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];s.crop=o.image.cropAndResize(e,[A],[0],[he[1][0],he[1][1]],"bilinear"),s.div=o.div(s.crop,O.tf255),[s.score,s.keypoints]=r0[1].execute(s.div,["Identity_1","Identity"]);let a=(await s.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(n.hand.minConfidence||0)){r.fingerScore=i,s.reshaped=o.reshape(s.keypoints,[-1,3]);let y=(await s.reshaped.array()).map(l=>[l[0]/he[1][1],l[1]/he[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);r.keypoints=y.map(l=>[ee[0]*(l[0]+t.boxRaw[0]),ee[1]*(l[1]+t.boxRaw[1]),l[2]||0]),r.landmarks=G2(r.keypoints);for(let l of Object.keys(pn))r.annotations[l]=pn[l].map(m=>r.landmarks&&r.keypoints[m]?r.keypoints[m]:null)}Object.keys(s).forEach(c=>o.dispose(s[c]))}return r}async function b5(e,t){var s,A;if(!((s=r0[0])!=null&&s.executor)||!((A=r0[1])!=null&&A.executor)||!r0[0].inputs[0].shape||!r0[1].inputs[0].shape)return[];ee=[e.shape[2]||0,e.shape[1]||0],Y2++;let n=(t.hand.skipTime||0)>R()-u5,r=Y2<(t.hand.skipFrames||0);return t.skipAllowed&&n&&r?o0.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>R()-u5,c=Y2<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(y=>h5(e,y,t))):t.skipAllowed&&i&&c&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(y=>h5(e,y,t))):(o0.boxes=await Ks(e,t),u5=R(),o0.hands=await Promise.all(o0.boxes.map(y=>h5(e,y,t))),Y2=0);let d=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let y=0;y.05&&l.box[3]/(e.shape[1]||1)>.05&&o0.hands[y].fingerScore&&o0.hands[y].fingerScore>(t.hand.minConfidence||0)){let m=k2(l.box,mn),x=k2(l.boxRaw,mn);o0.boxes.push({...d[y],box:m,boxRaw:x})}}for(let y=0;y({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var s2={};oe(s2,{connected:()=>J2,horizontal:()=>g5,kpt:()=>K2,relative:()=>v5,vertical:()=>T5});var K2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],g5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],T5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],v5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],J2={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var j=te(),R5=0;function gn(e,t){var a,i,c,d,y,l,m,x,p,f,h,v,T,u,g,E,k,N,W,V,D,Z,J,q,U,m0;let n=R();if(!e)return te();let r=Date.now()-e.timestamp,s=r<1e3?8-Math.log(r+1):1;if(e.canvas&&(j.canvas=e.canvas),e.error&&(j.error=e.error),!j.body||e.body.length!==j.body.length)j.body=JSON.parse(JSON.stringify(e.body));else for(let P=0;P((s-1)*j.body[P].box[F]+C)/s),g0=e.body[P].boxRaw.map((C,F)=>((s-1)*j.body[P].boxRaw[F]+C)/s),e0=e.body[P].keypoints.map((C,F)=>{var w0,t0,ne,Ke,Oe,H5,G5,V5,Z5;return{score:C.score,part:C.part,position:[j.body[P].keypoints[F]?((s-1)*(j.body[P].keypoints[F].position[0]||0)+(C.position[0]||0))/s:C.position[0],j.body[P].keypoints[F]?((s-1)*(j.body[P].keypoints[F].position[1]||0)+(C.position[1]||0))/s:C.position[1],j.body[P].keypoints[F]?((s-1)*(j.body[P].keypoints[F].position[2]||0)+(C.position[2]||0))/s:C.position[2]],positionRaw:[j.body[P].keypoints[F]?((s-1)*(j.body[P].keypoints[F].positionRaw[0]||0)+(C.positionRaw[0]||0))/s:C.positionRaw[0],j.body[P].keypoints[F]?((s-1)*(j.body[P].keypoints[F].positionRaw[1]||0)+(C.positionRaw[1]||0))/s:C.positionRaw[1],j.body[P].keypoints[F]?((s-1)*(j.body[P].keypoints[F].positionRaw[2]||0)+(C.positionRaw[2]||0))/s:C.positionRaw[2]],distance:[j.body[P].keypoints[F]?((s-1)*(((w0=j.body[P].keypoints[F].distance)==null?void 0:w0[0])||0)+(((t0=C.distance)==null?void 0:t0[0])||0))/s:(ne=C.distance)==null?void 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P=0;P((s-1)*j.hand[P].box[C]+S)/s),g0=e.hand[P].boxRaw.map((S,C)=>((s-1)*j.hand[P].boxRaw[C]+S)/s);j.hand[P].keypoints.length!==e.hand[P].keypoints.length&&(j.hand[P].keypoints=e.hand[P].keypoints);let e0=e.hand[P].keypoints&&e.hand[P].keypoints.length>0?e.hand[P].keypoints.map((S,C)=>S.map((F,w0)=>((s-1)*(j.hand[P].keypoints[C][w0]||1)+(F||0))/s)):[],G={};if(Object.keys(j.hand[P].annotations).length!==Object.keys(e.hand[P].annotations).length)j.hand[P].annotations=e.hand[P].annotations,G=j.hand[P].annotations;else if(e.hand[P].annotations)for(let S of Object.keys(e.hand[P].annotations))G[S]=(l=(y=(d=e.hand[P])==null?void 0:d.annotations)==null?void 0:y[S])!=null&&l[0]?e.hand[P].annotations[S].map((C,F)=>C.map((w0,t0)=>((s-1)*j.hand[P].annotations[S][F][t0]+w0)/s)):null;j.hand[P]={...e.hand[P],box:i0,boxRaw:g0,keypoints:e0,annotations:G}}if(!j.face||e.face.length!==j.face.length)j.face=JSON.parse(JSON.stringify(e.face));else for(let P=0;P((s-1)*j.face[P].box[S]+G)/s),g0=e.face[P].boxRaw.map((G,S)=>((s-1)*j.face[P].boxRaw[S]+G)/s),e0=e.face[P].annotations;if(Object.keys(j.face[P].annotations).length!==Object.keys(e.face[P].annotations).length)j.face[P].annotations=e.face[P].annotations,e0=j.face[P].annotations;else if(e.face[P].annotations)for(let G of Object.keys(e.face[P].annotations))e0[G]=(p=(x=(m=e.face[P])==null?void 0:m.annotations)==null?void 0:x[G])!=null&&p[0]?e.face[P].annotations[G].map((S,C)=>S.map((F,w0)=>((s-1)*j.face[P].annotations[G][C][w0]+F)/s)):null;if(e.face[P].rotation){let G={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};G.matrix=(f=e.face[P].rotation)==null?void 0:f.matrix,G.angle={roll:((s-1)*(((v=(h=j.face[P].rotation)==null?void 0:h.angle)==null?void 0:v.roll)||0)+(((u=(T=e.face[P].rotation)==null?void 0:T.angle)==null?void 0:u.roll)||0))/s,yaw:((s-1)*(((E=(g=j.face[P].rotation)==null?void 0:g.angle)==null?void 0:E.yaw)||0)+(((N=(k=e.face[P].rotation)==null?void 0:k.angle)==null?void 0:N.yaw)||0))/s,pitch:((s-1)*(((V=(W=j.face[P].rotation)==null?void 0:W.angle)==null?void 0:V.pitch)||0)+(((Z=(D=e.face[P].rotation)==null?void 0:D.angle)==null?void 0:Z.pitch)||0))/s},G.gaze={bearing:((s-1)*(((J=j.face[P].rotation)==null?void 0:J.gaze.bearing)||0)+(((q=e.face[P].rotation)==null?void 0:q.gaze.bearing)||0))/s,strength:((s-1)*(((U=j.face[P].rotation)==null?void 0:U.gaze.strength)||0)+(((m0=e.face[P].rotation)==null?void 0:m0.gaze.strength)||0))/s},j.face[P]={...e.face[P],rotation:G,box:i0,boxRaw:g0,annotations:e0}}else j.face[P]={...e.face[P],box:i0,boxRaw:g0,annotations:e0}}if(!j.object||e.object.length!==j.object.length)j.object=JSON.parse(JSON.stringify(e.object));else for(let P=0;P((s-1)*j.object[P].box[G]+e0)/s),g0=e.object[P].boxRaw.map((e0,G)=>((s-1)*j.object[P].boxRaw[G]+e0)/s);j.object[P]={...e.object[P],box:i0,boxRaw:g0}}if(e.persons){let P=e.persons;if(!j.persons||P.length!==j.persons.length)j.persons=JSON.parse(JSON.stringify(P));else for(let i0=0;i0((s-1)*j.persons[i0].box[e0]+g0)/s)}e.gesture&&(j.gesture=e.gesture),j.width=e.width,j.height=e.height;let A=R();return R5=M.perfadd?R5+Math.round(A-n):Math.round(A-n),e.performance&&(j.performance={...e.performance,interpolate:R5}),j}var h0;async function M5(e){return!h0||M.initial?h0=await L(e.segmentation.modelPath):e.debug&&b("cached model:",h0.modelUrl),h0}async function Tn(e,t){var s;if(h0||(h0=await M5(t)),!(h0!=null&&h0.executor)||!((s=h0==null?void 0:h0.inputs)!=null&&s[0].shape))return null;let n={};n.resize=o.image.resizeBilinear(e,[h0.inputs[0].shape?h0.inputs[0].shape[1]:0,h0.inputs[0].shape?h0.inputs[0].shape[2]:0],!1),n.norm=o.div(n.resize,O.tf255),n.res=h0.execute(n.norm),n.squeeze=o.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=o.unstack(n.squeeze,2),n.fg=o.softmax(n.fgRaw),n.mul=o.mul(n.fg,O.tf255),n.expand=o.expandDims(n.mul,2),n.output=o.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let r;switch(t.segmentation.mode||"default"){case"default":n.input=o.squeeze(e),n.concat=o.concat([n.input,n.output],-1),r=o.cast(n.concat,"int32");break;case"alpha":r=o.cast(n.output,"int32");break;default:r=o.tensor(0)}return Object.keys(n).forEach(A=>o.dispose(n[A])),r}var k5={};oe(k5,{distance:()=>P5,find:()=>_s,similarity:()=>Qs});function P5(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let r=0;for(let s=0;s{if(e===0)return 1;let A=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(r-n);return Math.max(Math.min(A,1),0)};function Qs(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let r=P5(e,t,n);return Rn(r,n.order||2,n.min||0,n.max||1)}function _s(e,t,n={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let r=Number.MAX_SAFE_INTEGER,s=-1;for(let a=0;ai2,validateModel:()=>ot});var Mn=.005,I0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function w5(e){for(let t of g5){let n=e.keypoints.findIndex(s=>s.part===t[0]),r=e.keypoints.findIndex(s=>s.part===t[1]);if(e.keypoints[n]&&e.keypoints[r]&&e.keypoints[n].position[0]s&&s.part===t[0]),r=e.keypoints.findIndex(s=>s&&s.part===t[1]);e.keypoints[n]&&e.keypoints[r]&&e.keypoints[n].position[1]d&&d.part===t[0]),s=e.keypoints.findIndex(d=>d&&d.part===t[1]),A=e.keypoints.findIndex(d=>d&&d.part===n[0]),a=e.keypoints.findIndex(d=>d&&d.part===n[1]);if(!e.keypoints[A]||!e.keypoints[a])continue;let i=e.keypoints[r]?[Math.abs(e.keypoints[A].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[r].position[0])]:[0,0],c=e.keypoints[s]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[s].position[0]),Math.abs(e.keypoints[A].position[0]-e.keypoints[s].position[0])]:[0,0];if(i[0]>i[1]||c[0]>c[1]){let d=e.keypoints[r];e.keypoints[r]=e.keypoints[s],e.keypoints[s]=d}}}function Pn(e){for(let t=0;te.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],n.pad=o.pad(e,I0.padding),n.resize=o.image.resizeBilinear(n.pad,[t,t]);let r=o.cast(n.resize,"int32");return Object.keys(n).forEach(a=>o.dispose(n[a])),r}function wn(e,t){e.keypoints=e.keypoints.filter(r=>r==null?void 0:r.position);for(let r of e.keypoints)r.position=[r.position[0]*(t[0]+I0.padding[2][0]+I0.padding[2][1])/t[0]-I0.padding[2][0],r.position[1]*(t[1]+I0.padding[1][0]+I0.padding[1][1])/t[1]-I0.padding[1][0]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1]];let n=ae(e.keypoints.map(r=>r.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var A0,Q2=0,E5=Number.MAX_SAFE_INTEGER,Le={boxes:[],bodies:[],last:0};async function En(e){var t;return M.initial&&(A0=null),A0?e.debug&&b("cached model:",A0.modelUrl):(h2(["size"],e),A0=await L(e.body.modelPath)),Q2=A0!=null&&A0.executor&&((t=A0==null?void 0:A0.inputs)!=null&&t[0].shape)?A0.inputs[0].shape[2]:0,Q2<64&&(Q2=256),o.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS&&o.env().set("WEBGL_USE_SHAPES_UNIFORMS",!1),A0}function eA(e,t,n){let r=e[0][0],s=[],A=0;for(let y=0;yt.body.minConfidence){let l=[r[y][1],r[y][0]];s.push({score:Math.round(100*A)/100,part:K2[y],positionRaw:l,position:[Math.round((n.shape[2]||0)*l[0]),Math.round((n.shape[1]||0)*l[1])]})}A=s.reduce((y,l)=>l.score>y?l.score:y,0);let a=[],i=ae(s.map(y=>y.position),[n.shape[2],n.shape[1]]),c={};for(let[y,l]of Object.entries(J2)){let m=[];for(let x=0;xh.part===l[x]),f=s.find(h=>h.part===l[x+1]);p&&f&&p.score>(t.body.minConfidence||0)&&f.score>(t.body.minConfidence||0)&&m.push([p.position,f.position])}c[y]=m}let d={id:0,score:A,box:i.box,boxRaw:i.boxRaw,keypoints:s,annotations:c};return w5(d),a.push(d),a}function tA(e,t,n){let r=[];for(let s=0;st.body.minConfidence){let i=[];for(let m=0;m<17;m++){let x=A[3*m+2];if(x>t.body.minConfidence){let p=[A[3*m+1],A[3*m+0]];i.push({part:K2[m],score:Math.round(100*x)/100,positionRaw:p,position:[Math.round((n.shape[2]||0)*p[0]),Math.round((n.shape[1]||0)*p[1])]})}}let c=[A[52],A[51],A[54]-A[52],A[53]-A[51]],d=[Math.trunc(c[0]*(n.shape[2]||0)),Math.trunc(c[1]*(n.shape[1]||0)),Math.trunc(c[2]*(n.shape[2]||0)),Math.trunc(c[3]*(n.shape[1]||0))],y={};for(let[m,x]of Object.entries(J2)){let p=[];for(let f=0;fT.part===x[f]),v=i.find(T=>T.part===x[f+1]);h&&v&&h.score>(t.body.minConfidence||0)&&v.score>(t.body.minConfidence||0)&&p.push([h.position,v.position])}y[m]=p}let l={id:s,score:a,box:d,boxRaw:c,keypoints:[...i],annotations:y};w5(l),r.push(l)}}return r.sort((s,A)=>A.score-s.score),r.length>t.body.maxDetected&&(r.length=t.body.maxDetected),r}async function z5(e,t){var s;if(!(A0!=null&&A0.executor)||!((s=A0==null?void 0:A0.inputs)!=null&&s[0].shape))return[];t.skipAllowed||(Le.boxes.length=0),E5++;let n=(t.body.skipTime||0)>R()-Le.last,r=E5<(t.body.skipFrames||0);return t.skipAllowed&&n&&r?Le.bodies:new Promise(async A=>{let a={};E5=0,a.input=kn(e,Q2),a.res=A0==null?void 0:A0.execute(a.input),Le.last=R();let i=await a.res.array();Le.bodies=a.res.shape[2]===17?eA(i,t,e):tA(i,t,e);for(let c of Le.bodies)wn(c,[e.shape[2]||1,e.shape[1]||1]),Pn(c.keypoints);Object.keys(a).forEach(c=>o.dispose(a[c])),A(Le.bodies)})}var H0,_2=[],Sn=0,S5=Number.MAX_SAFE_INTEGER,et=0,$2=2.5;async function jn(e){if(!H0||M.initial){H0=await L(e.object.modelPath);let t=H0!=null&&H0.executor?Object.values(H0.modelSignature.inputs):void 0;et=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&b("cached model:",H0.modelUrl);return H0}async function nA(e,t,n){var d,y;let r=0,s=[],A=et;for(let l of[1,2,4]){let m=l*13,x=o.squeeze(e.find(u=>u.shape[1]===m**2&&(u.shape[2]||0)===De.length)),p=await x.array(),f=o.squeeze(e.find(u=>u.shape[1]===m**2&&(u.shape[2]||0)(n.object.minConfidence||0)&&g!==61){let k=(.5+Math.trunc(u%m))/m,N=(.5+Math.trunc(u/m))/m,W=T[u].map(P=>P*(m/l/A)),[V,D]=[k-$2/l*W[0],N-$2/l*W[1]],[Z,J]=[k+$2/l*W[2]-V,N+$2/l*W[3]-D],q=[V,D,Z,J];q=q.map(P=>Math.max(0,Math.min(P,1)));let 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Ye,l2,c2,At,be,Kn=class{constructor(t){z(this,"version");z(this,"config");z(this,"result");z(this,"state");z(this,"process");z(this,"tf");z(this,"env",M);z(this,"draw",Tt);z(this,"match",P5);z(this,"models");z(this,"events");z(this,"faceTriangulation");z(this,"faceUVMap");z(this,"performance");q0(this,Ye,void 0);q0(this,l2,void 0);q0(this,c2,void 0);z(this,"analyze",(...t)=>{if(!E0(this,l2))return;let n=this.tf.engine().state.numTensors,o=E0(this,Ye);Q0(this,Ye,n);let s=n-o;s!==0&&b(...t,s)});q0(this,At,t=>{if(!E0(this,c2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof r.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});z(this,"webcam",new u2);z(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});q0(this,be,{});let n=(Je.tfjs||r.version_core).replace(/-(.*)/,"");Ce.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ce.modelBasePath=M.browser?"../models/":"file://models/",this.version=yt,Object.defineProperty(this,"version",{value:yt}),this.config=JSON.parse(JSON.stringify(Ce)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Q(this.config,t)),o1(this.config),this.tf=r,this.state="idle",Q0(this,Ye,0),Q0(this,l2,!1),Q0(this,c2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new i2(this),gt(),this.result=te(),this.process={tensor:null,canvas:null},this.faceTriangulation=J1,this.faceUVMap=Q1,ot(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&b(`version: ${this.version}`),this.config.debug&&b(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete 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m=this.config.body.maxDetected===-1?Q(this.config,{body:{maxDetected:this.config.face.enabled?1*c.length:1}}):this.config;this.config.async?((h=this.config.body.modelPath)!=null&&h.includes("posenet")?d=this.config.body.enabled?O5(i.tensor,m):[]:(v=this.config.body.modelPath)!=null&&v.includes("blazepose")?d=this.config.body.enabled?kt(i.tensor,m):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?d=this.config.body.enabled?Nt(i.tensor,m):[]:(u=this.config.body.modelPath)!=null&&u.includes("movenet")&&(d=this.config.body.enabled?E5(i.tensor,m):[]),this.performance.body&&delete this.performance.body):(s=R(),(g=this.config.body.modelPath)!=null&&g.includes("posenet")?d=this.config.body.enabled?await O5(i.tensor,m):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?d=this.config.body.enabled?await kt(i.tensor,m):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?d=this.config.body.enabled?await 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t=o.getBackend(),n=o.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;o.env().set("ENGINE_COMPILE_ONLY",!0);let r=o.engine().state.numTensors,s=[];for(let[l,m]of Object.entries(e.models.models)){if(!m)continue;let x=m!=null&&m.modelSignature&&((c=(i=m==null?void 0:m.inputs)==null?void 0:i[0])!=null&&c.shape)?[...m.inputs[0].shape]:[1,64,64,3],p=m!=null&&m.modelSignature&&((y=(d=m==null?void 0:m.inputs)==null?void 0:d[0])!=null&&y.dtype)?m.inputs[0].dtype:"float32";for(let h=0;ho.dispose(v)):o.dispose(h)}catch(h){e.config.debug&&b("compile fail model:",l)}o.dispose(f)}let A=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&b("compile pass:",{models:s,kernels:A.length}),o.env().set("ENGINE_COMPILE_ONLY",!1);let a=o.engine().state.numTensors;a-r>0&&b("tensor leak:",a-r)}async function Kn(e,t){await e2(e,!1);let n=R();return e.state="warmup",t&&(e.config=Q(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?te():new Promise(async r=>{await e.models.load(),await TA(e);let s=await gA(e),A=R();e.config.debug&&b("warmup",e.config.warmup,Math.round(A-n),"ms"),e.emit("warmup"),r(s)})}var Ye,l2,c2,At,be,Jn=class{constructor(t){z(this,"version");z(this,"config");z(this,"result");z(this,"state");z(this,"process");z(this,"tf");z(this,"env",M);z(this,"draw",vt);z(this,"match",k5);z(this,"models");z(this,"events");z(this,"faceTriangulation");z(this,"faceUVMap");z(this,"performance");q0(this,Ye,void 0);q0(this,l2,void 0);q0(this,c2,void 0);z(this,"analyze",(...t)=>{if(!E0(this,l2))return;let n=this.tf.engine().state.numTensors,r=E0(this,Ye);Q0(this,Ye,n);let s=n-r;s!==0&&b(...t,s)});q0(this,At,t=>{if(!E0(this,c2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof o.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});z(this,"webcam",new u2);z(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});q0(this,be,{});let n=(Je.tfjs||o.version_core).replace(/-(.*)/,"");Ce.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ce.modelBasePath=M.browser?"../models/":"file://models/",this.version=ft,Object.defineProperty(this,"version",{value:ft}),this.config=JSON.parse(JSON.stringify(Ce)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Q(this.config,t)),r1(this.config),this.tf=o,this.state="idle",Q0(this,Ye,0),Q0(this,l2,!1),Q0(this,c2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new i2(this),Tt(),this.result=te(),this.process={tensor:null,canvas:null},this.faceTriangulation=Q1,this.faceUVMap=_1,ot(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&b(`version: ${this.version}`),this.config.debug&&b(`tfjs version: ${this.tf.version["tfjs-core"]}`);let r=JSON.parse(JSON.stringify(this.env));delete r.kernels,delete r.initial,delete r.perfadd,this.config.debug&&b("environment:",r)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ce)),this.config.backend=t,xt(),M.initial=!0}validate(t){let n=lt(Ce,t||this.config);return n.length===0&&(this.config=Q(this.config,t)),n}now(){return R()}image(t,n=!1){return f2(t,this.config,n)}async segmentation(t,n){var A,a,i;if(n&&(this.config=Q(this.config,n)),!this.config.segmentation.enabled)return null;let r=await f2(t,this.config);if(!r.tensor)return null;let s=null;return(A=this.config.segmentation.modelPath)!=null&&A.includes("rvm")&&(s=await Vn(r.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("meet")&&(s=await Tn(r.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("selfie")&&(s=await Xn(r.tensor,this.config)),o.dispose(r.tensor),s}compare(t,n){return o1(this.config,t,n)}async init(){await e2(this,!0),await this.tf.ready(),xt()}async load(t){this.state="load";let n=R(),r=Object.values(this.models.models).filter(a=>a).length;t&&(this.config=Q(this.config,t)),this.env.initial&&(await e2(this,!1)||b("error: backend check failed"),await o.ready(),this.env.browser&&(this.config.debug&&b("configuration:",this.config),this.config.debug&&b("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&b("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(a=>a).length!==r&&(this.models.validate(),this.emit("load"));let A=Math.trunc(R()-n);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return gn(t,this.config)}async warmup(t){let n=R(),r=await Kn(this,t),s=R();return this.performance.warmup=Math.trunc(s-n),r}async profile(t,n){let r=await this.tf.profile(()=>this.detect(t,n)),s={},A=0;for(let i of r.kernels){let c=Number(i.kernelTimeMs)||0;s[i.name]?s[i.name]+=c:s[i.name]=c,A+=c}let a=[];Object.entries(s).forEach(i=>a.push({kernel:i[0],time:i[1],perc:0}));for(let i of a)i.perc=Math.round(1e3*i.time/A)/1e3,i.time=Math.round(1e3*i.time)/1e3;return a.sort((i,c)=>c.time-i.time),a.length=20,a}async detect(t,n){return this.state="detect",new Promise(async r=>{var h,v,T,u,g,E,k,N,W,V,D,Z,J,q,U,m0,P,i0,g0,e0,G;this.state="config";let s;this.config=Q(this.config,n),this.state="check";let A=E0(this,At).call(this,t);A&&(b(A,t),this.emit("error"),r(te(A)));let a=R();await this.load(),s=R(),this.state="image";let i=await f2(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(R()-s):Math.trunc(R()-s),this.analyze("Get Image:"),!i.tensor){this.config.debug&&b("could not convert input to tensor"),this.emit("error"),r(te("could not convert input to tensor"));return}this.emit("image"),s=R(),this.config.skipAllowed=await n1(this.config,i.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(R()-s):Math.trunc(R()-s),this.analyze("Check Changed:");let c=[],d=[],y=[],l=[];this.state="detect:face",this.config.async?(c=this.config.face.enabled?c5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(s=R(),c=this.config.face.enabled?await c5(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(c=await c),this.analyze("Start Body:"),this.state="detect:body";let m=this.config.body.maxDetected===-1?Q(this.config,{body:{maxDetected:this.config.face.enabled?1*c.length:1}}):this.config;this.config.async?((h=this.config.body.modelPath)!=null&&h.includes("posenet")?d=this.config.body.enabled?C5(i.tensor,m):[]:(v=this.config.body.modelPath)!=null&&v.includes("blazepose")?d=this.config.body.enabled?wt(i.tensor,m):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?d=this.config.body.enabled?Lt(i.tensor,m):[]:(u=this.config.body.modelPath)!=null&&u.includes("movenet")&&(d=this.config.body.enabled?z5(i.tensor,m):[]),this.performance.body&&delete this.performance.body):(s=R(),(g=this.config.body.modelPath)!=null&&g.includes("posenet")?d=this.config.body.enabled?await C5(i.tensor,m):[]:(E=this.config.body.modelPath)!=null&&E.includes("blazepose")?d=this.config.body.enabled?await wt(i.tensor,m):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?d=this.config.body.enabled?await Lt(i.tensor,m):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(d=this.config.body.enabled?await z5(i.tensor,m):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let x=this.config.hand.maxDetected===-1?Q(this.config,{hand:{maxDetected:this.config.face.enabled?2*c.length:1}}):this.config;this.config.async?((V=(W=this.config.hand.detector)==null?void 0:W.modelPath)!=null&&V.includes("handdetect")?y=this.config.hand.enabled?p5(i.tensor,x):[]:(Z=(D=this.config.hand.detector)==null?void 0:D.modelPath)!=null&&Z.includes("handtrack")&&(y=this.config.hand.enabled?b5(i.tensor,x):[]),this.performance.hand&&delete this.performance.hand):(s=R(),(q=(J=this.config.hand.detector)==null?void 0:J.modelPath)!=null&&q.includes("handdetect")?y=this.config.hand.enabled?await p5(i.tensor,x):[]:(m0=(U=this.config.hand.detector)==null?void 0:U.modelPath)!=null&&m0.includes("handtrack")&&(y=this.config.hand.enabled?await b5(i.tensor,x):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((P=this.config.object.modelPath)!=null&&P.includes("nanodet")?l=this.config.object.enabled?j5(i.tensor,this.config):[]:(i0=this.config.object.modelPath)!=null&&i0.includes("centernet")&&(l=this.config.object.enabled?St(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(s=R(),(g0=this.config.object.modelPath)!=null&&g0.includes("nanodet")?l=this.config.object.enabled?await j5(i.tensor,this.config):[]:(e0=this.config.object.modelPath)!=null&&e0.includes("centernet")&&(l=this.config.object.enabled?await St(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([c,d,y,l]=await Promise.all([c,d,y,l])),this.state="detect:gesture";let p=[];this.config.gesture.enabled&&(s=R(),p=[...K3(c),...Y3(d),...Q3(y),...J3(c)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(R()-a):Math.trunc(R()-a);let f=((G=this.process.tensor)==null?void 0:G.shape)||[0,0,0,0];this.result={face:c,body:d,hand:y,gesture:p,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return Yn(c,d,y,p,f)}},o.dispose(i.tensor),this.emit("detect"),this.state="idle",r(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,r=0){n?(E0(this,be)[t.id]||(this.config.debug&&b("video start",t.id),E0(this,be)[t.id]=!0),!t.paused&&E0(this,be)[t.id]&&t.readyState>=2&&await this.detect(t),r>0&&await this.sleep(r),E0(this,be)[t.id]&&requestAnimationFrame(()=>this.video(t,n,r))):(this.config.debug&&b("video stop",t.id),E0(this,be)[t.id]=!1)}};Ye=new WeakMap,l2=new WeakMap,c2=new WeakMap,At=new WeakMap,be=new WeakMap;export{p2 as Env,Jn as Human,Jn as default,Ce as defaults,vt as draw,te as empty,M as env,k5 as match,Un as models}; diff --git a/dist/human.esm.js b/dist/human.esm.js index c6b6e0d8..a87c081a 100644 --- a/dist/human.esm.js +++ b/dist/human.esm.js @@ -37,487 +37,488 @@ var __privateSet = (obj, member, value, setter) => { var tfjs_esm_exports = {}; __export(tfjs_esm_exports, { Abs: () => Xs, - Acos: () => Wo, - Acosh: () => Uo, - AdadeltaOptimizer: () => np, - AdagradOptimizer: () => sp, - AdamOptimizer: () => ap, - AdamaxOptimizer: () => ip, - Add: () => uo, - AddN: () => Go, - All: () => Ho, - Any: () => Ko, + Acos: () => Vo, + Acosh: () => Wo, + AdadeltaOptimizer: () => Qu, + AdagradOptimizer: () => Zu, + AdamOptimizer: () => Ju, + AdamaxOptimizer: () => ep, + Add: () => io, + AddN: () => Uo, + All: () => Go, + Any: () => Ho, ArgMax: () => Ys, ArgMin: () => Qs, - Asin: () => qo, - Asinh: () => jo, - Atan: () => Xo, - Atan2: () => Qo, - Atanh: () => Yo, - AvgPool: () => Zo, + Asin: () => Ko, + Asinh: () => qo, + Atan: () => jo, + Atan2: () => Yo, + Atanh: () => Xo, + AvgPool: () => Qo, AvgPool3D: () => Zs, - AvgPool3DGrad: () => Ai, - AvgPoolGrad: () => Di, - BackendWasm: () => pm, - BatchMatMul: () => Jo, + AvgPool3DGrad: () => Ei, + AvgPoolGrad: () => $i, + BackendWasm: () => im, + BatchMatMul: () => Zo, BatchToSpaceND: () => Js, - Bincount: () => en, - BitwiseAnd: () => ja, + Bincount: () => Jo, + BitwiseAnd: () => qa, BroadcastArgs: () => ea, BroadcastTo: () => Bce, - Cast: () => bo, - Ceil: () => tn, - ClipByValue: () => Co, - Complex: () => Fi, - ComplexAbs: () => Pi, + Cast: () => yo, + Ceil: () => en, + ClipByValue: () => bo, + Complex: () => Ri, + ComplexAbs: () => Di, Concat: () => ta, - Conv2D: () => rn, - Conv2DBackpropFilter: () => Oi, - Conv2DBackpropInput: () => on, - Conv3D: () => nn, - Conv3DBackpropFilterV2: () => Xa, - Conv3DBackpropInputV2: () => sn, - Cos: () => an, - Cosh: () => un, - CropAndResize: () => ln, - Cumprod: () => pn, - Cumsum: () => cn, - DataStorage: () => zo, + Conv2D: () => tn, + Conv2DBackpropFilter: () => Ai, + Conv2DBackpropInput: () => rn, + Conv3D: () => on, + Conv3DBackpropFilterV2: () => ja, + Conv3DBackpropInputV2: () => nn, + Cos: () => sn, + Cosh: () => an, + CropAndResize: () => cn, + Cumprod: () => un, + Cumsum: () => pn, + DataStorage: () => Bo, DenseBincount: () => ra, - DepthToSpace: () => mn, - DepthwiseConv2dNative: () => dn, - DepthwiseConv2dNativeBackpropFilter: () => Mi, - DepthwiseConv2dNativeBackpropInput: () => Li, + DepthToSpace: () => ln, + DepthwiseConv2dNative: () => mn, + DepthwiseConv2dNativeBackpropFilter: () => Fi, + DepthwiseConv2dNativeBackpropInput: () => Pi, Diag: () => oa, - Dilation2D: () => fn, - Dilation2DBackpropFilter: () => zi, - Dilation2DBackpropInput: () => Bi, - Draw: () => Pu, - ENV: () => nw, - Einsum: () => Vi, - Elu: () => gn, - EluGrad: () => Ya, - Environment: () => hl, - Equal: () => yn, - Erf: () => xn, - Exp: () => bn, + Dilation2D: () => dn, + Dilation2DBackpropFilter: () => Mi, + Dilation2DBackpropInput: () => Oi, + Draw: () => _u, + ENV: () => ow, + Einsum: () => Li, + Elu: () => hn, + EluGrad: () => Xa, + Environment: () => ll, + Equal: () => xn, + Erf: () => gn, + Exp: () => yn, ExpandDims: () => na, - Expm1: () => Cn, - FFT: () => Wi, + Expm1: () => bn, + FFT: () => Bi, Fill: () => sa, - FlipLeftRight: () => wn, - Floor: () => Sn, - FloorDiv: () => In, - FromPixels: () => Mu, - FusedBatchNorm: () => vn, - FusedConv2D: () => vo, - FusedDepthwiseConv2D: () => ko, - GPGPUContext: () => kp, - GatherNd: () => kn, + FlipLeftRight: () => Cn, + Floor: () => wn, + FloorDiv: () => Sn, + FromPixels: () => Eu, + FusedBatchNorm: () => In, + FusedConv2D: () => Io, + FusedDepthwiseConv2D: () => vo, + GPGPUContext: () => xp, + GatherNd: () => vn, GatherV2: () => aa, - GraphModel: () => Bl, - Greater: () => Nn, - GreaterEqual: () => Tn, - IFFT: () => Ui, - Identity: () => wo, - Imag: () => Gi, - IsFinite: () => _n, - IsInf: () => $n, - IsNan: () => En, - KernelBackend: () => ao, - LRN: () => zn, - LRNGrad: () => Qa, - LeakyRelu: () => Rn, - Less: () => Dn, - LessEqual: () => An, - LinSpace: () => Fn, - Log: () => Pn, - Log1p: () => On, + GraphModel: () => Ml, + Greater: () => kn, + GreaterEqual: () => Nn, + IFFT: () => zi, + Identity: () => Co, + Imag: () => Vi, + IsFinite: () => Tn, + IsInf: () => _n, + IsNan: () => $n, + KernelBackend: () => so, + LRN: () => Bn, + LRNGrad: () => Ya, + LeakyRelu: () => En, + Less: () => Rn, + LessEqual: () => Dn, + LinSpace: () => An, + Log: () => Fn, + Log1p: () => Pn, LogSoftmax: () => zce, - LogicalAnd: () => Mn, - LogicalNot: () => Ln, - LogicalOr: () => Bn, - LogicalXor: () => $0, + LogicalAnd: () => On, + LogicalNot: () => Mn, + LogicalOr: () => Ln, + LogicalXor: () => E0, LowerBound: () => Vce, - MathBackendCPU: () => xu, - MathBackendWebGL: () => wu, + MathBackendCPU: () => hc, + MathBackendWebGL: () => Oc, MatrixBandPart: () => Wce, - Max: () => Vn, - MaxPool: () => Un, + Max: () => zn, + MaxPool: () => Wn, MaxPool3D: () => ia, - MaxPool3DGrad: () => Ki, - MaxPoolGrad: () => Hi, + MaxPool3DGrad: () => Ui, + MaxPoolGrad: () => Wi, MaxPoolWithArgmax: () => ua, - Maximum: () => Wn, - Mean: () => Gn, - Min: () => Hn, - Minimum: () => Kn, - MirrorPad: () => qn, - Mod: () => jn, - MomentumOptimizer: () => up, - Multinomial: () => Xn, - Multiply: () => Yn, + Maximum: () => Vn, + Mean: () => Un, + Min: () => Gn, + Minimum: () => Hn, + MirrorPad: () => Kn, + Mod: () => qn, + MomentumOptimizer: () => tp, + Multinomial: () => jn, + Multiply: () => Xn, Neg: () => pa, - NonMaxSuppressionV3: () => Zn, - NonMaxSuppressionV4: () => Za, - NonMaxSuppressionV5: () => Jn, - NotEqual: () => Qn, + NonMaxSuppressionV3: () => Qn, + NonMaxSuppressionV4: () => Qa, + NonMaxSuppressionV5: () => Zn, + NotEqual: () => Yn, OP_SCOPE_SUFFIX: () => kw, - OneHot: () => es, + OneHot: () => Jn, OnesLike: () => ca, - Optimizer: () => Nr, - OptimizerConstructors: () => Pl, + Optimizer: () => kr, + OptimizerConstructors: () => Dl, Pack: () => la, - PadV2: () => ts, + PadV2: () => es, Pool: () => Uce, - Pow: () => rs, - Prelu: () => os, - Prod: () => ns, - RMSPropOptimizer: () => pp, - RaggedGather: () => Qp, - RaggedRange: () => Zp, - RaggedTensorToTensor: () => Jp, + Pow: () => ts, + Prelu: () => rs, + Prod: () => os, + RMSPropOptimizer: () => rp, + RaggedGather: () => Up, + RaggedRange: () => Gp, + RaggedTensorToTensor: () => Hp, Range: () => ma, Rank: () => hw, - Real: () => qi, - RealDiv: () => hn, - Reciprocal: () => ss, - Reduction: () => Rt, - Relu: () => as, - Relu6: () => ps, + Real: () => Gi, + RealDiv: () => fn, + Reciprocal: () => ns, + Reduction: () => Et, + Relu: () => ss, + Relu6: () => us, Reshape: () => da, - ResizeBilinear: () => us, - ResizeBilinearGrad: () => ei, - ResizeNearestNeighbor: () => is, - ResizeNearestNeighborGrad: () => Ja, - Reverse: () => cs, - RotateWithOffset: () => As, - Round: () => ls, - Rsqrt: () => ms, - SGDOptimizer: () => mi, - ScatterNd: () => ds, - SearchSorted: () => hs, + ResizeBilinear: () => is, + ResizeBilinearGrad: () => Ja, + ResizeNearestNeighbor: () => as, + ResizeNearestNeighborGrad: () => Za, + Reverse: () => ps, + RotateWithOffset: () => Ds, + Round: () => cs, + Rsqrt: () => ls, + SGDOptimizer: () => li, + ScatterNd: () => ms, + SearchSorted: () => fs, Select: () => fa, - Selu: () => gs, - Sigmoid: () => Cs, - Sign: () => bs, - Sin: () => xs, - Sinh: () => ys, + Selu: () => hs, + Sigmoid: () => bs, + Sign: () => ys, + Sin: () => gs, + Sinh: () => xs, Slice: () => ha, - Softmax: () => vs, - Softplus: () => ws, + Softmax: () => Is, + Softplus: () => Cs, SpaceToBatchND: () => ga, - SparseFillEmptyRows: () => ji, - SparseReshape: () => ti, + SparseFillEmptyRows: () => Hi, + SparseReshape: () => ei, SparseSegmentMean: () => ya, SparseSegmentSum: () => ba, - SparseToDense: () => ks, + SparseToDense: () => vs, SplitV: () => xa, - Sqrt: () => Ss, - Square: () => Xi, - SquaredDifference: () => Ns, - StaticRegexReplace: () => Ou, - Step: () => So, - StridedSlice: () => Ts, + Sqrt: () => ws, + Square: () => Ki, + SquaredDifference: () => ks, + StaticRegexReplace: () => $u, + Step: () => wo, + StridedSlice: () => Ns, StringNGrams: () => Ca, - StringSplit: () => Yi, - StringToHashBucketFast: () => Qi, - Sub: () => _s, - Sum: () => Is, - Tan: () => $s, - Tanh: () => Es, - Tensor: () => ut, + StringSplit: () => qi, + StringToHashBucketFast: () => ji, + Sub: () => Ts, + Sum: () => Ss, + Tan: () => _s, + Tanh: () => $s, + Tensor: () => mt, TensorBuffer: () => tt, - TensorScatterUpdate: () => fs, - Tile: () => po, - TopK: () => Rs, - Transform: () => Ds, - Transpose: () => co, - Unique: () => Zi, + TensorScatterUpdate: () => ds, + Tile: () => uo, + TopK: () => Es, + Transform: () => Rs, + Transpose: () => po, + Unique: () => Xi, Unpack: () => wa, - UnsortedSegmentSum: () => Ji, + UnsortedSegmentSum: () => Yi, UpperBound: () => Gce, - Variable: () => oi, - WebGPUBackend: () => Tu, + Variable: () => ri, + WebGPUBackend: () => Kc, ZerosLike: () => Sa, - _FusedMatMul: () => Io, - abs: () => Jt, - acos: () => kk, - acosh: () => Nk, + _FusedMatMul: () => So, + abs: () => Qt, + acos: () => Nk, + acosh: () => Tk, add: () => Ce, - addN: () => Tk, - all: () => _k, - any: () => $k, - argMax: () => Ek, - argMin: () => Rk, - asin: () => Dk, - asinh: () => Ak, - atan: () => Fk, - atan2: () => Pk, - atanh: () => Ok, - avgPool: () => fd, - avgPool3d: () => Bk, + addN: () => _k, + all: () => $k, + any: () => Ek, + argMax: () => Rk, + argMin: () => Dk, + asin: () => Ak, + asinh: () => Fk, + atan: () => Pk, + atan2: () => Ok, + atanh: () => Mk, + avgPool: () => md, + avgPool3d: () => zk, backend: () => vde, backend_util: () => w, - basicLSTMCell: () => zk, - batchNorm: () => au, - batchNorm2d: () => Wk, - batchNorm3d: () => Uk, - batchNorm4d: () => Gk, - batchToSpaceND: () => hd, - bincount: () => gd, - bitwiseAnd: () => Hk, + basicLSTMCell: () => Vk, + batchNorm: () => ru, + batchNorm2d: () => Uk, + batchNorm3d: () => Gk, + batchNorm4d: () => Hk, + batchToSpaceND: () => dd, + bincount: () => fd, + bitwiseAnd: () => Kk, booleanMaskAsync: () => E6, - broadcastArgs: () => Kk, - broadcastTo: () => iu, - broadcast_util: () => Ir, - browser: () => oT, + broadcastArgs: () => qk, + broadcastTo: () => ou, + broadcast_util: () => Sr, + browser: () => nT, buffer: () => me, cast: () => We, - ceil: () => qk, - clipByValue: () => jk, - clone: () => Ur, - complex: () => Er, - concat: () => bt, - concat1d: () => Xk, - concat2d: () => Yk, - concat3d: () => Qk, - concat4d: () => Zk, - conv1d: () => Jk, - conv2d: () => uu, - conv2dTranspose: () => e2, - conv3d: () => t2, - conv3dTranspose: () => o2, + ceil: () => jk, + clipByValue: () => Xk, + clone: () => Wr, + complex: () => $r, + concat: () => yt, + concat1d: () => Yk, + concat2d: () => Qk, + concat3d: () => Zk, + concat4d: () => Jk, + conv1d: () => e2, + conv2d: () => nu, + conv2dTranspose: () => t2, + conv3d: () => r22, + conv3dTranspose: () => n2, copyRegisteredKernels: () => Zce, - cos: () => n2, - cosh: () => s2, - cosineWindow: () => Rl, - cumprod: () => a2, - cumsum: () => i2, - customGrad: () => vr, - denseBincount: () => u2, + cos: () => s2, + cosh: () => a2, + cosineWindow: () => _l, + cumprod: () => i2, + cumsum: () => u2, + customGrad: () => Ir, + denseBincount: () => p2, deprecationWarn: () => Pw, - depthToSpace: () => p2, - depthwiseConv2d: () => lc, + depthToSpace: () => c2, + depthwiseConv2d: () => oc, deregisterOp: () => A5, - device_util: () => ou, - diag: () => c2, - dilation2d: () => l2, + device_util: () => Ji, + diag: () => l2, + dilation2d: () => m2, disableDeprecationWarnings: () => dde, - dispose: () => Mt, + dispose: () => Ot, disposeVariables: () => fde, div: () => je, - divNoNan: () => d2, - dot: () => f2, + divNoNan: () => f2, + dot: () => h2, dropout: () => W6, - einsum: () => pu, - elu: () => Cd, + einsum: () => su, + elu: () => yd, enableDebugMode: () => mde, enableProdMode: () => lde, enclosingPowerOfTwo: () => Qw, - engine: () => pr, - ensureShape: () => h2, + engine: () => ur, + ensureShape: () => g2, env: () => A, - equal: () => bd, - erf: () => g2, - euclideanNorm: () => b2, - exp: () => $o, + equal: () => xd, + erf: () => x2, + euclideanNorm: () => C2, + exp: () => _o, expandDims: () => Ms, - expm1: () => C2, - eye: () => wd, - fft: () => fc, + expm1: () => w2, + eye: () => bd, + fft: () => ac, fill: () => Ea, findBackend: () => Sde, findBackendFactory: () => Ide, - floor: () => Sd, - floorDiv: () => dd, - forceHalfFloat: () => MD, + floor: () => Cd, + floorDiv: () => ld, + forceHalfFloat: () => LD, fused: () => Zw, - gather: () => Id, + gather: () => wd, gatherND: () => z6, - gather_util: () => af, + gather_util: () => sf, getBackend: () => Cde, - getGradient: () => iw, - getKernel: () => tc, - getKernelsForBackend: () => Ym, + getGradient: () => aw, + getKernel: () => qp, + getKernelsForBackend: () => jm, getThreadsCount: () => Zse, - gpgpu_util: () => cv, + gpgpu_util: () => lv, grad: () => AK, grads: () => FK, - greater: () => qu, - greaterEqual: () => vd, - ifft: () => Ju, - imag: () => lu, + greater: () => zu, + greaterEqual: () => Sd, + ifft: () => Ku, + imag: () => iu, image: () => Kj, inTopKAsync: () => G6, - io: () => fi, - irfft: () => Kd, - isFinite: () => w2, - isInf: () => S2, - isNaN: () => I2, - keep: () => Rr, - kernel_impls: () => Wt, - leakyRelu: () => kd, - less: () => _l, - lessEqual: () => mc, + io: () => mi, + irfft: () => Gd, + isFinite: () => S2, + isInf: () => I2, + isNaN: () => v2, + keep: () => Er, + kernel_impls: () => Vt, + leakyRelu: () => Id, + less: () => kl, + lessEqual: () => nc, linalg: () => qj, - linspace: () => v2, + linspace: () => k2, loadGraphModel: () => $8, loadGraphModelSync: () => E8, - localResponseNormalization: () => k2, - log: () => pi, - log1p: () => Nd, - logSigmoid: () => N2, - logSoftmax: () => T2, - logSumExp: () => $d, - logicalAnd: () => ju, - logicalNot: () => Ed, - logicalOr: () => Rd, - logicalXor: () => _2, + localResponseNormalization: () => N2, + log: () => ui, + log1p: () => vd, + logSigmoid: () => T2, + logSoftmax: () => _2, + logSumExp: () => Td, + logicalAnd: () => Vu, + logicalNot: () => _d, + logicalOr: () => $d, + logicalXor: () => $2, losses: () => jj, - lowerBound: () => $2, + lowerBound: () => E2, matMul: () => Ze, - math: () => JN, + math: () => eT, max: () => Ra, - maxPool: () => Ad, - maxPool3d: () => E2, - maxPoolWithArgmax: () => R2, - maximum: () => Fd, - mean: () => Xu, + maxPool: () => Rd, + maxPool3d: () => R2, + maxPoolWithArgmax: () => D2, + maximum: () => Dd, + mean: () => Wu, memory: () => hde, - meshgrid: () => D2, - min: () => Tl, - minimum: () => Yu, - mirrorPad: () => A2, - mod: () => F2, - moments: () => P2, + meshgrid: () => A2, + min: () => vl, + minimum: () => Uu, + mirrorPad: () => F2, + mod: () => P2, + moments: () => O2, movingAverage: () => A6, mul: () => se, - multiRNNCell: () => O2, - multinomial: () => M2, - neg: () => cr, + multiRNNCell: () => M2, + multinomial: () => L2, + neg: () => pr, nextFrame: () => pS, - norm: () => Ku, - notEqual: () => Pd, - oneHot: () => El, + node: () => bQt, + norm: () => Bu, + notEqual: () => Ad, + oneHot: () => Tl, ones: () => Da, - onesLike: () => L2, + onesLike: () => B2, op: () => N, - outerProduct: () => B2, + outerProduct: () => z2, pad: () => Aa, - pad1d: () => z2, - pad2d: () => V2, - pad3d: () => W2, - pad4d: () => U2, - pool: () => G2, - pow: () => ui, - prelu: () => Md, - print: () => md, - prod: () => H2, + pad1d: () => V2, + pad2d: () => W2, + pad3d: () => U2, + pad4d: () => G2, + pool: () => H2, + pow: () => ii, + prelu: () => Pd, + print: () => cd, + prod: () => K2, profile: () => gde, - raggedGather: () => K2, - raggedRange: () => q2, - raggedTensorToTensor: () => j2, - rand: () => X2, - randomGamma: () => g1, - randomNormal: () => Ud, - randomStandardNormal: () => x1, - randomUniform: () => dc, - randomUniformInt: () => y1, - range: () => mu, + raggedGather: () => q2, + raggedRange: () => j2, + raggedTensorToTensor: () => X2, + rand: () => Y2, + randomGamma: () => x1, + randomNormal: () => Vd, + randomStandardNormal: () => y1, + randomUniform: () => sc, + randomUniformInt: () => b1, + range: () => uu, ready: () => bde, - real: () => ci, - reciprocal: () => b1, - registerBackend: () => su, + real: () => pi, + reciprocal: () => C1, + registerBackend: () => tu, registerGradient: () => Xce, - registerKernel: () => ri, + registerKernel: () => ti, registerOp: () => D5, - relu: () => du, - relu6: () => Gd, + relu: () => pu, + relu6: () => Wd, removeBackend: () => wde, reshape: () => W, - reverse: () => mo, - reverse1d: () => C1, - reverse2d: () => w1, - reverse3d: () => S1, - reverse4d: () => I1, - rfft: () => hc, - round: () => Hd, - rsqrt: () => v1, + reverse: () => lo, + reverse1d: () => w1, + reverse2d: () => S1, + reverse3d: () => I1, + reverse4d: () => v1, + rfft: () => ic, + round: () => Ud, + rsqrt: () => k1, scalar: () => ke, scatterND: () => P6, - scatter_util: () => hu, - searchSorted: () => $l, - selu: () => k1, - separableConv2d: () => N1, - serialization: () => WN, + scatter_util: () => lu, + searchSorted: () => Nl, + selu: () => N1, + separableConv2d: () => T1, + serialization: () => UN, setBackend: () => yde, setPlatform: () => kde, setThreadsCount: () => Qse, setWasmPath: () => Xse, setWasmPaths: () => Yse, - setWebGLContext: () => vI, - setdiff1dAsync: () => T1, - shared: () => Tc, + setWebGLContext: () => kI, + setdiff1dAsync: () => _1, + shared: () => wc, sigmoid: () => $a, - sign: () => _1, + sign: () => $1, signal: () => Hj, - sin: () => $1, - sinh: () => E1, + sin: () => E1, + sinh: () => R1, slice: () => Xe, - slice1d: () => R1, - slice2d: () => D1, - slice3d: () => A1, - slice4d: () => F1, - slice_util: () => ct, - softmax: () => P1, - softplus: () => _d, - spaceToBatchND: () => Od, + slice1d: () => D1, + slice2d: () => A1, + slice3d: () => F1, + slice4d: () => P1, + slice_util: () => pt, + softmax: () => O1, + softplus: () => Nd, + spaceToBatchND: () => Fd, sparse: () => Xj, sparseToDense: () => L6, spectral: () => Gj, - split: () => li, - sqrt: () => Dr, - square: () => er, - squaredDifference: () => qd, - squeeze: () => gc, - stack: () => kr, - step: () => jd, - stridedSlice: () => O1, + split: () => ci, + sqrt: () => Rr, + square: () => Zt, + squaredDifference: () => Hd, + squeeze: () => uc, + stack: () => vr, + step: () => Kd, + stridedSlice: () => M1, string: () => Yj, sub: () => Te, sum: () => ot, - sumOutType: () => ni, - tan: () => M1, - tanh: () => Nl, - tensor: () => ur, - tensor1d: () => tr, - tensor2d: () => fu, - tensor3d: () => Xd, - tensor4d: () => L1, - tensor5d: () => B1, - tensor6d: () => z1, - tensorScatterUpdate: () => W1, - tensor_util: () => ek, - test_util: () => h1, + sumOutType: () => oi, + tan: () => L1, + tanh: () => Il, + tensor: () => ar, + tensor1d: () => Jt, + tensor2d: () => cu, + tensor3d: () => qd, + tensor4d: () => B1, + tensor5d: () => z1, + tensor6d: () => V1, + tensorScatterUpdate: () => U1, + tensor_util: () => tk, + test_util: () => g1, tidy: () => De, - tile: () => cu, + tile: () => au, time: () => xde, - topk: () => U1, + topk: () => G1, train: () => TGe, - transpose: () => yc, - truncatedNormal: () => G1, - unique: () => H1, + transpose: () => cc, + truncatedNormal: () => H1, + unique: () => K1, unregisterGradient: () => Qce, unregisterKernel: () => Yce, - unsortedSegmentSum: () => K1, - unstack: () => fo, + unsortedSegmentSum: () => q1, + unstack: () => mo, upcastType: () => dt, - upperBound: () => q1, + upperBound: () => j1, util: () => y, valueAndGrad: () => PK, valueAndGrads: () => OK, - variable: () => j1, + variable: () => X1, variableGrads: () => zw, version: () => Ace, version_converter: () => D8, @@ -526,124 +527,124 @@ __export(tfjs_esm_exports, { version_wasm: () => Jse, version_webgl: () => s9, webgl: () => Cat, - webgl_util: () => Ac, - webgpu_util: () => Yv, - where: () => lo, - whereAsync: () => Qd, - zeros: () => Gr, - zerosLike: () => Ht + webgl_util: () => Tc, + webgpu_util: () => Qv, + where: () => co, + whereAsync: () => Xd, + zeros: () => Ur, + zerosLike: () => Gt }); -var wG = Object.create; -var QC = Object.defineProperty; -var SG = Object.getOwnPropertyDescriptor; -var IG = Object.getOwnPropertyNames; -var vG = Object.getPrototypeOf; -var kG = Object.prototype.hasOwnProperty; -var qt = (r, t8) => () => (t8 || r((t8 = { exports: {} }).exports, t8), t8.exports); -var qe = (r, t8) => { +var SG = Object.create; +var YC = Object.defineProperty; +var IG = Object.getOwnPropertyDescriptor; +var vG = Object.getOwnPropertyNames; +var kG = Object.getPrototypeOf; +var NG = Object.prototype.hasOwnProperty; +var Kt = (r15, t8) => () => (t8 || r15((t8 = { exports: {} }).exports, t8), t8.exports); +var qe = (r15, t8) => { for (var e in t8) - QC(r, e, { get: t8[e], enumerable: true }); + YC(r15, e, { get: t8[e], enumerable: true }); }; -var NG = (r, t8, e, o) => { +var TG = (r15, t8, e, o) => { if (t8 && typeof t8 == "object" || typeof t8 == "function") - for (let n of IG(t8)) - !kG.call(r, n) && n !== e && QC(r, n, { get: () => t8[n], enumerable: !(o = SG(t8, n)) || o.enumerable }); - return r; + for (let n of vG(t8)) + !NG.call(r15, n) && n !== e && YC(r15, n, { get: () => t8[n], enumerable: !(o = IG(t8, n)) || o.enumerable }); + return r15; }; -var Kp = (r, t8, e) => (e = r != null ? wG(vG(r)) : {}, NG(t8 || !r || !r.__esModule ? QC(e, "default", { value: r, enumerable: true }) : e, r)); -var V0 = qt((tle, z0) => { - z0.exports = Nt; - var No = null; +var Lp = (r15, t8, e) => (e = r15 != null ? SG(kG(r15)) : {}, TG(t8 || !r15 || !r15.__esModule ? YC(e, "default", { value: r15, enumerable: true }) : e, r15)); +var W0 = Kt((tle, V0) => { + V0.exports = kt; + var ko = null; try { - No = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 0, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 126, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11])), {}).exports; - } catch (r) { + ko = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 0, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 126, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11])), {}).exports; + } catch (r15) { } - function Nt(r, t8, e) { - this.low = r | 0, this.high = t8 | 0, this.unsigned = !!e; + function kt(r15, t8, e) { + this.low = r15 | 0, this.high = t8 | 0, this.unsigned = !!e; } - Nt.prototype.__isLong__; - Object.defineProperty(Nt.prototype, "__isLong__", { value: true }); - function Wr(r) { - return (r && r.__isLong__) === true; + kt.prototype.__isLong__; + Object.defineProperty(kt.prototype, "__isLong__", { value: true }); + function Vr(r15) { + return (r15 && r15.__isLong__) === true; } - Nt.isLong = Wr; - var R0 = {}, D0 = {}; - function Bu(r, t8) { + kt.isLong = Vr; + var D0 = {}, A0 = {}; + function Du(r15, t8) { var e, o, n; - return t8 ? (r >>>= 0, (n = 0 <= r && r < 256) && (o = D0[r], o) ? o : (e = Tt(r, (r | 0) < 0 ? -1 : 0, true), n && (D0[r] = e), e)) : (r |= 0, (n = -128 <= r && r < 128) && (o = R0[r], o) ? o : (e = Tt(r, r < 0 ? -1 : 0, false), n && (R0[r] = e), e)); + return t8 ? (r15 >>>= 0, (n = 0 <= r15 && r15 < 256) && (o = A0[r15], o) ? o : (e = Nt(r15, (r15 | 0) < 0 ? -1 : 0, true), n && (A0[r15] = e), e)) : (r15 |= 0, (n = -128 <= r15 && r15 < 128) && (o = D0[r15], o) ? o : (e = Nt(r15, r15 < 0 ? -1 : 0, false), n && (D0[r15] = e), e)); } - Nt.fromInt = Bu; - function To(r, t8) { - if (isNaN(r)) - return t8 ? Lu : _o; + kt.fromInt = Du; + function No(r15, t8) { + if (isNaN(r15)) + return t8 ? Ru : To; if (t8) { - if (r < 0) - return Lu; - if (r >= O0) - return B0; + if (r15 < 0) + return Ru; + if (r15 >= M0) + return z0; } else { - if (r <= -F0) - return Vr; - if (r + 1 >= F0) - return L0; + if (r15 <= -P0) + return zr; + if (r15 + 1 >= P0) + return B0; } - return r < 0 ? To(-r, t8).neg() : Tt(r % oc | 0, r / oc | 0, t8); + return r15 < 0 ? No(-r15, t8).neg() : Nt(r15 % Xp | 0, r15 / Xp | 0, t8); } - Nt.fromNumber = To; - function Tt(r, t8, e) { - return new Nt(r, t8, e); + kt.fromNumber = No; + function Nt(r15, t8, e) { + return new kt(r15, t8, e); } - Nt.fromBits = Tt; - var Zm = Math.pow; - function cw(r, t8, e) { - if (r.length === 0) + kt.fromBits = Nt; + var Ym = Math.pow; + function pw(r15, t8, e) { + if (r15.length === 0) throw Error("empty string"); - if (r === "NaN" || r === "Infinity" || r === "+Infinity" || r === "-Infinity") - return _o; + if (r15 === "NaN" || r15 === "Infinity" || r15 === "+Infinity" || r15 === "-Infinity") + return To; if (typeof t8 == "number" ? (e = t8, t8 = false) : t8 = !!t8, e = e || 10, e < 2 || 36 < e) throw RangeError("radix"); var o; - if ((o = r.indexOf("-")) > 0) + if ((o = r15.indexOf("-")) > 0) throw Error("interior hyphen"); if (o === 0) - return cw(r.substring(1), t8, e).neg(); - for (var n = To(Zm(e, 8)), s = _o, a = 0; a < r.length; a += 8) { - var i = Math.min(8, r.length - a), p = parseInt(r.substring(a, a + i), e); + return pw(r15.substring(1), t8, e).neg(); + for (var n = No(Ym(e, 8)), s = To, a = 0; a < r15.length; a += 8) { + var i = Math.min(8, r15.length - a), p = parseInt(r15.substring(a, a + i), e); if (i < 8) { - var u = To(Zm(e, i)); - s = s.mul(u).add(To(p)); + var u = No(Ym(e, i)); + s = s.mul(u).add(No(p)); } else - s = s.mul(n), s = s.add(To(p)); + s = s.mul(n), s = s.add(No(p)); } return s.unsigned = t8, s; } - Nt.fromString = cw; - function Fs(r, t8) { - return typeof r == "number" ? To(r, t8) : typeof r == "string" ? cw(r, t8) : Tt(r.low, r.high, typeof t8 == "boolean" ? t8 : r.unsigned); + kt.fromString = pw; + function As(r15, t8) { + return typeof r15 == "number" ? No(r15, t8) : typeof r15 == "string" ? pw(r15, t8) : Nt(r15.low, r15.high, typeof t8 == "boolean" ? t8 : r15.unsigned); } - Nt.fromValue = Fs; - var A0 = 65536, XG = 1 << 24, oc = A0 * A0, O0 = oc * oc, F0 = O0 / 2, P0 = Bu(XG), _o = Bu(0); - Nt.ZERO = _o; - var Lu = Bu(0, true); - Nt.UZERO = Lu; - var rc = Bu(1); - Nt.ONE = rc; - var M0 = Bu(1, true); - Nt.UONE = M0; - var pw = Bu(-1); - Nt.NEG_ONE = pw; - var L0 = Tt(-1, 2147483647, false); - Nt.MAX_VALUE = L0; - var B0 = Tt(-1, -1, true); - Nt.MAX_UNSIGNED_VALUE = B0; - var Vr = Tt(0, -2147483648, false); - Nt.MIN_VALUE = Vr; - var de = Nt.prototype; + kt.fromValue = As; + var F0 = 65536, YG = 1 << 24, Xp = F0 * F0, M0 = Xp * Xp, P0 = M0 / 2, O0 = Du(YG), To = Du(0); + kt.ZERO = To; + var Ru = Du(0, true); + kt.UZERO = Ru; + var jp = Du(1); + kt.ONE = jp; + var L0 = Du(1, true); + kt.UONE = L0; + var uw = Du(-1); + kt.NEG_ONE = uw; + var B0 = Nt(-1, 2147483647, false); + kt.MAX_VALUE = B0; + var z0 = Nt(-1, -1, true); + kt.MAX_UNSIGNED_VALUE = z0; + var zr = Nt(0, -2147483648, false); + kt.MIN_VALUE = zr; + var de = kt.prototype; de.toInt = function() { return this.unsigned ? this.low >>> 0 : this.low; }; de.toNumber = function() { - return this.unsigned ? (this.high >>> 0) * oc + (this.low >>> 0) : this.high * oc + (this.low >>> 0); + return this.unsigned ? (this.high >>> 0) * Xp + (this.low >>> 0) : this.high * Xp + (this.low >>> 0); }; de.toString = function(t8) { if (t8 = t8 || 10, t8 < 2 || 36 < t8) @@ -651,12 +652,12 @@ var V0 = qt((tle, z0) => { if (this.isZero()) return "0"; if (this.isNegative()) - if (this.eq(Vr)) { - var e = To(t8), o = this.div(e), n = o.mul(e).sub(this); + if (this.eq(zr)) { + var e = No(t8), o = this.div(e), n = o.mul(e).sub(this); return o.toString(t8) + n.toInt().toString(t8); } else return "-" + this.neg().toString(t8); - for (var s = To(Zm(t8, 6), this.unsigned), a = this, i = ""; ; ) { + for (var s = No(Ym(t8, 6), this.unsigned), a = this, i = ""; ; ) { var p = a.div(s), u = a.sub(p.mul(s)).toInt() >>> 0, c = u.toString(t8); if (a = p, a.isZero()) return c + i; @@ -679,7 +680,7 @@ var V0 = qt((tle, z0) => { }; de.getNumBitsAbs = function() { if (this.isNegative()) - return this.eq(Vr) ? 64 : this.neg().getNumBitsAbs(); + return this.eq(zr) ? 64 : this.neg().getNumBitsAbs(); for (var t8 = this.high != 0 ? this.high : this.low, e = 31; e > 0 && !(t8 & 1 << e); e--) ; return this.high != 0 ? e + 33 : e + 1; @@ -701,7 +702,7 @@ var V0 = qt((tle, z0) => { return (this.low & 1) === 0; }; de.equals = function(t8) { - return Wr(t8) || (t8 = Fs(t8)), this.unsigned !== t8.unsigned && this.high >>> 31 === 1 && t8.high >>> 31 === 1 ? false : this.high === t8.high && this.low === t8.low; + return Vr(t8) || (t8 = As(t8)), this.unsigned !== t8.unsigned && this.high >>> 31 === 1 && t8.high >>> 31 === 1 ? false : this.high === t8.high && this.low === t8.low; }; de.eq = de.equals; de.notEquals = function(t8) { @@ -728,137 +729,137 @@ var V0 = qt((tle, z0) => { de.gte = de.greaterThanOrEqual; de.ge = de.greaterThanOrEqual; de.compare = function(t8) { - if (Wr(t8) || (t8 = Fs(t8)), this.eq(t8)) + if (Vr(t8) || (t8 = As(t8)), this.eq(t8)) return 0; var e = this.isNegative(), o = t8.isNegative(); return e && !o ? -1 : !e && o ? 1 : this.unsigned ? t8.high >>> 0 > this.high >>> 0 || t8.high === this.high && t8.low >>> 0 > this.low >>> 0 ? -1 : 1 : this.sub(t8).isNegative() ? -1 : 1; }; de.comp = de.compare; de.negate = function() { - return !this.unsigned && this.eq(Vr) ? Vr : this.not().add(rc); + return !this.unsigned && this.eq(zr) ? zr : this.not().add(jp); }; de.neg = de.negate; de.add = function(t8) { - Wr(t8) || (t8 = Fs(t8)); + Vr(t8) || (t8 = As(t8)); var e = this.high >>> 16, o = this.high & 65535, n = this.low >>> 16, s = this.low & 65535, a = t8.high >>> 16, i = t8.high & 65535, p = t8.low >>> 16, u = t8.low & 65535, c = 0, l = 0, m = 0, d = 0; - return d += s + u, m += d >>> 16, d &= 65535, m += n + p, l += m >>> 16, m &= 65535, l += o + i, c += l >>> 16, l &= 65535, c += e + a, c &= 65535, Tt(m << 16 | d, c << 16 | l, this.unsigned); + return d += s + u, m += d >>> 16, d &= 65535, m += n + p, l += m >>> 16, m &= 65535, l += o + i, c += l >>> 16, l &= 65535, c += e + a, c &= 65535, Nt(m << 16 | d, c << 16 | l, this.unsigned); }; de.subtract = function(t8) { - return Wr(t8) || (t8 = Fs(t8)), this.add(t8.neg()); + return Vr(t8) || (t8 = As(t8)), this.add(t8.neg()); }; de.sub = de.subtract; de.multiply = function(t8) { if (this.isZero()) - return _o; - if (Wr(t8) || (t8 = Fs(t8)), No) { - var e = No.mul(this.low, this.high, t8.low, t8.high); - return Tt(e, No.get_high(), this.unsigned); + return To; + if (Vr(t8) || (t8 = As(t8)), ko) { + var e = ko.mul(this.low, this.high, t8.low, t8.high); + return Nt(e, ko.get_high(), this.unsigned); } if (t8.isZero()) - return _o; - if (this.eq(Vr)) - return t8.isOdd() ? Vr : _o; - if (t8.eq(Vr)) - return this.isOdd() ? Vr : _o; + return To; + if (this.eq(zr)) + return t8.isOdd() ? zr : To; + if (t8.eq(zr)) + return this.isOdd() ? zr : To; if (this.isNegative()) return t8.isNegative() ? this.neg().mul(t8.neg()) : this.neg().mul(t8).neg(); if (t8.isNegative()) return this.mul(t8.neg()).neg(); - if (this.lt(P0) && t8.lt(P0)) - return To(this.toNumber() * t8.toNumber(), this.unsigned); + if (this.lt(O0) && t8.lt(O0)) + return No(this.toNumber() * t8.toNumber(), this.unsigned); var o = this.high >>> 16, n = this.high & 65535, s = this.low >>> 16, a = this.low & 65535, i = t8.high >>> 16, p = t8.high & 65535, u = t8.low >>> 16, c = t8.low & 65535, l = 0, m = 0, d = 0, f = 0; - return f += a * c, d += f >>> 16, f &= 65535, d += s * c, m += d >>> 16, d &= 65535, d += a * u, m += d >>> 16, d &= 65535, m += n * c, l += m >>> 16, m &= 65535, m += s * u, l += m >>> 16, m &= 65535, m += a * p, l += m >>> 16, m &= 65535, l += o * c + n * u + s * p + a * i, l &= 65535, Tt(d << 16 | f, l << 16 | m, this.unsigned); + return f += a * c, d += f >>> 16, f &= 65535, d += s * c, m += d >>> 16, d &= 65535, d += a * u, m += d >>> 16, d &= 65535, m += n * c, l += m >>> 16, m &= 65535, m += s * u, l += m >>> 16, m &= 65535, m += a * p, l += m >>> 16, m &= 65535, l += o * c + n * u + s * p + a * i, l &= 65535, Nt(d << 16 | f, l << 16 | m, this.unsigned); }; de.mul = de.multiply; de.divide = function(t8) { - if (Wr(t8) || (t8 = Fs(t8)), t8.isZero()) + if (Vr(t8) || (t8 = As(t8)), t8.isZero()) throw Error("division by zero"); - if (No) { + if (ko) { if (!this.unsigned && this.high === -2147483648 && t8.low === -1 && t8.high === -1) return this; - var e = (this.unsigned ? No.div_u : No.div_s)(this.low, this.high, t8.low, t8.high); - return Tt(e, No.get_high(), this.unsigned); + var e = (this.unsigned ? ko.div_u : ko.div_s)(this.low, this.high, t8.low, t8.high); + return Nt(e, ko.get_high(), this.unsigned); } if (this.isZero()) - return this.unsigned ? Lu : _o; + return this.unsigned ? Ru : To; var o, n, s; if (this.unsigned) { if (t8.unsigned || (t8 = t8.toUnsigned()), t8.gt(this)) - return Lu; + return Ru; if (t8.gt(this.shru(1))) - return M0; - s = Lu; + return L0; + s = Ru; } else { - if (this.eq(Vr)) { - if (t8.eq(rc) || t8.eq(pw)) - return Vr; - if (t8.eq(Vr)) - return rc; + if (this.eq(zr)) { + if (t8.eq(jp) || t8.eq(uw)) + return zr; + if (t8.eq(zr)) + return jp; var a = this.shr(1); - return o = a.div(t8).shl(1), o.eq(_o) ? t8.isNegative() ? rc : pw : (n = this.sub(t8.mul(o)), s = o.add(n.div(t8)), s); - } else if (t8.eq(Vr)) - return this.unsigned ? Lu : _o; + return o = a.div(t8).shl(1), o.eq(To) ? t8.isNegative() ? jp : uw : (n = this.sub(t8.mul(o)), s = o.add(n.div(t8)), s); + } else if (t8.eq(zr)) + return this.unsigned ? Ru : To; if (this.isNegative()) return t8.isNegative() ? this.neg().div(t8.neg()) : this.neg().div(t8).neg(); if (t8.isNegative()) return this.div(t8.neg()).neg(); - s = _o; + s = To; } for (n = this; n.gte(t8); ) { o = Math.max(1, Math.floor(n.toNumber() / t8.toNumber())); - for (var i = Math.ceil(Math.log(o) / Math.LN2), p = i <= 48 ? 1 : Zm(2, i - 48), u = To(o), c = u.mul(t8); c.isNegative() || c.gt(n); ) - o -= p, u = To(o, this.unsigned), c = u.mul(t8); - u.isZero() && (u = rc), s = s.add(u), n = n.sub(c); + for (var i = Math.ceil(Math.log(o) / Math.LN2), p = i <= 48 ? 1 : Ym(2, i - 48), u = No(o), c = u.mul(t8); c.isNegative() || c.gt(n); ) + o -= p, u = No(o, this.unsigned), c = u.mul(t8); + u.isZero() && (u = jp), s = s.add(u), n = n.sub(c); } return s; }; de.div = de.divide; de.modulo = function(t8) { - if (Wr(t8) || (t8 = Fs(t8)), No) { - var e = (this.unsigned ? No.rem_u : No.rem_s)(this.low, this.high, t8.low, t8.high); - return Tt(e, No.get_high(), this.unsigned); + if (Vr(t8) || (t8 = As(t8)), ko) { + var e = (this.unsigned ? ko.rem_u : ko.rem_s)(this.low, this.high, t8.low, t8.high); + return Nt(e, ko.get_high(), this.unsigned); } return this.sub(this.div(t8).mul(t8)); }; de.mod = de.modulo; de.rem = de.modulo; de.not = function() { - return Tt(~this.low, ~this.high, this.unsigned); + return Nt(~this.low, ~this.high, this.unsigned); }; de.and = function(t8) { - return Wr(t8) || (t8 = Fs(t8)), Tt(this.low & t8.low, this.high & t8.high, this.unsigned); + return Vr(t8) || (t8 = As(t8)), Nt(this.low & t8.low, this.high & t8.high, this.unsigned); }; de.or = function(t8) { - return Wr(t8) || (t8 = Fs(t8)), Tt(this.low | t8.low, this.high | t8.high, this.unsigned); + return Vr(t8) || (t8 = As(t8)), Nt(this.low | t8.low, this.high | t8.high, this.unsigned); }; de.xor = function(t8) { - return Wr(t8) || (t8 = Fs(t8)), Tt(this.low ^ t8.low, this.high ^ t8.high, this.unsigned); + return Vr(t8) || (t8 = As(t8)), Nt(this.low ^ t8.low, this.high ^ t8.high, this.unsigned); }; de.shiftLeft = function(t8) { - return Wr(t8) && (t8 = t8.toInt()), (t8 &= 63) === 0 ? this : t8 < 32 ? Tt(this.low << t8, this.high << t8 | this.low >>> 32 - t8, this.unsigned) : Tt(0, this.low << t8 - 32, this.unsigned); + return Vr(t8) && (t8 = t8.toInt()), (t8 &= 63) === 0 ? this : t8 < 32 ? Nt(this.low << t8, this.high << t8 | this.low >>> 32 - t8, this.unsigned) : Nt(0, this.low << t8 - 32, this.unsigned); }; de.shl = de.shiftLeft; de.shiftRight = function(t8) { - return Wr(t8) && (t8 = t8.toInt()), (t8 &= 63) === 0 ? this : t8 < 32 ? Tt(this.low >>> t8 | this.high << 32 - t8, this.high >> t8, this.unsigned) : Tt(this.high >> t8 - 32, this.high >= 0 ? 0 : -1, this.unsigned); + return Vr(t8) && (t8 = t8.toInt()), (t8 &= 63) === 0 ? this : t8 < 32 ? Nt(this.low >>> t8 | this.high << 32 - t8, this.high >> t8, this.unsigned) : Nt(this.high >> t8 - 32, this.high >= 0 ? 0 : -1, this.unsigned); }; de.shr = de.shiftRight; de.shiftRightUnsigned = function(t8) { - if (Wr(t8) && (t8 = t8.toInt()), t8 &= 63, t8 === 0) + if (Vr(t8) && (t8 = t8.toInt()), t8 &= 63, t8 === 0) return this; var e = this.high; if (t8 < 32) { var o = this.low; - return Tt(o >>> t8 | e << 32 - t8, e >>> t8, this.unsigned); + return Nt(o >>> t8 | e << 32 - t8, e >>> t8, this.unsigned); } else - return t8 === 32 ? Tt(e, 0, this.unsigned) : Tt(e >>> t8 - 32, 0, this.unsigned); + return t8 === 32 ? Nt(e, 0, this.unsigned) : Nt(e >>> t8 - 32, 0, this.unsigned); }; de.shru = de.shiftRightUnsigned; de.shr_u = de.shiftRightUnsigned; de.toSigned = function() { - return this.unsigned ? Tt(this.low, this.high, false) : this; + return this.unsigned ? Nt(this.low, this.high, false) : this; }; de.toUnsigned = function() { - return this.unsigned ? this : Tt(this.low, this.high, true); + return this.unsigned ? this : Nt(this.low, this.high, true); }; de.toBytes = function(t8) { return t8 ? this.toBytesLE() : this.toBytesBE(); @@ -871,22 +872,22 @@ var V0 = qt((tle, z0) => { var t8 = this.high, e = this.low; return [t8 >>> 24, t8 >>> 16 & 255, t8 >>> 8 & 255, t8 & 255, e >>> 24, e >>> 16 & 255, e >>> 8 & 255, e & 255]; }; - Nt.fromBytes = function(t8, e, o) { - return o ? Nt.fromBytesLE(t8, e) : Nt.fromBytesBE(t8, e); + kt.fromBytes = function(t8, e, o) { + return o ? kt.fromBytesLE(t8, e) : kt.fromBytesBE(t8, e); }; - Nt.fromBytesLE = function(t8, e) { - return new Nt(t8[0] | t8[1] << 8 | t8[2] << 16 | t8[3] << 24, t8[4] | t8[5] << 8 | t8[6] << 16 | t8[7] << 24, e); + kt.fromBytesLE = function(t8, e) { + return new kt(t8[0] | t8[1] << 8 | t8[2] << 16 | t8[3] << 24, t8[4] | t8[5] << 8 | t8[6] << 16 | t8[7] << 24, e); }; - Nt.fromBytesBE = function(t8, e) { - return new Nt(t8[4] << 24 | t8[5] << 16 | t8[6] << 8 | t8[7], t8[0] << 24 | t8[1] << 16 | t8[2] << 8 | t8[3], e); + kt.fromBytesBE = function(t8, e) { + return new kt(t8[4] << 24 | t8[5] << 16 | t8[6] << 8 | t8[7], t8[0] << 24 | t8[1] << 16 | t8[2] << 8 | t8[3], e); }; }); -var Ik = qt(() => { +var vk = Kt(() => { }); -var vk = qt(() => { +var kk = Kt(() => { }); -var Q2 = qt((Y2, Vw) => { - (function(r, t8, e) { +var Z2 = Kt((Q2, Vw) => { + (function(r15, t8, e) { function o(i) { var p = this, u = a(); p.next = function() { @@ -922,10 +923,10 @@ var Q2 = qt((Y2, Vw) => { t8 && t8.exports ? t8.exports = s : e && e.amd ? e(function() { return s; }) : this.alea = s; - })(Y2, typeof Vw == "object" && Vw, typeof define == "function" && define); + })(Q2, typeof Vw == "object" && Vw, typeof define == "function" && define); }); -var J2 = qt((Z2, Ww) => { - (function(r, t8, e) { +var e1 = Kt((J2, Ww) => { + (function(r15, t8, e) { function o(a) { var i = this, p = ""; i.x = 0, i.y = 0, i.z = 0, i.w = 0, i.next = function() { @@ -954,10 +955,10 @@ var J2 = qt((Z2, Ww) => { t8 && t8.exports ? t8.exports = s : e && e.amd ? e(function() { return s; }) : this.xor128 = s; - })(Z2, typeof Ww == "object" && Ww, typeof define == "function" && define); + })(J2, typeof Ww == "object" && Ww, typeof define == "function" && define); }); -var t1 = qt((e1, Uw) => { - (function(r, t8, e) { +var r1 = Kt((t1, Uw) => { + (function(r15, t8, e) { function o(a) { var i = this, p = ""; i.next = function() { @@ -986,10 +987,10 @@ var t1 = qt((e1, Uw) => { t8 && t8.exports ? t8.exports = s : e && e.amd ? e(function() { return s; }) : this.xorwow = s; - })(e1, typeof Uw == "object" && Uw, typeof define == "function" && define); + })(t1, typeof Uw == "object" && Uw, typeof define == "function" && define); }); -var o1 = qt((r1, Gw) => { - (function(r, t8, e) { +var n1 = Kt((o1, Gw) => { + (function(r15, t8, e) { function o(a) { var i = this; i.next = function() { @@ -1032,10 +1033,10 @@ var o1 = qt((r1, Gw) => { t8 && t8.exports ? t8.exports = s : e && e.amd ? e(function() { return s; }) : this.xorshift7 = s; - })(r1, typeof Gw == "object" && Gw, typeof define == "function" && define); + })(o1, typeof Gw == "object" && Gw, typeof define == "function" && define); }); -var s1 = qt((n1, Hw) => { - (function(r, t8, e) { +var a1 = Kt((s1, Hw) => { + (function(r15, t8, e) { function o(a) { var i = this; i.next = function() { @@ -1072,10 +1073,10 @@ var s1 = qt((n1, Hw) => { t8 && t8.exports ? t8.exports = s : e && e.amd ? e(function() { return s; }) : this.xor4096 = s; - })(n1, typeof Hw == "object" && Hw, typeof define == "function" && define); + })(s1, typeof Hw == "object" && Hw, typeof define == "function" && define); }); -var i1 = qt((a1, Kw) => { - (function(r, t8, e) { +var u1 = Kt((i1, Kw) => { + (function(r15, t8, e) { function o(a) { var i = this, p = ""; i.next = function() { @@ -1104,12 +1105,12 @@ var i1 = qt((a1, Kw) => { t8 && t8.exports ? t8.exports = s : e && e.amd ? e(function() { return s; }) : this.tychei = s; - })(a1, typeof Kw == "object" && Kw, typeof define == "function" && define); + })(i1, typeof Kw == "object" && Kw, typeof define == "function" && define); }); -var u1 = qt(() => { +var p1 = Kt(() => { }); -var c1 = qt((p1, Ld) => { - (function(r, t8, e) { +var l1 = Kt((c1, Od) => { + (function(r15, t8, e) { var o = 256, n = 6, s = 52, a = "random", i = e.pow(o, n), p = e.pow(2, s), u = p * 2, c = o - 1, l; function m(C, S, k) { var _ = []; @@ -1164,72 +1165,72 @@ var c1 = qt((p1, Ld) => { function x() { try { var C; - return l && (C = l.randomBytes) ? C = C(o) : (C = new Uint8Array(o), (r.crypto || r.msCrypto).getRandomValues(C)), b(C); + return l && (C = l.randomBytes) ? C = C(o) : (C = new Uint8Array(o), (r15.crypto || r15.msCrypto).getRandomValues(C)), b(C); } catch (_) { - var S = r.navigator, k = S && S.plugins; - return [+/* @__PURE__ */ new Date(), r, k, r.screen, b(t8)]; + var S = r15.navigator, k = S && S.plugins; + return [+/* @__PURE__ */ new Date(), r15, k, r15.screen, b(t8)]; } } function b(C) { return String.fromCharCode.apply(0, C); } - if (g(e.random(), t8), typeof Ld == "object" && Ld.exports) { - Ld.exports = m; + if (g(e.random(), t8), typeof Od == "object" && Od.exports) { + Od.exports = m; try { - l = u1(); + l = p1(); } catch (C) { } } else typeof define == "function" && define.amd ? define(function() { return m; }) : e["seed" + a] = m; - })(typeof self != "undefined" ? self : p1, [], Math); + })(typeof self != "undefined" ? self : c1, [], Math); }); -var qw = qt((Y2e, l1) => { - var vq = Q2(), kq = J2(), Nq = t1(), Tq = o1(), _q = s1(), $q = i1(), Qu = c1(); - Qu.alea = vq; - Qu.xor128 = kq; - Qu.xorwow = Nq; - Qu.xorshift7 = Tq; - Qu.xor4096 = _q; - Qu.tychei = $q; - l1.exports = Qu; +var qw = Kt((Y2e, m1) => { + var vq = Z2(), kq = e1(), Nq = r1(), Tq = n1(), _q = a1(), $q = u1(), Gu = l1(); + Gu.alea = vq; + Gu.xor128 = kq; + Gu.xorwow = Nq; + Gu.xorshift7 = Tq; + Gu.xor4096 = _q; + Gu.tychei = $q; + m1.exports = Gu; }); -var Bv = qt(() => { +var zv = Kt(() => { }); -var zv = qt(() => { +var Vv = Kt(() => { }); -var RB = qt(() => { +var DB = Kt(() => { }); -var DB = qt(() => { +var AB = Kt(() => { }); -var AB = qt(() => { +var FB = Kt(() => { }); -var FB = qt((Wg, Wv) => { - var Vv = (() => { - var r = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0; - return typeof __filename != "undefined" && (r = r || __filename), function(t8) { +var PB = Kt((Vg, Uv) => { + var Wv = (() => { + var r15 = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0; + return typeof __filename != "undefined" && (r15 = r15 || __filename), function(t8) { t8 = t8 || {}; function e() { - return oe.buffer != Ge && _t(oe.buffer), mt; + return oe.buffer != Ge && Tt(oe.buffer), lt; } function o() { - return oe.buffer != Ge && _t(oe.buffer), it; + return oe.buffer != Ge && Tt(oe.buffer), it; } function n() { - return oe.buffer != Ge && _t(oe.buffer), gt; + return oe.buffer != Ge && Tt(oe.buffer), ht; } function s() { - return oe.buffer != Ge && _t(oe.buffer), Lr; + return oe.buffer != Ge && Tt(oe.buffer), Mr; } function a() { - return oe.buffer != Ge && _t(oe.buffer), Lt; + return oe.buffer != Ge && Tt(oe.buffer), Mt; } function i() { - return oe.buffer != Ge && _t(oe.buffer), to; + return oe.buffer != Ge && Tt(oe.buffer), eo; } function p() { - return oe.buffer != Ge && _t(oe.buffer), nr; + return oe.buffer != Ge && Tt(oe.buffer), rr; } var u = typeof t8 != "undefined" ? t8 : {}, c, l; u.ready = new Promise(function(F, V) { @@ -1245,26 +1246,26 @@ var FB = qt((Wg, Wv) => { } var E, R, D, P; function O(F) { - if (F instanceof Eu) + if (F instanceof Iu) return; j("exiting due to exception: " + F); } if (C) { - var M = Bv(), L = zv(); - b ? k = L.dirname(k) + "/" : k = __dirname + "/", E = (V, ue) => (V = zp(V) ? new URL(V) : L.normalize(V), M.readFileSync(V, ue ? void 0 : "utf8")), D = (V) => { + var M = zv(), L = Vv(); + b ? k = L.dirname(k) + "/" : k = __dirname + "/", E = (V, ue) => (V = Dp(V) ? new URL(V) : L.normalize(V), M.readFileSync(V, ue ? void 0 : "utf8")), D = (V) => { var ue = E(V, true); return ue.buffer || (ue = new Uint8Array(ue)), ue; }, R = (V, ue, Ee) => { - V = zp(V) ? new URL(V) : L.normalize(V), M.readFile(V, function(Be, Le) { + V = Dp(V) ? new URL(V) : L.normalize(V), M.readFile(V, function(Be, Le) { Be ? Ee(Be) : ue(Le.buffer); }); }, process.argv.length > 1 && (h = process.argv[1].replace(/\\/g, "/")), f = process.argv.slice(2), process.on("uncaughtException", function(V) { - if (!(V instanceof Eu)) + if (!(V instanceof Iu)) throw V; }), process.on("unhandledRejection", function(V) { throw V; }), g = (V, ue) => { - if (Bo()) + if (Lo()) throw process.exitCode = V, ue; O(ue), process.exit(V); }, u.inspect = function() { @@ -1272,13 +1273,13 @@ var FB = qt((Wg, Wv) => { }; let F; try { - F = RB(); + F = DB(); } catch (V) { throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'), V; } global.Worker = F.Worker; } else - (x || b) && (b ? k = self.location.href : typeof document != "undefined" && document.currentScript && (k = document.currentScript.src), typeof r != "undefined" && r && (k = r), k.indexOf("blob:") !== 0 ? k = k.substr(0, k.replace(/[?#].*/, "").lastIndexOf("/") + 1) : k = "", C || (E = (F) => { + (x || b) && (b ? k = self.location.href : typeof document != "undefined" && document.currentScript && (k = document.currentScript.src), typeof r15 != "undefined" && r15 && (k = r15), k.indexOf("blob:") !== 0 ? k = k.substr(0, k.replace(/[?#].*/, "").lastIndexOf("/") + 1) : k = "", C || (E = (F) => { var V = new XMLHttpRequest(); return V.open("GET", F, false), V.send(null), V.responseText; }, b && (D = (F) => { @@ -1294,7 +1295,7 @@ var FB = qt((Wg, Wv) => { ue(); }, Ee.onerror = ue, Ee.send(null); }), P = (F) => document.title = F); - C && typeof performance == "undefined" && (global.performance = DB().performance); + C && typeof performance == "undefined" && (global.performance = AB().performance); var B = console.log.bind(console), z = console.warn.bind(console); C && (B = (F) => M.writeSync(1, F + ` `), z = (F) => M.writeSync(2, F + ` @@ -1304,10 +1305,10 @@ var FB = qt((Wg, Wv) => { var q = 4, Y = Atomics.load, J = Atomics.store, re = Atomics.compareExchange, ne; u.wasmBinary && (ne = u.wasmBinary); var ee = u.noExitRuntime || true; - typeof WebAssembly != "object" && $u("no native wasm support detected"); + typeof WebAssembly != "object" && Su("no native wasm support detected"); var oe, ie, le = false, be; function _e(F, V) { - F || $u(V); + F || Su(V); } var ve = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0; function Fe(F, V, ue) { @@ -1327,12 +1328,12 @@ var FB = qt((Wg, Wv) => { Le += String.fromCharCode((ge & 31) << 6 | Ne); continue; } - var Pt = F[V++] & 63; - if ((ge & 240) == 224 ? ge = (ge & 15) << 12 | Ne << 6 | Pt : ge = (ge & 7) << 18 | Ne << 12 | Pt << 6 | F[V++] & 63, ge < 65536) + var Ft = F[V++] & 63; + if ((ge & 240) == 224 ? ge = (ge & 15) << 12 | Ne << 6 | Ft : ge = (ge & 7) << 18 | Ne << 12 | Ft << 6 | F[V++] & 63, ge < 65536) Le += String.fromCharCode(ge); else { - var so = ge - 65536; - Le += String.fromCharCode(55296 | so >> 10, 56320 | so & 1023); + var no = ge - 65536; + Le += String.fromCharCode(55296 | no >> 10, 56320 | no & 1023); } } return Le; @@ -1346,8 +1347,8 @@ var FB = qt((Wg, Wv) => { for (var Be = ue, Le = ue + Ee - 1, ge = 0; ge < F.length; ++ge) { var Ne = F.charCodeAt(ge); if (Ne >= 55296 && Ne <= 57343) { - var Pt = F.charCodeAt(++ge); - Ne = 65536 + ((Ne & 1023) << 10) | Pt & 1023; + var Ft = F.charCodeAt(++ge); + Ne = 65536 + ((Ne & 1023) << 10) | Ft & 1023; } if (Ne <= 127) { if (ue >= Le) @@ -1369,139 +1370,139 @@ var FB = qt((Wg, Wv) => { } return V[ue >>> 0] = 0, ue - Be; } - function lt(F, V, ue) { + function ct(F, V, ue) { return st(F, o(), V, ue); } - var Ge, mt, it, gt, xt, Lr, Lt, to, nr; + var Ge, lt, it, ht, gt, Mr, Mt, eo, rr; S && (Ge = u.buffer); - function _t(F) { - Ge = F, u.HEAP8 = mt = new Int8Array(F), u.HEAP16 = gt = new Int16Array(F), u.HEAP32 = Lr = new Int32Array(F), u.HEAPU8 = it = new Uint8Array(F), u.HEAPU16 = xt = new Uint16Array(F), u.HEAPU32 = Lt = new Uint32Array(F), u.HEAPF32 = to = new Float32Array(F), u.HEAPF64 = nr = new Float64Array(F); + function Tt(F) { + Ge = F, u.HEAP8 = lt = new Int8Array(F), u.HEAP16 = ht = new Int16Array(F), u.HEAP32 = Mr = new Int32Array(F), u.HEAPU8 = it = new Uint8Array(F), u.HEAPU16 = gt = new Uint16Array(F), u.HEAPU32 = Mt = new Uint32Array(F), u.HEAPF32 = eo = new Float32Array(F), u.HEAPF64 = rr = new Float64Array(F); } - var sr = u.INITIAL_MEMORY || 16777216; + var or = u.INITIAL_MEMORY || 16777216; if (S) oe = u.wasmMemory, Ge = u.buffer; else if (u.wasmMemory) oe = u.wasmMemory; - else if (oe = new WebAssembly.Memory({ initial: sr / 65536, maximum: 65536, shared: true }), !(oe.buffer instanceof SharedArrayBuffer)) + else if (oe = new WebAssembly.Memory({ initial: or / 65536, maximum: 65536, shared: true }), !(oe.buffer instanceof SharedArrayBuffer)) throw j("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"), C && j("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and/or recent version)"), Error("bad memory"); - oe && (Ge = oe.buffer), sr = Ge.byteLength, _t(Ge); - var ar, ro = [], oo = [], hr = [], Wa = false; - function Bo() { + oe && (Ge = oe.buffer), or = Ge.byteLength, Tt(Ge); + var nr, to = [], ro = [], fr = [], Va = false; + function Lo() { return ee; } function Ks() { if (u.preRun) for (typeof u.preRun == "function" && (u.preRun = [u.preRun]); u.preRun.length; ) - sl(u.preRun.shift()); - ul(ro); + tl(u.preRun.shift()); + nl(to); } - function Yt() { - Wa = true, !S && ul(oo); + function Xt() { + Va = true, !S && nl(ro); } - function Ua() { + function Wa() { if (!S) { if (u.postRun) for (typeof u.postRun == "function" && (u.postRun = [u.postRun]); u.postRun.length; ) - l0(u.postRun.shift()); - ul(hr); + m0(u.postRun.shift()); + nl(fr); } } - function sl(F) { + function tl(F) { + to.unshift(F); + } + function rl(F) { ro.unshift(F); } - function al(F) { - oo.unshift(F); + function m0(F) { + fr.unshift(F); } - function l0(F) { - hr.unshift(F); + var vi = 0, Rp = null, Ua = null; + function by(F) { + vi++, u.monitorRunDependencies && u.monitorRunDependencies(vi); } - var Ti = 0, Bp = null, Ga = null; - function Cy(F) { - Ti++, u.monitorRunDependencies && u.monitorRunDependencies(Ti); - } - function wm(F) { - if (Ti--, u.monitorRunDependencies && u.monitorRunDependencies(Ti), Ti == 0 && (Bp !== null && (clearInterval(Bp), Bp = null), Ga)) { - var V = Ga; - Ga = null, V(); + function bm(F) { + if (vi--, u.monitorRunDependencies && u.monitorRunDependencies(vi), vi == 0 && (Rp !== null && (clearInterval(Rp), Rp = null), Ua)) { + var V = Ua; + Ua = null, V(); } } - function $u(F) { + function Su(F) { u.onAbort && u.onAbort(F), F = "Aborted(" + F + ")", j(F), le = true, be = 1, F += ". Build with -sASSERTIONS for more info."; var V = new WebAssembly.RuntimeError(F); throw l(V), V; } - var wy = "data:application/octet-stream;base64,"; - function Sm(F) { - return F.startsWith(wy); + var Cy = "data:application/octet-stream;base64,"; + function Cm(F) { + return F.startsWith(Cy); } - function zp(F) { + function Dp(F) { return F.startsWith("file://"); } - var gr; - gr = "tfjs-backend-wasm-threaded-simd.wasm", Sm(gr) || (gr = _(gr)); - function Im(F) { + var hr; + hr = "tfjs-backend-wasm-threaded-simd.wasm", Cm(hr) || (hr = _(hr)); + function wm(F) { try { - if (F == gr && ne) + if (F == hr && ne) return new Uint8Array(ne); if (D) return D(F); throw "both async and sync fetching of the wasm failed"; } catch (V) { - $u(V); + Su(V); } } - function Sy() { + function wy() { if (!ne && (x || b)) { - if (typeof fetch == "function" && !zp(gr)) - return fetch(gr, { credentials: "same-origin" }).then(function(F) { + if (typeof fetch == "function" && !Dp(hr)) + return fetch(hr, { credentials: "same-origin" }).then(function(F) { if (!F.ok) - throw "failed to load wasm binary file at '" + gr + "'"; + throw "failed to load wasm binary file at '" + hr + "'"; return F.arrayBuffer(); }).catch(function() { - return Im(gr); + return wm(hr); }); if (R) return new Promise(function(F, V) { - R(gr, function(ue) { + R(hr, function(ue) { F(new Uint8Array(ue)); }, V); }); } return Promise.resolve().then(function() { - return Im(gr); + return wm(hr); }); } - function Iy() { - var F = { env: Om, wasi_snapshot_preview1: Om }; + function Sy() { + var F = { env: Fm, wasi_snapshot_preview1: Fm }; function V(ge, Ne) { - var Pt = ge.exports; - if (u.asm = Pt, Dy(u.asm._emscripten_tls_init), ar = u.asm.__indirect_function_table, al(u.asm.__wasm_call_ctors), ie = Ne, !S) { - var so = Me.unusedWorkers.length; - Me.unusedWorkers.forEach(function(Ka) { - Me.loadWasmModuleToWorker(Ka, function() { - --so || wm("wasm-instantiate"); + var Ft = ge.exports; + if (u.asm = Ft, Ry(u.asm._emscripten_tls_init), nr = u.asm.__indirect_function_table, rl(u.asm.__wasm_call_ctors), ie = Ne, !S) { + var no = Me.unusedWorkers.length; + Me.unusedWorkers.forEach(function(Ha) { + Me.loadWasmModuleToWorker(Ha, function() { + --no || bm("wasm-instantiate"); }); }); } } - S || Cy("wasm-instantiate"); + S || by("wasm-instantiate"); function ue(ge) { V(ge.instance, ge.module); } function Ee(ge) { - return Sy().then(function(Ne) { + return wy().then(function(Ne) { return WebAssembly.instantiate(Ne, F); }).then(function(Ne) { return Ne; }).then(ge, function(Ne) { - j("failed to asynchronously prepare wasm: " + Ne), $u(Ne); + j("failed to asynchronously prepare wasm: " + Ne), Su(Ne); }); } function Be() { - return !ne && typeof WebAssembly.instantiateStreaming == "function" && !Sm(gr) && !zp(gr) && !C && typeof fetch == "function" ? fetch(gr, { credentials: "same-origin" }).then(function(ge) { + return !ne && typeof WebAssembly.instantiateStreaming == "function" && !Cm(hr) && !Dp(hr) && !C && typeof fetch == "function" ? fetch(hr, { credentials: "same-origin" }).then(function(ge) { var Ne = WebAssembly.instantiateStreaming(ge, F); - return Ne.then(ue, function(Pt) { - return j("wasm streaming compile failed: " + Pt), j("falling back to ArrayBuffer instantiation"), Ee(ue); + return Ne.then(ue, function(Ft) { + return j("wasm streaming compile failed: " + Ft), j("falling back to ArrayBuffer instantiation"), Ee(ue); }); }) : Ee(ue); } @@ -1514,23 +1515,23 @@ var FB = qt((Wg, Wv) => { } return Be().catch(l), {}; } - var m0, d0, vm = {}; - function Eu(F) { + var d0, f0, Sm = {}; + function Iu(F) { this.name = "ExitStatus", this.message = "Program terminated with exit(" + F + ")", this.status = F; } + function Iy(F) { + var V = Me.pthreads[F]; + delete Me.pthreads[F], V.terminate(), qC(F), Me.runningWorkers.splice(Me.runningWorkers.indexOf(V), 1), V.pthread_ptr = 0; + } function vy(F) { - var V = Me.pthreads[F]; - delete Me.pthreads[F], V.terminate(), jC(F), Me.runningWorkers.splice(Me.runningWorkers.indexOf(V), 1), V.pthread_ptr = 0; - } - function ky(F) { var V = Me.pthreads[F]; V.postMessage({ cmd: "cancel" }); } - function il(F) { + function ol(F) { var V = Me.pthreads[F]; _e(V), Me.returnWorkerToPool(V); } - function Ny(F) { + function ky(F) { var V = Me.getNewWorker(); if (!V) return 6; @@ -1540,27 +1541,27 @@ var FB = qt((Wg, Wv) => { C && V.ref(), V.postMessage(ue, F.transferList), delete V.runPthread; }, V.loaded && V.runPthread(), 0; } - var km = { varargs: void 0, get: function() { - km.varargs += 4; - var F = s()[km.varargs - 4 >>> 2]; + var Im = { varargs: void 0, get: function() { + Im.varargs += 4; + var F = s()[Im.varargs - 4 >>> 2]; return F; }, getStr: function(F) { var V = Pe(F); return V; } }; - function Nm(F) { + function vm(F) { if (S) - return _i(1, 1, F); - be = F, Bo() || (Me.terminateAllThreads(), u.onExit && u.onExit(F), le = true), g(F, new Eu(F)); + return ki(1, 1, F); + be = F, Lo() || (Me.terminateAllThreads(), u.onExit && u.onExit(F), le = true), g(F, new Iu(F)); } - function Ty(F, V) { + function Ny(F, V) { if (be = F, !V && S) - throw _m(F), "unwind"; - Nm(F); + throw Nm(F), "unwind"; + vm(F); } - var Tm = Ty; - function _y(F) { - if (F instanceof Eu || F == "unwind") + var km = Ny; + function Ty(F) { + if (F instanceof Iu || F == "unwind") return be; g(1, F); } @@ -1581,19 +1582,19 @@ var FB = qt((Wg, Wv) => { Me.unusedWorkers = []; }, returnWorkerToPool: function(F) { var V = F.pthread_ptr; - delete Me.pthreads[V], Me.unusedWorkers.push(F), Me.runningWorkers.splice(Me.runningWorkers.indexOf(F), 1), F.pthread_ptr = 0, C && F.unref(), jC(V); + delete Me.pthreads[V], Me.unusedWorkers.push(F), Me.runningWorkers.splice(Me.runningWorkers.indexOf(F), 1), F.pthread_ptr = 0, C && F.unref(), qC(V); }, receiveObjectTransfer: function(F) { }, threadInitTLS: function() { Me.tlsInitFunctions.forEach((F) => F()); }, loadWasmModuleToWorker: function(F, V) { F.onmessage = (Le) => { var ge = Le.data, Ne = ge.cmd; - if (F.pthread_ptr && (Me.currentProxiedOperationCallerThread = F.pthread_ptr), ge.targetThread && ge.targetThread != Wm()) { - var Pt = Me.pthreads[ge.targetThread]; - Pt ? Pt.postMessage(ge, ge.transferList) : j('Internal error! Worker sent a message "' + Ne + '" to target pthread ' + ge.targetThread + ", but that thread no longer exists!"), Me.currentProxiedOperationCallerThread = void 0; + if (F.pthread_ptr && (Me.currentProxiedOperationCallerThread = F.pthread_ptr), ge.targetThread && ge.targetThread != zm()) { + var Ft = Me.pthreads[ge.targetThread]; + Ft ? Ft.postMessage(ge, ge.transferList) : j('Internal error! Worker sent a message "' + Ne + '" to target pthread ' + ge.targetThread + ", but that thread no longer exists!"), Me.currentProxiedOperationCallerThread = void 0; return; } - Ne === "processProxyingQueue" ? pl(ge.queue) : Ne === "spawnThread" ? Ny(ge) : Ne === "cleanupThread" ? il(ge.thread) : Ne === "killThread" ? vy(ge.thread) : Ne === "cancelThread" ? ky(ge.thread) : Ne === "loaded" ? (F.loaded = true, C && F.unref(), V && V(F), F.runPthread && F.runPthread()) : Ne === "print" ? U("Thread " + ge.threadId + ": " + ge.text) : Ne === "printErr" ? j("Thread " + ge.threadId + ": " + ge.text) : Ne === "alert" ? alert("Thread " + ge.threadId + ": " + ge.text) : ge.target === "setimmediate" ? F.postMessage(ge) : Ne === "callHandler" ? u[ge.handler](...ge.args) : Ne && j("worker sent an unknown command " + Ne), Me.currentProxiedOperationCallerThread = void 0; + Ne === "processProxyingQueue" ? sl(ge.queue) : Ne === "spawnThread" ? ky(ge) : Ne === "cleanupThread" ? ol(ge.thread) : Ne === "killThread" ? Iy(ge.thread) : Ne === "cancelThread" ? vy(ge.thread) : Ne === "loaded" ? (F.loaded = true, C && F.unref(), V && V(F), F.runPthread && F.runPthread()) : Ne === "print" ? U("Thread " + ge.threadId + ": " + ge.text) : Ne === "printErr" ? j("Thread " + ge.threadId + ": " + ge.text) : Ne === "alert" ? alert("Thread " + ge.threadId + ": " + ge.text) : ge.target === "setimmediate" ? F.postMessage(ge) : Ne === "callHandler" ? u[ge.handler](...ge.args) : Ne && j("worker sent an unknown command " + Ne), Me.currentProxiedOperationCallerThread = void 0; }, F.onerror = (Le) => { var ge = "worker sent an error!"; throw j(ge + " " + Le.filename + ":" + Le.lineno + ": " + Le.message), Le; @@ -1606,7 +1607,7 @@ var FB = qt((Wg, Wv) => { var ue = [], Ee = ["onExit", "onAbort", "print", "printErr"]; for (var Be of Ee) u.hasOwnProperty(Be) && ue.push(Be); - F.postMessage({ cmd: "load", handlers: ue, urlOrBlob: u.mainScriptUrlOrBlob || r, wasmMemory: oe, wasmModule: ie }); + F.postMessage({ cmd: "load", handlers: ue, urlOrBlob: u.mainScriptUrlOrBlob || r15, wasmMemory: oe, wasmModule: ie }); }, allocateUnusedWorker: function() { var F, V = _("tfjs-backend-wasm-threaded-simd.worker.js"); F = new Worker(V), Me.unusedWorkers.push(F); @@ -1614,71 +1615,71 @@ var FB = qt((Wg, Wv) => { return Me.unusedWorkers.length == 0 && (Me.allocateUnusedWorker(), Me.loadWasmModuleToWorker(Me.unusedWorkers[0])), Me.unusedWorkers.pop(); } }; u.PThread = Me; - function ul(F) { + function nl(F) { for (; F.length > 0; ) F.shift()(u); } - function $y() { - var F = Wm(), V = s()[F + 52 >>> 2], ue = s()[F + 56 >>> 2], Ee = V - ue; - b0(V, Ee), Um(V); + function _y() { + var F = zm(), V = s()[F + 52 >>> 2], ue = s()[F + 56 >>> 2], Ee = V - ue; + C0(V, Ee), Vm(V); } - u.establishStackSpace = $y; - function _m(F) { + u.establishStackSpace = _y; + function Nm(F) { if (S) - return _i(2, 0, F); + return ki(2, 0, F); try { - Tm(F); + km(F); } catch (V) { - _y(V); + Ty(V); } } - var Vp = []; - function Ey(F) { - var V = Vp[F]; - return V || (F >= Vp.length && (Vp.length = F + 1), Vp[F] = V = ar.get(F)), V; + var Ap = []; + function $y(F) { + var V = Ap[F]; + return V || (F >= Ap.length && (Ap.length = F + 1), Ap[F] = V = nr.get(F)), V; } - function Ry(F, V) { - var ue = Ey(F)(V); - Bo() ? Me.setExitStatus(ue) : y0(ue); + function Ey(F, V) { + var ue = $y(F)(V); + Lo() ? Me.setExitStatus(ue) : b0(ue); } - u.invokeEntryPoint = Ry; - function Dy(F) { + u.invokeEntryPoint = Ey; + function Ry(F) { Me.tlsInitFunctions.push(F); } + function Dy(F) { + g0(F, !b, 1, !x), Me.threadInitTLS(); + } function Ay(F) { - h0(F, !b, 1, !x), Me.threadInitTLS(); + S ? postMessage({ cmd: "cleanupThread", thread: F }) : ol(F); } - function Fy(F) { - S ? postMessage({ cmd: "cleanupThread", thread: F }) : il(F); + function Tm(F, V, ue, Ee) { + return S ? ki(3, 1, F, V, ue, Ee) : _m(F, V, ue, Ee); } - function $m(F, V, ue, Ee) { - return S ? _i(3, 1, F, V, ue, Ee) : Em(F, V, ue, Ee); - } - function Em(F, V, ue, Ee) { + function _m(F, V, ue, Ee) { if (typeof SharedArrayBuffer == "undefined") return j("Current environment does not support SharedArrayBuffer, pthreads are not available!"), 6; var Be = [], Le = 0; if (S && (Be.length === 0 || Le)) - return $m(F, V, ue, Ee); + return Tm(F, V, ue, Ee); if (Le) return Le; var ge = { startRoutine: ue, pthread_ptr: F, arg: Ee, transferList: Be }; - return S ? (ge.cmd = "spawnThread", postMessage(ge, Be), 0) : Ny(ge); + return S ? (ge.cmd = "spawnThread", postMessage(ge, Be), 0) : ky(ge); } - function Py() { + function Fy() { return 65536; } - var Oy = true; - function My() { - return Oy; + var Py = true; + function Oy() { + return Py; } - function pl(F) { - Atomics.store(s(), F >> 2, 1), Wm() && x0(F), Atomics.compareExchange(s(), F >> 2, 1, 0); + function sl(F) { + Atomics.store(s(), F >> 2, 1), zm() && y0(F), Atomics.compareExchange(s(), F >> 2, 1, 0); } - u.executeNotifiedProxyingQueue = pl; - function Ly(F, V, ue, Ee) { + u.executeNotifiedProxyingQueue = sl; + function My(F, V, ue, Ee) { if (F == V) - setTimeout(() => pl(Ee)); + setTimeout(() => sl(Ee)); else if (S) postMessage({ targetThread: F, cmd: "processProxyingQueue", queue: Ee }); else { @@ -1689,361 +1690,361 @@ var FB = qt((Wg, Wv) => { } return 1; } - function By(F, V, ue) { + function Ly(F, V, ue) { return -1; } - function zy() { - $u(""); + function By() { + Su(""); } - function Ru(F) { - Ru.shown || (Ru.shown = {}), Ru.shown[F] || (Ru.shown[F] = 1, C && (F = "warning: " + F), j(F)); + function vu(F) { + vu.shown || (vu.shown = {}), vu.shown[F] || (vu.shown[F] = 1, C && (F = "warning: " + F), j(F)); + } + function zy() { + C || b || vu("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread"); } function Vy() { - C || b || Ru("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread"); - } - function Wy() { return Date.now(); } - function Rm() { + function $m() { return 4294901760; } - function Uy() { - return Rm(); + function Wy() { + return $m(); } - var cl; - C ? cl = () => { + var al; + C ? al = () => { var F = process.hrtime(); return F[0] * 1e3 + F[1] / 1e6; - } : cl = () => performance.timeOrigin + performance.now(); - function Gy(F, V, ue) { + } : al = () => performance.timeOrigin + performance.now(); + function Uy(F, V, ue) { o().copyWithin(F >>> 0, V >>> 0, V + ue >>> 0); } - function Hy() { - return C ? AB().cpus().length : navigator.hardwareConcurrency; + function Gy() { + return C ? FB().cpus().length : navigator.hardwareConcurrency; } - function Ky(F) { - var V = XC(), ue = F(); - return Um(V), ue; + function Hy(F) { + var V = jC(), ue = F(); + return Vm(V), ue; } - function _i(F, V) { + function ki(F, V) { var ue = arguments.length - 2, Ee = arguments; - return Ky(() => { - for (var Be = ue, Le = Gm(Be * 8), ge = Le >> 3, Ne = 0; Ne < ue; Ne++) { - var Pt = Ee[2 + Ne]; - p()[ge + Ne >>> 0] = Pt; + return Hy(() => { + for (var Be = ue, Le = Wm(Be * 8), ge = Le >> 3, Ne = 0; Ne < ue; Ne++) { + var Ft = Ee[2 + Ne]; + p()[ge + Ne >>> 0] = Ft; } - return g0(F, Be, Le, V); + return x0(F, Be, Le, V); }); } - var ll = []; - function qy(F, V, ue) { - ll.length = V; + var il = []; + function Ky(F, V, ue) { + il.length = V; for (var Ee = ue >> 3, Be = 0; Be < V; Be++) - ll[Be] = p()[Ee + Be >>> 0]; - var Le = F < 0, ge = Le ? vm[-F - 1] : rb[F]; - return ge.apply(null, ll); + il[Be] = p()[Ee + Be >>> 0]; + var Le = F < 0, ge = Le ? Sm[-F - 1] : tb[F]; + return ge.apply(null, il); } - function jy(F) { + function qy(F) { try { - return oe.grow(F - Ge.byteLength + 65535 >>> 16), _t(oe.buffer), 1; + return oe.grow(F - Ge.byteLength + 65535 >>> 16), Tt(oe.buffer), 1; } catch (V) { } } - function Xy(F) { + function jy(F) { var V = o().length; if (F = F >>> 0, F <= V) return false; - var ue = Rm(); + var ue = $m(); if (F > ue) return false; - let Ee = (Pt, so) => Pt + (so - Pt % so) % so; + let Ee = (Ft, no) => Ft + (no - Ft % no) % no; for (var Be = 1; Be <= 4; Be *= 2) { var Le = V * (1 + 0.2 / Be); Le = Math.min(Le, F + 100663296); - var ge = Math.min(ue, Ee(Math.max(F, Le), 65536)), Ne = jy(ge); + var ge = Math.min(ue, Ee(Math.max(F, Le), 65536)), Ne = qy(ge); if (Ne) return true; } return false; } - function Yy() { + function Xy() { throw "unwind"; } - function Dm(F) { - return S ? _i(4, 1, F) : 52; + function Em(F) { + return S ? ki(4, 1, F) : 52; } - function Am(F, V, ue, Ee, Be) { - return S ? _i(5, 1, F, V, ue, Ee, Be) : 70; + function Rm(F, V, ue, Ee, Be) { + return S ? ki(5, 1, F, V, ue, Ee, Be) : 70; } - var Qy = [null, [], []]; - function Zy(F, V) { - var ue = Qy[F]; + var Yy = [null, [], []]; + function Qy(F, V) { + var ue = Yy[F]; V === 0 || V === 10 ? ((F === 1 ? U : j)(Fe(ue, 0)), ue.length = 0) : ue.push(V); } - function Fm(F, V, ue, Ee) { + function Dm(F, V, ue, Ee) { if (S) - return _i(6, 1, F, V, ue, Ee); + return ki(6, 1, F, V, ue, Ee); for (var Be = 0, Le = 0; Le < ue; Le++) { var ge = a()[V >>> 2], Ne = a()[V + 4 >>> 2]; V += 8; - for (var Pt = 0; Pt < Ne; Pt++) - Zy(F, o()[ge + Pt >>> 0]); + for (var Ft = 0; Ft < Ne; Ft++) + Qy(F, o()[ge + Ft >>> 0]); Be += Ne; } return a()[Ee >>> 2] = Be, 0; } - function Pm(F) { + function Am(F) { var V = u["_" + F]; return V; } - function Jy(F, V) { + function Zy(F, V) { e().set(F, V >>> 0); } - function eb(F, V, ue, Ee, Be) { - var Le = { string: (Br) => { - var Hp = 0; - if (Br != null && Br !== 0) { - var S0 = (Br.length << 2) + 1; - Hp = Gm(S0), lt(Br, Hp, S0); + function Jy(F, V, ue, Ee, Be) { + var Le = { string: (Lr) => { + var Mp = 0; + if (Lr != null && Lr !== 0) { + var I0 = (Lr.length << 2) + 1; + Mp = Wm(I0), ct(Lr, Mp, I0); } - return Hp; - }, array: (Br) => { - var Hp = Gm(Br.length); - return Jy(Br, Hp), Hp; + return Mp; + }, array: (Lr) => { + var Mp = Wm(Lr.length); + return Zy(Lr, Mp), Mp; } }; - function ge(Br) { - return V === "string" ? Pe(Br) : V === "boolean" ? !!Br : Br; + function ge(Lr) { + return V === "string" ? Pe(Lr) : V === "boolean" ? !!Lr : Lr; } - var Ne = Pm(F), Pt = [], so = 0; + var Ne = Am(F), Ft = [], no = 0; if (Ee) - for (var Ka = 0; Ka < Ee.length; Ka++) { - var w0 = Le[ue[Ka]]; - w0 ? (so === 0 && (so = XC()), Pt[Ka] = w0(Ee[Ka])) : Pt[Ka] = Ee[Ka]; + for (var Ha = 0; Ha < Ee.length; Ha++) { + var S0 = Le[ue[Ha]]; + S0 ? (no === 0 && (no = jC()), Ft[Ha] = S0(Ee[Ha])) : Ft[Ha] = Ee[Ha]; } - var YC = Ne.apply(null, Pt); - function CG(Br) { - return so !== 0 && Um(so), ge(Br); + var XC = Ne.apply(null, Ft); + function wG(Lr) { + return no !== 0 && Vm(no), ge(Lr); } - return YC = CG(YC), YC; + return XC = wG(XC), XC; } - function tb(F, V, ue, Ee) { + function eb(F, V, ue, Ee) { ue = ue || []; var Be = ue.every((ge) => ge === "number" || ge === "boolean"), Le = V !== "string"; - return Le && Be && !Ee ? Pm(F) : function() { - return eb(F, V, ue, arguments, Ee); + return Le && Be && !Ee ? Am(F) : function() { + return Jy(F, V, ue, arguments, Ee); }; } Me.init(); - var rb = [null, Nm, _m, $m, Dm, Am, Fm], Om = { __emscripten_init_main_thread_js: Ay, __emscripten_thread_cleanup: Fy, __pthread_create_js: Em, _emscripten_default_pthread_stack_size: Py, _emscripten_get_now_is_monotonic: My, _emscripten_notify_task_queue: Ly, _emscripten_set_offscreencanvas_size: By, abort: zy, emscripten_check_blocking_allowed: Vy, emscripten_date_now: Wy, emscripten_get_heap_max: Uy, emscripten_get_now: cl, emscripten_memcpy_big: Gy, emscripten_num_logical_cores: Hy, emscripten_receive_on_main_thread_js: qy, emscripten_resize_heap: Xy, emscripten_unwind_to_js_event_loop: Yy, exit: Tm, fd_close: Dm, fd_seek: Am, fd_write: Fm, memory: oe || u.wasmMemory }, f0 = Iy(), ob = u.___wasm_call_ctors = function() { - return (ob = u.___wasm_call_ctors = u.asm.__wasm_call_ctors).apply(null, arguments); - }, nb = u._init = function() { - return (nb = u._init = u.asm.init).apply(null, arguments); - }, sb = u._init_with_threads_count = function() { - return (sb = u._init_with_threads_count = u.asm.init_with_threads_count).apply(null, arguments); - }, ab = u._get_threads_count = function() { - return (ab = u._get_threads_count = u.asm.get_threads_count).apply(null, arguments); - }, ib = u._register_tensor = function() { - return (ib = u._register_tensor = u.asm.register_tensor).apply(null, arguments); - }, ub = u._dispose_data = function() { - return (ub = u._dispose_data = u.asm.dispose_data).apply(null, arguments); - }, pb = u._dispose = function() { - return (pb = u._dispose = u.asm.dispose).apply(null, arguments); - }, cb = u._Abs = function() { - return (cb = u._Abs = u.asm.Abs).apply(null, arguments); - }, lb = u._Acos = function() { - return (lb = u._Acos = u.asm.Acos).apply(null, arguments); - }, mb = u._Acosh = function() { - return (mb = u._Acosh = u.asm.Acosh).apply(null, arguments); - }, db = u._Add = function() { - return (db = u._Add = u.asm.Add).apply(null, arguments); - }, fb = u._AddN = function() { - return (fb = u._AddN = u.asm.AddN).apply(null, arguments); - }, hb = u._All = function() { - return (hb = u._All = u.asm.All).apply(null, arguments); - }, gb = u._Any = function() { - return (gb = u._Any = u.asm.Any).apply(null, arguments); - }, xb = u._ArgMax = function() { - return (xb = u._ArgMax = u.asm.ArgMax).apply(null, arguments); - }, yb = u._ArgMin = function() { - return (yb = u._ArgMin = u.asm.ArgMin).apply(null, arguments); - }, bb = u._Asin = function() { - return (bb = u._Asin = u.asm.Asin).apply(null, arguments); - }, Cb = u._Asinh = function() { - return (Cb = u._Asinh = u.asm.Asinh).apply(null, arguments); - }, wb = u._Atan = function() { - return (wb = u._Atan = u.asm.Atan).apply(null, arguments); - }, Sb = u._Atan2 = function() { - return (Sb = u._Atan2 = u.asm.Atan2).apply(null, arguments); - }, Ib = u._Atanh = function() { - return (Ib = u._Atanh = u.asm.Atanh).apply(null, arguments); - }, vb = u._AvgPool = function() { - return (vb = u._AvgPool = u.asm.AvgPool).apply(null, arguments); - }, kb = u._AvgPool3D = function() { - return (kb = u._AvgPool3D = u.asm.AvgPool3D).apply(null, arguments); - }, Nb = u._AvgPool3DGrad = function() { - return (Nb = u._AvgPool3DGrad = u.asm.AvgPool3DGrad).apply(null, arguments); - }, Tb = u._AvgPoolGrad = function() { - return (Tb = u._AvgPoolGrad = u.asm.AvgPoolGrad).apply(null, arguments); - }, _b = u._BatchMatMul = function() { - return (_b = u._BatchMatMul = u.asm.BatchMatMul).apply(null, arguments); - }, $b = u._Bincount = function() { - return ($b = u._Bincount = u.asm.Bincount).apply(null, arguments); - }, Eb = u._BitwiseAnd = function() { - return (Eb = u._BitwiseAnd = u.asm.BitwiseAnd).apply(null, arguments); - }, Rb = u._Ceil = function() { - return (Rb = u._Ceil = u.asm.Ceil).apply(null, arguments); - }, Db = u._ClipByValue = function() { - return (Db = u._ClipByValue = u.asm.ClipByValue).apply(null, arguments); - }, Ab = u._Conv2D = function() { - return (Ab = u._Conv2D = u.asm.Conv2D).apply(null, arguments); - }, Fb = u._Conv2DBackpropInput = function() { - return (Fb = u._Conv2DBackpropInput = u.asm.Conv2DBackpropInput).apply(null, arguments); - }, Pb = u._Conv3D = function() { - return (Pb = u._Conv3D = u.asm.Conv3D).apply(null, arguments); - }, Ob = u._Conv3DBackpropFilterV2 = function() { - return (Ob = u._Conv3DBackpropFilterV2 = u.asm.Conv3DBackpropFilterV2).apply(null, arguments); - }, Mb = u._Conv3DBackpropInputV2 = function() { - return (Mb = u._Conv3DBackpropInputV2 = u.asm.Conv3DBackpropInputV2).apply(null, arguments); - }, Lb = u._Cos = function() { - return (Lb = u._Cos = u.asm.Cos).apply(null, arguments); - }, Bb = u._Cosh = function() { - return (Bb = u._Cosh = u.asm.Cosh).apply(null, arguments); - }, zb = u._CropAndResize = function() { - return (zb = u._CropAndResize = u.asm.CropAndResize).apply(null, arguments); - }, Vb = u._Cumprod = function() { - return (Vb = u._Cumprod = u.asm.Cumprod).apply(null, arguments); - }, Wb = u._Cumsum = function() { - return (Wb = u._Cumsum = u.asm.Cumsum).apply(null, arguments); - }, Ub = u._DenseBincount = function() { - return (Ub = u._DenseBincount = u.asm.DenseBincount).apply(null, arguments); - }, Gb = u._DepthToSpace = function() { - return (Gb = u._DepthToSpace = u.asm.DepthToSpace).apply(null, arguments); - }, Hb = u._DepthwiseConv2dNative = function() { - return (Hb = u._DepthwiseConv2dNative = u.asm.DepthwiseConv2dNative).apply(null, arguments); - }, Kb = u._Diag = function() { - return (Kb = u._Diag = u.asm.Diag).apply(null, arguments); - }, qb = u._Dilation2D = function() { - return (qb = u._Dilation2D = u.asm.Dilation2D).apply(null, arguments); - }, jb = u._Dilation2DBackpropFilter = function() { - return (jb = u._Dilation2DBackpropFilter = u.asm.Dilation2DBackpropFilter).apply(null, arguments); - }, Xb = u._Dilation2DBackpropInput = function() { - return (Xb = u._Dilation2DBackpropInput = u.asm.Dilation2DBackpropInput).apply(null, arguments); - }, Yb = u._Elu = function() { - return (Yb = u._Elu = u.asm.Elu).apply(null, arguments); - }, Qb = u._EluGrad = function() { - return (Qb = u._EluGrad = u.asm.EluGrad).apply(null, arguments); - }, Zb = u._Equal = function() { - return (Zb = u._Equal = u.asm.Equal).apply(null, arguments); - }, Jb = u._Erf = function() { - return (Jb = u._Erf = u.asm.Erf).apply(null, arguments); - }, eC = u._Exp = function() { - return (eC = u._Exp = u.asm.Exp).apply(null, arguments); - }, tC = u._Expm1 = function() { - return (tC = u._Expm1 = u.asm.Expm1).apply(null, arguments); - }, rC = u._FlipLeftRight = function() { - return (rC = u._FlipLeftRight = u.asm.FlipLeftRight).apply(null, arguments); - }, oC = u._Floor = function() { - return (oC = u._Floor = u.asm.Floor).apply(null, arguments); - }, nC = u._FloorDiv = function() { - return (nC = u._FloorDiv = u.asm.FloorDiv).apply(null, arguments); - }, sC = u._FusedBatchNorm = function() { - return (sC = u._FusedBatchNorm = u.asm.FusedBatchNorm).apply(null, arguments); - }, aC = u._FusedConv2D = function() { - return (aC = u._FusedConv2D = u.asm.FusedConv2D).apply(null, arguments); - }, iC = u._FusedDepthwiseConv2D = function() { - return (iC = u._FusedDepthwiseConv2D = u.asm.FusedDepthwiseConv2D).apply(null, arguments); - }, uC = u._Gather = function() { - return (uC = u._Gather = u.asm.Gather).apply(null, arguments); - }, pC = u._GatherNd = function() { - return (pC = u._GatherNd = u.asm.GatherNd).apply(null, arguments); - }, cC = u._Greater = function() { - return (cC = u._Greater = u.asm.Greater).apply(null, arguments); - }, lC = u._GreaterEqual = function() { - return (lC = u._GreaterEqual = u.asm.GreaterEqual).apply(null, arguments); - }, mC = u._IsFinite = function() { - return (mC = u._IsFinite = u.asm.IsFinite).apply(null, arguments); - }, dC = u._IsInf = function() { - return (dC = u._IsInf = u.asm.IsInf).apply(null, arguments); - }, fC = u._IsNan = function() { - return (fC = u._IsNan = u.asm.IsNan).apply(null, arguments); - }, hC = u._LRN = function() { - return (hC = u._LRN = u.asm.LRN).apply(null, arguments); - }, gC = u._LRNGrad = function() { - return (gC = u._LRNGrad = u.asm.LRNGrad).apply(null, arguments); - }, xC = u._LeakyRelu = function() { - return (xC = u._LeakyRelu = u.asm.LeakyRelu).apply(null, arguments); - }, yC = u._Less = function() { - return (yC = u._Less = u.asm.Less).apply(null, arguments); - }, bC = u._LessEqual = function() { - return (bC = u._LessEqual = u.asm.LessEqual).apply(null, arguments); - }, CC = u._LinSpace = function() { - return (CC = u._LinSpace = u.asm.LinSpace).apply(null, arguments); - }, wC = u._Log = function() { - return (wC = u._Log = u.asm.Log).apply(null, arguments); - }, SC = u._Log1p = function() { - return (SC = u._Log1p = u.asm.Log1p).apply(null, arguments); - }, IC = u._LogicalAnd = function() { - return (IC = u._LogicalAnd = u.asm.LogicalAnd).apply(null, arguments); - }, vC = u._LogicalNot = function() { - return (vC = u._LogicalNot = u.asm.LogicalNot).apply(null, arguments); - }, kC = u._LogicalOr = function() { - return (kC = u._LogicalOr = u.asm.LogicalOr).apply(null, arguments); - }, NC = u._LogicalXor = function() { - return (NC = u._LogicalXor = u.asm.LogicalXor).apply(null, arguments); - }, TC = u._Max = function() { - return (TC = u._Max = u.asm.Max).apply(null, arguments); - }, _C = u._MaxPool = function() { - return (_C = u._MaxPool = u.asm.MaxPool).apply(null, arguments); - }, $C = u._MaxPool3D = function() { - return ($C = u._MaxPool3D = u.asm.MaxPool3D).apply(null, arguments); - }, EC = u._MaxPool3DGrad = function() { - return (EC = u._MaxPool3DGrad = u.asm.MaxPool3DGrad).apply(null, arguments); - }, RC = u._MaxPoolGrad = function() { - return (RC = u._MaxPoolGrad = u.asm.MaxPoolGrad).apply(null, arguments); - }, DC = u._MaxPoolWithArgmax = function() { - return (DC = u._MaxPoolWithArgmax = u.asm.MaxPoolWithArgmax).apply(null, arguments); - }, AC = u._Maximum = function() { - return (AC = u._Maximum = u.asm.Maximum).apply(null, arguments); - }, FC = u._Mean = function() { - return (FC = u._Mean = u.asm.Mean).apply(null, arguments); - }, PC = u._Min = function() { - return (PC = u._Min = u.asm.Min).apply(null, arguments); - }, OC = u._Minimum = function() { - return (OC = u._Minimum = u.asm.Minimum).apply(null, arguments); - }, MC = u._MirrorPad = function() { - return (MC = u._MirrorPad = u.asm.MirrorPad).apply(null, arguments); - }, LC = u._Mod = function() { - return (LC = u._Mod = u.asm.Mod).apply(null, arguments); - }, BC = u._Multinomial = function() { - return (BC = u._Multinomial = u.asm.Multinomial).apply(null, arguments); - }, zC = u._Multiply = function() { - return (zC = u._Multiply = u.asm.Multiply).apply(null, arguments); - }, VC = u._Neg = function() { - return (VC = u._Neg = u.asm.Neg).apply(null, arguments); - }, WC = u._NonMaxSuppressionV3 = function() { - return (WC = u._NonMaxSuppressionV3 = u.asm.NonMaxSuppressionV3).apply(null, arguments); - }, UC = u._NonMaxSuppressionV4 = function() { - return (UC = u._NonMaxSuppressionV4 = u.asm.NonMaxSuppressionV4).apply(null, arguments); - }, Mm = u._NonMaxSuppressionV5 = function() { - return (Mm = u._NonMaxSuppressionV5 = u.asm.NonMaxSuppressionV5).apply(null, arguments); - }, Lm = u._NotEqual = function() { - return (Lm = u._NotEqual = u.asm.NotEqual).apply(null, arguments); - }, ml = u._OneHot = function() { - return (ml = u._OneHot = u.asm.OneHot).apply(null, arguments); - }, GC = u._PadV2 = function() { - return (GC = u._PadV2 = u.asm.PadV2).apply(null, arguments); - }, HC = u._Pow = function() { - return (HC = u._Pow = u.asm.Pow).apply(null, arguments); - }, Wp = u._Prelu = function() { - return (Wp = u._Prelu = u.asm.Prelu).apply(null, arguments); - }, Bm = u._Prod = function() { - return (Bm = u._Prod = u.asm.Prod).apply(null, arguments); - }, Up = u._RealDiv = function() { - return (Up = u._RealDiv = u.asm.RealDiv).apply(null, arguments); - }, Gp = u._Reciprocal = function() { - return (Gp = u._Reciprocal = u.asm.Reciprocal).apply(null, arguments); - }, KC = u._Relu = function() { - return (KC = u._Relu = u.asm.Relu).apply(null, arguments); + var tb = [null, vm, Nm, Tm, Em, Rm, Dm], Fm = { __emscripten_init_main_thread_js: Dy, __emscripten_thread_cleanup: Ay, __pthread_create_js: _m, _emscripten_default_pthread_stack_size: Fy, _emscripten_get_now_is_monotonic: Oy, _emscripten_notify_task_queue: My, _emscripten_set_offscreencanvas_size: Ly, abort: By, emscripten_check_blocking_allowed: zy, emscripten_date_now: Vy, emscripten_get_heap_max: Wy, emscripten_get_now: al, emscripten_memcpy_big: Uy, emscripten_num_logical_cores: Gy, emscripten_receive_on_main_thread_js: Ky, emscripten_resize_heap: jy, emscripten_unwind_to_js_event_loop: Xy, exit: km, fd_close: Em, fd_seek: Rm, fd_write: Dm, memory: oe || u.wasmMemory }, h0 = Sy(), rb = u.___wasm_call_ctors = function() { + return (rb = u.___wasm_call_ctors = u.asm.__wasm_call_ctors).apply(null, arguments); + }, ob = u._init = function() { + return (ob = u._init = u.asm.init).apply(null, arguments); + }, nb = u._init_with_threads_count = function() { + return (nb = u._init_with_threads_count = u.asm.init_with_threads_count).apply(null, arguments); + }, sb = u._get_threads_count = function() { + return (sb = u._get_threads_count = u.asm.get_threads_count).apply(null, arguments); + }, ab = u._register_tensor = function() { + return (ab = u._register_tensor = u.asm.register_tensor).apply(null, arguments); + }, ib = u._dispose_data = function() { + return (ib = u._dispose_data = u.asm.dispose_data).apply(null, arguments); + }, ub = u._dispose = function() { + return (ub = u._dispose = u.asm.dispose).apply(null, arguments); + }, pb = u._Abs = function() { + return (pb = u._Abs = u.asm.Abs).apply(null, arguments); + }, cb = u._Acos = function() { + return (cb = u._Acos = u.asm.Acos).apply(null, arguments); + }, lb = u._Acosh = function() { + return (lb = u._Acosh = u.asm.Acosh).apply(null, arguments); + }, mb = u._Add = function() { + return (mb = u._Add = u.asm.Add).apply(null, arguments); + }, db = u._AddN = function() { + return (db = u._AddN = u.asm.AddN).apply(null, arguments); + }, fb = u._All = function() { + return (fb = u._All = u.asm.All).apply(null, arguments); + }, hb = u._Any = function() { + return (hb = u._Any = u.asm.Any).apply(null, arguments); + }, gb = u._ArgMax = function() { + return (gb = u._ArgMax = u.asm.ArgMax).apply(null, arguments); + }, xb = u._ArgMin = function() { + return (xb = u._ArgMin = u.asm.ArgMin).apply(null, arguments); + }, yb = u._Asin = function() { + return (yb = u._Asin = u.asm.Asin).apply(null, arguments); + }, bb = u._Asinh = function() { + return (bb = u._Asinh = u.asm.Asinh).apply(null, arguments); + }, Cb = u._Atan = function() { + return (Cb = u._Atan = u.asm.Atan).apply(null, arguments); + }, wb = u._Atan2 = function() { + return (wb = u._Atan2 = u.asm.Atan2).apply(null, arguments); + }, Sb = u._Atanh = function() { + return (Sb = u._Atanh = u.asm.Atanh).apply(null, arguments); + }, Ib = u._AvgPool = function() { + return (Ib = u._AvgPool = u.asm.AvgPool).apply(null, arguments); + }, vb = u._AvgPool3D = function() { + return (vb = u._AvgPool3D = u.asm.AvgPool3D).apply(null, arguments); + }, kb = u._AvgPool3DGrad = function() { + return (kb = u._AvgPool3DGrad = u.asm.AvgPool3DGrad).apply(null, arguments); + }, Nb = u._AvgPoolGrad = function() { + return (Nb = u._AvgPoolGrad = u.asm.AvgPoolGrad).apply(null, arguments); + }, Tb = u._BatchMatMul = function() { + return (Tb = u._BatchMatMul = u.asm.BatchMatMul).apply(null, arguments); + }, _b = u._Bincount = function() { + return (_b = u._Bincount = u.asm.Bincount).apply(null, arguments); + }, $b = u._BitwiseAnd = function() { + return ($b = u._BitwiseAnd = u.asm.BitwiseAnd).apply(null, arguments); + }, Eb = u._Ceil = function() { + return (Eb = u._Ceil = u.asm.Ceil).apply(null, arguments); + }, Rb = u._ClipByValue = function() { + return (Rb = u._ClipByValue = u.asm.ClipByValue).apply(null, arguments); + }, Db = u._Conv2D = function() { + return (Db = u._Conv2D = u.asm.Conv2D).apply(null, arguments); + }, Ab = u._Conv2DBackpropInput = function() { + return (Ab = u._Conv2DBackpropInput = u.asm.Conv2DBackpropInput).apply(null, arguments); + }, Fb = u._Conv3D = function() { + return (Fb = u._Conv3D = u.asm.Conv3D).apply(null, arguments); + }, Pb = u._Conv3DBackpropFilterV2 = function() { + return (Pb = u._Conv3DBackpropFilterV2 = u.asm.Conv3DBackpropFilterV2).apply(null, arguments); + }, Ob = u._Conv3DBackpropInputV2 = function() { + return (Ob = u._Conv3DBackpropInputV2 = u.asm.Conv3DBackpropInputV2).apply(null, arguments); + }, Mb = u._Cos = function() { + return (Mb = u._Cos = u.asm.Cos).apply(null, arguments); + }, Lb = u._Cosh = function() { + return (Lb = u._Cosh = u.asm.Cosh).apply(null, arguments); + }, Bb = u._CropAndResize = function() { + return (Bb = u._CropAndResize = u.asm.CropAndResize).apply(null, arguments); + }, zb = u._Cumprod = function() { + return (zb = u._Cumprod = u.asm.Cumprod).apply(null, arguments); + }, Vb = u._Cumsum = function() { + return (Vb = u._Cumsum = u.asm.Cumsum).apply(null, arguments); + }, Wb = u._DenseBincount = function() { + return (Wb = u._DenseBincount = u.asm.DenseBincount).apply(null, arguments); + }, Ub = u._DepthToSpace = function() { + return (Ub = u._DepthToSpace = u.asm.DepthToSpace).apply(null, arguments); + }, Gb = u._DepthwiseConv2dNative = function() { + return (Gb = u._DepthwiseConv2dNative = u.asm.DepthwiseConv2dNative).apply(null, arguments); + }, Hb = u._Diag = function() { + return (Hb = u._Diag = u.asm.Diag).apply(null, arguments); + }, Kb = u._Dilation2D = function() { + return (Kb = u._Dilation2D = u.asm.Dilation2D).apply(null, arguments); + }, qb = u._Dilation2DBackpropFilter = function() { + return (qb = u._Dilation2DBackpropFilter = u.asm.Dilation2DBackpropFilter).apply(null, arguments); + }, jb = u._Dilation2DBackpropInput = function() { + return (jb = u._Dilation2DBackpropInput = u.asm.Dilation2DBackpropInput).apply(null, arguments); + }, Xb = u._Elu = function() { + return (Xb = u._Elu = u.asm.Elu).apply(null, arguments); + }, Yb = u._EluGrad = function() { + return (Yb = u._EluGrad = u.asm.EluGrad).apply(null, arguments); + }, Qb = u._Equal = function() { + return (Qb = u._Equal = u.asm.Equal).apply(null, arguments); + }, Zb = u._Erf = function() { + return (Zb = u._Erf = u.asm.Erf).apply(null, arguments); + }, Jb = u._Exp = function() { + return (Jb = u._Exp = u.asm.Exp).apply(null, arguments); + }, eC = u._Expm1 = function() { + return (eC = u._Expm1 = u.asm.Expm1).apply(null, arguments); + }, tC = u._FlipLeftRight = function() { + return (tC = u._FlipLeftRight = u.asm.FlipLeftRight).apply(null, arguments); + }, rC = u._Floor = function() { + return (rC = u._Floor = u.asm.Floor).apply(null, arguments); + }, oC = u._FloorDiv = function() { + return (oC = u._FloorDiv = u.asm.FloorDiv).apply(null, arguments); + }, nC = u._FusedBatchNorm = function() { + return (nC = u._FusedBatchNorm = u.asm.FusedBatchNorm).apply(null, arguments); + }, sC = u._FusedConv2D = function() { + return (sC = u._FusedConv2D = u.asm.FusedConv2D).apply(null, arguments); + }, aC = u._FusedDepthwiseConv2D = function() { + return (aC = u._FusedDepthwiseConv2D = u.asm.FusedDepthwiseConv2D).apply(null, arguments); + }, iC = u._Gather = function() { + return (iC = u._Gather = u.asm.Gather).apply(null, arguments); + }, uC = u._GatherNd = function() { + return (uC = u._GatherNd = u.asm.GatherNd).apply(null, arguments); + }, pC = u._Greater = function() { + return (pC = u._Greater = u.asm.Greater).apply(null, arguments); + }, cC = u._GreaterEqual = function() { + return (cC = u._GreaterEqual = u.asm.GreaterEqual).apply(null, arguments); + }, lC = u._IsFinite = function() { + return (lC = u._IsFinite = u.asm.IsFinite).apply(null, arguments); + }, mC = u._IsInf = function() { + return (mC = u._IsInf = u.asm.IsInf).apply(null, arguments); + }, dC = u._IsNan = function() { + return (dC = u._IsNan = u.asm.IsNan).apply(null, arguments); + }, fC = u._LRN = function() { + return (fC = u._LRN = u.asm.LRN).apply(null, arguments); + }, hC = u._LRNGrad = function() { + return (hC = u._LRNGrad = u.asm.LRNGrad).apply(null, arguments); + }, gC = u._LeakyRelu = function() { + return (gC = u._LeakyRelu = u.asm.LeakyRelu).apply(null, arguments); + }, xC = u._Less = function() { + return (xC = u._Less = u.asm.Less).apply(null, arguments); + }, yC = u._LessEqual = function() { + return (yC = u._LessEqual = u.asm.LessEqual).apply(null, arguments); + }, bC = u._LinSpace = function() { + return (bC = u._LinSpace = u.asm.LinSpace).apply(null, arguments); + }, CC = u._Log = function() { + return (CC = u._Log = u.asm.Log).apply(null, arguments); + }, wC = u._Log1p = function() { + return (wC = u._Log1p = u.asm.Log1p).apply(null, arguments); + }, SC = u._LogicalAnd = function() { + return (SC = u._LogicalAnd = u.asm.LogicalAnd).apply(null, arguments); + }, IC = u._LogicalNot = function() { + return (IC = u._LogicalNot = u.asm.LogicalNot).apply(null, arguments); + }, vC = u._LogicalOr = function() { + return (vC = u._LogicalOr = u.asm.LogicalOr).apply(null, arguments); + }, kC = u._LogicalXor = function() { + return (kC = u._LogicalXor = u.asm.LogicalXor).apply(null, arguments); + }, NC = u._Max = function() { + return (NC = u._Max = u.asm.Max).apply(null, arguments); + }, TC = u._MaxPool = function() { + return (TC = u._MaxPool = u.asm.MaxPool).apply(null, arguments); + }, _C = u._MaxPool3D = function() { + return (_C = u._MaxPool3D = u.asm.MaxPool3D).apply(null, arguments); + }, $C = u._MaxPool3DGrad = function() { + return ($C = u._MaxPool3DGrad = u.asm.MaxPool3DGrad).apply(null, arguments); + }, EC = u._MaxPoolGrad = function() { + return (EC = u._MaxPoolGrad = u.asm.MaxPoolGrad).apply(null, arguments); + }, RC = u._MaxPoolWithArgmax = function() { + return (RC = u._MaxPoolWithArgmax = u.asm.MaxPoolWithArgmax).apply(null, arguments); + }, DC = u._Maximum = function() { + return (DC = u._Maximum = u.asm.Maximum).apply(null, arguments); + }, AC = u._Mean = function() { + return (AC = u._Mean = u.asm.Mean).apply(null, arguments); + }, FC = u._Min = function() { + return (FC = u._Min = u.asm.Min).apply(null, arguments); + }, PC = u._Minimum = function() { + return (PC = u._Minimum = u.asm.Minimum).apply(null, arguments); + }, OC = u._MirrorPad = function() { + return (OC = u._MirrorPad = u.asm.MirrorPad).apply(null, arguments); + }, MC = u._Mod = function() { + return (MC = u._Mod = u.asm.Mod).apply(null, arguments); + }, LC = u._Multinomial = function() { + return (LC = u._Multinomial = u.asm.Multinomial).apply(null, arguments); + }, BC = u._Multiply = function() { + return (BC = u._Multiply = u.asm.Multiply).apply(null, arguments); + }, zC = u._Neg = function() { + return (zC = u._Neg = u.asm.Neg).apply(null, arguments); + }, VC = u._NonMaxSuppressionV3 = function() { + return (VC = u._NonMaxSuppressionV3 = u.asm.NonMaxSuppressionV3).apply(null, arguments); + }, WC = u._NonMaxSuppressionV4 = function() { + return (WC = u._NonMaxSuppressionV4 = u.asm.NonMaxSuppressionV4).apply(null, arguments); + }, Pm = u._NonMaxSuppressionV5 = function() { + return (Pm = u._NonMaxSuppressionV5 = u.asm.NonMaxSuppressionV5).apply(null, arguments); + }, Om = u._NotEqual = function() { + return (Om = u._NotEqual = u.asm.NotEqual).apply(null, arguments); + }, ul = u._OneHot = function() { + return (ul = u._OneHot = u.asm.OneHot).apply(null, arguments); + }, UC = u._PadV2 = function() { + return (UC = u._PadV2 = u.asm.PadV2).apply(null, arguments); + }, GC = u._Pow = function() { + return (GC = u._Pow = u.asm.Pow).apply(null, arguments); + }, Fp = u._Prelu = function() { + return (Fp = u._Prelu = u.asm.Prelu).apply(null, arguments); + }, Mm = u._Prod = function() { + return (Mm = u._Prod = u.asm.Prod).apply(null, arguments); + }, Pp = u._RealDiv = function() { + return (Pp = u._RealDiv = u.asm.RealDiv).apply(null, arguments); + }, Op = u._Reciprocal = function() { + return (Op = u._Reciprocal = u.asm.Reciprocal).apply(null, arguments); + }, HC = u._Relu = function() { + return (HC = u._Relu = u.asm.Relu).apply(null, arguments); }, K = u._Relu6 = function() { return (K = u._Relu6 = u.asm.Relu6).apply(null, arguments); }, ae = u._ResizeBilinear = function() { @@ -2052,131 +2053,131 @@ var FB = qt((Wg, Wv) => { return ($e = u._ResizeBilinearGrad = u.asm.ResizeBilinearGrad).apply(null, arguments); }, at = u._ResizeNearestNeighbor = function() { return (at = u._ResizeNearestNeighbor = u.asm.ResizeNearestNeighbor).apply(null, arguments); - }, $t = u._ResizeNearestNeighborGrad = function() { - return ($t = u._ResizeNearestNeighborGrad = u.asm.ResizeNearestNeighborGrad).apply(null, arguments); - }, Et = u._Reverse = function() { - return (Et = u._Reverse = u.asm.Reverse).apply(null, arguments); + }, _t = u._ResizeNearestNeighborGrad = function() { + return (_t = u._ResizeNearestNeighborGrad = u.asm.ResizeNearestNeighborGrad).apply(null, arguments); + }, $t = u._Reverse = function() { + return ($t = u._Reverse = u.asm.Reverse).apply(null, arguments); }, Qe = u._RotateWithOffset = function() { return (Qe = u._RotateWithOffset = u.asm.RotateWithOffset).apply(null, arguments); }, Ke = u._Round = function() { return (Ke = u._Round = u.asm.Round).apply(null, arguments); - }, Gt = u._Rsqrt = function() { - return (Gt = u._Rsqrt = u.asm.Rsqrt).apply(null, arguments); - }, no = u._ScatterNd = function() { - return (no = u._ScatterNd = u.asm.ScatterNd).apply(null, arguments); - }, Ha = u._SearchSorted = function() { - return (Ha = u._SearchSorted = u.asm.SearchSorted).apply(null, arguments); - }, zm = u._SelectV2 = function() { - return (zm = u._SelectV2 = u.asm.SelectV2).apply(null, arguments); - }, dl = u._Selu = function() { - return (dl = u._Selu = u.asm.Selu).apply(null, arguments); - }, qC = u._Sigmoid = function() { - return (qC = u._Sigmoid = u.asm.Sigmoid).apply(null, arguments); - }, br = u._Sign = function() { - return (br = u._Sign = u.asm.Sign).apply(null, arguments); - }, $i = u._Sin = function() { - return ($i = u._Sin = u.asm.Sin).apply(null, arguments); - }, Vm = u._Sinh = function() { - return (Vm = u._Sinh = u.asm.Sinh).apply(null, arguments); - }, WU = u._Softmax = function() { - return (WU = u._Softmax = u.asm.Softmax).apply(null, arguments); - }, UU = u._Softplus = function() { - return (UU = u._Softplus = u.asm.Softplus).apply(null, arguments); - }, GU = u._SparseFillEmptyRows = function() { - return (GU = u._SparseFillEmptyRows = u.asm.SparseFillEmptyRows).apply(null, arguments); - }, HU = u._SparseReshape = function() { - return (HU = u._SparseReshape = u.asm.SparseReshape).apply(null, arguments); - }, KU = u._SparseSegmentReduction = function() { - return (KU = u._SparseSegmentReduction = u.asm.SparseSegmentReduction).apply(null, arguments); - }, qU = u._SparseToDense = function() { - return (qU = u._SparseToDense = u.asm.SparseToDense).apply(null, arguments); - }, jU = u._Sqrt = function() { - return (jU = u._Sqrt = u.asm.Sqrt).apply(null, arguments); - }, XU = u._Square = function() { - return (XU = u._Square = u.asm.Square).apply(null, arguments); - }, YU = u._SquaredDifference = function() { - return (YU = u._SquaredDifference = u.asm.SquaredDifference).apply(null, arguments); - }, QU = u._Step = function() { - return (QU = u._Step = u.asm.Step).apply(null, arguments); - }, ZU = u._StridedSlice = function() { - return (ZU = u._StridedSlice = u.asm.StridedSlice).apply(null, arguments); - }, JU = u._Sub = function() { - return (JU = u._Sub = u.asm.Sub).apply(null, arguments); - }, eG = u._Sum = function() { - return (eG = u._Sum = u.asm.Sum).apply(null, arguments); - }, tG = u._Tan = function() { - return (tG = u._Tan = u.asm.Tan).apply(null, arguments); - }, rG = u._Tanh = function() { - return (rG = u._Tanh = u.asm.Tanh).apply(null, arguments); - }, oG = u._TensorScatterUpdate = function() { - return (oG = u._TensorScatterUpdate = u.asm.TensorScatterUpdate).apply(null, arguments); - }, nG = u._Tile = function() { - return (nG = u._Tile = u.asm.Tile).apply(null, arguments); - }, sG = u._TopK = function() { - return (sG = u._TopK = u.asm.TopK).apply(null, arguments); - }, aG = u._Transform = function() { - return (aG = u._Transform = u.asm.Transform).apply(null, arguments); - }, iG = u._Transpose = function() { - return (iG = u._Transpose = u.asm.Transpose).apply(null, arguments); - }, uG = u.__FusedMatMul = function() { - return (uG = u.__FusedMatMul = u.asm._FusedMatMul).apply(null, arguments); - }, pG = u._malloc = function() { - return (pG = u._malloc = u.asm.malloc).apply(null, arguments); - }, cG = u._free = function() { - return (cG = u._free = u.asm.free).apply(null, arguments); - }, lG = u.__emscripten_tls_init = function() { - return (lG = u.__emscripten_tls_init = u.asm._emscripten_tls_init).apply(null, arguments); - }, Wm = u._pthread_self = function() { - return (Wm = u._pthread_self = u.asm.pthread_self).apply(null, arguments); - }, mG = u.___errno_location = function() { - return (mG = u.___errno_location = u.asm.__errno_location).apply(null, arguments); - }, h0 = u.__emscripten_thread_init = function() { - return (h0 = u.__emscripten_thread_init = u.asm._emscripten_thread_init).apply(null, arguments); - }, dG = u.__emscripten_thread_crashed = function() { - return (dG = u.__emscripten_thread_crashed = u.asm._emscripten_thread_crashed).apply(null, arguments); - }, fG = u._emscripten_main_thread_process_queued_calls = function() { - return (fG = u._emscripten_main_thread_process_queued_calls = u.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments); - }, hG = u._emscripten_main_browser_thread_id = function() { - return (hG = u._emscripten_main_browser_thread_id = u.asm.emscripten_main_browser_thread_id).apply(null, arguments); - }, g0 = u._emscripten_run_in_main_runtime_thread_js = function() { - return (g0 = u._emscripten_run_in_main_runtime_thread_js = u.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments); - }, gG = u._emscripten_dispatch_to_thread_ = function() { - return (gG = u._emscripten_dispatch_to_thread_ = u.asm.emscripten_dispatch_to_thread_).apply(null, arguments); - }, x0 = u.__emscripten_proxy_execute_task_queue = function() { - return (x0 = u.__emscripten_proxy_execute_task_queue = u.asm._emscripten_proxy_execute_task_queue).apply(null, arguments); - }, jC = u.__emscripten_thread_free_data = function() { - return (jC = u.__emscripten_thread_free_data = u.asm._emscripten_thread_free_data).apply(null, arguments); - }, y0 = u.__emscripten_thread_exit = function() { - return (y0 = u.__emscripten_thread_exit = u.asm._emscripten_thread_exit).apply(null, arguments); - }, b0 = u._emscripten_stack_set_limits = function() { - return (b0 = u._emscripten_stack_set_limits = u.asm.emscripten_stack_set_limits).apply(null, arguments); - }, XC = u.stackSave = function() { - return (XC = u.stackSave = u.asm.stackSave).apply(null, arguments); - }, Um = u.stackRestore = function() { - return (Um = u.stackRestore = u.asm.stackRestore).apply(null, arguments); - }, Gm = u.stackAlloc = function() { - return (Gm = u.stackAlloc = u.asm.stackAlloc).apply(null, arguments); - }, xG = u.dynCall_iijjiiii = function() { - return (xG = u.dynCall_iijjiiii = u.asm.dynCall_iijjiiii).apply(null, arguments); - }, yG = u.dynCall_jiji = function() { - return (yG = u.dynCall_jiji = u.asm.dynCall_jiji).apply(null, arguments); + }, Ut = u._Rsqrt = function() { + return (Ut = u._Rsqrt = u.asm.Rsqrt).apply(null, arguments); + }, oo = u._ScatterNd = function() { + return (oo = u._ScatterNd = u.asm.ScatterNd).apply(null, arguments); + }, Ga = u._SearchSorted = function() { + return (Ga = u._SearchSorted = u.asm.SearchSorted).apply(null, arguments); + }, Lm = u._SelectV2 = function() { + return (Lm = u._SelectV2 = u.asm.SelectV2).apply(null, arguments); + }, pl = u._Selu = function() { + return (pl = u._Selu = u.asm.Selu).apply(null, arguments); + }, KC = u._Sigmoid = function() { + return (KC = u._Sigmoid = u.asm.Sigmoid).apply(null, arguments); + }, yr = u._Sign = function() { + return (yr = u._Sign = u.asm.Sign).apply(null, arguments); + }, Ni = u._Sin = function() { + return (Ni = u._Sin = u.asm.Sin).apply(null, arguments); + }, Bm = u._Sinh = function() { + return (Bm = u._Sinh = u.asm.Sinh).apply(null, arguments); + }, UU = u._Softmax = function() { + return (UU = u._Softmax = u.asm.Softmax).apply(null, arguments); + }, GU = u._Softplus = function() { + return (GU = u._Softplus = u.asm.Softplus).apply(null, arguments); + }, HU = u._SparseFillEmptyRows = function() { + return (HU = u._SparseFillEmptyRows = u.asm.SparseFillEmptyRows).apply(null, arguments); + }, KU = u._SparseReshape = function() { + return (KU = u._SparseReshape = u.asm.SparseReshape).apply(null, arguments); + }, qU = u._SparseSegmentReduction = function() { + return (qU = u._SparseSegmentReduction = u.asm.SparseSegmentReduction).apply(null, arguments); + }, jU = u._SparseToDense = function() { + return (jU = u._SparseToDense = u.asm.SparseToDense).apply(null, arguments); + }, XU = u._Sqrt = function() { + return (XU = u._Sqrt = u.asm.Sqrt).apply(null, arguments); + }, YU = u._Square = function() { + return (YU = u._Square = u.asm.Square).apply(null, arguments); + }, QU = u._SquaredDifference = function() { + return (QU = u._SquaredDifference = u.asm.SquaredDifference).apply(null, arguments); + }, ZU = u._Step = function() { + return (ZU = u._Step = u.asm.Step).apply(null, arguments); + }, JU = u._StridedSlice = function() { + return (JU = u._StridedSlice = u.asm.StridedSlice).apply(null, arguments); + }, eG = u._Sub = function() { + return (eG = u._Sub = u.asm.Sub).apply(null, arguments); + }, tG = u._Sum = function() { + return (tG = u._Sum = u.asm.Sum).apply(null, arguments); + }, rG = u._Tan = function() { + return (rG = u._Tan = u.asm.Tan).apply(null, arguments); + }, oG = u._Tanh = function() { + return (oG = u._Tanh = u.asm.Tanh).apply(null, arguments); + }, nG = u._TensorScatterUpdate = function() { + return (nG = u._TensorScatterUpdate = u.asm.TensorScatterUpdate).apply(null, arguments); + }, sG = u._Tile = function() { + return (sG = u._Tile = u.asm.Tile).apply(null, arguments); + }, aG = u._TopK = function() { + return (aG = u._TopK = u.asm.TopK).apply(null, arguments); + }, iG = u._Transform = function() { + return (iG = u._Transform = u.asm.Transform).apply(null, arguments); + }, uG = u._Transpose = function() { + return (uG = u._Transpose = u.asm.Transpose).apply(null, arguments); + }, pG = u.__FusedMatMul = function() { + return (pG = u.__FusedMatMul = u.asm._FusedMatMul).apply(null, arguments); + }, cG = u._malloc = function() { + return (cG = u._malloc = u.asm.malloc).apply(null, arguments); + }, lG = u._free = function() { + return (lG = u._free = u.asm.free).apply(null, arguments); + }, mG = u.__emscripten_tls_init = function() { + return (mG = u.__emscripten_tls_init = u.asm._emscripten_tls_init).apply(null, arguments); + }, zm = u._pthread_self = function() { + return (zm = u._pthread_self = u.asm.pthread_self).apply(null, arguments); + }, dG = u.___errno_location = function() { + return (dG = u.___errno_location = u.asm.__errno_location).apply(null, arguments); + }, g0 = u.__emscripten_thread_init = function() { + return (g0 = u.__emscripten_thread_init = u.asm._emscripten_thread_init).apply(null, arguments); + }, fG = u.__emscripten_thread_crashed = function() { + return (fG = u.__emscripten_thread_crashed = u.asm._emscripten_thread_crashed).apply(null, arguments); + }, hG = u._emscripten_main_thread_process_queued_calls = function() { + return (hG = u._emscripten_main_thread_process_queued_calls = u.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments); + }, gG = u._emscripten_main_browser_thread_id = function() { + return (gG = u._emscripten_main_browser_thread_id = u.asm.emscripten_main_browser_thread_id).apply(null, arguments); + }, x0 = u._emscripten_run_in_main_runtime_thread_js = function() { + return (x0 = u._emscripten_run_in_main_runtime_thread_js = u.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments); + }, xG = u._emscripten_dispatch_to_thread_ = function() { + return (xG = u._emscripten_dispatch_to_thread_ = u.asm.emscripten_dispatch_to_thread_).apply(null, arguments); + }, y0 = u.__emscripten_proxy_execute_task_queue = function() { + return (y0 = u.__emscripten_proxy_execute_task_queue = u.asm._emscripten_proxy_execute_task_queue).apply(null, arguments); + }, qC = u.__emscripten_thread_free_data = function() { + return (qC = u.__emscripten_thread_free_data = u.asm._emscripten_thread_free_data).apply(null, arguments); + }, b0 = u.__emscripten_thread_exit = function() { + return (b0 = u.__emscripten_thread_exit = u.asm._emscripten_thread_exit).apply(null, arguments); + }, C0 = u._emscripten_stack_set_limits = function() { + return (C0 = u._emscripten_stack_set_limits = u.asm.emscripten_stack_set_limits).apply(null, arguments); + }, jC = u.stackSave = function() { + return (jC = u.stackSave = u.asm.stackSave).apply(null, arguments); + }, Vm = u.stackRestore = function() { + return (Vm = u.stackRestore = u.asm.stackRestore).apply(null, arguments); + }, Wm = u.stackAlloc = function() { + return (Wm = u.stackAlloc = u.asm.stackAlloc).apply(null, arguments); + }, yG = u.dynCall_iijjiiii = function() { + return (yG = u.dynCall_iijjiiii = u.asm.dynCall_iijjiiii).apply(null, arguments); + }, bG = u.dynCall_jiji = function() { + return (bG = u.dynCall_jiji = u.asm.dynCall_jiji).apply(null, arguments); }; - u.keepRuntimeAlive = Bo, u.wasmMemory = oe, u.cwrap = tb, u.ExitStatus = Eu, u.PThread = Me; - var Hm; - Ga = function F() { - Hm || C0(), Hm || (Ga = F); + u.keepRuntimeAlive = Lo, u.wasmMemory = oe, u.cwrap = eb, u.ExitStatus = Iu, u.PThread = Me; + var Um; + Ua = function F() { + Um || w0(), Um || (Ua = F); }; - function C0(F) { - if (F = F || f, Ti > 0) + function w0(F) { + if (F = F || f, vi > 0) return; if (S) { - c(u), Yt(), startWorker(u); + c(u), Xt(), startWorker(u); return; } - if (Ks(), Ti > 0) + if (Ks(), vi > 0) return; function V() { - Hm || (Hm = true, u.calledRun = true, !le && (Yt(), c(u), u.onRuntimeInitialized && u.onRuntimeInitialized(), Ua())); + Um || (Um = true, u.calledRun = true, !le && (Xt(), c(u), u.onRuntimeInitialized && u.onRuntimeInitialized(), Wa())); } u.setStatus ? (u.setStatus("Running..."), setTimeout(function() { setTimeout(function() { @@ -2187,26 +2188,26 @@ var FB = qt((Wg, Wv) => { if (u.preInit) for (typeof u.preInit == "function" && (u.preInit = [u.preInit]); u.preInit.length > 0; ) u.preInit.pop()(); - C0(); - var Km; - m && (Km = { uncaughtException: process.listeners("uncaughtException").filter(function(F) { + w0(); + var Gm; + m && (Gm = { uncaughtException: process.listeners("uncaughtException").filter(function(F) { return !m.uncaughtException.indexOf(F) > -1; }), unhandledRejection: process.listeners("unhandledRejection").filter(function(F) { return !m.unhandledRejection.indexOf(F) > -1; }) }); - var qm; + var Hm; if (typeof WasmBackendModule != "undefined") - qm = WasmBackendModule; + Hm = WasmBackendModule; else if (typeof t8 != "undefined") - qm = t8; + Hm = t8; else throw new Error("Could not find wasm module in post.js"); - if (Km) { - var bG = qm._dispose; - qm._dispose = function() { - bG(), Km.uncaughtException.forEach(function(F) { + if (Gm) { + var CG = Hm._dispose; + Hm._dispose = function() { + CG(), Gm.uncaughtException.forEach(function(F) { process.removeListener("uncaughtException", F); - }), Km.unhandledRejection.forEach(function(F) { + }), Gm.unhandledRejection.forEach(function(F) { process.removeListener("unhandledRejection", F); }); }; @@ -2214,18 +2215,18 @@ var FB = qt((Wg, Wv) => { return t8.ready; }; })(); - typeof Wg == "object" && typeof Wv == "object" ? Wv.exports = Vv : typeof define == "function" && define.amd ? define([], function() { - return Vv; - }) : typeof Wg == "object" && (Wg.WasmBackendModuleThreadedSimd = Vv); + typeof Vg == "object" && typeof Uv == "object" ? Uv.exports = Wv : typeof define == "function" && define.amd ? define([], function() { + return Wv; + }) : typeof Vg == "object" && (Vg.WasmBackendModuleThreadedSimd = Wv); }); -var OB = qt((UFt, PB) => { - PB.exports.wasmWorkerContents = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",data=>onmessage({data:data}));var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8")+"//# sourceURL="+f)},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}var initializedJS=false;var pendingNotifiedProxyingQueues=[];function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+" +var MB = Kt((GFt, OB) => { + OB.exports.wasmWorkerContents = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",data=>onmessage({data:data}));var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8")+"//# sourceURL="+f)},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}var initializedJS=false;var pendingNotifiedProxyingQueues=[];function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+" ");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=(info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports};self.onunhandledrejection=e=>{throw e.reason??e};self.startWorker=instance=>{Module=instance;postMessage({"cmd":"loaded"})};self.onmessage=e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;for(const handler of e.data.handlers){Module[handler]=function(){postMessage({cmd:"callHandler",handler:handler,args:[...arguments]})}}Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob=="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module)}else if(e.data.cmd==="run"){Module["__emscripten_thread_init"](e.data.pthread_ptr,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInitTLS();if(!initializedJS){pendingNotifiedProxyingQueues.forEach(queue=>{Module["executeNotifiedProxyingQueue"](queue)});pendingNotifiedProxyingQueues=[];initializedJS=true}try{Module["invokeEntryPoint"](e.data.start_routine,e.data.arg)}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processProxyingQueue"){if(initializedJS){Module["executeNotifiedProxyingQueue"](e.data.queue)}else{pendingNotifiedProxyingQueues.push(e.data.queue)}}else if(e.data.cmd){err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}};`; }); -var MB = qt((Ug, Gv) => { - var Uv = (() => { - var r = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0; - return typeof __filename != "undefined" && (r = r || __filename), function(t8) { +var LB = Kt((Wg, Hv) => { + var Gv = (() => { + var r15 = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0; + return typeof __filename != "undefined" && (r15 = r15 || __filename), function(t8) { t8 = t8 || {}; var e = typeof t8 != "undefined" ? t8 : {}, o, n; e.ready = new Promise(function(K, ae) { @@ -2241,21 +2242,21 @@ var MB = qt((Ug, Gv) => { } var h, g, x, b; function C(K) { - if (K instanceof Bp) + if (K instanceof Rp) return; E("exiting due to exception: " + K); } if (m) { - var S = Bv(), k = zv(); + var S = zv(), k = Vv(); l ? d = k.dirname(d) + "/" : d = __dirname + "/", h = (K, ae) => (K = Ks(K) ? new URL(K) : k.normalize(K), S.readFileSync(K, ae ? void 0 : "utf8")), x = (K) => { var ae = h(K, true); return ae.buffer || (ae = new Uint8Array(ae)), ae; }, g = (K, ae, $e) => { - K = Ks(K) ? new URL(K) : k.normalize(K), S.readFile(K, function(at, $t) { - at ? $e(at) : ae($t.buffer); + K = Ks(K) ? new URL(K) : k.normalize(K), S.readFile(K, function(at, _t) { + at ? $e(at) : ae(_t.buffer); }); }, process.argv.length > 1 && (p = process.argv[1].replace(/\\/g, "/")), i = process.argv.slice(2), process.on("uncaughtException", function(K) { - if (!(K instanceof Bp)) + if (!(K instanceof Rp)) throw K; }), process.on("unhandledRejection", function(K) { throw K; @@ -2267,7 +2268,7 @@ var MB = qt((Ug, Gv) => { return "[Emscripten Module object]"; }; } else - (c || l) && (l ? d = self.location.href : typeof document != "undefined" && document.currentScript && (d = document.currentScript.src), r && (d = r), d.indexOf("blob:") !== 0 ? d = d.substr(0, d.replace(/[?#].*/, "").lastIndexOf("/") + 1) : d = "", h = (K) => { + (c || l) && (l ? d = self.location.href : typeof document != "undefined" && document.currentScript && (d = document.currentScript.src), r15 && (d = r15), d.indexOf("blob:") !== 0 ? d = d.substr(0, d.replace(/[?#].*/, "").lastIndexOf("/") + 1) : d = "", h = (K) => { var ae = new XMLHttpRequest(); return ae.open("GET", K, false), ae.send(null), ae.responseText; }, l && (x = (K) => { @@ -2288,38 +2289,38 @@ var MB = qt((Ug, Gv) => { var R = 4, D; e.wasmBinary && (D = e.wasmBinary); var P = e.noExitRuntime || true; - typeof WebAssembly != "object" && hr("no native wasm support detected"); + typeof WebAssembly != "object" && fr("no native wasm support detected"); var O, M = false, L; function B(K, ae) { - K || hr(ae); + K || fr(ae); } var z = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0; function U(K, ae, $e) { ae >>>= 0; - for (var at = ae + $e, $t = ae; K[$t] && !($t >= at); ) - ++$t; - if ($t - ae > 16 && K.buffer && z) - return z.decode(K.subarray(ae, $t)); - for (var Et = ""; ae < $t; ) { + for (var at = ae + $e, _t = ae; K[_t] && !(_t >= at); ) + ++_t; + if (_t - ae > 16 && K.buffer && z) + return z.decode(K.subarray(ae, _t)); + for (var $t = ""; ae < _t; ) { var Qe = K[ae++]; if (!(Qe & 128)) { - Et += String.fromCharCode(Qe); + $t += String.fromCharCode(Qe); continue; } var Ke = K[ae++] & 63; if ((Qe & 224) == 192) { - Et += String.fromCharCode((Qe & 31) << 6 | Ke); + $t += String.fromCharCode((Qe & 31) << 6 | Ke); continue; } - var Gt = K[ae++] & 63; - if ((Qe & 240) == 224 ? Qe = (Qe & 15) << 12 | Ke << 6 | Gt : Qe = (Qe & 7) << 18 | Ke << 12 | Gt << 6 | K[ae++] & 63, Qe < 65536) - Et += String.fromCharCode(Qe); + var Ut = K[ae++] & 63; + if ((Qe & 240) == 224 ? Qe = (Qe & 15) << 12 | Ke << 6 | Ut : Qe = (Qe & 7) << 18 | Ke << 12 | Ut << 6 | K[ae++] & 63, Qe < 65536) + $t += String.fromCharCode(Qe); else { - var no = Qe - 65536; - Et += String.fromCharCode(55296 | no >> 10, 56320 | no & 1023); + var oo = Qe - 65536; + $t += String.fromCharCode(55296 | oo >> 10, 56320 | oo & 1023); } } - return Et; + return $t; } function j(K, ae) { return K >>>= 0, K ? U(ne, K, ae) : ""; @@ -2327,31 +2328,31 @@ var MB = qt((Ug, Gv) => { function q(K, ae, $e, at) { if ($e >>>= 0, !(at > 0)) return 0; - for (var $t = $e, Et = $e + at - 1, Qe = 0; Qe < K.length; ++Qe) { + for (var _t = $e, $t = $e + at - 1, Qe = 0; Qe < K.length; ++Qe) { var Ke = K.charCodeAt(Qe); if (Ke >= 55296 && Ke <= 57343) { - var Gt = K.charCodeAt(++Qe); - Ke = 65536 + ((Ke & 1023) << 10) | Gt & 1023; + var Ut = K.charCodeAt(++Qe); + Ke = 65536 + ((Ke & 1023) << 10) | Ut & 1023; } if (Ke <= 127) { - if ($e >= Et) + if ($e >= $t) break; ae[$e++ >>> 0] = Ke; } else if (Ke <= 2047) { - if ($e + 1 >= Et) + if ($e + 1 >= $t) break; ae[$e++ >>> 0] = 192 | Ke >> 6, ae[$e++ >>> 0] = 128 | Ke & 63; } else if (Ke <= 65535) { - if ($e + 2 >= Et) + if ($e + 2 >= $t) break; ae[$e++ >>> 0] = 224 | Ke >> 12, ae[$e++ >>> 0] = 128 | Ke >> 6 & 63, ae[$e++ >>> 0] = 128 | Ke & 63; } else { - if ($e + 3 >= Et) + if ($e + 3 >= $t) break; ae[$e++ >>> 0] = 240 | Ke >> 18, ae[$e++ >>> 0] = 128 | Ke >> 12 & 63, ae[$e++ >>> 0] = 128 | Ke >> 6 & 63, ae[$e++ >>> 0] = 128 | Ke & 63; } } - return ae[$e >>> 0] = 0, $e - $t; + return ae[$e >>> 0] = 0, $e - _t; } function Y(K, ae, $e) { return q(K, ne, ae, $e); @@ -2360,551 +2361,551 @@ var MB = qt((Ug, Gv) => { function ve(K) { J = K, e.HEAP8 = re = new Int8Array(K), e.HEAP16 = ee = new Int16Array(K), e.HEAP32 = ie = new Int32Array(K), e.HEAPU8 = ne = new Uint8Array(K), e.HEAPU16 = oe = new Uint16Array(K), e.HEAPU32 = le = new Uint32Array(K), e.HEAPF32 = be = new Float32Array(K), e.HEAPF64 = _e = new Float64Array(K); } - var Fe = e.INITIAL_MEMORY || 16777216, Pe, st = [], lt = [], Ge = [], mt = false; + var Fe = e.INITIAL_MEMORY || 16777216, Pe, st = [], ct = [], Ge = [], lt = false; function it() { return P; } - function gt() { + function ht() { if (e.preRun) for (typeof e.preRun == "function" && (e.preRun = [e.preRun]); e.preRun.length; ) - Lt(e.preRun.shift()); - Ga(st); + Mt(e.preRun.shift()); + Ua(st); } - function xt() { - mt = true, Ga(lt); + function gt() { + lt = true, Ua(ct); } - function Lr() { + function Mr() { if (e.postRun) for (typeof e.postRun == "function" && (e.postRun = [e.postRun]); e.postRun.length; ) - nr(e.postRun.shift()); - Ga(Ge); + rr(e.postRun.shift()); + Ua(Ge); } - function Lt(K) { + function Mt(K) { st.unshift(K); } - function to(K) { - lt.unshift(K); + function eo(K) { + ct.unshift(K); } - function nr(K) { + function rr(K) { Ge.unshift(K); } - var _t = 0, sr = null, ar = null; - function ro(K) { - _t++, e.monitorRunDependencies && e.monitorRunDependencies(_t); + var Tt = 0, or = null, nr = null; + function to(K) { + Tt++, e.monitorRunDependencies && e.monitorRunDependencies(Tt); } - function oo(K) { - if (_t--, e.monitorRunDependencies && e.monitorRunDependencies(_t), _t == 0 && (sr !== null && (clearInterval(sr), sr = null), ar)) { - var ae = ar; - ar = null, ae(); + function ro(K) { + if (Tt--, e.monitorRunDependencies && e.monitorRunDependencies(Tt), Tt == 0 && (or !== null && (clearInterval(or), or = null), nr)) { + var ae = nr; + nr = null, ae(); } } - function hr(K) { + function fr(K) { e.onAbort && e.onAbort(K), K = "Aborted(" + K + ")", E(K), M = true, L = 1, K += ". Build with -sASSERTIONS for more info."; var ae = new WebAssembly.RuntimeError(K); throw n(ae), ae; } - var Wa = "data:application/octet-stream;base64,"; - function Bo(K) { - return K.startsWith(Wa); + var Va = "data:application/octet-stream;base64,"; + function Lo(K) { + return K.startsWith(Va); } function Ks(K) { return K.startsWith("file://"); } - var Yt; - Yt = "tfjs-backend-wasm.wasm", Bo(Yt) || (Yt = f(Yt)); - function Ua(K) { + var Xt; + Xt = "tfjs-backend-wasm.wasm", Lo(Xt) || (Xt = f(Xt)); + function Wa(K) { try { - if (K == Yt && D) + if (K == Xt && D) return new Uint8Array(D); if (x) return x(K); throw "both async and sync fetching of the wasm failed"; } catch (ae) { - hr(ae); + fr(ae); } } - function sl() { + function tl() { if (!D && (c || l)) { - if (typeof fetch == "function" && !Ks(Yt)) - return fetch(Yt, { credentials: "same-origin" }).then(function(K) { + if (typeof fetch == "function" && !Ks(Xt)) + return fetch(Xt, { credentials: "same-origin" }).then(function(K) { if (!K.ok) - throw "failed to load wasm binary file at '" + Yt + "'"; + throw "failed to load wasm binary file at '" + Xt + "'"; return K.arrayBuffer(); }).catch(function() { - return Ua(Yt); + return Wa(Xt); }); if (g) return new Promise(function(K, ae) { - g(Yt, function($e) { + g(Xt, function($e) { K(new Uint8Array($e)); }, ae); }); } return Promise.resolve().then(function() { - return Ua(Yt); + return Wa(Xt); }); } - function al() { - var K = { env: il, wasi_snapshot_preview1: il }; + function rl() { + var K = { env: ol, wasi_snapshot_preview1: ol }; function ae(Qe, Ke) { - var Gt = Qe.exports; - e.asm = Gt, O = e.asm.memory, ve(O.buffer), Pe = e.asm.__indirect_function_table, to(e.asm.__wasm_call_ctors), oo("wasm-instantiate"); + var Ut = Qe.exports; + e.asm = Ut, O = e.asm.memory, ve(O.buffer), Pe = e.asm.__indirect_function_table, eo(e.asm.__wasm_call_ctors), ro("wasm-instantiate"); } - ro("wasm-instantiate"); + to("wasm-instantiate"); function $e(Qe) { ae(Qe.instance); } function at(Qe) { - return sl().then(function(Ke) { + return tl().then(function(Ke) { return WebAssembly.instantiate(Ke, K); }).then(function(Ke) { return Ke; }).then(Qe, function(Ke) { - E("failed to asynchronously prepare wasm: " + Ke), hr(Ke); + E("failed to asynchronously prepare wasm: " + Ke), fr(Ke); }); } - function $t() { - return !D && typeof WebAssembly.instantiateStreaming == "function" && !Bo(Yt) && !Ks(Yt) && !m && typeof fetch == "function" ? fetch(Yt, { credentials: "same-origin" }).then(function(Qe) { + function _t() { + return !D && typeof WebAssembly.instantiateStreaming == "function" && !Lo(Xt) && !Ks(Xt) && !m && typeof fetch == "function" ? fetch(Xt, { credentials: "same-origin" }).then(function(Qe) { var Ke = WebAssembly.instantiateStreaming(Qe, K); - return Ke.then($e, function(Gt) { - return E("wasm streaming compile failed: " + Gt), E("falling back to ArrayBuffer instantiation"), at($e); + return Ke.then($e, function(Ut) { + return E("wasm streaming compile failed: " + Ut), E("falling back to ArrayBuffer instantiation"), at($e); }); }) : at($e); } if (e.instantiateWasm) try { - var Et = e.instantiateWasm(K, ae); - return Et; + var $t = e.instantiateWasm(K, ae); + return $t; } catch (Qe) { E("Module.instantiateWasm callback failed with error: " + Qe), n(Qe); } - return $t().catch(n), {}; + return _t().catch(n), {}; } - var l0, Ti; - function Bp(K) { + var m0, vi; + function Rp(K) { this.name = "ExitStatus", this.message = "Program terminated with exit(" + K + ")", this.status = K; } - function Ga(K) { + function Ua(K) { for (; K.length > 0; ) K.shift()(e); } - function Cy() { - hr(""); + function by() { + fr(""); } - function wm() { + function bm() { return 4294901760; } - function $u() { - return wm(); + function Su() { + return bm(); } - function wy(K, ae, $e) { + function Cy(K, ae, $e) { ne.copyWithin(K >>> 0, ae >>> 0, ae + $e >>> 0); } - function Sm(K) { + function Cm(K) { try { return O.grow(K - J.byteLength + 65535 >>> 16), ve(O.buffer), 1; } catch (ae) { } } - function zp(K) { + function Dp(K) { var ae = ne.length; K = K >>> 0; - var $e = wm(); + var $e = bm(); if (K > $e) return false; - let at = (Gt, no) => Gt + (no - Gt % no) % no; - for (var $t = 1; $t <= 4; $t *= 2) { - var Et = ae * (1 + 0.2 / $t); - Et = Math.min(Et, K + 100663296); - var Qe = Math.min($e, at(Math.max(K, Et), 65536)), Ke = Sm(Qe); + let at = (Ut, oo) => Ut + (oo - Ut % oo) % oo; + for (var _t = 1; _t <= 4; _t *= 2) { + var $t = ae * (1 + 0.2 / _t); + $t = Math.min($t, K + 100663296); + var Qe = Math.min($e, at(Math.max(K, $t), 65536)), Ke = Cm(Qe); if (Ke) return true; } return false; } - var gr = { varargs: void 0, get: function() { - gr.varargs += 4; - var K = ie[gr.varargs - 4 >>> 2]; + var hr = { varargs: void 0, get: function() { + hr.varargs += 4; + var K = ie[hr.varargs - 4 >>> 2]; return K; }, getStr: function(K) { var ae = j(K); return ae; } }; - function Im(K) { + function wm(K) { return 52; } - function Sy(K, ae, $e, at, $t) { + function wy(K, ae, $e, at, _t) { return 70; } - var Iy = [null, [], []]; - function m0(K, ae) { - var $e = Iy[K]; + var Sy = [null, [], []]; + function d0(K, ae) { + var $e = Sy[K]; ae === 0 || ae === 10 ? ((K === 1 ? _ : E)(U($e, 0)), $e.length = 0) : $e.push(ae); } - function d0(K, ae, $e, at) { - for (var $t = 0, Et = 0; Et < $e; Et++) { + function f0(K, ae, $e, at) { + for (var _t = 0, $t = 0; $t < $e; $t++) { var Qe = le[ae >>> 2], Ke = le[ae + 4 >>> 2]; ae += 8; - for (var Gt = 0; Gt < Ke; Gt++) - m0(K, ne[Qe + Gt >>> 0]); - $t += Ke; + for (var Ut = 0; Ut < Ke; Ut++) + d0(K, ne[Qe + Ut >>> 0]); + _t += Ke; } - return le[at >>> 2] = $t, 0; + return le[at >>> 2] = _t, 0; } - function vm(K) { + function Sm(K) { var ae = e["_" + K]; return ae; } - function Eu(K, ae) { + function Iu(K, ae) { re.set(K, ae >>> 0); } - function vy(K, ae, $e, at, $t) { - var Et = { string: (br) => { - var $i = 0; - if (br != null && br !== 0) { - var Vm = (br.length << 2) + 1; - $i = ml(Vm), Y(br, $i, Vm); + function Iy(K, ae, $e, at, _t) { + var $t = { string: (yr) => { + var Ni = 0; + if (yr != null && yr !== 0) { + var Bm = (yr.length << 2) + 1; + Ni = ul(Bm), Y(yr, Ni, Bm); } - return $i; - }, array: (br) => { - var $i = ml(br.length); - return Eu(br, $i), $i; + return Ni; + }, array: (yr) => { + var Ni = ul(yr.length); + return Iu(yr, Ni), Ni; } }; - function Qe(br) { - return ae === "string" ? j(br) : ae === "boolean" ? !!br : br; + function Qe(yr) { + return ae === "string" ? j(yr) : ae === "boolean" ? !!yr : yr; } - var Ke = vm(K), Gt = [], no = 0; + var Ke = Sm(K), Ut = [], oo = 0; if (at) - for (var Ha = 0; Ha < at.length; Ha++) { - var zm = Et[$e[Ha]]; - zm ? (no === 0 && (no = Mm()), Gt[Ha] = zm(at[Ha])) : Gt[Ha] = at[Ha]; + for (var Ga = 0; Ga < at.length; Ga++) { + var Lm = $t[$e[Ga]]; + Lm ? (oo === 0 && (oo = Pm()), Ut[Ga] = Lm(at[Ga])) : Ut[Ga] = at[Ga]; } - var dl = Ke.apply(null, Gt); - function qC(br) { - return no !== 0 && Lm(no), Qe(br); + var pl = Ke.apply(null, Ut); + function KC(yr) { + return oo !== 0 && Om(oo), Qe(yr); } - return dl = qC(dl), dl; + return pl = KC(pl), pl; } - function ky(K, ae, $e, at) { + function vy(K, ae, $e, at) { $e = $e || []; - var $t = $e.every((Qe) => Qe === "number" || Qe === "boolean"), Et = ae !== "string"; - return Et && $t && !at ? vm(K) : function() { - return vy(K, ae, $e, arguments, at); + var _t = $e.every((Qe) => Qe === "number" || Qe === "boolean"), $t = ae !== "string"; + return $t && _t && !at ? Sm(K) : function() { + return Iy(K, ae, $e, arguments, at); }; } - var il = { abort: Cy, emscripten_get_heap_max: $u, emscripten_memcpy_big: wy, emscripten_resize_heap: zp, fd_close: Im, fd_seek: Sy, fd_write: d0 }, Ny = al(), km = e.___wasm_call_ctors = function() { - return (km = e.___wasm_call_ctors = e.asm.__wasm_call_ctors).apply(null, arguments); - }, Nm = e._init = function() { - return (Nm = e._init = e.asm.init).apply(null, arguments); - }, Ty = e._init_with_threads_count = function() { - return (Ty = e._init_with_threads_count = e.asm.init_with_threads_count).apply(null, arguments); - }, Tm = e._get_threads_count = function() { - return (Tm = e._get_threads_count = e.asm.get_threads_count).apply(null, arguments); - }, _y = e._register_tensor = function() { - return (_y = e._register_tensor = e.asm.register_tensor).apply(null, arguments); + var ol = { abort: by, emscripten_get_heap_max: Su, emscripten_memcpy_big: Cy, emscripten_resize_heap: Dp, fd_close: wm, fd_seek: wy, fd_write: f0 }, ky = rl(), Im = e.___wasm_call_ctors = function() { + return (Im = e.___wasm_call_ctors = e.asm.__wasm_call_ctors).apply(null, arguments); + }, vm = e._init = function() { + return (vm = e._init = e.asm.init).apply(null, arguments); + }, Ny = e._init_with_threads_count = function() { + return (Ny = e._init_with_threads_count = e.asm.init_with_threads_count).apply(null, arguments); + }, km = e._get_threads_count = function() { + return (km = e._get_threads_count = e.asm.get_threads_count).apply(null, arguments); + }, Ty = e._register_tensor = function() { + return (Ty = e._register_tensor = e.asm.register_tensor).apply(null, arguments); }, Me = e._dispose_data = function() { return (Me = e._dispose_data = e.asm.dispose_data).apply(null, arguments); - }, ul = e._dispose = function() { - return (ul = e._dispose = e.asm.dispose).apply(null, arguments); - }, $y = e._Abs = function() { - return ($y = e._Abs = e.asm.Abs).apply(null, arguments); - }, _m = e._Acos = function() { - return (_m = e._Acos = e.asm.Acos).apply(null, arguments); - }, Vp = e._Acosh = function() { - return (Vp = e._Acosh = e.asm.Acosh).apply(null, arguments); - }, Ey = e._Add = function() { - return (Ey = e._Add = e.asm.Add).apply(null, arguments); - }, Ry = e._AddN = function() { - return (Ry = e._AddN = e.asm.AddN).apply(null, arguments); - }, Dy = e._All = function() { - return (Dy = e._All = e.asm.All).apply(null, arguments); - }, Ay = e._Any = function() { - return (Ay = e._Any = e.asm.Any).apply(null, arguments); - }, Fy = e._ArgMax = function() { - return (Fy = e._ArgMax = e.asm.ArgMax).apply(null, arguments); - }, $m = e._ArgMin = function() { - return ($m = e._ArgMin = e.asm.ArgMin).apply(null, arguments); - }, Em = e._Asin = function() { - return (Em = e._Asin = e.asm.Asin).apply(null, arguments); - }, Py = e._Asinh = function() { - return (Py = e._Asinh = e.asm.Asinh).apply(null, arguments); - }, Oy = e._Atan = function() { - return (Oy = e._Atan = e.asm.Atan).apply(null, arguments); - }, My = e._Atan2 = function() { - return (My = e._Atan2 = e.asm.Atan2).apply(null, arguments); - }, pl = e._Atanh = function() { - return (pl = e._Atanh = e.asm.Atanh).apply(null, arguments); - }, Ly = e._AvgPool = function() { - return (Ly = e._AvgPool = e.asm.AvgPool).apply(null, arguments); - }, By = e._AvgPool3D = function() { - return (By = e._AvgPool3D = e.asm.AvgPool3D).apply(null, arguments); - }, zy = e._AvgPool3DGrad = function() { - return (zy = e._AvgPool3DGrad = e.asm.AvgPool3DGrad).apply(null, arguments); - }, Ru = e._AvgPoolGrad = function() { - return (Ru = e._AvgPoolGrad = e.asm.AvgPoolGrad).apply(null, arguments); - }, Vy = e._BatchMatMul = function() { - return (Vy = e._BatchMatMul = e.asm.BatchMatMul).apply(null, arguments); - }, Wy = e._Bincount = function() { - return (Wy = e._Bincount = e.asm.Bincount).apply(null, arguments); - }, Rm = e._BitwiseAnd = function() { - return (Rm = e._BitwiseAnd = e.asm.BitwiseAnd).apply(null, arguments); - }, Uy = e._Ceil = function() { - return (Uy = e._Ceil = e.asm.Ceil).apply(null, arguments); - }, cl = e._ClipByValue = function() { - return (cl = e._ClipByValue = e.asm.ClipByValue).apply(null, arguments); - }, Gy = e._Conv2D = function() { - return (Gy = e._Conv2D = e.asm.Conv2D).apply(null, arguments); - }, Hy = e._Conv2DBackpropInput = function() { - return (Hy = e._Conv2DBackpropInput = e.asm.Conv2DBackpropInput).apply(null, arguments); - }, Ky = e._Conv3D = function() { - return (Ky = e._Conv3D = e.asm.Conv3D).apply(null, arguments); - }, _i = e._Conv3DBackpropFilterV2 = function() { - return (_i = e._Conv3DBackpropFilterV2 = e.asm.Conv3DBackpropFilterV2).apply(null, arguments); - }, ll = e._Conv3DBackpropInputV2 = function() { - return (ll = e._Conv3DBackpropInputV2 = e.asm.Conv3DBackpropInputV2).apply(null, arguments); - }, qy = e._Cos = function() { - return (qy = e._Cos = e.asm.Cos).apply(null, arguments); - }, jy = e._Cosh = function() { - return (jy = e._Cosh = e.asm.Cosh).apply(null, arguments); - }, Xy = e._CropAndResize = function() { - return (Xy = e._CropAndResize = e.asm.CropAndResize).apply(null, arguments); - }, Yy = e._Cumprod = function() { - return (Yy = e._Cumprod = e.asm.Cumprod).apply(null, arguments); - }, Dm = e._Cumsum = function() { - return (Dm = e._Cumsum = e.asm.Cumsum).apply(null, arguments); - }, Am = e._DenseBincount = function() { - return (Am = e._DenseBincount = e.asm.DenseBincount).apply(null, arguments); - }, Qy = e._DepthToSpace = function() { - return (Qy = e._DepthToSpace = e.asm.DepthToSpace).apply(null, arguments); - }, Zy = e._DepthwiseConv2dNative = function() { - return (Zy = e._DepthwiseConv2dNative = e.asm.DepthwiseConv2dNative).apply(null, arguments); - }, Fm = e._Diag = function() { - return (Fm = e._Diag = e.asm.Diag).apply(null, arguments); - }, Pm = e._Dilation2D = function() { - return (Pm = e._Dilation2D = e.asm.Dilation2D).apply(null, arguments); - }, Jy = e._Dilation2DBackpropFilter = function() { - return (Jy = e._Dilation2DBackpropFilter = e.asm.Dilation2DBackpropFilter).apply(null, arguments); - }, eb = e._Dilation2DBackpropInput = function() { - return (eb = e._Dilation2DBackpropInput = e.asm.Dilation2DBackpropInput).apply(null, arguments); - }, tb = e._Elu = function() { - return (tb = e._Elu = e.asm.Elu).apply(null, arguments); - }, rb = e._EluGrad = function() { - return (rb = e._EluGrad = e.asm.EluGrad).apply(null, arguments); - }, Om = e._Equal = function() { - return (Om = e._Equal = e.asm.Equal).apply(null, arguments); - }, f0 = e._Erf = function() { - return (f0 = e._Erf = e.asm.Erf).apply(null, arguments); - }, ob = e._Exp = function() { - return (ob = e._Exp = e.asm.Exp).apply(null, arguments); - }, nb = e._Expm1 = function() { - return (nb = e._Expm1 = e.asm.Expm1).apply(null, arguments); - }, sb = e._FlipLeftRight = function() { - return (sb = e._FlipLeftRight = e.asm.FlipLeftRight).apply(null, arguments); - }, ab = e._Floor = function() { - return (ab = e._Floor = e.asm.Floor).apply(null, arguments); - }, ib = e._FloorDiv = function() { - return (ib = e._FloorDiv = e.asm.FloorDiv).apply(null, arguments); - }, ub = e._FusedBatchNorm = function() { - return (ub = e._FusedBatchNorm = e.asm.FusedBatchNorm).apply(null, arguments); - }, pb = e._FusedConv2D = function() { - return (pb = e._FusedConv2D = e.asm.FusedConv2D).apply(null, arguments); - }, cb = e._FusedDepthwiseConv2D = function() { - return (cb = e._FusedDepthwiseConv2D = e.asm.FusedDepthwiseConv2D).apply(null, arguments); - }, lb = e._Gather = function() { - return (lb = e._Gather = e.asm.Gather).apply(null, arguments); - }, mb = e._GatherNd = function() { - return (mb = e._GatherNd = e.asm.GatherNd).apply(null, arguments); - }, db = e._Greater = function() { - return (db = e._Greater = e.asm.Greater).apply(null, arguments); - }, fb = e._GreaterEqual = function() { - return (fb = e._GreaterEqual = e.asm.GreaterEqual).apply(null, arguments); - }, hb = e._IsFinite = function() { - return (hb = e._IsFinite = e.asm.IsFinite).apply(null, arguments); - }, gb = e._IsInf = function() { - return (gb = e._IsInf = e.asm.IsInf).apply(null, arguments); - }, xb = e._IsNan = function() { - return (xb = e._IsNan = e.asm.IsNan).apply(null, arguments); - }, yb = e._LRN = function() { - return (yb = e._LRN = e.asm.LRN).apply(null, arguments); - }, bb = e._LRNGrad = function() { - return (bb = e._LRNGrad = e.asm.LRNGrad).apply(null, arguments); - }, Cb = e._LeakyRelu = function() { - return (Cb = e._LeakyRelu = e.asm.LeakyRelu).apply(null, arguments); - }, wb = e._Less = function() { - return (wb = e._Less = e.asm.Less).apply(null, arguments); - }, Sb = e._LessEqual = function() { - return (Sb = e._LessEqual = e.asm.LessEqual).apply(null, arguments); - }, Ib = e._LinSpace = function() { - return (Ib = e._LinSpace = e.asm.LinSpace).apply(null, arguments); - }, vb = e._Log = function() { - return (vb = e._Log = e.asm.Log).apply(null, arguments); - }, kb = e._Log1p = function() { - return (kb = e._Log1p = e.asm.Log1p).apply(null, arguments); - }, Nb = e._LogicalAnd = function() { - return (Nb = e._LogicalAnd = e.asm.LogicalAnd).apply(null, arguments); - }, Tb = e._LogicalNot = function() { - return (Tb = e._LogicalNot = e.asm.LogicalNot).apply(null, arguments); - }, _b = e._LogicalOr = function() { - return (_b = e._LogicalOr = e.asm.LogicalOr).apply(null, arguments); - }, $b = e._LogicalXor = function() { - return ($b = e._LogicalXor = e.asm.LogicalXor).apply(null, arguments); - }, Eb = e._Max = function() { - return (Eb = e._Max = e.asm.Max).apply(null, arguments); - }, Rb = e._MaxPool = function() { - return (Rb = e._MaxPool = e.asm.MaxPool).apply(null, arguments); - }, Db = e._MaxPool3D = function() { - return (Db = e._MaxPool3D = e.asm.MaxPool3D).apply(null, arguments); - }, Ab = e._MaxPool3DGrad = function() { - return (Ab = e._MaxPool3DGrad = e.asm.MaxPool3DGrad).apply(null, arguments); - }, Fb = e._MaxPoolGrad = function() { - return (Fb = e._MaxPoolGrad = e.asm.MaxPoolGrad).apply(null, arguments); - }, Pb = e._MaxPoolWithArgmax = function() { - return (Pb = e._MaxPoolWithArgmax = e.asm.MaxPoolWithArgmax).apply(null, arguments); - }, Ob = e._Maximum = function() { - return (Ob = e._Maximum = e.asm.Maximum).apply(null, arguments); - }, Mb = e._Mean = function() { - return (Mb = e._Mean = e.asm.Mean).apply(null, arguments); - }, Lb = e._Min = function() { - return (Lb = e._Min = e.asm.Min).apply(null, arguments); - }, Bb = e._Minimum = function() { - return (Bb = e._Minimum = e.asm.Minimum).apply(null, arguments); - }, zb = e._MirrorPad = function() { - return (zb = e._MirrorPad = e.asm.MirrorPad).apply(null, arguments); - }, Vb = e._Mod = function() { - return (Vb = e._Mod = e.asm.Mod).apply(null, arguments); - }, Wb = e._Multinomial = function() { - return (Wb = e._Multinomial = e.asm.Multinomial).apply(null, arguments); - }, Ub = e._Multiply = function() { - return (Ub = e._Multiply = e.asm.Multiply).apply(null, arguments); - }, Gb = e._Neg = function() { - return (Gb = e._Neg = e.asm.Neg).apply(null, arguments); - }, Hb = e._NonMaxSuppressionV3 = function() { - return (Hb = e._NonMaxSuppressionV3 = e.asm.NonMaxSuppressionV3).apply(null, arguments); - }, Kb = e._NonMaxSuppressionV4 = function() { - return (Kb = e._NonMaxSuppressionV4 = e.asm.NonMaxSuppressionV4).apply(null, arguments); - }, qb = e._NonMaxSuppressionV5 = function() { - return (qb = e._NonMaxSuppressionV5 = e.asm.NonMaxSuppressionV5).apply(null, arguments); - }, jb = e._NotEqual = function() { - return (jb = e._NotEqual = e.asm.NotEqual).apply(null, arguments); - }, Xb = e._OneHot = function() { - return (Xb = e._OneHot = e.asm.OneHot).apply(null, arguments); - }, Yb = e._PadV2 = function() { - return (Yb = e._PadV2 = e.asm.PadV2).apply(null, arguments); - }, Qb = e._Pow = function() { - return (Qb = e._Pow = e.asm.Pow).apply(null, arguments); - }, Zb = e._Prelu = function() { - return (Zb = e._Prelu = e.asm.Prelu).apply(null, arguments); - }, Jb = e._Prod = function() { - return (Jb = e._Prod = e.asm.Prod).apply(null, arguments); - }, eC = e._RealDiv = function() { - return (eC = e._RealDiv = e.asm.RealDiv).apply(null, arguments); - }, tC = e._Reciprocal = function() { - return (tC = e._Reciprocal = e.asm.Reciprocal).apply(null, arguments); - }, rC = e._Relu = function() { - return (rC = e._Relu = e.asm.Relu).apply(null, arguments); - }, oC = e._Relu6 = function() { - return (oC = e._Relu6 = e.asm.Relu6).apply(null, arguments); - }, nC = e._ResizeBilinear = function() { - return (nC = e._ResizeBilinear = e.asm.ResizeBilinear).apply(null, arguments); - }, sC = e._ResizeBilinearGrad = function() { - return (sC = e._ResizeBilinearGrad = e.asm.ResizeBilinearGrad).apply(null, arguments); - }, aC = e._ResizeNearestNeighbor = function() { - return (aC = e._ResizeNearestNeighbor = e.asm.ResizeNearestNeighbor).apply(null, arguments); - }, iC = e._ResizeNearestNeighborGrad = function() { - return (iC = e._ResizeNearestNeighborGrad = e.asm.ResizeNearestNeighborGrad).apply(null, arguments); - }, uC = e._Reverse = function() { - return (uC = e._Reverse = e.asm.Reverse).apply(null, arguments); - }, pC = e._RotateWithOffset = function() { - return (pC = e._RotateWithOffset = e.asm.RotateWithOffset).apply(null, arguments); - }, cC = e._Round = function() { - return (cC = e._Round = e.asm.Round).apply(null, arguments); - }, lC = e._Rsqrt = function() { - return (lC = e._Rsqrt = e.asm.Rsqrt).apply(null, arguments); - }, mC = e._ScatterNd = function() { - return (mC = e._ScatterNd = e.asm.ScatterNd).apply(null, arguments); - }, dC = e._SearchSorted = function() { - return (dC = e._SearchSorted = e.asm.SearchSorted).apply(null, arguments); - }, fC = e._SelectV2 = function() { - return (fC = e._SelectV2 = e.asm.SelectV2).apply(null, arguments); - }, hC = e._Selu = function() { - return (hC = e._Selu = e.asm.Selu).apply(null, arguments); - }, gC = e._Sigmoid = function() { - return (gC = e._Sigmoid = e.asm.Sigmoid).apply(null, arguments); - }, xC = e._Sign = function() { - return (xC = e._Sign = e.asm.Sign).apply(null, arguments); - }, yC = e._Sin = function() { - return (yC = e._Sin = e.asm.Sin).apply(null, arguments); - }, bC = e._Sinh = function() { - return (bC = e._Sinh = e.asm.Sinh).apply(null, arguments); - }, CC = e._Softmax = function() { - return (CC = e._Softmax = e.asm.Softmax).apply(null, arguments); - }, wC = e._Softplus = function() { - return (wC = e._Softplus = e.asm.Softplus).apply(null, arguments); - }, SC = e._SparseFillEmptyRows = function() { - return (SC = e._SparseFillEmptyRows = e.asm.SparseFillEmptyRows).apply(null, arguments); - }, IC = e._SparseReshape = function() { - return (IC = e._SparseReshape = e.asm.SparseReshape).apply(null, arguments); - }, vC = e._SparseSegmentReduction = function() { - return (vC = e._SparseSegmentReduction = e.asm.SparseSegmentReduction).apply(null, arguments); - }, kC = e._SparseToDense = function() { - return (kC = e._SparseToDense = e.asm.SparseToDense).apply(null, arguments); - }, NC = e._Sqrt = function() { - return (NC = e._Sqrt = e.asm.Sqrt).apply(null, arguments); - }, TC = e._Square = function() { - return (TC = e._Square = e.asm.Square).apply(null, arguments); - }, _C = e._SquaredDifference = function() { - return (_C = e._SquaredDifference = e.asm.SquaredDifference).apply(null, arguments); - }, $C = e._Step = function() { - return ($C = e._Step = e.asm.Step).apply(null, arguments); - }, EC = e._StridedSlice = function() { - return (EC = e._StridedSlice = e.asm.StridedSlice).apply(null, arguments); - }, RC = e._Sub = function() { - return (RC = e._Sub = e.asm.Sub).apply(null, arguments); - }, DC = e._Sum = function() { - return (DC = e._Sum = e.asm.Sum).apply(null, arguments); - }, AC = e._Tan = function() { - return (AC = e._Tan = e.asm.Tan).apply(null, arguments); - }, FC = e._Tanh = function() { - return (FC = e._Tanh = e.asm.Tanh).apply(null, arguments); - }, PC = e._TensorScatterUpdate = function() { - return (PC = e._TensorScatterUpdate = e.asm.TensorScatterUpdate).apply(null, arguments); - }, OC = e._Tile = function() { - return (OC = e._Tile = e.asm.Tile).apply(null, arguments); - }, MC = e._TopK = function() { - return (MC = e._TopK = e.asm.TopK).apply(null, arguments); - }, LC = e._Transform = function() { - return (LC = e._Transform = e.asm.Transform).apply(null, arguments); - }, BC = e._Transpose = function() { - return (BC = e._Transpose = e.asm.Transpose).apply(null, arguments); - }, zC = e.__FusedMatMul = function() { - return (zC = e.__FusedMatMul = e.asm._FusedMatMul).apply(null, arguments); - }, VC = e._malloc = function() { - return (VC = e._malloc = e.asm.malloc).apply(null, arguments); - }, WC = e._free = function() { - return (WC = e._free = e.asm.free).apply(null, arguments); - }, UC = e.___errno_location = function() { - return (UC = e.___errno_location = e.asm.__errno_location).apply(null, arguments); - }, Mm = e.stackSave = function() { - return (Mm = e.stackSave = e.asm.stackSave).apply(null, arguments); - }, Lm = e.stackRestore = function() { - return (Lm = e.stackRestore = e.asm.stackRestore).apply(null, arguments); - }, ml = e.stackAlloc = function() { - return (ml = e.stackAlloc = e.asm.stackAlloc).apply(null, arguments); - }, GC = e.dynCall_iijjiiii = function() { - return (GC = e.dynCall_iijjiiii = e.asm.dynCall_iijjiiii).apply(null, arguments); - }, HC = e.dynCall_jiji = function() { - return (HC = e.dynCall_jiji = e.asm.dynCall_jiji).apply(null, arguments); + }, nl = e._dispose = function() { + return (nl = e._dispose = e.asm.dispose).apply(null, arguments); + }, _y = e._Abs = function() { + return (_y = e._Abs = e.asm.Abs).apply(null, arguments); + }, Nm = e._Acos = function() { + return (Nm = e._Acos = e.asm.Acos).apply(null, arguments); + }, Ap = e._Acosh = function() { + return (Ap = e._Acosh = e.asm.Acosh).apply(null, arguments); + }, $y = e._Add = function() { + return ($y = e._Add = e.asm.Add).apply(null, arguments); + }, Ey = e._AddN = function() { + return (Ey = e._AddN = e.asm.AddN).apply(null, arguments); + }, Ry = e._All = function() { + return (Ry = e._All = e.asm.All).apply(null, arguments); + }, Dy = e._Any = function() { + return (Dy = e._Any = e.asm.Any).apply(null, arguments); + }, Ay = e._ArgMax = function() { + return (Ay = e._ArgMax = e.asm.ArgMax).apply(null, arguments); + }, Tm = e._ArgMin = function() { + return (Tm = e._ArgMin = e.asm.ArgMin).apply(null, arguments); + }, _m = e._Asin = function() { + return (_m = e._Asin = e.asm.Asin).apply(null, arguments); + }, Fy = e._Asinh = function() { + return (Fy = e._Asinh = e.asm.Asinh).apply(null, arguments); + }, Py = e._Atan = function() { + return (Py = e._Atan = e.asm.Atan).apply(null, arguments); + }, Oy = e._Atan2 = function() { + return (Oy = e._Atan2 = e.asm.Atan2).apply(null, arguments); + }, sl = e._Atanh = function() { + return (sl = e._Atanh = e.asm.Atanh).apply(null, arguments); + }, My = e._AvgPool = function() { + return (My = e._AvgPool = e.asm.AvgPool).apply(null, arguments); + }, Ly = e._AvgPool3D = function() { + return (Ly = e._AvgPool3D = e.asm.AvgPool3D).apply(null, arguments); + }, By = e._AvgPool3DGrad = function() { + return (By = e._AvgPool3DGrad = e.asm.AvgPool3DGrad).apply(null, arguments); + }, vu = e._AvgPoolGrad = function() { + return (vu = e._AvgPoolGrad = e.asm.AvgPoolGrad).apply(null, arguments); + }, zy = e._BatchMatMul = function() { + return (zy = e._BatchMatMul = e.asm.BatchMatMul).apply(null, arguments); + }, Vy = e._Bincount = function() { + return (Vy = e._Bincount = e.asm.Bincount).apply(null, arguments); + }, $m = e._BitwiseAnd = function() { + return ($m = e._BitwiseAnd = e.asm.BitwiseAnd).apply(null, arguments); + }, Wy = e._Ceil = function() { + return (Wy = e._Ceil = e.asm.Ceil).apply(null, arguments); + }, al = e._ClipByValue = function() { + return (al = e._ClipByValue = e.asm.ClipByValue).apply(null, arguments); + }, Uy = e._Conv2D = function() { + return (Uy = e._Conv2D = e.asm.Conv2D).apply(null, arguments); + }, Gy = e._Conv2DBackpropInput = function() { + return (Gy = e._Conv2DBackpropInput = e.asm.Conv2DBackpropInput).apply(null, arguments); + }, Hy = e._Conv3D = function() { + return (Hy = e._Conv3D = e.asm.Conv3D).apply(null, arguments); + }, ki = e._Conv3DBackpropFilterV2 = function() { + return (ki = e._Conv3DBackpropFilterV2 = e.asm.Conv3DBackpropFilterV2).apply(null, arguments); + }, il = e._Conv3DBackpropInputV2 = function() { + return (il = e._Conv3DBackpropInputV2 = e.asm.Conv3DBackpropInputV2).apply(null, arguments); + }, Ky = e._Cos = function() { + return (Ky = e._Cos = e.asm.Cos).apply(null, arguments); + }, qy = e._Cosh = function() { + return (qy = e._Cosh = e.asm.Cosh).apply(null, arguments); + }, jy = e._CropAndResize = function() { + return (jy = e._CropAndResize = e.asm.CropAndResize).apply(null, arguments); + }, Xy = e._Cumprod = function() { + return (Xy = e._Cumprod = e.asm.Cumprod).apply(null, arguments); + }, Em = e._Cumsum = function() { + return (Em = e._Cumsum = e.asm.Cumsum).apply(null, arguments); + }, Rm = e._DenseBincount = function() { + return (Rm = e._DenseBincount = e.asm.DenseBincount).apply(null, arguments); + }, Yy = e._DepthToSpace = function() { + return (Yy = e._DepthToSpace = e.asm.DepthToSpace).apply(null, arguments); + }, Qy = e._DepthwiseConv2dNative = function() { + return (Qy = e._DepthwiseConv2dNative = e.asm.DepthwiseConv2dNative).apply(null, arguments); + }, Dm = e._Diag = function() { + return (Dm = e._Diag = e.asm.Diag).apply(null, arguments); + }, Am = e._Dilation2D = function() { + return (Am = e._Dilation2D = e.asm.Dilation2D).apply(null, arguments); + }, Zy = e._Dilation2DBackpropFilter = function() { + return (Zy = e._Dilation2DBackpropFilter = e.asm.Dilation2DBackpropFilter).apply(null, arguments); + }, Jy = e._Dilation2DBackpropInput = function() { + return (Jy = e._Dilation2DBackpropInput = e.asm.Dilation2DBackpropInput).apply(null, arguments); + }, eb = e._Elu = function() { + return (eb = e._Elu = e.asm.Elu).apply(null, arguments); + }, tb = e._EluGrad = function() { + return (tb = e._EluGrad = e.asm.EluGrad).apply(null, arguments); + }, Fm = e._Equal = function() { + return (Fm = e._Equal = e.asm.Equal).apply(null, arguments); + }, h0 = e._Erf = function() { + return (h0 = e._Erf = e.asm.Erf).apply(null, arguments); + }, rb = e._Exp = function() { + return (rb = e._Exp = e.asm.Exp).apply(null, arguments); + }, ob = e._Expm1 = function() { + return (ob = e._Expm1 = e.asm.Expm1).apply(null, arguments); + }, nb = e._FlipLeftRight = function() { + return (nb = e._FlipLeftRight = e.asm.FlipLeftRight).apply(null, arguments); + }, sb = e._Floor = function() { + return (sb = e._Floor = e.asm.Floor).apply(null, arguments); + }, ab = e._FloorDiv = function() { + return (ab = e._FloorDiv = e.asm.FloorDiv).apply(null, arguments); + }, ib = e._FusedBatchNorm = function() { + return (ib = e._FusedBatchNorm = e.asm.FusedBatchNorm).apply(null, arguments); + }, ub = e._FusedConv2D = function() { + return (ub = e._FusedConv2D = e.asm.FusedConv2D).apply(null, arguments); + }, pb = e._FusedDepthwiseConv2D = function() { + return (pb = e._FusedDepthwiseConv2D = e.asm.FusedDepthwiseConv2D).apply(null, arguments); + }, cb = e._Gather = function() { + return (cb = e._Gather = e.asm.Gather).apply(null, arguments); + }, lb = e._GatherNd = function() { + return (lb = e._GatherNd = e.asm.GatherNd).apply(null, arguments); + }, mb = e._Greater = function() { + return (mb = e._Greater = e.asm.Greater).apply(null, arguments); + }, db = e._GreaterEqual = function() { + return (db = e._GreaterEqual = e.asm.GreaterEqual).apply(null, arguments); + }, fb = e._IsFinite = function() { + return (fb = e._IsFinite = e.asm.IsFinite).apply(null, arguments); + }, hb = e._IsInf = function() { + return (hb = e._IsInf = e.asm.IsInf).apply(null, arguments); + }, gb = e._IsNan = function() { + return (gb = e._IsNan = e.asm.IsNan).apply(null, arguments); + }, xb = e._LRN = function() { + return (xb = e._LRN = e.asm.LRN).apply(null, arguments); + }, yb = e._LRNGrad = function() { + return (yb = e._LRNGrad = e.asm.LRNGrad).apply(null, arguments); + }, bb = e._LeakyRelu = function() { + return (bb = e._LeakyRelu = e.asm.LeakyRelu).apply(null, arguments); + }, Cb = e._Less = function() { + return (Cb = e._Less = e.asm.Less).apply(null, arguments); + }, wb = e._LessEqual = function() { + return (wb = e._LessEqual = e.asm.LessEqual).apply(null, arguments); + }, Sb = e._LinSpace = function() { + return (Sb = e._LinSpace = e.asm.LinSpace).apply(null, arguments); + }, Ib = e._Log = function() { + return (Ib = e._Log = e.asm.Log).apply(null, arguments); + }, vb = e._Log1p = function() { + return (vb = e._Log1p = e.asm.Log1p).apply(null, arguments); + }, kb = e._LogicalAnd = function() { + return (kb = e._LogicalAnd = e.asm.LogicalAnd).apply(null, arguments); + }, Nb = e._LogicalNot = function() { + return (Nb = e._LogicalNot = e.asm.LogicalNot).apply(null, arguments); + }, Tb = e._LogicalOr = function() { + return (Tb = e._LogicalOr = e.asm.LogicalOr).apply(null, arguments); + }, _b = e._LogicalXor = function() { + return (_b = e._LogicalXor = e.asm.LogicalXor).apply(null, arguments); + }, $b = e._Max = function() { + return ($b = e._Max = e.asm.Max).apply(null, arguments); + }, Eb = e._MaxPool = function() { + return (Eb = e._MaxPool = e.asm.MaxPool).apply(null, arguments); + }, Rb = e._MaxPool3D = function() { + return (Rb = e._MaxPool3D = e.asm.MaxPool3D).apply(null, arguments); + }, Db = e._MaxPool3DGrad = function() { + return (Db = e._MaxPool3DGrad = e.asm.MaxPool3DGrad).apply(null, arguments); + }, Ab = e._MaxPoolGrad = function() { + return (Ab = e._MaxPoolGrad = e.asm.MaxPoolGrad).apply(null, arguments); + }, Fb = e._MaxPoolWithArgmax = function() { + return (Fb = e._MaxPoolWithArgmax = e.asm.MaxPoolWithArgmax).apply(null, arguments); + }, Pb = e._Maximum = function() { + return (Pb = e._Maximum = e.asm.Maximum).apply(null, arguments); + }, Ob = e._Mean = function() { + return (Ob = e._Mean = e.asm.Mean).apply(null, arguments); + }, Mb = e._Min = function() { + return (Mb = e._Min = e.asm.Min).apply(null, arguments); + }, Lb = e._Minimum = function() { + return (Lb = e._Minimum = e.asm.Minimum).apply(null, arguments); + }, Bb = e._MirrorPad = function() { + return (Bb = e._MirrorPad = e.asm.MirrorPad).apply(null, arguments); + }, zb = e._Mod = function() { + return (zb = e._Mod = e.asm.Mod).apply(null, arguments); + }, Vb = e._Multinomial = function() { + return (Vb = e._Multinomial = e.asm.Multinomial).apply(null, arguments); + }, Wb = e._Multiply = function() { + return (Wb = e._Multiply = e.asm.Multiply).apply(null, arguments); + }, Ub = e._Neg = function() { + return (Ub = e._Neg = e.asm.Neg).apply(null, arguments); + }, Gb = e._NonMaxSuppressionV3 = function() { + return (Gb = e._NonMaxSuppressionV3 = e.asm.NonMaxSuppressionV3).apply(null, arguments); + }, Hb = e._NonMaxSuppressionV4 = function() { + return (Hb = e._NonMaxSuppressionV4 = e.asm.NonMaxSuppressionV4).apply(null, arguments); + }, Kb = e._NonMaxSuppressionV5 = function() { + return (Kb = e._NonMaxSuppressionV5 = e.asm.NonMaxSuppressionV5).apply(null, arguments); + }, qb = e._NotEqual = function() { + return (qb = e._NotEqual = e.asm.NotEqual).apply(null, arguments); + }, jb = e._OneHot = function() { + return (jb = e._OneHot = e.asm.OneHot).apply(null, arguments); + }, Xb = e._PadV2 = function() { + return (Xb = e._PadV2 = e.asm.PadV2).apply(null, arguments); + }, Yb = e._Pow = function() { + return (Yb = e._Pow = e.asm.Pow).apply(null, arguments); + }, Qb = e._Prelu = function() { + return (Qb = e._Prelu = e.asm.Prelu).apply(null, arguments); + }, Zb = e._Prod = function() { + return (Zb = e._Prod = e.asm.Prod).apply(null, arguments); + }, Jb = e._RealDiv = function() { + return (Jb = e._RealDiv = e.asm.RealDiv).apply(null, arguments); + }, eC = e._Reciprocal = function() { + return (eC = e._Reciprocal = e.asm.Reciprocal).apply(null, arguments); + }, tC = e._Relu = function() { + return (tC = e._Relu = e.asm.Relu).apply(null, arguments); + }, rC = e._Relu6 = function() { + return (rC = e._Relu6 = e.asm.Relu6).apply(null, arguments); + }, oC = e._ResizeBilinear = function() { + return (oC = e._ResizeBilinear = e.asm.ResizeBilinear).apply(null, arguments); + }, nC = e._ResizeBilinearGrad = function() { + return (nC = e._ResizeBilinearGrad = e.asm.ResizeBilinearGrad).apply(null, arguments); + }, sC = e._ResizeNearestNeighbor = function() { + return (sC = e._ResizeNearestNeighbor = e.asm.ResizeNearestNeighbor).apply(null, arguments); + }, aC = e._ResizeNearestNeighborGrad = function() { + return (aC = e._ResizeNearestNeighborGrad = e.asm.ResizeNearestNeighborGrad).apply(null, arguments); + }, iC = e._Reverse = function() { + return (iC = e._Reverse = e.asm.Reverse).apply(null, arguments); + }, uC = e._RotateWithOffset = function() { + return (uC = e._RotateWithOffset = e.asm.RotateWithOffset).apply(null, arguments); + }, pC = e._Round = function() { + return (pC = e._Round = e.asm.Round).apply(null, arguments); + }, cC = e._Rsqrt = function() { + return (cC = e._Rsqrt = e.asm.Rsqrt).apply(null, arguments); + }, lC = e._ScatterNd = function() { + return (lC = e._ScatterNd = e.asm.ScatterNd).apply(null, arguments); + }, mC = e._SearchSorted = function() { + return (mC = e._SearchSorted = e.asm.SearchSorted).apply(null, arguments); + }, dC = e._SelectV2 = function() { + return (dC = e._SelectV2 = e.asm.SelectV2).apply(null, arguments); + }, fC = e._Selu = function() { + return (fC = e._Selu = e.asm.Selu).apply(null, arguments); + }, hC = e._Sigmoid = function() { + return (hC = e._Sigmoid = e.asm.Sigmoid).apply(null, arguments); + }, gC = e._Sign = function() { + return (gC = e._Sign = e.asm.Sign).apply(null, arguments); + }, xC = e._Sin = function() { + return (xC = e._Sin = e.asm.Sin).apply(null, arguments); + }, yC = e._Sinh = function() { + return (yC = e._Sinh = e.asm.Sinh).apply(null, arguments); + }, bC = e._Softmax = function() { + return (bC = e._Softmax = e.asm.Softmax).apply(null, arguments); + }, CC = e._Softplus = function() { + return (CC = e._Softplus = e.asm.Softplus).apply(null, arguments); + }, wC = e._SparseFillEmptyRows = function() { + return (wC = e._SparseFillEmptyRows = e.asm.SparseFillEmptyRows).apply(null, arguments); + }, SC = e._SparseReshape = function() { + return (SC = e._SparseReshape = e.asm.SparseReshape).apply(null, arguments); + }, IC = e._SparseSegmentReduction = function() { + return (IC = e._SparseSegmentReduction = e.asm.SparseSegmentReduction).apply(null, arguments); + }, vC = e._SparseToDense = function() { + return (vC = e._SparseToDense = e.asm.SparseToDense).apply(null, arguments); + }, kC = e._Sqrt = function() { + return (kC = e._Sqrt = e.asm.Sqrt).apply(null, arguments); + }, NC = e._Square = function() { + return (NC = e._Square = e.asm.Square).apply(null, arguments); + }, TC = e._SquaredDifference = function() { + return (TC = e._SquaredDifference = e.asm.SquaredDifference).apply(null, arguments); + }, _C = e._Step = function() { + return (_C = e._Step = e.asm.Step).apply(null, arguments); + }, $C = e._StridedSlice = function() { + return ($C = e._StridedSlice = e.asm.StridedSlice).apply(null, arguments); + }, EC = e._Sub = function() { + return (EC = e._Sub = e.asm.Sub).apply(null, arguments); + }, RC = e._Sum = function() { + return (RC = e._Sum = e.asm.Sum).apply(null, arguments); + }, DC = e._Tan = function() { + return (DC = e._Tan = e.asm.Tan).apply(null, arguments); + }, AC = e._Tanh = function() { + return (AC = e._Tanh = e.asm.Tanh).apply(null, arguments); + }, FC = e._TensorScatterUpdate = function() { + return (FC = e._TensorScatterUpdate = e.asm.TensorScatterUpdate).apply(null, arguments); + }, PC = e._Tile = function() { + return (PC = e._Tile = e.asm.Tile).apply(null, arguments); + }, OC = e._TopK = function() { + return (OC = e._TopK = e.asm.TopK).apply(null, arguments); + }, MC = e._Transform = function() { + return (MC = e._Transform = e.asm.Transform).apply(null, arguments); + }, LC = e._Transpose = function() { + return (LC = e._Transpose = e.asm.Transpose).apply(null, arguments); + }, BC = e.__FusedMatMul = function() { + return (BC = e.__FusedMatMul = e.asm._FusedMatMul).apply(null, arguments); + }, zC = e._malloc = function() { + return (zC = e._malloc = e.asm.malloc).apply(null, arguments); + }, VC = e._free = function() { + return (VC = e._free = e.asm.free).apply(null, arguments); + }, WC = e.___errno_location = function() { + return (WC = e.___errno_location = e.asm.__errno_location).apply(null, arguments); + }, Pm = e.stackSave = function() { + return (Pm = e.stackSave = e.asm.stackSave).apply(null, arguments); + }, Om = e.stackRestore = function() { + return (Om = e.stackRestore = e.asm.stackRestore).apply(null, arguments); + }, ul = e.stackAlloc = function() { + return (ul = e.stackAlloc = e.asm.stackAlloc).apply(null, arguments); + }, UC = e.dynCall_iijjiiii = function() { + return (UC = e.dynCall_iijjiiii = e.asm.dynCall_iijjiiii).apply(null, arguments); + }, GC = e.dynCall_jiji = function() { + return (GC = e.dynCall_jiji = e.asm.dynCall_jiji).apply(null, arguments); }; - e.cwrap = ky; - var Wp; - ar = function K() { - Wp || Bm(), Wp || (ar = K); + e.cwrap = vy; + var Fp; + nr = function K() { + Fp || Mm(), Fp || (nr = K); }; - function Bm(K) { - if (K = K || i, _t > 0 || (gt(), _t > 0)) + function Mm(K) { + if (K = K || i, Tt > 0 || (ht(), Tt > 0)) return; function ae() { - Wp || (Wp = true, e.calledRun = true, !M && (xt(), o(e), e.onRuntimeInitialized && e.onRuntimeInitialized(), Lr())); + Fp || (Fp = true, e.calledRun = true, !M && (gt(), o(e), e.onRuntimeInitialized && e.onRuntimeInitialized(), Mr())); } e.setStatus ? (e.setStatus("Running..."), setTimeout(function() { setTimeout(function() { @@ -2915,26 +2916,26 @@ var MB = qt((Ug, Gv) => { if (e.preInit) for (typeof e.preInit == "function" && (e.preInit = [e.preInit]); e.preInit.length > 0; ) e.preInit.pop()(); - Bm(); - var Up; - s && (Up = { uncaughtException: process.listeners("uncaughtException").filter(function(K) { + Mm(); + var Pp; + s && (Pp = { uncaughtException: process.listeners("uncaughtException").filter(function(K) { return !s.uncaughtException.indexOf(K) > -1; }), unhandledRejection: process.listeners("unhandledRejection").filter(function(K) { return !s.unhandledRejection.indexOf(K) > -1; }) }); - var Gp; + var Op; if (typeof t8 != "undefined") - Gp = t8; + Op = t8; else if (typeof WasmBackendModuleThreadedSimd != "undefined") - Gp = WasmBackendModuleThreadedSimd; + Op = WasmBackendModuleThreadedSimd; else throw new Error("Could not find wasm module in post.js"); - if (Up) { - var KC = Gp._dispose; - Gp._dispose = function() { - KC(), Up.uncaughtException.forEach(function(K) { + if (Pp) { + var HC = Op._dispose; + Op._dispose = function() { + HC(), Pp.uncaughtException.forEach(function(K) { process.removeListener("uncaughtException", K); - }), Up.unhandledRejection.forEach(function(K) { + }), Pp.unhandledRejection.forEach(function(K) { process.removeListener("unhandledRejection", K); }); }; @@ -2942,11 +2943,11 @@ var MB = qt((Ug, Gv) => { return t8.ready; }; })(); - typeof Ug == "object" && typeof Gv == "object" ? Gv.exports = Uv : typeof define == "function" && define.amd ? define([], function() { - return Uv; - }) : typeof Ug == "object" && (Ug.WasmBackendModule = Uv); + typeof Wg == "object" && typeof Hv == "object" ? Hv.exports = Gv : typeof define == "function" && define.amd ? define([], function() { + return Gv; + }) : typeof Wg == "object" && (Wg.WasmBackendModule = Gv); }); -var zo = class { +var Bo = class { constructor(t8, e) { this.backend = t8, this.dataMover = e, this.data = /* @__PURE__ */ new WeakMap(), this.dataIdsCount = 0; } @@ -2966,172 +2967,172 @@ var zo = class { return this.dataIdsCount; } }; -var ao = class { +var so = class { refCount(t8) { - return zr("refCount"); + return Br("refCount"); } incRef(t8) { - return zr("incRef"); + return Br("incRef"); } timerAvailable() { return true; } time(t8) { - return zr("time"); + return Br("time"); } read(t8) { - return zr("read"); + return Br("read"); } readSync(t8) { - return zr("readSync"); + return Br("readSync"); } readToGPU(t8, e) { - return zr("readToGPU"); + return Br("readToGPU"); } numDataIds() { - return zr("numDataIds"); + return Br("numDataIds"); } disposeData(t8, e) { - return zr("disposeData"); + return Br("disposeData"); } write(t8, e, o) { - return zr("write"); + return Br("write"); } move(t8, e, o, n, s) { - return zr("move"); + return Br("move"); } createTensorFromGPUData(t8, e, o) { - return zr("createTensorFromGPUData"); + return Br("createTensorFromGPUData"); } memory() { - return zr("memory"); + return Br("memory"); } floatPrecision() { - return zr("floatPrecision"); + return Br("floatPrecision"); } epsilon() { return this.floatPrecision() === 32 ? 1e-7 : 1e-4; } dispose() { - return zr("dispose"); + return Br("dispose"); } }; -function zr(r) { - throw new Error(`'${r}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`); +function Br(r15) { + throw new Error(`'${r15}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`); } -function I0(r) { - let t8 = r.length, e = 0; +function v0(r15) { + let t8 = r15.length, e = 0; for (; t8 > 0; ) - e = Math.random() * t8 | 0, t8--, jm(r, t8, e); + e = Math.random() * t8 | 0, t8--, Km(r15, t8, e); } -function TG(r, t8) { - if (r.length !== t8.length) - throw new Error(`Array sizes must match to be shuffled together First array length was ${r.length}Second array length was ${t8.length}`); - let e = r.length, o = 0; +function _G(r15, t8) { + if (r15.length !== t8.length) + throw new Error(`Array sizes must match to be shuffled together First array length was ${r15.length}Second array length was ${t8.length}`); + let e = r15.length, o = 0; for (; e > 0; ) - o = Math.random() * e | 0, e--, jm(r, e, o), jm(t8, e, o); + o = Math.random() * e | 0, e--, Km(r15, e, o), Km(t8, e, o); } -function qp(r, t8, e) { - return Math.max(r, Math.min(t8, e)); +function Bp(r15, t8, e) { + return Math.max(r15, Math.min(t8, e)); } -function _G(r) { - return r % 2 === 0 ? r : r + 1; +function $G(r15) { + return r15 % 2 === 0 ? r15 : r15 + 1; } -function jm(r, t8, e) { - let o = r[t8]; - r[t8] = r[e], r[e] = o; +function Km(r15, t8, e) { + let o = r15[t8]; + r15[t8] = r15[e], r15[e] = o; } -function $G(r) { +function EG(r15) { let t8 = 0; - for (let e = 0; e < r.length; e++) - t8 += r[e]; + for (let e = 0; e < r15.length; e++) + t8 += r15[e]; return t8; } -function EG(r, t8) { +function RG(r15, t8) { let e = Math.random(); - return t8 * e + (1 - e) * r; + return t8 * e + (1 - e) * r15; } -function RG(r, t8) { +function DG(r15, t8) { let e = 0; - for (let o = 0; o < r.length; o++) { - let n = Number(r[o]) - Number(t8[o]); + for (let o = 0; o < r15.length; o++) { + let n = Number(r15[o]) - Number(t8[o]); e += n * n; } return e; } -function $(r, t8) { - if (!r) +function $(r15, t8) { + if (!r15) throw new Error(typeof t8 == "string" ? t8 : t8()); } -function yt(r, t8, e = "") { - $(Cr(r, t8), () => e + ` Shapes ${r} and ${t8} must match`); +function xt(r15, t8, e = "") { + $(br(r15, t8), () => e + ` Shapes ${r15} and ${t8} must match`); } -function io(r) { - $(r != null, () => "The input to the tensor constructor must be a non-null value."); +function ao(r15) { + $(r15 != null, () => "The input to the tensor constructor must be a non-null value."); } -function He(r) { - if (r.length === 0) +function He(r15) { + if (r15.length === 0) return 1; - let t8 = r[0]; - for (let e = 1; e < r.length; e++) - t8 *= r[e]; + let t8 = r15[0]; + for (let e = 1; e < r15.length; e++) + t8 *= r15[e]; return t8; } -function DG(r) { - return r.length === 0; +function AG(r15) { + return r15.length === 0; } -function ZC(r, t8) { - if (r === t8) +function QC(r15, t8) { + if (r15 === t8) return true; - if (r == null || t8 == null || r.length !== t8.length) + if (r15 == null || t8 == null || r15.length !== t8.length) return false; - for (let e = 0; e < r.length; e++) - if (r[e] !== null && t8[e] !== null && r[e] !== t8[e]) + for (let e = 0; e < r15.length; e++) + if (r15[e] !== null && t8[e] !== null && r15[e] !== t8[e]) return false; return true; } -function Cr(r, t8) { - if (r === t8) +function br(r15, t8) { + if (r15 === t8) return true; - if (r == null || t8 == null || r.length !== t8.length) + if (r15 == null || t8 == null || r15.length !== t8.length) return false; - for (let e = 0; e < r.length; e++) - if (r[e] !== t8[e]) + for (let e = 0; e < r15.length; e++) + if (r15[e] !== t8[e]) return false; return true; } -function qa(r) { - return r % 1 === 0; +function Ka(r15) { + return r15 % 1 === 0; } -function AG(r) { +function FG(r15) { if (Math.tanh != null) - return Math.tanh(r); - if (r === 1 / 0) + return Math.tanh(r15); + if (r15 === 1 / 0) return 1; - if (r === -1 / 0) + if (r15 === -1 / 0) return -1; { - let t8 = Math.exp(2 * r); + let t8 = Math.exp(2 * r15); return (t8 - 1) / (t8 + 1); } } -function FG(r) { - let t8 = Math.ceil(Math.sqrt(r)); - return [t8, Math.ceil(r / t8)]; +function PG(r15) { + let t8 = Math.ceil(Math.sqrt(r15)); + return [t8, Math.ceil(r15 / t8)]; } -function PG(r) { - let t8 = new Uint32Array(r); - for (let e = 0; e < r; ++e) +function OG(r15) { + let t8 = new Uint32Array(r15); + for (let e = 0; e < r15; ++e) t8[e] = e; - return I0(t8), t8; + return v0(t8), t8; } -function Au(r, t8) { - return t8 <= r.length ? r : r + " ".repeat(t8 - r.length); +function Nu(r15, t8) { + return t8 <= r15.length ? r15 : r15 + " ".repeat(t8 - r15.length); } -function OG(r, t8 = (n) => 0, e, o) { +function MG(r15, t8 = (n) => 0, e, o) { return new Promise((n, s) => { let a = 0, i = () => { - if (r()) { + if (r15()) { n(); return; } @@ -3146,212 +3147,212 @@ function OG(r, t8 = (n) => 0, e, o) { i(); }); } -function MG(r, t8) { +function LG(r15, t8) { let e = 1, o = -1; - for (let s = 0; s < r.length; ++s) - if (r[s] >= 0) - e *= r[s]; - else if (r[s] === -1) { + for (let s = 0; s < r15.length; ++s) + if (r15[s] >= 0) + e *= r15[s]; + else if (r15[s] === -1) { if (o !== -1) throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${o} and dim ${s}`); o = s; - } else if (r[s] < 0) - throw Error(`Shapes can not be < 0. Found ${r[s]} at dim ${s}`); + } else if (r15[s] < 0) + throw Error(`Shapes can not be < 0. Found ${r15[s]} at dim ${s}`); if (o === -1) { if (t8 > 0 && t8 !== e) - throw Error(`Size(${t8}) must match the product of shape ${r}`); - return r; + throw Error(`Size(${t8}) must match the product of shape ${r15}`); + return r15; } if (e === 0) - throw Error(`Cannot infer the missing size in [${r}] when there are 0 elements`); + throw Error(`Cannot infer the missing size in [${r15}] when there are 0 elements`); if (t8 % e !== 0) throw Error(`The implicit shape can't be a fractional number. Got ${t8} / ${e}`); - let n = r.slice(); + let n = r15.slice(); return n[o] = t8 / e, n; } -function Ei(r, t8) { +function Ti(r15, t8) { let e = t8.length; - return r = r == null ? t8.map((o, n) => n) : [].concat(r), $(r.every((o) => o >= -e && o < e), () => `All values in axis param must be in range [-${e}, ${e}) but got axis ${r}`), $(r.every((o) => qa(o)), () => `All values in axis param must be integers but got axis ${r}`), r.map((o) => o < 0 ? e + o : o); + return r15 = r15 == null ? t8.map((o, n) => n) : [].concat(r15), $(r15.every((o) => o >= -e && o < e), () => `All values in axis param must be in range [-${e}, ${e}) but got axis ${r15}`), $(r15.every((o) => Ka(o)), () => `All values in axis param must be integers but got axis ${r15}`), r15.map((o) => o < 0 ? e + o : o); } -function JC(r, t8) { - let e = [], o = [], n = t8 != null && Array.isArray(t8) && t8.length === 0, s = t8 == null || n ? null : Ei(t8, r).sort(), a = 0; - for (let i = 0; i < r.length; ++i) { +function ZC(r15, t8) { + let e = [], o = [], n = t8 != null && Array.isArray(t8) && t8.length === 0, s = t8 == null || n ? null : Ti(t8, r15).sort(), a = 0; + for (let i = 0; i < r15.length; ++i) { if (s != null) { - if (s[a] === i && r[i] !== 1) - throw new Error(`Can't squeeze axis ${i} since its dim '${r[i]}' is not 1`); - (s[a] == null || s[a] > i) && r[i] === 1 && (e.push(r[i]), o.push(i)), s[a] <= i && a++; + if (s[a] === i && r15[i] !== 1) + throw new Error(`Can't squeeze axis ${i} since its dim '${r15[i]}' is not 1`); + (s[a] == null || s[a] > i) && r15[i] === 1 && (e.push(r15[i]), o.push(i)), s[a] <= i && a++; } - r[i] !== 1 && (e.push(r[i]), o.push(i)); + r15[i] !== 1 && (e.push(r15[i]), o.push(i)); } return { newShape: e, keptDims: o }; } -function ew(r, t8) { - return Xm(r, t8); +function JC(r15, t8) { + return qm(r15, t8); } -function Xm(r, t8) { +function qm(r15, t8) { let e = null; - if (r == null || r === "float32") + if (r15 == null || r15 === "float32") e = new Float32Array(t8); - else if (r === "int32") + else if (r15 === "int32") e = new Int32Array(t8); - else if (r === "bool") + else if (r15 === "bool") e = new Uint8Array(t8); - else if (r === "string") + else if (r15 === "string") e = new Array(t8); else - throw new Error(`Unknown data type ${r}`); + throw new Error(`Unknown data type ${r15}`); return e; } -function tw(r, t8) { - for (let e = 0; e < r.length; e++) { - let o = r[e]; +function ew(r15, t8) { + for (let e = 0; e < r15.length; e++) { + let o = r15[e]; if (isNaN(o) || !isFinite(o)) throw Error(`A tensor of type ${t8} being uploaded contains ${o}.`); } } -function rw(r) { - return r === "bool" || r === "complex64" || r === "float32" || r === "int32" || r === "string"; +function tw(r15) { + return r15 === "bool" || r15 === "complex64" || r15 === "float32" || r15 === "int32" || r15 === "string"; } -function LG(r, t8) { - return !(t8 === "complex64" || t8 === "float32" && r !== "complex64" || t8 === "int32" && r !== "float32" && r !== "complex64" || t8 === "bool" && r === "bool"); +function BG(r15, t8) { + return !(t8 === "complex64" || t8 === "float32" && r15 !== "complex64" || t8 === "int32" && r15 !== "float32" && r15 !== "complex64" || t8 === "bool" && r15 === "bool"); } -function jp(r) { - if (r === "float32" || r === "int32") +function zp(r15) { + if (r15 === "float32" || r15 === "int32") return 4; - if (r === "complex64") + if (r15 === "complex64") return 8; - if (r === "bool") + if (r15 === "bool") return 1; - throw new Error(`Unknown dtype ${r}`); + throw new Error(`Unknown dtype ${r15}`); } -function ow(r) { - if (r == null) +function rw(r15) { + if (r15 == null) return 0; let t8 = 0; - return r.forEach((e) => t8 += e.length), t8; + return r15.forEach((e) => t8 += e.length), t8; } -function Vo(r) { - return typeof r == "string" || r instanceof String; +function zo(r15) { + return typeof r15 == "string" || r15 instanceof String; } -function v0(r) { - return typeof r == "boolean"; +function k0(r15) { + return typeof r15 == "boolean"; } -function k0(r) { - return typeof r == "number"; +function N0(r15) { + return typeof r15 == "number"; } -function Ri(r) { - return Array.isArray(r) ? Ri(r[0]) : r instanceof Float32Array ? "float32" : r instanceof Int32Array || r instanceof Uint8Array || r instanceof Uint8ClampedArray ? "int32" : k0(r) ? "float32" : Vo(r) ? "string" : v0(r) ? "bool" : "float32"; +function _i(r15) { + return Array.isArray(r15) ? _i(r15[0]) : r15 instanceof Float32Array ? "float32" : r15 instanceof Int32Array || r15 instanceof Uint8Array || r15 instanceof Uint8ClampedArray ? "int32" : N0(r15) ? "float32" : zo(r15) ? "string" : k0(r15) ? "bool" : "float32"; } -function qs(r) { - return !!(r && r.constructor && r.call && r.apply); +function qs(r15) { + return !!(r15 && r15.constructor && r15.call && r15.apply); } -function Xp(r, t8) { - for (let e = t8; e < r; ++e) - if (r % e === 0) +function Vp(r15, t8) { + for (let e = t8; e < r15; ++e) + if (r15 % e === 0) return e; - return r; + return r15; } -function js(r) { - let t8 = r.length; +function js(r15) { + let t8 = r15.length; if (t8 < 2) return []; let e = new Array(t8 - 1); - e[t8 - 2] = r[t8 - 1]; + e[t8 - 2] = r15[t8 - 1]; for (let o = t8 - 3; o >= 0; --o) - e[o] = e[o + 1] * r[o + 1]; + e[o] = e[o + 1] * r15[o + 1]; return e; } -function N0(r, t8, e, o = false) { +function T0(r15, t8, e, o = false) { let n = new Array(); if (t8.length === 1) { let s = t8[0] * (o ? 2 : 1); for (let a = 0; a < s; a++) - n[a] = e[r + a]; + n[a] = e[r15 + a]; } else { let s = t8[0], a = t8.slice(1), i = a.reduce((p, u) => p * u) * (o ? 2 : 1); for (let p = 0; p < s; p++) - n[p] = N0(r + p * i, a, e, o); + n[p] = T0(r15 + p * i, a, e, o); } return n; } -function Du(r, t8, e = false) { - if (r.length === 0) +function ku(r15, t8, e = false) { + if (r15.length === 0) return t8[0]; - let o = r.reduce((n, s) => n * s) * (e ? 2 : 1); + let o = r15.reduce((n, s) => n * s) * (e ? 2 : 1); if (o === 0) return []; if (o !== t8.length) - throw new Error(`[${r}] does not match the input size ${t8.length}${e ? " for a complex tensor" : ""}.`); - return N0(0, r, t8, e); + throw new Error(`[${r15}] does not match the input size ${t8.length}${e ? " for a complex tensor" : ""}.`); + return T0(0, r15, t8, e); } -function BG(r, t8) { - if (Array.isArray(r)) - return r; +function zG(r15, t8) { + if (Array.isArray(r15)) + return r15; if (t8 === "float32") - return r instanceof Float32Array ? r : new Float32Array(r); + return r15 instanceof Float32Array ? r15 : new Float32Array(r15); if (t8 === "int32") - return r instanceof Int32Array ? r : new Int32Array(r); + return r15 instanceof Int32Array ? r15 : new Int32Array(r15); if (t8 === "bool" || t8 === "string") - return Uint8Array.from(new Int32Array(r)); + return Uint8Array.from(new Int32Array(r15)); throw new Error(`Unknown dtype ${t8}`); } -function fl(r, t8) { - let e = Yp(r, t8); +function cl(r15, t8) { + let e = Wp(r15, t8); for (let o = 0; o < e.length; o++) e[o] = 1; return e; } -function Yp(r, t8) { +function Wp(r15, t8) { if (t8 == null || t8 === "float32" || t8 === "complex64") - return new Float32Array(r); + return new Float32Array(r15); if (t8 === "int32") - return new Int32Array(r); + return new Int32Array(r15); if (t8 === "bool") - return new Uint8Array(r); + return new Uint8Array(r15); throw new Error(`Unknown data type ${t8}`); } -function zG(r, t8) { - let e = r.reduce((o, n) => o * n, 1); +function VG(r15, t8) { + let e = r15.reduce((o, n) => o * n, 1); if (t8 == null || t8 === "float32") - return Du(r, new Float32Array(e)); + return ku(r15, new Float32Array(e)); if (t8 === "int32") - return Du(r, new Int32Array(e)); + return ku(r15, new Int32Array(e)); if (t8 === "bool") - return Du(r, new Uint8Array(e)); + return ku(r15, new Uint8Array(e)); throw new Error(`Unknown data type ${t8}`); } -function wt(r) { - r.forEach((t8) => { - $(Number.isInteger(t8) && t8 >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${r}].`); +function Ct(r15) { + r15.forEach((t8) => { + $(Number.isInteger(t8) && t8 >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${r15}].`); }); } -function VG(r, t8, e) { +function WG(r15, t8, e) { if (t8 === 0) return 0; if (t8 === 1) - return r[0]; - let o = r[r.length - 1]; - for (let n = 0; n < r.length - 1; ++n) - o += e[n] * r[n]; + return r15[0]; + let o = r15[r15.length - 1]; + for (let n = 0; n < r15.length - 1; ++n) + o += e[n] * r15[n]; return o; } -function WG(r, t8, e) { +function UG(r15, t8, e) { if (t8 === 0) return []; if (t8 === 1) - return [r]; + return [r15]; let o = new Array(t8); for (let n = 0; n < o.length - 1; ++n) - o[n] = Math.floor(r / e[n]), r -= o[n] * e[n]; - return o[o.length - 1] = r, o; + o[n] = Math.floor(r15 / e[n]), r15 -= o[n] * e[n]; + return o[o.length - 1] = r15, o; } -function Fu(r) { - return r && r.then && typeof r.then == "function"; +function Tu(r15) { + return r15 && r15.then && typeof r15.then == "function"; } -var T0 = "tfjsflags"; -var hl = class { +var _0 = "tfjsflags"; +var ll = class { constructor(t8) { - this.global = t8, this.flags = {}, this.flagRegistry = {}, this.urlFlags = {}, this.getQueryParams = GG, this.populateURLFlags(); + this.global = t8, this.flags = {}, this.flagRegistry = {}, this.urlFlags = {}, this.getQueryParams = HG, this.populateURLFlags(); } setPlatform(t8, e) { this.platform != null && (A().getBool("IS_TEST") || A().getBool("PROD") || console.warn(`Platform ${this.platformName} has already been set. 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Please use getAsync() instead.`); return this.flags[t8] = e, this.flags[t8]; } @@ -3408,450 +3409,450 @@ var hl = class { if (typeof this.global == "undefined" || typeof this.global.location == "undefined" || typeof this.global.location.search == "undefined") return; let t8 = this.getQueryParams(this.global.location.search); - T0 in t8 && t8[T0].split(",").forEach((o) => { + _0 in t8 && t8[_0].split(",").forEach((o) => { let [n, s] = o.split(":"); - this.urlFlags[n] = KG(n, s); + this.urlFlags[n] = qG(n, s); }); } }; -function GG(r) { +function HG(r15) { let t8 = {}; - return r.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (e, ...o) => (HG(t8, o[0], o[1]), o.join("="))), t8; + return r15.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (e, ...o) => (KG(t8, o[0], o[1]), o.join("="))), t8; } -function HG(r, t8, e) { - r[decodeURIComponent(t8)] = decodeURIComponent(e || ""); +function KG(r15, t8, e) { + r15[decodeURIComponent(t8)] = decodeURIComponent(e || ""); } -function KG(r, t8) { +function qG(r15, t8) { let e = t8.toLowerCase(); return e === "true" || e === "false" ? e === "true" : `${+e}` === e ? +e : t8; } function A() { - return nw; + return ow; } -var nw = null; -function _0(r) { - nw = r; +var ow = null; +function $0(r15) { + ow = r15; } -var sw; -function aw() { - if (sw == null) { - let r; +var nw; +function sw() { + if (nw == null) { + let r15; if (typeof window != "undefined") - r = window; + r15 = window; else if (typeof global != "undefined") - r = global; + r15 = global; else if (typeof process != "undefined") - r = process; + r15 = process; else if (typeof self != "undefined") - r = self; + r15 = self; else throw new Error("Could not find a global object"); - sw = r; + nw = r15; } - return sw; + return nw; } -function qG() { - let r = aw(); - return r._tfGlobals == null && (r._tfGlobals = /* @__PURE__ */ new Map()), r._tfGlobals; +function jG() { + let r15 = sw(); + return r15._tfGlobals == null && (r15._tfGlobals = /* @__PURE__ */ new Map()), r15._tfGlobals; } -function gl(r, t8) { - let e = qG(); - if (e.has(r)) - return e.get(r); +function ml(r15, t8) { + let e = jG(); + if (e.has(r15)) + return e.get(r15); { let o = t8(); - return e.set(r, o), e.get(r); + return e.set(r15, o), e.get(r15); } } var Xs = "Abs"; -var Wo = "Acos"; -var Uo = "Acosh"; -var uo = "Add"; -var Go = "AddN"; -var Ho = "All"; -var Ko = "Any"; +var Vo = "Acos"; +var Wo = "Acosh"; +var io = "Add"; +var Uo = "AddN"; +var Go = "All"; +var Ho = "Any"; var Ys = "ArgMax"; var Qs = "ArgMin"; -var qo = "Asin"; -var jo = "Asinh"; -var Xo = "Atan"; -var Yo = "Atanh"; -var Qo = "Atan2"; -var Zo = "AvgPool"; -var Di = "AvgPoolGrad"; +var Ko = "Asin"; +var qo = "Asinh"; +var jo = "Atan"; +var Xo = "Atanh"; +var Yo = "Atan2"; +var Qo = "AvgPool"; +var $i = "AvgPoolGrad"; var Zs = "AvgPool3D"; -var Ai = "AvgPool3DGrad"; -var Jo = "BatchMatMul"; +var Ei = "AvgPool3DGrad"; +var Zo = "BatchMatMul"; var Js = "BatchToSpaceND"; -var en = "Bincount"; -var ja = "BitwiseAnd"; +var Jo = "Bincount"; +var qa = "BitwiseAnd"; var Bce = "BroadcastTo"; var ea = "BroadcastArgs"; -var bo = "Cast"; -var tn = "Ceil"; -var Co = "ClipByValue"; -var Fi = "Complex"; -var Pi = "ComplexAbs"; +var yo = "Cast"; +var en = "Ceil"; +var bo = "ClipByValue"; +var Ri = "Complex"; +var Di = "ComplexAbs"; var ta = "Concat"; -var rn = "Conv2D"; -var Oi = "Conv2DBackpropFilter"; -var on = "Conv2DBackpropInput"; -var nn = "Conv3D"; -var Xa = "Conv3DBackpropFilterV2"; -var sn = "Conv3DBackpropInputV2"; -var an = "Cos"; -var un = "Cosh"; -var pn = "Cumprod"; -var cn = "Cumsum"; -var ln = "CropAndResize"; +var tn = "Conv2D"; +var Ai = "Conv2DBackpropFilter"; +var rn = "Conv2DBackpropInput"; +var on = "Conv3D"; +var ja = "Conv3DBackpropFilterV2"; +var nn = "Conv3DBackpropInputV2"; +var sn = "Cos"; +var an = "Cosh"; +var un = "Cumprod"; +var pn = "Cumsum"; +var cn = "CropAndResize"; var ra = "DenseBincount"; -var mn = "DepthToSpace"; -var dn = "DepthwiseConv2dNative"; -var Mi = "DepthwiseConv2dNativeBackpropFilter"; -var Li = "DepthwiseConv2dNativeBackpropInput"; +var ln = "DepthToSpace"; +var mn = "DepthwiseConv2dNative"; +var Fi = "DepthwiseConv2dNativeBackpropFilter"; +var Pi = "DepthwiseConv2dNativeBackpropInput"; var oa = "Diag"; -var fn = "Dilation2D"; -var Bi = "Dilation2DBackpropInput"; -var zi = "Dilation2DBackpropFilter"; -var Pu = "Draw"; -var hn = "RealDiv"; -var Vi = "Einsum"; -var gn = "Elu"; -var Ya = "EluGrad"; -var xn = "Erf"; -var yn = "Equal"; -var bn = "Exp"; +var dn = "Dilation2D"; +var Oi = "Dilation2DBackpropInput"; +var Mi = "Dilation2DBackpropFilter"; +var _u = "Draw"; +var fn = "RealDiv"; +var Li = "Einsum"; +var hn = "Elu"; +var Xa = "EluGrad"; +var gn = "Erf"; +var xn = "Equal"; +var yn = "Exp"; var na = "ExpandDims"; -var Cn = "Expm1"; -var Wi = "FFT"; +var bn = "Expm1"; +var Bi = "FFT"; var sa = "Fill"; -var wn = "FlipLeftRight"; -var Sn = "Floor"; -var In = "FloorDiv"; -var vn = "FusedBatchNorm"; +var Cn = "FlipLeftRight"; +var wn = "Floor"; +var Sn = "FloorDiv"; +var In = "FusedBatchNorm"; var aa = "GatherV2"; -var kn = "GatherNd"; -var Nn = "Greater"; -var Tn = "GreaterEqual"; -var wo = "Identity"; -var Ui = "IFFT"; -var Gi = "Imag"; -var _n = "IsFinite"; -var $n = "IsInf"; -var En = "IsNan"; -var Rn = "LeakyRelu"; -var Dn = "Less"; -var An = "LessEqual"; -var Fn = "LinSpace"; -var Pn = "Log"; -var On = "Log1p"; -var Mn = "LogicalAnd"; -var Ln = "LogicalNot"; -var Bn = "LogicalOr"; -var $0 = "LogicalXor"; +var vn = "GatherNd"; +var kn = "Greater"; +var Nn = "GreaterEqual"; +var Co = "Identity"; +var zi = "IFFT"; +var Vi = "Imag"; +var Tn = "IsFinite"; +var _n = "IsInf"; +var $n = "IsNan"; +var En = "LeakyRelu"; +var Rn = "Less"; +var Dn = "LessEqual"; +var An = "LinSpace"; +var Fn = "Log"; +var Pn = "Log1p"; +var On = "LogicalAnd"; +var Mn = "LogicalNot"; +var Ln = "LogicalOr"; +var E0 = "LogicalXor"; var zce = "LogSoftmax"; var Vce = "LowerBound"; -var zn = "LRN"; -var Qa = "LRNGrad"; +var Bn = "LRN"; +var Ya = "LRNGrad"; var Wce = "MatrixBandPart"; -var Vn = "Max"; -var Wn = "Maximum"; -var Un = "MaxPool"; -var Hi = "MaxPoolGrad"; +var zn = "Max"; +var Vn = "Maximum"; +var Wn = "MaxPool"; +var Wi = "MaxPoolGrad"; var ia = "MaxPool3D"; -var Ki = "MaxPool3DGrad"; +var Ui = "MaxPool3DGrad"; var ua = "MaxPoolWithArgmax"; -var Gn = "Mean"; -var Hn = "Min"; -var Kn = "Minimum"; -var qn = "MirrorPad"; -var jn = "Mod"; -var Xn = "Multinomial"; -var Yn = "Multiply"; +var Un = "Mean"; +var Gn = "Min"; +var Hn = "Minimum"; +var Kn = "MirrorPad"; +var qn = "Mod"; +var jn = "Multinomial"; +var Xn = "Multiply"; var pa = "Neg"; -var Qn = "NotEqual"; -var Zn = "NonMaxSuppressionV3"; -var Za = "NonMaxSuppressionV4"; -var Jn = "NonMaxSuppressionV5"; +var Yn = "NotEqual"; +var Qn = "NonMaxSuppressionV3"; +var Qa = "NonMaxSuppressionV4"; +var Zn = "NonMaxSuppressionV5"; var ca = "OnesLike"; -var es = "OneHot"; +var Jn = "OneHot"; var la = "Pack"; -var ts = "PadV2"; +var es = "PadV2"; var Uce = "Pool"; -var rs = "Pow"; -var os = "Prelu"; -var ns = "Prod"; -var Qp = "RaggedGather"; -var Zp = "RaggedRange"; -var Jp = "RaggedTensorToTensor"; +var ts = "Pow"; +var rs = "Prelu"; +var os = "Prod"; +var Up = "RaggedGather"; +var Gp = "RaggedRange"; +var Hp = "RaggedTensorToTensor"; var ma = "Range"; -var qi = "Real"; -var ss = "Reciprocal"; -var as = "Relu"; +var Gi = "Real"; +var ns = "Reciprocal"; +var ss = "Relu"; var da = "Reshape"; -var is = "ResizeNearestNeighbor"; -var Ja = "ResizeNearestNeighborGrad"; -var us = "ResizeBilinear"; -var ei = "ResizeBilinearGrad"; -var ps = "Relu6"; -var cs = "Reverse"; -var ls = "Round"; -var ms = "Rsqrt"; -var ds = "ScatterNd"; -var fs = "TensorScatterUpdate"; -var hs = "SearchSorted"; +var as = "ResizeNearestNeighbor"; +var Za = "ResizeNearestNeighborGrad"; +var is = "ResizeBilinear"; +var Ja = "ResizeBilinearGrad"; +var us = "Relu6"; +var ps = "Reverse"; +var cs = "Round"; +var ls = "Rsqrt"; +var ms = "ScatterNd"; +var ds = "TensorScatterUpdate"; +var fs = "SearchSorted"; var fa = "Select"; -var gs = "Selu"; +var hs = "Selu"; var ha = "Slice"; -var xs = "Sin"; -var ys = "Sinh"; -var bs = "Sign"; -var Cs = "Sigmoid"; -var ws = "Softplus"; -var Ss = "Sqrt"; -var Is = "Sum"; +var gs = "Sin"; +var xs = "Sinh"; +var ys = "Sign"; +var bs = "Sigmoid"; +var Cs = "Softplus"; +var ws = "Sqrt"; +var Ss = "Sum"; var ga = "SpaceToBatchND"; var xa = "SplitV"; -var vs = "Softmax"; -var ji = "SparseFillEmptyRows"; -var ti = "SparseReshape"; +var Is = "Softmax"; +var Hi = "SparseFillEmptyRows"; +var ei = "SparseReshape"; var ya = "SparseSegmentMean"; var ba = "SparseSegmentSum"; -var ks = "SparseToDense"; -var Ns = "SquaredDifference"; -var Xi = "Square"; -var Ou = "StaticRegexReplace"; -var Ts = "StridedSlice"; +var vs = "SparseToDense"; +var ks = "SquaredDifference"; +var Ki = "Square"; +var $u = "StaticRegexReplace"; +var Ns = "StridedSlice"; var Ca = "StringNGrams"; -var Yi = "StringSplit"; -var Qi = "StringToHashBucketFast"; -var _s = "Sub"; -var $s = "Tan"; -var Es = "Tanh"; -var po = "Tile"; -var Rs = "TopK"; -var Ds = "Transform"; -var co = "Transpose"; -var Zi = "Unique"; +var qi = "StringSplit"; 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+ Kp.has(o) && Ia(`The kernel '${t8}' for backend '${e}' is already registered`), Kp.set(o, r15); } -function Xce(r) { - let { kernelName: t8 } = r; - xl.has(t8) && A().getBool("DEBUG") && Ia(`Overriding the gradient for '${t8}'`), xl.set(t8, r); +function Xce(r15) { + let { kernelName: t8 } = r15; + dl.has(t8) && A().getBool("DEBUG") && Ia(`Overriding the gradient for '${t8}'`), dl.set(t8, r15); } -function Yce(r, t8) { - let e = uw(r, t8); - if (!ec.has(e)) - throw new Error(`The kernel '${r}' for backend '${t8}' is not registered`); - ec.delete(e); +function Yce(r15, t8) { + let e = iw(r15, t8); + if (!Kp.has(e)) + throw new Error(`The kernel '${r15}' for backend '${t8}' is not registered`); + Kp.delete(e); } -function Qce(r) { - if (!xl.has(r)) - throw new Error(`The gradient '${r}' for backend is not registered`); - xl.delete(r); +function Qce(r15) { + if (!dl.has(r15)) + throw new Error(`The gradient '${r15}' for backend is not registered`); + dl.delete(r15); } -function Zce(r, t8) { - Ym(r).forEach((o) => { +function Zce(r15, t8) { + jm(r15).forEach((o) => { let n = Object.assign({}, o, { backendName: t8 }); - ri(n); + ti(n); }); } -function uw(r, t8) { - return `${t8}_${r}`; +function iw(r15, t8) { + return `${t8}_${r15}`; } var y = {}; -qe(y, { arraysEqual: () => Cr, arraysEqualWithNull: () => ZC, assert: () => $, assertNonNegativeIntegerDimensions: () => wt, assertNonNull: () => io, assertShapesMatch: () => yt, bytesFromStringArray: () => ow, bytesPerElement: () => jp, checkConversionForErrors: () => tw, clamp: () => qp, computeStrides: () => js, convertBackendValuesAndArrayBuffer: () => BG, createScalarValue: () => t4, createShuffledIndices: () => PG, decodeString: () => sc, distSquared: () => RG, encodeString: () => tu, fetch: () => o4, fingerPrint64: () => e4, flatten: () => Ps, getArrayFromDType: () => Xm, getTypedArrayFromDType: () => ew, hasEncodingLoss: () => LG, hexToLong: () => yl, indexToLoc: () => WG, inferDtype: () => Ri, inferFromImplicitShape: () => MG, isBoolean: () => v0, isFunction: () => qs, isInt: () => qa, isNumber: () => k0, isPromise: () => Fu, isScalarShape: () => DG, isString: () => Vo, isTypedArray: () => Ot, isValidDtype: () => rw, locToIndex: () => VG, makeOnesTypedArray: () => fl, makeZerosNestedTypedArray: () => zG, makeZerosTypedArray: () => Yp, nearestDivisor: () => Xp, nearestLargerEven: () => _G, now: () => Wu, parseAxisParam: () => Ei, randUniform: () => EG, repeatedTry: () => OG, rightPad: () => Au, shuffle: () => I0, shuffleCombo: () => TG, sizeFromShape: () => He, sizeToSquarishShape: () => FG, squeezeShape: () => JC, sum: () => $G, swap: () => jm, tanh: () => AG, toNestedArray: () => Du, toTypedArray: () => nc }); -function Qm(r) { - return r instanceof Float32Array || r instanceof Int32Array || r instanceof Uint8Array || r instanceof Uint8ClampedArray; +qe(y, { arraysEqual: () => br, arraysEqualWithNull: () => QC, assert: () => $, assertNonNegativeIntegerDimensions: () => Ct, assertNonNull: () => ao, assertShapesMatch: () => xt, bytesFromStringArray: () => rw, bytesPerElement: () => zp, checkConversionForErrors: () => ew, clamp: () => Bp, computeStrides: () => js, convertBackendValuesAndArrayBuffer: () => zG, createScalarValue: () => r4, createShuffledIndices: () => OG, decodeString: () => Qp, distSquared: () => DG, encodeString: () => Zi, fetch: () => n4, fingerPrint64: () => t4, flatten: () => Fs, getArrayFromDType: () => qm, getTypedArrayFromDType: () => JC, hasEncodingLoss: () => BG, hexToLong: () => fl, indexToLoc: () => UG, inferDtype: () => _i, inferFromImplicitShape: () => LG, isBoolean: () => k0, isFunction: () => qs, isInt: () => Ka, isNumber: () => N0, isPromise: () => Tu, isScalarShape: () => AG, isString: () => zo, isTypedArray: () => Pt, isValidDtype: () => tw, locToIndex: () => WG, makeOnesTypedArray: () => cl, makeZerosNestedTypedArray: () => VG, makeZerosTypedArray: () => Wp, nearestDivisor: () => Vp, nearestLargerEven: () => $G, now: () => Pu, parseAxisParam: () => Ti, randUniform: () => RG, repeatedTry: () => MG, rightPad: () => Nu, shuffle: () => v0, shuffleCombo: () => _G, sizeFromShape: () => He, sizeToSquarishShape: () => PG, squeezeShape: () => ZC, sum: () => EG, swap: () => Km, tanh: () => FG, toNestedArray: () => ku, toTypedArray: () => Yp }); +function Xm(r15) { + return r15 instanceof Float32Array || r15 instanceof Int32Array || r15 instanceof Uint8Array || r15 instanceof Uint8ClampedArray; } -var mw = Kp(V0()); -var Vu = mw.default || mw; -function yl(r) { - return Vu.fromString(r, true, 16); +var lw = Lp(W0()); +var Fu = lw.default || lw; +function fl(r15) { + return Fu.fromString(r15, true, 16); } -var U0 = yl("c3a5c85c97cb3127"); -var zu = yl("b492b66fbe98f273"); -var wr = yl("9ae16a3b2f90404f"); -function lw(r) { - return r.xor(r.shru(47)); +var G0 = fl("c3a5c85c97cb3127"); +var Au = fl("b492b66fbe98f273"); +var Cr = fl("9ae16a3b2f90404f"); +function cw(r15) { + return r15.xor(r15.shru(47)); } -function G0(r, t8, e) { - let o = r.slice(t8, t8 + e); - return Vu.fromBytes(Array.from(o), true, true); +function H0(r15, t8, e) { + let o = r15.slice(t8, t8 + e); + return Fu.fromBytes(Array.from(o), true, true); } -function St(r, t8) { - return G0(r, t8, 8); +function wt(r15, t8) { + return H0(r15, t8, 8); } -function W0(r, t8) { - return G0(r, t8, 4); +function U0(r15, t8) { + return H0(r15, t8, 4); } -function Qt(r, t8) { - return t8 === 0 ? r : r.shru(t8).or(r.shl(64 - t8)); +function Yt(r15, t8) { + return t8 === 0 ? r15 : r15.shru(t8).or(r15.shl(64 - t8)); } -function eu(r, t8, e = yl("9ddfea08eb382d69")) { - let o = r.xor(t8).mul(e); +function Qi(r15, t8, e = fl("9ddfea08eb382d69")) { + let o = r15.xor(t8).mul(e); o = o.xor(o.shru(47)); let n = t8.xor(o).mul(e); return n = n.xor(n.shru(47)), n = n.mul(e), n; } -function YG(r, t8, e, o, n, s) { - n = n.add(r), s = Qt(s.add(n).add(o), 21); +function QG(r15, t8, e, o, n, s) { + n = n.add(r15), s = Yt(s.add(n).add(o), 21); let a = n; - return n = n.add(t8), n = n.add(e), s = s.add(Qt(n, 44)), [n.add(o), s.add(a)]; + return n = n.add(t8), n = n.add(e), s = s.add(Yt(n, 44)), [n.add(o), s.add(a)]; } -function Jm(r, t8, e, o) { - return YG(St(r, t8), St(r, t8 + 8), St(r, t8 + 16), St(r, t8 + 24), e, o); +function Qm(r15, t8, e, o) { + return QG(wt(r15, t8), wt(r15, t8 + 8), wt(r15, t8 + 16), wt(r15, t8 + 24), e, o); } -function QG(r, t8 = r.length) { +function ZG(r15, t8 = r15.length) { if (t8 >= 8) { - let e = wr.add(t8 * 2), o = St(r, 0).add(wr), n = St(r, t8 - 8), s = Qt(n, 37).mul(e).add(o), a = Qt(o, 25).add(n).mul(e); - return eu(s, a, e); + let e = Cr.add(t8 * 2), o = wt(r15, 0).add(Cr), n = wt(r15, t8 - 8), s = Yt(n, 37).mul(e).add(o), a = Yt(o, 25).add(n).mul(e); + return Qi(s, a, e); } if (t8 >= 4) { - let e = wr.add(t8 * 2), o = W0(r, 0); - return eu(o.shl(3).add(t8), W0(r, t8 - 4), e); + let e = Cr.add(t8 * 2), o = U0(r15, 0); + return Qi(o.shl(3).add(t8), U0(r15, t8 - 4), e); } if (t8 > 0) { - let e = r[0], o = r[t8 >> 1], n = r[t8 - 1], s = e + (o << 8), a = t8 + (n << 2); - return lw(wr.mul(s).xor(U0.mul(a))).mul(wr); + let e = r15[0], o = r15[t8 >> 1], n = r15[t8 - 1], s = e + (o << 8), a = t8 + (n << 2); + return cw(Cr.mul(s).xor(G0.mul(a))).mul(Cr); } - return wr; + return Cr; } -function ZG(r, t8 = r.length) { - let e = wr.add(t8 * 2), o = St(r, 0).mul(zu), n = St(r, 8), s = St(r, t8 - 8).mul(e), a = St(r, t8 - 16).mul(wr); - return eu(Qt(o.add(n), 43).add(Qt(s, 30)).add(a), o.add(Qt(n.add(wr), 18)).add(s), e); +function JG(r15, t8 = r15.length) { + let e = Cr.add(t8 * 2), o = wt(r15, 0).mul(Au), n = wt(r15, 8), s = wt(r15, t8 - 8).mul(e), a = wt(r15, t8 - 16).mul(Cr); + return Qi(Yt(o.add(n), 43).add(Yt(s, 30)).add(a), o.add(Yt(n.add(Cr), 18)).add(s), e); } -function JG(r, t8 = r.length) { - let e = wr.add(t8 * 2), o = St(r, 0).mul(wr), n = St(r, 8), s = St(r, t8 - 8).mul(e), a = St(r, t8 - 16).mul(wr), i = Qt(o.add(n), 43).add(Qt(s, 30)).add(a), p = eu(i, o.add(Qt(n.add(wr), 18)).add(s), e), u = St(r, 16).mul(e), c = St(r, 24), l = i.add(St(r, t8 - 32)).mul(e), m = p.add(St(r, t8 - 24)).mul(e); - return eu(Qt(u.add(c), 43).add(Qt(l, 30)).add(m), u.add(Qt(c.add(o), 18)).add(l), e); +function e4(r15, t8 = r15.length) { + let e = Cr.add(t8 * 2), o = wt(r15, 0).mul(Cr), n = wt(r15, 8), s = wt(r15, t8 - 8).mul(e), a = wt(r15, t8 - 16).mul(Cr), i = Yt(o.add(n), 43).add(Yt(s, 30)).add(a), p = Qi(i, o.add(Yt(n.add(Cr), 18)).add(s), e), u = wt(r15, 16).mul(e), c = wt(r15, 24), l = i.add(wt(r15, t8 - 32)).mul(e), m = p.add(wt(r15, t8 - 24)).mul(e); + return Qi(Yt(u.add(c), 43).add(Yt(l, 30)).add(m), u.add(Yt(c.add(o), 18)).add(l), e); } -function e4(r, t8 = r.length) { - let e = Vu.fromNumber(81, true); +function t4(r15, t8 = r15.length) { + let e = Fu.fromNumber(81, true); if (t8 <= 32) - return t8 <= 16 ? QG(r, t8) : ZG(r, t8); + return t8 <= 16 ? ZG(r15, t8) : JG(r15, t8); if (t8 <= 64) - return JG(r, t8); - let o = e, n = e.mul(zu).add(113), s = lw(n.mul(wr).add(113)).mul(wr), a = [Vu.UZERO, Vu.UZERO], i = [Vu.UZERO, Vu.UZERO]; - o = o.mul(wr).add(St(r, 0)); + return e4(r15, t8); + let o = e, n = e.mul(Au).add(113), s = cw(n.mul(Cr).add(113)).mul(Cr), a = [Fu.UZERO, Fu.UZERO], i = [Fu.UZERO, Fu.UZERO]; + o = o.mul(Cr).add(wt(r15, 0)); let p = 0, u = (t8 - 1 >> 6) * 64, c = u + (t8 - 1 & 63) - 63; do - o = Qt(o.add(n).add(a[0]).add(St(r, p + 8)), 37).mul(zu), n = Qt(n.add(a[1]).add(St(r, p + 48)), 42).mul(zu), o = o.xor(i[1]), n = n.add(a[0]).add(St(r, p + 40)), s = Qt(s.add(i[0]), 33).mul(zu), a = Jm(r, p, a[1].mul(zu), o.add(i[0])), i = Jm(r, p + 32, s.add(i[1]), n.add(St(r, p + 16))), [s, o] = [o, s], p += 64; + o = Yt(o.add(n).add(a[0]).add(wt(r15, p + 8)), 37).mul(Au), n = Yt(n.add(a[1]).add(wt(r15, p + 48)), 42).mul(Au), o = o.xor(i[1]), n = n.add(a[0]).add(wt(r15, p + 40)), s = Yt(s.add(i[0]), 33).mul(Au), a = Qm(r15, p, a[1].mul(Au), o.add(i[0])), i = Qm(r15, p + 32, s.add(i[1]), n.add(wt(r15, p + 16))), [s, o] = [o, s], p += 64; while (p !== u); - let l = zu.add(s.and(255).shl(1)); - return p = c, i[0] = i[0].add(t8 - 1 & 63), a[0] = a[0].add(i[0]), i[0] = i[0].add(a[0]), o = Qt(o.add(n).add(a[0]).add(St(r, p + 8)), 37).mul(l), n = Qt(n.add(a[1]).add(St(r, p + 48)), 42).mul(l), o = o.xor(i[1].mul(9)), n = n.add(a[0].mul(9).add(St(r, p + 40))), s = Qt(s.add(i[0]), 33).mul(l), a = Jm(r, p, a[1].mul(l), o.add(i[0])), i = Jm(r, p + 32, s.add(i[1]), n.add(St(r, p + 16))), [s, o] = [o, s], eu(eu(a[0], i[0], l).add(lw(n).mul(U0)).add(s), eu(a[1], i[1], l).add(o), l); + let l = Au.add(s.and(255).shl(1)); + return p = c, i[0] = i[0].add(t8 - 1 & 63), a[0] = a[0].add(i[0]), i[0] = i[0].add(a[0]), o = Yt(o.add(n).add(a[0]).add(wt(r15, p + 8)), 37).mul(l), n = Yt(n.add(a[1]).add(wt(r15, p + 48)), 42).mul(l), o = o.xor(i[1].mul(9)), n = n.add(a[0].mul(9).add(wt(r15, p + 40))), s = Yt(s.add(i[0]), 33).mul(l), a = Qm(r15, p, a[1].mul(l), o.add(i[0])), i = Qm(r15, p + 32, s.add(i[1]), n.add(wt(r15, p + 16))), [s, o] = [o, s], Qi(Qi(a[0], i[0], l).add(cw(n).mul(G0)).add(s), Qi(a[1], i[1], l).add(o), l); } -function t4(r, t8) { - return t8 === "string" ? tu(r) : nc([r], t8); +function r4(r15, t8) { + return t8 === "string" ? Zi(r15) : Yp([r15], t8); } -function r4(r, t8) { - return r instanceof Float32Array && t8 === "float32" || r instanceof Int32Array && t8 === "int32" || r instanceof Uint8Array && t8 === "bool"; +function o4(r15, t8) { + return r15 instanceof Float32Array && t8 === "float32" || r15 instanceof Int32Array && t8 === "int32" || r15 instanceof Uint8Array && t8 === "bool"; } -function nc(r, t8) { +function Yp(r15, t8) { if (t8 === "string") throw new Error("Cannot convert a string[] to a TypedArray"); - if (Array.isArray(r) && (r = Ps(r)), A().getBool("DEBUG") && tw(r, t8), r4(r, t8)) - return r; + if (Array.isArray(r15) && (r15 = Fs(r15)), A().getBool("DEBUG") && ew(r15, t8), o4(r15, t8)) + return r15; if (t8 == null || t8 === "float32" || t8 === "complex64") - return new Float32Array(r); + return new Float32Array(r15); if (t8 === "int32") - return new Int32Array(r); + return new Int32Array(r15); if (t8 === "bool") { - let e = new Uint8Array(r.length); + let e = new Uint8Array(r15.length); for (let o = 0; o < e.length; ++o) - Math.round(r[o]) !== 0 && (e[o] = 1); + Math.round(r15[o]) !== 0 && (e[o] = 1); return e; } else throw new Error(`Unknown data type ${t8}`); } -function Wu() { +function Pu() { return A().platform.now(); } -function o4(r, t8) { - return A().platform.fetch(r, t8); +function n4(r15, t8) { + return A().platform.fetch(r15, t8); } -function tu(r, t8 = "utf-8") { - return t8 = t8 || "utf-8", A().platform.encode(r, t8); +function Zi(r15, t8 = "utf-8") { + return t8 = t8 || "utf-8", A().platform.encode(r15, t8); } -function sc(r, t8 = "utf-8") { - return t8 = t8 || "utf-8", A().platform.decode(r, t8); +function Qp(r15, t8 = "utf-8") { + return t8 = t8 || "utf-8", A().platform.decode(r15, t8); } -function Ot(r) { - return A().platform.isTypedArray != null ? A().platform.isTypedArray(r) : Qm(r); +function Pt(r15) { + return A().platform.isTypedArray != null ? A().platform.isTypedArray(r15) : Xm(r15); } -function Ps(r, t8 = [], e = false) { - if (t8 == null && (t8 = []), typeof r == "boolean" || typeof r == "number" || typeof r == "string" || Fu(r) || r == null || Ot(r) && e) - t8.push(r); - else if (Array.isArray(r) || Ot(r)) - for (let o = 0; o < r.length; ++o) - Ps(r[o], t8, e); +function Fs(r15, t8 = [], e = false) { + if (t8 == null && (t8 = []), typeof r15 == "boolean" || typeof r15 == "number" || typeof r15 == "string" || Tu(r15) || r15 == null || Pt(r15) && e) + t8.push(r15); + else if (Array.isArray(r15) || Pt(r15)) + for (let o = 0; o < r15.length; ++o) + Fs(r15[o], t8, e); else { let o = -1; - for (let n of Object.keys(r)) + for (let n of Object.keys(r15)) /^([1-9]+[0-9]*|0)$/.test(n) && (o = Math.max(o, Number(n))); for (let n = 0; n <= o; n++) - Ps(r[n], t8, e); + Fs(r15[n], t8, e); } return t8; } -var ed = class { +var Zm = class { constructor(t8, e) { - this.backendTimer = t8, this.logger = e, e == null && (this.logger = new dw()); + this.backendTimer = t8, this.logger = e, e == null && (this.logger = new mw()); } profileKernel(t8, e, o) { let n, s = () => { n = o(); - }, a, i = Wu(); + }, a, i = Pu(); if (this.backendTimer.timerAvailable()) a = this.backendTimer.time(s); else { s(); for (let u of n) u.dataSync(); - a = Promise.resolve({ kernelMs: Wu() - i }); + a = Promise.resolve({ kernelMs: Pu() - i }); } if (A().getBool("CHECK_COMPUTATION_FOR_ERRORS")) for (let u = 0; u < n.length; u++) { let c = n[u]; c.data().then((l) => { - n4(l, c.dtype, t8); + s4(l, c.dtype, t8); }); } return { kernelName: t8, outputs: n, inputs: e, timeMs: a.then((u) => u.kernelMs), extraInfo: a.then((u) => u.getExtraProfileInfo != null ? u.getExtraProfileInfo() : "") }; @@ -3865,19 +3866,19 @@ var ed = class { }); } }; -function n4(r, t8, e) { +function s4(r15, t8, e) { if (t8 !== "float32") return false; - for (let o = 0; o < r.length; o++) { - let n = r[o]; + for (let o = 0; o < r15.length; o++) { + let n = r15[o]; if (isNaN(n) || !isFinite(n)) return console.warn(`Found ${n} in the result of '${e}'`), true; } return false; } -var dw = class { +var mw = class { logKernelProfile(t8, e, o, n, s, a) { - let i = typeof n == "number" ? Au(`${n}ms`, 9) : n.error, p = Au(t8, 25), u = e.rank, c = e.size, l = Au(e.shape.toString(), 14), m = ""; + let i = typeof n == "number" ? Nu(`${n}ms`, 9) : n.error, p = Nu(t8, 25), u = e.rank, c = e.size, l = Nu(e.shape.toString(), 14), m = ""; for (let d in s) { let f = s[d]; if (f != null) { @@ -3888,12 +3889,12 @@ var dw = class { console.log(`%c${p} %c${i} %c${u}D ${l} %c${c} %c${m} %c${a}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue"); } }; -function H0(r, t8, e) { +function K0(r15, t8, e) { let o = {}, n = {}; for (let p = 0; p < t8.length; p++) o[t8[p].id] = true; - for (let p = 0; p < r.length; p++) { - let u = r[p], c = u.inputs; + for (let p = 0; p < r15.length; p++) { + let u = r15[p], c = u.inputs; for (let l in c) { let m = c[l], d = false; for (let f = 0; f < t8.length; f++) @@ -3908,8 +3909,8 @@ function H0(r, t8, e) { let s = {}; s[e.id] = true; let a = {}; - for (let p = r.length - 1; p >= 0; p--) { - let u = r[p], c = u.inputs; + for (let p = r15.length - 1; p >= 0; p--) { + let u = r15[p], c = u.inputs; for (let l = 0; l < u.outputs.length; l++) if (s[u.outputs[l].id]) { for (let m in c) @@ -3918,8 +3919,8 @@ function H0(r, t8, e) { } } let i = []; - for (let p = 0; p < r.length; p++) { - let u = r[p]; + for (let p = 0; p < r15.length; p++) { + let u = r15[p]; if (n[u.id] && a[u.id]) { let c = {}; for (let m in u.inputs) { @@ -3932,11 +3933,11 @@ function H0(r, t8, e) { } return i; } -function K0(r, t8, e, o) { +function q0(r15, t8, e, o) { for (let n = t8.length - 1; n >= 0; n--) { let s = t8[n], a = []; if (s.outputs.forEach((p) => { - let u = r[p.id]; + let u = r15[p.id]; u != null ? a.push(u) : a.push(null); }), s.gradient == null) throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`); @@ -3948,74 +3949,74 @@ function K0(r, t8, e, o) { if (u.dtype !== "float32") throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${p} must have 'float32' dtype, but has '${u.dtype}'`); let c = s.inputs[p]; - if (!Cr(u.shape, c.shape)) + if (!br(u.shape, c.shape)) throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${p}' has shape '${u.shape}', which does not match the shape of the input '${c.shape}'`); - if (r[c.id] == null) - r[c.id] = u; + if (r15[c.id] == null) + r15[c.id] = u; else { - let l = r[c.id]; - r[c.id] = o(l, u), l.dispose(); + let l = r15[c.id]; + r15[c.id] = o(l, u), l.dispose(); } } } } -var q0 = 20; -var bl = 3; -var fw = 7; -function j0(r, t8, e, o) { - let n = js(t8), s = s4(r, t8, e, n), a = t8.length, i = td(r, t8, e, n, s), p = ["Tensor"]; +var j0 = 20; +var hl = 3; +var dw = 7; +function X0(r15, t8, e, o) { + let n = js(t8), s = a4(r15, t8, e, n), a = t8.length, i = Jm(r15, t8, e, n, s), p = ["Tensor"]; return o && (p.push(` dtype: ${e}`), p.push(` rank: ${a}`), p.push(` shape: [${t8}]`), p.push(" values:")), p.push(i.map((u) => " " + u).join(` `)), p.join(` `); } -function s4(r, t8, e, o) { - let n = He(t8), s = o[o.length - 1], a = new Array(s).fill(0), i = t8.length, p = e === "complex64" ? wl(r) : r; +function a4(r15, t8, e, o) { + let n = He(t8), s = o[o.length - 1], a = new Array(s).fill(0), i = t8.length, p = e === "complex64" ? xl(r15) : r15; if (i > 1) for (let u = 0; u < n / s; u++) { let c = u * s; for (let l = 0; l < s; l++) - a[l] = Math.max(a[l], Cl(p[c + l], 0, e).length); + a[l] = Math.max(a[l], gl(p[c + l], 0, e).length); } return a; } -function Cl(r, t8, e) { +function gl(r15, t8, e) { let o; - return Array.isArray(r) ? o = `${parseFloat(r[0].toFixed(fw))} + ${parseFloat(r[1].toFixed(fw))}j` : Vo(r) ? o = `'${r}'` : e === "bool" ? o = X0(r) : o = parseFloat(r.toFixed(fw)).toString(), Au(o, t8); + return Array.isArray(r15) ? o = `${parseFloat(r15[0].toFixed(dw))} + ${parseFloat(r15[1].toFixed(dw))}j` : zo(r15) ? o = `'${r15}'` : e === "bool" ? o = Y0(r15) : o = parseFloat(r15.toFixed(dw)).toString(), Nu(o, t8); } -function X0(r) { - return r === 0 ? "false" : "true"; +function Y0(r15) { + return r15 === 0 ? "false" : "true"; } -function td(r, t8, e, o, n, s = true) { +function Jm(r15, t8, e, o, n, s = true) { let a = e === "complex64" ? 2 : 1, i = t8[0], p = t8.length; if (p === 0) { if (e === "complex64") { - let h = wl(r); - return [Cl(h[0], 0, e)]; + let h = xl(r15); + return [gl(h[0], 0, e)]; } - return e === "bool" ? [X0(r[0])] : [r[0].toString()]; + return e === "bool" ? [Y0(r15[0])] : [r15[0].toString()]; } if (p === 1) { - if (i > q0) { - let g = bl * a, x = Array.from(r.slice(0, g)), b = Array.from(r.slice((i - bl) * a, i * a)); - return e === "complex64" && (x = wl(x), b = wl(b)), ["[" + x.map((C, S) => Cl(C, n[S], e)).join(", ") + ", ..., " + b.map((C, S) => Cl(C, n[i - bl + S], e)).join(", ") + "]"]; + if (i > j0) { + let g = hl * a, x = Array.from(r15.slice(0, g)), b = Array.from(r15.slice((i - hl) * a, i * a)); + return e === "complex64" && (x = xl(x), b = xl(b)), ["[" + x.map((C, S) => gl(C, n[S], e)).join(", ") + ", ..., " + b.map((C, S) => gl(C, n[i - hl + S], e)).join(", ") + "]"]; } - return ["[" + (e === "complex64" ? wl(r) : Array.from(r)).map((g, x) => Cl(g, n[x], e)).join(", ") + "]"]; + return ["[" + (e === "complex64" ? xl(r15) : Array.from(r15)).map((g, x) => gl(g, n[x], e)).join(", ") + "]"]; } let u = t8.slice(1), c = o.slice(1), l = o[0] * a, m = []; - if (i > q0) { - for (let h = 0; h < bl; h++) { + if (i > j0) { + for (let h = 0; h < hl; h++) { let g = h * l, x = g + l; - m.push(...td(r.slice(g, x), u, e, c, n, false)); + m.push(...Jm(r15.slice(g, x), u, e, c, n, false)); } m.push("..."); - for (let h = i - bl; h < i; h++) { + for (let h = i - hl; h < i; h++) { let g = h * l, x = g + l; - m.push(...td(r.slice(g, x), u, e, c, n, h === i - 1)); + m.push(...Jm(r15.slice(g, x), u, e, c, n, h === i - 1)); } } else for (let h = 0; h < i; h++) { let g = h * l, x = g + l; - m.push(...td(r.slice(g, x), u, e, c, n, h === i - 1)); + m.push(...Jm(r15.slice(g, x), u, e, c, n, h === i - 1)); } let d = p === 2 ? "," : ""; m[0] = "[" + (i > 0 ? m[0] + d : ""); @@ -4028,10 +4029,10 @@ function td(r, t8, e, o, n, s = true) { `; return m[m.length - 1] = " " + m[m.length - 1] + "]" + (s ? "" : f), m; } -function wl(r) { +function xl(r15) { let t8 = []; - for (let e = 0; e < r.length; e += 2) - t8.push([r[e], r[e + 1]]); + for (let e = 0; e < r15.length; e += 2) + t8.push([r15[e], r15[e + 1]]); return t8; } var tt = class { @@ -4042,7 +4043,7 @@ var tt = class { } if (e === "complex64") throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag)."); - this.values = o || Xm(e, this.size), this.strides = js(t8); + this.values = o || qm(e, this.size), this.strides = js(t8); } set(t8, ...e) { e.length === 0 && (e = [0]), $(e.length === this.rank, () => `The number of provided coordinates (${e.length}) must match the rank (${this.rank})`); @@ -4088,22 +4089,22 @@ var tt = class { return this.shape.length; } toTensor() { - return Os().makeTensor(this.values, this.shape, this.dtype); + return Ps().makeTensor(this.values, this.shape, this.dtype); } }; -var Os = null; -var ac = null; -var a4 = null; -function Y0(r) { - Os = r; +var Ps = null; +var Zp = null; +var i4 = null; +function Q0(r15) { + Ps = r15; } -function Q0(r) { - ac = r; +function Z0(r15) { + Zp = r15; } -function Z0(r) { - a4 = r; +function J0(r15) { + i4 = r15; } -var ut = class { +var mt = class { constructor(t8, e, o, n) { this.kept = false, this.isDisposedInternal = false, this.shape = t8.slice(), this.dtype = e || "float32", this.size = He(t8), this.strides = js(t8), this.dataId = o, this.id = n, this.rankType = this.rank < 5 ? this.rank.toString() : "higher"; } @@ -4112,25 +4113,25 @@ var ut = class { } async buffer() { let t8 = await this.data(); - return ac.buffer(this.shape, this.dtype, t8); + return Zp.buffer(this.shape, this.dtype, t8); } bufferSync() { - return ac.buffer(this.shape, this.dtype, this.dataSync()); + return Zp.buffer(this.shape, this.dtype, this.dataSync()); } async array() { let t8 = await this.data(); - return Du(this.shape, t8, this.dtype === "complex64"); + return ku(this.shape, t8, this.dtype === "complex64"); } arraySync() { - return Du(this.shape, this.dataSync(), this.dtype === "complex64"); + return ku(this.shape, this.dataSync(), this.dtype === "complex64"); } async data() { this.throwIfDisposed(); - let t8 = Os().read(this.dataId); + let t8 = Ps().read(this.dataId); if (this.dtype === "string") { let e = await t8; try { - return e.map((o) => sc(o)); + return e.map((o) => Qp(o)); } catch (o) { throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); } @@ -4138,14 +4139,14 @@ var ut = class { return t8; } dataToGPU(t8) { - return this.throwIfDisposed(), Os().readToGPU(this.dataId, t8); + return this.throwIfDisposed(), Ps().readToGPU(this.dataId, t8); } dataSync() { this.throwIfDisposed(); - let t8 = Os().readSync(this.dataId); + let t8 = Ps().readSync(this.dataId); if (this.dtype === "string") try { - return t8.map((e) => sc(e)); + return t8.map((e) => Qp(e)); } catch (e) { throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); } @@ -4153,11 +4154,11 @@ var ut = class { } async bytes() { this.throwIfDisposed(); - let t8 = await Os().read(this.dataId); + let t8 = await Ps().read(this.dataId); return this.dtype === "string" ? t8 : new Uint8Array(t8.buffer); } dispose() { - this.isDisposed || (this.kerasMask && this.kerasMask.dispose(), Os().disposeTensor(this), this.isDisposedInternal = true); + this.isDisposed || (this.kerasMask && this.kerasMask.dispose(), Ps().disposeTensor(this), this.isDisposedInternal = true); } get isDisposed() { return this.isDisposedInternal; @@ -4167,121 +4168,121 @@ var ut = class { throw new Error("Tensor is disposed."); } print(t8 = false) { - return ac.print(this, t8); + return Zp.print(this, t8); } clone() { - return this.throwIfDisposed(), ac.clone(this); + return this.throwIfDisposed(), Zp.clone(this); } toString(t8 = false) { let e = this.dataSync(); - return j0(e, this.shape, this.dtype, t8); + return X0(e, this.shape, this.dtype, t8); } cast(t8) { - return this.throwIfDisposed(), ac.cast(this, t8); + return this.throwIfDisposed(), Zp.cast(this, t8); } variable(t8 = true, e, o) { - return this.throwIfDisposed(), Os().makeVariable(this, t8, e, o); + return this.throwIfDisposed(), Ps().makeVariable(this, t8, e, o); } }; -Object.defineProperty(ut, Symbol.hasInstance, { value: (r) => !!r && r.data != null && r.dataSync != null && r.throwIfDisposed != null }); -function i4() { - return gl("Tensor", () => ut); +Object.defineProperty(mt, Symbol.hasInstance, { value: (r15) => !!r15 && r15.data != null && r15.dataSync != null && r15.throwIfDisposed != null }); +function fw() { + return ml("Tensor", () => mt); } -i4(); -var oi = class extends ut { +fw(); +var ri = class extends mt { constructor(t8, e, o, n) { super(t8.shape, t8.dtype, t8.dataId, n), this.trainable = e, this.name = o; } assign(t8) { if (t8.dtype !== this.dtype) throw new Error(`dtype of the new value (${t8.dtype}) and previous value (${this.dtype}) must match`); - if (!Cr(t8.shape, this.shape)) + if (!br(t8.shape, this.shape)) throw new Error(`shape of the new value (${t8.shape}) and previous value (${this.shape}) must match`); - Os().disposeTensor(this), this.dataId = t8.dataId, Os().incRef(this, null); + Ps().disposeTensor(this), this.dataId = t8.dataId, Ps().incRef(this, null); } dispose() { - Os().disposeVariable(this), this.isDisposedInternal = true; + Ps().disposeVariable(this), this.isDisposedInternal = true; } }; -Object.defineProperty(oi, Symbol.hasInstance, { value: (r) => r instanceof ut && r.assign != null && r.assign instanceof Function }); -var ek = {}; -qe(ek, { assertTypesMatch: () => Cw, getTensorsInContainer: () => Sl, isTensorInList: () => p4, makeTypesMatch: () => Oe }); +Object.defineProperty(ri, Symbol.hasInstance, { value: (r15) => r15 instanceof mt && r15.assign != null && r15.assign instanceof Function }); +var tk = {}; +qe(tk, { assertTypesMatch: () => Cw, getTensorsInContainer: () => yl, isTensorInList: () => p4, makeTypesMatch: () => Oe }); var hw; -(function(r) { - r.R0 = "R0", r.R1 = "R1", r.R2 = "R2", r.R3 = "R3", r.R4 = "R4", r.R5 = "R5", r.R6 = "R6"; +(function(r15) { + r15.R0 = "R0", r15.R1 = "R1", r15.R2 = "R2", r15.R3 = "R3", r15.R4 = "R4", r15.R5 = "R5", r15.R6 = "R6"; })(hw || (hw = {})); var gw; -(function(r) { - r.float32 = "float32", r.int32 = "int32", r.bool = "int32", r.complex64 = "complex64"; +(function(r15) { + r15.float32 = "float32", r15.int32 = "int32", r15.bool = "int32", r15.complex64 = "complex64"; })(gw || (gw = {})); var xw; -(function(r) { - r.float32 = "float32", r.int32 = "int32", r.bool = "bool", r.complex64 = "complex64"; +(function(r15) { + r15.float32 = "float32", r15.int32 = "int32", r15.bool = "bool", r15.complex64 = "complex64"; })(xw || (xw = {})); var yw; -(function(r) { - r.float32 = "float32", r.int32 = "float32", r.bool = "float32", r.complex64 = "complex64"; +(function(r15) { + r15.float32 = "float32", r15.int32 = "float32", r15.bool = "float32", r15.complex64 = "complex64"; })(yw || (yw = {})); var bw; -(function(r) { - r.float32 = "complex64", r.int32 = "complex64", r.bool = "complex64", r.complex64 = "complex64"; +(function(r15) { + r15.float32 = "complex64", r15.int32 = "complex64", r15.bool = "complex64", r15.complex64 = "complex64"; })(bw || (bw = {})); var u4 = { float32: yw, int32: gw, bool: xw, complex64: bw }; -function dt(r, t8) { - if (r === "string" || t8 === "string") { - if (r === "string" && t8 === "string") +function dt(r15, t8) { + if (r15 === "string" || t8 === "string") { + if (r15 === "string" && t8 === "string") return "string"; - throw new Error(`Can not upcast ${r} with ${t8}`); + throw new Error(`Can not upcast ${r15} with ${t8}`); } - return u4[r][t8]; + return u4[r15][t8]; } -function ni(r) { - return dt(r, "int32"); +function oi(r15) { + return dt(r15, "int32"); } -function rd(r) { - return r != null && typeof r == "object" && "texture" in r && r.texture instanceof WebGLTexture; +function ed(r15) { + return r15 != null && typeof r15 == "object" && "texture" in r15 && r15.texture instanceof WebGLTexture; } -function od(r) { - return typeof GPUBuffer != "undefined" && r != null && typeof r == "object" && "buffer" in r && r.buffer instanceof GPUBuffer; +function td(r15) { + return typeof GPUBuffer != "undefined" && r15 != null && typeof r15 == "object" && "buffer" in r15 && r15.buffer instanceof GPUBuffer; } -function Oe(r, t8) { - if (r.dtype === t8.dtype) - return [r, t8]; - let e = dt(r.dtype, t8.dtype); - return [r.cast(e), t8.cast(e)]; +function Oe(r15, t8) { + if (r15.dtype === t8.dtype) + return [r15, t8]; + let e = dt(r15.dtype, t8.dtype); + return [r15.cast(e), t8.cast(e)]; } -function Cw(r, t8) { - $(r.dtype === t8.dtype, () => `The dtypes of the first(${r.dtype}) and second(${t8.dtype}) input must match`); +function Cw(r15, t8) { + $(r15.dtype === t8.dtype, () => `The dtypes of the first(${r15.dtype}) and second(${t8.dtype}) input must match`); } -function p4(r, t8) { - return t8.some((e) => e.id === r.id); +function p4(r15, t8) { + return t8.some((e) => e.id === r15.id); } -function Sl(r) { +function yl(r15) { let t8 = []; - return J0(r, t8, /* @__PURE__ */ new Set()), t8; + return ek(r15, t8, /* @__PURE__ */ new Set()), t8; } -function J0(r, t8, e) { - if (r == null) +function ek(r15, t8, e) { + if (r15 == null) return; - if (r instanceof ut) { - t8.push(r); + if (r15 instanceof mt) { + t8.push(r15); return; } - if (!c4(r)) + if (!c4(r15)) return; - let o = r; + let o = r15; for (let n in o) { let s = o[n]; - e.has(s) || (e.add(s), J0(s, t8, e)); + e.has(s) || (e.add(s), ek(s, t8, e)); } } -function c4(r) { - return Array.isArray(r) || typeof r == "object"; +function c4(r15) { + return Array.isArray(r15) || typeof r15 == "object"; } -function ww(r) { - return r.kernelName != null; +function ww(r15) { + return r15.kernelName != null; } -var nd = class { +var rd = class { constructor() { this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.gradientDepth = 0, this.kernelDepth = 0, this.scopeStack = [], this.numDataMovesStack = [], this.nextScopeId = 0, this.tensorInfo = /* @__PURE__ */ new WeakMap(), this.profiling = false, this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() { return Array.from(new Set(this.kernels.map((t8) => t8.name))); @@ -4292,9 +4293,9 @@ var nd = class { this.registeredVariables[t8].dispose(); } }; -var ru = class { +var bl = class r { constructor(t8) { - this.ENV = t8, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new nd(); + this.ENV = t8, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new rd(); } async ready() { if (this.pendingBackendInit != null) @@ -4351,15 +4352,15 @@ var ru = class { if (!(o ? await e : e)) return false; } - return this.backendInstance = this.registry[t8], this.setupRegisteredKernels(), this.profiler = new ed(this.backendInstance), true; + return this.backendInstance = this.registry[t8], this.setupRegisteredKernels(), this.profiler = new Zm(this.backendInstance), true; } setupRegisteredKernels() { - Ym(this.backendName).forEach((e) => { + jm(this.backendName).forEach((e) => { e.setupFunc != null && e.setupFunc(this.backendInstance); }); } disposeRegisteredKernels(t8) { - Ym(t8).forEach((o) => { + jm(t8).forEach((o) => { o.disposeFunc != null && o.disposeFunc(this.registry[t8]); }); } @@ -4369,7 +4370,7 @@ var ru = class { throw new Error(`Cannot initialize backend ${t8}, no registration found.`); try { let o = e.factory(); - if (o && !(o instanceof ao) && typeof o.then == "function") { + if (o && !(o instanceof so) && typeof o.then == "function") { let n = ++this.pendingBackendInitId, s = o.then((a) => n < this.pendingBackendInitId ? false : (this.registry[t8] = a, this.pendingBackendInit = null, true)).catch((a) => (n < this.pendingBackendInitId || (this.pendingBackendInit = null, Ia(`Initialization of backend ${t8} failed`), Ia(a.stack || a.message)), false)); return this.pendingBackendInit = s, { success: s, asyncInit: true }; } else @@ -4427,20 +4428,20 @@ var ru = class { } } nextTensorId() { - return ru.nextTensorId++; + return r.nextTensorId++; } nextVariableId() { - return ru.nextVariableId++; + return r.nextVariableId++; } clone(t8) { - let e = T.runKernel(wo, { x: t8 }), o = { x: t8 }, n = (a) => ({ x: () => { + let e = T.runKernel(Co, { x: t8 }), o = { x: t8 }, n = (a) => ({ x: () => { let i = "float32", p = { x: a }, u = { dtype: i }; - return T.runKernel(bo, p, u); + return T.runKernel(yo, p, u); } }), s = []; return this.addTapeNode(this.state.activeScope.name, o, [e], n, s, {}), e; } runKernel(t8, e, o) { - if (this.backendName == null && this.backend, !(tc(t8, this.backendName) != null)) + if (this.backendName == null && this.backend, !(qp(t8, this.backendName) != null)) throw new Error(`Kernel '${t8}' not registered for backend '${this.backendName}'`); return this.runKernelFunc({ kernelName: t8, inputs: e, attrs: o }); } @@ -4465,7 +4466,7 @@ var ru = class { if (ww(t8)) { let { kernelName: f, inputs: h, attrs: g } = t8; this.backendName == null && this.backend; - let x = tc(f, this.backendName); + let x = qp(f, this.backendName); $(x != null, () => `Cannot find registered kernel '${f}' for backend '${this.backendName}'`), i = () => { let b = this.backend.numDataIds(); p = x.kernelFunc({ inputs: h, attrs: g, backend: this.backend }); @@ -4498,7 +4499,7 @@ var ru = class { return t8.map((o) => this.keep(this.clone(o))); } getTensorsForGradient(t8, e, o) { - let n = iw(t8); + let n = aw(t8); if (n != null) { let s = n.inputsToSave || [], a = n.outputsToSave || [], i; n.saveAllInputs ? ($(Array.isArray(e), () => "saveAllInputs is true, expected inputs to be an array."), i = Object.keys(e).map((u) => e[u])) : i = s.map((u) => e[u]); @@ -4512,10 +4513,10 @@ var ru = class { throw new Error("Values passed to engine.makeTensor() are null"); o = o || "float32", n = n || this.backend; let s = t8; - o === "string" && Vo(t8[0]) && (s = t8.map((p) => tu(p))); - let a = n.write(s, e, o), i = new ut(e, o, a, this.nextTensorId()); + o === "string" && zo(t8[0]) && (s = t8.map((p) => Zi(p))); + let a = n.write(s, e, o), i = new mt(e, o, a, this.nextTensorId()); if (this.trackTensor(i, n), o === "string") { - let p = this.state.tensorInfo.get(a), u = ow(s); + let p = this.state.tensorInfo.get(a), u = rw(s); this.state.numBytes += u - p.bytes, p.bytes = u; } return i; @@ -4526,12 +4527,12 @@ var ru = class { return this.makeTensorFromTensorInfo(s, n); } makeTensorFromTensorInfo(t8, e) { - let { dataId: o, shape: n, dtype: s } = t8, a = new ut(n, s, o, this.nextTensorId()); + let { dataId: o, shape: n, dtype: s } = t8, a = new mt(n, s, o, this.nextTensorId()); return this.trackTensor(a, e), a; } makeVariable(t8, e = true, o, n) { o = o || this.nextVariableId().toString(), n != null && n !== t8.dtype && (t8 = t8.cast(n)); - let s = new oi(t8, e, o, this.nextTensorId()); + let s = new ri(t8, e, o, this.nextTensorId()); if (this.state.registeredVariables[s.name] != null) throw new Error(`Variable with name ${s.name} was already registered`); return this.state.registeredVariables[s.name] = s, this.incRef(s, this.backend), s; @@ -4539,7 +4540,7 @@ var ru = class { trackTensor(t8, e) { this.state.numTensors++, t8.dtype === "string" && this.state.numStringTensors++; let o = 0; - t8.dtype !== "complex64" && t8.dtype !== "string" && (o = t8.size * jp(t8.dtype)), this.state.numBytes += o, this.state.tensorInfo.has(t8.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(t8.dataId, { backend: e || this.backend, dtype: t8.dtype, shape: t8.shape, bytes: o })), t8 instanceof oi || this.track(t8); + t8.dtype !== "complex64" && t8.dtype !== "string" && (o = t8.size * zp(t8.dtype)), this.state.numBytes += o, this.state.tensorInfo.has(t8.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(t8.dataId, { backend: e || this.backend, dtype: t8.dtype, shape: t8.shape, bytes: o })), t8 instanceof ri || this.track(t8); } incRef(t8, e) { this.trackTensor(t8, e), this.backend.incRef(t8.dataId); @@ -4552,7 +4553,7 @@ var ru = class { return; let e = this.state.tensorInfo.get(t8.dataId); if (this.state.numTensors--, t8.dtype === "string" && (this.state.numStringTensors--, this.state.numBytes -= e.bytes), t8.dtype !== "complex64" && t8.dtype !== "string") { - let o = t8.size * jp(t8.dtype); + let o = t8.size * zp(t8.dtype); this.state.numBytes -= o; } e.backend.disposeData(t8.dataId) && this.removeDataId(t8.dataId, e.backend); @@ -4582,10 +4583,10 @@ var ru = class { return this.state.gradientDepth > 0 && this.state.kernelDepth === 0; } addTapeNode(t8, e, o, n, s, a) { - let i = { id: this.state.nextTapeNodeId++, kernelName: t8, inputs: e, outputs: o, saved: s }, p = iw(t8); + let i = { id: this.state.nextTapeNodeId++, kernelName: t8, inputs: e, outputs: o, saved: s }, p = aw(t8); p != null && (n = p.gradFunc), n != null && (i.gradient = (u) => (u = u.map((c, l) => { if (c == null) { - let m = o[l], d = Yp(m.size, m.dtype); + let m = o[l], d = Wp(m.size, m.dtype); return this.makeTensor(d, m.shape, m.dtype); } return c; @@ -4605,7 +4606,7 @@ var ru = class { t8 && (e.name = t8), this.state.scopeStack.push(e), this.state.activeScope = e; } endScope(t8) { - let e = Sl(t8), o = new Set(e.map((s) => s.id)); + let e = yl(t8), o = new Set(e.map((s) => s.id)); for (let s = 0; s < this.state.activeScope.track.length; s++) { let a = this.state.activeScope.track[s]; !a.kept && !o.has(a.id) && a.dispose(); @@ -4619,13 +4620,13 @@ var ru = class { if ($(e.length > 0, () => "gradients() received an empty list of xs."), o != null && o.dtype !== "float32") throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`); let s = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", t8)); - $(s instanceof ut, () => "The result y returned by f() must be a tensor."); - let a = H0(this.state.activeTape, e, s); + $(s instanceof mt, () => "The result y returned by f() must be a tensor."); + let a = K0(this.state.activeTape, e, s); if (!n && a.length === 0 && e.length > 0) throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y."); return this.tidy("backward", () => { let i = {}; - i[s.id] = o == null ? l4(s.shape) : o, K0(i, a, (u) => this.tidy(u), m4); + i[s.id] = o == null ? l4(s.shape) : o, q0(i, a, (u) => this.tidy(u), m4); let p = e.map((u) => i[u.id]); return this.state.gradientDepth === 0 && (this.state.activeTape.forEach((u) => { for (let c of u.saved) @@ -4635,14 +4636,14 @@ var ru = class { } customGrad(t8) { return $(qs(t8), () => "The f passed in customGrad(f) must be a function."), (...e) => { - $(e.every((i) => i instanceof ut), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors"); + $(e.every((i) => i instanceof mt), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors"); let o, n = {}; e.forEach((i, p) => { n[p] = i; }); - let s = (i, p) => (o = t8(...e, p), $(o.value instanceof ut, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), $(qs(o.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), o.value), a = (i, p) => { + let s = (i, p) => (o = t8(...e, p), $(o.value instanceof mt, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), $(qs(o.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), o.value), a = (i, p) => { let u = o.gradFunc(i, p), c = Array.isArray(u) ? u : [u]; - $(c.length === e.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."), $(c.every((m) => m instanceof ut), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."); + $(c.length === e.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."), $(c.every((m) => m instanceof mt), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."); let l = {}; return c.forEach((m, d) => { l[d] = () => m; @@ -4661,8 +4662,8 @@ var ru = class { return this.state.tensorInfo.get(t8).backend.readToGPU(t8, e); } async time(t8) { - let e = Wu(), o = await this.backend.time(t8); - return o.wallMs = Wu() - e, o; + let e = Pu(), o = await this.backend.time(t8); + return o.wallMs = Pu() - e, o; } track(t8) { return this.state.activeScope != null && (t8.scopeId = this.state.activeScope.id, this.state.activeScope.track.push(t8)), t8; @@ -4671,49 +4672,49 @@ var ru = class { return this.state.registeredVariables; } reset() { - this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new nd(); + this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new rd(); for (let t8 in this.registry) this.disposeRegisteredKernels(t8), this.registry[t8].dispose(), delete this.registry[t8]; this.backendName = null, this.backendInstance = null, this.pendingBackendInit = null; } }; -ru.nextTensorId = 0; -ru.nextVariableId = 0; -function l4(r) { - let t8 = fl(He(r), "float32"); - return T.makeTensor(t8, r, "float32"); +bl.nextTensorId = 0; +bl.nextVariableId = 0; +function l4(r15) { + let t8 = cl(He(r15), "float32"); + return T.makeTensor(t8, r15, "float32"); } function Sw() { - let r = aw(); - if (r._tfengine == null) { - let t8 = new hl(r); - r._tfengine = new ru(t8); + let r15 = sw(); + if (r15._tfengine == null) { + let t8 = new ll(r15); + r15._tfengine = new bl(t8); } - return _0(r._tfengine.ENV), Y0(() => r._tfengine), r._tfengine; + return $0(r15._tfengine.ENV), Q0(() => r15._tfengine), r15._tfengine; } var T = Sw(); -function m4(r, t8) { - let e = { a: r, b: t8 }; - return T.runKernel(uo, e); +function m4(r15, t8) { + let e = { a: r15, b: t8 }; + return T.runKernel(io, e); } -var ou = {}; -qe(ou, { isBrowser: () => vw, isMobile: () => h4, mockIsMobile: () => f4 }); +var Ji = {}; +qe(Ji, { isBrowser: () => vw, isMobile: () => h4, mockIsMobile: () => f4 }); function d4() { return typeof navigator != "undefined" && navigator != null; } var Iw; -function f4(r) { - Iw = r; +function f4(r15) { + Iw = r15; } -function h4(r) { +function h4(r15) { if (Iw !== void 0) return Iw; - if (r || d4()) { - if (r || (r = navigator), r.product === "ReactNative") + if (r15 || d4()) { + if (r15 || (r15 = navigator), r15.product === "ReactNative") return true; - let t8 = r.userAgent || r.vendor || (typeof window != "undefined" ? window.opera : ""); + let t8 = r15.userAgent || r15.vendor || (typeof window != "undefined" ? window.opera : ""); if (!t8) { - let e = r; + let e = r15; return e.userAgentData && e.userAgentData.mobile; } return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t8) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t8.substr(0, 4)); @@ -4723,113 +4724,113 @@ function h4(r) { function vw() { return typeof window != "undefined" && window.document != null || typeof WorkerGlobalScope != "undefined"; } -var $r = A(); -$r.registerFlag("DEBUG", () => false, (r) => { - r && console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance."); +var _r = A(); +_r.registerFlag("DEBUG", () => false, (r15) => { + r15 && console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance."); }); -$r.registerFlag("IS_BROWSER", () => vw()); -$r.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined"); -$r.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor)); -$r.registerFlag("IS_SAFARI", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Safari/.test(navigator.userAgent) && /Apple/.test(navigator.vendor)); -$r.registerFlag("PROD", () => false); -$r.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => $r.getBool("DEBUG")); -$r.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true); -$r.registerFlag("IS_TEST", () => false); -$r.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => $r.getBool("DEBUG")); -$r.registerFlag("WRAP_TO_IMAGEBITMAP", () => false); -$r.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU", () => false); -$r.registerFlag("USE_SETTIMEOUTCUSTOM", () => false); -function ir(r, t8) { - let e = r; - if (Ot(r)) - return t8 === "string" ? [] : [r.length]; - if (rd(r)) { - let n = r.channels || "RGBA"; - return [r.height, r.width * n.length]; - } else if (od(r)) - return [r.buffer.size / (t8 == null ? 4 : jp(t8))]; - if (!Array.isArray(r)) +_r.registerFlag("IS_BROWSER", () => vw()); +_r.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined"); +_r.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor)); +_r.registerFlag("IS_SAFARI", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Safari/.test(navigator.userAgent) && /Apple/.test(navigator.vendor)); +_r.registerFlag("PROD", () => false); +_r.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => _r.getBool("DEBUG")); +_r.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true); +_r.registerFlag("IS_TEST", () => false); +_r.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => _r.getBool("DEBUG")); +_r.registerFlag("WRAP_TO_IMAGEBITMAP", () => false); +_r.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU", () => false); +_r.registerFlag("USE_SETTIMEOUTCUSTOM", () => false); +function sr(r15, t8) { + let e = r15; + if (Pt(r15)) + return t8 === "string" ? [] : [r15.length]; + if (ed(r15)) { + let n = r15.channels || "RGBA"; + return [r15.height, r15.width * n.length]; + } else if (td(r15)) + return [r15.buffer.size / (t8 == null ? 4 : zp(t8))]; + if (!Array.isArray(r15)) return []; let o = []; - for (; Array.isArray(e) || Ot(e) && t8 !== "string"; ) + for (; Array.isArray(e) || Pt(e) && t8 !== "string"; ) o.push(e.length), e = e[0]; - return Array.isArray(r) && A().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && rk(r, o, []), o; + return Array.isArray(r15) && A().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && ok(r15, o, []), o; } -function rk(r, t8, e) { - if (e = e || [], !Array.isArray(r) && !Ot(r)) { +function ok(r15, t8, e) { + if (e = e || [], !Array.isArray(r15) && !Pt(r15)) { $(t8.length === 0, () => `Element arr[${e.join("][")}] is a primitive, but should be an array/TypedArray of ${t8[0]} elements`); return; } - $(t8.length > 0, () => `Element arr[${e.join("][")}] should be a primitive, but is an array of ${r.length} elements`), $(r.length === t8[0], () => `Element arr[${e.join("][")}] should have ${t8[0]} elements, but has ${r.length} elements`); + $(t8.length > 0, () => `Element arr[${e.join("][")}] should be a primitive, but is an array of ${r15.length} elements`), $(r15.length === t8[0], () => `Element arr[${e.join("][")}] should have ${t8[0]} elements, but has ${r15.length} elements`); let o = t8.slice(1); - for (let n = 0; n < r.length; ++n) - rk(r[n], o, e.concat(n)); + for (let n = 0; n < r15.length; ++n) + ok(r15[n], o, e.concat(n)); } -function tk(r, t8, e, o) { - if (r !== "string_or_numeric") { - if (r == null) +function rk(r15, t8, e, o) { + if (r15 !== "string_or_numeric") { + if (r15 == null) throw new Error("Expected dtype cannot be null."); - if (r !== "numeric" && r !== t8 || r === "numeric" && t8 === "string") - throw new Error(`Argument '${e}' passed to '${o}' must be ${r} tensor, but got ${t8} tensor`); + if (r15 !== "numeric" && r15 !== t8 || r15 === "numeric" && t8 === "string") + throw new Error(`Argument '${e}' passed to '${o}' must be ${r15} tensor, but got ${t8} tensor`); } } -function v(r, t8, e, o = "numeric") { - if (r instanceof ut) - return tk(o, r.dtype, t8, e), r; - let n = Ri(r); - if (n !== "string" && ["bool", "int32", "float32"].indexOf(o) >= 0 && (n = o), tk(o, n, t8, e), r == null || !Ot(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") { - let p = r == null ? "null" : r.constructor.name; +function v(r15, t8, e, o = "numeric") { + if (r15 instanceof fw()) + return rk(o, r15.dtype, t8, e), r15; + let n = _i(r15); + if (n !== "string" && ["bool", "int32", "float32"].indexOf(o) >= 0 && (n = o), rk(o, n, t8, e), r15 == null || !Pt(r15) && !Array.isArray(r15) && typeof r15 != "number" && typeof r15 != "boolean" && typeof r15 != "string") { + let p = r15 == null ? "null" : r15.constructor.name; throw new Error(`Argument '${t8}' passed to '${e}' must be a Tensor or TensorLike, but got '${p}'`); } - let s = ir(r, n); - !Ot(r) && !Array.isArray(r) && (r = [r]); - let i = n !== "string" ? nc(r, n) : Ps(r, [], true); + let s = sr(r15, n); + !Pt(r15) && !Array.isArray(r15) && (r15 = [r15]); + let i = n !== "string" ? Yp(r15, n) : Fs(r15, [], true); return T.makeTensor(i, s, n); } -function si(r, t8, e, o = "numeric") { - if (!Array.isArray(r)) +function ni(r15, t8, e, o = "numeric") { + if (!Array.isArray(r15)) throw new Error(`Argument ${t8} passed to ${e} must be a \`Tensor[]\` or \`TensorLike[]\``); - return r.map((s, a) => v(s, `${t8}[${a}]`, e, o)); + return r15.map((s, a) => v(s, `${t8}[${a}]`, e, o)); } var kw = "__op"; -function N(r) { - let t8 = Object.keys(r); +function N(r15) { + let t8 = Object.keys(r15); if (t8.length !== 1) throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t8.length} keys.`); - let e = t8[0], o = r[e]; + let e = t8[0], o = r15[e]; e.endsWith("_") && (e = e.substring(0, e.length - 1)), e = e + kw; let n = (...s) => { T.startScope(e); try { let a = o(...s); - return Fu(a) && console.error("Cannot return a Promise inside of tidy."), T.endScope(a), a; + return Tu(a) && console.error("Cannot return a Promise inside of tidy."), T.endScope(a), a; } catch (a) { throw T.endScope(null), a; } }; return Object.defineProperty(n, "name", { value: e, configurable: true }), n; } -function g4(r, t8) { - let e = v(r, "real", "complex"), o = v(t8, "imag", "complex"); - yt(e.shape, o.shape, `real and imag shapes, ${e.shape} and ${o.shape}, must match in call to tf.complex().`); +function g4(r15, t8) { + let e = v(r15, "real", "complex"), o = v(t8, "imag", "complex"); + xt(e.shape, o.shape, `real and imag shapes, ${e.shape} and ${o.shape}, must match in call to tf.complex().`); let n = { real: e, imag: o }; - return T.runKernel(Fi, n); + return T.runKernel(Ri, n); } -var Er = N({ complex_: g4 }); -function Sr(r, t8, e, o) { +var $r = N({ complex_: g4 }); +function wr(r15, t8, e, o) { if (o == null) - o = Ri(r); + o = _i(r15); else if (o === "complex64") throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag)."); - if (od(r) || rd(r)) { + if (td(r15) || ed(r15)) { if (o !== "float32" && o !== "int32") throw new Error(`Creating tensor from GPU data only supports 'float32'|'int32' dtype, while the dtype is ${o}.`); - return T.backend.createTensorFromGPUData(r, t8 || e, o); + return T.backend.createTensorFromGPUData(r15, t8 || e, o); } - if (!Ot(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") + if (!Pt(r15) && !Array.isArray(r15) && typeof r15 != "number" && typeof r15 != "boolean" && typeof r15 != "string") throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray"); if (t8 != null) { - wt(t8); + Ct(t8); let n = He(t8), s = He(e); $(n === s, () => `Based on the provided shape, [${t8}], the tensor should have ${n} values but has ${s}`); for (let a = 0; a < e.length; ++a) { @@ -4837,19 +4838,19 @@ function Sr(r, t8, e, o) { $(e[a] === t8[a] || !p, () => `Error creating a new Tensor. Inferred shape (${e}) does not match the provided shape (${t8}). `); } } - return !Ot(r) && !Array.isArray(r) && (r = [r]), t8 = t8 || e, r = o !== "string" ? nc(r, o) : Ps(r, [], true), T.makeTensor(r, t8, o); + return !Pt(r15) && !Array.isArray(r15) && (r15 = [r15]), t8 = t8 || e, r15 = o !== "string" ? Yp(r15, o) : Fs(r15, [], true), T.makeTensor(r15, t8, o); } -function ur(r, t8, e) { - let o = ir(r, e); - return Sr(r, t8, o, e); +function ar(r15, t8, e) { + let o = sr(r15, e); + return wr(r15, t8, o, e); } -var Il = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 }; -var jt = class { +var Cl = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 }; +var ir = class r2 { static join(t8) { - return new jt(t8).slice(); + return new r2(t8).slice(); } constructor(t8) { - if (this.shards = [], this.previousShardIndex = 0, t8 == null || (t8 instanceof Array || (t8 = [t8]), t8 = t8.map((o) => Ot(o) ? o.buffer : o), t8.length === 0)) + if (this.shards = [], this.previousShardIndex = 0, t8 == null || (t8 instanceof Array || (t8 = [t8]), t8 = t8.map((o) => Pt(o) ? o.buffer : o), t8.length === 0)) return; this.bufferUniformSize = t8[0].byteLength; let e = 0; @@ -4891,30 +4892,30 @@ var jt = class { return o === -1 ? -1 : (this.previousShardIndex = o, this.previousShardIndex); } }; -function x4(r, t8) { - let e = 0, o = r.length; +function x4(r15, t8) { + let e = 0, o = r15.length; for (; e <= o; ) { - let n = Math.floor((o - e) / 2) + e, s = t8(r[n]); + let n = Math.floor((o - e) / 2) + e, s = t8(r15[n]); if (s === 0) return n; s < 0 ? o = n : e = n + 1; } return -1; } -var sd = 4; -async function nk(r, t8) { - let e = [], o = [], n = Array.isArray(r) ? r.map((a) => a.name) : Object.keys(r); +var od = 4; +async function sk(r15, t8) { + let e = [], o = [], n = Array.isArray(r15) ? r15.map((a) => a.name) : Object.keys(r15); for (let a = 0; a < n.length; ++a) { - let i = n[a], p = Array.isArray(r) ? r[a].tensor : r[i]; + let i = n[a], p = Array.isArray(r15) ? r15[a].tensor : r15[i]; if (p.dtype !== "float32" && p.dtype !== "int32" && p.dtype !== "bool" && p.dtype !== "string" && p.dtype !== "complex64") throw new Error(`Unsupported dtype in weight '${i}': ${p.dtype}`); let u = { name: i, shape: p.shape, dtype: p.dtype }; if (p.dtype === "string") { let c = new Promise(async (l) => { - let m = await p.bytes(), d = m.reduce((g, x) => g + x.length, 0) + sd * m.length, f = new Uint8Array(d), h = 0; + let m = await p.bytes(), d = m.reduce((g, x) => g + x.length, 0) + od * m.length, f = new Uint8Array(d), h = 0; for (let g = 0; g < m.length; g++) { let x = m[g], b = new Uint8Array(new Uint32Array([x.length]).buffer); - f.set(b, h), h += sd, f.set(x, h), h += x.length; + f.set(b, h), h += od, f.set(x, h), h += x.length; } l(f); }); @@ -4926,8 +4927,8 @@ async function nk(r, t8) { let s = await Promise.all(o); return { data: y4(s), specs: e }; } -function ad(r, t8) { - let e = new jt(r), o = {}, n, s = 0; +function nd(r15, t8) { + let e = new ir(r15), o = {}, n, s = 0; for (let a of t8) { let i = a.name, p = a.dtype, u = a.shape, c = He(u), l; if ("quantization" in a) { @@ -4940,7 +4941,7 @@ function ad(r, t8) { throw new Error(`Weight ${a.name} is quantized with ${m.dtype} which only supports weights of type float32 not ${p}.`); } else throw new Error(`Weight ${a.name} has unknown quantization dtype ${m.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`); - let d = Il[m.dtype], f = e.slice(s, s + c * d), h = m.dtype === "uint8" ? new Uint8Array(f) : new Uint16Array(f); + let d = Cl[m.dtype], f = e.slice(s, s + c * d), h = m.dtype === "uint8" ? new Uint8Array(f) : new Uint16Array(f); if (p === "float32") if (m.dtype === "uint8" || m.dtype === "uint16") { l = new Float32Array(h.length); @@ -4967,13 +4968,13 @@ function ad(r, t8) { let m = He(a.shape); l = []; for (let d = 0; d < m; d++) { - let f = new Uint32Array(e.slice(s, s + sd))[0]; - s += sd; + let f = new Uint32Array(e.slice(s, s + od))[0]; + s += od; let h = new Uint8Array(e.slice(s, s + f)); l.push(h), s += f; } } else { - let m = Il[p], d = e.slice(s, s + c * m); + let m = Cl[p], d = e.slice(s, s + c * m); if (p === "float32") l = new Float32Array(d); else if (p === "int32") @@ -4985,21 +4986,21 @@ function ad(r, t8) { let f = new Float32Array(l.length / 2), h = new Float32Array(l.length / 2); for (let b = 0; b < f.length; b++) f[b] = l[b * 2], h[b] = l[b * 2 + 1]; - let g = ur(f, u, "float32"), x = ur(h, u, "float32"); - o[i] = Er(g, x), g.dispose(), x.dispose(); + let g = ar(f, u, "float32"), x = ar(h, u, "float32"); + o[i] = $r(g, x), g.dispose(), x.dispose(); } else throw new Error(`Unsupported dtype in weight '${i}': ${p}`); s += c * m; } - p !== "complex64" && (o[i] = ur(l, u, p)); + p !== "complex64" && (o[i] = ar(l, u, p)); } return o; } -function y4(r) { - if (r === null) - throw new Error(`Invalid input value: ${JSON.stringify(r)}`); +function y4(r15) { + if (r15 === null) + throw new Error(`Invalid input value: ${JSON.stringify(r15)}`); let t8 = 0, e = []; - r.forEach((s) => { + r15.forEach((s) => { if (t8 += s.byteLength, e.push(s.byteLength === s.buffer.byteLength ? s : new s.constructor(s)), !(s instanceof Float32Array || s instanceof Int32Array || s instanceof Uint8Array)) throw new Error(`Unsupported TypedArray subtype: ${s.constructor.name}`); }); @@ -5009,69 +5010,69 @@ function y4(r) { }), o.buffer; } var Nw = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined"); -function ok(r) { - return Nw ? Buffer.byteLength(r, "utf8") : new Blob([r]).size; +function nk(r15) { + return Nw ? Buffer.byteLength(r15, "utf8") : new Blob([r15]).size; } -function sk(r) { +function ak(r15) { if (Nw) - return Buffer.from(r).toString("base64"); - let t8 = new Uint8Array(r), e = ""; + return Buffer.from(r15).toString("base64"); + let t8 = new Uint8Array(r15), e = ""; for (let o = 0, n = t8.length; o < n; o++) e += String.fromCharCode(t8[o]); return btoa(e); } -function ak(r) { +function ik(r15) { if (Nw) { - let o = Buffer.from(r, "base64"); + let o = Buffer.from(r15, "base64"); return o.buffer.slice(o.byteOffset, o.byteOffset + o.byteLength); } - let t8 = atob(r), e = new Uint8Array(t8.length); + let t8 = atob(r15), e = new Uint8Array(t8.length); for (let o = 0; o < t8.length; ++o) e.set([t8.charCodeAt(o)], o); return e.buffer; } -function ik(r) { - return jt.join(r); +function uk(r15) { + return ir.join(r15); } -function Tw(r) { +function Tw(r15) { let t8 = "/"; - for (r = r.trim(); r.endsWith(t8); ) - r = r.slice(0, r.length - 1); - let e = r.split(t8); + for (r15 = r15.trim(); r15.endsWith(t8); ) + r15 = r15.slice(0, r15.length - 1); + let e = r15.split(t8); return e[e.length - 1]; } -function id(r, t8) { - let e = { modelTopology: r.modelTopology, format: r.format, generatedBy: r.generatedBy, convertedBy: r.convertedBy, weightsManifest: t8 }; - return r.signature != null && (e.signature = r.signature), r.userDefinedMetadata != null && (e.userDefinedMetadata = r.userDefinedMetadata), r.modelInitializer != null && (e.modelInitializer = r.modelInitializer), r.initializerSignature != null && (e.initializerSignature = r.initializerSignature), r.trainingConfig != null && (e.trainingConfig = r.trainingConfig), e; +function sd(r15, t8) { + let e = { modelTopology: r15.modelTopology, format: r15.format, generatedBy: r15.generatedBy, convertedBy: r15.convertedBy, weightsManifest: t8 }; + return r15.signature != null && (e.signature = r15.signature), r15.userDefinedMetadata != null && (e.userDefinedMetadata = r15.userDefinedMetadata), r15.modelInitializer != null && (e.modelInitializer = r15.modelInitializer), r15.initializerSignature != null && (e.initializerSignature = r15.initializerSignature), r15.trainingConfig != null && (e.trainingConfig = r15.trainingConfig), e; } -function _w(r, t8, e) { - let o = { modelTopology: r.modelTopology, format: r.format, generatedBy: r.generatedBy, convertedBy: r.convertedBy }; - if (r.trainingConfig != null && (o.trainingConfig = r.trainingConfig), r.weightsManifest != null) { +function _w(r15, t8, e) { + let o = { modelTopology: r15.modelTopology, format: r15.format, generatedBy: r15.generatedBy, convertedBy: r15.convertedBy }; + if (r15.trainingConfig != null && (o.trainingConfig = r15.trainingConfig), r15.weightsManifest != null) { if (!t8) throw new Error("modelJSON has weightsManifest but weightSpecs is null"); if (!e) throw new Error("modelJSON has weightsManifest but weightData is null"); o.weightSpecs = t8, o.weightData = e; } - return r.signature != null && (o.signature = r.signature), r.userDefinedMetadata != null && (o.userDefinedMetadata = r.userDefinedMetadata), r.modelInitializer != null && (o.modelInitializer = r.modelInitializer), r.initializerSignature != null && (o.initializerSignature = r.initializerSignature), o; + return r15.signature != null && (o.signature = r15.signature), r15.userDefinedMetadata != null && (o.userDefinedMetadata = r15.userDefinedMetadata), r15.modelInitializer != null && (o.modelInitializer = r15.modelInitializer), r15.initializerSignature != null && (o.initializerSignature = r15.initializerSignature), o; } -async function ic(r, t8) { +async function Jp(r15, t8) { let e, o; - return r.weightsManifest != null && ([e, o] = await t8(r.weightsManifest)), _w(r, e, o); + return r15.weightsManifest != null && ([e, o] = await t8(r15.weightsManifest)), _w(r15, e, o); } -function va(r) { - if (r.modelTopology instanceof ArrayBuffer) +function va(r15) { + if (r15.modelTopology instanceof ArrayBuffer) throw new Error("Expected JSON model topology, received ArrayBuffer."); - return { dateSaved: /* @__PURE__ */ new Date(), modelTopologyType: "JSON", modelTopologyBytes: r.modelTopology == null ? 0 : ok(JSON.stringify(r.modelTopology)), weightSpecsBytes: r.weightSpecs == null ? 0 : ok(JSON.stringify(r.weightSpecs)), weightDataBytes: r.weightData == null ? 0 : new jt(r.weightData).byteLength }; + return { dateSaved: /* @__PURE__ */ new Date(), modelTopologyType: "JSON", modelTopologyBytes: r15.modelTopology == null ? 0 : nk(JSON.stringify(r15.modelTopology)), weightSpecsBytes: r15.weightSpecs == null ? 0 : nk(JSON.stringify(r15.weightSpecs)), weightDataBytes: r15.weightData == null ? 0 : new ir(r15.weightData).byteLength }; } -function ud(r) { +function ad(r15) { let t8 = []; - for (let e of r) + for (let e of r15) t8.push(...e.weights); return t8; } function b4() { - let r = (e) => { + let r15 = (e) => { let o = e << 13, n = 0; for (; !(o & 8388608); ) n -= 8388608, o <<= 1; @@ -5079,87 +5080,87 @@ function b4() { }, t8 = new Uint32Array(2048); t8[0] = 0; for (let e = 1; e < 1024; e++) - t8[e] = r(e); + t8[e] = r15(e); for (let e = 1024; e < 2048; e++) t8[e] = 939524096 + (e - 1024 << 13); return t8; } function C4() { - let r = new Uint32Array(64); - r[0] = 0, r[31] = 1199570944, r[32] = 2147483648, r[63] = 3347054592; + let r15 = new Uint32Array(64); + r15[0] = 0, r15[31] = 1199570944, r15[32] = 2147483648, r15[63] = 3347054592; for (let t8 = 1; t8 < 31; t8++) - r[t8] = t8 << 23; + r15[t8] = t8 << 23; for (let t8 = 33; t8 < 63; t8++) - r[t8] = 2147483648 + (t8 - 32 << 23); - return r; + r15[t8] = 2147483648 + (t8 - 32 << 23); + return r15; } function w4() { - let r = new Uint32Array(64); + let r15 = new Uint32Array(64); for (let t8 = 0; t8 < 64; t8++) - r[t8] = 1024; - return r[0] = r[32] = 0, r; + r15[t8] = 1024; + return r15[0] = r15[32] = 0, r15; } function S4() { - let r = b4(), t8 = C4(), e = w4(); + let r15 = b4(), t8 = C4(), e = w4(); return (o) => { let n = new ArrayBuffer(4 * o.length), s = new Uint32Array(n); for (let a = 0; a < o.length; a++) { - let i = o[a], p = r[e[i >> 10] + (i & 1023)] + t8[i >> 10]; + let i = o[a], p = r15[e[i >> 10] + (i & 1023)] + t8[i >> 10]; s[a] = p; } return new Float32Array(n); }; } -var ft = class { +var qt = class r3 { constructor() { this.saveRouters = [], this.loadRouters = []; } static getInstance() { - return ft.instance == null && (ft.instance = new ft()), ft.instance; + return r3.instance == null && (r3.instance = new r3()), r3.instance; } static registerSaveRouter(t8) { - ft.getInstance().saveRouters.push(t8); + r3.getInstance().saveRouters.push(t8); } static registerLoadRouter(t8) { - ft.getInstance().loadRouters.push(t8); + r3.getInstance().loadRouters.push(t8); } static getSaveHandlers(t8) { - return ft.getHandlers(t8, "save"); + return r3.getHandlers(t8, "save"); } static getLoadHandlers(t8, e) { - return ft.getHandlers(t8, "load", e); + return r3.getHandlers(t8, "load", e); } static getHandlers(t8, e, o) { let n = []; - return (e === "load" ? ft.getInstance().loadRouters : ft.getInstance().saveRouters).forEach((a) => { + return (e === "load" ? r3.getInstance().loadRouters : r3.getInstance().saveRouters).forEach((a) => { let i = a(t8, o); i !== null && n.push(i); }), n; } }; -var uk = (r) => ft.registerSaveRouter(r); -var pk = (r) => ft.registerLoadRouter(r); -var ck = (r) => ft.getSaveHandlers(r); -var lk = (r, t8) => ft.getLoadHandlers(r, t8); +var pk = (r15) => qt.registerSaveRouter(r15); +var ck = (r15) => qt.registerLoadRouter(r15); +var lk = (r15) => qt.getSaveHandlers(r15); +var mk = (r15, t8) => qt.getLoadHandlers(r15, t8); var $w = "tensorflowjs"; var Ew = 1; -var Uu = "models_store"; -var nu = "model_info_store"; -function mk() { +var Ou = "models_store"; +var eu = "model_info_store"; +function dk() { if (!A().getBool("IS_BROWSER")) throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser."); - let r = typeof window == "undefined" ? self : window, t8 = r.indexedDB || r.mozIndexedDB || r.webkitIndexedDB || r.msIndexedDB || r.shimIndexedDB; + let r15 = typeof window == "undefined" ? self : window, t8 = r15.indexedDB || r15.mozIndexedDB || r15.webkitIndexedDB || r15.msIndexedDB || r15.shimIndexedDB; if (t8 == null) throw new Error("The current browser does not appear to support IndexedDB."); return t8; } -function Rw(r) { - let t8 = r.result; - t8.createObjectStore(Uu, { keyPath: "modelPath" }), t8.createObjectStore(nu, { keyPath: "modelPath" }); +function Rw(r15) { + let t8 = r15.result; + t8.createObjectStore(Ou, { keyPath: "modelPath" }), t8.createObjectStore(eu, { keyPath: "modelPath" }); } var ka = class { constructor(t8) { - if (this.indexedDB = mk(), t8 == null || !t8) + if (this.indexedDB = dk(), t8 == null || !t8) throw new Error("For IndexedDB, modelPath must not be null, undefined or empty."); this.modelPath = t8; } @@ -5177,15 +5178,15 @@ var ka = class { s.onupgradeneeded = () => Rw(s), s.onsuccess = () => { let a = s.result; if (e == null) { - let i = a.transaction(Uu, "readonly"), u = i.objectStore(Uu).get(this.modelPath); + let i = a.transaction(Ou, "readonly"), u = i.objectStore(Ou).get(this.modelPath); u.onsuccess = () => { if (u.result == null) return a.close(), n(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`)); o(u.result.modelArtifacts); }, u.onerror = (c) => (a.close(), n(u.error)), i.oncomplete = () => a.close(); } else { - e.weightData = jt.join(e.weightData); - let i = va(e), p = a.transaction(nu, "readwrite"), u = p.objectStore(nu), c; + e.weightData = ir.join(e.weightData); + let i = va(e), p = a.transaction(eu, "readwrite"), u = p.objectStore(eu), c; try { c = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }); } catch (m) { @@ -5193,15 +5194,15 @@ var ka = class { } let l; c.onsuccess = () => { - l = a.transaction(Uu, "readwrite"); - let m = l.objectStore(Uu), d; + l = a.transaction(Ou, "readwrite"); + let m = l.objectStore(Ou), d; try { d = m.put({ modelPath: this.modelPath, modelArtifacts: e, modelArtifactsInfo: i }); } catch (f) { return n(f); } d.onsuccess = () => o({ modelArtifactsInfo: i }), d.onerror = (f) => { - u = p.objectStore(nu); + u = p.objectStore(eu); let h = u.delete(this.modelPath); h.onsuccess = () => (a.close(), n(d.error)), h.onerror = (g) => (a.close(), n(d.error)); }; @@ -5214,24 +5215,24 @@ var ka = class { } }; ka.URL_SCHEME = "indexeddb://"; -var dk = (r) => A().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(ka.URL_SCHEME) ? I4(r.slice(ka.URL_SCHEME.length)) : null; -ft.registerSaveRouter(dk); -ft.registerLoadRouter(dk); -function I4(r) { - return new ka(r); +var fk = (r15) => A().getBool("IS_BROWSER") && !Array.isArray(r15) && r15.startsWith(ka.URL_SCHEME) ? I4(r15.slice(ka.URL_SCHEME.length)) : null; +qt.registerSaveRouter(fk); +qt.registerLoadRouter(fk); +function I4(r15) { + return new ka(r15); } -function v4(r) { - return r.startsWith(ka.URL_SCHEME) ? r.slice(ka.URL_SCHEME.length) : r; +function v4(r15) { + return r15.startsWith(ka.URL_SCHEME) ? r15.slice(ka.URL_SCHEME.length) : r15; } -var pd = class { +var id = class { constructor() { - this.indexedDB = mk(); + this.indexedDB = dk(); } async listModels() { return new Promise((t8, e) => { let o = this.indexedDB.open($w, Ew); o.onupgradeneeded = () => Rw(o), o.onsuccess = () => { - let n = o.result, s = n.transaction(nu, "readonly"), i = s.objectStore(nu).getAll(); + let n = o.result, s = n.transaction(eu, "readonly"), i = s.objectStore(eu).getAll(); i.onsuccess = () => { let p = {}; for (let u of i.result) @@ -5245,14 +5246,14 @@ var pd = class { return t8 = v4(t8), new Promise((e, o) => { let n = this.indexedDB.open($w, Ew); n.onupgradeneeded = () => Rw(n), n.onsuccess = () => { - let s = n.result, a = s.transaction(nu, "readwrite"), i = a.objectStore(nu), p = i.get(t8), u; + let s = n.result, a = s.transaction(eu, "readwrite"), i = a.objectStore(eu), p = i.get(t8), u; p.onsuccess = () => { if (p.result == null) return s.close(), o(new Error(`Cannot find model with path '${t8}' in IndexedDB.`)); { let c = i.delete(t8), l = () => { - u = s.transaction(Uu, "readwrite"); - let d = u.objectStore(Uu).delete(t8); + u = s.transaction(Ou, "readwrite"); + let d = u.objectStore(Ou).delete(t8); d.onsuccess = () => e(p.result.modelArtifactsInfo), d.onerror = (f) => o(p.error); }; c.onsuccess = l, c.onerror = (m) => (l(), s.close(), o(p.error)); @@ -5264,28 +5265,28 @@ var pd = class { }); } }; -var ai = "/"; -var uc = "tensorflowjs_models"; -var fk = "info"; +var si = "/"; +var ec = "tensorflowjs_models"; +var hk = "info"; var k4 = "model_topology"; var N4 = "weight_specs"; var T4 = "weight_data"; var _4 = "model_metadata"; -function hk(r) { - return { info: [uc, r, fk].join(ai), topology: [uc, r, k4].join(ai), weightSpecs: [uc, r, N4].join(ai), weightData: [uc, r, T4].join(ai), modelMetadata: [uc, r, _4].join(ai) }; +function gk(r15) { + return { info: [ec, r15, hk].join(si), topology: [ec, r15, k4].join(si), weightSpecs: [ec, r15, N4].join(si), weightData: [ec, r15, T4].join(si), modelMetadata: [ec, r15, _4].join(si) }; } -function gk(r) { - for (let t8 of Object.values(r)) +function xk(r15) { + for (let t8 of Object.values(r15)) window.localStorage.removeItem(t8); } -function $4(r) { - let t8 = r.split(ai); +function $4(r15) { + let t8 = r15.split(si); if (t8.length < 3) - throw new Error(`Invalid key format: ${r}`); - return t8.slice(1, t8.length - 1).join(ai); + throw new Error(`Invalid key format: ${r15}`); + return t8.slice(1, t8.length - 1).join(si); } -function E4(r) { - return r.startsWith(Na.URL_SCHEME) ? r.slice(Na.URL_SCHEME.length) : r; +function E4(r15) { + return r15.startsWith(Na.URL_SCHEME) ? r15.slice(Na.URL_SCHEME.length) : r15; } var Na = class { constructor(t8) { @@ -5293,19 +5294,19 @@ var Na = class { throw new Error("The current environment does not support local storage."); if (this.LS = window.localStorage, t8 == null || !t8) throw new Error("For local storage, modelPath must not be null, undefined or empty."); - this.modelPath = t8, this.keys = hk(this.modelPath); + this.modelPath = t8, this.keys = gk(this.modelPath); } async save(t8) { if (t8.modelTopology instanceof ArrayBuffer) throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet."); { - let e = JSON.stringify(t8.modelTopology), o = JSON.stringify(t8.weightSpecs), n = va(t8), s = jt.join(t8.weightData); + let e = JSON.stringify(t8.modelTopology), o = JSON.stringify(t8.weightSpecs), n = va(t8), s = ir.join(t8.weightData); try { - this.LS.setItem(this.keys.info, JSON.stringify(n)), this.LS.setItem(this.keys.topology, e), this.LS.setItem(this.keys.weightSpecs, o), this.LS.setItem(this.keys.weightData, sk(s)); + this.LS.setItem(this.keys.info, JSON.stringify(n)), this.LS.setItem(this.keys.topology, e), this.LS.setItem(this.keys.weightSpecs, o), this.LS.setItem(this.keys.weightData, ak(s)); let a = { format: t8.format, generatedBy: t8.generatedBy, convertedBy: t8.convertedBy, signature: t8.signature != null ? t8.signature : void 0, userDefinedMetadata: t8.userDefinedMetadata != null ? t8.userDefinedMetadata : void 0, modelInitializer: t8.modelInitializer != null ? t8.modelInitializer : void 0, initializerSignature: t8.initializerSignature != null ? t8.initializerSignature : void 0, trainingConfig: t8.trainingConfig != null ? t8.trainingConfig : void 0 }; return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(a)), { modelArtifactsInfo: n }; } catch (a) { - throw gk(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${n.modelTopologyBytes}, weightSpecsBytes=${n.weightSpecsBytes}, weightDataBytes=${n.weightDataBytes}.`); + throw xk(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${n.modelTopologyBytes}, weightSpecsBytes=${n.weightSpecsBytes}, weightDataBytes=${n.weightDataBytes}.`); } } } @@ -5331,22 +5332,22 @@ var Na = class { let a = this.LS.getItem(this.keys.weightData); if (a == null) throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`); - return e.weightData = ak(a), e; + return e.weightData = ik(a), e; } }; Na.URL_SCHEME = "localstorage://"; -var xk = (r) => A().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Na.URL_SCHEME) ? R4(r.slice(Na.URL_SCHEME.length)) : null; -ft.registerSaveRouter(xk); -ft.registerLoadRouter(xk); -function R4(r) { - return new Na(r); +var yk = (r15) => A().getBool("IS_BROWSER") && !Array.isArray(r15) && r15.startsWith(Na.URL_SCHEME) ? R4(r15.slice(Na.URL_SCHEME.length)) : null; +qt.registerSaveRouter(yk); +qt.registerLoadRouter(yk); +function R4(r15) { + return new Na(r15); } -var cd = class { +var ud = class { constructor() { $(A().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), $(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage; } async listModels() { - let t8 = {}, e = uc + ai, o = ai + fk; + let t8 = {}, e = ec + si, o = si + hk; for (let n = 0; n < this.LS.length; ++n) { let s = this.LS.key(n); if (s.startsWith(e) && s.endsWith(o)) { @@ -5358,72 +5359,72 @@ var cd = class { } async removeModel(t8) { t8 = E4(t8); - let e = hk(t8); + let e = gk(t8); if (this.LS.getItem(e.info) == null) throw new Error(`Cannot find model at path '${t8}'`); let o = JSON.parse(this.LS.getItem(e.info)); - return gk(e), o; + return xk(e), o; } }; -var pc = "://"; -var Zt = class { +var tc = "://"; +var Os = class r5 { constructor() { this.managers = {}; } static getInstance() { - return Zt.instance == null && (Zt.instance = new Zt()), Zt.instance; + return r5.instance == null && (r5.instance = new r5()), r5.instance; } static registerManager(t8, e) { - $(t8 != null, () => "scheme must not be undefined or null."), t8.endsWith(pc) && (t8 = t8.slice(0, t8.indexOf(pc))), $(t8.length > 0, () => "scheme must not be an empty string."); - let o = Zt.getInstance(); + $(t8 != null, () => "scheme must not be undefined or null."), t8.endsWith(tc) && (t8 = t8.slice(0, t8.indexOf(tc))), $(t8.length > 0, () => "scheme must not be an empty string."); + let o = r5.getInstance(); $(o.managers[t8] == null, () => `A model store manager is already registered for scheme '${t8}'.`), o.managers[t8] = e; } static getManager(t8) { - let e = Zt.getInstance().managers[t8]; + let e = r5.getInstance().managers[t8]; if (e == null) throw new Error(`Cannot find model manager for scheme '${t8}'`); return e; } static getSchemes() { - return Object.keys(Zt.getInstance().managers); + return Object.keys(r5.getInstance().managers); } }; -function ld(r) { - if (r.indexOf(pc) === -1) - throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Zt.getSchemes().join(",")}`); - return { scheme: r.split(pc)[0], path: r.split(pc)[1] }; +function pd(r15) { + if (r15.indexOf(tc) === -1) + throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Os.getSchemes().join(",")}`); + return { scheme: r15.split(tc)[0], path: r15.split(tc)[1] }; } -async function yk(r, t8, e = false) { - $(r !== t8, () => `Old path and new path are the same: '${r}'`); - let o = ft.getLoadHandlers(r); - $(o.length > 0, () => `Copying failed because no load handler is found for source URL ${r}.`), $(o.length < 2, () => `Copying failed because more than one (${o.length}) load handlers for source URL ${r}.`); - let n = o[0], s = ft.getSaveHandlers(t8); +async function bk(r15, t8, e = false) { + $(r15 !== t8, () => `Old path and new path are the same: '${r15}'`); + let o = qt.getLoadHandlers(r15); + $(o.length > 0, () => `Copying failed because no load handler is found for source URL ${r15}.`), $(o.length < 2, () => `Copying failed because more than one (${o.length}) load handlers for source URL ${r15}.`); + let n = o[0], s = qt.getSaveHandlers(t8); $(s.length > 0, () => `Copying failed because no save handler is found for destination URL ${t8}.`), $(s.length < 2, () => `Copying failed because more than one (${o.length}) save handlers for destination URL ${t8}.`); - let a = s[0], i = ld(r).scheme, p = ld(r).path, u = i === ld(r).scheme, c = await n.load(); - e && u && await Zt.getManager(i).removeModel(p); + let a = s[0], i = pd(r15).scheme, p = pd(r15).path, u = i === pd(r15).scheme, c = await n.load(); + e && u && await Os.getManager(i).removeModel(p); let l = await a.save(c); - return e && !u && await Zt.getManager(i).removeModel(p), l.modelArtifactsInfo; + return e && !u && await Os.getManager(i).removeModel(p), l.modelArtifactsInfo; } -async function bk() { - let r = Zt.getSchemes(), t8 = {}; - for (let e of r) { - let o = await Zt.getManager(e).listModels(); +async function Ck() { + let r15 = Os.getSchemes(), t8 = {}; + for (let e of r15) { + let o = await Os.getManager(e).listModels(); for (let n in o) { - let s = e + pc + n; + let s = e + tc + n; t8[s] = o[n]; } } return t8; } -async function Ck(r) { - let t8 = ld(r); - return Zt.getManager(t8.scheme).removeModel(t8.path); +async function wk(r15) { + let t8 = pd(r15); + return Os.getManager(t8.scheme).removeModel(t8.path); } -async function wk(r, t8) { - return yk(r, t8, false); +async function Sk(r15, t8) { + return bk(r15, t8, false); } -async function Sk(r, t8) { - return yk(r, t8, true); +async function Ik(r15, t8) { + return bk(r15, t8, true); } var Dw = class { constructor() { @@ -5459,25 +5460,25 @@ var Dw = class { }, true)); } isTypedArray(t8) { - return Qm(t8); + return Xm(t8); } }; if (A().get("IS_BROWSER")) { A().setPlatform("browser", new Dw()); try { - Zt.registerManager(Na.URL_SCHEME, new cd()); - } catch (r) { + Os.registerManager(Na.URL_SCHEME, new ud()); + } catch (r15) { } try { - Zt.registerManager(ka.URL_SCHEME, new pd()); - } catch (r) { + Os.registerManager(ka.URL_SCHEME, new id()); + } catch (r15) { } } -var D4 = { importFetch: () => Ik() }; +var D4 = { importFetch: () => vk() }; var Aw; var Fw = class { constructor() { - this.util = vk(), this.textEncoder = new this.util.TextEncoder(); + this.util = kk(), this.textEncoder = new this.util.TextEncoder(); } fetch(t8, e) { return A().global.fetch != null ? A().global.fetch(t8, e) : (Aw == null && (Aw = D4.importFetch()), Aw(t8, e)); @@ -5499,30 +5500,30 @@ var Fw = class { } }; A().get("IS_NODE") && !A().get("IS_BROWSER") && A().setPlatform("node", new Fw()); -function me(r, t8 = "float32", e) { - return t8 = t8 || "float32", wt(r), new tt(r, t8, e); +function me(r15, t8 = "float32", e) { + return t8 = t8 || "float32", Ct(r15), new tt(r15, t8, e); } -function A4(r, t8) { - let e = v(r, "x", "cast"); - if (!rw(t8)) +function A4(r15, t8) { + let e = v(r15, "x", "cast"); + if (!tw(t8)) throw new Error(`Failed to cast to unknown dtype ${t8}`); if (t8 === "string" && e.dtype !== "string" || t8 !== "string" && e.dtype === "string") throw new Error("Only strings can be casted to strings"); let o = { x: e }, n = { dtype: t8 }; - return T.runKernel(bo, o, n); + return T.runKernel(yo, o, n); } var We = N({ cast_: A4 }); -function F4(r) { - let e = { x: v(r, "x", "clone", "string_or_numeric") }; - return T.runKernel(wo, e); +function F4(r15) { + let e = { x: v(r15, "x", "clone", "string_or_numeric") }; + return T.runKernel(Co, e); } -var Ur = N({ clone_: F4 }); -function md(r, t8 = false) { - console.log(r.toString(t8)); +var Wr = N({ clone_: F4 }); +function cd(r15, t8 = false) { + console.log(r15.toString(t8)); } Sw(); -var P4 = { buffer: me, cast: We, clone: Ur, print: md }; -Q0(P4); +var P4 = { buffer: me, cast: We, clone: Wr, print: cd }; +Z0(P4); function lde() { A().set("PROD", true); } @@ -5532,36 +5533,36 @@ function mde() { function dde() { A().set("DEPRECATION_WARNINGS_ENABLED", false), console.warn("TensorFlow.js deprecation warnings have been disabled."); } -function Pw(r) { - A().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(r + " You can disable deprecation warnings with tf.disableDeprecationWarnings()."); +function Pw(r15) { + A().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(r15 + " You can disable deprecation warnings with tf.disableDeprecationWarnings()."); } -Z0(Pw); +J0(Pw); function fde() { T.disposeVariables(); } -function pr() { +function ur() { return T; } function hde() { return T.memory(); } -function gde(r) { - return T.profile(r); +function gde(r15) { + return T.profile(r15); } -function De(r, t8) { - return T.tidy(r, t8); +function De(r15, t8) { + return T.tidy(r15, t8); } -function Mt(r) { - Sl(r).forEach((e) => e.dispose()); +function Ot(r15) { + yl(r15).forEach((e) => e.dispose()); } -function Rr(r) { - return T.keep(r); +function Er(r15) { + return T.keep(r15); } -function xde(r) { - return T.time(r); +function xde(r15) { + return T.time(r15); } -function yde(r) { - return T.setBackend(r); +function yde(r15) { + return T.setBackend(r15); } function bde() { return T.ready(); @@ -5569,411 +5570,411 @@ function bde() { function Cde() { return T.backendName; } -function wde(r) { - T.removeBackend(r); +function wde(r15) { + T.removeBackend(r15); } -function Sde(r) { - return T.findBackend(r); +function Sde(r15) { + return T.findBackend(r15); } -function Ide(r) { - return T.findBackendFactory(r); +function Ide(r15) { + return T.findBackendFactory(r15); } -function su(r, t8, e = 1) { - return T.registerBackend(r, t8, e); +function tu(r15, t8, e = 1) { + return T.registerBackend(r15, t8, e); } function vde() { return T.backend; } -function kde(r, t8) { - A().setPlatform(r, t8); +function kde(r15, t8) { + A().setPlatform(r15, t8); } -function O4(r, t8) { - let e = v(r, "a", "add"), o = v(t8, "b", "add"); +function O4(r15, t8) { + let e = v(r15, "a", "add"), o = v(t8, "b", "add"); [e, o] = Oe(e, o); let n = { a: e, b: o }; - return T.runKernel(uo, n); + return T.runKernel(io, n); } var Ce = N({ add_: O4 }); -function M4(r, t8) { - let e = v(r, "a", "floorDiv"), o = v(t8, "b", "floorDiv"); +function M4(r15, t8) { + let e = v(r15, "a", "floorDiv"), o = v(t8, "b", "floorDiv"); [e, o] = Oe(e, o); let n = { a: e, b: o }; - return T.runKernel(In, n); + return T.runKernel(Sn, n); } -var dd = N({ floorDiv_: M4 }); -function L4(r, t8) { - let e = v(r, "a", "div"), o = v(t8, "b", "div"); +var ld = N({ floorDiv_: M4 }); +function L4(r15, t8) { + let e = v(r15, "a", "div"), o = v(t8, "b", "div"); if ([e, o] = Oe(e, o), e.dtype === "int32" && o.dtype === "int32") - return dd(e, o); + return ld(e, o); let n = { a: e, b: o }, s = {}; - return T.runKernel(hn, n, s); + return T.runKernel(fn, n, s); } var je = N({ div_: L4 }); -function B4(r, t8) { - let e = v(r, "a", "mul"), o = v(t8, "b", "mul"); +function B4(r15, t8) { + let e = v(r15, "a", "mul"), o = v(t8, "b", "mul"); [e, o] = Oe(e, o); let n = { a: e, b: o }; - return T.runKernel(Yn, n); + return T.runKernel(Xn, n); } var se = N({ mul_: B4 }); -function z4(r) { - let t8 = v(r, "x", "abs"); +function z4(r15) { + let t8 = v(r15, "x", "abs"); if (t8.dtype === "complex64") { let e = { x: t8 }; - return T.runKernel(Pi, e); + return T.runKernel(Di, e); } else { let e = { x: t8 }; return T.runKernel(Xs, e); } } -var Jt = N({ abs_: z4 }); -function V4(r) { - let e = { x: v(r, "x", "acos") }; +var Qt = N({ abs_: z4 }); +function V4(r15) { + let e = { x: v(r15, "x", "acos") }; + return T.runKernel(Vo, e); +} +var Nk = N({ acos_: V4 }); +function W4(r15) { + let e = { x: v(r15, "x", "acosh") }; return T.runKernel(Wo, e); } -var kk = N({ acos_: V4 }); -function W4(r) { - let e = { x: v(r, "x", "acosh") }; - return T.runKernel(Uo, e); -} -var Nk = N({ acosh_: W4 }); -function U4(r) { - $(Array.isArray(r), () => "The argument passed to tf.addN() must be a list of tensors"), $(r.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${r.length}`); - let t8 = r.map((n, s) => v(n, `tensors${s}`, "addN")), e = t8[0]; +var Tk = N({ acosh_: W4 }); +function U4(r15) { + $(Array.isArray(r15), () => "The argument passed to tf.addN() must be a list of tensors"), $(r15.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${r15.length}`); + let t8 = r15.map((n, s) => v(n, `tensors${s}`, "addN")), e = t8[0]; t8.forEach((n) => { if (n.dtype !== e.dtype) throw new Error("All tensors passed to tf.addN() must have the same dtype"); }), t8.forEach((n) => { - if (!Cr(n.shape, e.shape)) + if (!br(n.shape, e.shape)) throw new Error("All tensors passed to tf.addN() must have the same shape"); }); let o = t8; - return T.runKernel(Go, o); + return T.runKernel(Uo, o); } -var Tk = N({ addN_: U4 }); -function G4(r, t8 = null, e = false) { - let n = { x: v(r, "x", "all", "bool") }, s = { axis: t8, keepDims: e }; +var _k = N({ addN_: U4 }); +function G4(r15, t8 = null, e = false) { + let n = { x: v(r15, "x", "all", "bool") }, s = { axis: t8, keepDims: e }; + return T.runKernel(Go, n, s); +} +var $k = N({ all_: G4 }); +function H4(r15, t8 = null, e = false) { + let n = { x: v(r15, "x", "any", "bool") }, s = { axis: t8, keepDims: e }; return T.runKernel(Ho, n, s); } -var _k = N({ all_: G4 }); -function H4(r, t8 = null, e = false) { - let n = { x: v(r, "x", "any", "bool") }, s = { axis: t8, keepDims: e }; - return T.runKernel(Ko, n, s); -} -var $k = N({ any_: H4 }); -function K4(r, t8 = 0) { - let o = { x: v(r, "x", "argMax") }, n = { axis: t8 }; +var Ek = N({ any_: H4 }); +function K4(r15, t8 = 0) { + let o = { x: v(r15, "x", "argMax") }, n = { axis: t8 }; return T.runKernel(Ys, o, n); } -var Ek = N({ argMax_: K4 }); -function q4(r, t8 = 0) { - let o = { x: v(r, "x", "argMin") }, n = { axis: t8 }; +var Rk = N({ argMax_: K4 }); +function q4(r15, t8 = 0) { + let o = { x: v(r15, "x", "argMin") }, n = { axis: t8 }; return T.runKernel(Qs, o, n); } -var Rk = N({ argMin_: q4 }); -function j4(r) { - let e = { x: v(r, "x", "asin") }; +var Dk = N({ argMin_: q4 }); +function j4(r15) { + let e = { x: v(r15, "x", "asin") }; + return T.runKernel(Ko, e); +} +var Ak = N({ asin_: j4 }); +function X4(r15) { + let e = { x: v(r15, "x", "asinh") }; return T.runKernel(qo, e); } -var Dk = N({ asin_: j4 }); -function X4(r) { - let e = { x: v(r, "x", "asinh") }; +var Fk = N({ asinh_: X4 }); +function Y4(r15) { + let e = { x: v(r15, "x", "atan") }; return T.runKernel(jo, e); } -var Ak = N({ asinh_: X4 }); -function Y4(r) { - let e = { x: v(r, "x", "atan") }; - return T.runKernel(Xo, e); -} -var Fk = N({ atan_: Y4 }); -function Q4(r, t8) { - let e = v(r, "a", "atan2"), o = v(t8, "b", "atan2"); +var Pk = N({ atan_: Y4 }); +function Q4(r15, t8) { + let e = v(r15, "a", "atan2"), o = v(t8, "b", "atan2"); [e, o] = Oe(e, o); let n = { a: e, b: o }; - return T.runKernel(Qo, n); + return T.runKernel(Yo, n); } -var Pk = N({ atan2_: Q4 }); -function Z4(r) { - let e = { x: v(r, "x", "atanh") }; - return T.runKernel(Yo, e); +var Ok = N({ atan2_: Q4 }); +function Z4(r15) { + let e = { x: v(r15, "x", "atanh") }; + return T.runKernel(Xo, e); } -var Ok = N({ atanh_: Z4 }); -function J4(r, t8, e, o, n = "NHWC", s) { - let a = r[3], i = [...t8, a], p = Lk(n); - return Hu(r, i, e, s, o, null, null, p); +var Mk = N({ atanh_: Z4 }); +function J4(r15, t8, e, o, n = "NHWC", s) { + let a = r15[3], i = [...t8, a], p = Bk(n); + return Lu(r15, i, e, s, o, null, null, p); } -function Mw(r, t8, e, o, n, s, a = "channelsLast") { - let [i, p] = vl(t8), u; +function Mw(r15, t8, e, o, n, s, a = "channelsLast") { + let [i, p] = wl(t8), u; if (a === "channelsLast") - u = [i, p, r[3], r[3]]; + u = [i, p, r15[3], r15[3]]; else if (a === "channelsFirst") - u = [i, p, r[1], r[1]]; + u = [i, p, r15[1], r15[1]]; else throw new Error(`Unknown dataFormat ${a}`); - return Hu(r, u, e, o, n, s, false, a); + return Lu(r15, u, e, o, n, s, false, a); } -function eH(r, t8, e, o, n, s, a = "NDHWC") { +function eH(r15, t8, e, o, n, s, a = "NDHWC") { let [i, p, u] = Ow(t8), c, l; if (a === "NDHWC") - l = "channelsLast", c = [i, p, u, r[4], r[4]]; + l = "channelsLast", c = [i, p, u, r15[4], r15[4]]; else if (a === "NCDHW") - l = "channelsFirst", c = [i, p, u, r[1], r[1]]; + l = "channelsFirst", c = [i, p, u, r15[1], r15[1]]; else throw new Error(`Unknown dataFormat ${a}`); - return Mk(r, c, e, o, n, false, l, s); + return Lk(r15, c, e, o, n, false, l, s); } -function Hu(r, t8, e, o, n, s, a = false, i = "channelsLast") { +function Lu(r15, t8, e, o, n, s, a = false, i = "channelsLast") { let [p, u, c, l] = [-1, -1, -1, -1]; if (i === "channelsLast") - [p, u, c, l] = r; + [p, u, c, l] = r15; else if (i === "channelsFirst") - [p, l, u, c] = r; + [p, l, u, c] = r15; else throw new Error(`Unknown dataFormat ${i}`); - let [m, d, , f] = t8, [h, g] = vl(e), [x, b] = vl(o), C = cc(m, x), S = cc(d, b), { padInfo: k, outHeight: _, outWidth: E } = oH(n, u, c, h, g, C, S, s, i), R = a ? f * l : f, D; - return i === "channelsFirst" ? D = [p, R, _, E] : i === "channelsLast" && (D = [p, _, E, R]), { batchSize: p, dataFormat: i, inHeight: u, inWidth: c, inChannels: l, outHeight: _, outWidth: E, outChannels: R, padInfo: k, strideHeight: h, strideWidth: g, filterHeight: m, filterWidth: d, effectiveFilterHeight: C, effectiveFilterWidth: S, dilationHeight: x, dilationWidth: b, inShape: r, outShape: D, filterShape: t8 }; + let [m, d, , f] = t8, [h, g] = wl(e), [x, b] = wl(o), C = rc(m, x), S = rc(d, b), { padInfo: k, outHeight: _, outWidth: E } = oH(n, u, c, h, g, C, S, s, i), R = a ? f * l : f, D; + return i === "channelsFirst" ? D = [p, R, _, E] : i === "channelsLast" && (D = [p, _, E, R]), { batchSize: p, dataFormat: i, inHeight: u, inWidth: c, inChannels: l, outHeight: _, outWidth: E, outChannels: R, padInfo: k, strideHeight: h, strideWidth: g, filterHeight: m, filterWidth: d, effectiveFilterHeight: C, effectiveFilterWidth: S, dilationHeight: x, dilationWidth: b, inShape: r15, outShape: D, filterShape: t8 }; } -function Mk(r, t8, e, o, n, s = false, a = "channelsLast", i) { +function Lk(r15, t8, e, o, n, s = false, a = "channelsLast", i) { let [p, u, c, l, m] = [-1, -1, -1, -1, -1]; if (a === "channelsLast") - [p, u, c, l, m] = r; + [p, u, c, l, m] = r15; else if (a === "channelsFirst") - [p, m, u, c, l] = r; + [p, m, u, c, l] = r15; else throw new Error(`Unknown dataFormat ${a}`); - let [d, f, h, , g] = t8, [x, b, C] = Ow(e), [S, k, _] = Ow(o), E = cc(d, S), R = cc(f, k), D = cc(h, _), { padInfo: P, outDepth: O, outHeight: M, outWidth: L } = nH(n, u, c, l, x, b, C, E, R, D, i), B = s ? g * m : g, z; - return a === "channelsFirst" ? z = [p, B, O, M, L] : a === "channelsLast" && (z = [p, O, M, L, B]), { batchSize: p, dataFormat: a, inDepth: u, inHeight: c, inWidth: l, inChannels: m, outDepth: O, outHeight: M, outWidth: L, outChannels: B, padInfo: P, strideDepth: x, strideHeight: b, strideWidth: C, filterDepth: d, filterHeight: f, filterWidth: h, effectiveFilterDepth: E, effectiveFilterHeight: R, effectiveFilterWidth: D, dilationDepth: S, dilationHeight: k, dilationWidth: _, inShape: r, outShape: z, filterShape: t8 }; + let [d, f, h, , g] = t8, [x, b, C] = Ow(e), [S, k, _] = Ow(o), E = rc(d, S), R = rc(f, k), D = rc(h, _), { padInfo: P, outDepth: O, outHeight: M, outWidth: L } = nH(n, u, c, l, x, b, C, E, R, D, i), B = s ? g * m : g, z; + return a === "channelsFirst" ? z = [p, B, O, M, L] : a === "channelsLast" && (z = [p, O, M, L, B]), { batchSize: p, dataFormat: a, inDepth: u, inHeight: c, inWidth: l, inChannels: m, outDepth: O, outHeight: M, outWidth: L, outChannels: B, padInfo: P, strideDepth: x, strideHeight: b, strideWidth: C, filterDepth: d, filterHeight: f, filterWidth: h, effectiveFilterDepth: E, effectiveFilterHeight: R, effectiveFilterWidth: D, dilationDepth: S, dilationHeight: k, dilationWidth: _, inShape: r15, outShape: z, filterShape: t8 }; } -function tH(r, t8, e, o, n) { - o == null && (o = Lw(r, t8, e)); - let s = r[0], a = r[1], i = kl((s - t8 + 2 * o) / e + 1, n), p = kl((a - t8 + 2 * o) / e + 1, n); +function tH(r15, t8, e, o, n) { + o == null && (o = Lw(r15, t8, e)); + let s = r15[0], a = r15[1], i = Sl((s - t8 + 2 * o) / e + 1, n), p = Sl((a - t8 + 2 * o) / e + 1, n); return [i, p]; } -function rH(r, t8, e, o, n, s) { - n == null && (n = Lw(r, t8[0], o[0])); +function rH(r15, t8, e, o, n, s) { + n == null && (n = Lw(r15, t8[0], o[0])); let a = [0, 0, 0, e]; for (let i = 0; i < 3; i++) - r[i] + 2 * n >= t8[i] && (a[i] = kl((r[i] - t8[i] + 2 * n) / o[i] + 1, s)); + r15[i] + 2 * n >= t8[i] && (a[i] = Sl((r15[i] - t8[i] + 2 * n) / o[i] + 1, s)); return a; } -function Lw(r, t8, e, o = 1) { - let n = cc(t8, o); - return Math.floor((r[0] * (e - 1) - e + n) / 2); +function Lw(r15, t8, e, o = 1) { + let n = rc(t8, o); + return Math.floor((r15[0] * (e - 1) - e + n) / 2); } -function vl(r) { - return typeof r == "number" ? [r, r, r] : r.length === 2 ? [r[0], r[1], 1] : r; +function wl(r15) { + return typeof r15 == "number" ? [r15, r15, r15] : r15.length === 2 ? [r15[0], r15[1], 1] : r15; } -function Ow(r) { - return typeof r == "number" ? [r, r, r] : r; +function Ow(r15) { + return typeof r15 == "number" ? [r15, r15, r15] : r15; } -function cc(r, t8) { - return t8 <= 1 ? r : r + (r - 1) * (t8 - 1); +function rc(r15, t8) { + return t8 <= 1 ? r15 : r15 + (r15 - 1) * (t8 - 1); } -function oH(r, t8, e, o, n, s, a, i, p) { +function oH(r15, t8, e, o, n, s, a, i, p) { let u, c, l; - if (typeof r == "number") { - u = { top: r, bottom: r, left: r, right: r, type: r === 0 ? "VALID" : "NUMBER" }; - let d = tH([t8, e], s, o, r, i); + if (typeof r15 == "number") { + u = { top: r15, bottom: r15, left: r15, right: r15, type: r15 === 0 ? "VALID" : "NUMBER" }; + let d = tH([t8, e], s, o, r15, i); c = d[0], l = d[1]; - } else if (r === "same") { + } else if (r15 === "same") { c = Math.ceil(t8 / o), l = Math.ceil(e / n); let m = Math.max(0, (c - 1) * o + s - t8), d = Math.max(0, (l - 1) * n + a - e), f = Math.floor(m / 2), h = m - f, g = Math.floor(d / 2), x = d - g; u = { top: f, bottom: h, left: g, right: x, type: "SAME" }; - } else if (r === "valid") + } else if (r15 === "valid") u = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" }, c = Math.ceil((t8 - s + 1) / o), l = Math.ceil((e - a + 1) / n); - else if (typeof r == "object") { - let m = p === "channelsLast" ? r[1][0] : r[2][0], d = p === "channelsLast" ? r[1][1] : r[2][1], f = p === "channelsLast" ? r[2][0] : r[3][0], h = p === "channelsLast" ? r[2][1] : r[3][1]; - u = { top: m, bottom: d, left: f, right: h, type: m === 0 && d === 0 && f === 0 && h === 0 ? "VALID" : "EXPLICIT" }, c = kl((t8 - s + m + d) / o + 1, i), l = kl((e - a + f + h) / n + 1, i); + else if (typeof r15 == "object") { + let m = p === "channelsLast" ? r15[1][0] : r15[2][0], d = p === "channelsLast" ? r15[1][1] : r15[2][1], f = p === "channelsLast" ? r15[2][0] : r15[3][0], h = p === "channelsLast" ? r15[2][1] : r15[3][1]; + u = { top: m, bottom: d, left: f, right: h, type: m === 0 && d === 0 && f === 0 && h === 0 ? "VALID" : "EXPLICIT" }, c = Sl((t8 - s + m + d) / o + 1, i), l = Sl((e - a + f + h) / n + 1, i); } else - throw Error(`Unknown padding parameter: ${r}`); + throw Error(`Unknown padding parameter: ${r15}`); return { padInfo: u, outHeight: c, outWidth: l }; } -function nH(r, t8, e, o, n, s, a, i, p, u, c) { +function nH(r15, t8, e, o, n, s, a, i, p, u, c) { let l, m, d, f; - if (r === "valid" && (r = 0), typeof r == "number") { - l = { top: r, bottom: r, left: r, right: r, front: r, back: r, type: r === 0 ? "VALID" : "NUMBER" }; - let g = rH([t8, e, o, 1], [i, p, u], 1, [n, s, a], r, c); + if (r15 === "valid" && (r15 = 0), typeof r15 == "number") { + l = { top: r15, bottom: r15, left: r15, right: r15, front: r15, back: r15, type: r15 === 0 ? "VALID" : "NUMBER" }; + let g = rH([t8, e, o, 1], [i, p, u], 1, [n, s, a], r15, c); m = g[0], d = g[1], f = g[2]; - } else if (r === "same") { + } else if (r15 === "same") { m = Math.ceil(t8 / n), d = Math.ceil(e / s), f = Math.ceil(o / a); let h = (m - 1) * n + i - t8, g = (d - 1) * s + p - e, x = (f - 1) * a + u - o, b = Math.floor(h / 2), C = h - b, S = Math.floor(g / 2), k = g - S, _ = Math.floor(x / 2), E = x - _; l = { top: S, bottom: k, left: _, right: E, front: b, back: C, type: "SAME" }; } else - throw Error(`Unknown padding parameter: ${r}`); + throw Error(`Unknown padding parameter: ${r15}`); return { padInfo: l, outDepth: m, outHeight: d, outWidth: f }; } -function kl(r, t8) { +function Sl(r15, t8) { if (!t8) - return Math.trunc(r); + return Math.trunc(r15); switch (t8) { case "round": - return Math.round(r); + return Math.round(r15); case "ceil": - return Math.ceil(r); + return Math.ceil(r15); case "floor": - return Math.floor(r); + return Math.floor(r15); default: throw new Error(`Unknown roundingMode ${t8}`); } } -function Gu(r) { - let [t8, e, o] = vl(r); +function Mu(r15) { + let [t8, e, o] = wl(r15); return t8 === 1 && e === 1 && o === 1; } -function xr(r, t8) { - return Gu(r) || Gu(t8); +function gr(r15, t8) { + return Mu(r15) || Mu(t8); } -function Ta(r) { - return vl(r).every((t8) => t8 > 0); +function Ta(r15) { + return wl(r15).every((t8) => t8 > 0); } -function Lk(r) { - if (r === "NHWC") +function Bk(r15) { + if (r15 === "NHWC") return "channelsLast"; - if (r === "NCHW") + if (r15 === "NCHW") return "channelsFirst"; - throw new Error(`Unknown dataFormat ${r}`); + throw new Error(`Unknown dataFormat ${r15}`); } -function Bt(r, t8, e) { +function Lt(r15, t8, e) { if (e != null) { if (typeof t8 == "string") - throw Error(`Error in ${r}: pad must be an integer when using dimRoundingMode ${e} but got pad ${t8}.`); + throw Error(`Error in ${r15}: pad must be an integer when using dimRoundingMode ${e} but got pad ${t8}.`); if (typeof t8 == "number") - $(qa(t8), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${e} but got pad ${t8}.`); + $(Ka(t8), () => `Error in ${r15}: pad must be an integer when using dimRoundingMode ${e} but got pad ${t8}.`); else if (typeof t8 == "object") t8.forEach((o) => { o.forEach((n) => { - $(qa(n), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${e} but got pad ${n}.`); + $(Ka(n), () => `Error in ${r15}: pad must be an integer when using dimRoundingMode ${e} but got pad ${n}.`); }); }); else - throw Error(`Error in ${r}: Unknown padding parameter: ${t8}`); + throw Error(`Error in ${r15}: Unknown padding parameter: ${t8}`); } } -function sH(r, t8) { - let o = { x: v(r, "x", "reshape", "string_or_numeric") }, n = { shape: t8 }; +function sH(r15, t8) { + let o = { x: v(r15, "x", "reshape", "string_or_numeric") }, n = { shape: t8 }; return T.runKernel(da, o, n); } var W = N({ reshape_: sH }); -function aH(r, t8, e, o, n) { - let s = v(r, "x", "avgPool", "float32"), a = 1; - $(xr(e, a), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${e} and dilations '${a}'`); +function aH(r15, t8, e, o, n) { + let s = v(r15, "x", "avgPool", "float32"), a = 1; + $(gr(e, a), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${e} and dilations '${a}'`); let i = s, p = false; - s.rank === 3 && (p = true, i = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])), $(i.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${i.rank}.`), Bt("avgPool", o, n); - let u = { x: i }, c = { filterSize: t8, strides: e, pad: o, dimRoundingMode: n }, l = T.runKernel(Zo, u, c); + s.rank === 3 && (p = true, i = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])), $(i.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${i.rank}.`), Lt("avgPool", o, n); + let u = { x: i }, c = { filterSize: t8, strides: e, pad: o, dimRoundingMode: n }, l = T.runKernel(Qo, u, c); return l = We(l, s.dtype), p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l; } -var fd = N({ avgPool_: aH }); -function iH(r, t8, e, o, n, s = "NDHWC") { - let a = v(r, "x", "avgPool3d", "float32"), i = a, p = false; - a.rank === 4 && (p = true, i = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(i.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`), $(s === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), $(typeof e == "number" && e > 0 || Array.isArray(e) && e[0] > 0 && e[1] > 0 && e[2] > 0, () => `Error in avgPool3d: Stride must be > 0, but got '${e}'`), Bt("avgPool3d", o, n); +var md = N({ avgPool_: aH }); +function iH(r15, t8, e, o, n, s = "NDHWC") { + let a = v(r15, "x", "avgPool3d", "float32"), i = a, p = false; + a.rank === 4 && (p = true, i = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(i.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`), $(s === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), $(typeof e == "number" && e > 0 || Array.isArray(e) && e[0] > 0 && e[1] > 0 && e[2] > 0, () => `Error in avgPool3d: Stride must be > 0, but got '${e}'`), Lt("avgPool3d", o, n); let u = { x: i }, c = { filterSize: t8, strides: e, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(Zs, u, c); return l = We(l, i.dtype), p ? W(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l; } -var Bk = N({ avgPool3d_: iH }); -function uH(r, t8 = 0) { - $(r.length >= 1, () => "Pass at least one tensor to concat"); - let e = si(r, "tensors", "concat", "string_or_numeric"); +var zk = N({ avgPool3d_: iH }); +function uH(r15, t8 = 0) { + $(r15.length >= 1, () => "Pass at least one tensor to concat"); + let e = ni(r15, "tensors", "concat", "string_or_numeric"); if (e[0].dtype === "complex64" && e.forEach((s) => { if (s.dtype !== "complex64") throw new Error(`Cannot concatenate complex64 tensors with a tensor with dtype ${s.dtype}. `); }), e.length === 1) - return Ur(e[0]); + return Wr(e[0]); let o = e, n = { axis: t8 }; return T.runKernel(ta, o, n); } -var bt = N({ concat_: uH }); -function pH(r, t8, e = false, o = false) { - let n = v(r, "a", "matMul"), s = v(t8, "b", "matMul"); +var yt = N({ concat_: uH }); +function pH(r15, t8, e = false, o = false) { + let n = v(r15, "a", "matMul"), s = v(t8, "b", "matMul"); [n, s] = Oe(n, s); let a = { a: n, b: s }, i = { transposeA: e, transposeB: o }; - return T.runKernel(Jo, a, i); + return T.runKernel(Zo, a, i); } var Ze = N({ matMul_: pH }); -function cH(r) { - let e = { x: v(r, "x", "sigmoid", "float32") }; - return T.runKernel(Cs, e); +function cH(r15) { + let e = { x: v(r15, "x", "sigmoid", "float32") }; + return T.runKernel(bs, e); } var $a = N({ sigmoid_: cH }); -function lH(r, t8, e) { - let o = v(r, "x", "slice", "string_or_numeric"); +function lH(r15, t8, e) { + let o = v(r15, "x", "slice", "string_or_numeric"); if (o.rank === 0) throw new Error("Slicing scalar is not possible"); let n = { x: o }, s = { begin: t8, size: e }; return T.runKernel(ha, n, s); } var Xe = N({ slice_: lH }); -function mH(r) { - let e = { x: v(r, "x", "tanh", "float32") }; - return T.runKernel(Es, e); +function mH(r15) { + let e = { x: v(r15, "x", "tanh", "float32") }; + return T.runKernel($s, e); } -var Nl = N({ tanh_: mH }); -function dH(r, t8, e, o, n, s) { - let a = v(r, "forgetBias", "basicLSTMCell"), i = v(t8, "lstmKernel", "basicLSTMCell"), p = v(e, "lstmBias", "basicLSTMCell"), u = v(o, "data", "basicLSTMCell"), c = v(n, "c", "basicLSTMCell"), l = v(s, "h", "basicLSTMCell"), m = bt([u, l], 1), d = Ze(m, i), f = Ce(d, p), h = f.shape[0], g = f.shape[1] / 4, x = [h, g], b = Xe(f, [0, 0], x), C = Xe(f, [0, g], x), S = Xe(f, [0, g * 2], x), k = Xe(f, [0, g * 3], x), _ = Ce(se($a(b), Nl(C)), se(c, $a(Ce(a, S)))), E = se(Nl(_), $a(k)); +var Il = N({ tanh_: mH }); +function dH(r15, t8, e, o, n, s) { + let a = v(r15, "forgetBias", "basicLSTMCell"), i = v(t8, "lstmKernel", "basicLSTMCell"), p = v(e, "lstmBias", "basicLSTMCell"), u = v(o, "data", "basicLSTMCell"), c = v(n, "c", "basicLSTMCell"), l = v(s, "h", "basicLSTMCell"), m = yt([u, l], 1), d = Ze(m, i), f = Ce(d, p), h = f.shape[0], g = f.shape[1] / 4, x = [h, g], b = Xe(f, [0, 0], x), C = Xe(f, [0, g], x), S = Xe(f, [0, g * 2], x), k = Xe(f, [0, g * 3], x), _ = Ce(se($a(b), Il(C)), se(c, $a(Ce(a, S)))), E = se(Il(_), $a(k)); return [_, E]; } -var zk = N({ basicLSTMCell_: dH }); -function fH(r, t8, e) { - let o = v(r, "x", "batchToSpaceND"), n = t8.reduce((i, p) => i * p); +var Vk = N({ basicLSTMCell_: dH }); +function fH(r15, t8, e) { + let o = v(r15, "x", "batchToSpaceND"), n = t8.reduce((i, p) => i * p); $(o.rank >= 1 + t8.length, () => `input rank is ${o.rank} but should be > than blockShape.length ${t8.length}`), $(e.length === t8.length, () => `crops.length is ${e.length} but should be equal to blockShape.length ${t8.length}`), $(o.shape[0] % n === 0, () => `input tensor batch is ${o.shape[0]} but is not divisible by the product of the elements of blockShape ${t8.join(" * ")} === ${n}`); let s = { x: o }, a = { blockShape: t8, crops: e }; return T.runKernel(Js, s, a); } -var hd = N({ batchToSpaceND_: fH }); -function Vk(r) { +var dd = N({ batchToSpaceND_: fH }); +function Wk(r15) { let t8; - return r.rank === 0 || r.rank === 1 ? t8 = W(r, [1, 1, 1, r.size]) : r.rank === 2 ? t8 = W(r, [1, 1, r.shape[0], r.shape[1]]) : r.rank === 3 ? t8 = W(r, [1, r.shape[0], r.shape[1], r.shape[2]]) : t8 = r, t8; + return r15.rank === 0 || r15.rank === 1 ? t8 = W(r15, [1, 1, 1, r15.size]) : r15.rank === 2 ? t8 = W(r15, [1, 1, r15.shape[0], r15.shape[1]]) : r15.rank === 3 ? t8 = W(r15, [1, r15.shape[0], r15.shape[1], r15.shape[2]]) : t8 = r15, t8; } -function hH(r, t8, e, o, n, s) { +function hH(r15, t8, e, o, n, s) { s == null && (s = 1e-3); - let a = v(r, "x", "batchNorm"), i = v(t8, "mean", "batchNorm"), p = v(e, "variance", "batchNorm"), u; + let a = v(r15, "x", "batchNorm"), i = v(t8, "mean", "batchNorm"), p = v(e, "variance", "batchNorm"), u; n != null && (u = v(n, "scale", "batchNorm")); let c; o != null && (c = v(o, "offset", "batchNorm")), $(i.rank === p.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."), $(c == null || i.rank === c.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."), $(u == null || i.rank === u.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks."); - let m = { x: Vk(a), scale: u, offset: c, mean: i, variance: p }, d = { varianceEpsilon: s }, f = T.runKernel(vn, m, d); + let m = { x: Wk(a), scale: u, offset: c, mean: i, variance: p }, d = { varianceEpsilon: s }, f = T.runKernel(In, m, d); return W(f, a.shape); } -var au = N({ batchNorm_: hH }); -function gH(r, t8, e, o, n, s) { - let a = v(r, "x", "batchNorm"), i = v(t8, "mean", "batchNorm"), p = v(e, "variance", "batchNorm"), u; +var ru = N({ batchNorm_: hH }); +function gH(r15, t8, e, o, n, s) { + let a = v(r15, "x", "batchNorm"), i = v(t8, "mean", "batchNorm"), p = v(e, "variance", "batchNorm"), u; n != null && (u = v(n, "scale", "batchNorm")); let c; - return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`), $(i.rank === 2 || i.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`), $(p.rank === 2 || p.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), au(a, i, p, c, u, s); + return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`), $(i.rank === 2 || i.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`), $(p.rank === 2 || p.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), ru(a, i, p, c, u, s); } -var Wk = N({ batchNorm2d_: gH }); -function xH(r, t8, e, o, n, s) { - let a = v(r, "x", "batchNorm"), i = v(t8, "mean", "batchNorm"), p = v(e, "variance", "batchNorm"), u; +var Uk = N({ batchNorm2d_: gH }); +function xH(r15, t8, e, o, n, s) { + let a = v(r15, "x", "batchNorm"), i = v(t8, "mean", "batchNorm"), p = v(e, "variance", "batchNorm"), u; n != null && (u = v(n, "scale", "batchNorm")); let c; - return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`), $(i.rank === 3 || i.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`), $(p.rank === 3 || p.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), au(a, i, p, c, u, s); + return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`), $(i.rank === 3 || i.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`), $(p.rank === 3 || p.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), ru(a, i, p, c, u, s); } -var Uk = N({ batchNorm3d_: xH }); -function yH(r, t8, e, o, n, s) { - let a = v(r, "x", "batchNorm"), i = v(t8, "mean", "batchNorm"), p = v(e, "variance", "batchNorm"), u; +var Gk = N({ batchNorm3d_: xH }); +function yH(r15, t8, e, o, n, s) { + let a = v(r15, "x", "batchNorm"), i = v(t8, "mean", "batchNorm"), p = v(e, "variance", "batchNorm"), u; n != null && (u = v(n, "scale", "batchNorm")); let c; - return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`), $(i.rank === 4 || i.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`), $(p.rank === 4 || p.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), au(a, i, p, c, u, s); + return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`), $(i.rank === 4 || i.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`), $(p.rank === 4 || p.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), ru(a, i, p, c, u, s); } -var Gk = N({ batchNorm4d_: yH }); -function bH(r, t8, e) { - let o = v(r, "x", "bincount"), n = v(t8, "weights", "bincount"); +var Hk = N({ batchNorm4d_: yH }); +function bH(r15, t8, e) { + let o = v(r15, "x", "bincount"), n = v(t8, "weights", "bincount"); $(o.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${o.dtype}`), $(e >= 0, () => `size must be non-negative, but got ${e}.`), $(n.size === o.size || n.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`); let s = { x: o, weights: n }, a = { size: e }; - return T.runKernel(en, s, a); + return T.runKernel(Jo, s, a); } -var gd = N({ bincount_: bH }); -function CH(r, t8) { - let e = v(r, "x", "bitwiseAnd"), o = v(t8, "y", "bitwiseAnd"); - if (!Cr(e.shape, o.shape)) +var fd = N({ bincount_: bH }); +function CH(r15, t8) { + let e = v(r15, "x", "bitwiseAnd"), o = v(t8, "y", "bitwiseAnd"); + if (!br(e.shape, o.shape)) throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${e.shape}, y: ${o.shape}`); if (e.dtype !== "int32" || o.dtype !== "int32") throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${e.dtype} and type of y: ${o.dtype}`); let n = { a: e, b: o }; - return T.runKernel(ja, n); + return T.runKernel(qa, n); } -var Hk = N({ bitwiseAnd_: CH }); -function wH(r, t8) { - let e = v(r, "s0", "broadcastArgs", "int32"), o = v(t8, "s1", "broadcastArgs", "int32"); +var Kk = N({ bitwiseAnd_: CH }); +function wH(r15, t8) { + let e = v(r15, "s0", "broadcastArgs", "int32"), o = v(t8, "s1", "broadcastArgs", "int32"); if (e.rank !== 1) throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${e.rank}`); if (o.rank !== 1) @@ -5981,10 +5982,10 @@ function wH(r, t8) { let n = { s0: e, s1: o }; return T.runKernel(ea, n); } -var Kk = N({ broadcastArgs_: wH }); -function SH(r, t8) { - let e = v(r, "broadcastTo", "x"), o = e.shape; - if (wt(t8), t8.length < e.rank) +var qk = N({ broadcastArgs_: wH }); +function SH(r15, t8) { + let e = v(r15, "broadcastTo", "x"), o = e.shape; + if (Ct(t8), t8.length < e.rank) throw new Error(`broadcastTo(): shape.length=${t8.length} < input.rank=${e.rank}.`); if (t8.length > e.rank) { let u = e.shape.slice(); @@ -5999,181 +6000,181 @@ function SH(r, t8) { else if (e.shape[u] !== 1) throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${t8}].`); if (s.map((u, c) => u > 1 ? c : -1).filter((u) => u >= 0).length === 0) - return Ur(e); + return Wr(e); let i = { x: e }, p = { reps: s }; - return T.runKernel(po, i, p); + return T.runKernel(uo, i, p); } -var iu = N({ broadcastTo_: SH }); -function IH(r) { - let e = { x: v(r, "x", "ceil", "float32") }; - return T.runKernel(tn, e); +var ou = N({ broadcastTo_: SH }); +function IH(r15) { + let e = { x: v(r15, "x", "ceil", "float32") }; + return T.runKernel(en, e); } -var qk = N({ ceil_: IH }); -function Ea(r, t8, e) { - wt(r), e = e || Ri(t8); - let o = { shape: r, value: t8, dtype: e }; +var jk = N({ ceil_: IH }); +function Ea(r15, t8, e) { + Ct(r15), e = e || _i(t8); + let o = { shape: r15, value: t8, dtype: e }; return T.runKernel(sa, {}, o); } -function vH(r, t8, e) { - let o = v(r, "x", "clipByValue"); +function vH(r15, t8, e) { + let o = v(r15, "x", "clipByValue"); if ($(t8 <= e, () => `Error in clip: min (${t8}) must be less than or equal to max (${e}).`), t8 === e) return Ea(o.shape, t8, o.dtype); let n = { x: o }, s = { clipValueMin: t8, clipValueMax: e }; - return T.runKernel(Co, n, s); + return T.runKernel(bo, n, s); } -var jk = N({ clipByValue_: vH }); -function kH(r) { - return bt(r, 0); +var Xk = N({ clipByValue_: vH }); +function kH(r15) { + return yt(r15, 0); } -var Xk = N({ concat1d_: kH }); -function NH(r, t8) { - return bt(r, t8); +var Yk = N({ concat1d_: kH }); +function NH(r15, t8) { + return yt(r15, t8); } -var Yk = N({ concat2d_: NH }); -function TH(r, t8) { - return bt(r, t8); +var Qk = N({ concat2d_: NH }); +function TH(r15, t8) { + return yt(r15, t8); } -var Qk = N({ concat3d_: TH }); -function _H(r, t8) { - return bt(r, t8); +var Zk = N({ concat3d_: TH }); +function _H(r15, t8) { + return yt(r15, t8); } -var Zk = N({ concat4d_: _H }); -function $H(r, t8, e, o, n = "NHWC", s = [1, 1], a) { - let i = v(r, "x", "conv2d", "float32"), p = v(t8, "filter", "conv2d", "float32"), u = i, c = false; - i.rank === 3 && (c = true, u = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(u.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${u.rank}.`), $(p.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`), Bt("conv2d", o, a); +var Jk = N({ concat4d_: _H }); +function $H(r15, t8, e, o, n = "NHWC", s = [1, 1], a) { + let i = v(r15, "x", "conv2d", "float32"), p = v(t8, "filter", "conv2d", "float32"), u = i, c = false; + i.rank === 3 && (c = true, u = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(u.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${u.rank}.`), $(p.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`), Lt("conv2d", o, a); let l = n === "NHWC" ? u.shape[3] : u.shape[1]; - $(l === p.shape[2], () => `Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`), $(xr(e, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`), $(Ta(s), () => "Error in conv2D: Dilated rates should be larger than 0."), $(Ta(e), () => "Error in conv2D: Strides should be larger than 0."); - let m = { x: u, filter: p }, d = { strides: e, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(rn, m, d); + $(l === p.shape[2], () => `Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`), $(gr(e, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`), $(Ta(s), () => "Error in conv2D: Dilated rates should be larger than 0."), $(Ta(e), () => "Error in conv2D: Strides should be larger than 0."); + let m = { x: u, filter: p }, d = { strides: e, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(tn, m, d); return c ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f; } -var uu = N({ conv2d_: $H }); -function EH(r, t8, e, o, n = "NWC", s = 1, a) { - let i = v(r, "x", "conv1d"), p = v(t8, "filter", "conv1d"), u = i, c = false; - i.rank === 2 && (c = true, u = W(i, [1, i.shape[0], i.shape[1]])), $(u.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${u.rank}.`), $(p.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${p.rank}.`), Bt("conv1d", o, a), $(u.shape[2] === p.shape[1], () => `Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${p.shape[1]}.`), $(xr(e, s), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${e} and dilation '${s}'`), $(Ta(s), () => "Error in conv1D: Dilated rates should be larger than 0."), $(Ta(e), () => "Error in conv1D: Stride should be larger than 0."), $(n === "NWC", () => `Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`); - let l = W(p, [1, p.shape[0], p.shape[1], p.shape[2]]), m = W(u, [u.shape[0], 1, u.shape[1], u.shape[2]]), g = uu(m, l, [1, e], o, "NHWC", [1, s], a); +var nu = N({ conv2d_: $H }); +function EH(r15, t8, e, o, n = "NWC", s = 1, a) { + let i = v(r15, "x", "conv1d"), p = v(t8, "filter", "conv1d"), u = i, c = false; + i.rank === 2 && (c = true, u = W(i, [1, i.shape[0], i.shape[1]])), $(u.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${u.rank}.`), $(p.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${p.rank}.`), Lt("conv1d", o, a), $(u.shape[2] === p.shape[1], () => `Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${p.shape[1]}.`), $(gr(e, s), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${e} and dilation '${s}'`), $(Ta(s), () => "Error in conv1D: Dilated rates should be larger than 0."), $(Ta(e), () => "Error in conv1D: Stride should be larger than 0."), $(n === "NWC", () => `Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`); + let l = W(p, [1, p.shape[0], p.shape[1], p.shape[2]]), m = W(u, [u.shape[0], 1, u.shape[1], u.shape[2]]), g = nu(m, l, [1, e], o, "NHWC", [1, s], a); return c ? W(g, [g.shape[2], g.shape[3]]) : W(g, [g.shape[0], g.shape[2], g.shape[3]]); } -var Jk = N({ conv1d_: EH }); -function RH(r, t8, e, o, n, s = "NHWC", a) { - $(r.length === t8.rank, () => `Length of inShape (${r.length}) and rank of dy (${t8.rank}) must match`); - let i = r, p = t8, u = false; - t8.rank === 3 && (u = true, p = W(t8, [1, t8.shape[0], t8.shape[1], t8.shape[2]]), i = [1, r[0], r[1], r[2]]), $(i.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`), $(p.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${p.rank}`), $(e.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${e.rank}`); +var e2 = N({ conv1d_: EH }); +function RH(r15, t8, e, o, n, s = "NHWC", a) { + $(r15.length === t8.rank, () => `Length of inShape (${r15.length}) and rank of dy (${t8.rank}) must match`); + let i = r15, p = t8, u = false; + t8.rank === 3 && (u = true, p = W(t8, [1, t8.shape[0], t8.shape[1], t8.shape[2]]), i = [1, r15[0], r15[1], r15[2]]), $(i.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`), $(p.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${p.rank}`), $(e.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${e.rank}`); let c = s === "NHWC" ? i[3] : i[1], l = s === "NHWC" ? p.shape[3] : p.shape[1]; - $(c === e.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${e.shape[2]}.`), $(l === e.shape[3], () => `Error in conv2dDerInput: depth of output (${l}) must match output depth for filter ${e.shape[3]}.`), Bt("conv2dDerInput", n, a); - let m = { dy: p, filter: e }, d = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, inputShape: i }, f = T.runKernel(on, m, d); + $(c === e.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${e.shape[2]}.`), $(l === e.shape[3], () => `Error in conv2dDerInput: depth of output (${l}) must match output depth for filter ${e.shape[3]}.`), Lt("conv2dDerInput", n, a); + let m = { dy: p, filter: e }, d = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, inputShape: i }, f = T.runKernel(rn, m, d); return u ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f; } -var xd = N({ conv2DBackpropInput_: RH }); -function DH(r, t8, e, o, n, s) { - let a = v(r, "x", "conv2dTranspose"), i = v(t8, "filter", "conv2dTranspose"); - return xd(e, a, i, o, n, "NHWC", s); +var hd = N({ conv2DBackpropInput_: RH }); +function DH(r15, t8, e, o, n, s) { + let a = v(r15, "x", "conv2dTranspose"), i = v(t8, "filter", "conv2dTranspose"); + return hd(e, a, i, o, n, "NHWC", s); } -var e2 = N({ conv2dTranspose_: DH }); -function AH(r, t8, e, o, n = "NDHWC", s = [1, 1, 1]) { - let a = v(r, "x", "conv3d"), i = v(t8, "filter", "conv3d"), p = a, u = false; - a.rank === 4 && (u = true, p = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(p.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${p.rank}.`), $(i.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`), $(p.shape[4] === i.shape[3], () => `Error in conv3d: depth of input (${p.shape[4]}) must match input depth for filter ${i.shape[3]}.`), $(xr(e, s), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`), $(n === "NDHWC", () => `Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`), $(Ta(s), () => "Error in conv3D: Dilated rates should be larger than 0."), $(Ta(e), () => "Error in conv3D: Strides should be larger than 0."); - let c = { x: p, filter: i }, l = { strides: e, pad: o, dataFormat: n, dilations: s }, m = T.runKernel(nn, c, l); +var t2 = N({ conv2dTranspose_: DH }); +function AH(r15, t8, e, o, n = "NDHWC", s = [1, 1, 1]) { + let a = v(r15, "x", "conv3d"), i = v(t8, "filter", "conv3d"), p = a, u = false; + a.rank === 4 && (u = true, p = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(p.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${p.rank}.`), $(i.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`), $(p.shape[4] === i.shape[3], () => `Error in conv3d: depth of input (${p.shape[4]}) must match input depth for filter ${i.shape[3]}.`), $(gr(e, s), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`), $(n === "NDHWC", () => `Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`), $(Ta(s), () => "Error in conv3D: Dilated rates should be larger than 0."), $(Ta(e), () => "Error in conv3D: Strides should be larger than 0."); + let c = { x: p, filter: i }, l = { strides: e, pad: o, dataFormat: n, dilations: s }, m = T.runKernel(on, c, l); return u ? W(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m; } -var t2 = N({ conv3d_: AH }); -function FH(r, t8, e, o, n) { - $(r.length === t8.rank, () => `Length of inShape (${r.length}) and rank of dy (${t8.rank}) must match`); - let s = r, a = t8, i = false; - t8.rank === 4 && (i = true, a = W(t8, [1, t8.shape[0], t8.shape[1], t8.shape[2], t8.shape[3]]), s = [1, r[0], r[1], r[2], r[3]]); +var r22 = N({ conv3d_: AH }); +function FH(r15, t8, e, o, n) { + $(r15.length === t8.rank, () => `Length of inShape (${r15.length}) and rank of dy (${t8.rank}) must match`); + let s = r15, a = t8, i = false; + t8.rank === 4 && (i = true, a = W(t8, [1, t8.shape[0], t8.shape[1], t8.shape[2], t8.shape[3]]), s = [1, r15[0], r15[1], r15[2], r15[3]]); let p = s[4], u = a.shape[4]; $(s.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`), $(a.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`), $(e.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${e.rank}`), $(p === e.shape[3], () => `Error in conv3dDerInput: depth of input (${p}) must match input depth for filter ${e.shape[3]}.`), $(u === e.shape[4], () => `Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${e.shape[4]}.`); - let c = { dy: a, filter: e }, l = { pad: n, strides: o, inputShape: s }, m = T.runKernel(sn, c, l); + let c = { dy: a, filter: e }, l = { pad: n, strides: o, inputShape: s }, m = T.runKernel(nn, c, l); return i ? W(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m; } -var r2 = N({ conv3DBackpropInput_: FH }); -function PH(r, t8, e, o, n) { - let s = v(r, "x", "conv3dTranspose"), a = v(t8, "filter", "conv3dTranspose"); - return r2(e, s, a, o, n); +var o2 = N({ conv3DBackpropInput_: FH }); +function PH(r15, t8, e, o, n) { + let s = v(r15, "x", "conv3dTranspose"), a = v(t8, "filter", "conv3dTranspose"); + return o2(e, s, a, o, n); } -var o2 = N({ conv3dTranspose_: PH }); -function OH(r) { - let e = { x: v(r, "x", "cos", "float32") }; +var n2 = N({ conv3dTranspose_: PH }); +function OH(r15) { + let e = { x: v(r15, "x", "cos", "float32") }; + return T.runKernel(sn, e); +} +var s2 = N({ cos_: OH }); +function MH(r15) { + let e = { x: v(r15, "x", "cosh", "float32") }; return T.runKernel(an, e); } -var n2 = N({ cos_: OH }); -function MH(r) { - let e = { x: v(r, "x", "cosh", "float32") }; - return T.runKernel(un, e); +var a2 = N({ cosh_: MH }); +function LH(r15, t8 = 0, e = false, o = false) { + let s = { x: v(r15, "x", "cumprod") }, a = { axis: t8, exclusive: e, reverse: o }; + return T.runKernel(un, s, a); } -var s2 = N({ cosh_: MH }); -function LH(r, t8 = 0, e = false, o = false) { - let s = { x: v(r, "x", "cumprod") }, a = { axis: t8, exclusive: e, reverse: o }; +var i2 = N({ cumprod_: LH }); +function BH(r15, t8 = 0, e = false, o = false) { + let s = { x: v(r15, "x", "cumsum") }, a = { axis: t8, exclusive: e, reverse: o }; return T.runKernel(pn, s, a); } -var a2 = N({ cumprod_: LH }); -function BH(r, t8 = 0, e = false, o = false) { - let s = { x: v(r, "x", "cumsum") }, a = { axis: t8, exclusive: e, reverse: o }; - return T.runKernel(cn, s, a); -} -var i2 = N({ cumsum_: BH }); -function zH(r, t8, e, o = false) { - let n = v(r, "x", "denseBincount"), s = v(t8, "weights", "denseBincount"); +var u2 = N({ cumsum_: BH }); +function zH(r15, t8, e, o = false) { + let n = v(r15, "x", "denseBincount"), s = v(t8, "weights", "denseBincount"); $(n.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${n.dtype}`), $(n.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`), $(e >= 0, () => `size must be non-negative, but got ${e}.`), $(s.size === n.size || s.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`); let a = { x: n, weights: s }, i = { size: e, binaryOutput: o }; return T.runKernel(ra, a, i); } -var u2 = N({ denseBincount_: zH }); -function VH(r, t8, e = "NHWC") { - let o = v(r, "x", "depthToSpace", "float32"), n = e === "NHWC" ? o.shape[1] : o.shape[2], s = e === "NHWC" ? o.shape[2] : o.shape[3], a = e === "NHWC" ? o.shape[3] : o.shape[1]; +var p2 = N({ denseBincount_: zH }); +function VH(r15, t8, e = "NHWC") { + let o = v(r15, "x", "depthToSpace", "float32"), n = e === "NHWC" ? o.shape[1] : o.shape[2], s = e === "NHWC" ? o.shape[2] : o.shape[3], a = e === "NHWC" ? o.shape[3] : o.shape[1]; $(t8 > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${t8}`), $(n * t8 >= 0, () => `Negative dimension size caused by overflow when multiplying ${n} and ${t8} for depthToSpace with input shape ${o.shape}`), $(s * t8 >= 0, () => `Negative dimension size caused by overflow when multiplying ${s} and ${t8} for depthToSpace with input shape ${o.shape}`), $(a % (t8 * t8) === 0, () => `Dimension size must be evenly divisible by ${t8 * t8} but is ${a} for depthToSpace with input shape ${o.shape}`); let i = { x: o }, p = { blockSize: t8, dataFormat: e }; - return T.runKernel(mn, i, p); + return T.runKernel(ln, i, p); } -var p2 = N({ depthToSpace_: VH }); -function WH(r, t8, e, o, n = "NHWC", s = [1, 1], a) { - let i = v(r, "x", "depthwiseConv2d", "float32"), p = v(t8, "filter", "depthwiseConv2d", "float32"), u = i, c = false; +var c2 = N({ depthToSpace_: VH }); +function WH(r15, t8, e, o, n = "NHWC", s = [1, 1], a) { + let i = v(r15, "x", "depthwiseConv2d", "float32"), p = v(t8, "filter", "depthwiseConv2d", "float32"), u = i, c = false; i.rank === 3 && (c = true, u = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(u.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`), $(p.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`); let l = n === "NHWC" ? u.shape[3] : u.shape[1]; - $(l === p.shape[2], () => `Error in depthwiseConv2d: number of input channels (${l}) must match the inChannels dimension in filter ${p.shape[2]}.`), Bt("depthwiseConv2d", o, a); - let m = { x: u, filter: p }, d = { strides: e, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(dn, m, d); + $(l === p.shape[2], () => `Error in depthwiseConv2d: number of input channels (${l}) must match the inChannels dimension in filter ${p.shape[2]}.`), Lt("depthwiseConv2d", o, a); + let m = { x: u, filter: p }, d = { strides: e, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(mn, m, d); return c ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f; } -var lc = N({ depthwiseConv2d_: WH }); -function UH(r) { - let e = { x: v(r, "x", "diag") }; +var oc = N({ depthwiseConv2d_: WH }); +function UH(r15) { + let e = { x: v(r15, "x", "diag") }; return T.runKernel(oa, e); } -var c2 = N({ diag_: UH }); -function GH(r, t8, e, o, n = [1, 1], s = "NHWC") { - let a = v(r, "x", "dilation2d"), i = v(t8, "filter", "dilation2d"); +var l2 = N({ diag_: UH }); +function GH(r15, t8, e, o, n = [1, 1], s = "NHWC") { + let a = v(r15, "x", "dilation2d"), i = v(t8, "filter", "dilation2d"); $(a.rank === 3 || a.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`), $(i.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`), $(s === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`); let p = a, u = false; a.rank === 3 && (p = W(a, [1, a.shape[0], a.shape[1], a.shape[2]]), u = true), $(p.shape[3] === i.shape[2], () => `Error in dilation2d: input and filter must have the same depth: ${p.shape[3]} vs ${i.shape[2]}`); - let c = { x: p, filter: i }, l = { strides: e, pad: o, dilations: n }, m = T.runKernel(fn, c, l); + let c = { x: p, filter: i }, l = { strides: e, pad: o, dilations: n }, m = T.runKernel(dn, c, l); return u ? W(m, [m.shape[1], m.shape[2], m.shape[3]]) : m; } -var l2 = N({ dilation2d_: GH }); -var Ir = {}; -qe(Ir, { assertAndGetBroadcastShape: () => rt, getBroadcastDims: () => m2, getReductionAxes: () => yd }); -function m2(r, t8) { - let e = r.length, o = []; +var m2 = N({ dilation2d_: GH }); +var Sr = {}; +qe(Sr, { assertAndGetBroadcastShape: () => rt, getBroadcastDims: () => d2, getReductionAxes: () => gd }); +function d2(r15, t8) { + let e = r15.length, o = []; for (let n = 0; n < e; n++) { - let s = e - 1 - n, a = r[s] || 1; + let s = e - 1 - n, a = r15[s] || 1; (t8[t8.length - 1 - n] || 1) > 1 && a === 1 && o.unshift(s); } return o; } -function yd(r, t8) { +function gd(r15, t8) { let e = []; for (let o = 0; o < t8.length; o++) { - let n = r[r.length - o - 1], s = t8.length - o - 1, a = t8[s]; + let n = r15[r15.length - o - 1], s = t8.length - o - 1, a = t8[s]; (n == null || n === 1 && a > 1) && e.unshift(s); } return e; } -function rt(r, t8) { - let e = Math.max(r.length, t8.length), o = new Array(e); +function rt(r15, t8) { + let e = Math.max(r15.length, t8.length), o = new Array(e); for (let n = 0; n < e; n++) { - let s = r[r.length - n - 1]; + let s = r15[r15.length - n - 1]; s == null && (s = 1); let a = t8[t8.length - n - 1]; if (a == null && (a = 1), s === 1) @@ -6181,39 +6182,39 @@ function rt(r, t8) { else if (a === 1) o[e - n - 1] = s; else if (s !== a) { - let i = `Operands could not be broadcast together with shapes ${r} and ${t8}.`; + let i = `Operands could not be broadcast together with shapes ${r15} and ${t8}.`; throw Error(i); } else o[e - n - 1] = s; } return o; } -function HH(r, t8) { - let e = v(r, "a", "equal", "string_or_numeric"), o = v(t8, "b", "equal", "string_or_numeric"); +function HH(r15, t8) { + let e = v(r15, "a", "equal", "string_or_numeric"), o = v(t8, "b", "equal", "string_or_numeric"); [e, o] = Oe(e, o), rt(e.shape, o.shape); let n = { a: e, b: o }; - return T.runKernel(yn, n); + return T.runKernel(xn, n); } -var bd = N({ equal_: HH }); -function KH(r, t8, e) { - let o = v(t8, "a", "where"), n = v(e, "b", "where"), s = v(r, "condition", "where", "bool"), a = rt(rt(s.shape, o.shape), n.shape), i = iu(s, a), p = iu(o, a), u = iu(n, a), c = { condition: i, t: p, e: u }; +var xd = N({ equal_: HH }); +function KH(r15, t8, e) { + let o = v(t8, "a", "where"), n = v(e, "b", "where"), s = v(r15, "condition", "where", "bool"), a = rt(rt(s.shape, o.shape), n.shape), i = ou(s, a), p = ou(o, a), u = ou(n, a), c = { condition: i, t: p, e: u }; return T.runKernel(fa, c); } -var lo = N({ where_: KH }); -function qH(r) { - let e = { x: v(r, "x", "zerosLike") }; +var co = N({ where_: KH }); +function qH(r15) { + let e = { x: v(r15, "x", "zerosLike") }; return T.runKernel(Sa, e); } -var Ht = N({ zerosLike_: qH }); -function jH(r, t8) { - let e = v(r, "a", "div"), o = v(t8, "b", "div"); +var Gt = N({ zerosLike_: qH }); +function jH(r15, t8) { + let e = v(r15, "a", "div"), o = v(t8, "b", "div"); [e, o] = Oe(e, o); - let n = je(e, o), s = Ht(n), a = bd(o, s); - return lo(a, s, n); + let n = je(e, o), s = Gt(n), a = xd(o, s); + return co(a, s, n); } -var d2 = N({ divNoNan_: jH }); -function XH(r, t8) { - let e = v(r, "t1", "dot"), o = v(t8, "t2", "dot"); +var f2 = N({ divNoNan_: jH }); +function XH(r15, t8) { + let e = v(r15, "t1", "dot"), o = v(t8, "t2", "dot"); $((e.rank === 1 || e.rank === 2) && (o.rank === 1 || o.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${e.rank} and ${o.rank}.`); let n = e.rank === 1 ? e.size : e.shape[1], s = o.rank === 1 ? o.size : o.shape[0]; if ($(n === s, () => `Error in dot: inner dimensions of inputs must match, but got ${n} and ${s}.`), e.rank === 1 && o.rank === 1) { @@ -6230,323 +6231,323 @@ function XH(r, t8) { return Ze(e, a); } } -var f2 = N({ dot_: XH }); -function YH(r, ...t8) { - let e = t8.map((n, s) => v(n, `tensors${s}`, "einsum")), o = { equation: r }; - return T.runKernel(Vi, e, o); +var h2 = N({ dot_: XH }); +function YH(r15, ...t8) { + let e = t8.map((n, s) => v(n, `tensors${s}`, "einsum")), o = { equation: r15 }; + return T.runKernel(Li, e, o); } -var pu = N({ einsum_: YH }); -function QH(r) { - let e = { x: v(r, "x", "elu", "float32") }; - return T.runKernel(gn, e); +var su = N({ einsum_: YH }); +function QH(r15) { + let e = { x: v(r15, "x", "elu", "float32") }; + return T.runKernel(hn, e); } -var Cd = N({ elu_: QH }); -function ZH(r, t8) { - let e = v(r, "x", "ensureShape", "string_or_numeric"); - if (!ZC(e.shape, t8)) +var yd = N({ elu_: QH }); +function ZH(r15, t8) { + let e = v(r15, "x", "ensureShape", "string_or_numeric"); + if (!QC(e.shape, t8)) throw new Error(`EnsureShape: Shape of tensor ${e.shape} is not compatible with expected shape ${t8}`); - return r; + return r15; } -var h2 = N({ ensureShape_: ZH }); -function JH(r) { - let t8 = v(r, "x", "erf"); +var g2 = N({ ensureShape_: ZH }); +function JH(r15) { + let t8 = v(r15, "x", "erf"); $(t8.dtype === "int32" || t8.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), t8.dtype === "int32" && (t8 = We(t8, "float32")); let e = { x: t8 }; - return T.runKernel(xn, e); + return T.runKernel(gn, e); } -var g2 = N({ erf_: JH }); -function Bw(r, t8) { - for (let e = 0; e < r.length; ++e) - if (r[r.length - e - 1] !== t8 - 1 - e) +var x2 = N({ erf_: JH }); +function Bw(r15, t8) { + for (let e = 0; e < r15.length; ++e) + if (r15[r15.length - e - 1] !== t8 - 1 - e) return false; return true; } -function x2(r, t8, e) { - let o = r.length + t8.length, n = [], s = 0, a = 0; +function y2(r15, t8, e) { + let o = r15.length + t8.length, n = [], s = 0, a = 0; for (let i = 0; i < o; i++) - e.indexOf(i) === -1 ? n.push(r[s++]) : n.push(t8[a++]); + e.indexOf(i) === -1 ? n.push(r15[s++]) : n.push(t8[a++]); return n; } -function eK(r, t8) { - let e = [], o = r.length; +function eK(r15, t8) { + let e = [], o = r15.length; for (let s = 0; s < o; s++) - t8.indexOf(s) === -1 && e.push(r[s]); - let n = t8.map((s) => r[s]); + t8.indexOf(s) === -1 && e.push(r15[s]); + let n = t8.map((s) => r15[s]); return [e, n]; } -function ii(r, t8) { +function ai(r15, t8) { let e = t8.map((o) => 1); - return x2(r, e, t8); + return y2(r15, e, t8); } -function tK(r, t8, e) { - $(Bw(t8, e), () => `${r} supports only inner-most axes for now. Got axes ${t8} and rank-${e} input.`); +function tK(r15, t8, e) { + $(Bw(t8, e), () => `${r15} supports only inner-most axes for now. Got axes ${t8} and rank-${e} input.`); } -function rK(r, t8) { - if (Bw(r, t8)) +function rK(r15, t8) { + if (Bw(r15, t8)) return null; let e = []; for (let o = 0; o < t8; ++o) - r.indexOf(o) === -1 && e.push(o); - return r.forEach((o) => e.push(o)), e; + r15.indexOf(o) === -1 && e.push(o); + return r15.forEach((o) => e.push(o)), e; } -function oK(r) { - return r.map((t8, e) => [e, t8]).sort((t8, e) => t8[1] - e[1]).map((t8) => t8[0]); +function oK(r15) { + return r15.map((t8, e) => [e, t8]).sort((t8, e) => t8[1] - e[1]).map((t8) => t8[0]); } -function nK(r, t8) { +function nK(r15, t8) { let e = []; - for (let o = t8 - r; o < t8; ++o) + for (let o = t8 - r15; o < t8; ++o) e.push(o); return e; } -function aK(r, t8 = null, e = false) { - let n = { x: v(r, "x", "max") }, s = { reductionIndices: t8, keepDims: e }; - return T.runKernel(Vn, n, s); +function aK(r15, t8 = null, e = false) { + let n = { x: v(r15, "x", "max") }, s = { reductionIndices: t8, keepDims: e }; + return T.runKernel(zn, n, s); } var Ra = N({ max_: aK }); -function iK(r, t8 = null, e = false) { - let n = { x: v(r, "x", "min") }, s = { axis: t8, keepDims: e }; - return T.runKernel(Hn, n, s); +function iK(r15, t8 = null, e = false) { + let n = { x: v(r15, "x", "min") }, s = { axis: t8, keepDims: e }; + return T.runKernel(Gn, n, s); } -var Tl = N({ min_: iK }); -function uK(r, t8) { - let e = v(r, "base", "pow"), o = v(t8, "exp", "pow"); +var vl = N({ min_: iK }); +function uK(r15, t8) { + let e = v(r15, "base", "pow"), o = v(t8, "exp", "pow"); [e, o] = Oe(e, o); let n = { a: e, b: o }; - return T.runKernel(rs, n); + return T.runKernel(ts, n); } -var ui = N({ pow_: uK }); -function ke(r, t8) { - if ((Ot(r) && t8 !== "string" || Array.isArray(r)) && t8 !== "complex64") +var ii = N({ pow_: uK }); +function ke(r15, t8) { + if ((Pt(r15) && t8 !== "string" || Array.isArray(r15)) && t8 !== "complex64") throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)"); - if (t8 === "string" && Ot(r) && !(r instanceof Uint8Array)) + if (t8 === "string" && Pt(r15) && !(r15 instanceof Uint8Array)) throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`."); - return Sr(r, [], [], t8); + return wr(r15, [], [], t8); } -function pK(r) { - let e = { x: v(r, "x", "sqrt", "float32") }; - return T.runKernel(Ss, e); +function pK(r15) { + let e = { x: v(r15, "x", "sqrt", "float32") }; + return T.runKernel(ws, e); } -var Dr = N({ sqrt_: pK }); -function cK(r) { - let t8 = v(r, "x", "square"), e = {}; +var Rr = N({ sqrt_: pK }); +function cK(r15) { + let t8 = v(r15, "x", "square"), e = {}; return T.runKernel("Square", { x: t8 }, e); } -var er = N({ square_: cK }); -function lK(r, t8 = null, e = false) { - let o = v(r, "x", "sum"); +var Zt = N({ square_: cK }); +function lK(r15, t8 = null, e = false) { + let o = v(r15, "x", "sum"); o.dtype === "bool" && (o = We(o, "int32")); let n = { x: o }, s = { axis: t8, keepDims: e }; - return T.runKernel(Is, n, s); + return T.runKernel(Ss, n, s); } var ot = N({ sum_: lK }); -function mK(r, t8 = "euclidean", e = null, o = false) { - r = v(r, "x", "norm"); - let n = y2(r, t8, e), s = n.shape; +function mK(r15, t8 = "euclidean", e = null, o = false) { + r15 = v(r15, "x", "norm"); + let n = b2(r15, t8, e), s = n.shape; if (o) { - let a = Ei(e, r.shape); - s = ii(n.shape, a); + let a = Ti(e, r15.shape); + s = ai(n.shape, a); } return W(n, s); } -function y2(r, t8, e = null) { - if (r.rank === 0) - return Jt(r); - if (r.rank !== 1 && e === null) - return y2(W(r, [-1]), t8, e); - if (r.rank === 1 || typeof e == "number" || Array.isArray(e) && e.length === 1) { +function b2(r15, t8, e = null) { + if (r15.rank === 0) + return Qt(r15); + if (r15.rank !== 1 && e === null) + return b2(W(r15, [-1]), t8, e); + if (r15.rank === 1 || typeof e == "number" || Array.isArray(e) && e.length === 1) { if (t8 === 1) - return ot(Jt(r), e); + return ot(Qt(r15), e); if (t8 === 1 / 0) - return Ra(Jt(r), e); + return Ra(Qt(r15), e); if (t8 === -1 / 0) - return Tl(Jt(r), e); + return vl(Qt(r15), e); if (t8 === "euclidean" || t8 === 2) - return Dr(ot(ui(Jt(r), ke(2, "int32")), e)); + return Rr(ot(ii(Qt(r15), ke(2, "int32")), e)); throw new Error(`Error in norm: invalid ord value: ${t8}`); } if (Array.isArray(e) && e.length === 2) { if (t8 === 1) - return Ra(ot(Jt(r), e[0]), e[1] - 1); + return Ra(ot(Qt(r15), e[0]), e[1] - 1); if (t8 === 1 / 0) - return Ra(ot(Jt(r), e[1]), e[0]); + return Ra(ot(Qt(r15), e[1]), e[0]); if (t8 === -1 / 0) - return Tl(ot(Jt(r), e[1]), e[0]); + return vl(ot(Qt(r15), e[1]), e[0]); if (t8 === "fro" || t8 === "euclidean") - return Dr(ot(er(r), e)); + return Rr(ot(Zt(r15), e)); throw new Error(`Error in norm: invalid ord value: ${t8}`); } throw new Error(`Error in norm: invalid axis: ${e}`); } -var Ku = N({ norm_: mK }); -function dK(r, t8 = null, e = false) { - return Ku(r, "euclidean", t8, e); +var Bu = N({ norm_: mK }); +function dK(r15, t8 = null, e = false) { + return Bu(r15, "euclidean", t8, e); } -var b2 = N({ euclideanNorm_: dK }); -function fK(r) { - let e = { x: v(r, "x", "exp") }; - return T.runKernel(bn, e); +var C2 = N({ euclideanNorm_: dK }); +function fK(r15) { + let e = { x: v(r15, "x", "exp") }; + return T.runKernel(yn, e); } -var $o = N({ exp_: fK }); -function hK(r, t8 = 0) { - let e = v(r, "x", "expandDims", "string_or_numeric"); +var _o = N({ exp_: fK }); +function hK(r15, t8 = 0) { + let e = v(r15, "x", "expandDims", "string_or_numeric"); $(t8 <= e.rank, () => "Axis must be <= rank of the tensor"); let o = { input: e }, n = { dim: t8 }; return T.runKernel(na, o, n); } var Ms = N({ expandDims_: hK }); -function gK(r) { - let e = { x: v(r, "x", "expm1") }; - return T.runKernel(Cn, e); +function gK(r15) { + let e = { x: v(r15, "x", "expm1") }; + return T.runKernel(bn, e); } -var C2 = N({ expm1_: gK }); -function xK(r, t8) { - let e = v(r, "x", "tile", "string_or_numeric"); +var w2 = N({ expm1_: gK }); +function xK(r15, t8) { + let e = v(r15, "x", "tile", "string_or_numeric"); $(e.rank === t8.length, () => `Error in transpose: rank of input ${e.rank} must match length of reps ${t8}.`); let o = { x: e }, n = { reps: t8 }; - return T.runKernel(po, o, n); + return T.runKernel(uo, o, n); } -var cu = N({ tile_: xK }); -function yK(r, t8, e, o = "float32") { - t8 == null && (t8 = r); - let n = me([r, t8], o), s = r <= t8 ? r : t8; +var au = N({ tile_: xK }); +function yK(r15, t8, e, o = "float32") { + t8 == null && (t8 = r15); + let n = me([r15, t8], o), s = r15 <= t8 ? r15 : t8; for (let i = 0; i < s; ++i) n.set(1, i, i); - let a = W(n.toTensor(), [r, t8]); + let a = W(n.toTensor(), [r15, t8]); if (e == null) return a; if (e.length === 1) - return cu(Ms(a, 0), [e[0], 1, 1]); + return au(Ms(a, 0), [e[0], 1, 1]); if (e.length === 2) - return cu(Ms(Ms(a, 0), 0), [e[0], e[1], 1, 1]); + return au(Ms(Ms(a, 0), 0), [e[0], e[1], 1, 1]); if (e.length === 3) - return cu(Ms(Ms(Ms(a, 0), 0), 0), [e[0], e[1], e[2], 1, 1]); + return au(Ms(Ms(Ms(a, 0), 0), 0), [e[0], e[1], e[2], 1, 1]); throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${e.length}D.`); } -var wd = N({ eye_: yK }); -function bK(r) { - let e = { x: v(r, "x", "floor", "float32") }; - return T.runKernel(Sn, e); +var bd = N({ eye_: yK }); +function bK(r15) { + let e = { x: v(r15, "x", "floor", "float32") }; + return T.runKernel(wn, e); } -var Sd = N({ floor_: bK }); -function CK(r, t8, e = 0, o = 0) { - let n = v(r, "x", "gather"), s = v(t8, "indices", "gather", "int32"), a = { x: n, indices: s }, i = { axis: e, batchDims: o }; +var Cd = N({ floor_: bK }); +function CK(r15, t8, e = 0, o = 0) { + let n = v(r15, "x", "gather"), s = v(t8, "indices", "gather", "int32"), a = { x: n, indices: s }, i = { axis: e, batchDims: o }; return T.runKernel(aa, a, i); } -var Id = N({ gather_: CK }); -function wK(r, t8) { - let e = v(r, "a", "greater", "string_or_numeric"), o = v(t8, "b", "greater", "string_or_numeric"); +var wd = N({ gather_: CK }); +function wK(r15, t8) { + let e = v(r15, "a", "greater", "string_or_numeric"), o = v(t8, "b", "greater", "string_or_numeric"); + [e, o] = Oe(e, o), rt(e.shape, o.shape); + let n = { a: e, b: o }; + return T.runKernel(kn, n); +} +var zu = N({ greater_: wK }); +function SK(r15, t8) { + let e = v(r15, "a", "greaterEqual", "string_or_numeric"), o = v(t8, "b", "greaterEqual", "string_or_numeric"); [e, o] = Oe(e, o), rt(e.shape, o.shape); let n = { a: e, b: o }; return T.runKernel(Nn, n); } -var qu = N({ greater_: wK }); -function SK(r, t8) { - let e = v(r, "a", "greaterEqual", "string_or_numeric"), o = v(t8, "b", "greaterEqual", "string_or_numeric"); - [e, o] = Oe(e, o), rt(e.shape, o.shape); - let n = { a: e, b: o }; - return T.runKernel(Tn, n); +var Sd = N({ greaterEqual_: SK }); +function IK(r15) { + let e = { input: v(r15, "input", "imag") }; + return T.runKernel(Vi, e); } -var vd = N({ greaterEqual_: SK }); -function IK(r) { - let e = { input: v(r, "input", "imag") }; - return T.runKernel(Gi, e); +var iu = N({ imag_: IK }); +function vK(r15) { + let e = { x: v(r15, "x", "isFinite") }; + return T.runKernel(Tn, e); } -var lu = N({ imag_: IK }); -function vK(r) { - let e = { x: v(r, "x", "isFinite") }; +var S2 = N({ isFinite_: vK }); +function kK(r15) { + let e = { x: v(r15, "x", "isInf") }; return T.runKernel(_n, e); } -var w2 = N({ isFinite_: vK }); -function kK(r) { - let e = { x: v(r, "x", "isInf") }; +var I2 = N({ isInf_: kK }); +function NK(r15) { + let e = { x: v(r15, "x", "isNaN") }; return T.runKernel($n, e); } -var S2 = N({ isInf_: kK }); -function NK(r) { - let e = { x: v(r, "x", "isNaN") }; - return T.runKernel(En, e); +var v2 = N({ isNaN_: NK }); +function TK(r15, t8 = 0.2) { + let o = { x: v(r15, "x", "leakyRelu") }, n = { alpha: t8 }; + return T.runKernel(En, o, n); } -var I2 = N({ isNaN_: NK }); -function TK(r, t8 = 0.2) { - let o = { x: v(r, "x", "leakyRelu") }, n = { alpha: t8 }; - return T.runKernel(Rn, o, n); +var Id = N({ leakyRelu_: TK }); +function _K(r15, t8) { + let e = v(r15, "a", "less", "string_or_numeric"), o = v(t8, "b", "less", "string_or_numeric"); + [e, o] = Oe(e, o), rt(e.shape, o.shape); + let n = { a: e, b: o }; + return T.runKernel(Rn, n); } -var kd = N({ leakyRelu_: TK }); -function _K(r, t8) { - let e = v(r, "a", "less", "string_or_numeric"), o = v(t8, "b", "less", "string_or_numeric"); +var kl = N({ less_: _K }); +function $K(r15, t8) { + let e = v(r15, "a", "lessEqual", "string_or_numeric"), o = v(t8, "b", "lessEqual", "string_or_numeric"); [e, o] = Oe(e, o), rt(e.shape, o.shape); let n = { a: e, b: o }; return T.runKernel(Dn, n); } -var _l = N({ less_: _K }); -function $K(r, t8) { - let e = v(r, "a", "lessEqual", "string_or_numeric"), o = v(t8, "b", "lessEqual", "string_or_numeric"); - [e, o] = Oe(e, o), rt(e.shape, o.shape); - let n = { a: e, b: o }; - return T.runKernel(An, n); -} -var mc = N({ lessEqual_: $K }); -function v2(r, t8, e) { +var nc = N({ lessEqual_: $K }); +function k2(r15, t8, e) { if (e <= 0) throw new Error("The number of values should be positive."); - let o = { start: r, stop: t8, num: e }; - return T.runKernel(Fn, {}, o); + let o = { start: r15, stop: t8, num: e }; + return T.runKernel(An, {}, o); } -function EK(r, t8 = 5, e = 1, o = 1, n = 0.5) { - let s = v(r, "x", "localResponseNormalization"); +function EK(r15, t8 = 5, e = 1, o = 1, n = 0.5) { + let s = v(r15, "x", "localResponseNormalization"); $(s.rank === 4 || s.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got - rank ${s.rank}.`), $(qa(t8), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t8}.`); + rank ${s.rank}.`), $(Ka(t8), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t8}.`); let a = s, i = false; s.rank === 3 && (i = true, a = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])); - let p = { x: a }, u = { depthRadius: t8, bias: e, alpha: o, beta: n }, c = T.runKernel(zn, p, u); + let p = { x: a }, u = { depthRadius: t8, bias: e, alpha: o, beta: n }, c = T.runKernel(Bn, p, u); return i ? W(c, [c.shape[1], c.shape[2], c.shape[3]]) : c; } -var k2 = N({ localResponseNormalization_: EK }); -function RK(r) { - let e = { x: v(r, "x", "log", "float32") }; +var N2 = N({ localResponseNormalization_: EK }); +function RK(r15) { + let e = { x: v(r15, "x", "log", "float32") }; + return T.runKernel(Fn, e); +} +var ui = N({ log_: RK }); +function DK(r15) { + let e = { x: v(r15, "x", "log1p") }; return T.runKernel(Pn, e); } -var pi = N({ log_: RK }); -function DK(r) { - let e = { x: v(r, "x", "log1p") }; - return T.runKernel(On, e); -} -var Nd = N({ log1p_: DK }); -function AK(r) { - return $(qs(r), () => "The f passed in grad(f) must be a function"), (t8, e) => { +var vd = N({ log1p_: DK }); +function AK(r15) { + return $(qs(r15), () => "The f passed in grad(f) must be a function"), (t8, e) => { let o = v(t8, "x", "tf.grad", "string_or_numeric"), n = e != null ? v(e, "dy", "tf.grad") : null; return T.tidy(() => { - let { value: s, grads: a } = T.gradients(() => r(o), [o], n); - return n != null && yt(s.shape, n.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), Td(a), a[0]; + let { value: s, grads: a } = T.gradients(() => r15(o), [o], n); + return n != null && xt(s.shape, n.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), kd(a), a[0]; }); }; } -function FK(r) { - return $(qs(r), () => "The f passed in grads(f) must be a function"), (t8, e) => { +function FK(r15) { + return $(qs(r15), () => "The f passed in grads(f) must be a function"), (t8, e) => { $(Array.isArray(t8), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s"); - let o = si(t8, "args", "tf.grads", "string_or_numeric"), n = e != null ? v(e, "dy", "tf.grads") : null; + let o = ni(t8, "args", "tf.grads", "string_or_numeric"), n = e != null ? v(e, "dy", "tf.grads") : null; return T.tidy(() => { - let { value: s, grads: a } = T.gradients(() => r(...o), o, n); - return n != null && yt(s.shape, n.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), Td(a), a; + let { value: s, grads: a } = T.gradients(() => r15(...o), o, n); + return n != null && xt(s.shape, n.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), kd(a), a; }); }; } -function PK(r) { - return $(qs(r), () => "The f passed in valueAndGrad(f) must be a function"), (t8, e) => { - $(t8 instanceof ut, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), $(e == null || e instanceof ut, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor"); - let { grads: o, value: n } = T.gradients(() => r(t8), [t8], e); - return Td(o), { grad: o[0], value: n }; +function PK(r15) { + return $(qs(r15), () => "The f passed in valueAndGrad(f) must be a function"), (t8, e) => { + $(t8 instanceof mt, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), $(e == null || e instanceof mt, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor"); + let { grads: o, value: n } = T.gradients(() => r15(t8), [t8], e); + return kd(o), { grad: o[0], value: n }; }; } -function OK(r) { - return $(qs(r), () => "The f passed in valueAndGrads(f) must be a function"), (t8, e) => { - $(Array.isArray(t8) && t8.every((n) => n instanceof ut), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), $(e == null || e instanceof ut, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor"); - let o = T.gradients(() => r(...t8), t8, e); - return e != null && yt(o.value.shape, e.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), Td(o.grads), o; +function OK(r15) { + return $(qs(r15), () => "The f passed in valueAndGrads(f) must be a function"), (t8, e) => { + $(Array.isArray(t8) && t8.every((n) => n instanceof mt), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), $(e == null || e instanceof mt, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor"); + let o = T.gradients(() => r15(...t8), t8, e); + return e != null && xt(o.value.shape, e.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), kd(o.grads), o; }; } -function zw(r, t8) { - $(qs(r), () => "The f passed in variableGrads(f) must be a function"), $(t8 == null || Array.isArray(t8) && t8.every((u) => u instanceof oi), () => "The varList passed in variableGrads(f, varList) must be an array of variables"); +function zw(r15, t8) { + $(qs(r15), () => "The f passed in variableGrads(f) must be a function"), $(t8 == null || Array.isArray(t8) && t8.every((u) => u instanceof ri), () => "The varList passed in variableGrads(f, varList) must be an array of variables"); let e = t8 != null; if (!e) { t8 = []; @@ -6555,175 +6556,175 @@ function zw(r, t8) { } let o = e ? t8.filter((u) => !u.trainable) : null, n = t8.length; t8 = t8.filter((u) => u.trainable), $(t8.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${n} variables is trainable.`); - let s = true, { value: a, grads: i } = T.gradients(r, t8, null, s); + let s = true, { value: a, grads: i } = T.gradients(r15, t8, null, s); $(i.some((u) => u != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."), $(a.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${a.rank} tensor`); let p = {}; return t8.forEach((u, c) => { i[c] != null && (p[u.name] = i[c]); }), o != null && o.forEach((u) => p[u.name] = null), { value: a, grads: p }; } -function vr(r) { - return T.customGrad(r); +function Ir(r15) { + return T.customGrad(r15); } -function Td(r) { - if (r.filter((e) => e == null).length > 0) +function kd(r15) { + if (r15.filter((e) => e == null).length > 0) throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.`); } -function MK(r) { - let e = { x: v(r, "x", "neg") }; +function MK(r15) { + let e = { x: v(r15, "x", "neg") }; return T.runKernel(pa, e); } -var cr = N({ neg_: MK }); -function LK(r) { - let e = { x: v(r, "x", "softplus") }; - return T.runKernel(ws, e); +var pr = N({ neg_: MK }); +function LK(r15) { + let e = { x: v(r15, "x", "softplus") }; + return T.runKernel(Cs, e); } -var _d = N({ softplus_: LK }); -function BK(r) { - let t8 = v(r, "x", "logSigmoid"); - return vr((o) => ({ value: cr(_d(cr(o))), gradFunc: (a) => se(a, $a(cr(o))) }))(t8); +var Nd = N({ softplus_: LK }); +function BK(r15) { + let t8 = v(r15, "x", "logSigmoid"); + return Ir((o) => ({ value: pr(Nd(pr(o))), gradFunc: (a) => se(a, $a(pr(o))) }))(t8); } -var N2 = N({ logSigmoid_: BK }); -function zK(r, t8) { - let e = v(r, "a", "sub"), o = v(t8, "b", "sub"); +var T2 = N({ logSigmoid_: BK }); +function zK(r15, t8) { + let e = v(r15, "a", "sub"), o = v(t8, "b", "sub"); [e, o] = Oe(e, o); let n = { a: e, b: o }; - return T.runKernel(_s, n); + return T.runKernel(Ts, n); } var Te = N({ sub_: zK }); -function VK(r, t8 = -1) { - let e = v(r, "logits", "logSoftmax"); +function VK(r15, t8 = -1) { + let e = v(r15, "logits", "logSoftmax"); if (t8 === -1 && (t8 = e.rank - 1), t8 !== e.rank - 1) throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${e.rank} and axis was ${t8}`); - return vr((n, s) => { - let i = Ra(n, t8, true), p = Te(n, i), u = Te(We(p, "float32"), pi(ot($o(p), t8, true))); + return Ir((n, s) => { + let i = Ra(n, t8, true), p = Te(n, i), u = Te(We(p, "float32"), ui(ot(_o(p), t8, true))); return s([u]), { value: u, gradFunc: (l, m) => { - let [d] = m, f = true, h = $o(d); + let [d] = m, f = true, h = _o(d); return Te(l, se(ot(l, t8, f), h)); } }; })(e); } -var T2 = N({ logSoftmax_: VK }); -function WK(r, t8 = null, e = false) { - let o = v(r, "x", "logSumExp"), n = Ei(t8, o.shape), s = Ra(o, n, true), a = Te(o, s), i = $o(a), p = ot(i, n), u = pi(p), c = Ce(W(s, u.shape), u); +var _2 = N({ logSoftmax_: VK }); +function WK(r15, t8 = null, e = false) { + let o = v(r15, "x", "logSumExp"), n = Ti(t8, o.shape), s = Ra(o, n, true), a = Te(o, s), i = _o(a), p = ot(i, n), u = ui(p), c = Ce(W(s, u.shape), u); if (e) { - let l = ii(c.shape, n); + let l = ai(c.shape, n); return W(c, l); } return c; } -var $d = N({ logSumExp_: WK }); -function UK(r, t8) { - let e = v(r, "a", "logicalAnd", "bool"), o = v(t8, "b", "logicalAnd", "bool"); +var Td = N({ logSumExp_: WK }); +function UK(r15, t8) { + let e = v(r15, "a", "logicalAnd", "bool"), o = v(t8, "b", "logicalAnd", "bool"); rt(e.shape, o.shape); let n = { a: e, b: o }; - return T.runKernel(Mn, n); + return T.runKernel(On, n); } -var ju = N({ logicalAnd_: UK }); -function GK(r) { - let e = { x: v(r, "x", "logicalNot", "bool") }; - return T.runKernel(Ln, e); +var Vu = N({ logicalAnd_: UK }); +function GK(r15) { + let e = { x: v(r15, "x", "logicalNot", "bool") }; + return T.runKernel(Mn, e); } -var Ed = N({ logicalNot_: GK }); -function HK(r, t8) { - let e = v(r, "a", "logicalOr", "bool"), o = v(t8, "b", "logicalOr", "bool"); +var _d = N({ logicalNot_: GK }); +function HK(r15, t8) { + let e = v(r15, "a", "logicalOr", "bool"), o = v(t8, "b", "logicalOr", "bool"); rt(e.shape, o.shape); let n = { a: e, b: o }; - return T.runKernel(Bn, n); + return T.runKernel(Ln, n); } -var Rd = N({ logicalOr_: HK }); -function KK(r, t8) { - let e = v(r, "a", "logicalXor", "bool"), o = v(t8, "b", "logicalXor", "bool"); - return rt(e.shape, o.shape), ju(Rd(r, t8), Ed(ju(r, t8))); +var $d = N({ logicalOr_: HK }); +function KK(r15, t8) { + let e = v(r15, "a", "logicalXor", "bool"), o = v(t8, "b", "logicalXor", "bool"); + return rt(e.shape, o.shape), Vu($d(r15, t8), _d(Vu(r15, t8))); } -var _2 = N({ logicalXor_: KK }); -var Dd = 2147483648; -function qK(r, t8, e = "left") { - let o = v(r, "sortedSequence", "searchSorted"), n = v(t8, "values", "searchSorted"), s = o.shape[o.shape.length - 1], a = n.shape[n.shape.length - 1], i = W(o, [-1, s]), p = W(n, [-1, a]); +var $2 = N({ logicalXor_: KK }); +var Ed = 2147483648; +function qK(r15, t8, e = "left") { + let o = v(r15, "sortedSequence", "searchSorted"), n = v(t8, "values", "searchSorted"), s = o.shape[o.shape.length - 1], a = n.shape[n.shape.length - 1], i = W(o, [-1, s]), p = W(n, [-1, a]); if (i.rank < 2) throw new Error("Sorted input argument must be at least 2-dimensional"); if (i.shape[0] !== p.shape[0]) throw new Error("Leading dimension of 'sortedSequence' and 'values' must match."); - if (He(p.shape) >= Dd) - throw new Error(`values tensor size must less than ${Dd}`); - if (i.shape[1] >= Dd) - throw new Error(`trailing dim_size must less than ${Dd} for int32 output type, was ${i.shape[1]}`); + if (He(p.shape) >= Ed) + throw new Error(`values tensor size must less than ${Ed}`); + if (i.shape[1] >= Ed) + throw new Error(`trailing dim_size must less than ${Ed} for int32 output type, was ${i.shape[1]}`); let u = { sortedSequence: i, values: p }, c = { side: e }; - return T.runKernel(hs, u, c); + return T.runKernel(fs, u, c); } -var $l = N({ searchSorted_: qK }); -function $2(r, t8) { - return $l(r, t8, "left"); +var Nl = N({ searchSorted_: qK }); +function E2(r15, t8) { + return Nl(r15, t8, "left"); } -function jK(r, t8, e, o, n) { - let s = v(r, "x", "maxPool"), a = 1, i = s, p = false; - s.rank === 3 && (p = true, i = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])), $(i.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${i.rank}.`), $(xr(e, a), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${e} and dilations '${a}'`), Bt("maxPool", o, n); - let u = { x: i }, c = { filterSize: t8, strides: e, pad: o, dimRoundingMode: n }, l = T.runKernel(Un, u, c); +function jK(r15, t8, e, o, n) { + let s = v(r15, "x", "maxPool"), a = 1, i = s, p = false; + s.rank === 3 && (p = true, i = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])), $(i.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${i.rank}.`), $(gr(e, a), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${e} and dilations '${a}'`), Lt("maxPool", o, n); + let u = { x: i }, c = { filterSize: t8, strides: e, pad: o, dimRoundingMode: n }, l = T.runKernel(Wn, u, c); return p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l; } -var Ad = N({ maxPool_: jK }); -function XK(r, t8 = [1, 1, 1], e, o, n, s = "NDHWC") { - let a = v(r, "x", "maxPool3d"), i = a, p = false; - a.rank === 4 && (p = true, i = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(i.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`), $(s === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), Bt("maxPool3d", o, n); +var Rd = N({ maxPool_: jK }); +function XK(r15, t8 = [1, 1, 1], e, o, n, s = "NDHWC") { + let a = v(r15, "x", "maxPool3d"), i = a, p = false; + a.rank === 4 && (p = true, i = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(i.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`), $(s === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), Lt("maxPool3d", o, n); let u = { x: i }, c = { filterSize: t8, strides: e, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(ia, u, c); return p ? W(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l; } -var E2 = N({ maxPool3d_: XK }); -function YK(r, t8, e, o, n = false) { - let a = { x: v(r, "x", "maxPoolWithArgmax") }, i = { filterSize: t8, strides: e, pad: o, includeBatchInIndex: n }, p = T.runKernel(ua, a, i); +var R2 = N({ maxPool3d_: XK }); +function YK(r15, t8, e, o, n = false) { + let a = { x: v(r15, "x", "maxPoolWithArgmax") }, i = { filterSize: t8, strides: e, pad: o, includeBatchInIndex: n }, p = T.runKernel(ua, a, i); return { result: p[0], indexes: p[1] }; } -var R2 = N({ maxPoolWithArgmax_: YK }); -function QK(r, t8) { - let e = v(r, "a", "maximum"), o = v(t8, "b", "maximum"); +var D2 = N({ maxPoolWithArgmax_: YK }); +function QK(r15, t8) { + let e = v(r15, "a", "maximum"), o = v(t8, "b", "maximum"); [e, o] = Oe(e, o), e.dtype === "bool" && (e = We(e, "int32"), o = We(o, "int32")), rt(e.shape, o.shape); let n = { a: e, b: o }; - return T.runKernel(Wn, n); + return T.runKernel(Vn, n); } -var Fd = N({ maximum_: QK }); -function ZK(r, t8 = null, e = false) { - let n = { x: v(r, "x", "mean") }, s = { axis: t8, keepDims: e }; - return T.runKernel(Gn, n, s); +var Dd = N({ maximum_: QK }); +function ZK(r15, t8 = null, e = false) { + let n = { x: v(r15, "x", "mean") }, s = { axis: t8, keepDims: e }; + return T.runKernel(Un, n, s); } -var Xu = N({ mean_: ZK }); -function Gr(r, t8 = "float32") { - if (wt(r), t8 === "complex64") { - let o = Gr(r, "float32"), n = Gr(r, "float32"); - return Er(o, n); +var Wu = N({ mean_: ZK }); +function Ur(r15, t8 = "float32") { + if (Ct(r15), t8 === "complex64") { + let o = Ur(r15, "float32"), n = Ur(r15, "float32"); + return $r(o, n); } - let e = Yp(He(r), t8); - return T.makeTensor(e, r, t8); + let e = Wp(He(r15), t8); + return T.makeTensor(e, r15, t8); } -function Da(r, t8 = "float32") { - if (wt(r), t8 === "complex64") { - let o = Da(r, "float32"), n = Gr(r, "float32"); - return Er(o, n); +function Da(r15, t8 = "float32") { + if (Ct(r15), t8 === "complex64") { + let o = Da(r15, "float32"), n = Ur(r15, "float32"); + return $r(o, n); } - let e = fl(He(r), t8); - return T.makeTensor(e, r, t8); + let e = cl(He(r15), t8); + return T.makeTensor(e, r15, t8); } -function D2(r, t8, { indexing: e = "xy" } = {}) { +function A2(r15, t8, { indexing: e = "xy" } = {}) { if (e !== "xy" && e !== "ij") throw new TypeError(`${e} is not a valid third argument to meshgrid`); - if (r === void 0) + if (r15 === void 0) return []; - let o = v(r, "x", "meshgrid", r instanceof ut ? r.dtype : "float32"); + let o = v(r15, "x", "meshgrid", r15 instanceof mt ? r15.dtype : "float32"); if (t8 === void 0) return [o]; - let n = v(t8, "y", "meshgrid", t8 instanceof ut ? t8.dtype : "float32"), s = He(o.shape), a = He(n.shape); + let n = v(t8, "y", "meshgrid", t8 instanceof mt ? t8.dtype : "float32"), s = He(o.shape), a = He(n.shape); return e === "xy" ? (o = W(o, [1, -1]), n = W(n, [-1, 1]), [Ze(Da([a, 1], o.dtype), o), Ze(n, Da([1, s], n.dtype))]) : (o = W(o, [-1, 1]), n = W(n, [1, -1]), [Ze(o, Da([1, a], o.dtype)), Ze(Da([s, 1], n.dtype), n)]); } -function JK(r, t8) { - let e = v(r, "a", "minimum"), o = v(t8, "b", "minimum"); +function JK(r15, t8) { + let e = v(r15, "a", "minimum"), o = v(t8, "b", "minimum"); [e, o] = Oe(e, o), e.dtype === "bool" && (e = We(e, "int32"), o = We(o, "int32")), rt(e.shape, o.shape); let n = { a: e, b: o }; - return T.runKernel(Kn, n); + return T.runKernel(Hn, n); } -var Yu = N({ minimum_: JK }); -function eq(r, t8, e) { +var Uu = N({ minimum_: JK }); +function eq(r15, t8, e) { $(e === "reflect" || e === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${e}.`); - let o = v(r, "x", "mirrorPad"); + let o = v(r15, "x", "mirrorPad"); if (o.rank === 0) throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad"); $(t8.length === o.rank, () => `Padding doesn't match input. Must be ${o.rank}. Got ${t8.length}.`); @@ -6731,28 +6732,28 @@ function eq(r, t8, e) { for (let i = 0; i < o.rank; i++) $(t8[i].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), $(t8[i][0] >= 0 && t8[i][0] <= o.shape[i] - n && t8[i][1] >= 0 && t8[i][1] <= o.shape[i] - n, () => `Padding in dimension ${i} cannot be greater than or equal to ${o.shape[i] - n} or less than 0 for input of shape ${o.shape}`); let s = { paddings: t8, mode: e }, a = { x: o }; - return T.runKernel(qn, a, s); + return T.runKernel(Kn, a, s); } -var A2 = N({ mirrorPad_: eq }); -function tq(r, t8) { - let e = v(r, "a", "mod"), o = v(t8, "b", "mod"); +var F2 = N({ mirrorPad_: eq }); +function tq(r15, t8) { + let e = v(r15, "a", "mod"), o = v(t8, "b", "mod"); [e, o] = Oe(e, o); let n = { a: e, b: o }; - return T.runKernel(jn, n); + return T.runKernel(qn, n); } -var F2 = N({ mod_: tq }); -function rq(r, t8 = null, e = false) { - r = v(r, "x", "moments"); - let o = Ei(t8, r.shape), n = Xu(r, o, e), s = n.shape; - e || (s = ii(n.shape, o)); - let a = er(Te(We(r, "float32"), W(n, s))), i = Xu(a, o, e); +var P2 = N({ mod_: tq }); +function rq(r15, t8 = null, e = false) { + r15 = v(r15, "x", "moments"); + let o = Ti(t8, r15.shape), n = Wu(r15, o, e), s = n.shape; + e || (s = ai(n.shape, o)); + let a = Zt(Te(We(r15, "float32"), W(n, s))), i = Wu(a, o, e); return { mean: n, variance: i }; } -var P2 = N({ moments_: rq }); -function oq(r, t8, e, o) { - let n = v(t8, "data", "multiRNNCell"), s = si(e, "c", "multiRNNCell"), a = si(o, "h", "multiRNNCell"), i = n, p = []; - for (let l = 0; l < r.length; l++) { - let m = r[l](i, s[l], a[l]); +var O2 = N({ moments_: rq }); +function oq(r15, t8, e, o) { + let n = v(t8, "data", "multiRNNCell"), s = ni(e, "c", "multiRNNCell"), a = ni(o, "h", "multiRNNCell"), i = n, p = []; + for (let l = 0; l < r15.length; l++) { + let m = r15[l](i, s[l], a[l]); p.push(m[0]), p.push(m[1]), i = m[1]; } let u = [], c = []; @@ -6760,123 +6761,123 @@ function oq(r, t8, e, o) { u.push(p[l]), c.push(p[l + 1]); return [u, c]; } -var O2 = N({ multiRNNCell_: oq }); -function nq(r, t8, e, o = false) { - let n = v(r, "logits", "multinomial"), s = n.size, a = n.rank; +var M2 = N({ multiRNNCell_: oq }); +function nq(r15, t8, e, o = false) { + let n = v(r15, "logits", "multinomial"), s = n.size, a = n.rank; if (s < 2) throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${s}.`); if (a > 2) throw new Error(`Rank of probabilities must be 1 or 2, but is ${a}`); e = e || Math.random(); - let p = { logits: a === 1 ? W(n, [1, -1]) : n }, u = { numSamples: t8, seed: e, normalized: o }, c = T.runKernel(Xn, p, u); + let p = { logits: a === 1 ? W(n, [1, -1]) : n }, u = { numSamples: t8, seed: e, normalized: o }, c = T.runKernel(jn, p, u); return a === 1 ? W(c, [c.size]) : c; } -var M2 = N({ multinomial_: nq }); -function sq(r, t8) { - let e = v(r, "a", "notEqual", "string_or_numeric"), o = v(t8, "b", "notEqual", "string_or_numeric"); +var L2 = N({ multinomial_: nq }); +function sq(r15, t8) { + let e = v(r15, "a", "notEqual", "string_or_numeric"), o = v(t8, "b", "notEqual", "string_or_numeric"); [e, o] = Oe(e, o), rt(e.shape, o.shape); let n = { a: e, b: o }; - return T.runKernel(Qn, n); + return T.runKernel(Yn, n); } -var Pd = N({ notEqual_: sq }); -function aq(r, t8, e = 1, o = 0, n = "int32") { +var Ad = N({ notEqual_: sq }); +function aq(r15, t8, e = 1, o = 0, n = "int32") { if (t8 < 2) throw new Error(`Error in oneHot: depth must be >=2, but it is ${t8}`); - let a = { indices: v(r, "indices", "oneHot", "int32") }, i = { dtype: n, depth: t8, onValue: e, offValue: o }; - return T.runKernel(es, a, i); + let a = { indices: v(r15, "indices", "oneHot", "int32") }, i = { dtype: n, depth: t8, onValue: e, offValue: o }; + return T.runKernel(Jn, a, i); } -var El = N({ oneHot_: aq }); -function iq(r) { - let e = { x: v(r, "x", "onesLike") }; +var Tl = N({ oneHot_: aq }); +function iq(r15) { + let e = { x: v(r15, "x", "onesLike") }; return T.runKernel(ca, e); } -var L2 = N({ onesLike_: iq }); -function uq(r, t8) { - let e = v(r, "v1", "outerProduct"), o = v(t8, "v2", "outerProduct"); +var B2 = N({ onesLike_: iq }); +function uq(r15, t8) { + let e = v(r15, "v1", "outerProduct"), o = v(t8, "v2", "outerProduct"); $(e.rank === 1 && o.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${e.rank} and ${o.rank}.`); let n = W(e, [-1, 1]), s = W(o, [1, -1]); return Ze(n, s); } -var B2 = N({ outerProduct_: uq }); -function pq(r, t8, e = 0) { - let o = v(r, "x", "pad"); +var z2 = N({ outerProduct_: uq }); +function pq(r15, t8, e = 0) { + let o = v(r15, "x", "pad"); if (o.rank === 0) throw new Error("pad(scalar) is not defined. Pass non-scalar to pad"); let n = { paddings: t8, constantValue: e }, s = { x: o }; - return T.runKernel(ts, s, n); + return T.runKernel(es, s, n); } var Aa = N({ pad_: pq }); -function cq(r, t8, e = 0) { - return $(t8.length === 2, () => "Invalid number of paddings. Must be length of 2."), Aa(r, [t8], e); +function cq(r15, t8, e = 0) { + return $(t8.length === 2, () => "Invalid number of paddings. Must be length of 2."), Aa(r15, [t8], e); } -var z2 = N({ pad1d_: cq }); -function lq(r, t8, e = 0) { - return $(t8.length === 2 && t8[0].length === 2 && t8[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Aa(r, t8, e); +var V2 = N({ pad1d_: cq }); +function lq(r15, t8, e = 0) { + return $(t8.length === 2 && t8[0].length === 2 && t8[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Aa(r15, t8, e); } -var V2 = N({ pad2d_: lq }); -function mq(r, t8, e = 0) { - return $(t8.length === 3 && t8[0].length === 2 && t8[1].length === 2 && t8[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Aa(r, t8, e); +var W2 = N({ pad2d_: lq }); +function mq(r15, t8, e = 0) { + return $(t8.length === 3 && t8[0].length === 2 && t8[1].length === 2 && t8[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Aa(r15, t8, e); } -var W2 = N({ pad3d_: mq }); -function dq(r, t8, e = 0) { - return $(t8.length === 4 && t8[0].length === 2 && t8[1].length === 2 && t8[2].length === 2 && t8[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Aa(r, t8, e); +var U2 = N({ pad3d_: mq }); +function dq(r15, t8, e = 0) { + return $(t8.length === 4 && t8[0].length === 2 && t8[1].length === 2 && t8[2].length === 2 && t8[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Aa(r15, t8, e); } -var U2 = N({ pad4d_: dq }); -function fq(r, t8, e) { - let o = v(r, "x", "spaceToBatchND"); +var G2 = N({ pad4d_: dq }); +function fq(r15, t8, e) { + let o = v(r15, "x", "spaceToBatchND"); $(o.rank >= 1 + t8.length, () => `input rank ${o.rank} should be > than [blockShape] ${t8.length}`), $(e.length === t8.length, () => `paddings.shape[0] ${e.length} must be equal to [blockShape] ${t8.length}`), $(o.shape.reduce((a, i, p) => p > 0 && p <= t8.length ? a && (i + e[p - 1][0] + e[p - 1][1]) % t8[p - 1] === 0 : a, true), () => `input spatial dimensions ${o.shape.slice(1)} with paddings ${e.toString()} must be divisible by blockShapes ${t8.toString()}`); let n = { x: o }, s = { blockShape: t8, paddings: e }; return T.runKernel(ga, n, s); } -var Od = N({ spaceToBatchND_: fq }); -function hq(r, t8, e, o, n, s, a) { +var Fd = N({ spaceToBatchND_: fq }); +function hq(r15, t8, e, o, n, s, a) { n == null && (n = [1, 1]), s == null && (s = 1), o === 0 && (o = "valid"); - let i = v(r, "x", "maxPool"), p = i, u = false; - i.rank === 3 && (u = true, p = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(xr(s, n), () => `Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${n}'`); + let i = v(r15, "x", "maxPool"), p = i, u = false; + i.rank === 3 && (u = true, p = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(gr(s, n), () => `Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${n}'`); let c = Mw(p.shape, t8, s, n, o), l = [c.dilationHeight, c.dilationWidth], m; o === "same" ? m = xq([c.filterHeight, c.filterWidth], l) : m = [[0, 0], [0, 0]]; - let d = l[0] === 1 && l[1] === 1, [f, h] = gq([c.inHeight, c.inWidth], l, m), g = d ? o : "valid", x = d ? p : Od(p, l, f), C = (e === "avg" ? () => fd(x, t8, s, g, a) : () => Ad(x, t8, s, g, a))(), S = d ? C : hd(C, l, h); + let d = l[0] === 1 && l[1] === 1, [f, h] = gq([c.inHeight, c.inWidth], l, m), g = d ? o : "valid", x = d ? p : Fd(p, l, f), C = (e === "avg" ? () => md(x, t8, s, g, a) : () => Rd(x, t8, s, g, a))(), S = d ? C : dd(C, l, h); return u ? W(S, [S.shape[1], S.shape[2], S.shape[3]]) : S; } -function gq(r, t8, e) { - let o = e.map((c) => c[0]), n = e.map((c) => c[1]), s = r.concat(o, n), a = t8.map((c, l) => (c - s[l] % c) % c), i = n.map((c, l) => c + a[l]), p = t8.map((c, l) => [o[l], i[l]]), u = t8.map((c, l) => [0, a[l]]); +function gq(r15, t8, e) { + let o = e.map((c) => c[0]), n = e.map((c) => c[1]), s = r15.concat(o, n), a = t8.map((c, l) => (c - s[l] % c) % c), i = n.map((c, l) => c + a[l]), p = t8.map((c, l) => [o[l], i[l]]), u = t8.map((c, l) => [0, a[l]]); return [p, u]; } -function xq(r, t8) { - let o = r.map((a, i) => a + (a - 1) * (t8[i] - 1)).map((a) => a - 1), n = o.map((a) => Math.floor(a / 2)), s = o.map((a, i) => a - n[i]); +function xq(r15, t8) { + let o = r15.map((a, i) => a + (a - 1) * (t8[i] - 1)).map((a) => a - 1), n = o.map((a) => Math.floor(a / 2)), s = o.map((a, i) => a - n[i]); return o.map((a, i) => [n[i], s[i]]); } -var G2 = N({ pool_: hq }); -function yq(r, t8) { - let e = v(r, "x", "prelu"), o = v(t8, "alpha", "prelu"), n = { x: e, alpha: o }; - return T.runKernel(os, n); +var H2 = N({ pool_: hq }); +function yq(r15, t8) { + let e = v(r15, "x", "prelu"), o = v(t8, "alpha", "prelu"), n = { x: e, alpha: o }; + return T.runKernel(rs, n); } -var Md = N({ prelu_: yq }); -function bq(r, t8 = null, e = false) { - let o = v(r, "x", "prod"); +var Pd = N({ prelu_: yq }); +function bq(r15, t8 = null, e = false) { + let o = v(r15, "x", "prod"); o.dtype === "bool" && (o = We(o, "int32")); let n = { x: o }, s = { axis: t8, keepDims: e }; - return T.runKernel(ns, n, s); + return T.runKernel(os, n, s); } -var H2 = N({ prod_: bq }); -function Cq(r, t8, e, o) { - let n = r.map((c, l) => v(c, `tensors${l}`, "raggedGather", "int32")), s = v(t8, "paramsDenseValues", "raggedGather"), a = v(e, "indices", "raggedGather", "int32"), i = { paramsNestedSplits: n, paramsDenseValues: s, indices: a }, p = { outputRaggedRank: o }, u = T.runKernel(Qp, i, p); +var K2 = N({ prod_: bq }); +function Cq(r15, t8, e, o) { + let n = r15.map((c, l) => v(c, `tensors${l}`, "raggedGather", "int32")), s = v(t8, "paramsDenseValues", "raggedGather"), a = v(e, "indices", "raggedGather", "int32"), i = { paramsNestedSplits: n, paramsDenseValues: s, indices: a }, p = { outputRaggedRank: o }, u = T.runKernel(Up, i, p); return { outputNestedSplits: u.slice(0, u.length - 1), outputDenseValues: u[u.length - 1] }; } -var K2 = N({ raggedGather_: Cq }); -function wq(r, t8, e) { - let o = v(r, "starts", "raggedRange"), n = v(t8, "limits", "raggedRange", o.dtype), s = v(e, "deltas", "raggedRange", o.dtype), a = { starts: o, limits: n, deltas: s }, i = T.runKernel(Zp, a); +var q2 = N({ raggedGather_: Cq }); +function wq(r15, t8, e) { + let o = v(r15, "starts", "raggedRange"), n = v(t8, "limits", "raggedRange", o.dtype), s = v(e, "deltas", "raggedRange", o.dtype), a = { starts: o, limits: n, deltas: s }, i = T.runKernel(Gp, a); return { rtNestedSplits: i[0], rtDenseValues: i[1] }; } -var q2 = N({ raggedRange_: wq }); -function Sq(r, t8, e, o, n) { - let s = v(r, "shape", "raggedTensorToTensor", "int32"), a = v(t8, "values", "raggedTensorToTensor"), i = v(e, "defaultValue", "raggedTensorToTensor", a.dtype), p = o.map((l, m) => v(l, `tensors${m}`, "raggedTensorToTensor", "int32")), u = { shape: s, values: a, defaultValue: i, rowPartitionTensors: p }, c = { rowPartitionTypes: n }; - return T.runKernel(Jp, u, c); +var j2 = N({ raggedRange_: wq }); +function Sq(r15, t8, e, o, n) { + let s = v(r15, "shape", "raggedTensorToTensor", "int32"), a = v(t8, "values", "raggedTensorToTensor"), i = v(e, "defaultValue", "raggedTensorToTensor", a.dtype), p = o.map((l, m) => v(l, `tensors${m}`, "raggedTensorToTensor", "int32")), u = { shape: s, values: a, defaultValue: i, rowPartitionTensors: p }, c = { rowPartitionTypes: n }; + return T.runKernel(Hp, u, c); } -var j2 = N({ raggedTensorToTensor_: Sq }); -function Iq(r, t8, e) { - wt(r); - let o = He(r), n = null; +var X2 = N({ raggedTensorToTensor_: Sq }); +function Iq(r15, t8, e) { + Ct(r15); + let o = He(r15), n = null; if (e == null || e === "float32") n = new Float32Array(o); else if (e === "int32") @@ -6887,33 +6888,33 @@ function Iq(r, t8, e) { throw new Error(`Unknown data type ${e}`); for (let s = 0; s < o; s++) n[s] = t8(); - return T.makeTensor(n, r, e); + return T.makeTensor(n, r15, e); } -var X2 = N({ rand_: Iq }); -var Wd = Kp(qw()); -var h1 = {}; -qe(h1, { TEST_EPSILON_FLOAT16: () => m1, createVideoElement: () => Oq, encodeStrings: () => f1, expectArrayBuffersEqual: () => Pq, expectArraysClose: () => Rq, expectArraysEqual: () => Aq, expectNumbersClose: () => d1, expectPromiseToFail: () => Dq, expectValuesInRange: () => Fq, play: () => Mq, testEpsilon: () => Bd }); +var Y2 = N({ rand_: Iq }); +var zd = Lp(qw()); +var g1 = {}; +qe(g1, { TEST_EPSILON_FLOAT16: () => d1, createVideoElement: () => Oq, encodeStrings: () => h1, expectArrayBuffersEqual: () => Pq, expectArraysClose: () => Rq, expectArraysEqual: () => Aq, expectNumbersClose: () => f1, expectPromiseToFail: () => Dq, expectValuesInRange: () => Fq, play: () => Mq, testEpsilon: () => Md }); var Eq = 1e-3; -var m1 = 0.1; -function Rq(r, t8, e) { - return e == null && (e = Bd()), jw(r, t8, (o, n) => Xw(o, n, e)); +var d1 = 0.1; +function Rq(r15, t8, e) { + return e == null && (e = Md()), jw(r15, t8, (o, n) => Xw(o, n, e)); } -function Bd() { - return T.backend.floatPrecision() === 32 ? Eq : m1; +function Md() { + return T.backend.floatPrecision() === 32 ? Eq : d1; } -function jw(r, t8, e) { +function jw(r15, t8, e) { let o = true; - if ((Ot(r) || Ot(t8)) && (o = false), Ot(r) && Ot(t8) && (o = true), o) { - let a = r.constructor.name, i = t8.constructor.name; + if ((Pt(r15) || Pt(t8)) && (o = false), Pt(r15) && Pt(t8) && (o = true), o) { + let a = r15.constructor.name, i = t8.constructor.name; if (a !== i) throw new Error(`Arrays are of different type. Actual: ${a}. Expected: ${i}`); } - if (Array.isArray(r) && Array.isArray(t8)) { - let a = ir(r), i = ir(t8); - if (!Cr(a, i)) + if (Array.isArray(r15) && Array.isArray(t8)) { + let a = sr(r15), i = sr(t8); + if (!br(a, i)) throw new Error(`Arrays have different shapes. Actual: [${a}]. Expected: [${i}]`); } - let n = Ot(r) ? r : Ps(r), s = Ot(t8) ? t8 : Ps(t8); + let n = Pt(r15) ? r15 : Fs(r15), s = Pt(t8) ? t8 : Fs(t8); if (n.length !== s.length) throw new Error(`Arrays have different lengths actual: ${n.length} vs expected: ${s.length}. Actual: ${n}. @@ -6927,57 +6928,57 @@ Expected: ${s}.`); } typeof expect != "undefined" && expect().nothing(); } -function Dq(r, t8) { - r().then(() => t8.fail(), () => t8()), typeof expect != "undefined" && expect().nothing(); +function Dq(r15, t8) { + r15().then(() => t8.fail(), () => t8()), typeof expect != "undefined" && expect().nothing(); } -function Aq(r, t8) { +function Aq(r15, t8) { let e = typeof t8 == "string" || typeof t8 == "number" || typeof t8 == "boolean" ? [t8] : t8; - return Vo(r) || Vo(r[0]) || Vo(t8) || Vo(t8[0]) ? jw(r, e, (o, n) => o == n) : jw(r, t8, (o, n) => Xw(o, n, 0)); + return zo(r15) || zo(r15[0]) || zo(t8) || zo(t8[0]) ? jw(r15, e, (o, n) => o == n) : jw(r15, t8, (o, n) => Xw(o, n, 0)); } -function d1(r, t8, e) { - if (e == null && (e = Bd()), !Xw(r, t8, e)) - throw new Error(`Numbers differ: actual === ${r}, expected === ${t8}`); +function f1(r15, t8, e) { + if (e == null && (e = Md()), !Xw(r15, t8, e)) + throw new Error(`Numbers differ: actual === ${r15}, expected === ${t8}`); typeof expect != "undefined" && expect().nothing(); } -function Xw(r, t8, e) { - return !isFinite(r) && !isFinite(t8) ? true : !(isNaN(r) || isNaN(t8) || Math.abs(r - t8) > e); +function Xw(r15, t8, e) { + return !isFinite(r15) && !isFinite(t8) ? true : !(isNaN(r15) || isNaN(t8) || Math.abs(r15 - t8) > e); } -function Fq(r, t8, e) { - for (let o = 0; o < r.length; o++) - if (r[o] < t8 || r[o] > e) - throw new Error(`Value out of range:${r[o]} low: ${t8}, high: ${e}`); +function Fq(r15, t8, e) { + for (let o = 0; o < r15.length; o++) + if (r15[o] < t8 || r15[o] > e) + throw new Error(`Value out of range:${r15[o]} low: ${t8}, high: ${e}`); } -function Pq(r, t8) { - let e = new Float32Array(r), o = new Float32Array(t8); +function Pq(r15, t8) { + let e = new Float32Array(r15), o = new Float32Array(t8); if (e.length !== o.length) throw new Error(`Expected ArrayBuffer to be of length ${o.length}, but it was ${e.length}`); for (let n = 0; n < o.length; n++) if (e[n] !== o[n]) throw new Error(`Expected ArrayBuffer value at ${n} to be ${o[n]} but got ${e[n]} instead`); } -function f1(r) { - for (let t8 = 0; t8 < r.length; t8++) { - let e = r[t8]; - Array.isArray(e) ? f1(e) : r[t8] = tu(e); +function h1(r15) { + for (let t8 = 0; t8 < r15.length; t8++) { + let e = r15[t8]; + Array.isArray(e) ? h1(e) : r15[t8] = Zi(e); } - return r; + return r15; } -function Oq(r) { +function Oq(r15) { let t8 = document.createElement("video"); - return "playsInline" in t8 && (t8.playsInline = true), t8.muted = true, t8.loop = true, t8.style.position = "fixed", t8.style.left = "0px", t8.style.top = "0px", t8.preload = "auto", t8.appendChild(r), new Promise((e) => { + return "playsInline" in t8 && (t8.playsInline = true), t8.muted = true, t8.loop = true, t8.style.position = "fixed", t8.style.left = "0px", t8.style.top = "0px", t8.preload = "auto", t8.appendChild(r15), new Promise((e) => { t8.addEventListener("loadeddata", (o) => e(t8)), t8.load(); }); } -async function Mq(r) { - await r.play(), "requestVideoFrameCallback" in r && await new Promise((t8) => { - r.requestVideoFrameCallback(t8); +async function Mq(r15) { + await r15.play(), "requestVideoFrameCallback" in r15 && await new Promise((t8) => { + r15.requestVideoFrameCallback(t8); }); } -var Zu = class { +var Hu = class { constructor(t8, e, o, n, s) { this.mean = t8, this.stdDev = e, this.dtype = o, this.nextVal = NaN, this.truncated = n, this.truncated && (this.upper = this.mean + this.stdDev * 2, this.lower = this.mean - this.stdDev * 2); let a = s || Math.random(); - this.random = Wd.alea(a.toString()); + this.random = zd.alea(a.toString()); } nextValue() { if (!isNaN(this.nextVal)) { @@ -7002,11 +7003,11 @@ var Zu = class { return t8 <= this.upper && t8 >= this.lower; } }; -var zd = class { +var Ld = class { constructor(t8, e, o, n) { this.alpha = t8, this.beta = 1 / e, this.dtype = o; let s = n || Math.random(); - this.randu = Wd.alea(s.toString()), this.randn = new Zu(0, 1, o, false, this.randu()), t8 < 1 ? this.d = t8 + 2 / 3 : this.d = t8 - 1 / 3, this.c = 1 / Math.sqrt(9 * this.d); + this.randu = zd.alea(s.toString()), this.randn = new Hu(0, 1, o, false, this.randu()), t8 < 1 ? this.d = t8 + 2 / 3 : this.d = t8 - 1 / 3, this.c = 1 / Math.sqrt(9 * this.d); } nextValue() { let t8, e, o, n, s, a; @@ -7023,11 +7024,11 @@ var zd = class { return this.dtype === "float32" ? t8 : Math.round(t8); } }; -var Vd = class { +var Bd = class { constructor(t8 = 0, e = 1, o, n) { if (this.canReturnFloat = () => this.dtype == null || this.dtype === "float32", this.min = t8, this.range = e - t8, this.dtype = o, n == null && (n = Math.random()), typeof n == "number" && (n = n.toString()), !this.canReturnFloat() && this.range <= 1) throw new Error(`The difference between ${t8} - ${e} <= 1 and dtype is not float`); - this.random = Wd.alea(n); + this.random = zd.alea(n); } convertValue(t8) { return this.canReturnFloat() ? t8 : Math.round(t8); @@ -7036,121 +7037,121 @@ var Vd = class { return this.convertValue(this.min + this.range * this.random()); } }; -function Lq(r, t8, e = 1, o = "float32", n) { - if (wt(r), e == null && (e = 1), o == null && (o = "float32"), o !== "float32" && o !== "int32") +function Lq(r15, t8, e = 1, o = "float32", n) { + if (Ct(r15), e == null && (e = 1), o == null && (o = "float32"), o !== "float32" && o !== "int32") throw new Error(`Unsupported data type ${o}`); - let s = new zd(t8, e, o, n), a = me(r, o); + let s = new Ld(t8, e, o, n), a = me(r15, o); for (let i = 0; i < a.values.length; i++) a.values[i] = s.nextValue(); return a.toTensor(); } -var g1 = N({ randomGamma_: Lq }); -function Bq(r, t8 = 0, e = 1, o, n) { - if (wt(r), o != null && o === "bool") +var x1 = N({ randomGamma_: Lq }); +function Bq(r15, t8 = 0, e = 1, o, n) { + if (Ct(r15), o != null && o === "bool") throw new Error(`Unsupported data type ${o}`); - let s = new Zu(t8, e, o, false, n), a = me(r, o); + let s = new Hu(t8, e, o, false, n), a = me(r15, o); for (let i = 0; i < a.values.length; i++) a.values[i] = s.nextValue(); return a.toTensor(); } -var Ud = N({ randomNormal_: Bq }); -function zq(r, t8, e) { +var Vd = N({ randomNormal_: Bq }); +function zq(r15, t8, e) { if (t8 != null && t8 === "bool") throw new Error(`Unsupported data type ${t8}`); - return Ud(r, 0, 1, t8, e); + return Vd(r15, 0, 1, t8, e); } -var x1 = N({ randomStandardNormal_: zq }); -function Vq(r, t8 = 0, e = 1, o = "float32", n) { - wt(r); - let s = me(r, o), a = new Vd(t8, e, null, n); +var y1 = N({ randomStandardNormal_: zq }); +function Vq(r15, t8 = 0, e = 1, o = "float32", n) { + Ct(r15); + let s = me(r15, o), a = new Bd(t8, e, null, n); for (let i = 0; i < s.values.length; i++) s.values[i] = a.nextValue(); return s.toTensor(); } -var dc = N({ randomUniform_: Vq }); -function Wq(r, t8, e, o) { - return dc(r, t8, e, "int32", o); +var sc = N({ randomUniform_: Vq }); +function Wq(r15, t8, e, o) { + return sc(r15, t8, e, "int32", o); } -var y1 = N({ randomUniformInt_: Wq }); -function mu(r, t8, e = 1, o = "float32") { +var b1 = N({ randomUniformInt_: Wq }); +function uu(r15, t8, e = 1, o = "float32") { if (e === 0) throw new Error("Cannot have a step of zero"); - let n = { start: r, stop: t8, step: e, dtype: o }; + let n = { start: r15, stop: t8, step: e, dtype: o }; return T.runKernel(ma, {}, n); } -function Uq(r) { - let e = { input: v(r, "input", "real") }; - return T.runKernel(qi, e); +function Uq(r15) { + let e = { input: v(r15, "input", "real") }; + return T.runKernel(Gi, e); } -var ci = N({ real_: Uq }); -function Gq(r) { - let e = { x: v(r, "x", "reciprocal") }; +var pi = N({ real_: Uq }); +function Gq(r15) { + let e = { x: v(r15, "x", "reciprocal") }; + return T.runKernel(ns, e); +} +var C1 = N({ reciprocal_: Gq }); +function Hq(r15) { + let e = { x: v(r15, "x", "relu") }; return T.runKernel(ss, e); } -var b1 = N({ reciprocal_: Gq }); -function Hq(r) { - let e = { x: v(r, "x", "relu") }; - return T.runKernel(as, e); +var pu = N({ relu_: Hq }); +function Kq(r15) { + let e = { x: v(r15, "x", "relu6") }; + return T.runKernel(us, e); } -var du = N({ relu_: Hq }); -function Kq(r) { - let e = { x: v(r, "x", "relu6") }; - return T.runKernel(ps, e); +var Wd = N({ relu6_: Kq }); +function qq(r15, t8) { + let o = { x: v(r15, "x", "reverse") }, n = { dims: t8 }; + return T.runKernel(ps, o, n); } -var Gd = N({ relu6_: Kq }); -function qq(r, t8) { - let o = { x: v(r, "x", "reverse") }, n = { dims: t8 }; - return T.runKernel(cs, o, n); +var lo = N({ reverse_: qq }); +function jq(r15) { + let t8 = v(r15, "x", "reverse"); + return $(t8.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${t8.rank}.`), lo(t8, 0); } -var mo = N({ reverse_: qq }); -function jq(r) { - let t8 = v(r, "x", "reverse"); - return $(t8.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${t8.rank}.`), mo(t8, 0); +var w1 = N({ reverse1d_: jq }); +function Xq(r15, t8) { + let e = v(r15, "x", "reverse"); + return $(e.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${e.rank}.`), lo(e, t8); } -var C1 = N({ reverse1d_: jq }); -function Xq(r, t8) { - let e = v(r, "x", "reverse"); - return $(e.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${e.rank}.`), mo(e, t8); +var S1 = N({ reverse2d_: Xq }); +function Yq(r15, t8) { + let e = v(r15, "x", "reverse"); + return $(e.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${e.rank}.`), lo(e, t8); } -var w1 = N({ reverse2d_: Xq }); -function Yq(r, t8) { - let e = v(r, "x", "reverse"); - return $(e.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${e.rank}.`), mo(e, t8); +var I1 = N({ reverse3d_: Yq }); +function Qq(r15, t8) { + let e = v(r15, "x", "reverse"); + return $(e.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${e.rank}.`), lo(e, t8); } -var S1 = N({ reverse3d_: Yq }); -function Qq(r, t8) { - let e = v(r, "x", "reverse"); - return $(e.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${e.rank}.`), mo(e, t8); +var v1 = N({ reverse4d_: Qq }); +function Zq(r15) { + let e = { x: v(r15, "x", "round") }; + return T.runKernel(cs, e); } -var I1 = N({ reverse4d_: Qq }); -function Zq(r) { - let e = { x: v(r, "x", "round") }; +var Ud = N({ round_: Zq }); +function Jq(r15) { + let e = { x: v(r15, "x", "rsqrt", "float32") }; return T.runKernel(ls, e); } -var Hd = N({ round_: Zq }); -function Jq(r) { - let e = { x: v(r, "x", "rsqrt", "float32") }; - return T.runKernel(ms, e); +var k1 = N({ rsqrt_: Jq }); +function e6(r15) { + let e = { x: v(r15, "x", "selu") }; + return T.runKernel(hs, e); } -var v1 = N({ rsqrt_: Jq }); -function e6(r) { - let e = { x: v(r, "x", "selu") }; - return T.runKernel(gs, e); -} -var k1 = N({ selu_: e6 }); -function t6(r, t8, e, o, n, s = [1, 1], a = "NHWC") { - let i = v(r, "x", "separableConv2d"), p = v(t8, "depthwiseFilter", "separableConv2d"), u = v(e, "pointwiseFilter", "separableConv2d"), c = i, l = false; +var N1 = N({ selu_: e6 }); +function t6(r15, t8, e, o, n, s = [1, 1], a = "NHWC") { + let i = v(r15, "x", "separableConv2d"), p = v(t8, "depthwiseFilter", "separableConv2d"), u = v(e, "pointwiseFilter", "separableConv2d"), c = i, l = false; if (i.rank === 3 && (l = true, c = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), a === "NCHW") throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported"); $(c.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`), $(p.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${p.rank}.`), $(u.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${p.rank}.`), $(u.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${u.shape[0]}.`), $(u.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`); let m = p.shape[2], d = p.shape[3]; $(u.shape[2] === m * d, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${m * d}, but got ${u.shape[2]}.`); - let f = lc(c, p, o, n, a, s), g = uu(f, u, 1, "valid", a); + let f = oc(c, p, o, n, a, s), g = nu(f, u, 1, "valid", a); return l ? W(g, [g.shape[1], g.shape[2], g.shape[3]]) : g; } -var N1 = N({ separableConv2d_: t6 }); -async function r6(r, t8) { - let e = v(r, "x", "setdiff1d"), o = v(t8, "y", "setdiff1d"); +var T1 = N({ separableConv2d_: t6 }); +async function r6(r15, t8) { + let e = v(r15, "x", "setdiff1d"), o = v(t8, "y", "setdiff1d"); $(e.dtype === o.dtype, () => `x and y should have the same dtype, but got x (${e.dtype}) and y (${o.dtype}).`), $(e.rank === 1, () => `x should be 1D tensor, but got x (${e.shape}).`), $(o.rank === 1, () => `y should be 1D tensor, but got y (${o.shape}).`); let n = await e.data(), s = await o.data(), a = new Set(s), i = 0; for (let c = 0; c < n.length; c++) @@ -7160,211 +7161,211 @@ async function r6(r, t8) { a.has(n[c]) || (p.values[l] = n[c], u.values[l] = c, l++); return [p.toTensor(), u.toTensor()]; } -var T1 = r6; -function o6(r) { - let e = { x: v(r, "x", "sign") }; - return T.runKernel(bs, e); -} -var _1 = N({ sign_: o6 }); -function n6(r) { - let e = { x: v(r, "x", "sin", "float32") }; - return T.runKernel(xs, e); -} -var $1 = N({ sin_: n6 }); -function s6(r) { - let e = { x: v(r, "x", "sinh") }; +var _1 = r6; +function o6(r15) { + let e = { x: v(r15, "x", "sign") }; return T.runKernel(ys, e); } -var E1 = N({ sinh_: s6 }); -function a6(r, t8, e) { - let o = v(r, "x", "slice1d"); +var $1 = N({ sign_: o6 }); +function n6(r15) { + let e = { x: v(r15, "x", "sin", "float32") }; + return T.runKernel(gs, e); +} +var E1 = N({ sin_: n6 }); +function s6(r15) { + let e = { x: v(r15, "x", "sinh") }; + return T.runKernel(xs, e); +} +var R1 = N({ sinh_: s6 }); +function a6(r15, t8, e) { + let o = v(r15, "x", "slice1d"); return $(o.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${o.rank} tensor`), Xe(o, [t8], [e]); } -var R1 = N({ slice1d_: a6 }); -function i6(r, t8, e) { - let o = v(r, "x", "slice2d"); +var D1 = N({ slice1d_: a6 }); +function i6(r15, t8, e) { + let o = v(r15, "x", "slice2d"); return $(o.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${o.rank} tensor`), Xe(o, t8, e); } -var D1 = N({ slice2d_: i6 }); -function u6(r, t8, e) { - let o = v(r, "x", "slice3d"); +var A1 = N({ slice2d_: i6 }); +function u6(r15, t8, e) { + let o = v(r15, "x", "slice3d"); return $(o.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${o.rank} tensor`), Xe(o, t8, e); } -var A1 = N({ slice3d_: u6 }); -function p6(r, t8, e) { - let o = v(r, "x", "slice4d"); +var F1 = N({ slice3d_: u6 }); +function p6(r15, t8, e) { + let o = v(r15, "x", "slice4d"); return $(o.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${o.rank} tensor`), Xe(o, t8, e); } -var F1 = N({ slice4d_: p6 }); -function c6(r, t8 = -1) { - let e = v(r, "logits", "softmax", "float32"); +var P1 = N({ slice4d_: p6 }); +function c6(r15, t8 = -1) { + let e = v(r15, "logits", "softmax", "float32"); if (t8 === -1 && (t8 = e.rank - 1), t8 !== e.rank - 1) throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${e.rank} and dim was ${t8}`); let o = { logits: e }, n = { dim: t8 }; - return T.runKernel(vs, o, n); + return T.runKernel(Is, o, n); } -var P1 = N({ softmax_: c6 }); -function l6(r) { - $(r.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${r.dtype}.`); - let t8 = { input: r }; - return T.runKernel(Wi, t8); +var O1 = N({ softmax_: c6 }); +function l6(r15) { + $(r15.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${r15.dtype}.`); + let t8 = { input: r15 }; + return T.runKernel(Bi, t8); } -var fc = N({ fft_: l6 }); -function m6(r) { - $(r.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${r.dtype}.`); - let t8 = { input: r }; - return T.runKernel(Ui, t8); +var ac = N({ fft_: l6 }); +function m6(r15) { + $(r15.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${r15.dtype}.`); + let t8 = { input: r15 }; + return T.runKernel(zi, t8); } -var Ju = N({ ifft_: m6 }); -function d6(r) { - let t8 = r.shape[r.shape.length - 1], e = r.size / t8, o; +var Ku = N({ ifft_: m6 }); +function d6(r15) { + let t8 = r15.shape[r15.shape.length - 1], e = r15.size / t8, o; if (t8 <= 2) { - let n = W(r, [e, t8]); - o = Ju(n); + let n = W(r15, [e, t8]); + o = Ku(n); } else { - let n = [e, 2 * (t8 - 1)], s = W(ci(r), [e, t8]), a = W(lu(r), [e, t8]), i = mo(Xe(s, [0, 1], [e, t8 - 2]), 1), p = se(mo(Xe(a, [0, 1], [e, t8 - 2]), 1), ke(-1)), u = bt([s, i], 1), c = bt([a, p], 1), l = W(Er(u, c), [n[0], n[1]]); - o = Ju(l); + let n = [e, 2 * (t8 - 1)], s = W(pi(r15), [e, t8]), a = W(iu(r15), [e, t8]), i = lo(Xe(s, [0, 1], [e, t8 - 2]), 1), p = se(lo(Xe(a, [0, 1], [e, t8 - 2]), 1), ke(-1)), u = yt([s, i], 1), c = yt([a, p], 1), l = W($r(u, c), [n[0], n[1]]); + o = Ku(l); } - if (o = ci(o), r.rank === 3 && r.shape[0] !== 0) { - let n = o, s = r.shape[0]; + if (o = pi(o), r15.rank === 3 && r15.shape[0] !== 0) { + let n = o, s = r15.shape[0]; o = W(o, [s, o.shape[0] / s, o.shape[1]]), n.dispose(); } return o; } -var Kd = N({ irfft_: d6 }); -function f6(r, t8, e = 0) { - let n = { x: v(r, "x", "split") }, s = { numOrSizeSplits: t8, axis: e }; +var Gd = N({ irfft_: d6 }); +function f6(r15, t8, e = 0) { + let n = { x: v(r15, "x", "split") }, s = { numOrSizeSplits: t8, axis: e }; return T.runKernel(xa, n, s); } -var li = N({ split_: f6 }); -function h6(r, t8) { - $(r.dtype === "float32", () => `The dtype for rfft() must be real value but got ${r.dtype}`); - let e = r.shape[r.shape.length - 1], o = r.size / e, n; +var ci = N({ split_: f6 }); +function h6(r15, t8) { + $(r15.dtype === "float32", () => `The dtype for rfft() must be real value but got ${r15.dtype}`); + let e = r15.shape[r15.shape.length - 1], o = r15.size / e, n; if (t8 != null && t8 < e) { - let f = r.shape.map((g) => 0), h = r.shape.map((g) => g); - h[r.shape.length - 1] = t8, n = Xe(r, f, h), e = t8; + let f = r15.shape.map((g) => 0), h = r15.shape.map((g) => g); + h[r15.shape.length - 1] = t8, n = Xe(r15, f, h), e = t8; } else if (t8 != null && t8 > e) { - let f = r.shape.map((h) => h); - f[r.shape.length - 1] = t8 - e, n = bt([r, Gr(f)], r.shape.length - 1), e = t8; + let f = r15.shape.map((h) => h); + f[r15.shape.length - 1] = t8 - e, n = yt([r15, Ur(f)], r15.shape.length - 1), e = t8; } else - n = r; - let s = Ht(n), a = W(Er(n, s), [o, e]), i = fc(a), p = Math.floor(e / 2) + 1, u = ci(i), c = lu(i), l = li(u, [p, e - p], u.shape.length - 1), m = li(c, [p, e - p], c.shape.length - 1), d = n.shape.slice(); - return d[n.shape.length - 1] = p, W(Er(l[0], m[0]), d); + n = r15; + let s = Gt(n), a = W($r(n, s), [o, e]), i = ac(a), p = Math.floor(e / 2) + 1, u = pi(i), c = iu(i), l = ci(u, [p, e - p], u.shape.length - 1), m = ci(c, [p, e - p], c.shape.length - 1), d = n.shape.slice(); + return d[n.shape.length - 1] = p, W($r(l[0], m[0]), d); } -var hc = N({ rfft_: h6 }); -function g6(r, t8) { - let e = v(r, "a", "squaredDifference"), o = v(t8, "b", "squaredDifference"); +var ic = N({ rfft_: h6 }); +function g6(r15, t8) { + let e = v(r15, "a", "squaredDifference"), o = v(t8, "b", "squaredDifference"); [e, o] = Oe(e, o), rt(e.shape, o.shape); let n = { a: e, b: o }, s = {}; - return T.runKernel(Ns, n, s); + return T.runKernel(ks, n, s); } -var qd = N({ squaredDifference_: g6 }); -function x6(r, t8) { - let e = v(r, "x", "squeeze", "string_or_numeric"); - return W(e, JC(e.shape, t8).newShape); +var Hd = N({ squaredDifference_: g6 }); +function x6(r15, t8) { + let e = v(r15, "x", "squeeze", "string_or_numeric"); + return W(e, ZC(e.shape, t8).newShape); } -var gc = N({ squeeze_: x6 }); -function y6(r, t8 = 0) { - let e = si(r, "tensors", "stack", "string_or_numeric"); +var uc = N({ squeeze_: x6 }); +function y6(r15, t8 = 0) { + let e = ni(r15, "tensors", "stack", "string_or_numeric"); $(e.length >= 1, () => "Pass at least one tensor to tf.stack"), e.length > 0 && $(t8 <= e[0].rank, () => "Axis must be <= rank of the tensor"); let o = e, n = { axis: t8 }; return T.runKernel(la, o, n); } -var kr = N({ stack_: y6 }); -function b6(r, t8 = 0) { - let o = { x: v(r, "x", "step") }, n = { alpha: t8 }; - return T.runKernel(So, o, n); +var vr = N({ stack_: y6 }); +function b6(r15, t8 = 0) { + let o = { x: v(r15, "x", "step") }, n = { alpha: t8 }; + return T.runKernel(wo, o, n); } -var jd = N({ step_: b6 }); -function C6(r, t8, e, o, n = 0, s = 0, a = 0, i = 0, p = 0) { - let c = { x: v(r, "x", "stridedSlice", "string_or_numeric") }, l = { begin: t8, end: e, strides: o, beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p }; - return T.runKernel(Ts, c, l); +var Kd = N({ step_: b6 }); +function C6(r15, t8, e, o, n = 0, s = 0, a = 0, i = 0, p = 0) { + let c = { x: v(r15, "x", "stridedSlice", "string_or_numeric") }, l = { begin: t8, end: e, strides: o, beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p }; + return T.runKernel(Ns, c, l); } -var O1 = N({ stridedSlice_: C6 }); -function w6(r) { - let e = { x: v(r, "x", "tan", "float32") }; - return T.runKernel($s, e); +var M1 = N({ stridedSlice_: C6 }); +function w6(r15) { + let e = { x: v(r15, "x", "tan", "float32") }; + return T.runKernel(_s, e); } -var M1 = N({ tan_: w6 }); -function tr(r, t8) { - io(r); - let e = ir(r, t8); +var L1 = N({ tan_: w6 }); +function Jt(r15, t8) { + ao(r15); + let e = sr(r15, t8); if (e.length !== 1) throw new Error("tensor1d() requires values to be a flat/TypedArray"); - return Sr(r, null, e, t8); + return wr(r15, null, e, t8); } -function fu(r, t8, e) { - if (io(r), t8 != null && t8.length !== 2) +function cu(r15, t8, e) { + if (ao(r15), t8 != null && t8.length !== 2) throw new Error("tensor2d() requires shape to have two numbers"); - let o = ir(r, e); + let o = sr(r15, e); if (o.length !== 2 && o.length !== 1) throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray"); if (o.length === 1 && t8 == null) throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray"); - return Sr(r, t8, o, e); + return wr(r15, t8, o, e); } -function Xd(r, t8, e) { - if (io(r), t8 != null && t8.length !== 3) +function qd(r15, t8, e) { + if (ao(r15), t8 != null && t8.length !== 3) throw new Error("tensor3d() requires shape to have three numbers"); - let o = ir(r, e); + let o = sr(r15, e); if (o.length !== 3 && o.length !== 1) throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray"); if (o.length === 1 && t8 == null) throw new Error("tensor3d() requires shape to be provided when `values` are a flat array"); - return Sr(r, t8, o, e); + return wr(r15, t8, o, e); } -function L1(r, t8, e) { - if (io(r), t8 != null && t8.length !== 4) +function B1(r15, t8, e) { + if (ao(r15), t8 != null && t8.length !== 4) throw new Error("tensor4d() requires shape to have four numbers"); - let o = ir(r, e); + let o = sr(r15, e); if (o.length !== 4 && o.length !== 1) throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray"); if (o.length === 1 && t8 == null) throw new Error("tensor4d() requires shape to be provided when `values` are a flat array"); - return Sr(r, t8, o, e); + return wr(r15, t8, o, e); } -function B1(r, t8, e) { - if (io(r), t8 != null && t8.length !== 5) +function z1(r15, t8, e) { + if (ao(r15), t8 != null && t8.length !== 5) throw new Error("tensor5d() requires shape to have five numbers"); - let o = ir(r, e); + let o = sr(r15, e); if (o.length !== 5 && o.length !== 1) throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray"); if (o.length === 1 && t8 == null) throw new Error("tensor5d() requires shape to be provided when `values` are a flat array"); - return Sr(r, t8, o, e); + return wr(r15, t8, o, e); } -function z1(r, t8, e) { - if (io(r), t8 != null && t8.length !== 6) +function V1(r15, t8, e) { + if (ao(r15), t8 != null && t8.length !== 6) throw new Error("tensor6d() requires shape to have six numbers"); - let o = ir(r, e); + let o = sr(r15, e); if (o.length !== 6 && o.length !== 1) throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray"); if (o.length === 1 && t8 == null) throw new Error("tensor6d() requires shape to be provided when `values` are a flat array"); - return t8 = t8 || o, Sr(r, t8, o, e); + return t8 = t8 || o, wr(r15, t8, o, e); } -var hu = {}; -qe(hu, { calculateShapes: () => V1, validateInput: () => xc, validateUpdateShape: () => Yw }); -function Yw(r, t8, e) { - let o = t8.rank > 1 ? t8.shape[t8.rank - 1] : 1, n = t8.rank > 1 ? t8.rank - 1 : 1, s = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${e.shape}, indices.shape: ${t8.shape}, shape: ${r}, sliceDim: ${o}, and batchDim: ${n}.`; +var lu = {}; +qe(lu, { calculateShapes: () => W1, validateInput: () => pc, validateUpdateShape: () => Yw }); +function Yw(r15, t8, e) { + let o = t8.rank > 1 ? t8.shape[t8.rank - 1] : 1, n = t8.rank > 1 ? t8.rank - 1 : 1, s = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${e.shape}, indices.shape: ${t8.shape}, shape: ${r15}, sliceDim: ${o}, and batchDim: ${n}.`; if (e.rank < n) throw new Error(s + ` update.rank < ${n}. `); - if (r.length < o + (e.rank - n)) + if (r15.length < o + (e.rank - n)) throw new Error(s + ` Output shape length < ${o + (e.rank - n)}`); - if (e.rank !== n + r.length - o) - throw new Error(s + ` update.rank != ${n + r.length - o}`); + if (e.rank !== n + r15.length - o) + throw new Error(s + ` update.rank != ${n + r15.length - o}`); for (let a = 0; a < n; ++a) if (e.shape[a] !== t8.shape[a]) throw new Error(s + ` updates.shape[${a}] (${e.shape[a]}) != indices.shape[${a}] (${t8.shape[a]}).`); for (let a = 0; a < e.rank - n; ++a) - if (e.shape[a + n] !== r[a + o]) - throw new Error(s + ` updates.shape[${a + n}] (${e.shape[a + n]}) != shape[${a + n}] (${r[a + n]})`); + if (e.shape[a + n] !== r15[a + o]) + throw new Error(s + ` updates.shape[${a + n}] (${e.shape[a + n]}) != shape[${a + n}] (${r15[a + n]})`); } -function xc(r, t8, e) { +function pc(r15, t8, e) { if (t8.rank < 1) throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${t8.rank}.`); - if (r.rank < 1) - throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${r.rank}.`); + if (r15.rank < 1) + throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${r15.rank}.`); if (t8.dtype !== "int32") throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${t8.dtype}`); if (e.length < 1) @@ -7372,28 +7373,28 @@ function xc(r, t8, e) { if (e.length === 0) { if (t8.size === 0) throw new Error(`Indices specified for empty output. indices shape: ${t8.shape}`); - if (r.size === 0) - throw new Error(`Updates specified for empty output. updates shape: ${r.shape}`); + if (r15.size === 0) + throw new Error(`Updates specified for empty output. updates shape: ${r15.shape}`); } - Yw(e, t8, r); + Yw(e, t8, r15); } -function V1(r, t8, e) { +function W1(r15, t8, e) { let o = t8.shape.length, n = o > 1 ? t8.shape[o - 1] : 1, s = e.length, a = 1; for (let l = n; l < s; ++l) a *= e[l]; let i = n < 1 ? 1 : n, p = He(t8.shape) / i, u = [...js(e.slice(0, n)), 1], c = He(e); return { sliceRank: n, numUpdates: p, sliceSize: a, strides: u, outputSize: c }; } -function S6(r, t8, e) { - let o = v(r, "tensor", "tensorScatterupdate"), n = v(t8, "indices", "tensorScatterupdate", "int32"), s = v(e, "updates", "tensorScatterupdate"); - if (xc(s, n, o.shape), o.dtype !== s.dtype) +function S6(r15, t8, e) { + let o = v(r15, "tensor", "tensorScatterupdate"), n = v(t8, "indices", "tensorScatterupdate", "int32"), s = v(e, "updates", "tensorScatterupdate"); + if (pc(s, n, o.shape), o.dtype !== s.dtype) throw new Error(`tensor and updates must have the same dtype, instead they are ${o.dtype} and ${s.dtype}.`); let a = { tensor: o, indices: n, updates: s }, i = {}; - return T.runKernel(fs, a, i); + return T.runKernel(ds, a, i); } -var W1 = N({ tensorScatterUpdate_: S6 }); -function I6(r, t8 = 1, e = true) { - let o = v(r, "x", "topk"); +var U1 = N({ tensorScatterUpdate_: S6 }); +function I6(r15, t8 = 1, e = true) { + let o = v(r15, "x", "topk"); if (o.rank === 0) throw new Error("topk() expects the input to be of rank 1 or higher"); let n = o.shape[o.shape.length - 1]; @@ -7401,111 +7402,111 @@ function I6(r, t8 = 1, e = true) { throw new Error(`'k' passed to topk() must be >= 0 but got ${t8}`); if (t8 > n) throw new Error(`'k' passed to topk() must be <= the last dimension (${n}) but got ${t8}`); - let s = { x: o }, a = { k: t8, sorted: e }, [i, p] = T.runKernel(Rs, s, a); + let s = { x: o }, a = { k: t8, sorted: e }, [i, p] = T.runKernel(Es, s, a); return { values: i, indices: p }; } -var U1 = N({ topk_: I6 }); -function v6(r, t8 = 0, e = 1, o, n) { - if (wt(r), o != null && o === "bool") +var G1 = N({ topk_: I6 }); +function v6(r15, t8 = 0, e = 1, o, n) { + if (Ct(r15), o != null && o === "bool") throw new Error("Unsupported data type $ { dtype }"); - let s = new Zu(t8, e, o, true, n), a = me(r, o); + let s = new Hu(t8, e, o, true, n), a = me(r15, o); for (let i = 0; i < a.values.length; i++) a.values[i] = s.nextValue(); return a.toTensor(); } -var G1 = N({ truncatedNormal_: v6 }); -function k6(r, t8 = 0) { - let e = v(r, "x", "unique", "string_or_numeric"); +var H1 = N({ truncatedNormal_: v6 }); +function k6(r15, t8 = 0) { + let e = v(r15, "x", "unique", "string_or_numeric"); $(e.rank > 0, () => "The input tensor must be at least 1D"); - let o = { x: e }, n = { axis: t8 }, [s, a] = T.runKernel(Zi, o, n); + let o = { x: e }, n = { axis: t8 }, [s, a] = T.runKernel(Xi, o, n); return { values: s, indices: a }; } -var H1 = N({ unique_: k6 }); -function N6(r, t8, e) { - let o = v(r, "x", "unsortedSegmentSum"), n = v(t8, "segmentIds", "unsortedSegmentSum", "int32"); - $(qa(e), () => "numSegments must be of dtype int"); +var K1 = N({ unique_: k6 }); +function N6(r15, t8, e) { + let o = v(r15, "x", "unsortedSegmentSum"), n = v(t8, "segmentIds", "unsortedSegmentSum", "int32"); + $(Ka(e), () => "numSegments must be of dtype int"); let s = { x: o, segmentIds: n }, a = { numSegments: e }; - return T.runKernel(Ji, s, a); + return T.runKernel(Yi, s, a); } -var K1 = N({ unsortedSegmentSum_: N6 }); -function T6(r, t8 = 0) { - let e = v(r, "x", "unstack", "string_or_numeric"); +var q1 = N({ unsortedSegmentSum_: N6 }); +function T6(r15, t8 = 0) { + let e = v(r15, "x", "unstack", "string_or_numeric"); $(t8 >= -e.shape.length && t8 < e.shape.length, () => `Axis = ${t8} is not in [-${e.shape.length}, ${e.shape.length})`); let o = { value: e }, n = { axis: t8 }; return T.runKernel(wa, o, n); } -var fo = N({ unstack_: T6 }); -function q1(r, t8) { - return $l(r, t8, "right"); +var mo = N({ unstack_: T6 }); +function j1(r15, t8) { + return Nl(r15, t8, "right"); } -function j1(r, t8 = true, e, o) { - return T.makeVariable(r, t8, e, o); +function X1(r15, t8 = true, e, o) { + return T.makeVariable(r15, t8, e, o); } -function Yd(r, t8) { +function jd(r15, t8) { let e = []; for (let s = 0; s < t8.length; s++) t8[s] && e.push(s); - let o = me(r, "int32"), n = me([e.length, r.length], "int32"); + let o = me(r15, "int32"), n = me([e.length, r15.length], "int32"); for (let s = 0; s < e.length; s++) { - let a = o.indexToLoc(e[s]), i = s * r.length; + let a = o.indexToLoc(e[s]), i = s * r15.length; n.values.set(a, i); } return n.toTensor(); } -async function _6(r) { - let t8 = v(r, "condition", "whereAsync", "bool"), e = await t8.data(), o = Yd(t8.shape, e); - return r !== t8 && t8.dispose(), o; +async function _6(r15) { + let t8 = v(r15, "condition", "whereAsync", "bool"), e = await t8.data(), o = jd(t8.shape, e); + return r15 !== t8 && t8.dispose(), o; } -var Qd = _6; -async function $6(r, t8, e) { - let o = v(r, "tensor", "boolMask"), n = v(t8, "mask", "boolMask", "bool"), s = e == null ? 0 : e, a = n.rank, i = o.shape; - $(a > 0, () => "mask cannot be scalar"), yt(i.slice(s, s + a), n.shape, "mask's shape must match the first K dimensions of tensor's shape,"); +var Xd = _6; +async function $6(r15, t8, e) { + let o = v(r15, "tensor", "boolMask"), n = v(t8, "mask", "boolMask", "bool"), s = e == null ? 0 : e, a = n.rank, i = o.shape; + $(a > 0, () => "mask cannot be scalar"), xt(i.slice(s, s + a), n.shape, "mask's shape must match the first K dimensions of tensor's shape,"); let p = 1; for (let h = s; h < s + a; h++) p *= i[h]; - let u = i.slice(0, s).concat([p], i.slice(s + a)), c = W(o, u), l = W(n, [-1]), m = await Qd(l), d = gc(m, [1]), f = Id(c, d, s); - return r !== o && o.dispose(), t8 !== n && n.dispose(), d.dispose(), c.dispose(), l.dispose(), m.dispose(), f; + let u = i.slice(0, s).concat([p], i.slice(s + a)), c = W(o, u), l = W(n, [-1]), m = await Xd(l), d = uc(m, [1]), f = wd(c, d, s); + return r15 !== o && o.dispose(), t8 !== n && n.dispose(), d.dispose(), c.dispose(), l.dispose(), m.dispose(), f; } var E6 = $6; -function R6(r, t8, e) { - let o = v(r, "x", "transpose"); +function R6(r15, t8, e) { + let o = v(r15, "x", "transpose"); if (t8 == null && (t8 = o.shape.map((a, i) => i).reverse()), $(o.rank === t8.length, () => `Error in transpose: rank of input ${o.rank} must match length of perm ${t8}.`), t8.forEach((a) => { $(a >= 0 && a < o.rank, () => `All entries in 'perm' must be between 0 and ${o.rank - 1} but got ${t8}`); }), o.rank <= 1) return o.clone(); let n = { x: o }, s = { perm: t8 }; return o.dtype === "complex64" ? De(() => { - let a = ci(o), i = lu(o); - return a = T.runKernel(co, { x: a }, s), i = T.runKernel(co, { x: i }, s), e && (i = cr(i)), Er(a, i); - }) : T.runKernel(co, n, s); + let a = pi(o), i = iu(o); + return a = T.runKernel(po, { x: a }, s), i = T.runKernel(po, { x: i }, s), e && (i = pr(i)), $r(a, i); + }) : T.runKernel(po, n, s); } -var yc = N({ transpose_: R6 }); -function D6(r, t8, e, o, n = true) { - let s = v(r, "v", "movingAverage"), a = v(t8, "x", "movingAverage"), i = v(e, "decay", "movingAverage"); - Cw(s, a), $(Cr(s.shape, a.shape), () => "Shape mismatch in v and x"); +var cc = N({ transpose_: R6 }); +function D6(r15, t8, e, o, n = true) { + let s = v(r15, "v", "movingAverage"), a = v(t8, "x", "movingAverage"), i = v(e, "decay", "movingAverage"); + Cw(s, a), $(br(s.shape, a.shape), () => "Shape mismatch in v and x"); let p = ke(1), u = Te(p, i), c = se(Te(a, s), u); if (n) { $(o != null, () => "When using zeroDebias: true, step is required."); let l = v(o, "step", "movingAverage"); - c = je(c, Te(p, ui(i, l))); + c = je(c, Te(p, ii(i, l))); } return Ce(s, c); } var A6 = N({ movingAverage_: D6 }); -function F6(r, t8, e) { - wt(e); - let o = v(r, "indices", "scatterND", "int32"), n = v(t8, "updates", "scatterND"); - xc(n, o, e); +function F6(r15, t8, e) { + Ct(e); + let o = v(r15, "indices", "scatterND", "int32"), n = v(t8, "updates", "scatterND"); + pc(n, o, e); let s = { indices: o, updates: n }, a = { shape: e }; - return T.runKernel(ds, s, a); + return T.runKernel(ms, s, a); } var P6 = N({ scatterND_: F6 }); -function X1(r, t8, e, o) { - if (r.dtype !== "int32") - throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${r.dtype}.`); - if (r.rank > 2) - throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${r.shape}.`); - let n = r.rank > 0 ? r.shape[0] : 1, s = r.rank > 1 ? r.shape[1] : 1; +function Y1(r15, t8, e, o) { + if (r15.dtype !== "int32") + throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${r15.dtype}.`); + if (r15.rank > 2) + throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${r15.shape}.`); + let n = r15.rank > 0 ? r15.shape[0] : 1, s = r15.rank > 1 ? r15.shape[1] : 1; if (e.length !== s) throw new Error(`outputShape has incorrect number of elements:, ${e.length}, should be: ${s}.`); let a = t8.size; @@ -7514,57 +7515,57 @@ function X1(r, t8, e, o) { if (t8.dtype !== o.dtype) throw new Error("sparseValues.dtype must match defaultValues.dtype"); } -function M6(r, t8, e, o = 0) { - wt(e); - let n = v(r, "sparseIndices", "sparseToDense", "int32"), s = v(t8, "sparseValues", "sparseToDense", "string_or_numeric"), a = v(o, "defaultValue", "sparseToDense", s.dtype); - X1(n, s, e, a); +function M6(r15, t8, e, o = 0) { + Ct(e); + let n = v(r15, "sparseIndices", "sparseToDense", "int32"), s = v(t8, "sparseValues", "sparseToDense", "string_or_numeric"), a = v(o, "defaultValue", "sparseToDense", s.dtype); + Y1(n, s, e, a); let i = { sparseIndices: n, sparseValues: s, defaultValue: a }, p = { outputShape: e }; - return T.runKernel(ks, i, p); + return T.runKernel(vs, i, p); } var L6 = N({ sparseToDense_: M6 }); -function B6(r, t8) { - let e = v(t8, "indices", "gatherND", "int32"), n = { params: v(r, "x", "gatherND", "string_or_numeric"), indices: e }; - return T.runKernel(kn, n); +function B6(r15, t8) { + let e = v(t8, "indices", "gatherND", "int32"), n = { params: v(r15, "x", "gatherND", "string_or_numeric"), indices: e }; + return T.runKernel(vn, n); } var z6 = N({ gatherND_: B6 }); -function Y1(r, t8) { +function Q1(r15, t8) { if (t8 == null) - return r.shape.slice(); - if (Cr(r.shape, t8)) + return r15.shape.slice(); + if (br(r15.shape, t8)) return t8; - if (r.shape.length === t8.length) { + if (r15.shape.length === t8.length) { let e = []; - for (let o = 0; o < r.shape.length; o++) - t8[o] == null && r.shape[o] != null ? e.push(r.shape[o]) : e.push(t8[o]); + for (let o = 0; o < r15.shape.length; o++) + t8[o] == null && r15.shape[o] != null ? e.push(r15.shape[o]) : e.push(t8[o]); return e; } return t8; } -function V6(r, t8, e, o) { - let n = v(r, "x", "dropout"); +function V6(r15, t8, e, o) { + let n = v(r15, "x", "dropout"); if ($(n.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${n.dtype} tensor instead.`), $(t8 >= 0 && t8 < 1, () => `rate must be a float in the range [0, 1), but got ${t8}.`), t8 === 0) - return r instanceof ut ? n.clone() : n; - let s = Y1(n, e), a = 1 - t8, i = je(Sd(Ce(dc(s, 0, 1, "float32", o), a)), a); + return r15 instanceof mt ? n.clone() : n; + let s = Q1(n, e), a = 1 - t8, i = je(Cd(Ce(sc(s, 0, 1, "float32", o), a)), a); return se(n, i); } var W6 = N({ dropout_: V6 }); -function Qw(r) { - return Math.floor(Math.pow(2, Math.ceil(Math.log(r) / Math.log(2)))); +function Qw(r15) { + return Math.floor(Math.pow(2, Math.ceil(Math.log(r15) / Math.log(2)))); } -function Rl(r, t8, e) { - let o = 1 - r % 2, n = new Float32Array(r); - for (let s = 0; s < r; ++s) { - let a = 2 * Math.PI * s / (r + o - 1); +function _l(r15, t8, e) { + let o = 1 - r15 % 2, n = new Float32Array(r15); + for (let s = 0; s < r15; ++s) { + let a = 2 * Math.PI * s / (r15 + o - 1); n[s] = t8 - e * Math.cos(a); } - return tr(n, "float32"); + return Jt(n, "float32"); } -async function U6(r, t8, e = 1) { - let o = v(r, "predictions", "inTopK"), n = v(t8, "targets", "inTopK"); - $(o.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${o.rank}`), $(o.rank - 1 === n.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${o.rank} and targets rank ${n.rank}`), yt(o.shape.slice(0, o.shape.length - 1), n.shape, "predictions's shape should be align with the targets' shape, except the last dimension."); +async function U6(r15, t8, e = 1) { + let o = v(r15, "predictions", "inTopK"), n = v(t8, "targets", "inTopK"); + $(o.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${o.rank}`), $(o.rank - 1 === n.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${o.rank} and targets rank ${n.rank}`), xt(o.shape.slice(0, o.shape.length - 1), n.shape, "predictions's shape should be align with the targets' shape, except the last dimension."); let s = o.shape[o.shape.length - 1]; $(e > 0 && e <= s, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${e}`); - let a = await o.data(), i = await n.data(), [p, u] = [a.length / s, s], c = ew("bool", p); + let a = await o.data(), i = await n.data(), [p, u] = [a.length / s, s], c = JC("bool", p); for (let l = 0; l < p; l++) { let m = l * u, d = a.subarray(m, m + u), f = []; for (let h = 0; h < d.length; h++) @@ -7576,62 +7577,62 @@ async function U6(r, t8, e = 1) { break; } } - return r !== o && o.dispose(), t8 !== n && n.dispose(), ur(c, n.shape, "bool"); + return r15 !== o && o.dispose(), t8 !== n && n.dispose(), ar(c, n.shape, "bool"); } var G6 = U6; var Zw = {}; -qe(Zw, { conv2d: () => Z1, depthwiseConv2d: () => tN, matMul: () => rN }); -function H6(r, t8, e, o, n, s = "NHWC", a) { - let i = r; - r.rank === 3 && (i = W(r, [1, r.shape[0], r.shape[1], r.shape[2]])); +qe(Zw, { conv2d: () => J1, depthwiseConv2d: () => rN, matMul: () => oN }); +function H6(r15, t8, e, o, n, s = "NHWC", a) { + let i = r15; + r15.rank === 3 && (i = W(r15, [1, r15.shape[0], r15.shape[1], r15.shape[2]])); let p = t8; p.rank === 3 && (p = W(t8, [1, t8.shape[0], t8.shape[1], t8.shape[2]])), $(i.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`), $(p.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${p.shape}.`), $(e.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${e}.`); let u = s === "NHWC" ? i.shape[3] : i.shape[1], c = s === "NHWC" ? p.shape[3] : p.shape[1]; - $(u === e[2], () => `Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${e[2]}.`), $(c === e[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${e[3]}).`), Bt("conv2dDerFilter", n, a); + $(u === e[2], () => `Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${e[2]}.`), $(c === e[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${e[3]}).`), Lt("conv2dDerFilter", n, a); let l = { x: i, dy: p }, m = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, filterShape: e }; - return T.runKernel(Oi, l, m); + return T.runKernel(Ai, l, m); } -var Q1 = N({ conv2DBackpropFilter_: H6 }); -function ep(r, t8, e) { +var Z1 = N({ conv2DBackpropFilter_: H6 }); +function qu(r15, t8, e) { if (e == null || e === "linear") - return r; + return r15; if (e === "relu") - return se(r, jd(t8)); + return se(r15, Kd(t8)); throw new Error(`Cannot compute gradient for fused activation ${e}.`); } -function tp(r, t8) { - let e = t8, o = yd(r.shape, t8.shape); - return o.length > 0 && (e = ot(e, o)), W(e, r.shape); +function ju(r15, t8) { + let e = t8, o = gd(r15.shape, t8.shape); + return o.length > 0 && (e = ot(e, o)), W(e, r15.shape); } -function rp(r, t8, e, o) { +function Xu(r15, t8, e, o) { if (t8 === "linear") - return r; + return r15; if (t8 === "relu") - return du(r); + return pu(r15); if (t8 === "elu") - return Cd(r); + return yd(r15); if (t8 === "relu6") - return Gd(r); + return Wd(r15); if (t8 === "prelu") - return Md(r, e); + return Pd(r15, e); if (t8 === "leakyrelu") - return kd(r, o); + return Id(r15, o); if (t8 === "sigmoid") - return $a(r); + return $a(r15); throw new Error(`Unknown fused activation ${t8}.`); } -var op = (r, t8) => !(r > 0) || t8 === "linear"; -function K6({ x: r, filter: t8, strides: e, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) { - if (p = p || "linear", op(T.state.gradientDepth, p) === false) { +var Yu = (r15, t8) => !(r15 > 0) || t8 === "linear"; +function K6({ x: r15, filter: t8, strides: e, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) { + if (p = p || "linear", Yu(T.state.gradientDepth, p) === false) { $(n === "NHWC", () => `Error in fused conv2d: got dataFormat of ${n} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`); - let _ = uu(r, t8, e, o, n, s, a); - return i != null && (_ = Ce(_, i)), rp(_, p, u, c); + let _ = nu(r15, t8, e, o, n, s, a); + return i != null && (_ = Ce(_, i)), Xu(_, p, u, c); } - let l = v(r, "x", "conv2d", "float32"), m = v(t8, "filter", "conv2d", "float32"), d = l, f = false; - l.rank === 3 && (f = true, d = W(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(d.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${d.rank}.`), $(m.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`), Bt("fused conv2d", o, a); + let l = v(r15, "x", "conv2d", "float32"), m = v(t8, "filter", "conv2d", "float32"), d = l, f = false; + l.rank === 3 && (f = true, d = W(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(d.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${d.rank}.`), $(m.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`), Lt("fused conv2d", o, a); let h = n === "NHWC" ? d.shape[3] : d.shape[1]; - $(m.shape[2] === h, () => `Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`), $(xr(e, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`); - let g = Hu(d.shape, m.shape, e, s, o, a), x; + $(m.shape[2] === h, () => `Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`), $(gr(e, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`); + let g = Lu(d.shape, m.shape, e, s, o, a), x; i != null && (x = v(i, "bias", "fused conv2d"), [x] = Oe(x, l), n === "NHWC" ? rt(g.outShape, x.shape) : ($(x.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${x.shape.length}.`), $(x.shape.length === 0 || x.shape[0] === g.outChannels || x.shape[0] === 1, () => `Error in fused conv2d: bias shape (${x.shape}) is not compatible with the number of output channels (${g.outChannels})`))); let b; if (u != null) { @@ -7649,75 +7650,75 @@ function K6({ x: r, filter: t8, strides: e, pad: o, dataFormat: n = "NHWC", dila } let C = (_, E) => { $(n === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${n} but only NHWC is currently supported.`); - let [R, D, P, O] = E, M = ep(_, P, p); - $(Gu(s), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`); - let L = xd(D.shape, M, R, e, o), B = Q1(D, M, R.shape, e, o), z = [L, B]; + let [R, D, P, O] = E, M = qu(_, P, p); + $(Mu(s), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`); + let L = hd(D.shape, M, R, e, o), B = Z1(D, M, R.shape, e, o), z = [L, B]; if (O != null) { - let U = tp(O, M); + let U = ju(O, M); z.push(U); } return z; }, S = { x: d, filter: m, bias: x, preluActivationWeights: b }, k = { strides: e, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c }; - return i == null ? vr((E, R, D) => { - let P = T.runKernel(vo, S, k); + return i == null ? Ir((E, R, D) => { + let P = T.runKernel(Io, S, k); return D([R, E, P]), f && (P = W(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: C }; - })(d, m) : vr((E, R, D, P) => { - let O = T.runKernel(vo, S, k); + })(d, m) : Ir((E, R, D, P) => { + let O = T.runKernel(Io, S, k); return P([R, E, O, D]), f && (O = W(O, [O.shape[1], O.shape[2], O.shape[3]])), { value: O, gradFunc: C }; })(d, m, x); } -var Z1 = N({ fusedConv2d_: K6 }); -function q6(r, t8, e, o, n, s = [1, 1], a) { - let i = r; - r.rank === 3 && (i = W(r, [1, r.shape[0], r.shape[1], r.shape[2]])); +var J1 = N({ fusedConv2d_: K6 }); +function q6(r15, t8, e, o, n, s = [1, 1], a) { + let i = r15; + r15.rank === 3 && (i = W(r15, [1, r15.shape[0], r15.shape[1], r15.shape[2]])); let p = t8; p.rank === 3 && (p = W(t8, [1, t8.shape[0], t8.shape[1], t8.shape[2]])); let u = { x: i, dy: p }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, filterShape: e }; - return T.runKernel(Mi, u, c); + return T.runKernel(Fi, u, c); } -var J1 = N({ depthwiseConv2dNativeBackpropFilter_: q6 }); -function j6(r, t8, e, o, n, s = [1, 1], a) { +var eN = N({ depthwiseConv2dNativeBackpropFilter_: q6 }); +function j6(r15, t8, e, o, n, s = [1, 1], a) { let i = t8, p = false; t8.rank === 3 && (p = true, i = W(t8, [1, t8.shape[0], t8.shape[1], t8.shape[2]])); - let u = { dy: i, filter: e }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, inputShape: r }, l = T.runKernel(Li, u, c); + let u = { dy: i, filter: e }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, inputShape: r15 }, l = T.runKernel(Pi, u, c); return p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l; } -var eN = N({ depthwiseConv2dNativeBackpropInput_: j6 }); -function X6({ x: r, filter: t8, strides: e, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) { - if (op(T.state.gradientDepth, p) === false) { - let k = lc(r, t8, e, o, n, s, a); - return i != null && (k = Ce(k, i)), rp(k, p, u, c); +var tN = N({ depthwiseConv2dNativeBackpropInput_: j6 }); +function X6({ x: r15, filter: t8, strides: e, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) { + if (Yu(T.state.gradientDepth, p) === false) { + let k = oc(r15, t8, e, o, n, s, a); + return i != null && (k = Ce(k, i)), Xu(k, p, u, c); } - let l = v(r, "x", "depthwiseConv2d", "float32"), m = v(t8, "filter", "depthwiseConv2d", "float32"), d = l, f = false; - l.rank === 3 && (f = true, d = W(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(d.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${d.rank}.`), $(m.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`), $(d.shape[3] === m.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${d.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`), s == null && (s = [1, 1]), $(xr(e, s), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`), Bt("fused depthwiseConv2d", o, a); - let h = Hu(d.shape, m.shape, e, s, o, a, true), g; + let l = v(r15, "x", "depthwiseConv2d", "float32"), m = v(t8, "filter", "depthwiseConv2d", "float32"), d = l, f = false; + l.rank === 3 && (f = true, d = W(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(d.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${d.rank}.`), $(m.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`), $(d.shape[3] === m.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${d.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`), s == null && (s = [1, 1]), $(gr(e, s), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`), Lt("fused depthwiseConv2d", o, a); + let h = Lu(d.shape, m.shape, e, s, o, a, true), g; i != null && (g = v(i, "bias", "fused conv2d"), [g] = Oe(g, l), rt(h.outShape, g.shape)); let x; u != null && (x = v(u, "prelu weights", "fused depthwiseConv2d")); let b = (k, _) => { - $(Gu(s), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`); - let [E, R, D, P] = _, O = ep(k, D, p), M = eN(R.shape, O, E, e, o, s, a), L = J1(R, O, E.shape, e, o, s, a); + $(Mu(s), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`); + let [E, R, D, P] = _, O = qu(k, D, p), M = tN(R.shape, O, E, e, o, s, a), L = eN(R, O, E.shape, e, o, s, a); if (P != null) { - let B = tp(g, O); + let B = ju(g, O); return [M, L, B]; } return [M, L]; }, C = { x: d, filter: m, bias: g, preluActivationWeights: x }, S = { strides: e, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c }; - return i == null ? vr((_, E, R) => { - let D = T.runKernel(ko, C, S); + return i == null ? Ir((_, E, R) => { + let D = T.runKernel(vo, C, S); return R([E, _, D]), f && (D = W(D, [D.shape[1], D.shape[2], D.shape[3]])), { value: D, gradFunc: b }; - })(d, m) : vr((_, E, R, D) => { - let P = T.runKernel(ko, C, S); + })(d, m) : Ir((_, E, R, D) => { + let P = T.runKernel(vo, C, S); return D([E, _, P, R]), f && (P = W(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: b }; })(d, m, g); } -var tN = N({ fusedDepthwiseConv2d_: X6 }); -function Y6({ a: r, b: t8, transposeA: e = false, transposeB: o = false, bias: n, activation: s = "linear", preluActivationWeights: a, leakyreluAlpha: i = 0.2 }) { - if (op(T.state.gradientDepth, s) === false) { - let O = Ze(r, t8, e, o); - return n != null && (O = Ce(O, n)), rp(O, s, a, i); +var rN = N({ fusedDepthwiseConv2d_: X6 }); +function Y6({ a: r15, b: t8, transposeA: e = false, transposeB: o = false, bias: n, activation: s = "linear", preluActivationWeights: a, leakyreluAlpha: i = 0.2 }) { + if (Yu(T.state.gradientDepth, s) === false) { + let O = Ze(r15, t8, e, o); + return n != null && (O = Ce(O, n)), Xu(O, s, a, i); } - let p = v(r, "a", "fused matMul"), u = v(t8, "b", "fused matMul"); + let p = v(r15, "a", "fused matMul"), u = v(t8, "b", "fused matMul"); [p, u] = Oe(p, u); let c = e ? p.shape[p.rank - 2] : p.shape[p.rank - 1], l = o ? u.shape[u.rank - 1] : u.shape[u.rank - 2], m = e ? p.shape[p.rank - 1] : p.shape[p.rank - 2], d = o ? u.shape[u.rank - 2] : u.shape[u.rank - 1], f = p.shape.slice(0, -2), h = u.shape.slice(0, -2), g = He(f), x = He(h); $(c === l, () => `Error in fused matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${p.shape} and ${u.shape} and transposeA=${e} and transposeB=${o} must match.`); @@ -7726,147 +7727,147 @@ function Y6({ a: r, b: t8, transposeA: e = false, transposeB: o = false, bias: n let E; a != null && (E = v(a, "prelu weights", "fused matMul")); let R = (O, M) => { - let [L, B, z, U] = M, j = ep(W(O, z.shape), z, s), q, Y; + let [L, B, z, U] = M, j = qu(W(O, z.shape), z, s), q, Y; if (!e && !o ? (q = Ze(j, B, false, true), Y = Ze(L, j, true, false)) : !e && o ? (q = Ze(j, B, false, false), Y = Ze(j, L, true, false)) : e && !o ? (q = Ze(B, j, false, true), Y = Ze(L, j, false, false)) : (q = Ze(B, j, true, true), Y = Ze(j, L, true, true)), n != null) { - let J = tp(U, j); + let J = ju(U, j); return [q, Y, J]; } else return [q, Y]; }, D = { a: S, b: k, bias: _, preluActivationWeights: E }, P = { transposeA: e, transposeB: o, activation: s, leakyreluAlpha: i }; - return n == null ? vr((M, L, B) => { - let z = T.runKernel(Io, D, P); + return n == null ? Ir((M, L, B) => { + let z = T.runKernel(So, D, P); return B([M, L, z]), { value: W(z, C), gradFunc: R }; - })(S, k) : vr((M, L, B, z) => { - let U = T.runKernel(Io, D, P); + })(S, k) : Ir((M, L, B, z) => { + let U = T.runKernel(So, D, P); return z([M, L, U, B]), { value: W(U, C), gradFunc: R }; })(S, k, _); } -var rN = N({ fusedMatMul_: Y6 }); -function Q6(r) { - return Rl(r, 0.54, 0.46); +var oN = N({ fusedMatMul_: Y6 }); +function Q6(r15) { + return _l(r15, 0.54, 0.46); } -var oN = N({ hammingWindow_: Q6 }); -function Z6(r) { - return Rl(r, 0.5, 0.5); +var nN = N({ hammingWindow_: Q6 }); +function Z6(r15) { + return _l(r15, 0.5, 0.5); } -var Zd = N({ hannWindow_: Z6 }); -function J6(r, t8, e, o = false, n = 0) { +var Yd = N({ hannWindow_: Z6 }); +function J6(r15, t8, e, o = false, n = 0) { let s = 0, a = []; - for (; s + t8 <= r.size; ) - a.push(Xe(r, s, t8)), s += e; + for (; s + t8 <= r15.size; ) + a.push(Xe(r15, s, t8)), s += e; if (o) - for (; s < r.size; ) { - let i = s + t8 - r.size, p = bt([Xe(r, s, t8 - i), Ea([i], n)]); + for (; s < r15.size; ) { + let i = s + t8 - r15.size, p = yt([Xe(r15, s, t8 - i), Ea([i], n)]); a.push(p), s += e; } - return a.length === 0 ? fu([], [0, t8]) : W(bt(a), [a.length, t8]); + return a.length === 0 ? cu([], [0, t8]) : W(yt(a), [a.length, t8]); } -var Jd = N({ frame_: J6 }); -function ej(r, t8, e, o, n = Zd) { +var Qd = N({ frame_: J6 }); +function ej(r15, t8, e, o, n = Yd) { o == null && (o = Qw(t8)); - let s = Jd(r, t8, e), a = se(s, n(t8)); - return hc(a, o); + let s = Qd(r15, t8, e), a = se(s, n(t8)); + return ic(a, o); } -var nN = N({ stft_: ej }); -function tj(r, t8, e, o, n = "bilinear", s = 0) { - let a = v(r, "image", "cropAndResize"), i = v(t8, "boxes", "cropAndResize", "float32"), p = v(e, "boxInd", "cropAndResize", "int32"), u = i.shape[0]; +var sN = N({ stft_: ej }); +function tj(r15, t8, e, o, n = "bilinear", s = 0) { + let a = v(r15, "image", "cropAndResize"), i = v(t8, "boxes", "cropAndResize", "float32"), p = v(e, "boxInd", "cropAndResize", "int32"), u = i.shape[0]; $(a.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${a.rank}.`), $(i.rank === 2 && i.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${u},4] but had shape ${i.shape}.`), $(p.rank === 1 && p.shape[0] === u, () => `Error in cropAndResize: boxInd must be have size [${u}] but had shape ${i.shape}.`), $(o.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${o.length}.`), $(o[0] >= 1 && o[1] >= 1, () => `cropSize must be atleast [1,1], but was ${o}`), $(n === "bilinear" || n === "nearest", () => `method must be bilinear or nearest, but was ${n}`); let c = { image: a, boxes: i, boxInd: p }, l = { method: n, extrapolationValue: s, cropSize: o }; - return T.runKernel(ln, c, l); + return T.runKernel(cn, c, l); } -var sN = N({ cropAndResize_: tj }); -function rj(r) { - let t8 = v(r, "image", "flipLeftRight", "float32"); +var aN = N({ cropAndResize_: tj }); +function rj(r15) { + let t8 = v(r15, "image", "flipLeftRight", "float32"); $(t8.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${t8.rank}.`); let e = { image: t8 }; - return T.runKernel(wn, e, {}); + return T.runKernel(Cn, e, {}); } -var aN = N({ flipLeftRight_: rj }); -function oj(r) { - let t8 = v(r, "image", "grayscaleToRGB"), e = t8.rank - 1, o = t8.shape[e]; +var iN = N({ flipLeftRight_: rj }); +function oj(r15) { + let t8 = v(r15, "image", "grayscaleToRGB"), e = t8.rank - 1, o = t8.shape[e]; $(t8.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t8.rank}.`), $(o === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${o}.`); let n = new Array(t8.rank); - return n.fill(1, 0, e), n[e] = 3, cu(t8, n); + return n.fill(1, 0, e), n[e] = 3, au(t8, n); } -var iN = N({ grayscaleToRGB_: oj }); -function nj(r) { - let t8 = v(r, "image", "RGBToGrayscale"), e = t8.rank - 1, o = t8.shape[e]; +var uN = N({ grayscaleToRGB_: oj }); +function nj(r15) { + let t8 = v(r15, "image", "RGBToGrayscale"), e = t8.rank - 1, o = t8.shape[e]; $(t8.rank >= 2, () => `Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t8.rank}.`), $(o === 3, () => `Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${o}.`); - let n = t8.dtype, s = We(t8, "float32"), a = tr([0.2989, 0.587, 0.114]), i; + let n = t8.dtype, s = We(t8, "float32"), a = Jt([0.2989, 0.587, 0.114]), i; switch (t8.rank) { case 2: - i = pu("ij,j->i", s, a); + i = su("ij,j->i", s, a); break; case 3: - i = pu("ijk,k->ij", s, a); + i = su("ijk,k->ij", s, a); break; case 4: - i = pu("ijkl,l->ijk", s, a); + i = su("ijkl,l->ijk", s, a); break; case 5: - i = pu("ijklm,m->ijkl", s, a); + i = su("ijklm,m->ijkl", s, a); break; case 6: - i = pu("ijklmn,n->ijklm", s, a); + i = su("ijklmn,n->ijklm", s, a); break; default: throw new Error("Not a valid tensor rank."); } return i = Ms(i, -1), We(i, n); } -var uN = N({ rgbToGrayscale_: nj }); -function sj(r, t8, e = 0, o = 0.5) { - let n = v(r, "image", "rotateWithOffset", "float32"); +var pN = N({ rgbToGrayscale_: nj }); +function sj(r15, t8, e = 0, o = 0.5) { + let n = v(r15, "image", "rotateWithOffset", "float32"); $(n.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${n.rank}.`); let s = { image: n }, a = { radians: t8, fillValue: e, center: o }; - return T.runKernel(As, s, a); + return T.runKernel(Ds, s, a); } -var pN = N({ rotateWithOffset_: sj }); -function Eo(r, t8, e, o, n, s) { +var cN = N({ rotateWithOffset_: sj }); +function $o(r15, t8, e, o, n, s) { o == null && (o = 0.5), n == null && (n = Number.NEGATIVE_INFINITY), s == null && (s = 0); - let a = r.shape[0]; - return e = Math.min(e, a), $(0 <= o && o <= 1, () => `iouThreshold must be in [0, 1], but was '${o}'`), $(r.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${r.rank}'`), $(r.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`), $(t8.rank === 1, () => "scores must be a 1D tensor"), $(t8.shape[0] === a, () => `scores has incompatible shape with boxes. Expected ${a}, but was ${t8.shape[0]}`), $(0 <= s && s <= 1, () => `softNmsSigma must be in [0, 1], but was '${s}'`), { maxOutputSize: e, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }; + let a = r15.shape[0]; + return e = Math.min(e, a), $(0 <= o && o <= 1, () => `iouThreshold must be in [0, 1], but was '${o}'`), $(r15.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${r15.rank}'`), $(r15.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${r15.shape[1]}`), $(t8.rank === 1, () => "scores must be a 1D tensor"), $(t8.shape[0] === a, () => `scores has incompatible shape with boxes. Expected ${a}, but was ${t8.shape[0]}`), $(0 <= s && s <= 1, () => `softNmsSigma must be in [0, 1], but was '${s}'`), { maxOutputSize: e, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }; } -function aj(r, t8, e, o = 0.5, n = Number.NEGATIVE_INFINITY) { - let s = v(r, "boxes", "nonMaxSuppression", "float32"), a = v(t8, "scores", "nonMaxSuppression", "float32"), i = Eo(s, a, e, o, n); +function aj(r15, t8, e, o = 0.5, n = Number.NEGATIVE_INFINITY) { + let s = v(r15, "boxes", "nonMaxSuppression", "float32"), a = v(t8, "scores", "nonMaxSuppression", "float32"), i = $o(s, a, e, o, n); e = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold; let p = { maxOutputSize: e, iouThreshold: o, scoreThreshold: n }; - return T.runKernel(Zn, { boxes: s, scores: a }, p); + return T.runKernel(Qn, { boxes: s, scores: a }, p); } -var cN = N({ nonMaxSuppression_: aj }); -function lN(r, t8, e) { - let o = ij(r, t8, e), n = o < 0 ? -(o + 1) : o; - r.splice(n, 0, t8); +var lN = N({ nonMaxSuppression_: aj }); +function mN(r15, t8, e) { + let o = ij(r15, t8, e), n = o < 0 ? -(o + 1) : o; + r15.splice(n, 0, t8); } -function ij(r, t8, e) { - return pj(r, t8, e || uj); +function ij(r15, t8, e) { + return pj(r15, t8, e || uj); } -function uj(r, t8) { - return r > t8 ? 1 : r < t8 ? -1 : 0; +function uj(r15, t8) { + return r15 > t8 ? 1 : r15 < t8 ? -1 : 0; } -function pj(r, t8, e) { - let o = 0, n = r.length, s = 0, a = false; +function pj(r15, t8, e) { + let o = 0, n = r15.length, s = 0, a = false; for (; o < n; ) { s = o + (n - o >>> 1); - let i = e(t8, r[s]); + let i = e(t8, r15[s]); i > 0 ? o = s + 1 : (n = s, a = !i); } return a ? o : -o - 1; } -function ef(r, t8, e, o, n) { - return Jw(r, t8, e, o, n, 0); +function Zd(r15, t8, e, o, n) { + return Jw(r15, t8, e, o, n, 0); } -function tf(r, t8, e, o, n, s) { - return Jw(r, t8, e, o, n, 0, false, s, true); +function Jd(r15, t8, e, o, n, s) { + return Jw(r15, t8, e, o, n, 0, false, s, true); } -function rf(r, t8, e, o, n, s) { - return Jw(r, t8, e, o, n, s, true); +function ef(r15, t8, e, o, n, s) { + return Jw(r15, t8, e, o, n, s, true); } -function Jw(r, t8, e, o, n, s, a = false, i = false, p = false) { +function Jw(r15, t8, e, o, n, s, a = false, i = false, p = false) { let u = []; for (let g = 0; g < t8.length; g++) t8[g] > n && u.push({ score: t8[g], boxIndex: g, suppressBeginIndex: 0 }); - u.sort(mN); + u.sort(dN); let c = s > 0 ? -0.5 / s : 0, l = [], m = []; for (; l.length < e && u.length > 0; ) { let g = u.pop(), { score: x, boxIndex: b, suppressBeginIndex: C } = g; @@ -7874,7 +7875,7 @@ function Jw(r, t8, e, o, n, s, a = false, i = false, p = false) { break; let S = false; for (let k = l.length - 1; k >= C; --k) { - let _ = cj(r, b, l[k]); + let _ = cj(r15, b, l[k]); if (_ >= o) { S = true; break; @@ -7882,135 +7883,135 @@ function Jw(r, t8, e, o, n, s, a = false, i = false, p = false) { if (g.score = g.score * lj(o, c, _), g.score <= n) break; } - g.suppressBeginIndex = l.length, S || (g.score === x ? (l.push(b), m.push(g.score)) : g.score > n && lN(u, g, mN)); + g.suppressBeginIndex = l.length, S || (g.score === x ? (l.push(b), m.push(g.score)) : g.score > n && mN(u, g, dN)); } let d = l.length, f = e - d; i && f > 0 && (l.push(...new Array(f).fill(0)), m.push(...new Array(f).fill(0))); let h = { selectedIndices: l }; return a && (h.selectedScores = m), p && (h.validOutputs = d), h; } -function cj(r, t8, e) { - let o = r.subarray(t8 * 4, t8 * 4 + 4), n = r.subarray(e * 4, e * 4 + 4), s = Math.min(o[0], o[2]), a = Math.min(o[1], o[3]), i = Math.max(o[0], o[2]), p = Math.max(o[1], o[3]), u = Math.min(n[0], n[2]), c = Math.min(n[1], n[3]), l = Math.max(n[0], n[2]), m = Math.max(n[1], n[3]), d = (i - s) * (p - a), f = (l - u) * (m - c); +function cj(r15, t8, e) { + let o = r15.subarray(t8 * 4, t8 * 4 + 4), n = r15.subarray(e * 4, e * 4 + 4), s = Math.min(o[0], o[2]), a = Math.min(o[1], o[3]), i = Math.max(o[0], o[2]), p = Math.max(o[1], o[3]), u = Math.min(n[0], n[2]), c = Math.min(n[1], n[3]), l = Math.max(n[0], n[2]), m = Math.max(n[1], n[3]), d = (i - s) * (p - a), f = (l - u) * (m - c); if (d <= 0 || f <= 0) return 0; let h = Math.max(s, u), g = Math.max(a, c), x = Math.min(i, l), b = Math.min(p, m), C = Math.max(x - h, 0) * Math.max(b - g, 0); return C / (d + f - C); } -function lj(r, t8, e) { +function lj(r15, t8, e) { let o = Math.exp(t8 * e * e); - return e <= r ? o : 0; + return e <= r15 ? o : 0; } -function mN(r, t8) { - return r.score - t8.score || r.score === t8.score && t8.boxIndex - r.boxIndex; +function dN(r15, t8) { + return r15.score - t8.score || r15.score === t8.score && t8.boxIndex - r15.boxIndex; } -async function mj(r, t8, e, o = 0.5, n = Number.NEGATIVE_INFINITY) { - let s = v(r, "boxes", "nonMaxSuppressionAsync"), a = v(t8, "scores", "nonMaxSuppressionAsync"), i = Eo(s, a, e, o, n); +async function mj(r15, t8, e, o = 0.5, n = Number.NEGATIVE_INFINITY) { + let s = v(r15, "boxes", "nonMaxSuppressionAsync"), a = v(t8, "scores", "nonMaxSuppressionAsync"), i = $o(s, a, e, o, n); e = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold; - let p = await Promise.all([s.data(), a.data()]), u = p[0], c = p[1], { selectedIndices: l } = ef(u, c, e, o, n); - return s !== r && s.dispose(), a !== t8 && a.dispose(), tr(l, "int32"); + let p = await Promise.all([s.data(), a.data()]), u = p[0], c = p[1], { selectedIndices: l } = Zd(u, c, e, o, n); + return s !== r15 && s.dispose(), a !== t8 && a.dispose(), Jt(l, "int32"); } -var dN = mj; -function dj(r, t8, e, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) { - let a = v(r, "boxes", "nonMaxSuppression"), i = v(t8, "scores", "nonMaxSuppression"), p = Eo(a, i, e, o, n, s); +var fN = mj; +function dj(r15, t8, e, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) { + let a = v(r15, "boxes", "nonMaxSuppression"), i = v(t8, "scores", "nonMaxSuppression"), p = $o(a, i, e, o, n, s); e = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma; - let u = { boxes: a, scores: i }, c = { maxOutputSize: e, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }, l = T.runKernel(Jn, u, c); + let u = { boxes: a, scores: i }, c = { maxOutputSize: e, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }, l = T.runKernel(Zn, u, c); return { selectedIndices: l[0], selectedScores: l[1] }; } -var fN = N({ nonMaxSuppressionWithScore_: dj }); -async function fj(r, t8, e, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) { - let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(t8, "scores", "nonMaxSuppressionAsync"), p = Eo(a, i, e, o, n, s); +var hN = N({ nonMaxSuppressionWithScore_: dj }); +async function fj(r15, t8, e, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) { + let a = v(r15, "boxes", "nonMaxSuppressionAsync"), i = v(t8, "scores", "nonMaxSuppressionAsync"), p = $o(a, i, e, o, n, s); e = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma; - let u = await Promise.all([a.data(), i.data()]), c = u[0], l = u[1], { selectedIndices: m, selectedScores: d } = rf(c, l, e, o, n, s); - return a !== r && a.dispose(), i !== t8 && i.dispose(), { selectedIndices: tr(m, "int32"), selectedScores: tr(d) }; + let u = await Promise.all([a.data(), i.data()]), c = u[0], l = u[1], { selectedIndices: m, selectedScores: d } = ef(c, l, e, o, n, s); + return a !== r15 && a.dispose(), i !== t8 && i.dispose(), { selectedIndices: Jt(m, "int32"), selectedScores: Jt(d) }; } -var hN = fj; -function hj(r, t8, e, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) { - let a = v(r, "boxes", "nonMaxSuppression"), i = v(t8, "scores", "nonMaxSuppression"), p = Eo(a, i, e, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, m = { boxes: a, scores: i }, d = { maxOutputSize: u, iouThreshold: c, scoreThreshold: l, padToMaxOutputSize: s }, f = T.runKernel(Za, m, d); +var gN = fj; +function hj(r15, t8, e, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) { + let a = v(r15, "boxes", "nonMaxSuppression"), i = v(t8, "scores", "nonMaxSuppression"), p = $o(a, i, e, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, m = { boxes: a, scores: i }, d = { maxOutputSize: u, iouThreshold: c, scoreThreshold: l, padToMaxOutputSize: s }, f = T.runKernel(Qa, m, d); return { selectedIndices: f[0], validOutputs: f[1] }; } -var gN = N({ nonMaxSuppressionPadded_: hj }); -async function gj(r, t8, e, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) { - let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(t8, "scores", "nonMaxSuppressionAsync"), p = Eo(a, i, e, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, [m, d] = await Promise.all([a.data(), i.data()]), { selectedIndices: f, validOutputs: h } = tf(m, d, u, c, l, s); - return a !== r && a.dispose(), i !== t8 && i.dispose(), { selectedIndices: tr(f, "int32"), validOutputs: ke(h, "int32") }; +var xN = N({ nonMaxSuppressionPadded_: hj }); +async function gj(r15, t8, e, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) { + let a = v(r15, "boxes", "nonMaxSuppressionAsync"), i = v(t8, "scores", "nonMaxSuppressionAsync"), p = $o(a, i, e, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, [m, d] = await Promise.all([a.data(), i.data()]), { selectedIndices: f, validOutputs: h } = Jd(m, d, u, c, l, s); + return a !== r15 && a.dispose(), i !== t8 && i.dispose(), { selectedIndices: Jt(f, "int32"), validOutputs: ke(h, "int32") }; } -var xN = gj; -function xj(r, t8, e = false, o = false) { - let n = v(r, "images", "resizeBilinear"); +var yN = gj; +function xj(r15, t8, e = false, o = false) { + let n = v(r15, "images", "resizeBilinear"); $(n.rank === 3 || n.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`), $(t8.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${t8}.`), $(o === false || e === false, () => "Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false."); let s = n, a = false; n.rank === 3 && (a = true, s = W(n, [1, n.shape[0], n.shape[1], n.shape[2]])); - let [] = t8, i = { images: s }, p = { alignCorners: e, halfPixelCenters: o, size: t8 }, u = T.runKernel(us, i, p); - return a ? W(u, [u.shape[1], u.shape[2], u.shape[3]]) : u; -} -var yN = N({ resizeBilinear_: xj }); -function yj(r, t8, e = false, o = false) { - let n = v(r, "images", "resizeNearestNeighbor"); - $(n.rank === 3 || n.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`), $(t8.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t8}.`), $(n.dtype === "float32" || n.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), $(o === false || e === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false."); - let s = n, a = false; - n.rank === 3 && (a = true, s = W(n, [1, n.shape[0], n.shape[1], n.shape[2]])); let [] = t8, i = { images: s }, p = { alignCorners: e, halfPixelCenters: o, size: t8 }, u = T.runKernel(is, i, p); return a ? W(u, [u.shape[1], u.shape[2], u.shape[3]]) : u; } -var bN = N({ resizeNearestNeighbor_: yj }); -function bj(r, t8 = "binary", e = false, o = 0.5) { - let n = v(r, "image", "threshold"), s = 0.2989, a = 0.587, i = 0.114, p = n.shape[0] * n.shape[1], u = se(tr([o]), 255), c, l, m, d; +var bN = N({ resizeBilinear_: xj }); +function yj(r15, t8, e = false, o = false) { + let n = v(r15, "images", "resizeNearestNeighbor"); + $(n.rank === 3 || n.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`), $(t8.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t8}.`), $(n.dtype === "float32" || n.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), $(o === false || e === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false."); + let s = n, a = false; + n.rank === 3 && (a = true, s = W(n, [1, n.shape[0], n.shape[1], n.shape[2]])); + let [] = t8, i = { images: s }, p = { alignCorners: e, halfPixelCenters: o, size: t8 }, u = T.runKernel(as, i, p); + return a ? W(u, [u.shape[1], u.shape[2], u.shape[3]]) : u; +} +var CN = N({ resizeNearestNeighbor_: yj }); +function bj(r15, t8 = "binary", e = false, o = 0.5) { + let n = v(r15, "image", "threshold"), s = 0.2989, a = 0.587, i = 0.114, p = n.shape[0] * n.shape[1], u = se(Jt([o]), 255), c, l, m, d; if ($(n.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${n.rank}.`), $(n.shape[2] === 3 || n.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${n.shape[2]}.`), $(n.dtype === "int32" || n.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${n.dtype}.`), $(t8 === "otsu" || t8 === "binary", () => `Method must be binary or otsu, but was ${t8}`), n.shape[2] === 3) { - [c, l, m] = li(n, [1, 1, 1], -1); + [c, l, m] = ci(n, [1, 1, 1], -1); let g = se(c, s), x = se(l, a), b = se(m, i); d = Ce(Ce(g, x), b); } else - d = r; + d = r15; if (t8 === "otsu") { - let g = gd(We(Hd(d), "int32"), ur([]), 256); + let g = fd(We(Ud(d), "int32"), ar([]), 256); u = Cj(g, p); } - let f = e ? mc(d, u) : qu(d, u); + let f = e ? nc(d, u) : zu(d, u); return We(se(f, 255), "int32"); } -function Cj(r, t8) { - let e = tr([-1]), o = tr([0]), n = tr([0]), s, a, i, p, u, c; - for (let l = 0; l < r.size - 1; l++) { - s = Xe(r, 0, l + 1), a = Xe(r, l + 1), u = je(ot(s), t8), c = je(ot(a), t8); - let m = ot(se(s, mu(0, s.size))); +function Cj(r15, t8) { + let e = Jt([-1]), o = Jt([0]), n = Jt([0]), s, a, i, p, u, c; + for (let l = 0; l < r15.size - 1; l++) { + s = Xe(r15, 0, l + 1), a = Xe(r15, l + 1), u = je(ot(s), t8), c = je(ot(a), t8); + let m = ot(se(s, uu(0, s.size))); i = je(m, ot(s)); - let d = Ea(a.shape, s.size), f = Ce(mu(0, a.size), d), h = se(a, f); + let d = Ea(a.shape, s.size), f = Ce(uu(0, a.size), d), h = se(a, f); p = je(ot(h), ot(a)); let g = Te(i, p), x = Te(i, p), b = se(u, c); n = se(se(b, g), x); - let C = qu(n, o); - o = lo(C, n, o), e = lo(C, tr([l]), e); + let C = zu(n, o); + o = co(C, n, o), e = co(C, Jt([l]), e); } return e; } -var CN = N({ threshold_: bj }); -function wj(r, t8, e = "nearest", o = "constant", n = 0, s) { - let a = v(r, "image", "transform", "float32"), i = v(t8, "transforms", "transform", "float32"); +var wN = N({ threshold_: bj }); +function wj(r15, t8, e = "nearest", o = "constant", n = 0, s) { + let a = v(r15, "image", "transform", "float32"), i = v(t8, "transforms", "transform", "float32"); $(a.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${a.rank}.`), $(i.rank === 2 && (i.shape[0] === a.shape[0] || i.shape[0] === 1) && i.shape[1] === 8, () => "Error in transform: Input transform should be batch x 8 or 1 x 8"), $(s == null || s.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${s}.`); let p = { image: a, transforms: i }, u = { interpolation: e, fillMode: o, fillValue: n, outputShape: s }; - return T.runKernel(Ds, p, u); + return T.runKernel(Rs, p, u); } -var wN = N({ transform_: wj }); -function Sj(r, t8, e) { - let o = v(r, "a", "bandPart"); +var SN = N({ transform_: wj }); +function Sj(r15, t8, e) { + let o = v(r15, "a", "bandPart"); $(o.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${o.rank}.`); let n = o.shape, [s, a] = o.shape.slice(-2), i, p; - typeof t8 == "number" ? ($(t8 % 1 === 0, () => `bandPart(): numLower must be an integer, got ${t8}.`), $(t8 <= s, () => `bandPart(): numLower (${t8}) must not be greater than the number of rows (${s}).`), i = v(t8 < 0 ? s : t8, "numLower", "bandPart")) : ($(t8.dtype === "int32", () => "bandPart(): numLower's dtype must be an int32."), i = lo(_l(t8, 0), s, Yu(t8, s))), typeof e == "number" ? ($(e % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${e}.`), $(e <= a, () => `bandPart(): numUpper (${e}) must not be greater than the number of columns (${a}).`), p = v(e < 0 ? a : e, "numUpper", "bandPart")) : ($(e.dtype === "int32", () => "bandPart(): numUpper's dtype must be an int32."), p = lo(_l(e, 0), a, Yu(e, a))); - let u = W(mu(0, s, 1, "int32"), [-1, 1]), c = mu(0, a, 1, "int32"), l = Te(u, c), m = ju(mc(l, i), vd(l, cr(p))), d = Gr([s, a], o.dtype); - return W(kr(fo(W(o, [-1, s, a])).map((f) => lo(m, f, d))), n); + typeof t8 == "number" ? ($(t8 % 1 === 0, () => `bandPart(): numLower must be an integer, got ${t8}.`), $(t8 <= s, () => `bandPart(): numLower (${t8}) must not be greater than the number of rows (${s}).`), i = v(t8 < 0 ? s : t8, "numLower", "bandPart")) : ($(t8.dtype === "int32", () => "bandPart(): numLower's dtype must be an int32."), i = co(kl(t8, 0), s, Uu(t8, s))), typeof e == "number" ? ($(e % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${e}.`), $(e <= a, () => `bandPart(): numUpper (${e}) must not be greater than the number of columns (${a}).`), p = v(e < 0 ? a : e, "numUpper", "bandPart")) : ($(e.dtype === "int32", () => "bandPart(): numUpper's dtype must be an int32."), p = co(kl(e, 0), a, Uu(e, a))); + let u = W(uu(0, s, 1, "int32"), [-1, 1]), c = uu(0, a, 1, "int32"), l = Te(u, c), m = Vu(nc(l, i), Sd(l, pr(p))), d = Ur([s, a], o.dtype); + return W(vr(mo(W(o, [-1, s, a])).map((f) => co(m, f, d))), n); } -var SN = N({ bandPart_: Sj }); -function Ij(r) { +var IN = N({ bandPart_: Sj }); +function Ij(r15) { let t8; - if (Array.isArray(r)) { - t8 = false, $(r != null && r.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty"); - let n = r[0].shape[0]; - for (let s = 1; s < r.length; ++s) - $(r[s].shape[0] === n, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`); + if (Array.isArray(r15)) { + t8 = false, $(r15 != null && r15.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty"); + let n = r15[0].shape[0]; + for (let s = 1; s < r15.length; ++s) + $(r15[s].shape[0] === n, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${r15[s].shape[0]} vs. ${n})`); } else - t8 = true, r = li(r, r.shape[0], 0).map((n) => gc(n, [0])); - $(r.length <= r[0].shape[0], () => `Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`); - let e = [], o = r; - for (let n = 0; n < r.length; ++n) + t8 = true, r15 = ci(r15, r15.shape[0], 0).map((n) => uc(n, [0])); + $(r15.length <= r15[0].shape[0], () => `Gram-Schmidt: Number of vectors (${r15.length}) exceeds number of dimensions (${r15[0].shape[0]}).`); + let e = [], o = r15; + for (let n = 0; n < r15.length; ++n) e.push(T.tidy(() => { let s = o[n]; if (n > 0) @@ -8018,170 +8019,170 @@ function Ij(r) { let i = se(ot(se(e[a], s)), e[a]); s = Te(s, i); } - return je(s, Ku(s, "euclidean")); + return je(s, Bu(s, "euclidean")); })); - return t8 ? kr(e, 0) : e; + return t8 ? vr(e, 0) : e; } -var IN = N({ gramSchmidt_: Ij }); -function vj(r, t8 = false) { - if ($(r.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`), r.rank === 2) - return vN(r, t8); +var vN = N({ gramSchmidt_: Ij }); +function vj(r15, t8 = false) { + if ($(r15.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${r15.rank}`), r15.rank === 2) + return kN(r15, t8); { - let e = r.shape.slice(0, r.shape.length - 2).reduce((p, u) => p * u), o = fo(W(r, [e, r.shape[r.shape.length - 2], r.shape[r.shape.length - 1]]), 0), n = [], s = []; + let e = r15.shape.slice(0, r15.shape.length - 2).reduce((p, u) => p * u), o = mo(W(r15, [e, r15.shape[r15.shape.length - 2], r15.shape[r15.shape.length - 1]]), 0), n = [], s = []; o.forEach((p) => { - let [u, c] = vN(p, t8); + let [u, c] = kN(p, t8); n.push(u), s.push(c); }); - let a = W(kr(n, 0), r.shape), i = W(kr(s, 0), r.shape); + let a = W(vr(n, 0), r15.shape), i = W(vr(s, 0), r15.shape); return [a, i]; } } -function vN(r, t8 = false) { +function kN(r15, t8 = false) { return T.tidy(() => { - $(r.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`); - let e = r.shape[0], o = r.shape[1], n = wd(e), s = Ur(r), a = fu([[1]], [1, 1]), i = Ur(a), p = e >= o ? o : e; + $(r15.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${r15.shape.length}D Tensor.`); + let e = r15.shape[0], o = r15.shape[1], n = bd(e), s = Wr(r15), a = cu([[1]], [1, 1]), i = Wr(a), p = e >= o ? o : e; for (let u = 0; u < p; ++u) { let c = s, l = i, m = n; [i, s, n] = T.tidy(() => { - let d = Xe(s, [u, u], [e - u, 1]), f = Ku(d), h = Xe(s, [u, u], [1, 1]), g = lo(qu(h, 0), fu([[-1]]), fu([[1]])), x = Te(h, se(g, f)), b = je(d, x); - b.shape[0] === 1 ? i = Ur(a) : i = bt([a, Xe(b, [1, 0], [b.shape[0] - 1, b.shape[1]])], 0); - let C = cr(je(Ze(g, x), f)), S = Xe(s, [u, 0], [e - u, o]), k = se(C, i), _ = yc(i); + let d = Xe(s, [u, u], [e - u, 1]), f = Bu(d), h = Xe(s, [u, u], [1, 1]), g = co(zu(h, 0), cu([[-1]]), cu([[1]])), x = Te(h, se(g, f)), b = je(d, x); + b.shape[0] === 1 ? i = Wr(a) : i = yt([a, Xe(b, [1, 0], [b.shape[0] - 1, b.shape[1]])], 0); + let C = pr(je(Ze(g, x), f)), S = Xe(s, [u, 0], [e - u, o]), k = se(C, i), _ = cc(i); if (u === 0) s = Te(S, Ze(k, Ze(_, S))); else { let D = Te(S, Ze(k, Ze(_, S))); - s = bt([Xe(s, [0, 0], [u, o]), D], 0); + s = yt([Xe(s, [0, 0], [u, o]), D], 0); } - let E = yc(k), R = Xe(n, [0, u], [e, n.shape[1] - u]); + let E = cc(k), R = Xe(n, [0, u], [e, n.shape[1] - u]); if (u === 0) n = Te(R, Ze(Ze(R, i), E)); else { let D = Te(R, Ze(Ze(R, i), E)); - n = bt([Xe(n, [0, 0], [e, u]), D], 1); + n = yt([Xe(n, [0, 0], [e, u]), D], 1); } return [i, s, n]; - }), Mt([c, l, m]); + }), Ot([c, l, m]); } return !t8 && e > o && (n = Xe(n, [0, 0], [e, o]), s = Xe(s, [0, 0], [o, o])), [n, s]; }); } -var kN = N({ qr_: vj }); -var Rt; -(function(r) { - r[r.NONE = 0] = "NONE", r[r.MEAN = 1] = "MEAN", r[r.SUM = 2] = "SUM", r[r.SUM_BY_NONZERO_WEIGHTS = 3] = "SUM_BY_NONZERO_WEIGHTS"; -})(Rt || (Rt = {})); -function kj(r, t8, e = Rt.SUM_BY_NONZERO_WEIGHTS) { - let o = v(r, "losses", "computeWeightedLoss"), n = null; +var NN = N({ qr_: vj }); +var Et; +(function(r15) { + r15[r15.NONE = 0] = "NONE", r15[r15.MEAN = 1] = "MEAN", r15[r15.SUM = 2] = "SUM", r15[r15.SUM_BY_NONZERO_WEIGHTS = 3] = "SUM_BY_NONZERO_WEIGHTS"; +})(Et || (Et = {})); +function kj(r15, t8, e = Et.SUM_BY_NONZERO_WEIGHTS) { + let o = v(r15, "losses", "computeWeightedLoss"), n = null; t8 != null && (n = v(t8, "weights", "computeWeightedLoss")); let s = n == null ? o : se(o, n); - if (e === Rt.NONE) + if (e === Et.NONE) return s; - if (e === Rt.SUM) + if (e === Et.SUM) return ot(s); - if (e === Rt.MEAN) { + if (e === Et.MEAN) { if (n == null) - return Xu(s); + return Wu(s); { let a = o.size / n.size, i = je(ot(s), ot(n)); return a > 1 ? je(i, ke(a)) : i; } } - if (e === Rt.SUM_BY_NONZERO_WEIGHTS) { + if (e === Et.SUM_BY_NONZERO_WEIGHTS) { if (n == null) return je(ot(s), ke(o.size)); { - let a = se(n, Da(o.shape)), i = We(ot(Pd(a, ke(0))), "float32"); + let a = se(n, Da(o.shape)), i = We(ot(Ad(a, ke(0))), "float32"); return je(ot(s), i); } } throw Error(`Unknown reduction: ${e}`); } -var lr = N({ computeWeightedLoss_: kj }); -function Nj(r, t8, e, o = Rt.SUM_BY_NONZERO_WEIGHTS) { - let n = v(r, "labels", "absoluteDifference"), s = v(t8, "predictions", "absoluteDifference"), a = null; - e != null && (a = v(e, "weights", "absoluteDifference")), yt(n.shape, s.shape, "Error in absoluteDifference: "); - let i = Jt(Te(n, s)); - return lr(i, a, o); +var cr = N({ computeWeightedLoss_: kj }); +function Nj(r15, t8, e, o = Et.SUM_BY_NONZERO_WEIGHTS) { + let n = v(r15, "labels", "absoluteDifference"), s = v(t8, "predictions", "absoluteDifference"), a = null; + e != null && (a = v(e, "weights", "absoluteDifference")), xt(n.shape, s.shape, "Error in absoluteDifference: "); + let i = Qt(Te(n, s)); + return cr(i, a, o); } -var NN = N({ absoluteDifference_: Nj }); -function Tj(r, t8, e, o, n = Rt.SUM_BY_NONZERO_WEIGHTS) { - let s = v(r, "labels", "cosineDistance"), a = v(t8, "predictions", "cosineDistance"), i = null; - o != null && (i = v(o, "weights", "cosineDistance")), yt(s.shape, a.shape, "Error in cosineDistance: "); +var TN = N({ absoluteDifference_: Nj }); +function Tj(r15, t8, e, o, n = Et.SUM_BY_NONZERO_WEIGHTS) { + let s = v(r15, "labels", "cosineDistance"), a = v(t8, "predictions", "cosineDistance"), i = null; + o != null && (i = v(o, "weights", "cosineDistance")), xt(s.shape, a.shape, "Error in cosineDistance: "); let p = ke(1), u = Te(p, ot(se(s, a), e, true)); - return lr(u, i, n); + return cr(u, i, n); } -var TN = N({ cosineDistance_: Tj }); -function _j(r, t8, e, o = Rt.SUM_BY_NONZERO_WEIGHTS) { - let n = v(r, "labels", "hingeLoss"), s = v(t8, "predictions", "hingeLoss"), a = null; - e != null && (a = v(e, "weights", "hingeLoss")), yt(n.shape, s.shape, "Error in hingeLoss: "); +var _N = N({ cosineDistance_: Tj }); +function _j(r15, t8, e, o = Et.SUM_BY_NONZERO_WEIGHTS) { + let n = v(r15, "labels", "hingeLoss"), s = v(t8, "predictions", "hingeLoss"), a = null; + e != null && (a = v(e, "weights", "hingeLoss")), xt(n.shape, s.shape, "Error in hingeLoss: "); let i = ke(1); n = Te(se(ke(2), n), i); - let p = du(Te(i, se(n, s))); - return lr(p, a, o); + let p = pu(Te(i, se(n, s))); + return cr(p, a, o); } -var _N = N({ hingeLoss_: _j }); -function $j(r, t8, e, o = 1, n = Rt.SUM_BY_NONZERO_WEIGHTS) { - let s = v(r, "labels", "huberLoss"), a = v(t8, "predictions", "huberLoss"), i = null; - e != null && (i = v(e, "weights", "huberLoss")), yt(s.shape, a.shape, "Error in huberLoss: "); - let p = ke(o), u = Jt(Te(a, s)), c = Yu(u, p), l = Te(u, c), m = Ce(se(ke(0.5), er(c)), se(p, l)); - return lr(m, i, n); +var $N = N({ hingeLoss_: _j }); +function $j(r15, t8, e, o = 1, n = Et.SUM_BY_NONZERO_WEIGHTS) { + let s = v(r15, "labels", "huberLoss"), a = v(t8, "predictions", "huberLoss"), i = null; + e != null && (i = v(e, "weights", "huberLoss")), xt(s.shape, a.shape, "Error in huberLoss: "); + let p = ke(o), u = Qt(Te(a, s)), c = Uu(u, p), l = Te(u, c), m = Ce(se(ke(0.5), Zt(c)), se(p, l)); + return cr(m, i, n); } -var $N = N({ huberLoss_: $j }); -function Ej(r, t8, e, o = 1e-7, n = Rt.SUM_BY_NONZERO_WEIGHTS) { - let s = v(r, "labels", "logLoss"), a = v(t8, "predictions", "logLoss"), i = null; - e != null && (i = v(e, "weights", "logLoss")), yt(s.shape, a.shape, "Error in logLoss: "); - let p = ke(1), u = ke(o), c = cr(se(s, pi(Ce(a, u)))), l = se(Te(p, s), pi(Ce(Te(p, a), u))), m = Te(c, l); - return lr(m, i, n); +var EN = N({ huberLoss_: $j }); +function Ej(r15, t8, e, o = 1e-7, n = Et.SUM_BY_NONZERO_WEIGHTS) { + let s = v(r15, "labels", "logLoss"), a = v(t8, "predictions", "logLoss"), i = null; + e != null && (i = v(e, "weights", "logLoss")), xt(s.shape, a.shape, "Error in logLoss: "); + let p = ke(1), u = ke(o), c = pr(se(s, ui(Ce(a, u)))), l = se(Te(p, s), ui(Ce(Te(p, a), u))), m = Te(c, l); + return cr(m, i, n); } -var EN = N({ logLoss_: Ej }); -function Rj(r, t8, e, o = Rt.SUM_BY_NONZERO_WEIGHTS) { - let n = v(r, "labels", "meanSquaredError"), s = v(t8, "predictions", "meanSquaredError"), a = null; - e != null && (a = v(e, "weights", "meanSquaredError")), yt(n.shape, s.shape, "Error in meanSquaredError: "); - let i = qd(n, s); - return lr(i, a, o); +var RN = N({ logLoss_: Ej }); +function Rj(r15, t8, e, o = Et.SUM_BY_NONZERO_WEIGHTS) { + let n = v(r15, "labels", "meanSquaredError"), s = v(t8, "predictions", "meanSquaredError"), a = null; + e != null && (a = v(e, "weights", "meanSquaredError")), xt(n.shape, s.shape, "Error in meanSquaredError: "); + let i = Hd(n, s); + return cr(i, a, o); } -var RN = N({ meanSquaredError_: Rj }); -function Dj(r, t8) { - let e = v(r, "labels", "sigmoidCrossEntropyWithLogits"), o = v(t8, "logits", "sigmoidCrossEntropyWithLogits"); - yt(e.shape, o.shape, "Error in sigmoidCrossEntropyWithLogits: "); - let n = du(o), s = se(o, e), a = Nd($o(cr(Jt(o)))); +var DN = N({ meanSquaredError_: Rj }); +function Dj(r15, t8) { + let e = v(r15, "labels", "sigmoidCrossEntropyWithLogits"), o = v(t8, "logits", "sigmoidCrossEntropyWithLogits"); + xt(e.shape, o.shape, "Error in sigmoidCrossEntropyWithLogits: "); + let n = pu(o), s = se(o, e), a = vd(_o(pr(Qt(o)))); return Ce(Te(n, s), a); } -function Aj(r, t8, e, o = 0, n = Rt.SUM_BY_NONZERO_WEIGHTS) { - let s = v(r, "multiClassLabels", "sigmoidCrossEntropy"), a = v(t8, "logits", "sigmoidCrossEntropy"), i = null; - if (e != null && (i = v(e, "weights", "sigmoidCrossEntropy")), yt(s.shape, a.shape, "Error in sigmoidCrossEntropy: "), o > 0) { +function Aj(r15, t8, e, o = 0, n = Et.SUM_BY_NONZERO_WEIGHTS) { + let s = v(r15, "multiClassLabels", "sigmoidCrossEntropy"), a = v(t8, "logits", "sigmoidCrossEntropy"), i = null; + if (e != null && (i = v(e, "weights", "sigmoidCrossEntropy")), xt(s.shape, a.shape, "Error in sigmoidCrossEntropy: "), o > 0) { let u = ke(o), c = ke(1), l = ke(0.5); s = Ce(se(s, Te(c, u)), se(l, u)); } let p = Dj(s, a); - return lr(p, i, n); + return cr(p, i, n); } -var DN = N({ sigmoidCrossEntropy_: Aj }); -function Fj(r, t8, e = -1) { +var AN = N({ sigmoidCrossEntropy_: Aj }); +function Fj(r15, t8, e = -1) { if (e === -1 && (e = t8.rank - 1), e !== t8.rank - 1) throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t8.rank} and dim was ${e}`); - return vr((n, s, a) => { - let p = $d(s, [e], true), u = Te(We(s, "float32"), p); + return Ir((n, s, a) => { + let p = Td(s, [e], true), u = Te(We(s, "float32"), p); a([n, u]); - let c = cr(se(u, n)); + let c = pr(se(u, n)); return { value: ot(c, [e]), gradFunc: (d, f) => { - let [h, g] = f, x = ii(d.shape, [e]); - return [se(W(d, x), Te(We(h, "float32"), $o(g))), se(W(d, x), Te($o(g), We(h, "float32")))]; + let [h, g] = f, x = ai(d.shape, [e]); + return [se(W(d, x), Te(We(h, "float32"), _o(g))), se(W(d, x), Te(_o(g), We(h, "float32")))]; } }; - })(r, t8); + })(r15, t8); } -function Pj(r, t8, e, o = 0, n = Rt.SUM_BY_NONZERO_WEIGHTS) { - let s = v(r, "onehotLabels", "softmaxCrossEntropy"), a = v(t8, "logits", "softmaxCrossEntropy"), i = null; - if (e != null && (i = v(e, "weights", "softmaxCrossEntropy")), yt(s.shape, a.shape, "Error in softmaxCrossEntropy: "), o > 0) { +function Pj(r15, t8, e, o = 0, n = Et.SUM_BY_NONZERO_WEIGHTS) { + let s = v(r15, "onehotLabels", "softmaxCrossEntropy"), a = v(t8, "logits", "softmaxCrossEntropy"), i = null; + if (e != null && (i = v(e, "weights", "softmaxCrossEntropy")), xt(s.shape, a.shape, "Error in softmaxCrossEntropy: "), o > 0) { let u = ke(o), c = ke(1), l = ke(s.shape[1]); s = Ce(se(s, Te(c, u)), je(u, l)); } let p = Fj(s, a); - return lr(p, i, n); + return cr(p, i, n); } -var AN = N({ softmaxCrossEntropy_: Pj }); -function Oj(r, t8, e, o) { - let n = v(r, "indices", "sparseFillEmptyRows", "int32"), s = v(t8, "values", "sparseFillEmptyRows"), a = v(e, "denseShape", "sparseFillEmptyRows", "int32"), i = v(o, "defaultValue", "sparseFillEmptyRows", s.dtype); +var FN = N({ softmaxCrossEntropy_: Pj }); +function Oj(r15, t8, e, o) { + let n = v(r15, "indices", "sparseFillEmptyRows", "int32"), s = v(t8, "values", "sparseFillEmptyRows"), a = v(e, "denseShape", "sparseFillEmptyRows", "int32"), i = v(o, "defaultValue", "sparseFillEmptyRows", s.dtype); if (n.rank !== 2) throw new Error(`Indices should be Tensor2D but received shape ${n.shape}`); @@ -8191,12 +8192,12 @@ function Oj(r, t8, e, o) { throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`); if (i.rank !== 0) throw new Error(`Default value should be a scalar but received shape ${i.shape}`); - let p = { indices: n, values: s, denseShape: a, defaultValue: i }, u = T.runKernel(ji, p); + let p = { indices: n, values: s, denseShape: a, defaultValue: i }, u = T.runKernel(Hi, p); return { outputIndices: u[0], outputValues: u[1], emptyRowIndicator: u[2], reverseIndexMap: u[3] }; } -var FN = N({ sparseFillEmptyRows_: Oj }); -function Mj(r, t8, e) { - let o = v(r, "inputIndices", "sparseReshape", "int32"), n = v(t8, "inputShape", "sparseReshape", "int32"), s = v(e, "newShape", "sparseReshape", "int32"); +var PN = N({ sparseFillEmptyRows_: Oj }); +function Mj(r15, t8, e) { + let o = v(r15, "inputIndices", "sparseReshape", "int32"), n = v(t8, "inputShape", "sparseReshape", "int32"), s = v(e, "newShape", "sparseReshape", "int32"); if (o.rank !== 2) throw new Error(`Input indices should be Tensor2D but received shape ${o.shape}`); @@ -8204,12 +8205,12 @@ function Mj(r, t8, e) { throw new Error(`Input shape should be Tensor1D but received shape ${n.shape}`); if (s.rank !== 1) throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`); - let a = { inputIndices: o, inputShape: n, newShape: s }, i = T.runKernel(ti, a); + let a = { inputIndices: o, inputShape: n, newShape: s }, i = T.runKernel(ei, a); return { outputIndices: i[0], outputShape: i[1] }; } -var PN = N({ sparseReshape_: Mj }); -function Lj(r, t8, e) { - let o = v(r, "data", "sparseSegmentMean"), n = v(t8, "indices", "sparseSegmentMean", "int32"), s = v(e, "segmentIds", "sparseSegmentMean", "int32"); +var ON = N({ sparseReshape_: Mj }); +function Lj(r15, t8, e) { + let o = v(r15, "data", "sparseSegmentMean"), n = v(t8, "indices", "sparseSegmentMean", "int32"), s = v(e, "segmentIds", "sparseSegmentMean", "int32"); if (o.rank < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.rank !== 1) @@ -8221,9 +8222,9 @@ function Lj(r, t8, e) { let a = { data: o, indices: n, segmentIds: s }; return T.runKernel(ya, a); } -var ON = N({ sparseSegmentMean_: Lj }); -function Bj(r, t8, e) { - let o = v(r, "data", "sparseSegmentSum"), n = v(t8, "indices", "sparseSegmentSum", "int32"), s = v(e, "segmentIds", "sparseSegmentSum", "int32"); +var MN = N({ sparseSegmentMean_: Lj }); +function Bj(r15, t8, e) { + let o = v(r15, "data", "sparseSegmentSum"), n = v(t8, "indices", "sparseSegmentSum", "int32"), s = v(e, "segmentIds", "sparseSegmentSum", "int32"); if (o.rank < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.rank !== 1) @@ -8235,9 +8236,9 @@ function Bj(r, t8, e) { let a = { data: o, indices: n, segmentIds: s }; return T.runKernel(ba, a); } -var MN = N({ sparseSegmentSum_: Bj }); -function zj(r, t8, e, o, n, s, a, i) { - let p = v(r, "data", "stringNGrams", "string"); +var LN = N({ sparseSegmentSum_: Bj }); +function zj(r15, t8, e, o, n, s, a, i) { + let p = v(r15, "data", "stringNGrams", "string"); if (p.dtype !== "string") throw new Error("Data must be of datatype string"); if (p.shape.length !== 1) @@ -8248,42 +8249,42 @@ function zj(r, t8, e, o, n, s, a, i) { let c = { separator: e, nGramWidths: o, leftPad: n, rightPad: s, padWidth: a, preserveShortSequences: i }, l = { data: p, dataSplits: u }, m = T.runKernel(Ca, l, c); return { nGrams: m[0], nGramsSplits: m[1] }; } -var LN = N({ stringNGrams_: zj }); -function Vj(r, t8, e = true) { - let o = v(r, "input", "stringSplit", "string"), n = v(t8, "delimiter", "stringSplit", "string"); +var BN = N({ stringNGrams_: zj }); +function Vj(r15, t8, e = true) { + let o = v(r15, "input", "stringSplit", "string"), n = v(t8, "delimiter", "stringSplit", "string"); if (o.rank !== 1) throw new Error(`Input should be Tensor1D but received shape ${o.shape}`); if (n.rank !== 0) throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`); - let s = { skipEmpty: e }, a = { input: o, delimiter: n }, i = T.runKernel(Yi, a, s); + let s = { skipEmpty: e }, a = { input: o, delimiter: n }, i = T.runKernel(qi, a, s); return { indices: i[0], values: i[1], shape: i[2] }; } -var BN = N({ stringSplit_: Vj }); -function Wj(r, t8) { - let e = v(r, "input", "stringToHashBucketFast", "string"), o = { numBuckets: t8 }; +var zN = N({ stringSplit_: Vj }); +function Wj(r15, t8) { + let e = v(r15, "input", "stringToHashBucketFast", "string"), o = { numBuckets: t8 }; if (t8 <= 0) throw new Error("Number of buckets must be at least 1"); let n = { input: e }; - return T.runKernel(Qi, n, o); + return T.runKernel(ji, n, o); } -var zN = N({ stringToHashBucketFast_: Wj }); -function Uj(r, t8, e, o = true) { - let n = v(r, "input", "staticRegexReplace", "string"), s = { pattern: t8, rewrite: e, replaceGlobal: o }; - return T.runKernel(Ou, { x: n }, s); +var VN = N({ stringToHashBucketFast_: Wj }); +function Uj(r15, t8, e, o = true) { + let n = v(r15, "input", "staticRegexReplace", "string"), s = { pattern: t8, rewrite: e, replaceGlobal: o }; + return T.runKernel($u, { x: n }, s); } -var VN = N({ staticRegexReplace_: Uj }); -var Gj = { fft: fc, ifft: Ju, rfft: hc, irfft: Kd }; -var Hj = { hammingWindow: oN, hannWindow: Zd, frame: Jd, stft: nN }; -var Kj = { flipLeftRight: aN, grayscaleToRGB: iN, resizeNearestNeighbor: bN, resizeBilinear: yN, rgbToGrayscale: uN, rotateWithOffset: pN, cropAndResize: sN, nonMaxSuppression: cN, nonMaxSuppressionAsync: dN, nonMaxSuppressionWithScore: fN, nonMaxSuppressionWithScoreAsync: hN, nonMaxSuppressionPadded: gN, nonMaxSuppressionPaddedAsync: xN, threshold: CN, transform: wN }; -var qj = { bandPart: SN, gramSchmidt: IN, qr: kN }; -var jj = { absoluteDifference: NN, computeWeightedLoss: lr, cosineDistance: TN, hingeLoss: _N, huberLoss: $N, logLoss: EN, meanSquaredError: RN, sigmoidCrossEntropy: DN, softmaxCrossEntropy: AN }; -var Xj = { sparseFillEmptyRows: FN, sparseReshape: PN, sparseSegmentMean: ON, sparseSegmentSum: MN }; -var Yj = { stringNGrams: LN, stringSplit: BN, stringToHashBucketFast: zN, staticRegexReplace: VN }; -var WN = {}; -qe(WN, { Serializable: () => Dl, SerializationMap: () => Fa, getRegisteredName: () => Zj, registerClass: () => tS }); +var WN = N({ staticRegexReplace_: Uj }); +var Gj = { fft: ac, ifft: Ku, rfft: ic, irfft: Gd }; +var Hj = { hammingWindow: nN, hannWindow: Yd, frame: Qd, stft: sN }; +var Kj = { flipLeftRight: iN, grayscaleToRGB: uN, resizeNearestNeighbor: CN, resizeBilinear: bN, rgbToGrayscale: pN, rotateWithOffset: cN, cropAndResize: aN, nonMaxSuppression: lN, nonMaxSuppressionAsync: fN, nonMaxSuppressionWithScore: hN, nonMaxSuppressionWithScoreAsync: gN, nonMaxSuppressionPadded: xN, nonMaxSuppressionPaddedAsync: yN, threshold: wN, transform: SN }; +var qj = { bandPart: IN, gramSchmidt: vN, qr: NN }; +var jj = { absoluteDifference: TN, computeWeightedLoss: cr, cosineDistance: _N, hingeLoss: $N, huberLoss: EN, logLoss: RN, meanSquaredError: DN, sigmoidCrossEntropy: AN, softmaxCrossEntropy: FN }; +var Xj = { sparseFillEmptyRows: PN, sparseReshape: ON, sparseSegmentMean: MN, sparseSegmentSum: LN }; +var Yj = { stringNGrams: BN, stringSplit: zN, stringToHashBucketFast: VN, staticRegexReplace: WN }; +var UN = {}; +qe(UN, { Serializable: () => $l, SerializationMap: () => tf, getRegisteredName: () => Zj, registerClass: () => tS }); var Qj = /* @__PURE__ */ new Map(); var eS = /* @__PURE__ */ new Map(); -var Dl = class { +var $l = class { getClassName() { return this.constructor.className; } @@ -8291,26 +8292,26 @@ var Dl = class { return new t8(e); } }; -var Fa = class { +var tf = class r7 { constructor() { this.classNameMap = {}; } static getMap() { - return Fa.instance == null && (Fa.instance = new Fa()), Fa.instance; + return r7.instance == null && (r7.instance = new r7()), r7.instance; } static register(t8) { - Fa.getMap().classNameMap[t8.className] = [t8, t8.fromConfig]; + r7.getMap().classNameMap[t8.className] = [t8, t8.fromConfig]; } }; -function tS(r, t8, e) { - $(r.className != null, () => "Class being registered does not have the static className property defined."), $(typeof r.className == "string", () => "className is required to be a string, but got type " + typeof r.className), $(r.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), typeof t8 == "undefined" && (t8 = "Custom"), typeof e == "undefined" && (e = r.className); +function tS(r15, t8, e) { + $(r15.className != null, () => "Class being registered does not have the static className property defined."), $(typeof r15.className == "string", () => "className is required to be a string, but got type " + typeof r15.className), $(r15.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), typeof t8 == "undefined" && (t8 = "Custom"), typeof e == "undefined" && (e = r15.className); let o = e, n = t8 + ">" + o; - return Fa.register(r), Qj.set(n, r), eS.set(r, n), r; + return tf.register(r15), Qj.set(n, r15), eS.set(r15, n), r15; } -function Zj(r) { - return eS.has(r) ? eS.get(r) : r.className; +function Zj(r15) { + return eS.has(r15) ? eS.get(r15) : r15.className; } -var Nr = class extends Dl { +var kr = class extends $l { minimize(t8, e = false, o) { let { value: n, grads: s } = this.computeGradients(t8, o); if (o != null) { @@ -8318,7 +8319,7 @@ var Nr = class extends Dl { this.applyGradients(a); } else this.applyGradients(s); - return Mt(s), e ? n : (n.dispose(), null); + return Ot(s), e ? n : (n.dispose(), null); } get iterations() { return this.iterations_ == null && (this.iterations_ = 0), this.iterations_; @@ -8330,7 +8331,7 @@ var Nr = class extends Dl { return zw(t8, e); } dispose() { - this.iterations_ != null && Mt(this.iterations_); + this.iterations_ != null && Ot(this.iterations_); } async saveIterations() { return this.iterations_ == null && (this.iterations_ = 0), { name: "iter", tensor: ke(this.iterations_, "int32") }; @@ -8345,8 +8346,8 @@ var Nr = class extends Dl { return this.iterations_ = (await t8[0].tensor.data())[0], t8.slice(1); } }; -Object.defineProperty(Nr, Symbol.hasInstance, { value: (r) => r.minimize != null && r.computeGradients != null && r.applyGradients != null }); -var np = class extends Nr { +Object.defineProperty(kr, Symbol.hasInstance, { value: (r15) => r15.minimize != null && r15.computeGradients != null && r15.applyGradients != null }); +var Qu = class extends kr { static get className() { return "Adadelta"; } @@ -8356,13 +8357,13 @@ var np = class extends Nr { applyGradients(t8) { (Array.isArray(t8) ? t8.map((o) => o.name) : Object.keys(t8)).forEach((o, n) => { let s = T.registeredVariables[o], a = false; - this.accumulatedGrads[n] == null && (this.accumulatedGrads[n] = { originalName: `${o}/accum_grad`, variable: De(() => Ht(s).variable(a)) }), this.accumulatedUpdates[n] == null && (this.accumulatedUpdates[n] = { originalName: `${o}/accum_var`, variable: De(() => Ht(s).variable(a)) }); + this.accumulatedGrads[n] == null && (this.accumulatedGrads[n] = { originalName: `${o}/accum_grad`, variable: De(() => Gt(s).variable(a)) }), this.accumulatedUpdates[n] == null && (this.accumulatedUpdates[n] = { originalName: `${o}/accum_var`, variable: De(() => Gt(s).variable(a)) }); let i = Array.isArray(t8) ? t8[n].tensor : t8[o]; if (i == null) return; let p = this.accumulatedGrads[n].variable, u = this.accumulatedUpdates[n].variable; De(() => { - let c = Ce(se(p, this.rho), se(er(i), 1 - this.rho)), l = se(je(Dr(Ce(u, this.epsilon)), Dr(Ce(p, this.epsilon))), i), m = Ce(se(u, this.rho), se(er(l), 1 - this.rho)); + let c = Ce(se(p, this.rho), se(Zt(i), 1 - this.rho)), l = se(je(Rr(Ce(u, this.epsilon)), Rr(Ce(p, this.epsilon))), i), m = Ce(se(u, this.rho), se(Zt(l), 1 - this.rho)); p.assign(c), u.assign(m); let d = Ce(se(l, -this.learningRate), s); s.assign(d); @@ -8370,7 +8371,7 @@ var np = class extends Nr { }), this.incrementIterations(); } dispose() { - this.accumulatedUpdates != null && (Mt(this.accumulatedGrads.map((t8) => t8.variable)), Mt(this.accumulatedUpdates.map((t8) => t8.variable))); + this.accumulatedUpdates != null && (Ot(this.accumulatedGrads.map((t8) => t8.variable)), Ot(this.accumulatedUpdates.map((t8) => t8.variable))); } async getWeights() { let t8 = [...this.accumulatedGrads, ...this.accumulatedUpdates]; @@ -8388,7 +8389,7 @@ var np = class extends Nr { return new t8(e.learningRate, e.rho, e.epsilon); } }; -var sp = class extends Nr { +var Zu = class extends kr { static get className() { return "Adagrad"; } @@ -8404,15 +8405,15 @@ var sp = class extends Nr { return; let i = this.accumulatedGrads[n].variable; De(() => { - let p = Ce(i, er(a)); + let p = Ce(i, Zt(a)); i.assign(p); - let u = Ce(se(je(a, Dr(Ce(p, T.backend.epsilon()))), -this.learningRate), s); + let u = Ce(se(je(a, Rr(Ce(p, T.backend.epsilon()))), -this.learningRate), s); s.assign(u); }); }), this.incrementIterations(); } dispose() { - this.accumulatedGrads != null && Mt(this.accumulatedGrads.map((t8) => t8.variable)); + this.accumulatedGrads != null && Ot(this.accumulatedGrads.map((t8) => t8.variable)); } async getWeights() { return [await this.saveIterations()].concat(this.accumulatedGrads.map((t8) => ({ name: t8.originalName, tensor: t8.variable }))); @@ -8429,7 +8430,7 @@ var sp = class extends Nr { return new t8(e.learningRate, e.initialAccumulatorValue); } }; -var ap = class extends Nr { +var Ju = class extends kr { static get className() { return "Adam"; } @@ -8444,19 +8445,19 @@ var ap = class extends Nr { let o = Te(1, this.accBeta1), n = Te(1, this.accBeta2); e.forEach((s, a) => { let i = T.registeredVariables[s], p = false; - this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${s}/m`, variable: De(() => Ht(i).variable(p)) }), this.accumulatedSecondMoment[a] == null && (this.accumulatedSecondMoment[a] = { originalName: `${s}/v`, variable: De(() => Ht(i).variable(p)) }); + this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${s}/m`, variable: De(() => Gt(i).variable(p)) }), this.accumulatedSecondMoment[a] == null && (this.accumulatedSecondMoment[a] = { originalName: `${s}/v`, variable: De(() => Gt(i).variable(p)) }); let u = Array.isArray(t8) ? t8[a].tensor : t8[s]; if (u == null) return; - let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedSecondMoment[a].variable, m = Ce(se(c, this.beta1), se(u, 1 - this.beta1)), d = Ce(se(l, this.beta2), se(er(u), 1 - this.beta2)), f = je(m, o), h = je(d, n); + let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedSecondMoment[a].variable, m = Ce(se(c, this.beta1), se(u, 1 - this.beta1)), d = Ce(se(l, this.beta2), se(Zt(u), 1 - this.beta2)), f = je(m, o), h = je(d, n); c.assign(m), l.assign(d); - let g = Ce(se(je(f, Ce(Dr(h), this.epsilon)), -this.learningRate), i); + let g = Ce(se(je(f, Ce(Rr(h), this.epsilon)), -this.learningRate), i); i.assign(g); }), this.accBeta1.assign(se(this.accBeta1, this.beta1)), this.accBeta2.assign(se(this.accBeta2, this.beta2)); }), this.incrementIterations(); } dispose() { - this.accBeta1.dispose(), this.accBeta2.dispose(), this.accumulatedFirstMoment != null && Mt(this.accumulatedFirstMoment.map((t8) => t8.variable)), this.accumulatedSecondMoment != null && Mt(this.accumulatedSecondMoment.map((t8) => t8.variable)); + this.accBeta1.dispose(), this.accBeta2.dispose(), this.accumulatedFirstMoment != null && Ot(this.accumulatedFirstMoment.map((t8) => t8.variable)), this.accumulatedSecondMoment != null && Ot(this.accumulatedSecondMoment.map((t8) => t8.variable)); } async getWeights() { let t8 = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment]; @@ -8464,7 +8465,7 @@ var ap = class extends Nr { } async setWeights(t8) { t8 = await this.extractIterations(t8), De(() => { - this.accBeta1.assign(ui(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(ui(this.beta2, this.iterations_ + 1)); + this.accBeta1.assign(ii(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(ii(this.beta2, this.iterations_ + 1)); }); let e = t8.length / 2, o = false; this.accumulatedFirstMoment = t8.slice(0, e).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedSecondMoment = t8.slice(e, e * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })); @@ -8476,7 +8477,7 @@ var ap = class extends Nr { return new t8(e.learningRate, e.beta1, e.beta2, e.epsilon); } }; -var ip = class extends Nr { +var ep = class extends kr { static get className() { return "Adamax"; } @@ -8491,11 +8492,11 @@ var ip = class extends Nr { let o = Te(1, this.accBeta1), n = je(-this.learningRate, Ce(se(this.iteration, this.decay), 1)); e.forEach((s, a) => { let i = T.registeredVariables[s], p = false; - this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${s}/m`, variable: Ht(i).variable(p) }), this.accumulatedWeightedInfNorm[a] == null && (this.accumulatedWeightedInfNorm[a] = { originalName: `${s}/v`, variable: Ht(i).variable(p) }); + this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${s}/m`, variable: Gt(i).variable(p) }), this.accumulatedWeightedInfNorm[a] == null && (this.accumulatedWeightedInfNorm[a] = { originalName: `${s}/v`, variable: Gt(i).variable(p) }); let u = Array.isArray(t8) ? t8[a].tensor : t8[s]; if (u == null) return; - let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedWeightedInfNorm[a].variable, m = Ce(se(c, this.beta1), se(u, 1 - this.beta1)), d = se(l, this.beta2), f = Jt(u), h = Fd(d, f); + let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedWeightedInfNorm[a].variable, m = Ce(se(c, this.beta1), se(u, 1 - this.beta1)), d = se(l, this.beta2), f = Qt(u), h = Dd(d, f); c.assign(m), l.assign(h); let g = Ce(se(je(n, o), je(m, Ce(h, this.epsilon))), i); i.assign(g); @@ -8503,7 +8504,7 @@ var ip = class extends Nr { }), this.incrementIterations(); } dispose() { - this.accBeta1.dispose(), this.iteration.dispose(), this.accumulatedFirstMoment != null && Mt(this.accumulatedFirstMoment.map((t8) => t8.variable)), this.accumulatedWeightedInfNorm != null && Mt(this.accumulatedWeightedInfNorm.map((t8) => t8.variable)); + this.accBeta1.dispose(), this.iteration.dispose(), this.accumulatedFirstMoment != null && Ot(this.accumulatedFirstMoment.map((t8) => t8.variable)), this.accumulatedWeightedInfNorm != null && Ot(this.accumulatedWeightedInfNorm.map((t8) => t8.variable)); } async getWeights() { throw new Error("getWeights() is not implemented for Adamax yet."); @@ -8518,7 +8519,7 @@ var ip = class extends Nr { return new t8(e.learningRate, e.beta1, e.beta2, e.epsilon, e.decay); } }; -var mi = class extends Nr { +var li = class extends kr { static get className() { return "SGD"; } @@ -8538,7 +8539,7 @@ var mi = class extends Nr { }), this.incrementIterations(); } setLearningRate(t8) { - this.learningRate = t8, this.c != null && this.c.dispose(), this.c = Rr(ke(-t8)); + this.learningRate = t8, this.c != null && this.c.dispose(), this.c = Er(ke(-t8)); } dispose() { this.c.dispose(); @@ -8557,7 +8558,7 @@ var mi = class extends Nr { return new t8(e.learningRate); } }; -var up = class extends mi { +var tp = class extends li { static get className() { return "Momentum"; } @@ -8567,7 +8568,7 @@ var up = class extends mi { applyGradients(t8) { (Array.isArray(t8) ? t8.map((o) => o.name) : Object.keys(t8)).forEach((o, n) => { let s = T.registeredVariables[o]; - this.accumulations[n] == null && (this.accumulations[n] = { originalName: `${o}/momentum`, variable: De(() => Ht(s).variable(false)) }); + this.accumulations[n] == null && (this.accumulations[n] = { originalName: `${o}/momentum`, variable: De(() => Gt(s).variable(false)) }); let a = this.accumulations[n].variable, i = Array.isArray(t8) ? t8[n].tensor : t8[o]; i != null && De(() => { let p, u = Ce(se(this.m, a), i); @@ -8576,7 +8577,7 @@ var up = class extends mi { }), this.incrementIterations(); } dispose() { - this.m.dispose(), this.accumulations != null && Mt(this.accumulations.map((t8) => t8.variable)); + this.m.dispose(), this.accumulations != null && Ot(this.accumulations.map((t8) => t8.variable)); } setMomentum(t8) { this.momentum = t8; @@ -8596,7 +8597,7 @@ var up = class extends mi { return new t8(e.learningRate, e.momentum, e.useNesterov); } }; -var pp = class extends Nr { +var rp = class extends kr { static get className() { return "RMSProp"; } @@ -8607,20 +8608,20 @@ var pp = class extends Nr { applyGradients(t8) { (Array.isArray(t8) ? t8.map((o) => o.name) : Object.keys(t8)).forEach((o, n) => { let s = T.registeredVariables[o], a = false; - this.accumulatedMeanSquares[n] == null && (this.accumulatedMeanSquares[n] = { originalName: `${o}/rms`, variable: De(() => Ht(s).variable(a)) }), this.accumulatedMoments[n] == null && (this.accumulatedMoments[n] = { originalName: `${o}/momentum`, variable: De(() => Ht(s).variable(a)) }), this.accumulatedMeanGrads[n] == null && this.centered && (this.accumulatedMeanGrads[n] = { originalName: `${o}/mg`, variable: De(() => Ht(s).variable(a)) }); + this.accumulatedMeanSquares[n] == null && (this.accumulatedMeanSquares[n] = { originalName: `${o}/rms`, variable: De(() => Gt(s).variable(a)) }), this.accumulatedMoments[n] == null && (this.accumulatedMoments[n] = { originalName: `${o}/momentum`, variable: De(() => Gt(s).variable(a)) }), this.accumulatedMeanGrads[n] == null && this.centered && (this.accumulatedMeanGrads[n] = { originalName: `${o}/mg`, variable: De(() => Gt(s).variable(a)) }); let i = Array.isArray(t8) ? t8[n].tensor : t8[o]; if (i == null) return; let p = this.accumulatedMeanSquares[n].variable, u = this.accumulatedMoments[n].variable; De(() => { - let c = Ce(se(p, this.decay), se(er(i), 1 - this.decay)); + let c = Ce(se(p, this.decay), se(Zt(i), 1 - this.decay)); if (this.centered) { - let l = this.accumulatedMeanGrads[n].variable, m = Ce(se(l, this.decay), se(i, 1 - this.decay)), d = je(se(i, this.learningRate), Dr(Te(c, Ce(er(m), this.epsilon)))), f = Ce(se(u, this.momentum), d); + let l = this.accumulatedMeanGrads[n].variable, m = Ce(se(l, this.decay), se(i, 1 - this.decay)), d = je(se(i, this.learningRate), Rr(Te(c, Ce(Zt(m), this.epsilon)))), f = Ce(se(u, this.momentum), d); p.assign(c), l.assign(m), u.assign(f); let h = Te(s, f); s.assign(h); } else { - let l = Ce(se(p, this.decay), se(er(i), 1 - this.decay)), m = Ce(se(u, this.momentum), je(se(i, this.learningRate), Dr(Ce(l, this.epsilon)))); + let l = Ce(se(p, this.decay), se(Zt(i), 1 - this.decay)), m = Ce(se(u, this.momentum), je(se(i, this.learningRate), Rr(Ce(l, this.epsilon)))); p.assign(l), u.assign(m); let d = Te(s, m); s.assign(d); @@ -8629,7 +8630,7 @@ var pp = class extends Nr { }), this.incrementIterations(); } dispose() { - this.accumulatedMeanSquares != null && Mt(this.accumulatedMeanSquares.map((t8) => t8.variable)), this.accumulatedMeanGrads != null && this.centered && Mt(this.accumulatedMeanGrads.map((t8) => t8.variable)), this.accumulatedMoments != null && Mt(this.accumulatedMoments.map((t8) => t8.variable)); + this.accumulatedMeanSquares != null && Ot(this.accumulatedMeanSquares.map((t8) => t8.variable)), this.accumulatedMeanGrads != null && this.centered && Ot(this.accumulatedMeanGrads.map((t8) => t8.variable)), this.accumulatedMoments != null && Ot(this.accumulatedMoments.map((t8) => t8.variable)); } async getWeights() { let t8 = [...this.accumulatedMeanSquares, ...this.accumulatedMoments]; @@ -8647,42 +8648,42 @@ var pp = class extends Nr { return new t8(e.learningRate, e.decay, e.momentum, e.epsilon, e.centered); } }; -var Jj = [np, sp, ap, ip, up, pp, mi]; -function UN() { - for (let r of Jj) - tS(r); +var Jj = [Qu, Zu, Ju, ep, tp, rp, li]; +function GN() { + for (let r15 of Jj) + tS(r15); } -var fi = {}; -qe(fi, { CompositeArrayBuffer: () => jt, browserFiles: () => HN, browserHTTPRequest: () => jN, concatenateArrayBuffers: () => ik, copyModel: () => wk, decodeWeights: () => ad, encodeWeights: () => nk, fromMemory: () => XN, fromMemorySync: () => iS, getLoadHandlers: () => lk, getModelArtifactsForJSON: () => ic, getModelArtifactsForJSONSync: () => _w, getModelArtifactsInfoForJSON: () => va, getSaveHandlers: () => ck, getWeightSpecs: () => ud, http: () => nf, isHTTPScheme: () => of, listModels: () => bk, loadWeights: () => KN, moveModel: () => Sk, registerLoadRouter: () => pk, registerSaveRouter: () => uk, removeModel: () => Ck, weightsLoaderFactory: () => sS, withSaveHandler: () => YN, withSaveHandlerSync: () => QN }); +var mi = {}; +qe(mi, { CompositeArrayBuffer: () => ir, browserFiles: () => KN, browserHTTPRequest: () => XN, concatenateArrayBuffers: () => uk, copyModel: () => Sk, decodeWeights: () => nd, encodeWeights: () => sk, fromMemory: () => YN, fromMemorySync: () => iS, getLoadHandlers: () => mk, getModelArtifactsForJSON: () => Jp, getModelArtifactsForJSONSync: () => _w, getModelArtifactsInfoForJSON: () => va, getSaveHandlers: () => lk, getWeightSpecs: () => ad, http: () => of, isHTTPScheme: () => rf, listModels: () => Ck, loadWeights: () => qN, moveModel: () => Ik, registerLoadRouter: () => ck, registerSaveRouter: () => pk, removeModel: () => wk, weightsLoaderFactory: () => sS, withSaveHandler: () => QN, withSaveHandlerSync: () => ZN }); var eX = "model"; var tX = ".json"; var rX = ".weights.bin"; -function GN(r) { - return new Promise((t8) => setTimeout(t8)).then(r); +function HN(r15) { + return new Promise((t8) => setTimeout(t8)).then(r15); } -var di = class { +var lc = class r8 { constructor(t8) { if (!A().getBool("IS_BROWSER")) throw new Error("browserDownloads() cannot proceed because the current environment is not a browser."); - t8.startsWith(di.URL_SCHEME) && (t8 = t8.slice(di.URL_SCHEME.length)), (t8 == null || t8.length === 0) && (t8 = eX), this.modelJsonFileName = t8 + tX, this.weightDataFileName = t8 + rX; + t8.startsWith(r8.URL_SCHEME) && (t8 = t8.slice(r8.URL_SCHEME.length)), (t8 == null || t8.length === 0) && (t8 = eX), this.modelJsonFileName = t8 + tX, this.weightDataFileName = t8 + rX; } async save(t8) { if (typeof document == "undefined") throw new Error("Browser downloads are not supported in this environment since `document` is not present"); - let e = jt.join(t8.weightData), o = window.URL.createObjectURL(new Blob([e], { type: "application/octet-stream" })); + let e = ir.join(t8.weightData), o = window.URL.createObjectURL(new Blob([e], { type: "application/octet-stream" })); if (t8.modelTopology instanceof ArrayBuffer) throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet."); { - let n = [{ paths: ["./" + this.weightDataFileName], weights: t8.weightSpecs }], s = id(t8, n), a = window.URL.createObjectURL(new Blob([JSON.stringify(s)], { type: "application/json" })), i = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor; - if (i.download = this.modelJsonFileName, i.href = a, await GN(() => i.dispatchEvent(new MouseEvent("click"))), t8.weightData != null) { + let n = [{ paths: ["./" + this.weightDataFileName], weights: t8.weightSpecs }], s = sd(t8, n), a = window.URL.createObjectURL(new Blob([JSON.stringify(s)], { type: "application/json" })), i = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor; + if (i.download = this.modelJsonFileName, i.href = a, await HN(() => i.dispatchEvent(new MouseEvent("click"))), t8.weightData != null) { let p = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor; - p.download = this.weightDataFileName, p.href = o, await GN(() => p.dispatchEvent(new MouseEvent("click"))); + p.download = this.weightDataFileName, p.href = o, await HN(() => p.dispatchEvent(new MouseEvent("click"))); } return { modelArtifactsInfo: va(t8) }; } } }; -di.URL_SCHEME = "downloads://"; +lc.URL_SCHEME = "downloads://"; var rS = class { constructor(t8) { if (t8 == null || t8.length < 1) @@ -8706,7 +8707,7 @@ var rS = class { t8({ modelTopology: a }); return; } - let p = ic(s, (u) => this.loadWeights(u)); + let p = Jp(s, (u) => this.loadWeights(u)); t8(p); }, o.onerror = (n) => e(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`), o.readAsText(this.jsonFile); }); @@ -8743,18 +8744,18 @@ var rS = class { return n; } }; -var oX = (r) => A().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(di.URL_SCHEME) ? nX(r.slice(di.URL_SCHEME.length)) : null; -ft.registerSaveRouter(oX); -function nX(r = "model") { - return new di(r); +var oX = (r15) => A().getBool("IS_BROWSER") && !Array.isArray(r15) && r15.startsWith(lc.URL_SCHEME) ? nX(r15.slice(lc.URL_SCHEME.length)) : null; +qt.registerSaveRouter(oX); +function nX(r15 = "model") { + return new lc(r15); } -function HN(r) { - return new rS(r); +function KN(r15) { + return new rS(r15); } -function oS(r, t8, e, o) { - a(r), e = e == null ? 0 : e, o = o == null ? 1 : o, i(e, o); +function oS(r15, t8, e, o) { + a(r15), e = e == null ? 0 : e, o = o == null ? 1 : o, i(e, o); let n = 0, s = (p) => (p.then((u) => { - let c = e + ++n / r.length * (o - e); + let c = e + ++n / r15.length * (o - e); return t8(c), u; }), p); function a(p) { @@ -8763,23 +8764,23 @@ function oS(r, t8, e, o) { function i(p, u) { $(p >= 0 && p <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${p}`), $(u >= 0 && u <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${u}`), $(u >= p, () => `startFraction must be no more than endFraction, but got startFraction ${p} and endFraction ${u}`); } - return Promise.all(r.map(s)); + return Promise.all(r15.map(s)); } -async function nS(r, t8) { +async function nS(r15, t8) { t8 == null && (t8 = {}); - let e = t8.fetchFunc == null ? A().platform.fetch : t8.fetchFunc, o = r.map((l) => e(l, t8.requestInit, { isBinary: true })), n = 0, s = 0.5, i = (t8.onProgress == null ? await Promise.all(o) : await oS(o, t8.onProgress, n, s)).map((l) => l.arrayBuffer()), p = 0.5, u = 1; + let e = t8.fetchFunc == null ? A().platform.fetch : t8.fetchFunc, o = r15.map((l) => e(l, t8.requestInit, { isBinary: true })), n = 0, s = 0.5, i = (t8.onProgress == null ? await Promise.all(o) : await oS(o, t8.onProgress, n, s)).map((l) => l.arrayBuffer()), p = 0.5, u = 1; return t8.onProgress == null ? await Promise.all(i) : await oS(i, t8.onProgress, p, u); } -async function KN(r, t8 = "", e, o) { - return sS((a) => nS(a, { requestInit: o }))(r, t8, e); +async function qN(r15, t8 = "", e, o) { + return sS((a) => nS(a, { requestInit: o }))(r15, t8, e); } -function sS(r) { +function sS(r15) { return async (t8, e = "", o) => { let n = t8.map(() => false), s = {}, a = o != null ? o.map(() => false) : [], i = []; if (t8.forEach((d, f) => { let h = 0; d.weights.forEach((g) => { - let x = "quantization" in g ? g.quantization.dtype : g.dtype, b = Il[x] * He(g.shape), C = () => { + let x = "quantization" in g ? g.quantization.dtype : g.dtype, b = Cl[x] * He(g.shape), C = () => { n[f] = true, s[f] == null && (s[f] = []), s[f].push({ manifestEntry: g, groupOffset: h, sizeBytes: b }); }; o != null ? o.forEach((S, k) => { @@ -8798,11 +8799,11 @@ Manifest JSON has weights with names: ${i.join(", ")}.`); u.push(h); }); }); - let c = await r(u), l = {}, m = 0; + let c = await r15(u), l = {}, m = 0; return p.forEach((d) => { - let f = t8[d].paths.length, h = new jt(c.slice(m, m + f)); + let f = t8[d].paths.length, h = new ir(c.slice(m, m + f)); s[d].forEach((x) => { - let b = h.slice(x.groupOffset, x.groupOffset + x.sizeBytes), C = ad(b, [x.manifestEntry]); + let b = h.slice(x.groupOffset, x.groupOffset + x.sizeBytes), C = nd(b, [x.manifestEntry]); for (let S in C) l[S] = C[S]; }), m += f; @@ -8811,7 +8812,7 @@ Manifest JSON has weights with names: ${i.join(", ")}.`); } var sX = "application/octet-stream"; var aX = "application/json"; -var Al = class { +var El = class { constructor(t8, e) { if (this.DEFAULT_METHOD = "POST", e == null && (e = {}), this.weightPathPrefix = e.weightPathPrefix, this.onProgress = e.onProgress, this.weightUrlConverter = e.weightUrlConverter, e.fetchFunc != null ? ($(typeof e.fetchFunc == "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"), this.fetch = e.fetchFunc) : this.fetch = A().platform.fetch, $(t8 != null && t8.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(t8) && $(t8.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${t8.length}).`), this.path = t8, e.requestInit != null && e.requestInit.body != null) throw new Error("requestInit is expected to have no pre-existing body, but has one."); @@ -8822,9 +8823,9 @@ var Al = class { throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet."); let e = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit); e.body = new FormData(); - let o = [{ paths: ["./model.weights.bin"], weights: t8.weightSpecs }], n = id(t8, o); + let o = [{ paths: ["./model.weights.bin"], weights: t8.weightSpecs }], n = sd(t8, o); if (e.body.append("model.json", new Blob([JSON.stringify(n)], { type: aX }), "model.json"), t8.weightData != null) { - let a = jt.join(t8.weightData); + let a = ir.join(t8.weightData); e.body.append("model.weights.bin", new Blob([a], { type: sX }), "model.weights.bin"); } let s = await this.fetch(this.path, e); @@ -8846,10 +8847,10 @@ var Al = class { let o = e.modelTopology, n = e.weightsManifest; if (o == null && n == null) throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`); - return ic(e, (s) => this.loadWeights(s)); + return Jp(e, (s) => this.loadWeights(s)); } async loadWeights(t8) { - let e = Array.isArray(this.path) ? this.path[1] : this.path, [o, n] = iX(e), s = this.weightPathPrefix || o, a = ud(t8), i = [], p = []; + let e = Array.isArray(this.path) ? this.path[1] : this.path, [o, n] = iX(e), s = this.weightPathPrefix || o, a = ad(t8), i = [], p = []; for (let c of t8) for (let l of c.paths) this.weightUrlConverter != null ? p.push(this.weightUrlConverter(l)) : i.push(s + l + n); @@ -8858,33 +8859,33 @@ var Al = class { return [a, u]; } }; -Al.URL_SCHEME_REGEX = /^https?:\/\//; -function iX(r) { - let t8 = r.lastIndexOf("/"), e = r.lastIndexOf("?"), o = r.substring(0, t8), n = e > t8 ? r.substring(e) : ""; +El.URL_SCHEME_REGEX = /^https?:\/\//; +function iX(r15) { + let t8 = r15.lastIndexOf("/"), e = r15.lastIndexOf("?"), o = r15.substring(0, t8), n = e > t8 ? r15.substring(e) : ""; return [o + "/", n]; } -function of(r) { - return r.match(Al.URL_SCHEME_REGEX) != null; +function rf(r15) { + return r15.match(El.URL_SCHEME_REGEX) != null; } -var qN = (r, t8) => { +var jN = (r15, t8) => { if (typeof fetch == "undefined" && (t8 == null || t8.fetchFunc == null)) return null; { let e = true; - if (Array.isArray(r) ? e = r.every((o) => of(o)) : e = of(r), e) - return nf(r, t8); + if (Array.isArray(r15) ? e = r15.every((o) => rf(o)) : e = rf(r15), e) + return of(r15, t8); } return null; }; -ft.registerSaveRouter(qN); -ft.registerLoadRouter(qN); -function nf(r, t8) { - return new Al(r, t8); +qt.registerSaveRouter(jN); +qt.registerLoadRouter(jN); +function of(r15, t8) { + return new El(r15, t8); } -function jN(r, t8) { - return nf(r, t8); +function XN(r15, t8) { + return of(r15, t8); } -var Fl = class { +var Rl = class { constructor(t8) { this.modelArtifacts = t8; } @@ -8892,7 +8893,7 @@ var Fl = class { return this.modelArtifacts; } }; -var sf = class { +var nf = class { constructor(t8) { this.saveHandler = t8; } @@ -8905,71 +8906,71 @@ var aS = class { t8.load && (this.load = () => Promise.resolve(t8.load())), t8.save && (this.save = (e) => Promise.resolve(t8.save(e))); } }; -function XN(r, t8, e, o) { +function YN(r15, t8, e, o) { let n = arguments; return new aS(iS(...n)); } -function iS(r, t8, e, o) { - return arguments.length === 1 ? r.modelTopology != null || r.weightSpecs != null ? new Fl(r) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Fl({ modelTopology: r })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Fl({ modelTopology: r, weightSpecs: t8, weightData: e, trainingConfig: o })); +function iS(r15, t8, e, o) { + return arguments.length === 1 ? r15.modelTopology != null || r15.weightSpecs != null ? new Rl(r15) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Rl({ modelTopology: r15 })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Rl({ modelTopology: r15, weightSpecs: t8, weightData: e, trainingConfig: o })); } -function YN(r) { - return new sf(r); +function QN(r15) { + return new nf(r15); } -function QN(r) { - return new sf(r); +function ZN(r15) { + return new nf(r15); } -var JN = {}; -qe(JN, { confusionMatrix: () => ZN }); -function uX(r, t8, e) { - let o = v(r, "labels", "confusionMatrix"), n = v(t8, "predictions", "confusionMatrix"); +var eT = {}; +qe(eT, { confusionMatrix: () => JN }); +function uX(r15, t8, e) { + let o = v(r15, "labels", "confusionMatrix"), n = v(t8, "predictions", "confusionMatrix"); $(e == null || e > 0 && Number.isInteger(e), () => `If provided, numClasses must be a positive integer, but got ${e}`), $(o.rank === 1, () => `Expected the rank of labels to be 1, but got ${o.rank}`), $(n.rank === 1, () => `Expected the rank of predictions to be 1, but got ${n.rank}`), $(o.shape[0] === n.shape[0], () => `Mismatch in the number of examples: ${o.shape[0]} vs. ${n.shape[0]}. Labels and predictions should have the same number of elements.`), $(e > 0 && Number.isInteger(e), () => `numClasses is required to be a positive integer, but got ${e}`); - let s = El(We(o, "int32"), e), a = El(We(n, "int32"), e), i = yc(s), p = Ze(i, a); + let s = Tl(We(o, "int32"), e), a = Tl(We(n, "int32"), e), i = cc(s), p = Ze(i, a); return We(p, "int32"); } -var ZN = N({ confusionMatrix_: uX }); -var oT = {}; -qe(oT, { draw: () => gX, fromPixels: () => xX, fromPixelsAsync: () => dX, toPixels: () => hX }); -var cp; -var eT = false; -function tT(r, t8 = 3) { +var JN = N({ confusionMatrix_: uX }); +var nT = {}; +qe(nT, { draw: () => gX, fromPixels: () => xX, fromPixelsAsync: () => dX, toPixels: () => hX }); +var op; +var tT = false; +function rT(r15, t8 = 3) { if (t8 > 4) throw new Error("Cannot construct Tensor with more than 4 channels from pixels."); - if (r == null) + if (r15 == null) throw new Error("pixels passed to tf.browser.fromPixels() can not be null"); let e = false, o = false, n = false, s = false, a = false, i = false; - if (r.data instanceof Uint8Array) + if (r15.data instanceof Uint8Array) e = true; - else if (typeof ImageData != "undefined" && r instanceof ImageData) + else if (typeof ImageData != "undefined" && r15 instanceof ImageData) o = true; - else if (typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement) + else if (typeof HTMLVideoElement != "undefined" && r15 instanceof HTMLVideoElement) n = true; - else if (typeof HTMLImageElement != "undefined" && r instanceof HTMLImageElement) + else if (typeof HTMLImageElement != "undefined" && r15 instanceof HTMLImageElement) s = true; - else if (r.getContext != null) + else if (r15.getContext != null) a = true; - else if (typeof ImageBitmap != "undefined" && r instanceof ImageBitmap) + else if (typeof ImageBitmap != "undefined" && r15 instanceof ImageBitmap) i = true; else - throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${r.constructor.name}`); - if (tc(Mu, T.backendName) != null) { - let f = { pixels: r }, h = { numChannels: t8 }; - return T.runKernel(Mu, f, h); + throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${r15.constructor.name}`); + if (qp(Eu, T.backendName) != null) { + let f = { pixels: r15 }, h = { numChannels: t8 }; + return T.runKernel(Eu, f, h); } - let [u, c] = n ? [r.videoWidth, r.videoHeight] : [r.width, r.height], l; + let [u, c] = n ? [r15.videoWidth, r15.videoHeight] : [r15.width, r15.height], l; if (a) - l = r.getContext("2d").getImageData(0, 0, u, c).data; + l = r15.getContext("2d").getImageData(0, 0, u, c).data; else if (o || e) - l = r.data; + l = r15.data; else if (s || n || i) { - if (cp == null) + if (op == null) if (typeof document == "undefined") if (typeof OffscreenCanvas != "undefined" && typeof OffscreenCanvasRenderingContext2D != "undefined") - cp = new OffscreenCanvas(1, 1).getContext("2d"); + op = new OffscreenCanvas(1, 1).getContext("2d"); else throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported."); else - cp = document.createElement("canvas").getContext("2d", { willReadFrequently: true }); - cp.canvas.width = u, cp.canvas.height = c, cp.drawImage(r, 0, 0, u, c), l = cp.getImageData(0, 0, u, c).data; + op = document.createElement("canvas").getContext("2d", { willReadFrequently: true }); + op.canvas.width = u, op.canvas.height = c, op.drawImage(r15, 0, 0, u, c), l = op.getImageData(0, 0, u, c).data; } let m; if (t8 === 4) @@ -8981,55 +8982,55 @@ function tT(r, t8 = 3) { for (let g = 0; g < t8; ++g) m[h * t8 + g] = l[h * 4 + g]; } - return Xd(m, [c, u, t8], "int32"); + return qd(m, [c, u, t8], "int32"); } -function pX(r) { - return r != null && r.data instanceof Uint8Array; +function pX(r15) { + return r15 != null && r15.data instanceof Uint8Array; } function cX() { return typeof window != "undefined" && typeof ImageBitmap != "undefined" && window.hasOwnProperty("createImageBitmap"); } -function lX(r) { - return r != null && r.width !== 0 && r.height !== 0; +function lX(r15) { + return r15 != null && r15.width !== 0 && r15.height !== 0; } -function mX(r) { - return cX() && !(r instanceof ImageBitmap) && lX(r) && !pX(r); +function mX(r15) { + return cX() && !(r15 instanceof ImageBitmap) && lX(r15) && !pX(r15); } -async function dX(r, t8 = 3) { +async function dX(r15, t8 = 3) { let e = null; - if (A().getBool("WRAP_TO_IMAGEBITMAP") && mX(r)) { + if (A().getBool("WRAP_TO_IMAGEBITMAP") && mX(r15)) { let o; try { - o = await createImageBitmap(r, { premultiplyAlpha: "none" }); + o = await createImageBitmap(r15, { premultiplyAlpha: "none" }); } catch (n) { o = null; } - o != null && o.width === r.width && o.height === r.height ? e = o : e = r; + o != null && o.width === r15.width && o.height === r15.height ? e = o : e = r15; } else - e = r; - return tT(e, t8); + e = r15; + return rT(e, t8); } -function rT(r) { - if (r.rank !== 2 && r.rank !== 3) - throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${r.rank}.`); - let t8 = r.rank === 2 ? 1 : r.shape[2]; +function oT(r15) { + if (r15.rank !== 2 && r15.rank !== 3) + throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${r15.rank}.`); + let t8 = r15.rank === 2 ? 1 : r15.shape[2]; if (t8 > 4 || t8 === 2) throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${t8}`); - if (r.dtype !== "float32" && r.dtype !== "int32") - throw new Error(`Unsupported type for toPixels: ${r.dtype}. Please use float32 or int32 tensors.`); + if (r15.dtype !== "float32" && r15.dtype !== "int32") + throw new Error(`Unsupported type for toPixels: ${r15.dtype}. Please use float32 or int32 tensors.`); } -function fX(r) { - let t8 = (r == null ? void 0 : r.alpha) || 1; +function fX(r15) { + let t8 = (r15 == null ? void 0 : r15.alpha) || 1; if (t8 > 1 || t8 < 0) throw new Error(`Alpha value ${t8} is suppoed to be in range [0 - 1].`); } -async function hX(r, t8) { - let e = v(r, "img", "toPixels"); - if (!(r instanceof ut)) { +async function hX(r15, t8) { + let e = v(r15, "img", "toPixels"); + if (!(r15 instanceof mt)) { let u = e; e = We(u, "int32"), u.dispose(); } - rT(e); + oT(e); let [o, n] = e.shape.slice(0, 2), s = e.rank === 2 ? 1 : e.shape[2], a = await e.data(), i = e.dtype === "float32" ? 255 : 1, p = new Uint8ClampedArray(n * o * 4); for (let u = 0; u < o * n; ++u) { let c = [0, 0, 0, 255]; @@ -9046,27 +9047,27 @@ async function hX(r, t8) { p[l + 0] = Math.round(c[0]), p[l + 1] = Math.round(c[1]), p[l + 2] = Math.round(c[2]), p[l + 3] = Math.round(c[3]); } if (t8 != null) { - eT || tc(Pu, T.backendName) != null && (console.warn("tf.browser.toPixels is not efficient to draw tensor on canvas. Please try tf.browser.draw instead."), eT = true), t8.width = n, t8.height = o; + tT || qp(_u, T.backendName) != null && (console.warn("tf.browser.toPixels is not efficient to draw tensor on canvas. Please try tf.browser.draw instead."), tT = true), t8.width = n, t8.height = o; let u = t8.getContext("2d"), c = new ImageData(p, n, o); u.putImageData(c, 0, 0); } - return e !== r && e.dispose(), p; + return e !== r15 && e.dispose(), p; } -function gX(r, t8, e) { - let o = v(r, "img", "draw"); - if (!(r instanceof ut)) { +function gX(r15, t8, e) { + let o = v(r15, "img", "draw"); + if (!(r15 instanceof mt)) { let a = o; o = We(a, "int32"), a.dispose(); } - rT(o), fX(e == null ? void 0 : e.imageOptions); + oT(o), fX(e == null ? void 0 : e.imageOptions); let n = { image: o }, s = { canvas: t8, options: e }; - T.runKernel(Pu, n, s); + T.runKernel(_u, n, s); } -var xX = N({ fromPixels_: tT }); -var af = {}; -qe(af, { prepareAndValidate: () => nT }); -function nT(r, t8) { - let e = r.shape.length, o = t8.shape.length; +var xX = N({ fromPixels_: rT }); +var sf = {}; +qe(sf, { prepareAndValidate: () => sT }); +function sT(r15, t8) { + let e = r15.shape.length, o = t8.shape.length; if (e < 1) throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${e}.`); if (o < 1) @@ -9075,111 +9076,111 @@ function nT(r, t8) { throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t8.dtype}.`); if (t8.shape[o - 1] > e) throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t8.shape[o - 1]} vs. ${e}`); - if (He(r.shape) === 0) - throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${r.shape}.`); + if (He(r15.shape) === 0) + throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${r15.shape}.`); let n = t8.shape, s = n[n.length - 1], a = 1; for (let l = 0; l < n.length - 1; ++l) a *= n[l]; - let i = r.shape, p = n.slice(); + let i = r15.shape, p = n.slice(); p.pop(); let u = 1; for (let l = s; l < e; ++l) u *= i[l], p.push(i[l]); - let c = [...js(r.shape).map((l) => l / u), 1].slice(0, s); + let c = [...js(r15.shape).map((l) => l / u), 1].slice(0, s); return [p, a, u, c]; } -var ct = {}; -qe(ct, { assertParamsValid: () => bX, computeFlatOffset: () => vX, computeOutShape: () => wX, getNormalizedAxes: () => SX, isSliceContinous: () => IX, maskToAxes: () => CX, parseSliceParams: () => kX, sliceInfo: () => NX, startForAxis: () => mT, startIndicesWithElidedDims: () => pT, stopForAxis: () => dT, stopIndicesWithElidedDims: () => cT, stridesForAxis: () => lT, stridesWithElidedDims: () => aT }); +var pt = {}; +qe(pt, { assertParamsValid: () => bX, computeFlatOffset: () => vX, computeOutShape: () => wX, getNormalizedAxes: () => SX, isSliceContinous: () => IX, maskToAxes: () => CX, parseSliceParams: () => kX, sliceInfo: () => NX, startForAxis: () => dT, startIndicesWithElidedDims: () => cT, stopForAxis: () => fT, stopIndicesWithElidedDims: () => lT, stridesForAxis: () => mT, stridesWithElidedDims: () => iT }); var uS = -2; var yX = -1; -function bX(r, t8, e) { - let o = r.shape.length; +function bX(r15, t8, e) { + let o = r15.shape.length; $(o === t8.length, () => `Error in slice${o}D: Length of begin ${t8} must match the rank of the array (${o}).`), $(o === e.length, () => `Error in slice${o}D: Length of size ${e} must match the rank of the array (${o}).`); for (let n = 0; n < o; ++n) - $(t8[n] + e[n] <= r.shape[n], () => `Error in slice${o}D: begin[${n}] + size[${n}] (${t8[n] + e[n]}) would overflow input.shape[${n}] (${r.shape[n]})`); + $(t8[n] + e[n] <= r15.shape[n], () => `Error in slice${o}D: begin[${n}] + size[${n}] (${t8[n] + e[n]}) would overflow input.shape[${n}] (${r15.shape[n]})`); } -function CX(r) { +function CX(r15) { let t8 = [], e = 0; - for (; r > 0; ) - r & 1 && t8.push(e), r /= 2, e++; + for (; r15 > 0; ) + r15 & 1 && t8.push(e), r15 /= 2, e++; return t8; } -function wX(r, t8, e) { +function wX(r15, t8, e) { let o = []; - for (let n = 0; n < r.length; n++) - o[n] = Math.ceil((t8[n] - r[n]) / e[n]); + for (let n = 0; n < r15.length; n++) + o[n] = Math.ceil((t8[n] - r15[n]) / e[n]); return o; } -function aT(r, t8, e, o) { - let n = [...r]; +function iT(r15, t8, e, o) { + let n = [...r15]; for (let s = n.length; s < o.length; s++) n.push(1); for (let s = 0; s < e; s++) s === 0 ? n[t8] = 1 : (n.splice(t8, 0, 1), n.pop()); return n; } -function iT(r, t8, e) { - return e <= r ? e : e - (t8 - 1); +function uT(r15, t8, e) { + return e <= r15 ? e : e - (t8 - 1); } -function uT(r, t8) { +function pT(r15, t8) { let e = []; - for (let o = 0; o < r; o++) + for (let o = 0; o < r15; o++) e.push(t8 + o); return e; } -function SX(r, t8, e, o, n, s, a, i, p) { - let u = r.length, c = new Array(u), l = new Array(u), m = new Array(u); +function SX(r15, t8, e, o, n, s, a, i, p) { + let u = r15.length, c = new Array(u), l = new Array(u), m = new Array(u); if (t8.length && e > 0) { let d = t8[0], f = e + 1; - c = pT(a, d, f, o, r), l = cT(i, d, f, n, r), m = aT(s, d, f, r); + c = cT(a, d, f, o, r15), l = lT(i, d, f, n, r15), m = iT(s, d, f, r15); } else for (let d = 0; d < u; d++) - c[d] = mT(a, o, s, r, d, p), l[d] = dT(i, n, s, r, d, p), m[d] = lT(s, d, p); + c[d] = dT(a, o, s, r15, d, p), l[d] = fT(i, n, s, r15, d, p), m[d] = mT(s, d, p); return { begin: c, end: l, strides: m }; } -function pT(r, t8, e, o, n) { - let s = [...n], a = uT(e, t8); +function cT(r15, t8, e, o, n) { + let s = [...n], a = pT(e, t8); for (let i = 0; i < s.length; i++) if (a.indexOf(i) > -1) s[i] = 0; else { - let p = iT(t8, e, i), u = o[p]; - r & 1 << p && (u = 0), s[i] = u; + let p = uT(t8, e, i), u = o[p]; + r15 & 1 << p && (u = 0), s[i] = u; } return s; } -function cT(r, t8, e, o, n) { - let s = [...n], a = uT(e, t8); +function lT(r15, t8, e, o, n) { + let s = [...n], a = pT(e, t8); for (let i = 0; i < s.length; i++) if (a.indexOf(i) > -1) s[i] = Number.MAX_SAFE_INTEGER; else { - let p = iT(t8, e, i), u = o[p]; - r & 1 << p && (u = Number.MAX_SAFE_INTEGER), s[i] = u; + let p = uT(t8, e, i), u = o[p]; + r15 & 1 << p && (u = Number.MAX_SAFE_INTEGER), s[i] = u; } for (let i = 0; i < s.length; i++) { let p = n[i]; - s[i] < 0 && (s[i] += p), s[i] = qp(0, s[i], n[i]); + s[i] < 0 && (s[i] += p), s[i] = Bp(0, s[i], n[i]); } return s; } -function lT(r, t8, e) { - let o = r[t8]; +function mT(r15, t8, e) { + let o = r15[t8]; return (e & 1 << t8 || o == null) && (o = 1), o; } -function mT(r, t8, e, o, n, s) { +function dT(r15, t8, e, o, n, s) { let a = t8[n], i = e[n] || 1; - (r & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MIN_SAFE_INTEGER : a = Number.MAX_SAFE_INTEGER); + (r15 & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MIN_SAFE_INTEGER : a = Number.MAX_SAFE_INTEGER); let p = o[n]; - return a < 0 && (a += p), a = qp(0, a, p - 1), a; + return a < 0 && (a += p), a = Bp(0, a, p - 1), a; } -function dT(r, t8, e, o, n, s) { +function fT(r15, t8, e, o, n, s) { let a = t8[n], i = e[n] || 1; - (r & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MAX_SAFE_INTEGER : a = Number.MIN_SAFE_INTEGER); + (r15 & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MAX_SAFE_INTEGER : a = Number.MIN_SAFE_INTEGER); let p = o[n]; - return a < 0 && (a += p), i > 0 ? a = qp(0, a, p) : a = qp(-1, a, p - 1), a; + return a < 0 && (a += p), i > 0 ? a = Bp(0, a, p) : a = Bp(-1, a, p - 1), a; } -function IX(r, t8, e) { +function IX(r15, t8, e) { let o = e.length; for (let n = 0; n < e.length; n++) if (e[n] > 1) { @@ -9187,25 +9188,25 @@ function IX(r, t8, e) { break; } for (let n = o + 1; n < e.length; n++) - if (t8[n] > 0 || e[n] !== r[n]) + if (t8[n] > 0 || e[n] !== r15[n]) return false; return true; } -function vX(r, t8) { - let e = r.length > 0 ? r[r.length - 1] : 1; - for (let o = 0; o < r.length - 1; o++) - e += r[o] * t8[o]; +function vX(r15, t8) { + let e = r15.length > 0 ? r15[r15.length - 1] : 1; + for (let o = 0; o < r15.length - 1; o++) + e += r15[o] * t8[o]; return e; } -function kX(r, t8, e) { - let o, n = r.shape.length; +function kX(r15, t8, e) { + let o, n = r15.shape.length; typeof t8 == "number" ? o = [t8, ...new Array(n - 1).fill(0)] : t8.length < n ? o = t8.concat(new Array(n - t8.length).fill(0)) : o = t8.slice(), o.forEach((a) => { $(a !== -1, () => "slice() does not support negative begin indexing."); }); let s; - return e == null ? s = new Array(n).fill(-1) : typeof e == "number" ? s = [e, ...new Array(n - 1).fill(-1)] : e.length < n ? s = e.concat(new Array(n - e.length).fill(-1)) : s = e, s = s.map((a, i) => a >= 0 ? a : ($(a === -1, () => `Negative size values should be exactly -1 but got ${a} for the slice() size at index ${i}.`), r.shape[i] - o[i])), [o, s]; + return e == null ? s = new Array(n).fill(-1) : typeof e == "number" ? s = [e, ...new Array(n - 1).fill(-1)] : e.length < n ? s = e.concat(new Array(n - e.length).fill(-1)) : s = e, s = s.map((a, i) => a >= 0 ? a : ($(a === -1, () => `Negative size values should be exactly -1 but got ${a} for the slice() size at index ${i}.`), r15.shape[i] - o[i])), [o, s]; } -function NX(r, t8, e, o, n, s, a, i, p) { +function NX(r15, t8, e, o, n, s, a, i, p) { let u; if (o == null ? (u = new Array(t8.length), u.fill(1)) : u = o, a != null && a & a - 1) throw new Error("Multiple ellipses in slice is not allowed."); @@ -9213,13 +9214,13 @@ function NX(r, t8, e, o, n, s, a, i, p) { for (let C = 0; C < l.dims; C++) c && 1 << C & i && l.numAddAxisAfterEllipsis++, 1 << C & a && (c = true); c || (l.ellipsisMask |= 1 << l.dims, l.dims++); - let m = { dims: r.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false }; + let m = { dims: r15.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false }; TX(l, m); let d = true, f = true, h = true, g = [], x = []; - for (let C = 0; C < r.length; ++C) { + for (let C = 0; C < r15.length; ++C) { if (m.strides[C] === 0) throw Error(`strides[${C}] must be non-zero`); - let S = !!(m.shrinkAxisMask & 1 << C), k = r[C]; + let S = !!(m.shrinkAxisMask & 1 << C), k = r15[C]; if (k === -1) { g.push(S ? 1 : -1); continue; @@ -9235,7 +9236,7 @@ function NX(r, t8, e, o, n, s, a, i, p) { if (m.begin[C] = M, m.end[C] = m.begin[C] + 1, M < 0 || M >= k) throw Error(`slice index ${m.begin[C]} of dimension ${C} out of bounds.`); } else - m.begin[C] = sT(m.begin[C], 0, m.strides[C], k, _, E), m.end[C] = sT(m.end[C], 1, m.strides[C], k, _, E); + m.begin[C] = aT(m.begin[C], 0, m.strides[C], k, _, E), m.end[C] = aT(m.end[C], 1, m.strides[C], k, _, E); let O = m.strides[C] === 1 && m.begin[C] === 0 && m.end[C] === k; d = d && O, f = f && (C === 0 && m.strides[C] === 1 || O); } else @@ -9253,183 +9254,183 @@ function NX(r, t8, e, o, n, s, a, i, p) { } return { finalShapeSparse: x.filter((C, S) => m.finalShapeGatherIndices[S] !== uS), finalShape: x, isIdentity: d, sliceDim0: f, isSimpleSlice: h, begin: m.begin, end: m.end, strides: m.strides }; } -function TX(r, t8) { +function TX(r15, t8) { t8.beginMask = 0, t8.endMask = 0, t8.shrinkAxisMask = 0; let e = 0; - t8.beginValid = r.begin != null, t8.endValid = r.end != null, t8.begin = new Array(t8.dims), t8.end = new Array(t8.dims), t8.strides = new Array(t8.dims), t8.finalShapeGatherIndices = [], t8.finalShapeGatherIndicesSparse = [], t8.inputShapeGatherIndicesSparse = new Array(t8.dims); - for (let o = 0; o < r.dims; o++) - if (1 << o & r.ellipsisMask) { - let n = Math.min(t8.dims - (r.dims - o) + 1 + r.numAddAxisAfterEllipsis, t8.dims); + t8.beginValid = r15.begin != null, t8.endValid = r15.end != null, t8.begin = new Array(t8.dims), t8.end = new Array(t8.dims), t8.strides = new Array(t8.dims), t8.finalShapeGatherIndices = [], t8.finalShapeGatherIndicesSparse = [], t8.inputShapeGatherIndicesSparse = new Array(t8.dims); + for (let o = 0; o < r15.dims; o++) + if (1 << o & r15.ellipsisMask) { + let n = Math.min(t8.dims - (r15.dims - o) + 1 + r15.numAddAxisAfterEllipsis, t8.dims); for (; e < n; e++) t8.begin[e] = 0, t8.end[e] = 0, t8.strides[e] = 1, t8.beginMask |= 1 << e, t8.endMask |= 1 << e, t8.finalShapeGatherIndices.push(e), t8.finalShapeGatherIndicesSparse.push(-1), t8.inputShapeGatherIndicesSparse[e] = o; - } else if (1 << o & r.newAxisMask) + } else if (1 << o & r15.newAxisMask) t8.finalShapeGatherIndices.push(uS), t8.finalShapeGatherIndicesSparse.push(-1); else { if (e === t8.begin.length) throw Error(`Index out of range using input dim ${e}; input has only ${t8.dims} dims, ${t8.begin.length}.`); - r.begin != null && (t8.begin[e] = r.begin[o]), r.end != null && (t8.end[e] = r.end[o]), t8.strides[e] = r.strides[o], r.beginMask & 1 << o && (t8.beginMask |= 1 << e), r.endMask & 1 << o && (t8.endMask |= 1 << e), r.shrinkAxisMask & 1 << o ? (t8.finalShapeGatherIndices.push(yX), t8.finalShapeGatherIndicesSparse.push(-1), t8.shrinkAxisMask |= 1 << e) : (t8.finalShapeGatherIndices.push(e), t8.finalShapeGatherIndicesSparse.push(o)), t8.inputShapeGatherIndicesSparse[e] = o, e++; + r15.begin != null && (t8.begin[e] = r15.begin[o]), r15.end != null && (t8.end[e] = r15.end[o]), t8.strides[e] = r15.strides[o], r15.beginMask & 1 << o && (t8.beginMask |= 1 << e), r15.endMask & 1 << o && (t8.endMask |= 1 << e), r15.shrinkAxisMask & 1 << o ? (t8.finalShapeGatherIndices.push(yX), t8.finalShapeGatherIndicesSparse.push(-1), t8.shrinkAxisMask |= 1 << e) : (t8.finalShapeGatherIndices.push(e), t8.finalShapeGatherIndicesSparse.push(o)), t8.inputShapeGatherIndicesSparse[e] = o, e++; } } -function sT(r, t8, e, o, n, s) { +function aT(r15, t8, e, o, n, s) { if (n[t8]) return e > 0 ? s[t8] : s[t8 + 1 & 1]; { - let a = r < 0 ? o + r : r; + let a = r15 < 0 ? o + r15 : r15; return a < s[0] ? s[0] : a > s[1] ? s[1] : a; } } -var _X = "4.11.0"; -var Pl = class { +var _X = "4.14.0"; +var Dl = class { static sgd(t8) { - return new mi(t8); + return new li(t8); } static momentum(t8, e, o = false) { - return new up(t8, e, o); + return new tp(t8, e, o); } static rmsprop(t8, e = 0.9, o = 0, n = null, s = false) { - return new pp(t8, e, o, n, s); + return new rp(t8, e, o, n, s); } static adam(t8 = 1e-3, e = 0.9, o = 0.999, n = null) { - return new ap(t8, e, o, n); + return new Ju(t8, e, o, n); } static adadelta(t8 = 1e-3, e = 0.95, o = null) { - return new np(t8, e, o); + return new Qu(t8, e, o); } static adamax(t8 = 2e-3, e = 0.9, o = 0.999, n = null, s = 0) { - return new ip(t8, e, o, n, s); + return new ep(t8, e, o, n, s); } static adagrad(t8, e = 0.1) { - return new sp(t8, e); + return new Zu(t8, e); } }; -var TGe = Pl; -var $X = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (r) => r())(); +var TGe = Dl; +var $X = typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (r15) => r15(); function pS() { - return new Promise((r) => $X(() => r())); + return new Promise((r15) => $X(() => r15())); } var w = {}; -qe(w, { ERF_A1: () => KX, ERF_A2: () => qX, ERF_A3: () => jX, ERF_A4: () => XX, ERF_A5: () => YX, ERF_P: () => HX, PARALLELIZE_THRESHOLD: () => uf, RowPartitionType: () => Pa, SELU_SCALE: () => GX, SELU_SCALEALPHA: () => UX, applyActivation: () => rp, assertAndGetBroadcastShape: () => rt, assertAxesAreInnerMostDims: () => tK, assertParamsConsistent: () => EX, assignToTypedArray: () => r5, axesAreInnerMostDims: () => Bw, calculateShapes: () => V1, checkEinsumDimSizes: () => u5, checkPadOnDimRoundingMode: () => Bt, combineLocations: () => x2, combineRaggedTensorToTensorShapes: () => DX, complexWithEvenIndex: () => JX, complexWithOddIndex: () => e5, computeConv2DInfo: () => Hu, computeConv3DInfo: () => Mk, computeDefaultPad: () => Lw, computeDilation2DInfo: () => J4, computeOptimalWindowSize: () => OX, computeOutAndReduceShapes: () => eK, computeOutShape: () => RX, computePool2DInfo: () => Mw, computePool3DInfo: () => eH, convertConv2DDataFormat: () => Lk, decodeEinsumEquation: () => a5, eitherStridesOrDilationsAreOne: () => xr, expandShapeToKeepDim: () => ii, exponent: () => n5, exponents: () => o5, fromStringArrayToUint8: () => $5, fromUint8ToStringArray: () => _5, getAxesPermutation: () => rK, getBroadcastDims: () => m2, getComplexWithIndex: () => t5, getEinsumComputePath: () => p5, getEinsumPermutation: () => i5, getFusedBiasGradient: () => tp, getFusedDyActivation: () => ep, getImageCenter: () => MX, getInnerMostAxes: () => nK, getPermuted: () => BX, getRaggedRank: () => FX, getReductionAxes: () => yd, getReshaped: () => LX, getReshapedPermuted: () => zX, getRowPartitionTypesHelper: () => AX, getSliceBeginCoords: () => VX, getSliceSize: () => WX, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => d5, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => f5, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => h5, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => y5, getSparseReshapeInputOutputMismatchErrorMessage: () => C5, getSparseReshapeInputOutputMultipleErrorMessage: () => b5, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => g5, getSparseReshapeNegativeOutputDimErrorMessage: () => x5, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => v5, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => w5, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => S5, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => I5, getUndoAxesPermutation: () => oK, isIdentityPermutation: () => c5, log: () => jG, mergeRealAndImagArrays: () => QX, prepareAndValidate: () => nT, prepareSplitSize: () => m5, segment_util: () => lS, shouldFuse: () => op, slice_util: () => ct, splitRealAndImagArrays: () => ZX, stridesOrDilationsArePositive: () => Ta, tupleValuesAreOne: () => Gu, upcastType: () => dt, validateDefaultValueShape: () => PX, validateInput: () => xc, validateUpdateShape: () => Yw, warn: () => Ia }); -function EX(r, t8) { - let e = r[0].length; - r.forEach((n, s) => { +qe(w, { ERF_A1: () => KX, ERF_A2: () => qX, ERF_A3: () => jX, ERF_A4: () => XX, ERF_A5: () => YX, ERF_P: () => HX, PARALLELIZE_THRESHOLD: () => af, RowPartitionType: () => Fa, SELU_SCALE: () => GX, SELU_SCALEALPHA: () => UX, applyActivation: () => Xu, assertAndGetBroadcastShape: () => rt, assertAxesAreInnerMostDims: () => tK, assertParamsConsistent: () => EX, assignToTypedArray: () => r52, axesAreInnerMostDims: () => Bw, calculateShapes: () => W1, checkEinsumDimSizes: () => u5, checkPadOnDimRoundingMode: () => Lt, combineLocations: () => y2, combineRaggedTensorToTensorShapes: () => DX, complexWithEvenIndex: () => JX, complexWithOddIndex: () => e5, computeConv2DInfo: () => Lu, computeConv3DInfo: () => Lk, computeDefaultPad: () => Lw, computeDilation2DInfo: () => J4, computeOptimalWindowSize: () => OX, computeOutAndReduceShapes: () => eK, computeOutShape: () => RX, computePool2DInfo: () => Mw, computePool3DInfo: () => eH, convertConv2DDataFormat: () => Bk, decodeEinsumEquation: () => a5, eitherStridesOrDilationsAreOne: () => gr, expandShapeToKeepDim: () => ai, exponent: () => n5, exponents: () => o5, fromStringArrayToUint8: () => $5, fromUint8ToStringArray: () => _5, getAxesPermutation: () => rK, getBroadcastDims: () => d2, getComplexWithIndex: () => t5, getEinsumComputePath: () => p5, getEinsumPermutation: () => i5, getFusedBiasGradient: () => ju, getFusedDyActivation: () => qu, getImageCenter: () => MX, getInnerMostAxes: () => nK, getPermuted: () => BX, getRaggedRank: () => FX, getReductionAxes: () => gd, getReshaped: () => LX, getReshapedPermuted: () => zX, getRowPartitionTypesHelper: () => AX, getSliceBeginCoords: () => VX, getSliceSize: () => WX, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => d5, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => f5, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => h5, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => y5, getSparseReshapeInputOutputMismatchErrorMessage: () => C5, getSparseReshapeInputOutputMultipleErrorMessage: () => b5, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => g5, getSparseReshapeNegativeOutputDimErrorMessage: () => x5, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => v5, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => w5, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => S5, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => I5, getUndoAxesPermutation: () => oK, isIdentityPermutation: () => c5, log: () => XG, mergeRealAndImagArrays: () => QX, prepareAndValidate: () => sT, prepareSplitSize: () => m5, segment_util: () => lS, shouldFuse: () => Yu, slice_util: () => pt, splitRealAndImagArrays: () => ZX, stridesOrDilationsArePositive: () => Ta, tupleValuesAreOne: () => Mu, upcastType: () => dt, validateDefaultValueShape: () => PX, validateInput: () => pc, validateUpdateShape: () => Yw, warn: () => Ia }); +function EX(r15, t8) { + let e = r15[0].length; + r15.forEach((n, s) => { $(n.length === e, () => `Error in concat${e}D: rank of tensors[${s}] must be the same as the rank of the rest (${e})`); }), $(t8 >= 0 && t8 < e, () => `Error in concat${e}D: axis must be between 0 and ${e - 1}.`); - let o = r[0]; - r.forEach((n, s) => { + let o = r15[0]; + r15.forEach((n, s) => { for (let a = 0; a < e; a++) $(a === t8 || n[a] === o[a], () => `Error in concat${e}D: Shape of tensors[${s}] (${n}) does not match the shape of the rest (${o}) along the non-concatenated axis ${s}.`); }); } -function RX(r, t8) { - let e = r[0].slice(); - for (let o = 1; o < r.length; o++) - e[t8] += r[o][t8]; +function RX(r15, t8) { + let e = r15[0].slice(); + for (let o = 1; o < r15.length; o++) + e[t8] += r15[o][t8]; return e; } -var Pa; -(function(r) { - r[r.FIRST_DIM_SIZE = 0] = "FIRST_DIM_SIZE", r[r.VALUE_ROWIDS = 1] = "VALUE_ROWIDS", r[r.ROW_LENGTHS = 2] = "ROW_LENGTHS", r[r.ROW_SPLITS = 3] = "ROW_SPLITS", r[r.ROW_LIMITS = 4] = "ROW_LIMITS", r[r.ROW_STARTS = 5] = "ROW_STARTS"; -})(Pa || (Pa = {})); -function DX(r, t8, e) { +var Fa; +(function(r15) { + r15[r15.FIRST_DIM_SIZE = 0] = "FIRST_DIM_SIZE", r15[r15.VALUE_ROWIDS = 1] = "VALUE_ROWIDS", r15[r15.ROW_LENGTHS = 2] = "ROW_LENGTHS", r15[r15.ROW_SPLITS = 3] = "ROW_SPLITS", r15[r15.ROW_LIMITS = 4] = "ROW_LIMITS", r15[r15.ROW_STARTS = 5] = "ROW_STARTS"; +})(Fa || (Fa = {})); +function DX(r15, t8, e) { let o = new Array(); if (e == null && t8 == null) return o; if (t8 == null) - for (; o.length < r + e.length; ) + for (; o.length < r15 + e.length; ) o.push(-1); else o = t8.slice(); if (e == null) return o; - if (r + e.length !== o.length) - throw new Error(`rt input.shape and shape=${t8} are incompatible: rt input.rank = ${r + e.length}, but shape.rank = ${o.length}`); + if (r15 + e.length !== o.length) + throw new Error(`rt input.shape and shape=${t8} are incompatible: rt input.rank = ${r15 + e.length}, but shape.rank = ${o.length}`); for (let n = 1; n < e.length; ++n) { let s = e[n], a = o[o.length - e.length + n], i = o[a]; if (s >= 0) if (i >= 0) { if (i !== s) - throw new Error(`rt input.shape and shape=${t8} are incompatible: rt input.shape[${n + r}] = ${s} but shape[${n + r}] = ${i}`); + throw new Error(`rt input.shape and shape=${t8} are incompatible: rt input.shape[${n + r15}] = ${s} but shape[${n + r15}] = ${i}`); } else o[a] = s; } return o; } -function AX(r) { - let t8 = { FIRST_DIM_SIZE: Pa.FIRST_DIM_SIZE, VALUE_ROWIDS: Pa.VALUE_ROWIDS, ROW_LENGTHS: Pa.ROW_LENGTHS, ROW_SPLITS: Pa.ROW_SPLITS, ROW_LIMITS: Pa.ROW_LIMITS, ROW_STARTS: Pa.ROW_STARTS }, e = []; - for (let o of r) +function AX(r15) { + let t8 = { FIRST_DIM_SIZE: Fa.FIRST_DIM_SIZE, VALUE_ROWIDS: Fa.VALUE_ROWIDS, ROW_LENGTHS: Fa.ROW_LENGTHS, ROW_SPLITS: Fa.ROW_SPLITS, ROW_LIMITS: Fa.ROW_LIMITS, ROW_STARTS: Fa.ROW_STARTS }, e = []; + for (let o of r15) if (o in t8) e.push(t8[o]); else break; return e; } -function FX(r) { - return r.length === 0 ? 0 : r[0] === Pa.FIRST_DIM_SIZE ? r.length - 1 : r.length; +function FX(r15) { + return r15.length === 0 ? 0 : r15[0] === Fa.FIRST_DIM_SIZE ? r15.length - 1 : r15.length; } -function PX(r, t8) { - if (r == null || t8 == null) +function PX(r15, t8) { + if (r15 == null || t8 == null) return; - let e = r.length, o = t8.length; + let e = r15.length, o = t8.length; if (e >= o) - throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${t8}, are incompatible: defaultValue.rank = ${e} must be less than ragged tensor input flatValues.rank = ${o})`); + throw new Error(`defaultValue.shape=${r15} and ragged tensor flatValues.shape=${t8}, are incompatible: defaultValue.rank = ${e} must be less than ragged tensor input flatValues.rank = ${o})`); for (let n = 0; n < Math.min(e, o - 1); ++n) { - let s = r[n], a = t8[n + 1]; + let s = r15[n], a = t8[n + 1]; if (s >= 0 && a >= 0 && s !== 1 && s !== a) - throw new Error(`defaultValue.shape=${r}, and ragged tensor input flatValues.shape=${t8} are incompatible: defaultValue.shape[${n - r.length}] = ${s} but ragged tensor input.flatValues.shape[${n - r.length}] = ${a}`); + throw new Error(`defaultValue.shape=${r15}, and ragged tensor input flatValues.shape=${t8} are incompatible: defaultValue.shape[${n - r15.length}] = ${s} but ragged tensor input.flatValues.shape[${n - r15.length}] = ${a}`); } } -var uf = 30; -function OX(r) { - return r <= uf ? r : Xp(r, Math.floor(Math.sqrt(r))); +var af = 30; +function OX(r15) { + return r15 <= af ? r15 : Vp(r15, Math.floor(Math.sqrt(r15))); } -function MX(r, t8, e) { - let o = e * (typeof r == "number" ? r : r[0]), n = t8 * (typeof r == "number" ? r : r[1]); +function MX(r15, t8, e) { + let o = e * (typeof r15 == "number" ? r15 : r15[0]), n = t8 * (typeof r15 == "number" ? r15 : r15[1]); return [o, n]; } -function LX(r, t8, e, o = true) { +function LX(r15, t8, e, o = true) { let n = []; if (o) - n = n.concat(t8.slice(0)), n.push(r[0] / e), n = n.concat(r.slice(1)); + n = n.concat(t8.slice(0)), n.push(r15[0] / e), n = n.concat(r15.slice(1)); else { - n = n.concat(r[0]); + n = n.concat(r15[0]); let s = t8.length; for (let a = 0; a < s; ++a) - n = n.concat([r[a + 1] / t8[a], t8[a]]); - n = n.concat(r.slice(s + 1)); + n = n.concat([r15[a + 1] / t8[a], t8[a]]); + n = n.concat(r15.slice(s + 1)); } return n; } -function BX(r, t8, e = true) { +function BX(r15, t8, e = true) { let o = []; if (e) { o.push(t8); - for (let n = t8 + 1; n < r; ++n) + for (let n = t8 + 1; n < r15; ++n) n <= 2 * t8 ? (o.push(n), o.push(n - (t8 + 1))) : o.push(n); } else { let n = [], s = []; - for (let a = 1; a < r; ++a) + for (let a = 1; a < r15; ++a) a >= t8 * 2 + 1 || a % 2 === 1 ? s.push(a) : n.push(a); o.push(...n), o.push(0), o.push(...s); } return o; } -function zX(r, t8, e, o = true) { +function zX(r15, t8, e, o = true) { let n = []; - o ? n.push(r[0] / e) : n.push(r[0] * e); - for (let s = 1; s < r.length; ++s) - s <= t8.length ? o ? n.push(t8[s - 1] * r[s]) : n.push(r[s] / t8[s - 1]) : n.push(r[s]); + o ? n.push(r15[0] / e) : n.push(r15[0] * e); + for (let s = 1; s < r15.length; ++s) + s <= t8.length ? o ? n.push(t8[s - 1] * r15[s]) : n.push(r15[s] / t8[s - 1]) : n.push(r15[s]); return n; } -function VX(r, t8) { +function VX(r15, t8) { let e = [0]; for (let o = 0; o < t8; ++o) - e.push(r[o][0]); + e.push(r15[o][0]); return e; } -function WX(r, t8, e) { - let o = r.slice(0, 1); +function WX(r15, t8, e) { + let o = r15.slice(0, 1); for (let n = 0; n < e; ++n) - o.push(r[n + 1] - t8[n][0] - t8[n][1]); + o.push(r15[n + 1] - t8[n][0] - t8[n][1]); return o; } var UX = 1.7580993408473768; @@ -9440,65 +9441,65 @@ var qX = -0.284496736; var jX = 1.421413741; var XX = -1.453152027; var YX = 1.061405429; -function QX(r, t8) { - if (r.length !== t8.length) - throw new Error(`Cannot merge real and imag arrays of different lengths. real:${r.length}, imag: ${t8.length}.`); - let e = new Float32Array(r.length * 2); +function QX(r15, t8) { + if (r15.length !== t8.length) + throw new Error(`Cannot merge real and imag arrays of different lengths. real:${r15.length}, imag: ${t8.length}.`); + let e = new Float32Array(r15.length * 2); for (let o = 0; o < e.length; o += 2) - e[o] = r[o / 2], e[o + 1] = t8[o / 2]; + e[o] = r15[o / 2], e[o + 1] = t8[o / 2]; return e; } -function ZX(r) { - let t8 = new Float32Array(r.length / 2), e = new Float32Array(r.length / 2); - for (let o = 0; o < r.length; o += 2) - t8[o / 2] = r[o], e[o / 2] = r[o + 1]; +function ZX(r15) { + let t8 = new Float32Array(r15.length / 2), e = new Float32Array(r15.length / 2); + for (let o = 0; o < r15.length; o += 2) + t8[o / 2] = r15[o], e[o / 2] = r15[o + 1]; return { real: t8, imag: e }; } -function JX(r) { - let t8 = Math.ceil(r.length / 4), e = new Float32Array(t8), o = new Float32Array(t8); - for (let n = 0; n < r.length; n += 4) - e[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1]; +function JX(r15) { + let t8 = Math.ceil(r15.length / 4), e = new Float32Array(t8), o = new Float32Array(t8); + for (let n = 0; n < r15.length; n += 4) + e[Math.floor(n / 4)] = r15[n], o[Math.floor(n / 4)] = r15[n + 1]; return { real: e, imag: o }; } -function e5(r) { - let t8 = Math.floor(r.length / 4), e = new Float32Array(t8), o = new Float32Array(t8); - for (let n = 2; n < r.length; n += 4) - e[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1]; +function e5(r15) { + let t8 = Math.floor(r15.length / 4), e = new Float32Array(t8), o = new Float32Array(t8); + for (let n = 2; n < r15.length; n += 4) + e[Math.floor(n / 4)] = r15[n], o[Math.floor(n / 4)] = r15[n + 1]; return { real: e, imag: o }; } -function t5(r, t8) { - let e = r[t8 * 2], o = r[t8 * 2 + 1]; +function t5(r15, t8) { + let e = r15[t8 * 2], o = r15[t8 * 2 + 1]; return { real: e, imag: o }; } -function r5(r, t8, e, o) { - r[o * 2] = t8, r[o * 2 + 1] = e; +function r52(r15, t8, e, o) { + r15[o * 2] = t8, r15[o * 2 + 1] = e; } -function o5(r, t8) { - let e = new Float32Array(r / 2), o = new Float32Array(r / 2); - for (let n = 0; n < Math.ceil(r / 2); n++) { - let s = (t8 ? 2 : -2) * Math.PI * (n / r); +function o5(r15, t8) { + let e = new Float32Array(r15 / 2), o = new Float32Array(r15 / 2); + for (let n = 0; n < Math.ceil(r15 / 2); n++) { + let s = (t8 ? 2 : -2) * Math.PI * (n / r15); e[n] = Math.cos(s), o[n] = Math.sin(s); } return { real: e, imag: o }; } -function n5(r, t8, e) { - let o = (e ? 2 : -2) * Math.PI * (r / t8), n = Math.cos(o), s = Math.sin(o); +function n5(r15, t8, e) { + let o = (e ? 2 : -2) * Math.PI * (r15 / t8), n = Math.cos(o), s = Math.sin(o); return { real: n, imag: s }; } var cS = "->"; var s5 = /->/g; -var fT = ","; -var hT = "..."; -function a5(r, t8) { - r = r.replace(/\s/g, ""); - let e = (r.length - r.replace(s5, "").length) / cS.length; +var hT = ","; +var gT = "..."; +function a5(r15, t8) { + r15 = r15.replace(/\s/g, ""); + let e = (r15.length - r15.replace(s5, "").length) / cS.length; if (e < 1) throw new Error("Equations without an arrow are not supported."); if (e > 1) throw new Error(`Equation must contain exactly one arrow ("${cS}").`); - let [o, n] = r.split(cS); - $(o.indexOf(hT) === -1, () => `The ellipsis notation ("${hT}") is not supported yet.`); - let s = o.split(fT), a = s.length; + let [o, n] = r15.split(cS); + $(o.indexOf(gT) === -1, () => `The ellipsis notation ("${gT}") is not supported yet.`); + let s = o.split(hT), a = s.length; if (t8 !== a) throw new Error(`Expected ${a} input tensors, received ${t8}`); if (a > 2) @@ -9512,7 +9513,7 @@ function a5(r, t8) { } for (let m = 0; m < o.length; ++m) { let d = o[m]; - i.indexOf(d) === -1 && d !== fT && i.push(d); + i.indexOf(d) === -1 && d !== hT && i.push(d); } let p = new Array(s.length); for (let m = 0; m < a; ++m) { @@ -9527,27 +9528,27 @@ function a5(r, t8) { l.push(m); return { allDims: i, summedDims: l, idDims: p }; } -function i5(r, t8) { - let e = new Array(r); +function i5(r15, t8) { + let e = new Array(r15); e.fill(-1); for (let n = 0; n < t8.length; ++n) e[t8[n]] = n; let o = []; - for (let n = 0; n < r; ++n) + for (let n = 0; n < r15; ++n) e[n] === -1 && o.push(n); return e = e.filter((n) => n !== -1), { permutationIndices: e, expandDims: o }; } -function u5(r, t8, e) { - let o = new Array(r); +function u5(r15, t8, e) { + let o = new Array(r15); for (let n = 0; n < e.length; ++n) { let s = e[n].shape; for (let a = 0; a < t8[n].length; ++a) o[t8[n][a]] === void 0 ? o[t8[n][a]] = s[a] : $(o[t8[n][a]] === s[a], () => `Expected dimension ${o[t8[n][a]]} at axis ${a} of input shaped ${JSON.stringify(s)}, but got dimension ${s[a]}`); } } -function p5(r, t8) { - let e = r, o = [], n = 0; - r.length === 0 && e.push(-1), n = r.length + 1; +function p5(r15, t8) { + let e = r15, o = [], n = 0; + r15.length === 0 && e.push(-1), n = r15.length + 1; for (let a = 0; a < n; ++a) o.push([]); let s = []; @@ -9558,58 +9559,58 @@ function p5(r, t8) { } return { path: e, steps: o }; } -function c5(r) { - return r.every((t8, e) => t8 === e); +function c5(r15) { + return r15.every((t8, e) => t8 === e); } -function l5(r, t8) { +function l5(r15, t8) { let e = []; - for (let o = 0; o < r.length; ++o) - (r[o].length === 0 || r[o].indexOf(t8) !== -1 || t8 === -1) && e.push(o); + for (let o = 0; o < r15.length; ++o) + (r15[o].length === 0 || r15[o].indexOf(t8) !== -1 || t8 === -1) && e.push(o); return e; } -function m5(r, t8, e = 0) { +function m5(r15, t8, e = 0) { let o = []; if (typeof t8 == "number") - $(r.shape[e] % t8 === 0, () => "Number of splits must evenly divide the axis."), o = new Array(t8).fill(r.shape[e] / t8); + $(r15.shape[e] % t8 === 0, () => "Number of splits must evenly divide the axis."), o = new Array(t8).fill(r15.shape[e] / t8); else { let n = t8.reduce((a, i) => (i === -1 && (a += 1), a), 0); $(n <= 1, () => "There should be only one negative value in split array."); let s = t8.indexOf(-1); if (s !== -1) { let a = t8.reduce((i, p) => p > 0 ? i + p : i); - t8[s] = r.shape[e] - a; + t8[s] = r15.shape[e] - a; } - $(r.shape[e] === t8.reduce((a, i) => a + i), () => "The sum of sizes must match the size of the axis dimension."), o = t8; + $(r15.shape[e] === t8.reduce((a, i) => a + i), () => "The sum of sizes must match the size of the axis dimension."), o = t8; } return o; } -function d5(r) { +function d5(r15) { return `Received SparseTensor with denseShape[0] = 0 but - indices.shape[0] = ${r}`; + indices.shape[0] = ${r15}`; } -function f5(r, t8) { - return `indices(${r}, 0) is invalid: ${t8} < 0`; +function f5(r15, t8) { + return `indices(${r15}, 0) is invalid: ${t8} < 0`; } -function h5(r, t8, e) { - return `indices(${r}, 0) is invalid: ${t8} >= ${e}`; +function h5(r15, t8, e) { + return `indices(${r15}, 0) is invalid: ${t8} >= ${e}`; } -function g5(r, t8) { - return `only one output dimension may be -1, not both ${r} and ${t8}`; +function g5(r15, t8) { + return `only one output dimension may be -1, not both ${r15} and ${t8}`; } -function x5(r, t8) { - return `size ${r} must be non-negative, not ${t8}`; +function x5(r15, t8) { + return `size ${r15} must be non-negative, not ${t8}`; } function y5() { return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"; } -function b5(r, t8) { - let e = He(r), o = He(t8); +function b5(r15, t8) { + let e = He(r15), o = He(t8); return `Input to reshape is a SparseTensor with ${e} - dense values, but the requested shape requires a multiple of ${o}. inputShape=${r} outputShape= ${t8}`; + dense values, but the requested shape requires a multiple of ${o}. inputShape=${r15} outputShape= ${t8}`; } -function C5(r, t8) { - let e = He(r), o = He(t8); - return `Input to reshape is a tensor with ${e} dense values, but the requested shape has ${o}. inputShape=${r} outputShape=${t8}`; +function C5(r15, t8) { + let e = He(r15), o = He(t8); + return `Input to reshape is a tensor with ${e} dense values, but the requested shape has ${o}. inputShape=${r15} outputShape=${t8}`; } function w5() { return "segment ids must be >= 0"; @@ -9617,28 +9618,28 @@ function w5() { function S5() { return "segment ids are not increasing"; } -function I5(r, t8) { - return `Segment id ${r} out of range [0, ${t8}), possibly because segmentIds input is not sorted.`; +function I5(r15, t8) { + return `Segment id ${r15} out of range [0, ${t8}), possibly because segmentIds input is not sorted.`; } -function v5(r, t8, e) { - return `Bad: indices[${r}] == ${t8} out of range [0, ${e})`; +function v5(r15, t8, e) { + return `Bad: indices[${r15}] == ${t8} out of range [0, ${e})`; } var lS = {}; qe(lS, { collectGatherOpShapeInfo: () => T5, computeOutShape: () => N5, segOpComputeOptimalWindowSize: () => k5 }); -function k5(r, t8) { +function k5(r15, t8) { let e = false, o; - for (r <= uf ? (o = r, e = true) : o = Xp(r, Math.floor(Math.sqrt(r))); !e; ) - o > t8 || o === r ? e = true : o = Xp(r, o + 1); + for (r15 <= af ? (o = r15, e = true) : o = Vp(r15, Math.floor(Math.sqrt(r15))); !e; ) + o > t8 || o === r15 ? e = true : o = Vp(r15, o + 1); return o; } -function N5(r, t8, e) { - let o = [], n = r.length; +function N5(r15, t8, e) { + let o = [], n = r15.length; for (let s = 0; s < n; s++) - s !== t8 ? o.push(r[s]) : o.push(e); + s !== t8 ? o.push(r15[s]) : o.push(e); return o; } -function T5(r, t8, e, o) { - let n = t8.shape.length, s = r.shape.length; +function T5(r15, t8, e, o) { + let n = t8.shape.length, s = r15.shape.length; if (o !== 0 && (o < -n || o > n)) throw new Error(`Expect batchDims in the range of [-${n}, ${n}], but got ${o}`); if (o < 0 && (o += n), o > s) @@ -9647,119 +9648,119 @@ function T5(r, t8, e, o) { if (e < o) throw new Error(`batchDims (${o}) must be less than or equal to axis (${e}).`); for (let l = 0; l < o; ++l) - if (r.shape[l] !== t8.shape[l]) - throw new Error(`x.shape[${l}]: ${r.shape[l]} should be equal to indices.shape[${l}]: ${t8.shape[l]}.`); - let a = r.shape[e], i = [], p = 1, u = 1, c = 1; + if (r15.shape[l] !== t8.shape[l]) + throw new Error(`x.shape[${l}]: ${r15.shape[l]} should be equal to indices.shape[${l}]: ${t8.shape[l]}.`); + let a = r15.shape[e], i = [], p = 1, u = 1, c = 1; for (let l = 0; l < o; ++l) - i.push(r.shape[l]), p *= r.shape[l]; + i.push(r15.shape[l]), p *= r15.shape[l]; for (let l = o; l < e; l++) - i.push(r.shape[l]), u *= r.shape[l]; + i.push(r15.shape[l]), u *= r15.shape[l]; for (let l = o; l < n; l++) i.push(t8.shape[l]); for (let l = e + 1; l < s; l++) - i.push(r.shape[l]), c *= r.shape[l]; + i.push(r15.shape[l]), c *= r15.shape[l]; return { batchSize: p, sliceSize: c, outerSize: u, dimSize: a, outputShape: i }; } -function _5(r) { +function _5(r15) { try { - return r.map((t8) => sc(t8)); + return r15.map((t8) => Qp(t8)); } catch (t8) { throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t8}`); } } -function $5(r) { - return r.map((t8) => tu(t8)); +function $5(r15) { + return r15.map((t8) => Zi(t8)); } -var Wt = {}; -qe(Wt, { nonMaxSuppressionV3Impl: () => ef, nonMaxSuppressionV4Impl: () => tf, nonMaxSuppressionV5Impl: () => rf, whereImpl: () => Yd }); -UN(); +var Vt = {}; +qe(Vt, { nonMaxSuppressionV3Impl: () => Zd, nonMaxSuppressionV4Impl: () => Jd, nonMaxSuppressionV5Impl: () => ef, whereImpl: () => jd }); +GN(); var E5 = A(); -E5.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (r) => { - r && console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance."); +E5.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (r15) => { + r15 && console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance."); }); -var ho; -(function(r) { - r[r.DT_INVALID = 0] = "DT_INVALID", r[r.DT_FLOAT = 1] = "DT_FLOAT", r[r.DT_DOUBLE = 2] = "DT_DOUBLE", r[r.DT_INT32 = 3] = "DT_INT32", r[r.DT_UINT8 = 4] = "DT_UINT8", r[r.DT_INT16 = 5] = "DT_INT16", r[r.DT_INT8 = 6] = "DT_INT8", r[r.DT_STRING = 7] = "DT_STRING", r[r.DT_COMPLEX64 = 8] = "DT_COMPLEX64", r[r.DT_INT64 = 9] = "DT_INT64", r[r.DT_BOOL = 10] = "DT_BOOL", r[r.DT_QINT8 = 11] = "DT_QINT8", r[r.DT_QUINT8 = 12] = "DT_QUINT8", r[r.DT_QINT32 = 13] = "DT_QINT32", r[r.DT_BFLOAT16 = 14] = "DT_BFLOAT16", r[r.DT_QINT16 = 15] = "DT_QINT16", r[r.DT_QUINT16 = 16] = "DT_QUINT16", r[r.DT_UINT16 = 17] = "DT_UINT16", r[r.DT_COMPLEX128 = 18] = "DT_COMPLEX128", r[r.DT_HALF = 19] = "DT_HALF", r[r.DT_RESOURCE = 20] = "DT_RESOURCE", r[r.DT_VARIANT = 21] = "DT_VARIANT", r[r.DT_UINT32 = 22] = "DT_UINT32", r[r.DT_UINT64 = 23] = "DT_UINT64", r[r.DT_FLOAT_REF = 101] = "DT_FLOAT_REF", r[r.DT_DOUBLE_REF = 102] = "DT_DOUBLE_REF", r[r.DT_INT32_REF = 103] = "DT_INT32_REF", r[r.DT_UINT8_REF = 104] = "DT_UINT8_REF", r[r.DT_INT16_REF = 105] = "DT_INT16_REF", r[r.DT_INT8_REF = 106] = "DT_INT8_REF", r[r.DT_STRING_REF = 107] = "DT_STRING_REF", r[r.DT_COMPLEX64_REF = 108] = "DT_COMPLEX64_REF", r[r.DT_INT64_REF = 109] = "DT_INT64_REF", r[r.DT_BOOL_REF = 110] = "DT_BOOL_REF", r[r.DT_QINT8_REF = 111] = "DT_QINT8_REF", r[r.DT_QUINT8_REF = 112] = "DT_QUINT8_REF", r[r.DT_QINT32_REF = 113] = "DT_QINT32_REF", r[r.DT_BFLOAT16_REF = 114] = "DT_BFLOAT16_REF", r[r.DT_QINT16_REF = 115] = "DT_QINT16_REF", r[r.DT_QUINT16_REF = 116] = "DT_QUINT16_REF", r[r.DT_UINT16_REF = 117] = "DT_UINT16_REF", r[r.DT_COMPLEX128_REF = 118] = "DT_COMPLEX128_REF", r[r.DT_HALF_REF = 119] = "DT_HALF_REF", r[r.DT_RESOURCE_REF = 120] = "DT_RESOURCE_REF", r[r.DT_VARIANT_REF = 121] = "DT_VARIANT_REF", r[r.DT_UINT32_REF = 122] = "DT_UINT32_REF", r[r.DT_UINT64_REF = 123] = "DT_UINT64_REF"; -})(ho || (ho = {})); -var gT; -(function(r) { +var fo; +(function(r15) { + r15[r15.DT_INVALID = 0] = "DT_INVALID", r15[r15.DT_FLOAT = 1] = "DT_FLOAT", r15[r15.DT_DOUBLE = 2] = "DT_DOUBLE", r15[r15.DT_INT32 = 3] = "DT_INT32", r15[r15.DT_UINT8 = 4] = "DT_UINT8", r15[r15.DT_INT16 = 5] = "DT_INT16", r15[r15.DT_INT8 = 6] = "DT_INT8", r15[r15.DT_STRING = 7] = "DT_STRING", r15[r15.DT_COMPLEX64 = 8] = "DT_COMPLEX64", r15[r15.DT_INT64 = 9] = "DT_INT64", r15[r15.DT_BOOL = 10] = "DT_BOOL", r15[r15.DT_QINT8 = 11] = "DT_QINT8", r15[r15.DT_QUINT8 = 12] = "DT_QUINT8", r15[r15.DT_QINT32 = 13] = "DT_QINT32", r15[r15.DT_BFLOAT16 = 14] = "DT_BFLOAT16", r15[r15.DT_QINT16 = 15] = "DT_QINT16", r15[r15.DT_QUINT16 = 16] = "DT_QUINT16", r15[r15.DT_UINT16 = 17] = "DT_UINT16", r15[r15.DT_COMPLEX128 = 18] = "DT_COMPLEX128", r15[r15.DT_HALF = 19] = "DT_HALF", r15[r15.DT_RESOURCE = 20] = "DT_RESOURCE", r15[r15.DT_VARIANT = 21] = "DT_VARIANT", r15[r15.DT_UINT32 = 22] = "DT_UINT32", r15[r15.DT_UINT64 = 23] = "DT_UINT64", r15[r15.DT_FLOAT_REF = 101] = "DT_FLOAT_REF", r15[r15.DT_DOUBLE_REF = 102] = "DT_DOUBLE_REF", r15[r15.DT_INT32_REF = 103] = "DT_INT32_REF", r15[r15.DT_UINT8_REF = 104] = "DT_UINT8_REF", r15[r15.DT_INT16_REF = 105] = "DT_INT16_REF", r15[r15.DT_INT8_REF = 106] = "DT_INT8_REF", r15[r15.DT_STRING_REF = 107] = "DT_STRING_REF", r15[r15.DT_COMPLEX64_REF = 108] = "DT_COMPLEX64_REF", r15[r15.DT_INT64_REF = 109] = "DT_INT64_REF", r15[r15.DT_BOOL_REF = 110] = "DT_BOOL_REF", r15[r15.DT_QINT8_REF = 111] = "DT_QINT8_REF", r15[r15.DT_QUINT8_REF = 112] = "DT_QUINT8_REF", r15[r15.DT_QINT32_REF = 113] = "DT_QINT32_REF", r15[r15.DT_BFLOAT16_REF = 114] = "DT_BFLOAT16_REF", r15[r15.DT_QINT16_REF = 115] = "DT_QINT16_REF", r15[r15.DT_QUINT16_REF = 116] = "DT_QUINT16_REF", r15[r15.DT_UINT16_REF = 117] = "DT_UINT16_REF", r15[r15.DT_COMPLEX128_REF = 118] = "DT_COMPLEX128_REF", r15[r15.DT_HALF_REF = 119] = "DT_HALF_REF", r15[r15.DT_RESOURCE_REF = 120] = "DT_RESOURCE_REF", r15[r15.DT_VARIANT_REF = 121] = "DT_VARIANT_REF", r15[r15.DT_UINT32_REF = 122] = "DT_UINT32_REF", r15[r15.DT_UINT64_REF = 123] = "DT_UINT64_REF"; +})(fo || (fo = {})); +var xT; +(function(r15) { let t8; (function(e) { e[e.LEGACY = 0] = "LEGACY", e[e.V1 = 1] = "V1", e[e.V2 = 2] = "V2"; - })(t8 = r.CheckpointFormatVersion || (r.CheckpointFormatVersion = {})); -})(gT || (gT = {})); + })(t8 = r15.CheckpointFormatVersion || (r15.CheckpointFormatVersion = {})); +})(xT || (xT = {})); var dS = {}; -function D5(r, t8) { - let e = { tfOpName: r, category: "custom", inputs: [], attrs: [], customExecutor: t8 }; - dS[r] = e; +function D5(r15, t8) { + let e = { tfOpName: r15, category: "custom", inputs: [], attrs: [], customExecutor: t8 }; + dS[r15] = e; } -function pf(r) { - return dS[r]; +function uf(r15) { + return dS[r15]; } -function A5(r) { - delete dS[r]; +function A5(r15) { + delete dS[r15]; } -function I(r, t8, e, o, n) { - let s = t8.inputParams[r]; +function I(r15, t8, e, o, n) { + let s = t8.inputParams[r15]; if (s && s.inputIndexStart !== void 0) { let i = s.inputIndexStart, p = s.inputIndexEnd === 0 ? void 0 : s.inputIndexEnd === void 0 ? i + 1 : s.inputIndexEnd, u = i < 0 ? t8.inputNames.length + i : i; if (s.type === "tensor") - return zt(t8.inputNames[u], e, o, n); + return Bt(t8.inputNames[u], e, o, n); if (s.type === "tensors") { let m = t8.inputs.slice(i, p); return t8.inputNames.slice(i, p).filter((f, h) => { var g; return ((g = m[h]) === null || g === void 0 ? void 0 : g.op) !== "NoOp"; - }).map((f) => zt(f, e, o, n)); + }).map((f) => Bt(f, e, o, n)); } - let c = zt(t8.inputNames[u], e, o, n), l = c.dataSync(); + let c = Bt(t8.inputNames[u], e, o, n), l = c.dataSync(); return s.type === "number" ? l[0] : y.toNestedArray(c.shape, l); } - let a = t8.attrParams[r]; + let a = t8.attrParams[r15]; return a && a.value; } -function zt(r, t8, e, o) { - let [n, s] = Tr(r, e); +function Bt(r15, t8, e, o) { + let [n, s] = Nr(r15, e); if (o != null) { let i = o.getHashTableHandleByName(n); if (i != null) return i; } - let a = e.currentContextIds.find((i) => !!t8[cf(n, i)]); - return a !== void 0 ? t8[cf(n, a)][s] : void 0; + let a = e.currentContextIds.find((i) => !!t8[pf(n, i)]); + return a !== void 0 ? t8[pf(n, a)][s] : void 0; } -function fS(r, t8, e) { - return t8[cf(r, e.currentContextId)]; +function fS(r15, t8, e) { + return t8[pf(r15, e.currentContextId)]; } -function Ls(r, t8) { - let [e, o, n] = Tr(r, t8); - return [cf(e, t8 && t8.currentContextId), o, n]; +function Ls(r15, t8) { + let [e, o, n] = Nr(r15, t8); + return [pf(e, t8 && t8.currentContextId), o, n]; } -function cf(r, t8) { - return t8 ? `${r}-${t8}` : r; +function pf(r15, t8) { + return t8 ? `${r15}-${t8}` : r15; } -function Tr(r, t8) { - if (r === "") +function Nr(r15, t8) { + if (r15 === "") return ["", 0, void 0]; let e = t8 != null && t8.parseNodeNameCache != null; if (e) { - let s = t8.parseNodeNameCache.get(r); + let s = t8.parseNodeNameCache.get(r15); if (s != null) return s; } - let o = r.split(":"), n; + let o = r15.split(":"), n; if (o.length === 1) - n = [r, 0, void 0]; + n = [r15, 0, void 0]; else { let s = o[0], a = o.length === 3 ? o[1] : void 0, i = Number(o[o.length - 1]); n = [s, i, a]; } - return e && t8.parseNodeNameCache.set(r, n), n; + return e && t8.parseNodeNameCache.set(r15, n), n; } -function Ol(r, t8, e) { - let o = I("pad", r, t8, e); +function Al(r15, t8, e) { + let o = I("pad", r15, t8, e); if (o === "explicit") { - o = I("explicitPaddings", r, t8, e); + o = I("explicitPaddings", r15, t8, e); let n = [[0, 0], [0, 0], [0, 0], [0, 0]]; for (let s = 0; s < 4; s++) n[s][0] = o[s * 2], n[s][1] = o[s * 2 + 1]; @@ -9767,8 +9768,8 @@ function Ol(r, t8, e) { } return o; } -function Bs(r) { - return r.kept ? r : Ur(r); +function Bs(r15) { + return r15.kept ? r15 : Wr(r15); } var hS = {}; qe(hS, { json: () => F5 }); @@ -9827,7 +9828,7 @@ var Q5 = [{ tfOpName: "StaticRegexReplace", category: "string", inputs: [{ start var AS = {}; qe(AS, { json: () => Z5 }); var Z5 = [{ tfOpName: "Cast", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "SrcT", name: "sdtype", type: "dtype", notSupported: true }, { tfName: "DstT", name: "dtype", type: "dtype" }] }, { tfOpName: "ExpandDims", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "MirrorPad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "mode", name: "mode", type: "string" }] }, { tfOpName: "Pad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "constant_value", name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "PadV2", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }, { start: 2, name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "Reshape", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }] }, { tfOpName: "EnsureShape", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }] }, { tfOpName: "Squeeze", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "axis", tfDeprecatedName: "squeeze_dims", name: "axis", type: "number[]" }] }, { tfOpName: "SpaceToBatchND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "paddings", type: "number[]" }] }, { tfOpName: "BatchToSpaceND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "crops", type: "number[]" }] }, { tfOpName: "DepthToSpace", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "block_size", name: "blockSize", type: "number" }, { tfName: "data_format", name: "dataFormat", type: "string" }] }, { tfOpName: "BroadcastTo", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }], attrs: [] }, { tfOpName: "BroadcastArgs", category: "transformation", inputs: [{ start: 0, name: "s0", type: "tensor" }, { start: 1, name: "s1", type: "tensor" }], attrs: [] }]; -var Ml = class { +var Fl = class { static get Instance() { return this._instance || (this._instance = new this()); } @@ -9871,44 +9872,44 @@ var Ml = class { return Object.keys(t8 || {}).reduce((e, o) => (e[t8[o].name] = o, e), {}); } mapNode(t8) { - let e = pf(t8.op) || this.opMappers[t8.op] || {}; + let e = uf(t8.op) || this.opMappers[t8.op] || {}; t8.attr == null && (t8.attr = {}); let o = { name: t8.name, op: t8.op, category: e.category, inputNames: (t8.input || []).map((n) => n.startsWith("^") ? n.slice(1) : n), inputs: [], children: [], inputParams: {}, attrParams: {}, rawAttrs: t8.attr, outputs: e.outputs }; return e.inputs != null && (o.inputParams = e.inputs.reduce((n, s) => (n[s.name] = { type: s.type, inputIndexStart: s.start, inputIndexEnd: s.end }, n), {})), e.attrs != null && (o.attrParams = e.attrs.reduce((n, s) => { let a = s.type, i; switch (s.type) { case "string": - i = lf(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = lf(t8.attr, s.tfDeprecatedName, s.defaultValue)); + i = cf(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = cf(t8.attr, s.tfDeprecatedName, s.defaultValue)); break; case "string[]": - i = yf(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = yf(t8.attr, s.tfDeprecatedName, s.defaultValue)); - break; - case "number": - i = df(t8.attr, s.tfName, s.defaultValue || 0), i === void 0 && s.tfDeprecatedName && (i = df(t8.attr, s.tfDeprecatedName, s.defaultValue)); - break; - case "number[]": i = xf(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = xf(t8.attr, s.tfDeprecatedName, s.defaultValue)); break; - case "bool": - i = mf(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = mf(t8.attr, s.tfDeprecatedName, s.defaultValue)); + case "number": + i = mf(t8.attr, s.tfName, s.defaultValue || 0), i === void 0 && s.tfDeprecatedName && (i = mf(t8.attr, s.tfDeprecatedName, s.defaultValue)); break; - case "bool[]": - i = Cf(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = Cf(t8.attr, s.tfDeprecatedName, s.defaultValue)); - break; - case "shape": + case "number[]": i = gf(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = gf(t8.attr, s.tfDeprecatedName, s.defaultValue)); break; - case "shape[]": + case "bool": + i = lf(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = lf(t8.attr, s.tfDeprecatedName, s.defaultValue)); + break; + case "bool[]": i = bf(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = bf(t8.attr, s.tfDeprecatedName, s.defaultValue)); break; - case "dtype": - i = ff(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = ff(t8.attr, s.tfDeprecatedName, s.defaultValue)); - break; - case "dtype[]": + case "shape": i = hf(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = hf(t8.attr, s.tfDeprecatedName, s.defaultValue)); break; + case "shape[]": + i = yf(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = yf(t8.attr, s.tfDeprecatedName, s.defaultValue)); + break; + case "dtype": + i = df(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = df(t8.attr, s.tfDeprecatedName, s.defaultValue)); + break; + case "dtype[]": + i = ff(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = ff(t8.attr, s.tfDeprecatedName, s.defaultValue)); + break; case "func": - i = xT(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = xT(t8.attr, s.tfDeprecatedName, s.defaultValue)); + i = yT(t8.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = yT(t8.attr, s.tfDeprecatedName, s.defaultValue)); break; case "tensor": case "tensors": @@ -9956,292 +9957,292 @@ var Ml = class { return e != null && (o = e[o]), { name: o, dtype: t8.type }; } }; -function J5(r) { +function J5(r15) { let t8 = A().global; if (typeof t8.atob != "undefined") - return t8.atob(r); + return t8.atob(r15); if (typeof Buffer != "undefined") - return new Buffer(r, "base64").toString(); + return new Buffer(r15, "base64").toString(); throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()"); } -function yT(r, t8) { - let e = Array.isArray(r) ? String.fromCharCode.apply(null, r) : J5(r); +function bT(r15, t8) { + let e = Array.isArray(r15) ? String.fromCharCode.apply(null, r15) : J5(r15); return t8 ? e : e.toLowerCase(); } -function lf(r, t8, e, o = false) { - let n = r[t8]; - return n != null ? yT(n.s, o) : e; +function cf(r15, t8, e, o = false) { + let n = r15[t8]; + return n != null ? bT(n.s, o) : e; } -function mf(r, t8, e) { - let o = r[t8]; +function lf(r15, t8, e) { + let o = r15[t8]; return o ? o.b : e; } -function df(r, t8, e) { - let o = r[t8] || {}, n = o.i != null ? o.i : o.f != null ? o.f : e; +function mf(r15, t8, e) { + let o = r15[t8] || {}, n = o.i != null ? o.i : o.f != null ? o.f : e; return typeof n == "number" ? n : parseInt(n, 10); } -function FS(r) { - switch (typeof r == "string" && (r = ho[r]), r) { - case ho.DT_FLOAT: - case ho.DT_HALF: +function FS(r15) { + switch (typeof r15 == "string" && (r15 = fo[r15]), r15) { + case fo.DT_FLOAT: + case fo.DT_HALF: return "float32"; - case ho.DT_INT32: - case ho.DT_INT64: - case ho.DT_INT8: - case ho.DT_UINT8: + case fo.DT_INT32: + case fo.DT_INT64: + case fo.DT_INT8: + case fo.DT_UINT8: return "int32"; - case ho.DT_BOOL: + case fo.DT_BOOL: return "bool"; - case ho.DT_DOUBLE: + case fo.DT_DOUBLE: return "float32"; - case ho.DT_STRING: + case fo.DT_STRING: return "string"; default: return null; } } -function xT(r, t8, e) { - let o = r[t8]; +function yT(r15, t8, e) { + let o = r15[t8]; return o && o.func ? o.func.name : e; } -function ff(r, t8, e) { - let o = r[t8]; +function df(r15, t8, e) { + let o = r15[t8]; return o && o.type ? FS(o.type) : e; } -function hf(r, t8, e) { - let o = r[t8]; +function ff(r15, t8, e) { + let o = r15[t8]; return o && o.list && o.list.type ? o.list.type.map((n) => FS(n)) : e; } -function bT(r) { - if (!r.unknownRank) - return r.dim != null ? r.dim.map((t8) => typeof t8.size == "number" ? t8.size : parseInt(t8.size, 10)) : []; +function CT(r15) { + if (!r15.unknownRank) + return r15.dim != null ? r15.dim.map((t8) => typeof t8.size == "number" ? t8.size : parseInt(t8.size, 10)) : []; } -function gf(r, t8, e) { - let o = r[t8]; - return o && o.shape ? bT(o.shape) : e; +function hf(r15, t8, e) { + let o = r15[t8]; + return o && o.shape ? CT(o.shape) : e; } -function xf(r, t8, e) { - let o = r[t8]; +function gf(r15, t8, e) { + let o = r15[t8]; return o ? ((o.list.f && o.list.f.length ? o.list.f : o.list.i) || []).map((n) => typeof n == "number" ? n : parseInt(n, 10)) : e; } -function yf(r, t8, e, o = false) { - let n = r[t8]; - return n && n.list && n.list.s ? n.list.s.map((s) => yT(s, o)) : e; +function xf(r15, t8, e, o = false) { + let n = r15[t8]; + return n && n.list && n.list.s ? n.list.s.map((s) => bT(s, o)) : e; } -function bf(r, t8, e) { - let o = r[t8]; - return o && o.list && o.list.shape ? o.list.shape.map((n) => bT(n)) : e; +function yf(r15, t8, e) { + let o = r15[t8]; + return o && o.list && o.list.shape ? o.list.shape.map((n) => CT(n)) : e; } -function Cf(r, t8, e) { - let o = r[t8]; +function bf(r15, t8, e) { + let o = r15[t8]; return o && o.list && o.list.b ? o.list.b : e; } -var wf = class { +var Cf = class { constructor(t8, e, o) { this.node = t8, this.tensorMap = e, this.context = o, this.inputs = [], this.attrs = {}, this.inputs = t8.inputNames.map((n) => this.getInput(n)), t8.rawAttrs != null && (this.attrs = Object.keys(t8.rawAttrs).reduce((n, s) => (n[s] = this.getAttr(s), n), {})); } getInput(t8) { - return zt(t8, this.tensorMap, this.context); + return Bt(t8, this.tensorMap, this.context); } getAttr(t8, e) { let o = this.node.rawAttrs[t8]; if (o.tensor != null) - return zt(t8, this.tensorMap, this.context); + return Bt(t8, this.tensorMap, this.context); if (o.i != null || o.f != null) - return df(this.node.rawAttrs, t8, e); - if (o.s != null) - return lf(this.node.rawAttrs, t8, e); - if (o.b != null) return mf(this.node.rawAttrs, t8, e); + if (o.s != null) + return cf(this.node.rawAttrs, t8, e); + if (o.b != null) + return lf(this.node.rawAttrs, t8, e); if (o.shape != null) - return gf(this.node.rawAttrs, t8, e); + return hf(this.node.rawAttrs, t8, e); if (o.type != null) - return ff(this.node.rawAttrs, t8, e); + return df(this.node.rawAttrs, t8, e); if (o.list != null) { if (o.list.i != null || o.list.f != null) - return xf(this.node.rawAttrs, t8, e); + return gf(this.node.rawAttrs, t8, e); if (o.list.s != null) - return yf(this.node.rawAttrs, t8, e); + return xf(this.node.rawAttrs, t8, e); if (o.list.shape != null) - return bf(this.node.rawAttrs, t8, e); + return yf(this.node.rawAttrs, t8, e); if (o.list.b != null) - return Cf(this.node.rawAttrs, t8, e); + return bf(this.node.rawAttrs, t8, e); if (o.list.type != null) - return hf(this.node.rawAttrs, t8, e); + return ff(this.node.rawAttrs, t8, e); } return e; } }; var Je = {}; -qe(Je, { OP_SCOPE_SUFFIX: () => kw, abs: () => Jt, acos: () => kk, acosh: () => Nk, add: () => Ce, addN: () => Tk, all: () => _k, any: () => $k, argMax: () => Ek, argMin: () => Rk, asin: () => Dk, asinh: () => Ak, atan: () => Fk, atan2: () => Pk, atanh: () => Ok, avgPool: () => fd, avgPool3d: () => Bk, basicLSTMCell: () => zk, batchNorm: () => au, batchNorm2d: () => Wk, batchNorm3d: () => Uk, batchNorm4d: () => Gk, batchToSpaceND: () => hd, bincount: () => gd, bitwiseAnd: () => Hk, booleanMaskAsync: () => E6, broadcastArgs: () => Kk, broadcastTo: () => iu, buffer: () => me, cast: () => We, ceil: () => qk, clipByValue: () => jk, clone: () => Ur, complex: () => Er, concat: () => bt, concat1d: () => Xk, concat2d: () => Yk, concat3d: () => Qk, concat4d: () => Zk, conv1d: () => Jk, conv2d: () => uu, conv2dTranspose: () => e2, conv3d: () => t2, conv3dTranspose: () => o2, cos: () => n2, cosh: () => s2, cosineWindow: () => Rl, cumprod: () => a2, cumsum: () => i2, denseBincount: () => u2, depthToSpace: () => p2, depthwiseConv2d: () => lc, diag: () => c2, dilation2d: () => l2, div: () => je, divNoNan: () => d2, dot: () => f2, dropout: () => W6, einsum: () => pu, elu: () => Cd, enclosingPowerOfTwo: () => Qw, ensureShape: () => h2, equal: () => bd, erf: () => g2, euclideanNorm: () => b2, exp: () => $o, expandDims: () => Ms, expm1: () => C2, eye: () => wd, fft: () => fc, fill: () => Ea, floor: () => Sd, floorDiv: () => dd, fused: () => Zw, gather: () => Id, gatherND: () => z6, greater: () => qu, greaterEqual: () => vd, ifft: () => Ju, imag: () => lu, image: () => Kj, inTopKAsync: () => G6, irfft: () => Kd, isFinite: () => w2, isInf: () => S2, isNaN: () => I2, leakyRelu: () => kd, less: () => _l, lessEqual: () => mc, linalg: () => qj, linspace: () => v2, localResponseNormalization: () => k2, log: () => pi, log1p: () => Nd, logSigmoid: () => N2, logSoftmax: () => T2, logSumExp: () => $d, logicalAnd: () => ju, logicalNot: () => Ed, logicalOr: () => Rd, logicalXor: () => _2, losses: () => jj, lowerBound: () => $2, matMul: () => Ze, max: () => Ra, maxPool: () => Ad, maxPool3d: () => E2, maxPoolWithArgmax: () => R2, maximum: () => Fd, mean: () => Xu, meshgrid: () => D2, min: () => Tl, minimum: () => Yu, mirrorPad: () => A2, mod: () => F2, moments: () => P2, movingAverage: () => A6, mul: () => se, multiRNNCell: () => O2, multinomial: () => M2, neg: () => cr, norm: () => Ku, notEqual: () => Pd, oneHot: () => El, ones: () => Da, onesLike: () => L2, op: () => N, outerProduct: () => B2, pad: () => Aa, pad1d: () => z2, pad2d: () => V2, pad3d: () => W2, pad4d: () => U2, pool: () => G2, pow: () => ui, prelu: () => Md, print: () => md, prod: () => H2, raggedGather: () => K2, raggedRange: () => q2, raggedTensorToTensor: () => j2, rand: () => X2, randomGamma: () => g1, randomNormal: () => Ud, randomStandardNormal: () => x1, randomUniform: () => dc, randomUniformInt: () => y1, range: () => mu, real: () => ci, reciprocal: () => b1, relu: () => du, relu6: () => Gd, reshape: () => W, reverse: () => mo, reverse1d: () => C1, reverse2d: () => w1, reverse3d: () => S1, reverse4d: () => I1, rfft: () => hc, round: () => Hd, rsqrt: () => v1, scalar: () => ke, scatterND: () => P6, searchSorted: () => $l, selu: () => k1, separableConv2d: () => N1, setdiff1dAsync: () => T1, sigmoid: () => $a, sign: () => _1, signal: () => Hj, sin: () => $1, sinh: () => E1, slice: () => Xe, slice1d: () => R1, slice2d: () => D1, slice3d: () => A1, slice4d: () => F1, softmax: () => P1, softplus: () => _d, spaceToBatchND: () => Od, sparse: () => Xj, sparseToDense: () => L6, spectral: () => Gj, split: () => li, sqrt: () => Dr, square: () => er, squaredDifference: () => qd, squeeze: () => gc, stack: () => kr, step: () => jd, stridedSlice: () => O1, string: () => Yj, sub: () => Te, sum: () => ot, tan: () => M1, tanh: () => Nl, tensor: () => ur, tensor1d: () => tr, tensor2d: () => fu, tensor3d: () => Xd, tensor4d: () => L1, tensor5d: () => B1, tensor6d: () => z1, tensorScatterUpdate: () => W1, tile: () => cu, topk: () => U1, transpose: () => yc, truncatedNormal: () => G1, unique: () => H1, unsortedSegmentSum: () => K1, unstack: () => fo, upperBound: () => q1, variable: () => j1, where: () => lo, whereAsync: () => Qd, zeros: () => Gr, zerosLike: () => Ht }); -var CT = (r, t8, e, o = Je) => { - switch (r.op) { +qe(Je, { OP_SCOPE_SUFFIX: () => kw, abs: () => Qt, acos: () => Nk, acosh: () => Tk, add: () => Ce, addN: () => _k, all: () => $k, any: () => Ek, argMax: () => Rk, argMin: () => Dk, asin: () => Ak, asinh: () => Fk, atan: () => Pk, atan2: () => Ok, atanh: () => Mk, avgPool: () => md, avgPool3d: () => zk, basicLSTMCell: () => Vk, batchNorm: () => ru, batchNorm2d: () => Uk, batchNorm3d: () => Gk, batchNorm4d: () => Hk, batchToSpaceND: () => dd, bincount: () => fd, bitwiseAnd: () => Kk, booleanMaskAsync: () => E6, broadcastArgs: () => qk, broadcastTo: () => ou, buffer: () => me, cast: () => We, ceil: () => jk, clipByValue: () => Xk, clone: () => Wr, complex: () => $r, concat: () => yt, concat1d: () => Yk, concat2d: () => Qk, concat3d: () => Zk, concat4d: () => Jk, conv1d: () => e2, conv2d: () => nu, conv2dTranspose: () => t2, conv3d: () => r22, conv3dTranspose: () => n2, cos: () => s2, cosh: () => a2, cosineWindow: () => _l, cumprod: () => i2, cumsum: () => u2, denseBincount: () => p2, depthToSpace: () => c2, depthwiseConv2d: () => oc, diag: () => l2, dilation2d: () => m2, div: () => je, divNoNan: () => f2, dot: () => h2, dropout: () => W6, einsum: () => su, elu: () => yd, enclosingPowerOfTwo: () => Qw, ensureShape: () => g2, equal: () => xd, erf: () => x2, euclideanNorm: () => C2, exp: () => _o, expandDims: () => Ms, expm1: () => w2, eye: () => bd, fft: () => ac, fill: () => Ea, floor: () => Cd, floorDiv: () => ld, fused: () => Zw, gather: () => wd, gatherND: () => z6, greater: () => zu, greaterEqual: () => Sd, ifft: () => Ku, imag: () => iu, image: () => Kj, inTopKAsync: () => G6, irfft: () => Gd, isFinite: () => S2, isInf: () => I2, isNaN: () => v2, leakyRelu: () => Id, less: () => kl, lessEqual: () => nc, linalg: () => qj, linspace: () => k2, localResponseNormalization: () => N2, log: () => ui, log1p: () => vd, logSigmoid: () => T2, logSoftmax: () => _2, logSumExp: () => Td, logicalAnd: () => Vu, logicalNot: () => _d, logicalOr: () => $d, logicalXor: () => $2, losses: () => jj, lowerBound: () => E2, matMul: () => Ze, max: () => Ra, maxPool: () => Rd, maxPool3d: () => R2, maxPoolWithArgmax: () => D2, maximum: () => Dd, mean: () => Wu, meshgrid: () => A2, min: () => vl, minimum: () => Uu, mirrorPad: () => F2, mod: () => P2, moments: () => O2, movingAverage: () => A6, mul: () => se, multiRNNCell: () => M2, multinomial: () => L2, neg: () => pr, norm: () => Bu, notEqual: () => Ad, oneHot: () => Tl, ones: () => Da, onesLike: () => B2, op: () => N, outerProduct: () => z2, pad: () => Aa, pad1d: () => V2, pad2d: () => W2, pad3d: () => U2, pad4d: () => G2, pool: () => H2, pow: () => ii, prelu: () => Pd, print: () => cd, prod: () => K2, raggedGather: () => q2, raggedRange: () => j2, raggedTensorToTensor: () => X2, rand: () => Y2, randomGamma: () => x1, randomNormal: () => Vd, randomStandardNormal: () => y1, randomUniform: () => sc, randomUniformInt: () => b1, range: () => uu, real: () => pi, reciprocal: () => C1, relu: () => pu, relu6: () => Wd, reshape: () => W, reverse: () => lo, reverse1d: () => w1, reverse2d: () => S1, reverse3d: () => I1, reverse4d: () => v1, rfft: () => ic, round: () => Ud, rsqrt: () => k1, scalar: () => ke, scatterND: () => P6, searchSorted: () => Nl, selu: () => N1, separableConv2d: () => T1, setdiff1dAsync: () => _1, sigmoid: () => $a, sign: () => $1, signal: () => Hj, sin: () => E1, sinh: () => R1, slice: () => Xe, slice1d: () => D1, slice2d: () => A1, slice3d: () => F1, slice4d: () => P1, softmax: () => O1, softplus: () => Nd, spaceToBatchND: () => Fd, sparse: () => Xj, sparseToDense: () => L6, spectral: () => Gj, split: () => ci, sqrt: () => Rr, square: () => Zt, squaredDifference: () => Hd, squeeze: () => uc, stack: () => vr, step: () => Kd, stridedSlice: () => M1, string: () => Yj, sub: () => Te, sum: () => ot, tan: () => L1, tanh: () => Il, tensor: () => ar, tensor1d: () => Jt, tensor2d: () => cu, tensor3d: () => qd, tensor4d: () => B1, tensor5d: () => z1, tensor6d: () => V1, tensorScatterUpdate: () => U1, tile: () => au, topk: () => G1, transpose: () => cc, truncatedNormal: () => H1, unique: () => K1, unsortedSegmentSum: () => q1, unstack: () => mo, upperBound: () => j1, variable: () => X1, where: () => co, whereAsync: () => Xd, zeros: () => Ur, zerosLike: () => Gt }); +var wT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "BiasAdd": case "AddV2": case "Add": - return [o.add(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.add(I("a", r15, t8, e), I("b", r15, t8, e))]; case "AddN": - return [o.addN(I("tensors", r, t8, e))]; + return [o.addN(I("tensors", r15, t8, e))]; case "FloorMod": case "Mod": - return [o.mod(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.mod(I("a", r15, t8, e), I("b", r15, t8, e))]; case "Mul": - return [o.mul(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.mul(I("a", r15, t8, e), I("b", r15, t8, e))]; case "RealDiv": case "Div": - return [o.div(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.div(I("a", r15, t8, e), I("b", r15, t8, e))]; case "DivNoNan": - return [o.divNoNan(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.divNoNan(I("a", r15, t8, e), I("b", r15, t8, e))]; case "FloorDiv": - return [o.floorDiv(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.floorDiv(I("a", r15, t8, e), I("b", r15, t8, e))]; case "Sub": - return [o.sub(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.sub(I("a", r15, t8, e), I("b", r15, t8, e))]; case "Minimum": - return [o.minimum(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.minimum(I("a", r15, t8, e), I("b", r15, t8, e))]; case "Maximum": - return [o.maximum(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.maximum(I("a", r15, t8, e), I("b", r15, t8, e))]; case "Pow": - return [o.pow(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.pow(I("a", r15, t8, e), I("b", r15, t8, e))]; case "SquaredDifference": - return [o.squaredDifference(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.squaredDifference(I("a", r15, t8, e), I("b", r15, t8, e))]; default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var wT = (r, t8, e, o = Je) => { - switch (r.op) { +var ST = (r15, t8, e, o = Je) => { + switch (r15.op) { case "Abs": case "ComplexAbs": - return [o.abs(I("x", r, t8, e))]; + return [o.abs(I("x", r15, t8, e))]; case "Acos": - return [o.acos(I("x", r, t8, e))]; + return [o.acos(I("x", r15, t8, e))]; case "Acosh": - return [o.acosh(I("x", r, t8, e))]; + return [o.acosh(I("x", r15, t8, e))]; case "Asin": - return [o.asin(I("x", r, t8, e))]; + return [o.asin(I("x", r15, t8, e))]; case "Asinh": - return [o.asinh(I("x", r, t8, e))]; + return [o.asinh(I("x", r15, t8, e))]; case "Atan": - return [o.atan(I("x", r, t8, e))]; + return [o.atan(I("x", r15, t8, e))]; case "Atan2": - return [o.atan2(I("x", r, t8, e), I("y", r, t8, e))]; + return [o.atan2(I("x", r15, t8, e), I("y", r15, t8, e))]; case "Atanh": - return [o.atanh(I("x", r, t8, e))]; + return [o.atanh(I("x", r15, t8, e))]; case "Ceil": - return [o.ceil(I("x", r, t8, e))]; + return [o.ceil(I("x", r15, t8, e))]; case "Complex": - return [o.complex(I("real", r, t8, e), I("imag", r, t8, e))]; + return [o.complex(I("real", r15, t8, e), I("imag", r15, t8, e))]; case "Cos": - return [o.cos(I("x", r, t8, e))]; + return [o.cos(I("x", r15, t8, e))]; case "Cosh": - return [o.cosh(I("x", r, t8, e))]; + return [o.cosh(I("x", r15, t8, e))]; case "Elu": - return [o.elu(I("x", r, t8, e))]; + return [o.elu(I("x", r15, t8, e))]; case "Erf": - return [o.erf(I("x", r, t8, e))]; + return [o.erf(I("x", r15, t8, e))]; case "Exp": - return [o.exp(I("x", r, t8, e))]; + return [o.exp(I("x", r15, t8, e))]; case "Expm1": - return [o.expm1(I("x", r, t8, e))]; + return [o.expm1(I("x", r15, t8, e))]; case "Floor": - return [o.floor(I("x", r, t8, e))]; + return [o.floor(I("x", r15, t8, e))]; case "Log": - return [o.log(I("x", r, t8, e))]; + return [o.log(I("x", r15, t8, e))]; case "Log1p": - return [o.log1p(I("x", r, t8, e))]; + return [o.log1p(I("x", r15, t8, e))]; case "Imag": - return [o.imag(I("x", r, t8, e))]; + return [o.imag(I("x", r15, t8, e))]; case "Neg": - return [o.neg(I("x", r, t8, e))]; + return [o.neg(I("x", r15, t8, e))]; case "Reciprocal": - return [o.reciprocal(I("x", r, t8, e))]; + return [o.reciprocal(I("x", r15, t8, e))]; case "Real": - return [o.real(I("x", r, t8, e))]; + return [o.real(I("x", r15, t8, e))]; case "Relu": - return [o.relu(I("x", r, t8, e))]; + return [o.relu(I("x", r15, t8, e))]; case "Round": - return [o.round(I("x", r, t8, e))]; + return [o.round(I("x", r15, t8, e))]; case "Selu": - return [o.selu(I("x", r, t8, e))]; + return [o.selu(I("x", r15, t8, e))]; case "Sigmoid": - return [o.sigmoid(I("x", r, t8, e))]; + return [o.sigmoid(I("x", r15, t8, e))]; case "Sin": - return [o.sin(I("x", r, t8, e))]; + return [o.sin(I("x", r15, t8, e))]; case "Sign": - return [o.sign(I("x", r, t8, e))]; + return [o.sign(I("x", r15, t8, e))]; case "Sinh": - return [o.sinh(I("x", r, t8, e))]; + return [o.sinh(I("x", r15, t8, e))]; case "Softplus": - return [o.softplus(I("x", r, t8, e))]; + return [o.softplus(I("x", r15, t8, e))]; case "Sqrt": - return [o.sqrt(I("x", r, t8, e))]; + return [o.sqrt(I("x", r15, t8, e))]; case "Square": - return [o.square(I("x", r, t8, e))]; + return [o.square(I("x", r15, t8, e))]; case "Tanh": - return [o.tanh(I("x", r, t8, e))]; + return [o.tanh(I("x", r15, t8, e))]; case "Tan": - return [o.tan(I("x", r, t8, e))]; + return [o.tan(I("x", r15, t8, e))]; case "ClipByValue": - return [o.clipByValue(I("x", r, t8, e), I("clipValueMin", r, t8, e), I("clipValueMax", r, t8, e))]; + return [o.clipByValue(I("x", r15, t8, e), I("clipValueMin", r15, t8, e), I("clipValueMax", r15, t8, e))]; case "Relu6": - return [o.relu6(I("x", r, t8, e))]; + return [o.relu6(I("x", r15, t8, e))]; case "Rsqrt": - return [o.rsqrt(zt(r.inputNames[0], t8, e))]; + return [o.rsqrt(Bt(r15.inputNames[0], t8, e))]; case "LeakyRelu": - return [o.leakyRelu(I("x", r, t8, e), I("alpha", r, t8, e))]; + return [o.leakyRelu(I("x", r15, t8, e), I("alpha", r15, t8, e))]; case "Prelu": - return [o.prelu(I("x", r, t8, e), I("alpha", r, t8, e))]; + return [o.prelu(I("x", r15, t8, e), I("alpha", r15, t8, e))]; case "IsNan": - return [o.isNaN(zt(r.inputNames[0], t8, e))]; + return [o.isNaN(Bt(r15.inputNames[0], t8, e))]; case "IsInf": - return [o.isInf(zt(r.inputNames[0], t8, e))]; + return [o.isInf(Bt(r15.inputNames[0], t8, e))]; case "IsFinite": - return [o.isFinite(zt(r.inputNames[0], t8, e))]; + return [o.isFinite(Bt(r15.inputNames[0], t8, e))]; default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -function Hr(r, t8, e = "") { - if (!(typeof r == "number" || typeof t8 == "number")) { - y.assert(r.length === t8.length, () => e + ` Shapes ${r} and ${t8} must match`); - for (let o = 0; o < r.length; o++) { - let n = r[o], s = t8[o]; - y.assert(n < 0 || s < 0 || n === s, () => e + ` Shapes ${r} and ${t8} must match`); +function Gr(r15, t8, e = "") { + if (!(typeof r15 == "number" || typeof t8 == "number")) { + y.assert(r15.length === t8.length, () => e + ` Shapes ${r15} and ${t8} must match`); + for (let o = 0; o < r15.length; o++) { + let n = r15[o], s = t8[o]; + y.assert(n < 0 || s < 0 || n === s, () => e + ` Shapes ${r15} and ${t8} must match`); } } } -function ST(r) { - return !(typeof r == "number" || r.some((t8) => t8 < 0)); +function IT(r15) { + return !(typeof r15 == "number" || r15.some((t8) => t8 < 0)); } -function bc(r, t8, e) { - let o = Sf(r, e), n = !ST(o); +function mc(r15, t8, e) { + let o = wf(r15, e), n = !IT(o); if (n && t8.length === 0) throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${o}`); if (n && t8.forEach((s) => { - o = Sf(s.shape, o); - }), !ST(o)) + o = wf(s.shape, o); + }), !IT(o)) throw new Error(`Non-fully-defined elementShape: ${o}`); return o; } -function Sf(r, t8) { - if (typeof r == "number") +function wf(r15, t8) { + if (typeof r15 == "number") return t8; if (typeof t8 == "number") - return r; - if (r.length !== t8.length) - throw new Error(`Incompatible ranks during merge: ${r} vs. ${t8}`); + return r15; + if (r15.length !== t8.length) + throw new Error(`Incompatible ranks during merge: ${r15} vs. ${t8}`); let e = []; - for (let o = 0; o < r.length; ++o) { - let n = r[o], s = t8[o]; + for (let o = 0; o < r15.length; ++o) { + let n = r15[o], s = t8[o]; if (n >= 0 && s >= 0 && n !== s) - throw new Error(`Incompatible shape during merge: ${r} vs. ${t8}`); + throw new Error(`Incompatible shape during merge: ${r15} vs. ${t8}`); e[o] = n >= 0 ? n : s; } return e; } -var If = class { +var Sf = class { constructor(t8, e, o, n, s, a, i) { - this.name = t8, this.dtype = e, this.maxSize = o, this.elementShape = n, this.identicalElementShapes = s, this.dynamicSize = a, this.clearAfterRead = i, this.tensors = [], this.closed_ = false, this.idTensor = ke(0), Rr(this.idTensor); + this.name = t8, this.dtype = e, this.maxSize = o, this.elementShape = n, this.identicalElementShapes = s, this.dynamicSize = a, this.clearAfterRead = i, this.tensors = [], this.closed_ = false, this.idTensor = ke(0), Er(this.idTensor); } get id() { return this.idTensor.id; @@ -10279,11 +10280,11 @@ var If = class { if (e.dtype !== this.dtype) throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t8}, because the value dtype is ${e.dtype}, but TensorArray dtype is ${this.dtype}.`); - if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0) && (this.elementShape = e.shape), Hr(this.elementShape, e.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${t8}.`), o.read) + if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0) && (this.elementShape = e.shape), Gr(this.elementShape, e.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${t8}.`), o.read) throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t8}, because it has already been read.`); if (o.written) throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t8}, because it has already been written.`); - o.tensor = e, Rr(e), o.written = true, this.tensors[t8] = o; + o.tensor = e, Er(e), o.written = true, this.tensors[t8] = o; } writeMany(t8, e) { if (t8.length !== e.length) @@ -10301,20 +10302,20 @@ var If = class { t8.push(n); } if (t8.length === 0) - return ur([], [0].concat(this.elementShape)); + return ar([], [0].concat(this.elementShape)); let o = this.readMany(t8); - return Hr(this.elementShape, o[0].shape, "TensorArray shape mismatch: "), kr(o, 0); + return Gr(this.elementShape, o[0].shape, "TensorArray shape mismatch: "), vr(o, 0); } concat(t8) { if (t8 && t8 !== this.dtype) throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${t8}`); if (this.size() === 0) - return ur([], [0].concat(this.elementShape)); + return ar([], [0].concat(this.elementShape)); let e = []; for (let n = 0; n < this.size(); n++) e.push(n); let o = this.readMany(e); - return Hr(this.elementShape, o[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${o[0].shape})`), bt(o, 0); + return Gr(this.elementShape, o[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${o[0].shape})`), yt(o, 0); } scatter(t8, e) { if (e.dtype !== this.dtype) @@ -10324,7 +10325,7 @@ var If = class { let o = Math.max(...t8); if (!this.dynamicSize && o >= this.maxSize) throw new Error(`Max index must be < array size (${o} vs. ${this.maxSize})`); - this.writeMany(t8, fo(e, 0)); + this.writeMany(t8, mo(e, 0)); } split(t8, e) { if (e.dtype !== this.dtype) @@ -10351,7 +10352,7 @@ var If = class { this.writeMany(i, a); } }; -var hi = class { +var dc = class r9 { get id() { return this.idTensor.id; } @@ -10359,11 +10360,11 @@ var hi = class { this.tensors = t8, this.elementShape = e, this.elementDtype = o, t8 != null && t8.forEach((s) => { if (o !== s.dtype) throw new Error(`Invalid data types; op elements ${o}, but list elements ${s.dtype}`); - Hr(e, s.shape, "TensorList shape mismatch: "), Rr(s); - }), this.idTensor = ke(0), this.maxNumElements = n, Rr(this.idTensor); + Gr(e, s.shape, "TensorList shape mismatch: "), Er(s); + }), this.idTensor = ke(0), this.maxNumElements = n, Er(this.idTensor); } copy() { - return new hi([...this.tensors], this.elementShape, this.elementDtype); + return new r9([...this.tensors], this.elementShape, this.elementDtype); } clearAndClose(t8) { this.tensors.forEach((e) => { @@ -10378,11 +10379,11 @@ var hi = class { throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`); if (o !== -1 && this.tensors.length !== o) throw new Error(`Operation expected a list with ${o} elements but got a list with ${this.tensors.length} elements.`); - Hr(t8, this.elementShape, "TensorList shape mismatch: "); - let n = bc(this.elementShape, this.tensors, t8); + Gr(t8, this.elementShape, "TensorList shape mismatch: "); + let n = mc(this.elementShape, this.tensors, t8); return De(() => { let s = this.tensors.map((a) => W(a, n)); - return kr(s, 0); + return vr(s, 0); }); } popBack(t8, e) { @@ -10390,22 +10391,22 @@ var hi = class { throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`); if (this.size() === 0) throw new Error("Trying to pop from an empty list."); - let o = bc(this.elementShape, this.tensors, t8), n = this.tensors.pop(); - return n.kept = false, Hr(n.shape, t8, "TensorList shape mismatch: "), W(n, o); + let o = mc(this.elementShape, this.tensors, t8), n = this.tensors.pop(); + return n.kept = false, Gr(n.shape, t8, "TensorList shape mismatch: "), W(n, o); } pushBack(t8) { if (t8.dtype !== this.elementDtype) throw new Error(`Invalid data types; op elements ${t8.dtype}, but list elements ${this.elementDtype}`); - if (Hr(t8.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size()) + if (Gr(t8.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size()) throw new Error("Trying to push element into a full list."); - Rr(t8), this.tensors.push(t8); + Er(t8), this.tensors.push(t8); } resize(t8) { if (t8 < 0) throw new Error(`TensorListResize expects size to be non-negative. Got: ${t8}`); if (this.maxNumElements !== -1 && t8 > this.maxNumElements) throw new Error(`TensorListResize input size ${t8} is greater maxNumElement ${this.maxNumElements}.`); - let e = new hi([], this.elementShape, this.elementDtype, this.maxNumElements); + let e = new r9([], this.elementShape, this.elementDtype, this.maxNumElements); e.tensors.length = t8; for (let o = 0; o < Math.min(this.tensors.length, t8); ++o) e.tensors[o] = this.tensors[o]; @@ -10418,8 +10419,8 @@ var hi = class { throw new Error(`Trying to access element ${t8} in a list with ${this.tensors.length} elements.`); if (this.tensors[t8] == null) throw new Error(`element at index ${t8} is null.`); - Hr(this.tensors[t8].shape, e, "TensorList shape mismatch: "); - let n = bc(this.elementShape, this.tensors, e); + Gr(this.tensors[t8].shape, e, "TensorList shape mismatch: "); + let n = mc(this.elementShape, this.tensors, e); return W(this.tensors[t8], n); } setItem(t8, e) { @@ -10427,83 +10428,83 @@ var hi = class { throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`); if (t8 < 0 || this.maxNumElements !== -1 && t8 >= this.maxNumElements) throw new Error(`Trying to set element ${t8} in a list with max ${this.maxNumElements} elements.`); - Hr(this.elementShape, e.shape, "TensorList shape mismatch: "), Rr(e), this.tensors[t8] != null && (this.tensors[t8].kept = false), this.tensors[t8] = e; + Gr(this.elementShape, e.shape, "TensorList shape mismatch: "), Er(e), this.tensors[t8] != null && (this.tensors[t8].kept = false), this.tensors[t8] = e; } gather(t8, e, o) { if (e !== this.elementDtype) throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`); - Hr(this.elementShape, o, "TensorList shape mismatch: "), t8 = t8.slice(0, this.size()); - let n = bc(this.elementShape, this.tensors, o); - return t8.length === 0 ? ur([], [0].concat(n)) : De(() => { + Gr(this.elementShape, o, "TensorList shape mismatch: "), t8 = t8.slice(0, this.size()); + let n = mc(this.elementShape, this.tensors, o); + return t8.length === 0 ? ar([], [0].concat(n)) : De(() => { let s = t8.map((a) => W(this.tensors[a], n)); - return kr(s, 0); + return vr(s, 0); }); } concat(t8, e) { if (t8 && t8 !== this.elementDtype) throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${t8}`); - Hr(this.elementShape, e, "TensorList shape mismatch: "); - let o = bc(this.elementShape, this.tensors, e); - return this.size() === 0 ? ur([], [0].concat(o)) : De(() => { + Gr(this.elementShape, e, "TensorList shape mismatch: "); + let o = mc(this.elementShape, this.tensors, e); + return this.size() === 0 ? ar([], [0].concat(o)) : De(() => { let n = this.tensors.map((s) => W(s, o)); - return bt(n, 0); + return yt(n, 0); }); } }; -function IT(r, t8, e) { - let o = r.dtype; - if (r.shape.length < 1) - throw new Error(`Tensor must be at least a vector, but saw shape: ${r.shape}`); - if (r.dtype !== e) - throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${e}`); - let n = r.shape.slice(1); - Hr(n, t8, "TensorList shape mismatch: "); - let s = fo(r); - return new hi(s, t8, o); +function vT(r15, t8, e) { + let o = r15.dtype; + if (r15.shape.length < 1) + throw new Error(`Tensor must be at least a vector, but saw shape: ${r15.shape}`); + if (r15.dtype !== e) + throw new Error(`Invalid data types; op elements ${r15.dtype}, but list elements ${e}`); + let n = r15.shape.slice(1); + Gr(n, t8, "TensorList shape mismatch: "); + let s = mo(r15); + return new dc(s, t8, o); } -function vT(r, t8, e, o) { - return new hi([], r, t8, o); +function kT(r15, t8, e, o) { + return new dc([], r15, t8, o); } -function kT(r, t8, e, o) { - if (t8.length !== r.shape[0]) - throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t8.length} vs. ${r.shape[0]}`); +function NT(r15, t8, e, o) { + if (t8.length !== r15.shape[0]) + throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t8.length} vs. ${r15.shape[0]}`); let n = Math.max(...t8); if (o != null && o !== -1 && n >= o) throw new Error(`Max index must be < array size (${n} vs. ${o})`); - let s = new hi([], e, r.dtype, o), a = fo(r, 0); + let s = new dc([], e, r15.dtype, o), a = mo(r15, 0); return t8.forEach((i, p) => { s.setItem(i, a[p]); }), s; } -function NT(r, t8, e) { +function TT(r15, t8, e) { let o = 0, n = t8.map((c) => (o += c, o)); - if (o !== r.shape[0]) + if (o !== r15.shape[0]) throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is - ${o}, and tensor's shape is: ${r.shape}`); - let s = r.shape.slice(1), a = Sf(s, e), i = o === 0 ? 0 : r.size / o, p = De(() => { + ${o}, and tensor's shape is: ${r15.shape}`); + let s = r15.shape.slice(1), a = wf(s, e), i = o === 0 ? 0 : r15.size / o, p = De(() => { let c = []; - r = W(r, [1, o, i]); + r15 = W(r15, [1, o, i]); for (let l = 0; l < t8.length; ++l) { let d = [0, l === 0 ? 0 : n[l - 1], 0], f = [1, t8[l], i]; - c[l] = W(Xe(r, d, f), a); + c[l] = W(Xe(r15, d, f), a); } - return r.dispose(), c; - }), u = new hi([], e, r.dtype, t8.length); + return r15.dispose(), c; + }), u = new dc([], e, r15.dtype, t8.length); for (let c = 0; c < p.length; c++) u.setItem(c, p[c]); return u; } -var TT = async (r, t8, e) => { - switch (r.op) { +var _T = async (r15, t8, e) => { + switch (r15.op) { case "If": case "StatelessIf": { - let o = I("thenBranch", r, t8, e), n = I("elseBranch", r, t8, e), s = I("cond", r, t8, e), a = I("args", r, t8, e); + let o = I("thenBranch", r15, t8, e), n = I("elseBranch", r15, t8, e), s = I("cond", r15, t8, e), a = I("args", r15, t8, e); return (await s.data())[0] ? e.functionMap[o].executeFunctionAsync(a, e.tensorArrayMap, e.tensorListMap) : e.functionMap[n].executeFunctionAsync(a, e.tensorArrayMap, e.tensorListMap); } case "While": case "StatelessWhile": { - let o = I("body", r, t8, e), n = I("cond", r, t8, e), s = I("args", r, t8, e), a = await e.functionMap[n].executeFunctionAsync(s, e.tensorArrayMap, e.tensorListMap), i = s.map((c) => c.id), p = await a[0].data(); + let o = I("body", r15, t8, e), n = I("cond", r15, t8, e), s = I("args", r15, t8, e), a = await e.functionMap[n].executeFunctionAsync(s, e.tensorArrayMap, e.tensorListMap), i = s.map((c) => c.id), p = await a[0].data(); a.forEach((c) => { !c.kept && i.indexOf(c.id) === -1 && c.dispose(); }); @@ -10523,132 +10524,132 @@ var TT = async (r, t8, e) => { return u; } case "LoopCond": { - let o = I("pred", r, t8, e); + let o = I("pred", r15, t8, e); return [Bs(o)]; } case "Switch": { - let o = I("pred", r, t8, e), n = I("data", r, t8, e); + let o = I("pred", r15, t8, e), n = I("data", r15, t8, e); return n.kept || (n = Bs(n)), (await o.data())[0] ? [void 0, n] : [n, void 0]; } case "Merge": { - let o = r.inputNames.find((n) => zt(n, t8, e) !== void 0); + let o = r15.inputNames.find((n) => Bt(n, t8, e) !== void 0); if (o) { - let n = zt(o, t8, e); + let n = Bt(o, t8, e); return [Bs(n)]; } return; } case "Enter": { - let o = I("frameName", r, t8, e), n = I("tensor", r, t8, e); + let o = I("frameName", r15, t8, e), n = I("tensor", r15, t8, e); return e.enterFrame(o), [Bs(n)]; } case "Exit": { - let o = I("tensor", r, t8, e); + let o = I("tensor", r15, t8, e); return e.exitFrame(), [Bs(o)]; } case "NextIteration": { - let o = I("tensor", r, t8, e); + let o = I("tensor", r15, t8, e); return e.nextIteration(), [Bs(o)]; } case "TensorArrayV3": { - let o = I("size", r, t8, e), n = I("dtype", r, t8, e), s = I("elementShape", r, t8, e), a = I("dynamicSize", r, t8, e), i = I("clearAfterRead", r, t8, e), p = I("identicalElementShapes", r, t8, e), u = I("name", r, t8, e), c = new If(u, n, o, s, p, a, i); + let o = I("size", r15, t8, e), n = I("dtype", r15, t8, e), s = I("elementShape", r15, t8, e), a = I("dynamicSize", r15, t8, e), i = I("clearAfterRead", r15, t8, e), p = I("identicalElementShapes", r15, t8, e), u = I("name", r15, t8, e), c = new Sf(u, n, o, s, p, a, i); return e.addTensorArray(c), [c.idTensor, ke(1)]; } case "TensorArrayWriteV3": { - let o = I("tensorArrayId", r, t8, e), n = I("index", r, t8, e), s = I("tensor", r, t8, e), a = e.getTensorArray(o.id); + let o = I("tensorArrayId", r15, t8, e), n = I("index", r15, t8, e), s = I("tensor", r15, t8, e), a = e.getTensorArray(o.id); return a.write(n, s), [a.idTensor]; } case "TensorArrayReadV3": { - let o = I("tensorArrayId", r, t8, e), n = I("index", r, t8, e); + let o = I("tensorArrayId", r15, t8, e), n = I("index", r15, t8, e); return [e.getTensorArray(o.id).read(n)]; } case "TensorArrayGatherV3": { - let o = I("tensorArrayId", r, t8, e), n = I("indices", r, t8, e), s = I("dtype", r, t8, e); + let o = I("tensorArrayId", r15, t8, e), n = I("indices", r15, t8, e), s = I("dtype", r15, t8, e); return [e.getTensorArray(o.id).gather(n, s)]; } case "TensorArrayScatterV3": { - let o = I("tensorArrayId", r, t8, e), n = I("indices", r, t8, e), s = I("tensor", r, t8, e), a = e.getTensorArray(o.id); + let o = I("tensorArrayId", r15, t8, e), n = I("indices", r15, t8, e), s = I("tensor", r15, t8, e), a = e.getTensorArray(o.id); return a.scatter(n, s), [a.idTensor]; } case "TensorArrayConcatV3": { - let o = I("tensorArrayId", r, t8, e), n = e.getTensorArray(o.id), s = I("dtype", r, t8, e); + let o = I("tensorArrayId", r15, t8, e), n = e.getTensorArray(o.id), s = I("dtype", r15, t8, e); return [n.concat(s)]; } case "TensorArraySplitV3": { - let o = I("tensorArrayId", r, t8, e), n = I("tensor", r, t8, e), s = I("lengths", r, t8, e), a = e.getTensorArray(o.id); + let o = I("tensorArrayId", r15, t8, e), n = I("tensor", r15, t8, e), s = I("lengths", r15, t8, e), a = e.getTensorArray(o.id); return a.split(s, n), [a.idTensor]; } case "TensorArraySizeV3": { - let o = I("tensorArrayId", r, t8, e), n = e.getTensorArray(o.id); + let o = I("tensorArrayId", r15, t8, e), n = e.getTensorArray(o.id); return [ke(n.size(), "int32")]; } case "TensorArrayCloseV3": { - let o = I("tensorArrayId", r, t8, e), n = e.getTensorArray(o.id); + let o = I("tensorArrayId", r15, t8, e), n = e.getTensorArray(o.id); return n.clearAndClose(), [n.idTensor]; } case "TensorListSetItem": { - let o = I("tensorListId", r, t8, e), n = I("index", r, t8, e), s = I("tensor", r, t8, e), a = e.getTensorList(o.id); + let o = I("tensorListId", r15, t8, e), n = I("index", r15, t8, e), s = I("tensor", r15, t8, e), a = e.getTensorList(o.id); return a.setItem(n, s), [a.idTensor]; } case "TensorListGetItem": { - let o = I("tensorListId", r, t8, e), n = I("index", r, t8, e), s = I("elementShape", r, t8, e), a = I("elementDType", r, t8, e); + let o = I("tensorListId", r15, t8, e), n = I("index", r15, t8, e), s = I("elementShape", r15, t8, e), a = I("elementDType", r15, t8, e); return [e.getTensorList(o.id).getItem(n, s, a)]; } case "TensorListScatterV2": case "TensorListScatter": { - let o = I("indices", r, t8, e), n = I("tensor", r, t8, e), s = I("elementShape", r, t8, e), a = I("numElements", r, t8, e), i = kT(n, o, s, a); + let o = I("indices", r15, t8, e), n = I("tensor", r15, t8, e), s = I("elementShape", r15, t8, e), a = I("numElements", r15, t8, e), i = NT(n, o, s, a); return e.addTensorList(i), [i.idTensor]; } case "TensorListReserve": case "EmptyTensorList": { - let o = I("elementShape", r, t8, e), n = I("elementDType", r, t8, e), s; - r.op === "TensorListReserve" ? s = "numElements" : s = "maxNumElements"; - let a = I(s, r, t8, e), i = r.op === "TensorListReserve" ? -1 : a, p = vT(o, n, a, i); + let o = I("elementShape", r15, t8, e), n = I("elementDType", r15, t8, e), s; + r15.op === "TensorListReserve" ? s = "numElements" : s = "maxNumElements"; + let a = I(s, r15, t8, e), i = r15.op === "TensorListReserve" ? -1 : a, p = kT(o, n, a, i); return e.addTensorList(p), [p.idTensor]; } case "TensorListGather": { - let o = I("tensorListId", r, t8, e), n = I("indices", r, t8, e), s = I("elementShape", r, t8, e), a = I("elementDType", r, t8, e); + let o = I("tensorListId", r15, t8, e), n = I("indices", r15, t8, e), s = I("elementShape", r15, t8, e), a = I("elementDType", r15, t8, e); return [e.getTensorList(o.id).gather(n, a, s)]; } case "TensorListStack": { - let o = I("tensorListId", r, t8, e), n = I("elementShape", r, t8, e), s = I("elementDType", r, t8, e), a = I("numElements", r, t8, e); + let o = I("tensorListId", r15, t8, e), n = I("elementShape", r15, t8, e), s = I("elementDType", r15, t8, e), a = I("numElements", r15, t8, e); return [e.getTensorList(o.id).stack(n, s, a)]; } case "TensorListFromTensor": { - let o = I("tensor", r, t8, e), n = I("elementShape", r, t8, e), s = I("elementDType", r, t8, e), a = IT(o, n, s); + let o = I("tensor", r15, t8, e), n = I("elementShape", r15, t8, e), s = I("elementDType", r15, t8, e), a = vT(o, n, s); return e.addTensorList(a), [a.idTensor]; } case "TensorListConcat": case "TensorListConcatV2": { - let o = I("tensorListId", r, t8, e), n = e.getTensorList(o.id), s = I("dtype", r, t8, e), a = I("elementShape", r, t8, e); + let o = I("tensorListId", r15, t8, e), n = e.getTensorList(o.id), s = I("dtype", r15, t8, e), a = I("elementShape", r15, t8, e); return [n.concat(s, a)]; } case "TensorListPushBack": { - let o = I("tensorListId", r, t8, e), n = I("tensor", r, t8, e), s = e.getTensorList(o.id); + let o = I("tensorListId", r15, t8, e), n = I("tensor", r15, t8, e), s = e.getTensorList(o.id); return s.pushBack(n), [s.idTensor]; } case "TensorListPopBack": { - let o = I("tensorListId", r, t8, e), n = I("elementShape", r, t8, e), s = I("elementDType", r, t8, e); + let o = I("tensorListId", r15, t8, e), n = I("elementShape", r15, t8, e), s = I("elementDType", r15, t8, e); return [e.getTensorList(o.id).popBack(n, s)]; } case "TensorListSplit": { - let o = I("tensor", r, t8, e), n = I("elementShape", r, t8, e), s = I("lengths", r, t8, e), a = NT(o, s, n); + let o = I("tensor", r15, t8, e), n = I("elementShape", r15, t8, e), s = I("lengths", r15, t8, e), a = TT(o, s, n); return e.addTensorList(a), [a.idTensor]; } case "TensorListLength": { - let o = I("tensorListId", r, t8, e), n = e.getTensorList(o.id); + let o = I("tensorListId", r15, t8, e), n = e.getTensorList(o.id); return [ke(n.size(), "int32")]; } case "TensorListResize": { - let o = I("tensorListId", r, t8, e), n = I("size", r, t8, e), a = e.getTensorList(o.id).resize(n); + let o = I("tensorListId", r15, t8, e), n = I("size", r15, t8, e), a = e.getTensorList(o.id).resize(n); return e.addTensorList(a), [a.idTensor]; } default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -function _T(r, t8, e) { - let [o, n] = I("fusedOps", r, t8, e), s = o === "biasadd", a = !s, i = n === "prelu", p = o === "fusedbatchnorm", u = I("numArgs", r, t8, e); +function $T(r15, t8, e) { + let [o, n] = I("fusedOps", r15, t8, e), s = o === "biasadd", a = !s, i = n === "prelu", p = o === "fusedbatchnorm", u = I("numArgs", r15, t8, e); if (s) { if (i && u !== 2) throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha."); @@ -10657,216 +10658,216 @@ function _T(r, t8, e) { } if (p) throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported"); - let c = I("strides", r, t8, e), l = Ol(r, t8, e), m = I("dataFormat", r, t8, e).toUpperCase(), d = I("dilations", r, t8, e), [f, h] = I("args", r, t8, e); + let c = I("strides", r15, t8, e), l = Al(r15, t8, e), m = I("dataFormat", r15, t8, e).toUpperCase(), d = I("dilations", r15, t8, e), [f, h] = I("args", r15, t8, e); a && (h = f, f = void 0); - let g = I("leakyreluAlpha", r, t8, e); + let g = I("leakyreluAlpha", r15, t8, e); return { stride: c, pad: l, dataFormat: m, dilations: d, biasArg: f, preluArg: h, activationFunc: n, leakyreluAlpha: g }; } -var $T = (r, t8, e, o = Je) => { - switch (r.op) { +var ET = (r15, t8, e, o = Je) => { + switch (r15.op) { case "Conv1D": { - let n = I("stride", r, t8, e), s = I("pad", r, t8, e), a = I("dataFormat", r, t8, e).toUpperCase(), i = I("dilation", r, t8, e); - return [o.conv1d(I("x", r, t8, e), I("filter", r, t8, e), n, s, a, i)]; + let n = I("stride", r15, t8, e), s = I("pad", r15, t8, e), a = I("dataFormat", r15, t8, e).toUpperCase(), i = I("dilation", r15, t8, e); + return [o.conv1d(I("x", r15, t8, e), I("filter", r15, t8, e), n, s, a, i)]; } case "Conv2D": { - let n = I("strides", r, t8, e), s = Ol(r, t8, e), a = I("dataFormat", r, t8, e).toUpperCase(), i = I("dilations", r, t8, e); - return [o.conv2d(I("x", r, t8, e), I("filter", r, t8, e), [n[1], n[2]], s, a, [i[1], i[2]])]; + let n = I("strides", r15, t8, e), s = Al(r15, t8, e), a = I("dataFormat", r15, t8, e).toUpperCase(), i = I("dilations", r15, t8, e); + return [o.conv2d(I("x", r15, t8, e), I("filter", r15, t8, e), [n[1], n[2]], s, a, [i[1], i[2]])]; } case "_FusedConv2D": { - let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = _T(r, t8, e); - return [o.fused.conv2d({ x: I("x", r, t8, e), filter: I("filter", r, t8, e), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })]; + let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = $T(r15, t8, e); + return [o.fused.conv2d({ x: I("x", r15, t8, e), filter: I("filter", r15, t8, e), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })]; } case "FusedDepthwiseConv2dNative": { - let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = _T(r, t8, e); - return [o.fused.depthwiseConv2d({ x: I("x", r, t8, e), filter: I("filter", r, t8, e), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })]; + let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = $T(r15, t8, e); + return [o.fused.depthwiseConv2d({ x: I("x", r15, t8, e), filter: I("filter", r15, t8, e), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })]; } case "Conv2DBackpropInput": case "Conv2dTranspose": { - let n = I("outputShape", r, t8, e), s = I("strides", r, t8, e), a = Ol(r, t8, e); - return [o.conv2dTranspose(I("x", r, t8, e), I("filter", r, t8, e), n, [s[1], s[2]], a)]; + let n = I("outputShape", r15, t8, e), s = I("strides", r15, t8, e), a = Al(r15, t8, e); + return [o.conv2dTranspose(I("x", r15, t8, e), I("filter", r15, t8, e), n, [s[1], s[2]], a)]; } case "DepthwiseConv2dNative": case "DepthwiseConv2d": { - let n = I("strides", r, t8, e), s = Ol(r, t8, e), a = I("dilations", r, t8, e), i = I("dataFormat", r, t8, e).toUpperCase(); - return [o.depthwiseConv2d(I("input", r, t8, e), I("filter", r, t8, e), [n[1], n[2]], s, i, [a[1], a[2]])]; + let n = I("strides", r15, t8, e), s = Al(r15, t8, e), a = I("dilations", r15, t8, e), i = I("dataFormat", r15, t8, e).toUpperCase(); + return [o.depthwiseConv2d(I("input", r15, t8, e), I("filter", r15, t8, e), [n[1], n[2]], s, i, [a[1], a[2]])]; } case "Conv3D": { - let n = I("strides", r, t8, e), s = I("pad", r, t8, e), a = I("dataFormat", r, t8, e).toUpperCase(), i = I("dilations", r, t8, e); - return [o.conv3d(I("x", r, t8, e), I("filter", r, t8, e), [n[1], n[2], n[3]], s, a, [i[1], i[2], i[3]])]; + let n = I("strides", r15, t8, e), s = I("pad", r15, t8, e), a = I("dataFormat", r15, t8, e).toUpperCase(), i = I("dilations", r15, t8, e); + return [o.conv3d(I("x", r15, t8, e), I("filter", r15, t8, e), [n[1], n[2], n[3]], s, a, [i[1], i[2], i[3]])]; } case "AvgPool": { - let n = I("strides", r, t8, e), s = I("pad", r, t8, e), a = I("kernelSize", r, t8, e); - return [o.avgPool(I("x", r, t8, e), [a[1], a[2]], [n[1], n[2]], s)]; + let n = I("strides", r15, t8, e), s = I("pad", r15, t8, e), a = I("kernelSize", r15, t8, e); + return [o.avgPool(I("x", r15, t8, e), [a[1], a[2]], [n[1], n[2]], s)]; } case "MaxPool": { - let n = I("strides", r, t8, e), s = I("pad", r, t8, e), a = I("kernelSize", r, t8, e); - return [o.maxPool(I("x", r, t8, e), [a[1], a[2]], [n[1], n[2]], s)]; + let n = I("strides", r15, t8, e), s = I("pad", r15, t8, e), a = I("kernelSize", r15, t8, e); + return [o.maxPool(I("x", r15, t8, e), [a[1], a[2]], [n[1], n[2]], s)]; } case "MaxPoolWithArgmax": { - let n = I("strides", r, t8, e), s = I("pad", r, t8, e), a = I("kernelSize", r, t8, e), i = I("includeBatchInIndex", r, t8, e), { result: p, indexes: u } = o.maxPoolWithArgmax(I("x", r, t8, e), [a[1], a[2]], [n[1], n[2]], s, i); + let n = I("strides", r15, t8, e), s = I("pad", r15, t8, e), a = I("kernelSize", r15, t8, e), i = I("includeBatchInIndex", r15, t8, e), { result: p, indexes: u } = o.maxPoolWithArgmax(I("x", r15, t8, e), [a[1], a[2]], [n[1], n[2]], s, i); return [p, u]; } case "AvgPool3D": { - let n = I("strides", r, t8, e), s = I("pad", r, t8, e), a = I("kernelSize", r, t8, e); - return [o.avgPool3d(I("x", r, t8, e), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)]; + let n = I("strides", r15, t8, e), s = I("pad", r15, t8, e), a = I("kernelSize", r15, t8, e); + return [o.avgPool3d(I("x", r15, t8, e), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)]; } case "MaxPool3D": { - let n = I("strides", r, t8, e), s = I("pad", r, t8, e), a = I("kernelSize", r, t8, e); - return [o.maxPool3d(I("x", r, t8, e), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)]; + let n = I("strides", r15, t8, e), s = I("pad", r15, t8, e), a = I("kernelSize", r15, t8, e); + return [o.maxPool3d(I("x", r15, t8, e), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)]; } case "Dilation2D": { - let n = I("strides", r, t8, e), s = I("pad", r, t8, e), a = I("dilations", r, t8, e), i = n[1], p = n[2], u = a[1], c = a[2]; - return [o.dilation2d(I("x", r, t8, e), I("filter", r, t8, e), [i, p], s, [u, c], "NHWC")]; + let n = I("strides", r15, t8, e), s = I("pad", r15, t8, e), a = I("dilations", r15, t8, e), i = n[1], p = n[2], u = a[1], c = a[2]; + return [o.dilation2d(I("x", r15, t8, e), I("filter", r15, t8, e), [i, p], s, [u, c], "NHWC")]; } default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var ET = (r, t8, e, o = Je) => { - switch (r.op) { +var RT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "Fill": { - let n = I("shape", r, t8, e), s = I("dtype", r, t8, e), a = I("value", r, t8, e); + let n = I("shape", r15, t8, e), s = I("dtype", r15, t8, e), a = I("value", r15, t8, e); return [o.fill(n, a, s)]; } case "LinSpace": { - let n = I("start", r, t8, e), s = I("stop", r, t8, e), a = I("num", r, t8, e); + let n = I("start", r15, t8, e), s = I("stop", r15, t8, e), a = I("num", r15, t8, e); return [o.linspace(n, s, a)]; } case "Multinomial": { - let n = I("logits", r, t8, e), s = I("numSamples", r, t8, e), a = I("seed", r, t8, e); + let n = I("logits", r15, t8, e), s = I("numSamples", r15, t8, e), a = I("seed", r15, t8, e); return [o.multinomial(n, s, a)]; } case "OneHot": { - let n = I("indices", r, t8, e), s = I("depth", r, t8, e), a = I("onValue", r, t8, e), i = I("offValue", r, t8, e), p = I("dtype", r, t8, e); + let n = I("indices", r15, t8, e), s = I("depth", r15, t8, e), a = I("onValue", r15, t8, e), i = I("offValue", r15, t8, e), p = I("dtype", r15, t8, e); return [o.oneHot(n, s, a, i, p)]; } case "Ones": - return [o.ones(I("shape", r, t8, e), I("dtype", r, t8, e))]; + return [o.ones(I("shape", r15, t8, e), I("dtype", r15, t8, e))]; case "OnesLike": - return [o.onesLike(I("x", r, t8, e))]; + return [o.onesLike(I("x", r15, t8, e))]; case "RandomStandardNormal": - return [o.randomStandardNormal(I("shape", r, t8, e), I("dtype", r, t8, e), I("seed", r, t8, e))]; + return [o.randomStandardNormal(I("shape", r15, t8, e), I("dtype", r15, t8, e), I("seed", r15, t8, e))]; case "RandomUniform": - return [o.randomUniform(I("shape", r, t8, e), I("minval", r, t8, e), I("maxval", r, t8, e), I("dtype", r, t8, e))]; + return [o.randomUniform(I("shape", r15, t8, e), I("minval", r15, t8, e), I("maxval", r15, t8, e), I("dtype", r15, t8, e))]; case "RandomUniformInt": - return [o.randomUniformInt(I("shape", r, t8, e), I("minval", r, t8, e), I("maxval", r, t8, e), I("seed", r, t8, e))]; + return [o.randomUniformInt(I("shape", r15, t8, e), I("minval", r15, t8, e), I("maxval", r15, t8, e), I("seed", r15, t8, e))]; case "Range": { - let n = I("start", r, t8, e), s = I("stop", r, t8, e), a = I("step", r, t8, e); - return [o.range(n, s, a, I("dtype", r, t8, e))]; + let n = I("start", r15, t8, e), s = I("stop", r15, t8, e), a = I("step", r15, t8, e); + return [o.range(n, s, a, I("dtype", r15, t8, e))]; } case "TruncatedNormal": { - let n = I("shape", r, t8, e), s = I("mean", r, t8, e), a = I("stdDev", r, t8, e), i = I("seed", r, t8, e); - return [o.truncatedNormal(n, s, a, I("dtype", r, t8, e), i)]; + let n = I("shape", r15, t8, e), s = I("mean", r15, t8, e), a = I("stdDev", r15, t8, e), i = I("seed", r15, t8, e); + return [o.truncatedNormal(n, s, a, I("dtype", r15, t8, e), i)]; } case "Zeros": - return [o.zeros(I("shape", r, t8, e), I("dtype", r, t8, e))]; + return [o.zeros(I("shape", r15, t8, e), I("dtype", r15, t8, e))]; case "ZerosLike": - return [o.zerosLike(I("x", r, t8, e))]; + return [o.zerosLike(I("x", r15, t8, e))]; default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -function PS(r, t8, e) { - let o = I("boxes", r, t8, e), n = I("scores", r, t8, e), s = I("maxOutputSize", r, t8, e), a = I("iouThreshold", r, t8, e), i = I("scoreThreshold", r, t8, e), p = I("softNmsSigma", r, t8, e); +function PS(r15, t8, e) { + let o = I("boxes", r15, t8, e), n = I("scores", r15, t8, e), s = I("maxOutputSize", r15, t8, e), a = I("iouThreshold", r15, t8, e), i = I("scoreThreshold", r15, t8, e), p = I("softNmsSigma", r15, t8, e); return { boxes: o, scores: n, maxOutputSize: s, iouThreshold: a, scoreThreshold: i, softNmsSigma: p }; } -var RT = async (r, t8, e, o, n = Je) => { - switch (r.op) { +var DT = async (r15, t8, e, o, n = Je) => { + switch (r15.op) { case "NonMaxSuppressionV5": { - let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u, softNmsSigma: c } = PS(r, t8, e), l = await n.image.nonMaxSuppressionWithScoreAsync(s, a, i, p, u, c); + let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u, softNmsSigma: c } = PS(r15, t8, e), l = await n.image.nonMaxSuppressionWithScoreAsync(s, a, i, p, u, c); return [l.selectedIndices, l.selectedScores]; } case "NonMaxSuppressionV4": { - let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = PS(r, t8, e), c = I("padToMaxOutputSize", r, t8, e), l = await n.image.nonMaxSuppressionPaddedAsync(s, a, i, p, u, c); + let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = PS(r15, t8, e), c = I("padToMaxOutputSize", r15, t8, e), l = await n.image.nonMaxSuppressionPaddedAsync(s, a, i, p, u, c); return [l.selectedIndices, l.validOutputs]; } case "NonMaxSuppressionV3": case "NonMaxSuppressionV2": { - let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = PS(r, t8, e); + let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = PS(r15, t8, e); return [await n.image.nonMaxSuppressionAsync(s, a, i, p, u)]; } case "Where": { - let s = n.cast(I("condition", r, t8, e), "bool"), a = [await n.whereAsync(s)]; + let s = n.cast(I("condition", r15, t8, e), "bool"), a = [await n.whereAsync(s)]; return s.dispose(), a; } case "ListDiff": - return n.setdiff1dAsync(I("x", r, t8, e), I("y", r, t8, e)); + return n.setdiff1dAsync(I("x", r15, t8, e), I("y", r15, t8, e)); default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var DT = (r, t8, e, o = Je) => { - switch (r.op) { +var AT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "LowerBound": { - let n = I("sortedSequence", r, t8, e), s = I("values", r, t8, e); + let n = I("sortedSequence", r15, t8, e), s = I("values", r15, t8, e); return [o.lowerBound(n, s)]; } case "TopKV2": { - let n = I("x", r, t8, e), s = I("k", r, t8, e), a = I("sorted", r, t8, e), i = o.topk(n, s, a); + let n = I("x", r15, t8, e), s = I("k", r15, t8, e), a = I("sorted", r15, t8, e), i = o.topk(n, s, a); return [i.values, i.indices]; } case "UpperBound": { - let n = I("sortedSequence", r, t8, e), s = I("values", r, t8, e); + let n = I("sortedSequence", r15, t8, e), s = I("values", r15, t8, e); return [o.upperBound(n, s)]; } case "Unique": { - let n = I("x", r, t8, e), s = o.unique(n); + let n = I("x", r15, t8, e), s = o.unique(n); return [s.values, s.indices]; } case "UniqueV2": { - let n = I("x", r, t8, e), s = I("axis", r, t8, e), a = o.unique(n, s); + let n = I("x", r15, t8, e), s = I("axis", r15, t8, e), a = o.unique(n, s); return [a.values, a.indices]; } default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var AT = (r, t8, e, o = Je) => { - switch (r.op) { +var FT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "Const": - return t8[r.name]; + return t8[r15.name]; case "PlaceholderWithDefault": - let n = I("default", r, t8, e); - return [zt(r.name, t8, e) || n]; + let n = I("default", r15, t8, e); + return [Bt(r15.name, t8, e) || n]; case "Placeholder": - return [zt(r.name, t8, e)]; + return [Bt(r15.name, t8, e)]; case "Identity": case "StopGradient": case "FakeQuantWithMinMaxVars": { - let c = I("x", r, t8, e); + let c = I("x", r15, t8, e); return [Bs(c)]; } case "IdentityN": - return I("x", r, t8, e).map((c) => Bs(c)); + return I("x", r15, t8, e).map((c) => Bs(c)); case "Snapshot": - let s = I("x", r, t8, e); + let s = I("x", r15, t8, e); return [Bs(s)]; case "Shape": - return [o.tensor1d(I("x", r, t8, e).shape, "int32")]; + return [o.tensor1d(I("x", r15, t8, e).shape, "int32")]; case "ShapeN": - return I("x", r, t8, e).map((c) => o.tensor1d(c.shape)); + return I("x", r15, t8, e).map((c) => o.tensor1d(c.shape)); case "Size": - return [o.scalar(I("x", r, t8, e).size, "int32")]; + return [o.scalar(I("x", r15, t8, e).size, "int32")]; case "Rank": - return [o.scalar(I("x", r, t8, e).rank, "int32")]; + return [o.scalar(I("x", r15, t8, e).rank, "int32")]; case "NoOp": return [o.scalar(1)]; case "Print": - let a = I("x", r, t8, e), i = I("data", r, t8, e), p = I("message", r, t8, e), u = I("summarize", r, t8, e); + let a = I("x", r15, t8, e), i = I("data", r15, t8, e), p = I("message", r15, t8, e), u = I("summarize", r15, t8, e); console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."), console.log(p); for (let c = 0; c < i.length; c++) console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0, u)); return [a]; default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var vf = class { +var If = class { get id() { return this.handle.id; } constructor(t8, e) { - this.keyDType = t8, this.valueDType = e, this.handle = ke(0), this.tensorMap = /* @__PURE__ */ new Map(), Rr(this.handle); + this.keyDType = t8, this.valueDType = e, this.handle = ke(0), this.tensorMap = /* @__PURE__ */ new Map(), Er(this.handle); } clearAndClose() { this.tensorMap.forEach((t8) => t8.dispose()), this.tensorMap.clear(), this.handle.dispose(); @@ -10881,11 +10882,11 @@ var vf = class { this.checkKeyAndValueTensor(t8, e); let o = await t8.data(); return this.tensorMap.forEach((n) => n.dispose()), this.tensorMap.clear(), De(() => { - let n = fo(e), s = o.length, a = n.length; + let n = mo(e), s = o.length, a = n.length; y.assert(s === a, () => `The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`); for (let i = 0; i < s; i++) { let p = o[i], u = n[i]; - Rr(u), this.tensorMap.set(p, u); + Er(u), this.tensorMap.set(p, u); } return this.handle; }); @@ -10899,7 +10900,7 @@ var vf = class { let a = o[s], i = this.findWithDefault(a, e); n.push(i); } - return kr(n); + return vr(n); }); } findWithDefault(t8, e) { @@ -10913,245 +10914,245 @@ var vf = class { throw new Error(`Expect value dtype ${this.valueDType}, but got ${e.dtype}`); } }; -var FT = async (r, t8, e, o) => { - switch (r.op) { +var PT = async (r15, t8, e, o) => { + switch (r15.op) { case "HashTable": case "HashTableV2": { - let n = o.getHashTableHandleByName(r.name); + let n = o.getHashTableHandleByName(r15.name); if (n != null) return [n]; { - let s = I("keyDType", r, t8, e), a = I("valueDType", r, t8, e), i = new vf(s, a); - return o.addHashTable(r.name, i), [i.handle]; + let s = I("keyDType", r15, t8, e), a = I("valueDType", r15, t8, e), i = new If(s, a); + return o.addHashTable(r15.name, i), [i.handle]; } } case "InitializeTable": case "InitializeTableV2": case "LookupTableImport": case "LookupTableImportV2": { - let n = I("tableHandle", r, t8, e, o), s = I("keys", r, t8, e), a = I("values", r, t8, e); + let n = I("tableHandle", r15, t8, e, o), s = I("keys", r15, t8, e), a = I("values", r15, t8, e); return [await o.getHashTableById(n.id).import(s, a)]; } case "LookupTableFind": case "LookupTableFindV2": { - let n = I("tableHandle", r, t8, e, o), s = I("keys", r, t8, e), a = I("defaultValue", r, t8, e); + let n = I("tableHandle", r15, t8, e, o), s = I("keys", r15, t8, e), a = I("defaultValue", r15, t8, e); return [await o.getHashTableById(n.id).find(s, a)]; } case "LookupTableSize": case "LookupTableSizeV2": { - let n = I("tableHandle", r, t8, e, o); + let n = I("tableHandle", r15, t8, e, o); return [o.getHashTableById(n.id).tensorSize()]; } default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var PT = (r, t8, e, o = Je) => { - switch (r.op) { +var OT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "ResizeBilinear": { - let n = I("images", r, t8, e), s = I("size", r, t8, e), a = I("alignCorners", r, t8, e), i = I("halfPixelCenters", r, t8, e); + let n = I("images", r15, t8, e), s = I("size", r15, t8, e), a = I("alignCorners", r15, t8, e), i = I("halfPixelCenters", r15, t8, e); return [o.image.resizeBilinear(n, [s[0], s[1]], a, i)]; } case "ResizeNearestNeighbor": { - let n = I("images", r, t8, e), s = I("size", r, t8, e), a = I("alignCorners", r, t8, e), i = I("halfPixelCenters", r, t8, e); + let n = I("images", r15, t8, e), s = I("size", r15, t8, e), a = I("alignCorners", r15, t8, e), i = I("halfPixelCenters", r15, t8, e); return [o.image.resizeNearestNeighbor(n, [s[0], s[1]], a, i)]; } case "CropAndResize": { - let n = I("image", r, t8, e), s = I("boxes", r, t8, e), a = I("boxInd", r, t8, e), i = I("cropSize", r, t8, e), p = I("method", r, t8, e), u = I("extrapolationValue", r, t8, e); + let n = I("image", r15, t8, e), s = I("boxes", r15, t8, e), a = I("boxInd", r15, t8, e), i = I("cropSize", r15, t8, e), p = I("method", r15, t8, e), u = I("extrapolationValue", r15, t8, e); return [o.image.cropAndResize(n, s, a, i, p, u)]; } case "ImageProjectiveTransformV3": { - let n = I("images", r, t8, e), s = I("transforms", r, t8, e), a = I("outputShape", r, t8, e), i = I("fillValue", r, t8, e), p = I("interpolation", r, t8, e), u = I("fillMode", r, t8, e); + let n = I("images", r15, t8, e), s = I("transforms", r15, t8, e), a = I("outputShape", r15, t8, e), i = I("fillValue", r15, t8, e), p = I("interpolation", r15, t8, e), u = I("fillMode", r15, t8, e); return [o.image.transform(n, s, p.toLowerCase(), u.toLowerCase(), i, a)]; } default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var OT = (r, t8, e, o = Je) => { - switch (r.op) { +var MT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "Equal": - return [o.equal(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.equal(I("a", r15, t8, e), I("b", r15, t8, e))]; case "NotEqual": - return [o.notEqual(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.notEqual(I("a", r15, t8, e), I("b", r15, t8, e))]; case "Greater": - return [o.greater(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.greater(I("a", r15, t8, e), I("b", r15, t8, e))]; case "GreaterEqual": - return [o.greaterEqual(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.greaterEqual(I("a", r15, t8, e), I("b", r15, t8, e))]; case "Less": - return [o.less(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.less(I("a", r15, t8, e), I("b", r15, t8, e))]; case "LessEqual": - return [o.lessEqual(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.lessEqual(I("a", r15, t8, e), I("b", r15, t8, e))]; case "LogicalAnd": - return [o.logicalAnd(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.logicalAnd(I("a", r15, t8, e), I("b", r15, t8, e))]; case "LogicalNot": - return [o.logicalNot(I("a", r, t8, e))]; + return [o.logicalNot(I("a", r15, t8, e))]; case "LogicalOr": - return [o.logicalOr(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.logicalOr(I("a", r15, t8, e), I("b", r15, t8, e))]; case "Select": case "SelectV2": - return [o.where(I("condition", r, t8, e), I("a", r, t8, e), I("b", r, t8, e))]; + return [o.where(I("condition", r15, t8, e), I("a", r15, t8, e), I("b", r15, t8, e))]; case "BitwiseAnd": - return [o.bitwiseAnd(I("a", r, t8, e), I("b", r, t8, e))]; + return [o.bitwiseAnd(I("a", r15, t8, e), I("b", r15, t8, e))]; default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var MT = (r, t8, e, o = Je) => { - switch (r.op) { +var LT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "BatchMatMul": case "BatchMatMulV2": case "MatMul": - return [o.matMul(I("a", r, t8, e), I("b", r, t8, e), I("transposeA", r, t8, e), I("transposeB", r, t8, e))]; + return [o.matMul(I("a", r15, t8, e), I("b", r15, t8, e), I("transposeA", r15, t8, e), I("transposeB", r15, t8, e))]; case "Einsum": - return [o.einsum(I("equation", r, t8, e), ...I("tensors", r, t8, e))]; + return [o.einsum(I("equation", r15, t8, e), ...I("tensors", r15, t8, e))]; case "Transpose": - return [o.transpose(I("x", r, t8, e), I("perm", r, t8, e))]; + return [o.transpose(I("x", r15, t8, e), I("perm", r15, t8, e))]; case "_FusedMatMul": - let [n, s] = I("fusedOps", r, t8, e), a = n === "biasadd", i = s === "prelu", p = I("numArgs", r, t8, e), u = I("leakyreluAlpha", r, t8, e); + let [n, s] = I("fusedOps", r15, t8, e), a = n === "biasadd", i = s === "prelu", p = I("numArgs", r15, t8, e), u = I("leakyreluAlpha", r15, t8, e); if (a) { if (i && p !== 2) throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha."); if (!i && p !== 1) throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias."); } - let [c, l] = I("args", r, t8, e); - return [o.fused.matMul({ a: I("a", r, t8, e), b: I("b", r, t8, e), transposeA: I("transposeA", r, t8, e), transposeB: I("transposeB", r, t8, e), bias: c, activation: s, preluActivationWeights: l, leakyreluAlpha: u })]; + let [c, l] = I("args", r15, t8, e); + return [o.fused.matMul({ a: I("a", r15, t8, e), b: I("b", r15, t8, e), transposeA: I("transposeA", r15, t8, e), transposeB: I("transposeB", r15, t8, e), bias: c, activation: s, preluActivationWeights: l, leakyreluAlpha: u })]; case "MatrixBandPart": - return [o.linalg.bandPart(I("a", r, t8, e), I("numLower", r, t8, e), I("numUpper", r, t8, e))]; + return [o.linalg.bandPart(I("a", r15, t8, e), I("numLower", r15, t8, e), I("numUpper", r15, t8, e))]; default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var LT = (r, t8, e, o = Je) => { - switch (r.op) { +var BT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "EuclideanNorm": - return [o.euclideanNorm(I("x", r, t8, e), I("axis", r, t8, e), I("keepDims", r, t8, e))]; + return [o.euclideanNorm(I("x", r15, t8, e), I("axis", r15, t8, e), I("keepDims", r15, t8, e))]; case "FusedBatchNorm": case "FusedBatchNormV2": - return [o.batchNorm(I("x", r, t8, e), I("mean", r, t8, e), I("variance", r, t8, e), I("offset", r, t8, e), I("scale", r, t8, e), I("epsilon", r, t8, e))]; + return [o.batchNorm(I("x", r15, t8, e), I("mean", r15, t8, e), I("variance", r15, t8, e), I("offset", r15, t8, e), I("scale", r15, t8, e), I("epsilon", r15, t8, e))]; case "FusedBatchNormV3": - return [o.batchNorm(I("x", r, t8, e), I("mean", r, t8, e), I("variance", r, t8, e), I("offset", r, t8, e), I("scale", r, t8, e), I("epsilon", r, t8, e))]; + return [o.batchNorm(I("x", r15, t8, e), I("mean", r15, t8, e), I("variance", r15, t8, e), I("offset", r15, t8, e), I("scale", r15, t8, e), I("epsilon", r15, t8, e))]; case "LRN": - return [o.localResponseNormalization(I("x", r, t8, e), I("radius", r, t8, e), I("bias", r, t8, e), I("alpha", r, t8, e), I("beta", r, t8, e))]; + return [o.localResponseNormalization(I("x", r15, t8, e), I("radius", r15, t8, e), I("bias", r15, t8, e), I("alpha", r15, t8, e), I("beta", r15, t8, e))]; case "Softmax": - return [o.softmax(I("x", r, t8, e))]; + return [o.softmax(I("x", r15, t8, e))]; case "LogSoftmax": - return [o.logSoftmax(I("x", r, t8, e))]; + return [o.logSoftmax(I("x", r15, t8, e))]; default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var BT = (r, t8, e, o = Je) => { - switch (r.op) { +var zT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "RaggedGather": { - let { outputNestedSplits: n, outputDenseValues: s } = o.raggedGather(I("paramsNestedSplits", r, t8, e), I("paramsDenseValues", r, t8, e), I("indices", r, t8, e), I("outputRaggedRank", r, t8, e)); + let { outputNestedSplits: n, outputDenseValues: s } = o.raggedGather(I("paramsNestedSplits", r15, t8, e), I("paramsDenseValues", r15, t8, e), I("indices", r15, t8, e), I("outputRaggedRank", r15, t8, e)); return n.concat(s); } case "RaggedRange": { - let { rtNestedSplits: n, rtDenseValues: s } = o.raggedRange(I("starts", r, t8, e), I("limits", r, t8, e), I("splits", r, t8, e)); + let { rtNestedSplits: n, rtDenseValues: s } = o.raggedRange(I("starts", r15, t8, e), I("limits", r15, t8, e), I("splits", r15, t8, e)); return [n, s]; } case "RaggedTensorToTensor": - return [o.raggedTensorToTensor(I("shape", r, t8, e), I("values", r, t8, e), I("defaultValue", r, t8, e), I("rowPartitionTensors", r, t8, e), I("rowPartitionTypes", r, t8, e))]; + return [o.raggedTensorToTensor(I("shape", r15, t8, e), I("values", r15, t8, e), I("defaultValue", r15, t8, e), I("rowPartitionTensors", r15, t8, e), I("rowPartitionTypes", r15, t8, e))]; default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var zT = (r, t8, e, o = Je) => { - switch (r.op) { +var VT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "Max": { - let i = I("axis", r, t8, e), p = I("keepDims", r, t8, e); - return [o.max(I("x", r, t8, e), i, p)]; + let i = I("axis", r15, t8, e), p = I("keepDims", r15, t8, e); + return [o.max(I("x", r15, t8, e), i, p)]; } case "Mean": { - let i = I("axis", r, t8, e), p = I("keepDims", r, t8, e); - return [o.mean(I("x", r, t8, e), i, p)]; + let i = I("axis", r15, t8, e), p = I("keepDims", r15, t8, e); + return [o.mean(I("x", r15, t8, e), i, p)]; } case "Min": { - let i = I("axis", r, t8, e), p = I("keepDims", r, t8, e); - return [o.min(I("x", r, t8, e), i, p)]; + let i = I("axis", r15, t8, e), p = I("keepDims", r15, t8, e); + return [o.min(I("x", r15, t8, e), i, p)]; } case "Sum": { - let i = I("axis", r, t8, e), p = I("keepDims", r, t8, e); - return [o.sum(I("x", r, t8, e), i, p)]; + let i = I("axis", r15, t8, e), p = I("keepDims", r15, t8, e); + return [o.sum(I("x", r15, t8, e), i, p)]; } case "All": { - let i = I("axis", r, t8, e), p = I("keepDims", r, t8, e); - return [o.all(I("x", r, t8, e), i, p)]; + let i = I("axis", r15, t8, e), p = I("keepDims", r15, t8, e); + return [o.all(I("x", r15, t8, e), i, p)]; } case "Any": { - let i = I("axis", r, t8, e), p = I("keepDims", r, t8, e); - return [o.any(I("x", r, t8, e), i, p)]; + let i = I("axis", r15, t8, e), p = I("keepDims", r15, t8, e); + return [o.any(I("x", r15, t8, e), i, p)]; } case "ArgMax": { - let i = I("axis", r, t8, e); - return [o.argMax(I("x", r, t8, e), i)]; + let i = I("axis", r15, t8, e); + return [o.argMax(I("x", r15, t8, e), i)]; } case "ArgMin": { - let i = I("axis", r, t8, e); - return [o.argMin(I("x", r, t8, e), i)]; + let i = I("axis", r15, t8, e); + return [o.argMin(I("x", r15, t8, e), i)]; } case "Prod": { - let i = I("axis", r, t8, e), p = I("keepDims", r, t8, e); - return [o.prod(I("x", r, t8, e), i, p)]; + let i = I("axis", r15, t8, e), p = I("keepDims", r15, t8, e); + return [o.prod(I("x", r15, t8, e), i, p)]; } case "Cumprod": { - let i = I("axis", r, t8, e), p = I("exclusive", r, t8, e), u = I("reverse", r, t8, e); - return [o.cumprod(I("x", r, t8, e), i, p, u)]; + let i = I("axis", r15, t8, e), p = I("exclusive", r15, t8, e), u = I("reverse", r15, t8, e); + return [o.cumprod(I("x", r15, t8, e), i, p, u)]; } case "Cumsum": { - let i = I("axis", r, t8, e), p = I("exclusive", r, t8, e), u = I("reverse", r, t8, e); - return [o.cumsum(I("x", r, t8, e), i, p, u)]; + let i = I("axis", r15, t8, e), p = I("exclusive", r15, t8, e), u = I("reverse", r15, t8, e); + return [o.cumsum(I("x", r15, t8, e), i, p, u)]; } case "Bincount": - let n = I("x", r, t8, e), s = I("weights", r, t8, e), a = I("size", r, t8, e); + let n = I("x", r15, t8, e), s = I("weights", r15, t8, e), a = I("size", r15, t8, e); return [o.bincount(n, s, a)]; case "DenseBincount": { - let i = I("x", r, t8, e), p = I("weights", r, t8, e), u = I("size", r, t8, e), c = I("binaryOutput", r, t8, e); + let i = I("x", r15, t8, e), p = I("weights", r15, t8, e), u = I("size", r15, t8, e), c = I("binaryOutput", r15, t8, e); return [o.denseBincount(i, p, u, c)]; } default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var VT = (r, t8, e, o = Je) => { - switch (r.op) { +var WT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "ConcatV2": case "Concat": { - let n = I("n", r, t8, e), s = I("axis", r, t8, e), a = I("tensors", r, t8, e); + let n = I("n", r15, t8, e), s = I("axis", r15, t8, e), a = I("tensors", r15, t8, e); return a = a.slice(0, n), [o.concat(a, s)]; } case "Gather": { - let n = I("x", r, t8, e), s = I("indices", r, t8, e); + let n = I("x", r15, t8, e), s = I("indices", r15, t8, e); return [o.gather(n, o.cast(s, "int32"), 0)]; } case "GatherV2": { - let n = I("axis", r, t8, e), s = I("batchDims", r, t8, e), a = I("x", r, t8, e), i = I("indices", r, t8, e); + let n = I("axis", r15, t8, e), s = I("batchDims", r15, t8, e), a = I("x", r15, t8, e), i = I("indices", r15, t8, e); return [o.gather(a, o.cast(i, "int32"), n, s)]; } case "Reverse": { - let n = I("dims", r, t8, e), s = []; + let n = I("dims", r15, t8, e), s = []; for (let i = 0; i < n.length; i++) n[i] && s.push(i); - let a = I("x", r, t8, e); + let a = I("x", r15, t8, e); return [o.reverse(a, s)]; } case "ReverseV2": { - let n = I("axis", r, t8, e), s = I("x", r, t8, e); + let n = I("axis", r15, t8, e), s = I("x", r15, t8, e); return [o.reverse(s, n)]; } case "Slice": { - let n = I("begin", r, t8, e), s = I("size", r, t8, e); - return [o.slice(I("x", r, t8, e), n, s)]; + let n = I("begin", r15, t8, e), s = I("size", r15, t8, e); + return [o.slice(I("x", r15, t8, e), n, s)]; } case "StridedSlice": { - let n = I("begin", r, t8, e), s = I("end", r, t8, e), a = I("strides", r, t8, e), i = I("beginMask", r, t8, e), p = I("endMask", r, t8, e), u = I("ellipsisMask", r, t8, e), c = I("newAxisMask", r, t8, e), l = I("shrinkAxisMask", r, t8, e), m = I("x", r, t8, e); + let n = I("begin", r15, t8, e), s = I("end", r15, t8, e), a = I("strides", r15, t8, e), i = I("beginMask", r15, t8, e), p = I("endMask", r15, t8, e), u = I("ellipsisMask", r15, t8, e), c = I("newAxisMask", r15, t8, e), l = I("shrinkAxisMask", r15, t8, e), m = I("x", r15, t8, e); return [o.stridedSlice(m, n, s, a, i, p, u, c, l)]; } case "Pack": return De(() => { - let n = I("axis", r, t8, e), s = I("tensors", r, t8, e), a = s[0].shape, i = o.squeeze(s[0]).shape, p = s.map((u) => { + let n = I("axis", r15, t8, e), s = I("tensors", r15, t8, e), a = s[0].shape, i = o.squeeze(s[0]).shape, p = s.map((u) => { let c = y.arraysEqual(u.shape, a); if (!c && !y.arraysEqual(o.squeeze(u).shape, i)) throw new Error("the input tensors shape does not match"); @@ -11160,184 +11161,184 @@ var VT = (r, t8, e, o = Je) => { return [o.stack(p, n)]; }); case "Unpack": { - let n = I("axis", r, t8, e), s = I("tensor", r, t8, e); + let n = I("axis", r15, t8, e), s = I("tensor", r15, t8, e); return o.unstack(s, n); } case "Tile": { - let n = I("reps", r, t8, e); - return [o.tile(I("x", r, t8, e), n)]; + let n = I("reps", r15, t8, e); + return [o.tile(I("x", r15, t8, e), n)]; } case "Split": case "SplitV": { - let n = I("axis", r, t8, e), s = I("numOrSizeSplits", r, t8, e), a = I("x", r, t8, e); + let n = I("axis", r15, t8, e), s = I("numOrSizeSplits", r15, t8, e), a = I("x", r15, t8, e); return o.split(a, s, n); } case "ScatterNd": { - let n = I("indices", r, t8, e), s = I("values", r, t8, e), a = I("shape", r, t8, e); + let n = I("indices", r15, t8, e), s = I("values", r15, t8, e), a = I("shape", r15, t8, e); return [o.scatterND(n, s, a)]; } case "GatherNd": { - let n = I("x", r, t8, e), s = I("indices", r, t8, e); + let n = I("x", r15, t8, e), s = I("indices", r15, t8, e); return [o.gatherND(n, s)]; } case "SparseToDense": { - let n = I("sparseIndices", r, t8, e), s = I("outputShape", r, t8, e), a = I("sparseValues", r, t8, e), i = I("defaultValue", r, t8, e); + let n = I("sparseIndices", r15, t8, e), s = I("outputShape", r15, t8, e), a = I("sparseValues", r15, t8, e), i = I("defaultValue", r15, t8, e); return [o.sparseToDense(n, a, s, a.dtype === i.dtype ? i : o.cast(i, a.dtype))]; } case "TensorScatterUpdate": { - let n = I("indices", r, t8, e), s = I("values", r, t8, e), a = I("tensor", r, t8, e); + let n = I("indices", r15, t8, e), s = I("values", r15, t8, e), a = I("tensor", r15, t8, e); return [o.tensorScatterUpdate(a, n, s)]; } default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var WT = (r, t8, e, o = Je) => { - switch (r.op) { +var UT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "SparseFillEmptyRows": { - let { outputIndices: n, outputValues: s, emptyRowIndicator: a, reverseIndexMap: i } = o.sparse.sparseFillEmptyRows(I("indices", r, t8, e), I("values", r, t8, e), I("denseShape", r, t8, e), I("defaultValue", r, t8, e)); + let { outputIndices: n, outputValues: s, emptyRowIndicator: a, reverseIndexMap: i } = o.sparse.sparseFillEmptyRows(I("indices", r15, t8, e), I("values", r15, t8, e), I("denseShape", r15, t8, e), I("defaultValue", r15, t8, e)); return [n, s, a, i]; } case "SparseReshape": { - let { outputIndices: n, outputShape: s } = o.sparse.sparseReshape(I("inputIndices", r, t8, e), I("inputShape", r, t8, e), I("newShape", r, t8, e)); + let { outputIndices: n, outputShape: s } = o.sparse.sparseReshape(I("inputIndices", r15, t8, e), I("inputShape", r15, t8, e), I("newShape", r15, t8, e)); return [n, s]; } case "SparseSegmentMean": - return [o.sparse.sparseSegmentMean(I("data", r, t8, e), I("indices", r, t8, e), I("segmentIds", r, t8, e))]; + return [o.sparse.sparseSegmentMean(I("data", r15, t8, e), I("indices", r15, t8, e), I("segmentIds", r15, t8, e))]; case "SparseSegmentSum": - return [o.sparse.sparseSegmentSum(I("data", r, t8, e), I("indices", r, t8, e), I("segmentIds", r, t8, e))]; + return [o.sparse.sparseSegmentSum(I("data", r15, t8, e), I("indices", r15, t8, e), I("segmentIds", r15, t8, e))]; default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var UT = (r, t8, e, o = Je) => { - switch (r.op) { +var GT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "FFT": - return [o.fft(I("x", r, t8, e))]; + return [o.fft(I("x", r15, t8, e))]; case "IFFT": - return [o.ifft(I("x", r, t8, e))]; + return [o.ifft(I("x", r15, t8, e))]; case "RFFT": - return [o.rfft(I("x", r, t8, e))]; + return [o.rfft(I("x", r15, t8, e))]; case "IRFFT": - return [o.irfft(I("x", r, t8, e))]; + return [o.irfft(I("x", r15, t8, e))]; default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var GT = (r, t8, e, o = Je) => { - switch (r.op) { +var HT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "StaticRegexReplace": - return [o.string.staticRegexReplace(I("input", r, t8, e), I("pattern", r, t8, e), I("rewrite", r, t8, e), I("replaceGlobal", r, t8, e))]; + return [o.string.staticRegexReplace(I("input", r15, t8, e), I("pattern", r15, t8, e), I("rewrite", r15, t8, e), I("replaceGlobal", r15, t8, e))]; case "StringNGrams": { - let { nGrams: n, nGramsSplits: s } = o.string.stringNGrams(I("data", r, t8, e), I("dataSplits", r, t8, e), I("separator", r, t8, e), I("nGramWidths", r, t8, e), I("leftPad", r, t8, e), I("rightPad", r, t8, e), I("padWidth", r, t8, e), I("preserveShortSequences", r, t8, e)); + let { nGrams: n, nGramsSplits: s } = o.string.stringNGrams(I("data", r15, t8, e), I("dataSplits", r15, t8, e), I("separator", r15, t8, e), I("nGramWidths", r15, t8, e), I("leftPad", r15, t8, e), I("rightPad", r15, t8, e), I("padWidth", r15, t8, e), I("preserveShortSequences", r15, t8, e)); return [n, s]; } case "StringSplit": { - let { indices: n, values: s, shape: a } = o.string.stringSplit(I("input", r, t8, e), I("delimiter", r, t8, e), I("skipEmpty", r, t8, e)); + let { indices: n, values: s, shape: a } = o.string.stringSplit(I("input", r15, t8, e), I("delimiter", r15, t8, e), I("skipEmpty", r15, t8, e)); return [n, s, a]; } case "StringToHashBucketFast": - return [o.string.stringToHashBucketFast(I("input", r, t8, e), I("numBuckets", r, t8, e))]; + return [o.string.stringToHashBucketFast(I("input", r15, t8, e), I("numBuckets", r15, t8, e))]; default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -var HT = (r, t8, e, o = Je) => { - switch (r.op) { +var KT = (r15, t8, e, o = Je) => { + switch (r15.op) { case "Cast": - return [o.cast(I("x", r, t8, e), I("dtype", r, t8, e))]; + return [o.cast(I("x", r15, t8, e), I("dtype", r15, t8, e))]; case "ExpandDims": { - let n = I("axis", r, t8, e); - return [o.expandDims(I("x", r, t8, e), n)]; + let n = I("axis", r15, t8, e); + return [o.expandDims(I("x", r15, t8, e), n)]; } case "Squeeze": { - let n = I("axis", r, t8, e); - return [o.squeeze(I("x", r, t8, e), n)]; + let n = I("axis", r15, t8, e); + return [o.squeeze(I("x", r15, t8, e), n)]; } case "Reshape": - return [o.reshape(I("x", r, t8, e), I("shape", r, t8, e))]; + return [o.reshape(I("x", r15, t8, e), I("shape", r15, t8, e))]; case "EnsureShape": - return [o.ensureShape(I("x", r, t8, e), I("shape", r, t8, e))]; + return [o.ensureShape(I("x", r15, t8, e), I("shape", r15, t8, e))]; case "MirrorPad": - return [o.mirrorPad(I("x", r, t8, e), I("padding", r, t8, e), I("mode", r, t8, e))]; + return [o.mirrorPad(I("x", r15, t8, e), I("padding", r15, t8, e), I("mode", r15, t8, e))]; case "PadV2": case "Pad": - return [o.pad(I("x", r, t8, e), I("padding", r, t8, e), I("constantValue", r, t8, e))]; + return [o.pad(I("x", r15, t8, e), I("padding", r15, t8, e), I("constantValue", r15, t8, e))]; case "SpaceToBatchND": { - let n = I("blockShape", r, t8, e), s = I("paddings", r, t8, e); - return [o.spaceToBatchND(I("x", r, t8, e), n, s)]; + let n = I("blockShape", r15, t8, e), s = I("paddings", r15, t8, e); + return [o.spaceToBatchND(I("x", r15, t8, e), n, s)]; } case "BatchToSpaceND": { - let n = I("blockShape", r, t8, e), s = I("crops", r, t8, e); - return [o.batchToSpaceND(I("x", r, t8, e), n, s)]; + let n = I("blockShape", r15, t8, e), s = I("crops", r15, t8, e); + return [o.batchToSpaceND(I("x", r15, t8, e), n, s)]; } case "DepthToSpace": { - let n = I("blockSize", r, t8, e), s = I("dataFormat", r, t8, e).toUpperCase(); - return [o.depthToSpace(I("x", r, t8, e), n, s)]; + let n = I("blockSize", r15, t8, e), s = I("dataFormat", r15, t8, e).toUpperCase(); + return [o.depthToSpace(I("x", r15, t8, e), n, s)]; } case "BroadcastTo": - return [o.broadcastTo(I("x", r, t8, e), I("shape", r, t8, e))]; + return [o.broadcastTo(I("x", r15, t8, e), I("shape", r15, t8, e))]; case "BroadcastArgs": - return [o.broadcastArgs(I("s0", r, t8, e), I("s1", r, t8, e))]; + return [o.broadcastArgs(I("s0", r15, t8, e), I("s1", r15, t8, e))]; default: - throw TypeError(`Node type ${r.op} is not implemented`); + throw TypeError(`Node type ${r15.op} is not implemented`); } }; -function OS(r, t8, e, o, n = De) { +function OS(r15, t8, e, o, n = De) { let s = ((a, i, p) => { switch (a.category) { case "arithmetic": - return n(() => CT(a, i, p)); - case "basic_math": return n(() => wT(a, i, p)); + case "basic_math": + return n(() => ST(a, i, p)); case "control": - return TT(a, i, p); + return _T(a, i, p); case "convolution": - return n(() => $T(a, i, p)); - case "creation": return n(() => ET(a, i, p)); + case "creation": + return n(() => RT(a, i, p)); case "dynamic": - return RT(a, i, p); + return DT(a, i, p); case "evaluation": - return n(() => DT(a, i, p)); - case "image": - return n(() => PT(a, i, p)); - case "graph": return n(() => AT(a, i, p)); - case "logical": + case "image": return n(() => OT(a, i, p)); - case "matrices": + case "graph": + return n(() => FT(a, i, p)); + case "logical": return n(() => MT(a, i, p)); - case "normalization": + case "matrices": return n(() => LT(a, i, p)); - case "ragged": + case "normalization": return n(() => BT(a, i, p)); - case "reduction": + case "ragged": return n(() => zT(a, i, p)); - case "slice_join": + case "reduction": return n(() => VT(a, i, p)); - case "sparse": + case "slice_join": return n(() => WT(a, i, p)); - case "spectral": + case "sparse": return n(() => UT(a, i, p)); - case "string": + case "spectral": return n(() => GT(a, i, p)); - case "transformation": + case "string": return n(() => HT(a, i, p)); + case "transformation": + return n(() => KT(a, i, p)); case "hash_table": - return FT(a, i, p, o); + return PT(a, i, p, o); case "custom": - let u = pf(a.op); + let u = uf(a.op); if (u && u.customExecutor) - return u.customExecutor(new wf(a, i, p)); + return u.customExecutor(new Cf(a, i, p)); throw TypeError(`Custom op ${a.op} is not registered.`); default: throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`); } - })(r, t8, e); + })(r15, t8, e); return y.isPromise(s) ? s.then((a) => [].concat(a)) : [].concat(s); } -var Ll = class { +var Pl = class { constructor(t8 = {}, e = {}, o = {}, n = {}, s) { this.weightMap = t8, this.tensorArrayMap = e, this.tensorListMap = o, this.functionMap = n, this.parseNodeNameCache = s, this.rootContext = { id: 0, frameName: "", iterationId: 0 }, this.contexts = [this.rootContext], this.lastId = 0, this.generateCurrentContextIds(); } @@ -11406,13 +11407,13 @@ var Ll = class { this.tensorListMap[e].clearAndClose(t8); } }; -function MS(r, t8, e, o) { - let n = /* @__PURE__ */ new Set(), s = [], a = null, i = null, p = /* @__PURE__ */ new Set(), u = new Set(Object.keys(r).map((m) => Tr(m)[0])); +function MS(r15, t8, e, o) { + let n = /* @__PURE__ */ new Set(), s = [], a = null, i = null, p = /* @__PURE__ */ new Set(), u = new Set(Object.keys(r15).map((m) => Nr(m)[0])); o = o || []; - let c = new Set(o.map((m) => Tr(m.name)[0])), l = [...t8]; + let c = new Set(o.map((m) => Nr(m.name)[0])), l = [...t8]; for (; l.length > 0; ) { let m = l.pop(); - if ((gu(m) || k8(m) || N8(m)) && a == null && (a = m, i = a.children.map((d) => d.name).filter((d) => n.has(d))), n.add(m.name), e[m.name] == null && !u.has(m.name) && !c.has(m.name)) { + if ((mu(m) || k8(m) || N8(m)) && a == null && (a = m, i = a.children.map((d) => d.name).filter((d) => n.has(d))), n.add(m.name), e[m.name] == null && !u.has(m.name) && !c.has(m.name)) { if (m.inputs.length === 0) { s.push(m.name); continue; @@ -11422,14 +11423,14 @@ function MS(r, t8, e, o) { }); } } - return { inputs: r, outputs: t8, usedNodes: n, missingInputs: s, dynamicNode: a, syncInputs: i }; + return { inputs: r15, outputs: t8, usedNodes: n, missingInputs: s, dynamicNode: a, syncInputs: i }; } -function KT(r, t8) { - let { usedNodes: e, inputs: o } = t8, n = Object.keys(o).map((g) => Tr(g)[0]).map((g) => r.nodes[g]), s = r.initNodes || [], a = (g) => e.has(typeof g == "string" ? g : g.name); +function qT(r15, t8) { + let { usedNodes: e, inputs: o } = t8, n = Object.keys(o).map((g) => Nr(g)[0]).map((g) => r15.nodes[g]), s = r15.initNodes || [], a = (g) => e.has(typeof g == "string" ? g : g.name); function i(g) { return [...new Map(g.map((x) => [x.name, x])).values()]; } - let p = i([...n, ...r.weights, ...s]).filter(a), u = i([...p, ...Object.values(r.nodes)]).filter(a), c = new Map(u.map((g) => [g.name, g])), l = {}; + let p = i([...n, ...r15.weights, ...s]).filter(a), u = i([...p, ...Object.values(r15.nodes)]).filter(a), c = new Map(u.map((g) => [g.name, g])), l = {}; for (let g of u) { l[g.name] = l[g.name] || 0; for (let x of g.children) @@ -11444,48 +11445,48 @@ function KT(r, t8) { let f = d.map((g) => c.get(g)), h = C8(f, p); return w8(h, p), h; } -function C8(r, t8) { - let e = new Map(r.map((a) => [a.name, a])), o = t8.map((a) => a.name), n = new Set(o); +function C8(r15, t8) { + let e = new Map(r15.map((a) => [a.name, a])), o = t8.map((a) => a.name), n = new Set(o); for (; o.length > 0; ) { let a = o.pop(), i = e.get(a); for (let p of i.children) !e.has(p.name) || n.has(p.name) || (n.add(p.name), o.push(p.name)); } - return r.filter((a) => n.has(a.name)); + return r15.filter((a) => n.has(a.name)); } -var Cc = class extends Error { +var fc = class extends Error { constructor(t8) { super(`NodesExecutionOrderError: ${t8}`); } }; -function w8(r, t8) { - let e = new Map(r.map((i, p) => [i.name, p])), o = new Set(t8.map((i) => i.name)), n = (i) => o.has(typeof i == "string" ? i : i.name), s = new Set(r.map((i) => i.name)), a = (i) => s.has(typeof i == "string" ? i : i.name); - for (let i of r) { +function w8(r15, t8) { + let e = new Map(r15.map((i, p) => [i.name, p])), o = new Set(t8.map((i) => i.name)), n = (i) => o.has(typeof i == "string" ? i : i.name), s = new Set(r15.map((i) => i.name)), a = (i) => s.has(typeof i == "string" ? i : i.name); + for (let i of r15) { for (let p of i.children.filter(a)) { if (!e.has(p.name)) - throw new Cc(`Child ${p.name} of node ${i.name} is unreachable.`); + throw new fc(`Child ${p.name} of node ${i.name} is unreachable.`); if (e.get(i.name) > e.get(p.name)) - throw new Cc(`Node ${i.name} is scheduled to run after its child ${p.name}.`); + throw new fc(`Node ${i.name} is scheduled to run after its child ${p.name}.`); } if (!n(i)) for (let p of i.inputs) { if (!e.has(p.name)) - throw new Cc(`Input ${p.name} of node ${i.name} is unreachable.`); + throw new fc(`Input ${p.name} of node ${i.name} is unreachable.`); if (e.get(p.name) > e.get(i.name)) - throw new Cc(`Node ${i.name} is scheduled to run before its input ${p.name}.`); + throw new fc(`Node ${i.name} is scheduled to run before its input ${p.name}.`); } } } -function qT(r) { - let t8 = new Map(r.map((i, p) => [i.name, p])), e = Number.MAX_SAFE_INTEGER, o = r.map((i, p) => gu(i) ? e : p), n = (i) => { +function jT(r15) { + let t8 = new Map(r15.map((i, p) => [i.name, p])), e = Number.MAX_SAFE_INTEGER, o = r15.map((i, p) => mu(i) ? e : p), n = (i) => { let p = o[t8.get(i.name)]; return p == null ? -1 : p; - }, s = r.map((i, p) => i.children.map(n).reduce((u, c) => Math.max(u, c), o[p])), a = /* @__PURE__ */ new Map(); - for (let i = 0; i < r.length; ++i) { + }, s = r15.map((i, p) => i.children.map(n).reduce((u, c) => Math.max(u, c), o[p])), a = /* @__PURE__ */ new Map(); + for (let i = 0; i < r15.length; ++i) { let p = s[i]; if (p === e) continue; - let u = r[i], c = r[p]; + let u = r15[i], c = r15[p]; a.has(c.name) || a.set(c.name, []), a.get(c.name).push(u); } return a; @@ -11493,16 +11494,16 @@ function qT(r) { var S8 = /* @__PURE__ */ new Set(["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"]); var I8 = /* @__PURE__ */ new Set(["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"]); var v8 = /* @__PURE__ */ new Set(["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"]); -function gu(r) { - return S8.has(r.op); +function mu(r15) { + return S8.has(r15.op); } -function k8(r) { - return I8.has(r.op); +function k8(r15) { + return I8.has(r15.op); } -function N8(r) { - return v8.has(r.op); +function N8(r15) { + return v8.has(r15.op); } -var lp = class { +var Ol = class r10 { get weightIds() { return this.parent ? this.parent.weightIds : this._weightIds; } @@ -11539,7 +11540,7 @@ var lp = class { } constructor(t8, e) { this.graph = t8, this.parent = e, this.compiledMap = /* @__PURE__ */ new Map(), this.parseNodeNameCache = /* @__PURE__ */ new Map(), this._weightMap = {}, this.SEPARATOR = ",", this._functions = {}, this._functionExecutorMap = {}, this.keepIntermediateTensors = false, this._outputs = t8.outputs, this._inputs = t8.inputs, this._initNodes = t8.initNodes, this._signature = t8.signature, this._functions = t8.functions, t8.functions != null && Object.keys(t8.functions).forEach((o) => { - this._functionExecutorMap[o] = new lp(t8.functions[o], this); + this._functionExecutorMap[o] = new r10(t8.functions[o], this); }); } getCompilationKey(t8, e) { @@ -11554,14 +11555,14 @@ var lp = class { let u = e.map((l) => l.name), c = Object.keys(t8); throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`); } - let i = KT(this.graph, o), p = qT(i); + let i = qT(this.graph, o), p = jT(i); return { orderedNodes: i, nodeLiveUntilMap: p }; } cloneAndKeepTensor(t8) { if (t8 == null) return null; let e = t8.clone(); - return Rr(e), e; + return Er(e), e; } cloneTensorList(t8) { return t8 ? t8.map((o) => this.cloneAndKeepTensor(o)) : null; @@ -11573,7 +11574,7 @@ var lp = class { this.disposeIntermediateTensors(), t8 = this.mapInputs(t8); let o = Object.keys(t8).sort(); this.checkInputs(t8), this.checkInputShapeAndType(t8), e = this.mapOutputs(e), this.checkOutputs(e); - let n = o.map((m) => this.graph.nodes[Tr(m)[0]]), s = e.map((m) => Tr(m)[0]), a = new Set(s), i = s.map((m) => this.graph.nodes[m]); + let n = o.map((m) => this.graph.nodes[Nr(m)[0]]), s = e.map((m) => Nr(m)[0]), a = new Set(s), i = s.map((m) => this.graph.nodes[m]); i.length === 0 && (i = this._outputs); let p = this.getCompilationKey(n, i), u = this.compiledMap.get(p); u == null && (u = this.compile(t8, i), this.compiledMap.set(p, u)); @@ -11584,9 +11585,9 @@ var lp = class { } let c = {}, l = {}; return De(() => { - let m = new Ll(this.weightMap, c, l, this.functionExecutorMap, this.parseNodeNameCache), d = Object.assign({}, this.weightMap); + let m = new Pl(this.weightMap, c, l, this.functionExecutorMap, this.parseNodeNameCache), d = Object.assign({}, this.weightMap); this.keepIntermediateTensors && (this.clonedTensorsMap = this.cloneTensorMap(this.weightMap)), Object.keys(t8).forEach((x) => { - let [b, C] = Tr(x, m), S = []; + let [b, C] = Nr(x, m), S = []; S[C] = t8[x], d[b] = S, this.keepIntermediateTensors && (this.clonedTensorsMap[b] = this.cloneTensorList(S)); }); let f = this.getFrozenTensorIds(d), { orderedNodes: h, nodeLiveUntilMap: g } = u; @@ -11598,7 +11599,7 @@ var lp = class { throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`); d[x.name] = b, this.keepIntermediateTensors && (this.clonedTensorsMap[x.name] = this.cloneTensorList(b)), this.checkTensorForDisposalWithNodeLiveUntilInfo(x, d, m, f, a, g.get(x.name)); } - return this.parent == null && m.dispose(f), e.map((x) => zt(x, d, m)); + return this.parent == null && m.dispose(f), e.map((x) => Bt(x, d, m)); }); } getFrozenTensorIds(t8) { @@ -11606,11 +11607,11 @@ var lp = class { return new Set(e); } checkTensorForDisposal(t8, e, o, n, s, a, i) { - if (!(gu(e) || a.has(t8))) { + if (!(mu(e) || a.has(t8))) { for (let p of o[t8]) p != null && (i[p.id] = (i[p.id] || 0) + e.children.length); for (let p of e.inputs) { - if (gu(p)) + if (mu(p)) continue; let u = fS(p.name, o, n); if (u != null) @@ -11625,9 +11626,9 @@ var lp = class { } checkTensorForDisposalWithNodeLiveUntilInfo(t8, e, o, n, s, a) { function i(p) { - return gu(p) || s.has(p.name); + return mu(p) || s.has(p.name); } - if (!(gu(t8) || a == null)) + if (!(mu(t8) || a == null)) for (let p of a) { if (i(p)) continue; @@ -11655,9 +11656,9 @@ var lp = class { } catch (m) { this.keepIntermediateTensors = false, console.warn(m.message); } - let a = new Ll(this.weightMap, n, s, this.functionExecutorMap, this.parseNodeNameCache); + let a = new Pl(this.weightMap, n, s, this.functionExecutorMap, this.parseNodeNameCache); this.keepIntermediateTensors && (this.clonedTensorsMap = this.cloneTensorMap(this.weightMap)); - let i = await this.executeWithControlFlow(t8, a, e, o), p = e.map((m) => zt(m, i, a)), u = p.map((m) => m.id), c = Object.keys(t8).map((m) => t8[m].id), l = /* @__PURE__ */ new Set([...u, ...c, ...this.weightIds]); + let i = await this.executeWithControlFlow(t8, a, e, o), p = e.map((m) => Bt(m, i, a)), u = p.map((m) => m.id), c = Object.keys(t8).map((m) => t8[m].id), l = /* @__PURE__ */ new Set([...u, ...c, ...this.weightIds]); return Object.values(i).forEach((m) => { m.forEach((d) => { d && !d.isDisposed && !l.has(d.id) && d.dispose(); @@ -11669,11 +11670,11 @@ var lp = class { return this._executeAsync(n, this.outputNodes, true, e, o); } async executeWithControlFlow(t8, e, o, n) { - let s = Object.keys(t8), a = s.map((S) => this.graph.nodes[Tr(S)[0]]), i = o.map((S) => Tr(S)[0]), p = new Set(i), u = i.map((S) => this.graph.nodes[S]); + let s = Object.keys(t8), a = s.map((S) => this.graph.nodes[Nr(S)[0]]), i = o.map((S) => Nr(S)[0]), p = new Set(i), u = i.map((S) => this.graph.nodes[S]); u.length === 0 && (u = this._outputs); let { usedNodes: c, missingInputs: l, dynamicNode: m, syncInputs: d } = MS(t8, u, this.weightMap, this._initNodes), f = [...a, ...this.graph.weights, ...this._initNodes || []].map((S) => ({ node: S, contexts: e.currentContext })), h = Object.assign({}, this.weightMap); Object.keys(t8).forEach((S) => { - let [k, _] = Tr(S), E = []; + let [k, _] = Nr(S), E = []; E[_] = t8[S], h[k] = E; }); let g = {}, x = this.getFrozenTensorIds(h), b = {}; @@ -11682,7 +11683,7 @@ var lp = class { await Promise.all(S); } m == null && !n && console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead."); - let C = u.filter((S) => !gu(S) && !zt(S.name, h, e)).map((S) => S.name); + let C = u.filter((S) => !mu(S) && !Bt(S.name, h, e)).map((S) => S.name); if (C.length > 0) { let S = ""; throw m != null && (S = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`), new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`); @@ -11708,7 +11709,7 @@ var lp = class { processChildNodes(t8, e, o, n, s, a) { t8.children.forEach((i) => { let [p] = Ls(i.name, o); - s[p] || !a.has(i.name) || (i.op === "Merge" ? i.inputNames.some((u) => !!zt(u, n, o)) && (s[p] = true, e.push({ contexts: o.currentContext, node: i })) : i.inputNames.every((u) => !!zt(u, n, o)) && (s[p] = true, e.push({ contexts: o.currentContext, node: i }))); + s[p] || !a.has(i.name) || (i.op === "Merge" ? i.inputNames.some((u) => !!Bt(u, n, o)) && (s[p] = true, e.push({ contexts: o.currentContext, node: i })) : i.inputNames.every((u) => !!Bt(u, n, o)) && (s[p] = true, e.push({ contexts: o.currentContext, node: i }))); }); } dispose() { @@ -11716,7 +11717,7 @@ var lp = class { } checkInputShapeAndType(t8) { Object.keys(t8).forEach((e) => { - let o = t8[e], [n] = Tr(e), s = this.graph.nodes[n]; + let o = t8[e], [n] = Nr(e), s = this.graph.nodes[n]; if (s.attrParams.shape && s.attrParams.shape.value) { let a = s.attrParams.shape.value, i = a.length === o.shape.length && o.shape.every((p, u) => a[u] === -1 || a[u] === p); y.assert(i, () => `The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`); @@ -11735,7 +11736,7 @@ var lp = class { } checkInputs(t8) { let e = Object.keys(t8).filter((o) => { - let [n] = Tr(o); + let [n] = Nr(o); return this.graph.nodes[n] == null; }); if (e.length > 0) @@ -11750,13 +11751,13 @@ var lp = class { } checkOutputs(t8) { t8.forEach((e) => { - let [o] = Tr(e); + let [o] = Nr(e); if (!this.graph.nodes[o]) throw new Error(`The output '${e}' is not found in the graph`); }); } }; -var kf = class { +var vf = class { constructor(t8 = {}, e = {}) { this.hashTableNameToHandle = t8, this.hashTableMap = e; } @@ -11778,7 +11779,7 @@ var kf = class { }; var T8 = "?tfjs-format=file"; var _8 = "model.json"; -var Bl = class { +var Ml = class { get modelVersion() { return this.version; } @@ -11806,8 +11807,8 @@ var Bl = class { get modelStructuredOutputKeys() { return this.structuredOutputKeys; } - constructor(t8, e = {}, o = fi) { - this.modelUrl = t8, this.loadOptions = e, this.version = "n/a", this.io = o, e == null && (this.loadOptions = {}), this.resourceManager = new kf(); + constructor(t8, e = {}, o = mi) { + this.modelUrl = t8, this.loadOptions = e, this.version = "n/a", this.io = o, e == null && (this.loadOptions = {}), this.resourceManager = new vf(); } findIOHandler() { let t8 = this.modelUrl; @@ -11839,9 +11840,9 @@ var Bl = class { } this.signature = o, this.version = `${e.versions.producer}.${e.versions.minConsumer}`; let n = this.io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs); - if (this.executor = new lp(Ml.Instance.transformGraph(e, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(n), this.executor.resourceManager = this.resourceManager, t8.modelInitializer != null && t8.modelInitializer.node != null) { - let s = Ml.Instance.transformGraph(t8.modelInitializer); - this.initializer = new lp(s), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializerSignature = t8.initializerSignature; + if (this.executor = new Ol(Fl.Instance.transformGraph(e, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(n), this.executor.resourceManager = this.resourceManager, t8.modelInitializer != null && t8.modelInitializer.node != null) { + let s = Fl.Instance.transformGraph(t8.modelInitializer); + this.initializer = new Ol(s), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializerSignature = t8.initializerSignature; } return true; } @@ -11860,7 +11861,7 @@ var Bl = class { } addStructuredOutputNames(t8) { if (this.structuredOutputKeys) { - let e = t8 instanceof ut ? [t8] : t8, o = {}; + let e = t8 instanceof mt ? [t8] : t8, o = {}; return e.forEach((n, s) => o[this.structuredOutputKeys[s]] = n), o; } return t8; @@ -11875,7 +11876,7 @@ var Bl = class { } normalizeInputs(t8) { var e; - if (!(t8 instanceof ut) && !Array.isArray(t8)) { + if (!(t8 instanceof mt) && !Array.isArray(t8)) { let s = (e = this.signature) === null || e === void 0 ? void 0 : e.inputs; if (s != null) for (let a in s) { @@ -11933,22 +11934,22 @@ var Bl = class { return Object.keys(t8).reduce((e, o) => (e[o] = [t8[o]], e), {}); } dispose() { - this.executor.dispose(), this.initializer && (this.initializer.dispose(), this.resourceIdToCapturedInput && Mt(this.resourceIdToCapturedInput)), this.resourceManager.dispose(); + this.executor.dispose(), this.initializer && (this.initializer.dispose(), this.resourceIdToCapturedInput && Ot(this.resourceIdToCapturedInput)), this.resourceManager.dispose(); } }; -async function $8(r, t8 = {}, e = fi) { - if (r == null) +async function $8(r15, t8 = {}, e = mi) { + if (r15 == null) throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model"); - t8 == null && (t8 = {}), t8.fromTFHub && typeof r == "string" && (r = R8(r)); - let o = new Bl(r, t8, e); + t8 == null && (t8 = {}), t8.fromTFHub && typeof r15 == "string" && (r15 = R8(r15)); + let o = new Ml(r15, t8, e); return await o.load(), o; } -function E8(r) { - if (r == null) +function E8(r15) { + if (r15 == null) throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model"); let t8; - if (r instanceof Array) { - let [o, n] = r; + if (r15 instanceof Array) { + let [o, n] = r15; if (!o) throw new Error("modelJSON must be the first element of the array"); if (!n || !(n instanceof ArrayBuffer)) @@ -11957,33 +11958,33 @@ function E8(r) { throw new Error("Model JSON is missing 'modelTopology'"); if (!("weightsManifest" in o)) throw new Error("Model JSON is missing 'weightsManifest'"); - let s = fi.getWeightSpecs(o.weightsManifest), a = fi.getModelArtifactsForJSONSync(o, s, n); - t8 = fi.fromMemorySync(a); - } else if ("load" in r) - t8 = r; - else if ("modelTopology" in r && "weightSpecs" in r && "weightData" in r) - t8 = fi.fromMemorySync(r); + let s = mi.getWeightSpecs(o.weightsManifest), a = mi.getModelArtifactsForJSONSync(o, s, n); + t8 = mi.fromMemorySync(a); + } else if ("load" in r15) + t8 = r15; + else if ("modelTopology" in r15 && "weightSpecs" in r15 && "weightData" in r15) + t8 = mi.fromMemorySync(r15); else throw new Error("Unknown model format"); - let e = new Bl(t8); + let e = new Ml(t8); return e.load(), e; } -function R8(r) { - return r.endsWith("/") || (r = r + "/"), `${r}${_8}${T8}`; +function R8(r15) { + return r15.endsWith("/") || (r15 = r15 + "/"), `${r15}${_8}${T8}`; } -var D8 = "4.11.0"; -function Q(r, t8) { - Array.isArray(r) || (r = [r]), r.forEach((e) => { +var D8 = "4.14.0"; +function Q(r15, t8) { + Array.isArray(r15) || (r15 = [r15]), r15.forEach((e) => { e != null && y.assert(e.dtype !== "complex64", () => `${t8} does not support complex64 tensors in the CPU backend.`); }); } -var A8 = Wt.whereImpl; -var xu = class extends ao { +var A8 = Vt.whereImpl; +var hc = class r11 extends so { nextDataId() { - return xu.nextDataId++; + return r11.nextDataId++; } constructor() { - super(), this.blockSize = 48, this.firstUse = true, this.data = new zo(this, pr()); + super(), this.blockSize = 48, this.firstUse = true, this.data = new Bo(this, ur()); } write(t8, e, o) { this.firstUse && (this.firstUse = false, A().get("IS_NODE") && w.warn(` @@ -12044,7 +12045,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return me(t8.shape, t8.dtype, e); } makeOutput(t8, e, o) { - return pr().makeTensorFromTensorInfo(this.makeTensorInfo(e, o, t8), this); + return ur().makeTensorFromTensorInfo(this.makeTensorInfo(e, o, t8), this); } disposeData(t8, e = false) { if (this.data.has(t8)) { @@ -12079,28 +12080,28 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return super.epsilon(); } }; -xu.nextDataId = 0; -var Tc = {}; -qe(Tc, { addImpl: () => zS, bincountImpl: () => Ic, bincountReduceImpl: () => Nf, bitwiseAndImpl: () => VS, castImpl: () => BS, ceilImpl: () => WS, concatImpl: () => mp, equalImpl: () => US, expImpl: () => HS, expm1Impl: () => qS, floorDivImpl: () => XS, floorImpl: () => jS, gatherNdImpl: () => Tf, gatherV2Impl: () => _f, greaterEqualImpl: () => QS, greaterImpl: () => YS, lessEqualImpl: () => JS, lessImpl: () => ZS, linSpaceImpl: () => $f, logImpl: () => eI, maxImpl: () => Ef, maximumImpl: () => tI, minimumImpl: () => rI, multiplyImpl: () => zl, negImpl: () => oI, notEqualImpl: () => nI, prodImpl: () => sI, raggedGatherImpl: () => Rf, raggedRangeImpl: () => Df, raggedTensorToTensorImpl: () => Af, rangeImpl: () => fp, rsqrtImpl: () => aI, scatterImpl: () => zs, sigmoidImpl: () => v_, simpleAbsImpl: () => LS, sliceImpl: () => hp, sparseFillEmptyRowsImpl: () => Ff, sparseReshapeImpl: () => Pf, sparseSegmentReductionImpl: () => Nc, sqrtImpl: () => T_, squaredDifferenceImpl: () => uI, staticRegexReplaceImpl: () => pI, stridedSliceImpl: () => Of, stringNGramsImpl: () => gp, stringSplitImpl: () => xp, stringToHashBucketFastImpl: () => yp, subImpl: () => lI, tileImpl: () => Mf, topKImpl: () => Lf, transposeImpl: () => vc, uniqueImpl: () => bp }); -function LS(r) { - let t8 = new Float32Array(r.length); - for (let e = 0; e < r.length; ++e) - t8[e] = Math.abs(r[e]); +hc.nextDataId = 0; +var wc = {}; +qe(wc, { addImpl: () => zS, bincountImpl: () => yc, bincountReduceImpl: () => kf, bitwiseAndImpl: () => VS, castImpl: () => BS, ceilImpl: () => WS, concatImpl: () => np, equalImpl: () => US, expImpl: () => HS, expm1Impl: () => qS, floorDivImpl: () => XS, floorImpl: () => jS, gatherNdImpl: () => Nf, gatherV2Impl: () => Tf, greaterEqualImpl: () => QS, greaterImpl: () => YS, lessEqualImpl: () => JS, lessImpl: () => ZS, linSpaceImpl: () => _f, logImpl: () => eI, maxImpl: () => $f, maximumImpl: () => tI, minimumImpl: () => rI, multiplyImpl: () => Ll, negImpl: () => oI, notEqualImpl: () => nI, prodImpl: () => sI, raggedGatherImpl: () => Ef, raggedRangeImpl: () => Rf, raggedTensorToTensorImpl: () => Df, rangeImpl: () => ap, rsqrtImpl: () => iI, scatterImpl: () => zs, sigmoidImpl: () => k_, simpleAbsImpl: () => LS, sliceImpl: () => ip, sparseFillEmptyRowsImpl: () => Af, sparseReshapeImpl: () => Ff, sparseSegmentReductionImpl: () => Cc, sqrtImpl: () => __, squaredDifferenceImpl: () => pI, staticRegexReplaceImpl: () => cI, stridedSliceImpl: () => Pf, stringNGramsImpl: () => up, stringSplitImpl: () => pp, stringToHashBucketFastImpl: () => cp, subImpl: () => mI, tileImpl: () => Of, topKImpl: () => Mf, transposeImpl: () => bc, uniqueImpl: () => lp }); +function LS(r15) { + let t8 = new Float32Array(r15.length); + for (let e = 0; e < r15.length; ++e) + t8[e] = Math.abs(r15[e]); return t8; } -var F8 = (r) => { - let { x: t8 } = r.inputs, e = r.backend; +var F8 = (r15) => { + let { x: t8 } = r15.inputs, e = r15.backend; Q(t8, "abs"); let o = new Float32Array(y.sizeFromShape(t8.shape)), n = e.data.get(t8.dataId).values; return o = LS(n), e.makeOutput(o, t8.shape, t8.dtype); }; -var jT = { kernelName: Xs, backendName: "cpu", kernelFunc: F8 }; -function ze(r) { +var XT = { kernelName: Xs, backendName: "cpu", kernelFunc: F8 }; +function ze(r15) { return (t8, e, o, n, s) => { let a = w.assertAndGetBroadcastShape(t8, e), i = a.length, p = y.computeStrides(a), u = y.sizeFromShape(a), c = y.getTypedArrayFromDType(s, u), l = t8.length, m = e.length, d = y.computeStrides(t8), f = y.computeStrides(e), h = w.getBroadcastDims(t8, a), g = w.getBroadcastDims(e, a); if (h.length + g.length === 0) for (let x = 0; x < c.length; ++x) - c[x] = r(o[x % o.length], n[x % n.length]); + c[x] = r15(o[x % o.length], n[x % n.length]); else for (let x = 0; x < c.length; ++x) { let b = y.indexToLoc(x, i, p), C = b.slice(-l); @@ -12108,75 +12109,75 @@ function ze(r) { let S = y.locToIndex(C, l, d), k = b.slice(-m); g.forEach((E) => k[E] = 0); let _ = y.locToIndex(k, m, f); - c[x] = r(o[S], n[_]); + c[x] = r15(o[S], n[_]); } return [c, a]; }; } -function Kt(r) { - let { inputs: t8, backend: e } = r, { real: o, imag: n } = t8, s = e.data.get(o.dataId).values, a = e.data.get(n.dataId).values, i = e.makeTensorInfo(o.shape, "complex64"), p = e.data.get(i.dataId); +function Ht(r15) { + let { inputs: t8, backend: e } = r15, { real: o, imag: n } = t8, s = e.data.get(o.dataId).values, a = e.data.get(n.dataId).values, i = e.makeTensorInfo(o.shape, "complex64"), p = e.data.get(i.dataId); return p.complexTensorInfos = { real: e.makeTensorInfo(o.shape, "float32", s), imag: e.makeTensorInfo(n.shape, "float32", a) }, i; } -var XT = { kernelName: Fi, backendName: "cpu", kernelFunc: Kt }; -function wc(r, t8, e = "float32") { +var YT = { kernelName: Ri, backendName: "cpu", kernelFunc: Ht }; +function gc(r15, t8, e = "float32") { if (e === "complex64") { - let n = wc(r, t8, "float32"), s = wc(r, t8, "float32"); - return Kt({ inputs: { real: n, imag: s }, backend: r }); + let n = gc(r15, t8, "float32"), s = gc(r15, t8, "float32"); + return Ht({ inputs: { real: n, imag: s }, backend: r15 }); } let o = y.makeZerosTypedArray(y.sizeFromShape(t8), e); - return r.makeTensorInfo(t8, e, o); + return r15.makeTensorInfo(t8, e, o); } -function mr(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8; +function lr(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8; return e.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; } -var YT = { kernelName: wo, backendName: "cpu", kernelFunc: mr }; -function Ro(r) { - let { inputs: t8, backend: e } = r, { input: o } = t8, n = e.data.get(o.dataId).complexTensorInfos.real, s = e.data.get(n.dataId).values; +var QT = { kernelName: Co, backendName: "cpu", kernelFunc: lr }; +function Eo(r15) { + let { inputs: t8, backend: e } = r15, { input: o } = t8, n = e.data.get(o.dataId).complexTensorInfos.real, s = e.data.get(n.dataId).values; return e.makeTensorInfo(n.shape, n.dtype, s); } -var QT = { kernelName: qi, backendName: "cpu", kernelFunc: Ro }; -function BS(r, t8, e, o) { +var ZT = { kernelName: Gi, backendName: "cpu", kernelFunc: Eo }; +function BS(r15, t8, e, o) { if (o === "int32") { - let n = Int32Array.from(r); + let n = Int32Array.from(r15); return [t8, "int32", n]; } if (o === "bool") { - let n = y.toTypedArray([0], e), [s, a] = ze((i, p) => i !== p ? 1 : 0)(t8, [], r, n, "bool"); + let n = y.toTypedArray([0], e), [s, a] = ze((i, p) => i !== p ? 1 : 0)(t8, [], r15, n, "bool"); return [a, "bool", s]; } throw new Error(`Error in Cast: failed to cast ${e} to ${o}`); } -function Do(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { dtype: s } = o; +function Ro(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { dtype: s } = o; if (s === "complex64") { if (n.dtype === "complex64") - return mr({ inputs: { x: n }, backend: e }); - let c = wc(e, n.shape, n.dtype), l = Do({ inputs: { x: n }, backend: e, attrs: { dtype: "float32" } }), m = Kt({ inputs: { real: l, imag: c }, backend: e }); + return lr({ inputs: { x: n }, backend: e }); + let c = gc(e, n.shape, n.dtype), l = Ro({ inputs: { x: n }, backend: e, attrs: { dtype: "float32" } }), m = Ht({ inputs: { real: l, imag: c }, backend: e }); return e.disposeIntermediateTensorInfo(c), e.disposeIntermediateTensorInfo(l), m; } if (n.dtype === "complex64") { - let c = Ro({ inputs: { input: n }, backend: e }), l = Do({ inputs: { x: c }, backend: e, attrs: { dtype: s } }); + let c = Eo({ inputs: { input: n }, backend: e }), l = Ro({ inputs: { x: c }, backend: e, attrs: { dtype: s } }); return e.disposeIntermediateTensorInfo(c), l; } if (!y.hasEncodingLoss(n.dtype, s)) { - let c = mr({ inputs: { x: n }, backend: e }); + let c = lr({ inputs: { x: n }, backend: e }); return { dataId: c.dataId, shape: c.shape, dtype: s }; } let a = e.data.get(n.dataId).values, [i, p, u] = BS(a, n.shape, n.dtype, s); return e.makeTensorInfo(i, p, u); } -var ZT = { kernelName: bo, backendName: "cpu", kernelFunc: Do }; -function Ye(r, t8, e, o) { +var JT = { kernelName: yo, backendName: "cpu", kernelFunc: Ro }; +function Ye(r15, t8, e, o) { return e == null ? ({ inputs: n, backend: s }) => { let { a, b: i } = n, p = s; - Q([a, i], r); + Q([a, i], r15); let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = a.dtype === "string" ? w.fromUint8ToStringArray(u) : u, m = a.dtype === "string" ? w.fromUint8ToStringArray(c) : c, d = o || a.dtype, [f, h] = t8(a.shape, i.shape, l, m, d); return p.makeTensorInfo(h, d, f); } : ({ inputs: n, backend: s }) => { let { a, b: i } = n, p = s; if (a.dtype === "complex64" || i.dtype === "complex64") { - let u = Do({ inputs: { x: a }, backend: p, attrs: { dtype: "complex64" } }), c = p.data.get(u.dataId), l = c.complexTensorInfos.real, m = c.complexTensorInfos.imag, d = p.data.get(l.dataId).values, f = p.data.get(m.dataId).values, h = Do({ inputs: { x: i }, backend: p, attrs: { dtype: "complex64" } }), g = p.data.get(h.dataId), x = g.complexTensorInfos.real, b = g.complexTensorInfos.imag, C = p.data.get(x.dataId).values, S = p.data.get(b.dataId).values, [k, _, E] = e(a.shape, i.shape, d, f, C, S), R = p.makeTensorInfo(E, "float32", k), D = p.makeTensorInfo(E, "float32", _), P = Kt({ inputs: { real: R, imag: D }, backend: p }); + let u = Ro({ inputs: { x: a }, backend: p, attrs: { dtype: "complex64" } }), c = p.data.get(u.dataId), l = c.complexTensorInfos.real, m = c.complexTensorInfos.imag, d = p.data.get(l.dataId).values, f = p.data.get(m.dataId).values, h = Ro({ inputs: { x: i }, backend: p, attrs: { dtype: "complex64" } }), g = p.data.get(h.dataId), x = g.complexTensorInfos.real, b = g.complexTensorInfos.imag, C = p.data.get(x.dataId).values, S = p.data.get(b.dataId).values, [k, _, E] = e(a.shape, i.shape, d, f, C, S), R = p.makeTensorInfo(E, "float32", k), D = p.makeTensorInfo(E, "float32", _), P = Ht({ inputs: { real: R, imag: D }, backend: p }); return p.disposeIntermediateTensorInfo(u), p.disposeIntermediateTensorInfo(h), p.disposeIntermediateTensorInfo(R), p.disposeIntermediateTensorInfo(D), P; } else { let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = o || a.dtype, [m, d] = t8(a.shape, i.shape, u, c, l); @@ -12184,12 +12185,12 @@ function Ye(r, t8, e, o) { } }; } -function Sc(r) { +function xc(r15) { return (t8, e, o, n, s, a) => { let i = w.assertAndGetBroadcastShape(t8, e), p = y.sizeFromShape(i), u = i.length, c = y.computeStrides(i), l = y.getTypedArrayFromDType("float32", p), m = y.getTypedArrayFromDType("float32", p), d = w.getBroadcastDims(t8, i), f = w.getBroadcastDims(e, i), h = w.mergeRealAndImagArrays(o, n), g = w.mergeRealAndImagArrays(s, a), x = t8.length, b = y.computeStrides(t8), C = e.length, S = y.computeStrides(e); if (d.length + f.length === 0) for (let k = 0; k < l.length; k++) { - let _ = k % h.length, E = k % g.length, R = r(h[_ * 2], h[_ * 2 + 1], g[E * 2], g[E * 2 + 1]); + let _ = k % h.length, E = k % g.length, R = r15(h[_ * 2], h[_ * 2 + 1], g[E * 2], g[E * 2 + 1]); l[k] = R.real, m[k] = R.imag; } else @@ -12198,56 +12199,56 @@ function Sc(r) { d.forEach((M) => E[M] = 0); let R = y.locToIndex(E, x, b), D = _.slice(-C); f.forEach((M) => D[M] = 0); - let P = y.locToIndex(D, C, S), O = r(h[R * 2], h[R * 2 + 1], g[P * 2], g[P * 2 + 1]); + let P = y.locToIndex(D, C, S), O = r15(h[R * 2], h[R * 2 + 1], g[P * 2], g[P * 2 + 1]); l[k] = O.real, m[k] = O.imag; } return [l, m, i]; }; } -var zS = ze((r, t8) => r + t8); -var P8 = Sc((r, t8, e, o) => ({ real: r + e, imag: t8 + o })); -var Oa = Ye(uo, zS, P8); -var JT = { kernelName: uo, backendName: "cpu", kernelFunc: Oa }; -function Ic(r, t8, e, o, n) { +var zS = ze((r15, t8) => r15 + t8); +var P8 = xc((r15, t8, e, o) => ({ real: r15 + e, imag: t8 + o })); +var Pa = Ye(io, zS, P8); +var e_ = { kernelName: io, backendName: "cpu", kernelFunc: Pa }; +function yc(r15, t8, e, o, n) { let s = y.sizeFromShape(o), a = y.makeZerosTypedArray(n, e); - for (let i = 0; i < r.length; i++) { - let p = r[i]; + for (let i = 0; i < r15.length; i++) { + let p = r15[i]; if (p < 0) throw new Error("Input x must be non-negative!"); p >= n || (s > 0 ? a[p] += t8[i] : a[p] += 1); } return a; } -function Nf(r, t8, e, o = false) { - let n = r.shape[0], s = r.shape[1], a = me([n, e], t8.dtype); +function kf(r15, t8, e, o = false) { + let n = r15.shape[0], s = r15.shape[1], a = me([n, e], t8.dtype); for (let i = 0; i < n; i++) for (let p = 0; p < s; p++) { - let u = r.get(i, p); + let u = r15.get(i, p); if (u < 0) throw new Error("Input x must be non-negative!"); u >= e || (o ? a.set(1, i, u) : t8.size > 0 ? a.set(a.get(i, u) + t8.get(i, p), i, u) : a.set(a.get(i, u) + 1, i, u)); } return a; } -var VS = ze((r, t8) => r & t8); -var O8 = Ye(ja, VS); -var e_ = { kernelName: ja, backendName: "cpu", kernelFunc: O8 }; -function Xt(r) { +var VS = ze((r15, t8) => r15 & t8); +var O8 = Ye(qa, VS); +var t_ = { kernelName: qa, backendName: "cpu", kernelFunc: O8 }; +function jt(r15) { return (t8, e, o) => { let n = y.getArrayFromDType(e, t8.length); for (let s = 0; s < t8.length; ++s) - n[s] = r(t8[s], o); + n[s] = r15(t8[s], o); return n; }; } -function Ie(r, t8, e) { - let o = Xt(t8); - return Ar(r, o, e); +function Ie(r15, t8, e) { + let o = jt(t8); + return Dr(r15, o, e); } -function Ar(r, t8, e) { +function Dr(r15, t8, e) { return ({ inputs: o, attrs: n, backend: s }) => { let { x: a } = o; - Q(a, r); + Q(a, r15); let i = s, p = i.data.get(a.dataId).values, u; if (a.dtype === "string") { if (!Array.isArray(p)) @@ -12259,20 +12260,20 @@ function Ar(r, t8, e) { return i.makeTensorInfo(a.shape, c, l); }; } -var WS = Xt((r) => Math.ceil(r)); -var M8 = Ar(tn, WS); -var t_ = { kernelName: tn, backendName: "cpu", kernelFunc: M8 }; -function mp(r, t8, e, o) { +var WS = jt((r15) => Math.ceil(r15)); +var M8 = Dr(en, WS); +var r_ = { kernelName: en, backendName: "cpu", kernelFunc: M8 }; +function np(r15, t8, e, o) { let n = y.getArrayFromDType(e, y.sizeFromShape(t8)); if (o && e !== "string") { let s = 0; - r.forEach((a) => { + r15.forEach((a) => { let i = y.sizeFromShape(a.shape); n.set(a.vals, s), s += i; }); } else { let s = 0; - r.forEach((a) => { + r15.forEach((a) => { let i = e === "string" ? w.fromUint8ToStringArray(a.vals) : a.vals, p = 0; for (let u = 0; u < a.shape[0]; ++u) { let c = u * t8[1] + s; @@ -12284,27 +12285,27 @@ function mp(r, t8, e, o) { } return n; } -var US = ze((r, t8) => r === t8 ? 1 : 0); -var GS = Ye(yn, US, null, "bool"); -var r_ = { kernelName: yn, backendName: "cpu", kernelFunc: GS }; -var HS = Xt((r) => Math.exp(r)); -var KS = Ar(bn, HS, "float32"); -var o_ = { kernelName: bn, backendName: "cpu", kernelFunc: KS }; -var qS = Xt((r) => Math.expm1(r)); -var L8 = Ar(Cn, qS); -var n_ = { kernelName: Cn, backendName: "cpu", kernelFunc: L8 }; -var jS = Xt((r) => Math.floor(r)); -var B8 = Ar(Sn, jS); -var s_ = { kernelName: Sn, backendName: "cpu", kernelFunc: B8 }; -var XS = ze((r, t8) => Math.floor(r / t8)); -var z8 = Ye(In, XS, null, "int32"); -var a_ = { kernelName: In, backendName: "cpu", kernelFunc: z8 }; -function Tf(r, t8, e, o, n, s, a, i, p) { +var US = ze((r15, t8) => r15 === t8 ? 1 : 0); +var GS = Ye(xn, US, null, "bool"); +var o_ = { kernelName: xn, backendName: "cpu", kernelFunc: GS }; +var HS = jt((r15) => Math.exp(r15)); +var KS = Dr(yn, HS, "float32"); +var n_ = { kernelName: yn, backendName: "cpu", kernelFunc: KS }; +var qS = jt((r15) => Math.expm1(r15)); +var L8 = Dr(bn, qS); +var s_ = { kernelName: bn, backendName: "cpu", kernelFunc: L8 }; +var jS = jt((r15) => Math.floor(r15)); +var B8 = Dr(wn, jS); +var a_ = { kernelName: wn, backendName: "cpu", kernelFunc: B8 }; +var XS = ze((r15, t8) => Math.floor(r15 / t8)); +var z8 = Ye(Sn, XS, null, "int32"); +var i_ = { kernelName: Sn, backendName: "cpu", kernelFunc: z8 }; +function Nf(r15, t8, e, o, n, s, a, i, p) { let u = me([o, s], e); for (let c = 0; c < o; c++) { let l = [], m = 0; for (let d = 0; d < n; d++) { - let f = r[c * n + d]; + let f = r15[c * n + d]; m += f * a[d], l.push(f); } if (m < 0 || m >= p / s) @@ -12314,97 +12315,97 @@ function Tf(r, t8, e, o, n, s, a, i, p) { } return u; } -function _f(r, t8, e) { - let o = me(e, r.dtype); +function Tf(r15, t8, e) { + let o = me(e, r15.dtype); for (let n = 0; n < o.size; ++n) { let a = o.indexToLoc(n).slice(), i = a[0], p = a[2], u = t8.locToIndex([i, p]); a[2] = t8.values[u]; - let c = r.locToIndex(a); - 0 <= c && c < r.values.length && (o.values[n] = r.values[c]); + let c = r15.locToIndex(a); + 0 <= c && c < r15.values.length && (o.values[n] = r15.values[c]); } return o; } -var YS = ze((r, t8) => r > t8 ? 1 : 0); -var V8 = Ye(Nn, YS, null, "bool"); -var i_ = { kernelName: Nn, backendName: "cpu", kernelFunc: V8 }; -var QS = ze((r, t8) => r >= t8 ? 1 : 0); -var W8 = Ye(Tn, QS, null, "bool"); -var u_ = { kernelName: Tn, backendName: "cpu", kernelFunc: W8 }; -var ZS = ze((r, t8) => r < t8 ? 1 : 0); -var U8 = Ye(Dn, ZS, null, "bool"); -var p_ = { kernelName: Dn, backendName: "cpu", kernelFunc: U8 }; -var JS = ze((r, t8) => r <= t8 ? 1 : 0); -var G8 = Ye(An, JS, null, "bool"); -var c_ = { kernelName: An, backendName: "cpu", kernelFunc: G8 }; -function $f(r, t8, e) { - let o = (t8 - r) / (e - 1), n = y.makeZerosTypedArray(e, "float32"); - n[0] = r; +var YS = ze((r15, t8) => r15 > t8 ? 1 : 0); +var V8 = Ye(kn, YS, null, "bool"); +var u_ = { kernelName: kn, backendName: "cpu", kernelFunc: V8 }; +var QS = ze((r15, t8) => r15 >= t8 ? 1 : 0); +var W8 = Ye(Nn, QS, null, "bool"); +var p_ = { kernelName: Nn, backendName: "cpu", kernelFunc: W8 }; +var ZS = ze((r15, t8) => r15 < t8 ? 1 : 0); +var U8 = Ye(Rn, ZS, null, "bool"); +var c_ = { kernelName: Rn, backendName: "cpu", kernelFunc: U8 }; +var JS = ze((r15, t8) => r15 <= t8 ? 1 : 0); +var G8 = Ye(Dn, JS, null, "bool"); +var l_ = { kernelName: Dn, backendName: "cpu", kernelFunc: G8 }; +function _f(r15, t8, e) { + let o = (t8 - r15) / (e - 1), n = y.makeZerosTypedArray(e, "float32"); + n[0] = r15; for (let s = 1; s < n.length; s++) n[s] = n[s - 1] + o; return n; } -var eI = Xt((r) => Math.log(r)); -var H8 = Ar(Pn, eI); -var l_ = { kernelName: Pn, backendName: "cpu", kernelFunc: H8 }; -function Ef(r, t8, e, o) { +var eI = jt((r15) => Math.log(r15)); +var H8 = Dr(Fn, eI); +var m_ = { kernelName: Fn, backendName: "cpu", kernelFunc: H8 }; +function $f(r15, t8, e, o) { let n = y.getTypedArrayFromDType(o, y.sizeFromShape(e)); for (let s = 0; s < n.length; ++s) { - let a = s * t8, i = r[a]; + let a = s * t8, i = r15[a]; for (let p = 0; p < t8; ++p) { - let u = r[a + p]; + let u = r15[a + p]; (Number.isNaN(u) || u > i) && (i = u); } n[s] = i; } return n; } -var tI = ze((r, t8) => Math.max(r, t8)); -var K8 = Ye(Wn, tI); -var m_ = { kernelName: Wn, backendName: "cpu", kernelFunc: K8 }; -var rI = ze((r, t8) => Math.min(r, t8)); -var q8 = Ye(Kn, rI); -var d_ = { kernelName: Kn, backendName: "cpu", kernelFunc: q8 }; -var zl = ze((r, t8) => r * t8); -var j8 = Sc((r, t8, e, o) => ({ real: r * e - t8 * o, imag: r * o + t8 * e })); -var dp = Ye(Yn, zl, j8); -var f_ = { kernelName: Yn, backendName: "cpu", kernelFunc: dp }; -function oI(r, t8, e) { +var tI = ze((r15, t8) => Math.max(r15, t8)); +var K8 = Ye(Vn, tI); +var d_ = { kernelName: Vn, backendName: "cpu", kernelFunc: K8 }; +var rI = ze((r15, t8) => Math.min(r15, t8)); +var q8 = Ye(Hn, rI); +var f_ = { kernelName: Hn, backendName: "cpu", kernelFunc: q8 }; +var Ll = ze((r15, t8) => r15 * t8); +var j8 = xc((r15, t8, e, o) => ({ real: r15 * e - t8 * o, imag: r15 * o + t8 * e })); +var sp = Ye(Xn, Ll, j8); +var h_ = { kernelName: Xn, backendName: "cpu", kernelFunc: sp }; +function oI(r15, t8, e) { let o = y.createScalarValue(-1, e); - return zl([], t8, o, r, e); + return Ll([], t8, o, r15, e); } -function X8(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8; +function X8(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8; Q(o, "neg"); let n = e.data.get(o.dataId).values, [s, a] = oI(n, o.shape, o.dtype); return e.makeTensorInfo(a, o.dtype, s); } -var h_ = { kernelName: pa, backendName: "cpu", kernelFunc: X8 }; -var nI = ze((r, t8) => r !== t8 ? 1 : 0); -var Y8 = Ye(Qn, nI, null, "bool"); -var g_ = { kernelName: Qn, backendName: "cpu", kernelFunc: Y8 }; -function vc(r, t8, e, o, n) { +var g_ = { kernelName: pa, backendName: "cpu", kernelFunc: X8 }; +var nI = ze((r15, t8) => r15 !== t8 ? 1 : 0); +var Y8 = Ye(Yn, nI, null, "bool"); +var x_ = { kernelName: Yn, backendName: "cpu", kernelFunc: Y8 }; +function bc(r15, t8, e, o, n) { let s = t8.length, a = y.sizeFromShape(t8), i = y.computeStrides(t8), p = y.computeStrides(n), u = y.getTypedArrayFromDType(e, y.sizeFromShape(n)); for (let c = 0; c < a; ++c) { let l = y.indexToLoc(c, s, i), m = new Array(l.length); for (let f = 0; f < m.length; f++) m[f] = l[o[f]]; let d = y.locToIndex(m, s, p); - u[d] = r[c]; + u[d] = r15[c]; } return u; } -function It(r) { - let { inputs: t8, attrs: e, backend: o } = r, { x: n } = t8, { perm: s } = e; +function St(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, { x: n } = t8, { perm: s } = e; Q(n, "transpose"); let a = n.shape.length, i = new Array(a); for (let l = 0; l < i.length; l++) i[l] = n.shape[s[l]]; - let p = o.data.get(n.dataId).values, u = vc(p, n.shape, n.dtype, s, i); + let p = o.data.get(n.dataId).values, u = bc(p, n.shape, n.dtype, s, i); return { dataId: o.write(u, i, n.dtype), shape: i, dtype: n.dtype }; } -var x_ = { kernelName: co, backendName: "cpu", kernelFunc: It }; -function sI(r, t8, e, o) { - let [n, s] = w.computeOutAndReduceShapes(r, o), a = dt(t8, "int32"), i = y.makeZerosTypedArray(y.sizeFromShape(n), a), p = y.sizeFromShape(s); +var y_ = { kernelName: po, backendName: "cpu", kernelFunc: St }; +function sI(r15, t8, e, o) { + let [n, s] = w.computeOutAndReduceShapes(r15, o), a = dt(t8, "int32"), i = y.makeZerosTypedArray(y.sizeFromShape(n), a), p = y.sizeFromShape(s); for (let u = 0; u < i.length; ++u) { let c = u * p, l = 1; for (let m = 0; m < p; ++m) @@ -12413,26 +12414,26 @@ function sI(r, t8, e, o) { } return { outVals: i, outShape: n, outDtype: a }; } -function Q8(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, keepDims: a } = o; +function Q8(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, keepDims: a } = o; Q(n, "prod"); let i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = w.getAxesPermutation(p, i), c = p, l = n, m = []; - u != null && (l = It({ inputs: { x: n }, backend: e, attrs: { perm: u } }), m.push(l), c = w.getInnerMostAxes(c.length, i)); + u != null && (l = St({ inputs: { x: n }, backend: e, attrs: { perm: u } }), m.push(l), c = w.getInnerMostAxes(c.length, i)); let d = e.data.get(l.dataId).values, { outVals: f, outShape: h, outDtype: g } = sI(l.shape, l.dtype, d, c), x = h; return a && (x = w.expandShapeToKeepDim(h, p)), m.forEach((b) => e.disposeIntermediateTensorInfo(b)), e.makeTensorInfo(x, g, f); } -var y_ = { kernelName: ns, backendName: "cpu", kernelFunc: Q8 }; -function Z8(r, t8, e) { - r.forEach((o, n) => { +var b_ = { kernelName: os, backendName: "cpu", kernelFunc: Q8 }; +function Z8(r15, t8, e) { + r15.forEach((o, n) => { if (o < 0 || o >= e) { let s = y.indexToLoc(n, t8.length, y.computeStrides(t8)).join(","); throw new Error(`indices[${s}] = ${o} is not in [0, ${e})`); } }); } -function J8(r, t8) { - for (let e = 0; e < r.length; ++e) { - let o = r[e], n = e === r.length - 1 ? t8 : r[e + 1].length; +function J8(r15, t8) { + for (let e = 0; e < r15.length; ++e) { + let o = r15[e], n = e === r15.length - 1 ? t8 : r15[e + 1].length; if (o.length === 0) throw new Error("Ragged splits may not be empty"); if (o[0] < 0) @@ -12444,7 +12445,7 @@ function J8(r, t8) { throw new Error("Ragged splits must be sorted in ascending order"); } } -function eY(r, t8, e, o) { +function eY(r15, t8, e, o) { let n = [], s = 0, a = t8.length - 1 + e.length, i = new Array(a).fill(null).map(() => [0]); J8(e, o); let p = 1; @@ -12454,8 +12455,8 @@ function eY(r, t8, e, o) { for (let l = 1; l < p + 1; ++l) i[u].push(l * c); } - for (let u = 0; u < r.length; ++u) { - let c = r[u], l = r[u] + 1; + for (let u = 0; u < r15.length; ++u) { + let c = r15[u], l = r15[u] + 1; for (let m = 0; m < e.length; ++m) { let d = e[m], f = m + t8.length - 1; if (f >= 0) { @@ -12469,50 +12470,50 @@ function eY(r, t8, e, o) { } return { outSplits: i, valueSlices: n, numValues: s }; } -function tY(r) { +function tY(r15) { let t8 = []; - for (let e = 0; e < r.length; ++e) { - let o = r[e].length, n = y.getArrayFromDType("int32", o); - t8.push(n), r[e].forEach((s, a) => n[a] = s); + for (let e = 0; e < r15.length; ++e) { + let o = r15[e].length, n = y.getArrayFromDType("int32", o); + t8.push(n), r15[e].forEach((s, a) => n[a] = s); } return t8; } -function b_(r, t8) { - let e = r.slice(0, t8); +function C_(r15, t8) { + let e = r15.slice(0, t8); for (; e.length < t8; ) e.push(1); - for (let o = t8; o < r.length; o++) - e[t8 - 1] *= r[o]; + for (let o = t8; o < r15.length; o++) + e[t8 - 1] *= r15[o]; return e; } -function rY(r, t8, e, o, n, s) { - let a = b_(t8, 2)[1], i = b_(s, 2)[1], p = 0; +function rY(r15, t8, e, o, n, s) { + let a = C_(t8, 2)[1], i = C_(s, 2)[1], p = 0; for (let u of e) for (let c = u[0]; c < u[1]; ++c) { for (let l = 0; l < o; ++l) - n[p * i + l] = r[c * a + l]; + n[p * i + l] = r15[c * a + l]; ++p; } } -function oY(r, t8, e, o, n) { +function oY(r15, t8, e, o, n) { let s = t8.slice(); s[0] = n; - let a = y.getArrayFromDType(e, y.sizeFromShape(s)), i = r.length, p = i === 0 ? 0 : i / t8[0]; - return rY(r, t8, o, p, a, s), [a, s]; + let a = y.getArrayFromDType(e, y.sizeFromShape(s)), i = r15.length, p = i === 0 ? 0 : i / t8[0]; + return rY(r15, t8, o, p, a, s), [a, s]; } -function Rf(r, t8, e, o, n, s, a, i) { - if (r.length === 0) +function Ef(r15, t8, e, o, n, s, a, i) { + if (r15.length === 0) throw new Error("paramsNestedSplits must be non empty"); if (t8[0].length === 0) throw new Error("Split tensors must not be scalars"); let p = t8[0][0] - 1; if (Z8(s, a, p), o.length === 0) throw new Error("params.rank must be nonzero"); - let u = o[0], { outSplits: c, valueSlices: l, numValues: m } = eY(s, a, r, u), d = tY(c), f = oY(e, o, n, l, m); + let u = o[0], { outSplits: c, valueSlices: l, numValues: m } = eY(s, a, r15, u), d = tY(c), f = oY(e, o, n, l, m); return [d, f[0], f[1]]; } -var C_ = 2147483647; -function Df(r, t8, e, o, n, s, a) { +var w_ = 2147483647; +function Rf(r15, t8, e, o, n, s, a) { if (t8.length > 1) throw new Error("starts must be a scalar or vector"); if (n.length > 1) @@ -12527,44 +12528,44 @@ function Df(r, t8, e, o, n, s, a) { let l = c.length === 0 ? 1 : c[0], m = y.getArrayFromDType("int32", l + 1); m[0] = 0; for (let g = 0; g < l; ++g) { - let x = i ? r[0] : r[g], b = p ? o[0] : o[g], C = u ? s[0] : s[g]; + let x = i ? r15[0] : r15[g], b = p ? o[0] : o[g], C = u ? s[0] : s[g]; if (C === 0) throw new Error("Requires delta != 0"); let S; if (C > 0 && b < x || C < 0 && b > x) S = 0; - else if (S = Math.ceil(Math.abs((b - x) / C)), S > C_) - throw new Error(`Requires ((limit - start) / delta) <= ${C_}`); + else if (S = Math.ceil(Math.abs((b - x) / C)), S > w_) + throw new Error(`Requires ((limit - start) / delta) <= ${w_}`); m[g + 1] = m[g] + S; } let d = m[l], f = y.getArrayFromDType(e, d), h = 0; for (let g = 0; g < l; ++g) { - let x = m[g + 1] - m[g], b = i ? r[0] : r[g], C = u ? s[0] : s[g]; + let x = m[g + 1] - m[g], b = i ? r15[0] : r15[g], C = u ? s[0] : s[g]; for (let S = 0; S < x; ++S) f[h++] = b, b += C; } return [m, f]; } -var Ao = w.RowPartitionType; -var kc = class { +var Do = w.RowPartitionType; +var aI = class r12 { constructor(t8, e, o, n, s, a, i, p, u, c) { this.shape = t8, this.shapeShape = e, this.values = o, this.valuesShape = n, this.valuesDType = s, this.defaultValue = a, this.defaultValueShape = i, this.rowPartitionValues = p, this.rowPartitionValuesShapes = u, this.rowPartitionTypes = w.getRowPartitionTypesHelper(c), this.raggedRank = w.getRaggedRank(this.rowPartitionTypes); } getRowPartitionTypeByDimension(t8) { - return this.rowPartitionTypes[0] === Ao.FIRST_DIM_SIZE ? this.rowPartitionTypes[t8 + 1] : this.rowPartitionTypes[t8]; + return this.rowPartitionTypes[0] === Do.FIRST_DIM_SIZE ? this.rowPartitionTypes[t8 + 1] : this.rowPartitionTypes[t8]; } getRowPartitionTensor(t8) { - return this.rowPartitionTypes[0] === Ao.FIRST_DIM_SIZE ? this.rowPartitionValues[t8 + 1] : this.rowPartitionValues[t8]; + return this.rowPartitionTypes[0] === Do.FIRST_DIM_SIZE ? this.rowPartitionValues[t8 + 1] : this.rowPartitionValues[t8]; } getMaxWidth(t8) { let e = this.getRowPartitionTensor(t8 - 1); switch (this.getRowPartitionTypeByDimension(t8 - 1)) { - case Ao.VALUE_ROWIDS: - return kc.getMaxWidthValueRowID(e); - case Ao.ROW_SPLITS: - return kc.getMaxWidthRowSplit(e); + case Do.VALUE_ROWIDS: + return r12.getMaxWidthValueRowID(e); + case Do.ROW_SPLITS: + return r12.getMaxWidthRowSplit(e); default: - throw new Error(`Cannot handle partition type ${Ao[this.getRowPartitionTypeByDimension(t8 - 1)]}`); + throw new Error(`Cannot handle partition type ${Do[this.getRowPartitionTypeByDimension(t8 - 1)]}`); } } static getMaxWidthRowSplit(t8) { @@ -12595,7 +12596,7 @@ var kc = class { return []; throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1."); } - return S_(t8, o); + return I_(t8, o); } calculateOutputSize(t8) { let e = this.valuesShape, o = this.defaultValueShape; @@ -12655,14 +12656,14 @@ var kc = class { calculateOutputIndex(t8, e, o, n) { let s = this.getRowPartitionTensor(t8), a = this.getRowPartitionTypeByDimension(t8); switch (a) { - case Ao.VALUE_ROWIDS: + case Do.VALUE_ROWIDS: return this.calculateOutputIndexValueRowID(s, e, o, n); - case Ao.ROW_SPLITS: + case Do.ROW_SPLITS: if (s.length - 1 > e.length) throw new Error(`Row partition size is greater than output size: ${s.length - 1} > ${e.length}`); return this.calculateOutputIndexRowSplit(s, e, o, n); default: - throw new Error(`Unsupported partition type: ${Ao[a]}`); + throw new Error(`Unsupported partition type: ${Do[a]}`); } } getFirstDimensionSize() { @@ -12671,14 +12672,14 @@ var kc = class { throw new Error("No row_partition_types given."); let e = this.rowPartitionTypes[0]; switch (e) { - case Ao.FIRST_DIM_SIZE: + case Do.FIRST_DIM_SIZE: return t8[0]; - case Ao.VALUE_ROWIDS: + case Do.VALUE_ROWIDS: throw new Error("Cannot handle VALUE_ROWIDS in first dimension."); - case Ao.ROW_SPLITS: + case Do.ROW_SPLITS: return this.rowPartitionValuesShapes[0][0] - 1; default: - throw new Error(`Cannot handle type ${Ao[e]}`); + throw new Error(`Cannot handle type ${Do[e]}`); } } compute() { @@ -12688,7 +12689,7 @@ var kc = class { n[n.length - 1] = 1; for (let p = n.length - 2; p >= 0; --p) n[p] = n[p + 1] * o[p + 1]; - let s = S_(o, false), a = y.getArrayFromDType(this.valuesDType, y.sizeFromShape(s)); + let s = I_(o, false), a = y.getArrayFromDType(this.valuesDType, y.sizeFromShape(s)); if (n[0] * o[0] > 0) { let p = this.calculateFirstParentOutputIndex(e, n[0], o[0]); for (let u = 1; u <= this.raggedRank; ++u) @@ -12707,7 +12708,7 @@ var kc = class { let f = this.defaultValueShape; De(() => { let h = W(c, f); - c = iu(h, i).dataSync(); + c = ou(h, i).dataSync(); }); } let l = 0, m = 0, d = 0; @@ -12719,7 +12720,7 @@ var kc = class { } if (m < d) { let g = s.subarray(l * p), x = a.subarray(m * p), b = (d - m) * p; - w_(x, g, b); + S_(x, g, b); } if (f >= u) { let g = o.length; @@ -12731,19 +12732,19 @@ var kc = class { else for (; h > d; ) { let g = a.slice(d * p); - w_(g, c, p), ++d; + S_(g, c, p), ++d; } h < 0 ? (l = f + 1, m = d) : (l = f, m = d, d = m + 1); } } }; -function w_(r, t8, e) { +function S_(r15, t8, e) { for (let o = 0; o < e; o++) - r[o] = t8[o]; + r15[o] = t8[o]; } -function S_(r, t8) { +function I_(r15, t8) { let e = []; - for (let o of r) { + for (let o of r15) { if (o < 0) { if (!t8) throw new Error(`Dimension ${o} must be >= 0`); @@ -12755,24 +12756,24 @@ function S_(r, t8) { } return e; } -function Af(r, t8, e, o, n, s, a, i, p, u) { - return new kc(r, t8, e, o, n, s, a, i, p, u).compute(); +function Df(r15, t8, e, o, n, s, a, i, p, u) { + return new aI(r15, t8, e, o, n, s, a, i, p, u).compute(); } -function fp(r, t8, e, o) { - let n = r === t8, s = r < t8 && e < 0, a = t8 < r && e > 1; +function ap(r15, t8, e, o) { + let n = r15 === t8, s = r15 < t8 && e < 0, a = t8 < r15 && e > 1; if (n || s || a) return y.makeZerosTypedArray(0, o); - let i = Math.abs(Math.ceil((t8 - r) / e)), p = y.makeZerosTypedArray(i, o); - t8 < r && e === 1 && (e = -1), p[0] = r; + let i = Math.abs(Math.ceil((t8 - r15) / e)), p = y.makeZerosTypedArray(i, o); + t8 < r15 && e === 1 && (e = -1), p[0] = r15; for (let u = 1; u < p.length; u++) p[u] = p[u - 1] + e; return p; } -var aI = Xt((r) => 1 / Math.sqrt(r)); -var nY = Ar(ms, aI); -var I_ = { kernelName: ms, backendName: "cpu", kernelFunc: nY }; -function zs(r, t8, e, o, n, s, a, i, p, u) { - let c = [o / n, n], l = r.values, m = t8.values; +var iI = jt((r15) => 1 / Math.sqrt(r15)); +var nY = Dr(ls, iI); +var v_ = { kernelName: ls, backendName: "cpu", kernelFunc: nY }; +function zs(r15, t8, e, o, n, s, a, i, p, u) { + let c = [o / n, n], l = r15.values, m = t8.values; if (o === 0) return me(e, t8.dtype); let d = p instanceof tt ? p : me(c, t8.dtype); @@ -12790,32 +12791,32 @@ function zs(r, t8, e, o, n, s, a, i, p, u) { } return d; } -var v_ = Xt((r) => 1 / (1 + Math.exp(-r))); -var iI = Ie(Cs, (r) => 1 / (1 + Math.exp(-r))); -var k_ = { kernelName: Cs, backendName: "cpu", kernelFunc: iI }; -function hp(r, t8, e, o, n) { - let s = ct.isSliceContinous(o, t8, e), a = y.sizeFromShape(e), i = y.computeStrides(o); +var k_ = jt((r15) => 1 / (1 + Math.exp(-r15))); +var uI = Ie(bs, (r15) => 1 / (1 + Math.exp(-r15))); +var N_ = { kernelName: bs, backendName: "cpu", kernelFunc: uI }; +function ip(r15, t8, e, o, n) { + let s = pt.isSliceContinous(o, t8, e), a = y.sizeFromShape(e), i = y.computeStrides(o); if (s) { - let l = ct.computeFlatOffset(t8, i); - return n === "string" ? r.slice(l, l + a) : r.subarray(l, l + a); + let l = pt.computeFlatOffset(t8, i); + return n === "string" ? r15.slice(l, l + a) : r15.subarray(l, l + a); } - let p = n === "string" ? w.fromUint8ToStringArray(r) : r, u = me(o, n, p), c = me(e, n); + let p = n === "string" ? w.fromUint8ToStringArray(r15) : r15, u = me(o, n, p), c = me(e, n); for (let l = 0; l < c.size; ++l) { let m = c.indexToLoc(l), d = m.map((f, h) => f + t8[h]); c.set(u.get(...d), ...m); } return n === "string" ? w.fromStringArrayToUint8(c.values) : c.values; } -function Fo(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { begin: s, size: a } = o; +function Ao(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { begin: s, size: a } = o; Q(n, "slice"); - let [i, p] = ct.parseSliceParams(n, s, a); - ct.assertParamsValid(n, i, p); - let u = e.data.get(n.dataId).values, c = hp(u, i, p, n.shape, n.dtype); + let [i, p] = pt.parseSliceParams(n, s, a); + pt.assertParamsValid(n, i, p); + let u = e.data.get(n.dataId).values, c = ip(u, i, p, n.shape, n.dtype); return e.makeTensorInfo(p, n.dtype, c); } -var N_ = { kernelName: ha, backendName: "cpu", kernelFunc: Fo }; -function Ff(r, t8, e, o, n, s, a) { +var T_ = { kernelName: ha, backendName: "cpu", kernelFunc: Ao }; +function Af(r15, t8, e, o, n, s, a) { let i = t8[0], p = s[0], u = new Array(p), c = new Array(i), l = t8[1]; if (p === 0) { if (i !== 0) @@ -12825,7 +12826,7 @@ function Ff(r, t8, e, o, n, s, a) { } let m = true, d = 0, f = new Array(p).fill(0); for (let g = 0; g < i; ++g) { - let x = r[g * l]; + let x = r15[g * l]; if (x < 0) throw new Error(w.getSparseFillEmptyRowsNegativeIndexErrorMessage(g, x)); if (x >= p) @@ -12838,17 +12839,17 @@ function Ff(r, t8, e, o, n, s, a) { u[g] = x, h = h && !x, f[g] = Math.max(f[g], 1), g > 0 && (f[g] += f[g - 1]); } if (h && m) { - let g = r, x = o; + let g = r15, x = o; for (let b = 0; b < i; ++b) c[b] = b; return [g, [i, l], x, u, c]; } else { let g = f[p - 1], x = y.getArrayFromDType(e, g * l), b = y.getArrayFromDType(n, g), C = new Array(p).fill(0); for (let S = 0; S < i; ++S) { - let k = r[S * l], _ = C[k], E = (k === 0 ? 0 : f[k - 1]) + _; + let k = r15[S * l], _ = C[k], E = (k === 0 ? 0 : f[k - 1]) + _; C[k]++; for (let R = 0; R < l; ++R) - x[E * l + R] = r[S * l + R]; + x[E * l + R] = r15[S * l + R]; b[E] = o[S], c[S] = E; } for (let S = 0; S < p; ++S) @@ -12862,7 +12863,7 @@ function Ff(r, t8, e, o, n, s, a) { return [x, [g, l], b, u, c]; } } -function Pf(r, t8, e, o, n) { +function Ff(r15, t8, e, o, n) { let s = y.sizeFromShape(o), a = t8[0], i = n.length, p = [], u = 1, c = -1; for (let g = 0; g < i; ++g) { let x = n[g]; @@ -12902,14 +12903,14 @@ function Pf(r, t8, e, o, n) { for (let g = 0; g < a; ++g) { let x = 0; for (let b = 0; b < m; ++b) - x += r[g * m + b] * d[b]; + x += r15[g * m + b] * d[b]; for (let b = 0; b < i; ++b) h[g * i + b] = Math.trunc(x / f[b]), x %= f[b]; } return [h, [a, i], p]; } -function Nc(r, t8, e, o, n, s = false, a = 0) { - let i = o.length, p = [t8[0], r.length / t8[0]], u = p[1], l = i > 0 ? n[i - 1] + 1 : 0; +function Cc(r15, t8, e, o, n, s = false, a = 0) { + let i = o.length, p = [t8[0], r15.length / t8[0]], u = p[1], l = i > 0 ? n[i - 1] + 1 : 0; if (l < 0) throw new Error(w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); let m = t8.slice(); @@ -12938,7 +12939,7 @@ function Nc(r, t8, e, o, n, s = false, a = 0) { if (k < 0 || k >= p[0]) throw new Error(w.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(S, o[S], p[0])); for (let _ = 0; _ < u; _++) - f[b * u + _] += r[k * u + _]; + f[b * u + _] += r15[k * u + _]; } if (s) for (let S = 0; S < u; S++) @@ -12948,23 +12949,23 @@ function Nc(r, t8, e, o, n, s = false, a = 0) { } return x < l && f.fill(a, x * u, l * u), [f, m]; } -var T_ = Xt((r) => Math.sqrt(r)); -var sY = Ie(Ss, (r) => Math.sqrt(r)); -var __ = { kernelName: Ss, backendName: "cpu", kernelFunc: sY }; -var uI = ze((r, t8) => { - let e = r - t8; +var __ = jt((r15) => Math.sqrt(r15)); +var sY = Ie(ws, (r15) => Math.sqrt(r15)); +var $_ = { kernelName: ws, backendName: "cpu", kernelFunc: sY }; +var pI = ze((r15, t8) => { + let e = r15 - t8; return e * e; }); -var aY = Ye(Ns, uI); -var $_ = { kernelName: Ns, backendName: "cpu", kernelFunc: aY }; -var pI = Xt((r, t8) => { +var aY = Ye(ks, pI); +var E_ = { kernelName: ks, backendName: "cpu", kernelFunc: aY }; +var cI = jt((r15, t8) => { let { pattern: e, replaceGlobal: o, rewrite: n } = t8; - return r.replace(new RegExp(e, o ? "g" : ""), n); + return r15.replace(new RegExp(e, o ? "g" : ""), n); }); -var iY = Ar(Ou, pI); -var E_ = { kernelName: Ou, backendName: "cpu", kernelFunc: iY }; -function Of(r, t8, e, o) { - let n = me(r, t8.dtype); +var iY = Dr($u, cI); +var R_ = { kernelName: $u, backendName: "cpu", kernelFunc: iY }; +function Pf(r15, t8, e, o) { + let n = me(r15, t8.dtype); for (let s = 0; s < n.size; s++) { let a = n.indexToLoc(s), i = new Array(a.length); for (let p = 0; p < i.length; p++) @@ -12973,7 +12974,7 @@ function Of(r, t8, e, o) { } return n; } -var cI = class { +var lI = class { constructor(t8, e, o, n, s, a) { this.separator = y.encodeString(t8), this.nGramWidths = e, this.leftPad = y.encodeString(o), this.rightPad = y.encodeString(n), this.padWidth = s, this.preserveShort = a; } @@ -13055,38 +13056,38 @@ var cI = class { return [i, a]; } }; -function gp(r, t8, e, o, n, s, a, i) { - return new cI(e, o, n, s, a, i).compute(r, t8); +function up(r15, t8, e, o, n, s, a, i) { + return new lI(e, o, n, s, a, i).compute(r15, t8); } -function uY(r, t8, e, o) { - if (!r.length) +function uY(r15, t8, e, o) { + if (!r15.length) return; if (t8.length === 0) { - for (let s = 0; s < r.length; ++s) - o.push(r.subarray(s, s + 1)); + for (let s = 0; s < r15.length; ++s) + o.push(r15.subarray(s, s + 1)); return; } if (t8.length === 1) { - let s = t8[0], a = r.indexOf(s); + let s = t8[0], a = r15.indexOf(s); for (; a !== -1; ) { - let i = r.subarray(0, a); - (!e || i.length !== 0) && o.push(i), r = r.subarray(a + 1), a = r.indexOf(s); + let i = r15.subarray(0, a); + (!e || i.length !== 0) && o.push(i), r15 = r15.subarray(a + 1), a = r15.indexOf(s); } - (!e || r.length !== 0) && o.push(r); + (!e || r15.length !== 0) && o.push(r15); return; } let n = 0; - for (let s = 0; s < r.length + 1; s++) - if (s === r.length || t8.indexOf(r[s]) !== -1) { - let a = r.subarray(n, s); + for (let s = 0; s < r15.length + 1; s++) + if (s === r15.length || t8.indexOf(r15[s]) !== -1) { + let a = r15.subarray(n, s); (!e || a.length !== 0) && o.push(a), n = s + 1; } } -function xp(r, t8, e) { - let o = r.length, n = [], s = 0, a = 0, i = new Array(o); +function pp(r15, t8, e) { + let o = r15.length, n = [], s = 0, a = 0, i = new Array(o); for (let m = 0; m < o; ++m) { let d = n.length; - uY(r[m], t8, e, n); + uY(r15[m], t8, e, n); let f = n.length - d; i[m] = f, s += f, a = Math.max(a, f); } @@ -13096,55 +13097,55 @@ function xp(r, t8, e) { p[l * 2] = m, p[l * 2 + 1] = d, u[l] = n[l], ++l; return [p, u, c]; } -function yp(r, t8) { - let e = y.getArrayFromDType("int32", r.length); - for (let o = 0; o < r.length; ++o) - e[o] = y.fingerPrint64(r[o]).modulo(t8).getLowBitsUnsigned(); +function cp(r15, t8) { + let e = y.getArrayFromDType("int32", r15.length); + for (let o = 0; o < r15.length; ++o) + e[o] = y.fingerPrint64(r15[o]).modulo(t8).getLowBitsUnsigned(); return e; } -var lI = ze((r, t8) => r - t8); -var pY = Sc((r, t8, e, o) => ({ real: r - e, imag: t8 - o })); -var Vl = Ye(_s, lI, pY); -var R_ = { kernelName: _s, backendName: "cpu", kernelFunc: Vl }; -function Mf(r, t8) { - let e = new Array(r.rank); +var mI = ze((r15, t8) => r15 - t8); +var pY = xc((r15, t8, e, o) => ({ real: r15 - e, imag: t8 - o })); +var Bl = Ye(Ts, mI, pY); +var D_ = { kernelName: Ts, backendName: "cpu", kernelFunc: Bl }; +function Of(r15, t8) { + let e = new Array(r15.rank); for (let n = 0; n < e.length; n++) - e[n] = r.shape[n] * t8[n]; - let o = me(e, r.dtype); + e[n] = r15.shape[n] * t8[n]; + let o = me(e, r15.dtype); for (let n = 0; n < o.values.length; ++n) { - let s = o.indexToLoc(n), a = new Array(r.rank); + let s = o.indexToLoc(n), a = new Array(r15.rank); for (let p = 0; p < a.length; p++) - a[p] = s[p] % r.shape[p]; - let i = r.locToIndex(a); - o.values[n] = r.values[i]; + a[p] = s[p] % r15.shape[p]; + let i = r15.locToIndex(a); + o.values[n] = r15.values[i]; } return o; } -var Wl = (r, t8) => { - let e = t8.value - r.value; - return e === 0 ? r.index - t8.index : e; +var zl = (r15, t8) => { + let e = t8.value - r15.value; + return e === 0 ? r15.index - t8.index : e; }; -function D_(r, t8, e = 0, o = r.length - 1) { +function A_(r15, t8, e = 0, o = r15.length - 1) { for (; o > e; ) { if (o - e > 600) { let i = o - e + 1, p = t8 - e + 1, u = Math.log(i), c = 0.5 * Math.exp(2 * u / 3), l = 0.5 * Math.sqrt(u * c * (i - c) / i) * Math.sign(p - i / 2), m = Math.max(e, Math.floor(t8 - p * c / i + l)), d = Math.min(o, Math.floor(t8 + (i - p) * c / i + l)); - D_(r, t8, m, d); + A_(r15, t8, m, d); } - let n = r[t8], s = e, a = o; - for (y.swap(r, e, t8), Wl(r[o], n) > 0 && y.swap(r, e, o); s < a; ) { - for (y.swap(r, s, a), s++, a--; Wl(r[s], n) < 0; ) + let n = r15[t8], s = e, a = o; + for (y.swap(r15, e, t8), zl(r15[o], n) > 0 && y.swap(r15, e, o); s < a; ) { + for (y.swap(r15, s, a), s++, a--; zl(r15[s], n) < 0; ) s = s + 1; - for (; Wl(r[a], n) > 0; ) + for (; zl(r15[a], n) > 0; ) a = a - 1; } - Wl(r[e], n) === 0 ? y.swap(r, e, a) : (a = a + 1, y.swap(r, a, o)), a <= t8 && (e = a + 1), t8 <= a && (o = a - 1); + zl(r15[e], n) === 0 ? y.swap(r15, e, a) : (a = a + 1, y.swap(r15, a, o)), a <= t8 && (e = a + 1), t8 <= a && (o = a - 1); } } -function Lf(r, t8, e, o, n) { - let s = t8[t8.length - 1], [a, i] = [r.length / s, s], p = y.getTypedArrayFromDType(e, a * o), u = y.getTypedArrayFromDType("int32", a * o); +function Mf(r15, t8, e, o, n) { + let s = t8[t8.length - 1], [a, i] = [r15.length / s, s], p = y.getTypedArrayFromDType(e, a * o), u = y.getTypedArrayFromDType("int32", a * o); for (let l = 0; l < a; l++) { - let m = l * i, d = r.subarray(m, m + i), f = new Array(d.length); - d.forEach((b, C) => f[C] = { value: b, index: C }), o < f.length && (D_(f, o), f = f.slice(0, o)), n && f.sort(Wl); + let m = l * i, d = r15.subarray(m, m + i), f = new Array(d.length); + d.forEach((b, C) => f[C] = { value: b, index: C }), o < f.length && (A_(f, o), f = f.slice(0, o)), n && f.sort(zl); let h = l * o, g = p.subarray(h, h + o), x = u.subarray(h, h + o); for (let b = 0; b < o; b++) g[b] = f[b].value, x[b] = f[b].index; @@ -13152,18 +13153,18 @@ function Lf(r, t8, e, o, n) { let c = t8.slice(); return c[c.length - 1] = o, [me(c, e, p), me(c, "int32", u)]; } -function bp(r, t8, e, o) { +function lp(r15, t8, e, o) { let n = y.parseAxisParam(t8, e)[0], s = [1, e[0], 1]; for (let f = 0; f < n; f++) s[0] *= e[f]; s[1] = e[n]; for (let f = n + 1; f < e.length; f++) s[2] *= e[f]; - let a = /* @__PURE__ */ new Map(), i = new Int32Array(e[n]), p = new tt(s, o, r), u = [], c = s[0] === 1 && s[2] === 1; + let a = /* @__PURE__ */ new Map(), i = new Int32Array(e[n]), p = new tt(s, o, r15), u = [], c = s[0] === 1 && s[2] === 1; for (let f = 0; f < e[n]; f++) { let h; if (c) - h = r[f].toString(); + h = r15[f].toString(); else { let x = []; for (let b = 0; b < s[0]; b++) @@ -13190,50 +13191,50 @@ function bp(r, t8, e, o) { let d = e.slice(); return d[n] = l[1], { outputValues: m.values, outputShape: d, indices: i }; } -var cY = "4.11.0"; -su("cpu", () => new xu(), 1); -var mI = Ie(gn, (r) => r >= 0 ? r : Math.exp(r) - 1); -var A_ = { kernelName: gn, backendName: "cpu", kernelFunc: mI }; -function dI(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { alpha: s } = o; +var cY = "4.14.0"; +tu("cpu", () => new hc(), 1); +var dI = Ie(hn, (r15) => r15 >= 0 ? r15 : Math.exp(r15) - 1); +var F_ = { kernelName: hn, backendName: "cpu", kernelFunc: dI }; +function fI(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { alpha: s } = o; Q([n], "leakyRelu"); let a = y.sizeFromShape(n.shape), i = e.data.get(n.dataId).values, p = y.getTypedArrayFromDType("float32", a); for (let u = 0; u < i.length; u++) p[u] = i[u] < 0 ? s * i[u] : i[u]; return e.makeTensorInfo(n.shape, "float32", p); } -var F_ = { kernelName: Rn, backendName: "cpu", kernelFunc: dI }; -var lY = ze((r, t8) => r < 0 ? t8 * r : r); -function fI(r) { - let { inputs: t8, backend: e } = r, { x: o, alpha: n } = t8; +var P_ = { kernelName: En, backendName: "cpu", kernelFunc: fI }; +var lY = ze((r15, t8) => r15 < 0 ? t8 * r15 : r15); +function hI(r15) { + let { inputs: t8, backend: e } = r15, { x: o, alpha: n } = t8; Q([o, n], "prelu"); let s = e.data.get(o.dataId).values, a = e.data.get(n.dataId).values, [i, p] = lY(o.shape, n.shape, s, a, "float32"); return e.makeTensorInfo(p, "float32", i); } -var P_ = { kernelName: os, backendName: "cpu", kernelFunc: fI }; -var hI = Ie(as, (r) => Math.max(0, r)); -var O_ = { kernelName: as, backendName: "cpu", kernelFunc: hI }; -var gI = Ie(ps, (r) => Math.min(Math.max(0, r), 6)); -var M_ = { kernelName: ps, backendName: "cpu", kernelFunc: gI }; -function Cp(r, t8, e, o, n) { +var O_ = { kernelName: rs, backendName: "cpu", kernelFunc: hI }; +var gI = Ie(ss, (r15) => Math.max(0, r15)); +var M_ = { kernelName: ss, backendName: "cpu", kernelFunc: gI }; +var xI = Ie(us, (r15) => Math.min(Math.max(0, r15), 6)); +var L_ = { kernelName: us, backendName: "cpu", kernelFunc: xI }; +function mp(r15, t8, e, o, n) { if (e === "linear") - return mr({ inputs: { x: t8 }, backend: r }); + return lr({ inputs: { x: t8 }, backend: r15 }); if (e === "relu") - return hI({ inputs: { x: t8 }, backend: r }); + return gI({ inputs: { x: t8 }, backend: r15 }); if (e === "elu") - return mI({ inputs: { x: t8 }, backend: r }); + return dI({ inputs: { x: t8 }, backend: r15 }); if (e === "relu6") - return gI({ inputs: { x: t8 }, backend: r }); + return xI({ inputs: { x: t8 }, backend: r15 }); if (e === "prelu") - return fI({ inputs: { x: t8, alpha: o }, backend: r }); + return hI({ inputs: { x: t8, alpha: o }, backend: r15 }); if (e === "leakyrelu") - return dI({ inputs: { x: t8 }, backend: r, attrs: { alpha: n } }); + return fI({ inputs: { x: t8 }, backend: r15, attrs: { alpha: n } }); if (e === "sigmoid") - return iI({ inputs: { x: t8 }, backend: r }); + return uI({ inputs: { x: t8 }, backend: r15 }); throw new Error(`Activation ${e} has not been implemented for the CPU backend.`); } -function Ve(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { shape: s } = o, a = y.sizeFromShape(n.shape), i = y.inferFromImplicitShape(s, a), p = y.sizeFromShape(i); +function Ve(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { shape: s } = o, a = y.sizeFromShape(n.shape), i = y.inferFromImplicitShape(s, a), p = y.sizeFromShape(i); y.assert(a === p, () => `The new shape (${i}) has ${p} elements and the old shape (${n.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), e.incRef(n.dataId); let u = e.data.get(n.dataId); if (u.complexTensorInfos != null) { @@ -13242,11 +13243,11 @@ function Ve(r) { } return { dataId: n.dataId, shape: i, dtype: n.dtype }; } -var L_ = { kernelName: da, backendName: "cpu", kernelFunc: Ve }; -function xI(r) { - let { inputs: t8, backend: e, attrs: o } = r, { a: n, b: s } = t8, { transposeA: a, transposeB: i } = o; +var B_ = { kernelName: da, backendName: "cpu", kernelFunc: Ve }; +function yI(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { a: n, b: s } = t8, { transposeA: a, transposeB: i } = o; Q([n, s], "matMul"); - let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), C = Ir.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]); + let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), C = Sr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]); y.assert(c === l, () => `Error in matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`); let S = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = Ve({ inputs: { x: n }, backend: e, attrs: { shape: S } }), E = Ve({ inputs: { x: s }, backend: e, attrs: { shape: k } }), R = a ? _.shape[1] : _.shape[2], D = a ? _.shape[2] : _.shape[1], P = i ? E.shape[1] : E.shape[2], O = Math.max(g, x), M = e.data.get(_.dataId).values, L = e.data.get(E.dataId).values, B = y.computeStrides(_.shape), z = y.computeStrides(E.shape), [U, j, q] = a ? [B[0], 1, B[1]] : [B[0], B[1], 1], [Y, J, re] = i ? [1, z[1], z[0]] : [z[1], 1, z[0]], ne = D * P, ee = me([O, D, P], _.dtype), oe = ee.values, ie = e.blockSize; for (let le = 0; le < O; le++) { @@ -13255,16 +13256,16 @@ function xI(r) { let Fe = Math.min(ve + ie, D); for (let Pe = 0; Pe < P; Pe += ie) { let st = Math.min(Pe + ie, P); - for (let lt = 0; lt < R; lt += ie) { - let Ge = Math.min(lt + ie, R); - for (let mt = ve; mt < Fe; mt++) + for (let ct = 0; ct < R; ct += ie) { + let Ge = Math.min(ct + ie, R); + for (let lt = ve; lt < Fe; lt++) for (let it = Pe; it < st; it++) { - let gt = 0; - for (let xt = lt; xt < Ge; xt++) { - let Lr = M[be * U + mt * j + xt * q], Lt = L[xt * Y + it * J + _e * re]; - gt += Lr * Lt; + let ht = 0; + for (let gt = ct; gt < Ge; gt++) { + let Mr = M[be * U + lt * j + gt * q], Mt = L[gt * Y + it * J + _e * re]; + ht += Mr * Mt; } - oe[le * ne + (mt * P + it)] += gt; + oe[le * ne + (lt * P + it)] += ht; } } } @@ -13272,21 +13273,21 @@ function xI(r) { } return e.disposeIntermediateTensorInfo(_), e.disposeIntermediateTensorInfo(E), e.makeTensorInfo(C, ee.dtype, ee.values); } -var B_ = { kernelName: Jo, backendName: "cpu", kernelFunc: xI }; -function mY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = t8, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m, d, f, h = []; - m = xI({ inputs: { a: n, b: s }, attrs: { transposeA: p, transposeB: u }, backend: e }), a && (d = Oa({ inputs: { a: m, b: a }, backend: e }), h.push(m), m = d), c && (f = Cp(e, m, c, i, l), h.push(m), m = f); +var z_ = { kernelName: Zo, backendName: "cpu", kernelFunc: yI }; +function mY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { a: n, b: s, bias: a, preluActivationWeights: i } = t8, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m, d, f, h = []; + m = yI({ inputs: { a: n, b: s }, attrs: { transposeA: p, transposeB: u }, backend: e }), a && (d = Pa({ inputs: { a: m, b: a }, backend: e }), h.push(m), m = d), c && (f = mp(e, m, c, i, l), h.push(m), m = f); for (let x of h) e.disposeIntermediateTensorInfo(x); return m; } -var z_ = { kernelName: Io, backendName: "cpu", kernelFunc: mY }; -var dY = Ie(Wo, (r) => Math.acos(r)); -var V_ = { kernelName: Wo, backendName: "cpu", kernelFunc: dY }; -var fY = Ie(Uo, (r) => Math.acosh(r)); -var W_ = { kernelName: Uo, backendName: "cpu", kernelFunc: fY }; -function hY(r) { - let { inputs: t8, backend: e } = r, o = t8; +var V_ = { kernelName: So, backendName: "cpu", kernelFunc: mY }; +var dY = Ie(Vo, (r15) => Math.acos(r15)); +var W_ = { kernelName: Vo, backendName: "cpu", kernelFunc: dY }; +var fY = Ie(Wo, (r15) => Math.acosh(r15)); +var U_ = { kernelName: Wo, backendName: "cpu", kernelFunc: fY }; +function hY(r15) { + let { inputs: t8, backend: e } = r15, o = t8; Q(t8, "addN"); let n = o.map((i) => e.data.get(i.dataId).values), s = me(o[0].shape, o[0].dtype), a = s.values; for (let i = 0; i < o.length; i++) { @@ -13296,12 +13297,12 @@ function hY(r) { } return e.makeTensorInfo(s.shape, s.dtype, s.values); } -var U_ = { kernelName: Go, backendName: "cpu", kernelFunc: hY }; -function gY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, keepDims: a } = o; +var G_ = { kernelName: Uo, backendName: "cpu", kernelFunc: hY }; +function gY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, keepDims: a } = o; Q(n, "all"); let i = y.parseAxisParam(s, n.shape), p = i, u = w.getAxesPermutation(p, n.shape.length), c = n; - u != null && (c = It({ inputs: { x: n }, backend: e, attrs: { perm: u } }), p = w.getInnerMostAxes(p.length, n.shape.length)), w.assertAxesAreInnerMostDims("all", p, c.shape.length); + u != null && (c = St({ inputs: { x: n }, backend: e, attrs: { perm: u } }), p = w.getInnerMostAxes(p.length, n.shape.length)), w.assertAxesAreInnerMostDims("all", p, c.shape.length); let [l, m] = w.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = e.data.get(c.dataId).values; for (let x = 0; x < f.length; ++x) { let b = x * d, C = h[b]; @@ -13319,12 +13320,12 @@ function gY(r) { } return g; } -var G_ = { kernelName: Ho, backendName: "cpu", kernelFunc: gY }; -function xY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, keepDims: a } = o; +var H_ = { kernelName: Go, backendName: "cpu", kernelFunc: gY }; +function xY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, keepDims: a } = o; Q(n, "any"); let i = y.parseAxisParam(s, n.shape), p = i, u = w.getAxesPermutation(p, n.shape.length), c = n; - u != null && (c = It({ inputs: { x: n }, backend: e, attrs: { perm: u } }), p = w.getInnerMostAxes(p.length, n.shape.length)), w.assertAxesAreInnerMostDims("any", p, c.shape.length); + u != null && (c = St({ inputs: { x: n }, backend: e, attrs: { perm: u } }), p = w.getInnerMostAxes(p.length, n.shape.length)), w.assertAxesAreInnerMostDims("any", p, c.shape.length); let [l, m] = w.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = e.data.get(c.dataId).values; for (let x = 0; x < f.length; ++x) { let b = x * d, C = h[b]; @@ -13342,12 +13343,12 @@ function xY(r) { } return g; } -var H_ = { kernelName: Ko, backendName: "cpu", kernelFunc: xY }; -function yY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s } = o; +var K_ = { kernelName: Ho, backendName: "cpu", kernelFunc: xY }; +function yY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s } = o; Q(n, "argMax"); let a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = It({ inputs: { x: n }, backend: e, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], w.assertAxesAreInnerMostDims("argMax", a, p.shape.length); + i != null && (p = St({ inputs: { x: n }, backend: e, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], w.assertAxesAreInnerMostDims("argMax", a, p.shape.length); let [c, l] = w.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = e.data.get(p.dataId).values; for (let g = 0; g < d.length; ++g) { let x = g * f, b = h[x], C = 0; @@ -13359,12 +13360,12 @@ function yY(r) { } return u.forEach((g) => e.disposeIntermediateTensorInfo(g)), e.makeTensorInfo(c, "int32", d); } -var K_ = { kernelName: Ys, backendName: "cpu", kernelFunc: yY }; -function bY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s } = o; +var q_ = { kernelName: Ys, backendName: "cpu", kernelFunc: yY }; +function bY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s } = o; Q(n, "argMin"); let a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = It({ inputs: { x: n }, backend: e, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], w.assertAxesAreInnerMostDims("argMin", a, p.shape.length); + i != null && (p = St({ inputs: { x: n }, backend: e, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], w.assertAxesAreInnerMostDims("argMin", a, p.shape.length); let [c, l] = w.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = e.data.get(p.dataId).values; for (let g = 0; g < d.length; ++g) { let x = g * f, b = h[x], C = 0; @@ -13376,19 +13377,19 @@ function bY(r) { } return u.forEach((g) => e.disposeIntermediateTensorInfo(g)), e.makeTensorInfo(c, "int32", d); } -var q_ = { kernelName: Qs, backendName: "cpu", kernelFunc: bY }; -var CY = Ie(qo, (r) => Math.asin(r)); -var j_ = { kernelName: qo, backendName: "cpu", kernelFunc: CY }; -var wY = Ie(jo, (r) => Math.asinh(r)); -var X_ = { kernelName: jo, backendName: "cpu", kernelFunc: wY }; -var SY = Ie(Xo, (r) => Math.atan(r)); -var Y_ = { kernelName: Xo, backendName: "cpu", kernelFunc: SY }; -var IY = ze((r, t8) => Math.atan2(r, t8)); -var vY = Ye(Qo, IY); -var Q_ = { kernelName: Qo, backendName: "cpu", kernelFunc: vY }; -var kY = Ie(Yo, (r) => Math.atanh(r)); -var Z_ = { kernelName: Yo, backendName: "cpu", kernelFunc: kY }; -function _c(r, t8, e, o, n, s) { +var j_ = { kernelName: Qs, backendName: "cpu", kernelFunc: bY }; +var CY = Ie(Ko, (r15) => Math.asin(r15)); +var X_ = { kernelName: Ko, backendName: "cpu", kernelFunc: CY }; +var wY = Ie(qo, (r15) => Math.asinh(r15)); +var Y_ = { kernelName: qo, backendName: "cpu", kernelFunc: wY }; +var SY = Ie(jo, (r15) => Math.atan(r15)); +var Q_ = { kernelName: jo, backendName: "cpu", kernelFunc: SY }; +var IY = ze((r15, t8) => Math.atan2(r15, t8)); +var vY = Ye(Yo, IY); +var Z_ = { kernelName: Yo, backendName: "cpu", kernelFunc: vY }; +var kY = Ie(Xo, (r15) => Math.atanh(r15)); +var J_ = { kernelName: Xo, backendName: "cpu", kernelFunc: kY }; +function Sc(r15, t8, e, o, n, s) { let a = n.strideHeight, i = n.strideWidth, p = n.dilationHeight, u = n.dilationWidth, c = n.effectiveFilterHeight, l = n.effectiveFilterWidth, m = n.padInfo.top, d = n.padInfo.left, f = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, h = me(n.outShape, e), g = h.values, x = n.outShape[1] * n.outShape[2] * n.outShape[3], b = n.outShape[2] * n.outShape[3], C = n.outShape[3]; for (let S = 0; S < n.batchSize; ++S) { let k = S * x, _ = S * o[0]; @@ -13400,7 +13401,7 @@ function _c(r, t8, e, o, n, s) { for (let re = P; re < O; re += p) { let ne = _ + re * o[1]; for (let ee = z; ee < U; ee += u) { - let oe = ne + ee * o[2], ie = r[oe + E]; + let oe = ne + ee * o[2], ie = r15[oe + E]; s === "max" && ie > j ? j = ie : s === "avg" && (q += ie, Y++); } if (isNaN(j)) @@ -13413,8 +13414,8 @@ function _c(r, t8, e, o, n, s) { } return h; } -function Bf(r, t8, e, o, n = false, s = false) { - let a = me(o.outShape, "int32"), i = o.strideHeight, p = o.strideWidth, u = o.dilationHeight, c = o.dilationWidth, l = o.effectiveFilterHeight, m = o.effectiveFilterWidth, d = o.padInfo.top, f = o.padInfo.left, h = me(t8, e, r); +function Lf(r15, t8, e, o, n = false, s = false) { + let a = me(o.outShape, "int32"), i = o.strideHeight, p = o.strideWidth, u = o.dilationHeight, c = o.dilationWidth, l = o.effectiveFilterHeight, m = o.effectiveFilterWidth, d = o.padInfo.top, f = o.padInfo.left, h = me(t8, e, r15); for (let g = 0; g < o.batchSize; ++g) for (let x = 0; x < o.inChannels; ++x) for (let b = 0; b < o.outHeight; ++b) { @@ -13439,7 +13440,7 @@ function Bf(r, t8, e, o, n = false, s = false) { } return a; } -function zf(r, t8, e, o, n, s) { +function Bf(r15, t8, e, o, n, s) { let a = n.strideDepth, i = n.strideHeight, p = n.strideWidth, u = n.dilationDepth, c = n.dilationHeight, l = n.dilationWidth, m = n.effectiveFilterDepth, d = n.effectiveFilterHeight, f = n.effectiveFilterWidth, h = n.padInfo.front, g = n.padInfo.top, x = n.padInfo.left, b = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, C = me(n.outShape, e), S = C.values, k = n.outShape[1] * n.outShape[2] * n.outShape[3] * n.outShape[4], _ = n.outShape[2] * n.outShape[3] * n.outShape[4], E = n.outShape[3] * n.outShape[4], R = n.outShape[4]; for (let D = 0; D < n.batchSize; ++D) { let P = D * k, O = D * o[0]; @@ -13460,12 +13461,12 @@ function zf(r, t8, e, o, n, s) { ie += l; let le = Math.min(n.inWidth, f + oe), be = ne + ee * R, _e = b, ve = 0, Fe = 0; for (let st = z; st < U; st += u) { - let lt = O + st * o[1]; + let ct = O + st * o[1]; for (let Ge = J; Ge < re; Ge += c) { - let mt = lt + Ge * o[2]; + let lt = ct + Ge * o[2]; for (let it = ie; it < le; it += l) { - let gt = mt + it * o[3], xt = r[gt + M]; - if (s === "max" && xt > _e ? _e = xt : s === "avg" && (ve += xt, Fe++), isNaN(_e)) + let ht = lt + it * o[3], gt = r15[ht + M]; + if (s === "max" && gt > _e ? _e = gt : s === "avg" && (ve += gt, Fe++), isNaN(_e)) break; } if (isNaN(_e)) @@ -13482,7 +13483,7 @@ function zf(r, t8, e, o, n, s) { } return C; } -function J_(r, t8) { +function e$(r15, t8) { let e = me(t8.outShape, "int32"), o = t8.strideDepth, n = t8.strideHeight, s = t8.strideWidth, a = t8.dilationDepth, i = t8.dilationHeight, p = t8.dilationWidth, u = t8.effectiveFilterDepth, c = t8.effectiveFilterHeight, l = t8.effectiveFilterWidth, m = t8.padInfo.front, d = t8.padInfo.top, f = t8.padInfo.left; for (let h = 0; h < t8.batchSize; ++h) for (let g = 0; g < t8.inChannels; ++g) @@ -13506,7 +13507,7 @@ function J_(r, t8) { for (let j = E; j < R; j += i) { let q = j - _; for (let Y = O; Y < M; Y += p) { - let J = Y - P, re = r.get(h, z, j, Y, g); + let J = Y - P, re = r15.get(h, z, j, Y, g); re >= L && (L = re, B = U * c * l + q * c + J); } } @@ -13517,30 +13518,30 @@ function J_(r, t8) { } return e; } -function NY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8; +function NY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8; Q(n, "avgPool"); let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1; y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); let c = w.computePool2DInfo(n.shape, s, a, u, i, p), l; if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape)) - l = mr({ inputs: { x: n }, backend: e }); + l = lr({ inputs: { x: n }, backend: e }); else { - let m = e.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = _c(m, n.shape, n.dtype, d, c, "avg"); + let m = e.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = Sc(m, n.shape, n.dtype, d, c, "avg"); l = e.makeTensorInfo(c.outShape, n.dtype, f.values); } return l; } -var e$ = { kernelName: Zo, backendName: "cpu", kernelFunc: NY }; -function TY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o; +var t$ = { kernelName: Qo, backendName: "cpu", kernelFunc: NY }; +function TY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o; Q(n, "avgPool3d"); - let c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = e.data.get(n.dataId).values, m = zf(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "avg"); + let c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = e.data.get(n.dataId).values, m = Bf(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "avg"); return e.makeTensorInfo(m.shape, "float32", m.values); } -var t$ = { kernelName: Zs, backendName: "cpu", kernelFunc: TY }; -function _Y(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s } = t8, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o; +var r$ = { kernelName: Zs, backendName: "cpu", kernelFunc: TY }; +function _Y(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s } = t8, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o; Q([n, s], "avgPool3DGrad"); let c = w.computePool3DInfo(s.shape, a, i, 1, p, u), l = c.strideDepth, m = c.strideHeight, d = c.strideWidth, f = c.filterDepth, h = c.filterHeight, g = c.filterWidth, x = c.dilationDepth, b = c.dilationHeight, C = c.dilationWidth, S = c.effectiveFilterDepth, k = c.effectiveFilterHeight, _ = c.effectiveFilterWidth, E = S - 1 - c.padInfo.front, R = _ - 1 - c.padInfo.left, D = k - 1 - c.padInfo.top, P = me(s.shape, "float32"), O = 1 / (f * h * g), M = e.bufferSync(n); for (let L = 0; L < c.batchSize; ++L) @@ -13568,9 +13569,9 @@ function _Y(r) { } return e.makeTensorInfo(P.shape, P.dtype, P.values); } -var r$ = { kernelName: Ai, backendName: "cpu", kernelFunc: _Y }; -function $Y(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s } = t8, a = s; +var o$ = { kernelName: Ei, backendName: "cpu", kernelFunc: _Y }; +function $Y(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s } = t8, a = s; Q([n, s], "avgPoolGrad"); let { filterSize: i, strides: p, pad: u } = o, c = w.computePool2DInfo(a.shape, i, p, 1, u), l = c.strideHeight, m = c.strideWidth, d = c.filterHeight, f = c.filterWidth, h = c.dilationHeight, g = c.dilationWidth, x = c.effectiveFilterHeight, b = c.effectiveFilterWidth, C = b - 1 - c.padInfo.left, S = x - 1 - c.padInfo.top, k = me(a.shape, "float32"), _ = 1 / (d * f), E = e.data.get(n.dataId).values, R = me(n.shape, "float32", E); for (let D = 0; D < c.batchSize; ++D) @@ -13593,9 +13594,9 @@ function $Y(r) { } return e.makeTensorInfo(k.shape, k.dtype, k.values); } -var o$ = { kernelName: Di, backendName: "cpu", kernelFunc: $Y }; -function EY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, scale: s, offset: a, mean: i, variance: p } = t8; +var n$ = { kernelName: $i, backendName: "cpu", kernelFunc: $Y }; +function EY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, scale: s, offset: a, mean: i, variance: p } = t8; y.assert(i.shape.length === p.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), y.assert(a == null || i.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), y.assert(s == null || i.shape.length === s.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."), Q([n, i, p, s, a], "batchNorm"); let { varianceEpsilon: u } = o; u == null && (u = 1e-3); @@ -13604,54 +13605,54 @@ function EY(r) { h[R] = f[S++] + (c[R] - l[k++]) * d[_++] / Math.sqrt(m[E++] + u), S >= g && (S = 0), k >= C && (k = 0), _ >= x && (_ = 0), E >= b && (E = 0); return e.makeTensorInfo(n.shape, n.dtype, h); } -var n$ = { kernelName: vn, backendName: "cpu", kernelFunc: EY }; -function RY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { blockShape: s, crops: a } = o; +var s$ = { kernelName: In, backendName: "cpu", kernelFunc: EY }; +function RY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { blockShape: s, crops: a } = o; Q([n], "batchToSpaceND"); - let i = s.reduce((x, b) => x * b), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = Ve({ inputs: { x: n }, backend: e, attrs: { shape: p } }), f = It({ inputs: { x: d }, backend: e, attrs: { perm: u } }), h = Ve({ inputs: { x: f }, backend: e, attrs: { shape: c } }), g = Fo({ inputs: { x: h }, backend: e, attrs: { begin: l, size: m } }); + let i = s.reduce((x, b) => x * b), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = Ve({ inputs: { x: n }, backend: e, attrs: { shape: p } }), f = St({ inputs: { x: d }, backend: e, attrs: { perm: u } }), h = Ve({ inputs: { x: f }, backend: e, attrs: { shape: c } }), g = Ao({ inputs: { x: h }, backend: e, attrs: { begin: l, size: m } }); return e.disposeIntermediateTensorInfo(d), e.disposeIntermediateTensorInfo(f), e.disposeIntermediateTensorInfo(h), g; } -var s$ = { kernelName: Js, backendName: "cpu", kernelFunc: RY }; -function DY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, weights: s } = t8, { size: a } = o, i = e.data.get(n.dataId).values, p = e.data.get(s.dataId).values, u = Ic(i, p, s.dtype, s.shape, a); +var a$ = { kernelName: Js, backendName: "cpu", kernelFunc: RY }; +function DY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, weights: s } = t8, { size: a } = o, i = e.data.get(n.dataId).values, p = e.data.get(s.dataId).values, u = yc(i, p, s.dtype, s.shape, a); return e.makeTensorInfo([a], s.dtype, u); } -var a$ = { kernelName: en, backendName: "cpu", kernelFunc: DY }; -function AY(r) { - let { inputs: t8, backend: e } = r, { s0: o, s1: n } = t8, s = e.data.get(o.dataId).values, a = e.data.get(n.dataId).values, i = w.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); +var i$ = { kernelName: Jo, backendName: "cpu", kernelFunc: DY }; +function AY(r15) { + let { inputs: t8, backend: e } = r15, { s0: o, s1: n } = t8, s = e.data.get(o.dataId).values, a = e.data.get(n.dataId).values, i = w.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); return e.makeTensorInfo([i.length], "int32", Int32Array.from(i)); } -var i$ = { kernelName: ea, backendName: "cpu", kernelFunc: AY }; -var FY = Ie(Co, (r, t8) => { +var u$ = { kernelName: ea, backendName: "cpu", kernelFunc: AY }; +var FY = Ie(bo, (r15, t8) => { let e = t8; - return r > e.clipValueMax ? e.clipValueMax : r < e.clipValueMin ? e.clipValueMin : r; + return r15 > e.clipValueMax ? e.clipValueMax : r15 < e.clipValueMin ? e.clipValueMin : r15; }); -var u$ = { kernelName: Co, backendName: "cpu", kernelFunc: FY }; -var PY = (r) => { - let { x: t8 } = r.inputs, e = r.backend, o = new Float32Array(y.sizeFromShape(t8.shape)), n = e.data.get(t8.dataId), s = n.complexTensorInfos.real, a = n.complexTensorInfos.imag, i = e.data.get(s.dataId).values, p = e.data.get(a.dataId).values; +var p$ = { kernelName: bo, backendName: "cpu", kernelFunc: FY }; +var PY = (r15) => { + let { x: t8 } = r15.inputs, e = r15.backend, o = new Float32Array(y.sizeFromShape(t8.shape)), n = e.data.get(t8.dataId), s = n.complexTensorInfos.real, a = n.complexTensorInfos.imag, i = e.data.get(s.dataId).values, p = e.data.get(a.dataId).values; for (let u = 0; u < i.length; u++) { let c = i[u], l = p[u]; o[u] = Math.hypot(c, l); } return e.makeOutput(o, t8.shape, "float32"); }; -var p$ = { kernelName: Pi, backendName: "cpu", kernelFunc: PY }; -function Ma(r) { - let { inputs: t8, backend: e } = r, { input: o } = t8, n = e.data.get(o.dataId).complexTensorInfos.imag, s = e.data.get(n.dataId).values; +var c$ = { kernelName: Di, backendName: "cpu", kernelFunc: PY }; +function Oa(r15) { + let { inputs: t8, backend: e } = r15, { input: o } = t8, n = e.data.get(o.dataId).complexTensorInfos.imag, s = e.data.get(n.dataId).values; return e.makeTensorInfo(n.shape, n.dtype, s); } -var c$ = { kernelName: Gi, backendName: "cpu", kernelFunc: Ma }; -function yu(r) { - let { inputs: t8, backend: e, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, t8[0].shape)[0], a = t8.map((h) => h.shape); +var l$ = { kernelName: Vi, backendName: "cpu", kernelFunc: Oa }; +function du(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { axis: n } = o, s = y.parseAxisParam(n, t8[0].shape)[0], a = t8.map((h) => h.shape); w.assertParamsConsistent(a, s); let i = w.computeOutShape(t8.map((h) => h.shape), s); if (y.sizeFromShape(i) === 0) return e.makeTensorInfo(i, t8[0].dtype, []); let p = t8.filter((h) => y.sizeFromShape(h.shape) > 0); if (p.length === 1) - return mr({ inputs: { x: p[0] }, backend: e }); + return lr({ inputs: { x: p[0] }, backend: e }); if (p[0].dtype === "complex64") { - let h = p.map((S) => Ro({ inputs: { input: S }, backend: e })), g = p.map((S) => Ma({ inputs: { input: S }, backend: e })), x = yu({ inputs: h, backend: e, attrs: { axis: s } }), b = yu({ inputs: g, backend: e, attrs: { axis: s } }), C = Kt({ inputs: { real: x, imag: b }, backend: e }); + let h = p.map((S) => Eo({ inputs: { input: S }, backend: e })), g = p.map((S) => Oa({ inputs: { input: S }, backend: e })), x = du({ inputs: h, backend: e, attrs: { axis: s } }), b = du({ inputs: g, backend: e, attrs: { axis: s } }), C = Ht({ inputs: { real: x, imag: b }, backend: e }); return h.forEach((S) => e.disposeIntermediateTensorInfo(S)), g.forEach((S) => e.disposeIntermediateTensorInfo(S)), e.disposeIntermediateTensorInfo(x), e.disposeIntermediateTensorInfo(b), C; } let u = p.map((h) => { @@ -13659,12 +13660,12 @@ function yu(r) { return Ve({ inputs: { x: h }, backend: e, attrs: { shape: x } }); }), c = u.map((h) => ({ vals: e.data.get(h.dataId).values, shape: h.shape })); i = w.computeOutShape(u.map((h) => h.shape), 1); - let l = u[0].shape[0] === 1, m = mp(c, i, t8[0].dtype, l), d = w.computeOutShape(p.map((h) => h.shape), s), f = e.makeTensorInfo(d, t8[0].dtype, m); + let l = u[0].shape[0] === 1, m = np(c, i, t8[0].dtype, l), d = w.computeOutShape(p.map((h) => h.shape), s), f = e.makeTensorInfo(d, t8[0].dtype, m); return u.forEach((h) => e.disposeIntermediateTensorInfo(h)), f; } -var l$ = { kernelName: ta, backendName: "cpu", kernelFunc: yu }; -function yI(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s } = t8, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o; +var m$ = { kernelName: ta, backendName: "cpu", kernelFunc: du }; +function bI(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s } = t8, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o; Q([n, s], "conv2d"); let l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d = m.filterHeight, f = m.filterWidth, h = m.dilationHeight, g = m.dilationWidth, x = m.padInfo.left, b = m.padInfo.top, C = m.dataFormat === "channelsLast", S = new tt(m.outShape, n.dtype), k = y.computeStrides(n.shape), _ = y.computeStrides(s.shape), E = k[0], R = C ? k[1] : k[2], D = C ? k[2] : 1, P = C ? 1 : k[1], O = S.strides[0], M = C ? S.strides[1] : S.strides[2], L = C ? S.strides[2] : 1, B = C ? 1 : S.strides[1], z = e.data.get(n.dataId).values, U = e.data.get(s.dataId).values, j = S.values; for (let q = 0; q < m.batchSize; ++q) { @@ -13682,12 +13683,12 @@ function yI(r) { let st = Fe + Pe * g; if (st < 0 || st >= m.inWidth) continue; - let lt = le + Pe * _[1], Ge = be + st * D, mt = lt; + let ct = le + Pe * _[1], Ge = be + st * D, lt = ct; for (let it = 0; it < m.inChannels; ++it) { - let gt = z[Ge + it * P]; - for (let xt = 0; xt < m.outChannels; ++xt) - j[ve + xt * B] += gt * U[mt + xt]; - mt += m.outChannels; + let ht = z[Ge + it * P]; + for (let gt = 0; gt < m.outChannels; ++gt) + j[ve + gt * B] += ht * U[lt + gt]; + lt += m.outChannels; } } } @@ -13696,9 +13697,9 @@ function yI(r) { } return e.makeTensorInfo(S.shape, S.dtype, j); } -var m$ = { kernelName: rn, backendName: "cpu", kernelFunc: yI }; -function OY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, dy: s } = t8, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o; +var d$ = { kernelName: tn, backendName: "cpu", kernelFunc: bI }; +function OY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, dy: s } = t8, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o; Q([n, s], "conv2dBackpropFilter"); let l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), { strideHeight: d, strideWidth: f, filterHeight: h, filterWidth: g } = m, x = m.dataFormat === "channelsLast", b = new tt(m.filterShape, "float32"), C = m.padInfo.left, S = m.padInfo.top, k = e.data.get(n.dataId).values, _ = e.data.get(s.dataId).values, E = new tt(n.shape, n.dtype, k), R = new tt(s.shape, s.dtype, _); for (let D = 0; D < h; ++D) { @@ -13722,9 +13723,9 @@ function OY(r) { } return e.makeTensorInfo(b.shape, b.dtype, b.values); } -var d$ = { kernelName: Oi, backendName: "cpu", kernelFunc: OY }; -function MY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, filter: s } = t8, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o; +var f$ = { kernelName: Ai, backendName: "cpu", kernelFunc: OY }; +function MY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, filter: s } = t8, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o; Q([n, s], "conv2dBackpropInput"); let l = y.computeStrides(s.shape), m = y.computeStrides(n.shape), d = w.convertConv2DDataFormat(u), f = w.computeConv2DInfo(a, s.shape, i, 1, p, c, false, d), h = new tt(f.inShape, "float32"), g = h.values, x = e.data.get(n.dataId).values, b = e.data.get(s.dataId).values, [C, S, k] = l, { batchSize: _, filterHeight: E, filterWidth: R, inChannels: D, inHeight: P, inWidth: O, outChannels: M, outHeight: L, outWidth: B, strideHeight: z, strideWidth: U } = f; d = f.dataFormat; @@ -13732,28 +13733,28 @@ function MY(r) { for (let _e = 0; _e < _; ++_e) for (let ve = 0; ve < D; ++ve) for (let Fe = 0; Fe < P; ++Fe) { - let Pe = Fe - j, st = Math.max(0, Math.ceil(Pe / z)), lt = Math.min(L, (E + Pe) / z); + let Pe = Fe - j, st = Math.max(0, Math.ceil(Pe / z)), ct = Math.min(L, (E + Pe) / z); for (let Ge = 0; Ge < O; ++Ge) { - let mt = Ge - q, it = Math.max(0, Math.ceil(mt / U)), gt = Math.min(B, (R + mt) / U), xt = 0; - for (let Lt = st; Lt < lt; ++Lt) { - let to = Lt * z - Pe; - for (let nr = it; nr < gt; ++nr) { - let _t = nr * U - mt, sr = oe * _e + ie * Lt + le * nr, ar = C * (E - 1 - to) + S * (R - 1 - _t) + k * ve; - for (let ro = 0; ro < M; ++ro) { - let oo = x[sr + be * ro], hr = b[ar + ro]; - xt += oo * hr; + let lt = Ge - q, it = Math.max(0, Math.ceil(lt / U)), ht = Math.min(B, (R + lt) / U), gt = 0; + for (let Mt = st; Mt < ct; ++Mt) { + let eo = Mt * z - Pe; + for (let rr = it; rr < ht; ++rr) { + let Tt = rr * U - lt, or = oe * _e + ie * Mt + le * rr, nr = C * (E - 1 - eo) + S * (R - 1 - Tt) + k * ve; + for (let to = 0; to < M; ++to) { + let ro = x[or + be * to], fr = b[nr + to]; + gt += ro * fr; } } } - let Lr = J * _e + re * Fe + ne * Ge + ee * ve; - g[Lr] = xt; + let Mr = J * _e + re * Fe + ne * Ge + ee * ve; + g[Mr] = gt; } } return e.makeTensorInfo(h.shape, h.dtype, h.values); } -var f$ = { kernelName: on, backendName: "cpu", kernelFunc: MY }; -function LY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p } = o; +var h$ = { kernelName: rn, backendName: "cpu", kernelFunc: MY }; +function LY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p } = o; Q([n, s], "conv3d"); let u = w.computeConv3DInfo(n.shape, s.shape, a, p, i), { filterDepth: c, filterHeight: l, filterWidth: m, dilationDepth: d, dilationHeight: f, dilationWidth: h, padInfo: g } = u, x = g.front, b = g.left, C = g.top, S = new tt(u.outShape, n.dtype), k = e.data.get(n.dataId).values, _ = e.data.get(s.dataId).values, E = S.values, R = y.computeStrides(n.shape), D = y.computeStrides(s.shape); for (let P = 0; P < u.batchSize; ++P) { @@ -13778,11 +13779,11 @@ function LY(r) { let Pe = ve + Fe * h; if (Pe < 0 || Pe >= u.inWidth) continue; - let st = ie + Fe * D[2], lt = le + Pe * u.inChannels, Ge = st; - for (let mt = 0; mt < u.inChannels; ++mt) { - let it = k[lt + mt]; - for (let gt = 0; gt < u.outChannels; ++gt) - E[_e + gt] += it * _[Ge + gt]; + let st = ie + Fe * D[2], ct = le + Pe * u.inChannels, Ge = st; + for (let lt = 0; lt < u.inChannels; ++lt) { + let it = k[ct + lt]; + for (let ht = 0; ht < u.outChannels; ++ht) + E[_e + ht] += it * _[Ge + ht]; Ge += u.outChannels; } } @@ -13794,9 +13795,9 @@ function LY(r) { } return e.makeTensorInfo(S.shape, S.dtype, S.values); } -var h$ = { kernelName: nn, backendName: "cpu", kernelFunc: LY }; -function BY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, dy: s } = t8, { strides: a, pad: i, filterShape: p } = o; +var g$ = { kernelName: on, backendName: "cpu", kernelFunc: LY }; +function BY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, dy: s } = t8, { strides: a, pad: i, filterShape: p } = o; Q([n, s], "conv3dBackpropFilterV2"); let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = w.computeConv3DInfo(n.shape, p, a, 1, i), m = l.strideDepth, d = l.strideHeight, f = l.strideWidth, h = l.filterDepth, g = l.filterHeight, x = l.filterWidth, b = new tt(l.filterShape, "float32"), C = b.values, [S, k, _, E] = b.strides, R = e.data.get(s.dataId).values, [D, P, O, M] = c, L = e.data.get(n.dataId).values, [B, z, U, j] = u, q = l.padInfo.front, Y = l.padInfo.left, J = l.padInfo.top; for (let re = 0; re < h; ++re) { @@ -13805,24 +13806,24 @@ function BY(r) { let le = Math.max(0, Math.ceil((J - ie) / d)), be = Math.min(l.outHeight, (l.inHeight + J - ie) / d), _e = ie * k + oe; for (let ve = 0; ve < x; ++ve) { let Fe = Math.max(0, Math.ceil((Y - ve) / f)), Pe = Math.min(l.outWidth, (l.inWidth + Y - ve) / f), st = ve * _ + _e; - for (let lt = 0; lt < l.inChannels; ++lt) { - let Ge = lt * E + st; - for (let mt = 0; mt < l.outChannels; ++mt) { + for (let ct = 0; ct < l.inChannels; ++ct) { + let Ge = ct * E + st; + for (let lt = 0; lt < l.outChannels; ++lt) { let it = 0; - for (let gt = 0; gt < l.batchSize; ++gt) { - let xt = gt * B, Lr = gt * D; - for (let Lt = ne; Lt < ee; ++Lt) { - let nr = (re + Lt * m - q) * z + xt, _t = Lt * P + Lr; - for (let sr = le; sr < be; ++sr) { - let ro = (ie + sr * d - J) * U + nr, oo = sr * O + _t; - for (let hr = Fe; hr < Pe; ++hr) { - let Bo = (ve + hr * f - Y) * j + ro, Ks = hr * M + oo; - it += L[Bo + lt] * R[Ks + mt]; + for (let ht = 0; ht < l.batchSize; ++ht) { + let gt = ht * B, Mr = ht * D; + for (let Mt = ne; Mt < ee; ++Mt) { + let rr = (re + Mt * m - q) * z + gt, Tt = Mt * P + Mr; + for (let or = le; or < be; ++or) { + let to = (ie + or * d - J) * U + rr, ro = or * O + Tt; + for (let fr = Fe; fr < Pe; ++fr) { + let Lo = (ve + fr * f - Y) * j + to, Ks = fr * M + ro; + it += L[Lo + ct] * R[Ks + lt]; } } } } - C[Ge + mt] = it; + C[Ge + lt] = it; } } } @@ -13830,45 +13831,45 @@ function BY(r) { } return e.makeTensorInfo(b.shape, b.dtype, b.values); } -var g$ = { kernelName: Xa, backendName: "cpu", kernelFunc: BY }; -function zY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, filter: s } = t8, { pad: a, strides: i, inputShape: p } = o; +var x$ = { kernelName: ja, backendName: "cpu", kernelFunc: BY }; +function zY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, filter: s } = t8, { pad: a, strides: i, inputShape: p } = o; Q([n], "conv3dBackpropInputV2"); let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = w.computeConv3DInfo(p, s.shape, i, 1, a), m = new tt(l.inShape, "float32"), d = m.values, [f, h, g, x] = m.strides, b = e.data.get(n.dataId).values, [C, S, k, _] = u, E = e.data.get(s.dataId).values, [R, D, P, O] = c, { batchSize: M, filterDepth: L, filterHeight: B, filterWidth: z, inChannels: U, inDepth: j, inHeight: q, inWidth: Y, outChannels: J, outDepth: re, outHeight: ne, outWidth: ee, strideDepth: oe, strideHeight: ie, strideWidth: le } = l, be = L - 1 - l.padInfo.front, _e = B - 1 - l.padInfo.top, ve = z - 1 - l.padInfo.left; for (let Fe = 0; Fe < M; ++Fe) for (let Pe = 0; Pe < U; ++Pe) for (let st = 0; st < j; ++st) { - let lt = st - be, Ge = Math.max(0, Math.ceil(lt / oe)), mt = Math.min(re, (L + lt) / oe); + let ct = st - be, Ge = Math.max(0, Math.ceil(ct / oe)), lt = Math.min(re, (L + ct) / oe); for (let it = 0; it < q; ++it) { - let gt = it - _e, xt = Math.max(0, Math.ceil(gt / ie)), Lr = Math.min(ne, (B + gt) / ie); - for (let Lt = 0; Lt < Y; ++Lt) { - let to = Lt - ve, nr = Math.max(0, Math.ceil(to / le)), _t = Math.min(ee, (z + to) / le), sr = 0; - for (let ar = Ge; ar < mt; ++ar) { - let ro = ar * oe - lt; - for (let oo = xt; oo < Lr; ++oo) { - let hr = oo * ie - gt; - for (let Wa = nr; Wa < _t; ++Wa) { - let Bo = Wa * le - to, Ks = C * Fe + S * ar + k * oo + _ * Wa, Yt = R * (L - 1 - ro) + D * (B - 1 - hr) + P * (z - 1 - Bo) + O * Pe; - for (let Ua = 0; Ua < J; ++Ua) { - let sl = b[Ks + Ua], al = E[Yt + Ua]; - sr += sl * al; + let ht = it - _e, gt = Math.max(0, Math.ceil(ht / ie)), Mr = Math.min(ne, (B + ht) / ie); + for (let Mt = 0; Mt < Y; ++Mt) { + let eo = Mt - ve, rr = Math.max(0, Math.ceil(eo / le)), Tt = Math.min(ee, (z + eo) / le), or = 0; + for (let nr = Ge; nr < lt; ++nr) { + let to = nr * oe - ct; + for (let ro = gt; ro < Mr; ++ro) { + let fr = ro * ie - ht; + for (let Va = rr; Va < Tt; ++Va) { + let Lo = Va * le - eo, Ks = C * Fe + S * nr + k * ro + _ * Va, Xt = R * (L - 1 - to) + D * (B - 1 - fr) + P * (z - 1 - Lo) + O * Pe; + for (let Wa = 0; Wa < J; ++Wa) { + let tl = b[Ks + Wa], rl = E[Xt + Wa]; + or += tl * rl; } } } } - d[f * Fe + h * st + g * it + x * Lt + Pe] = sr; + d[f * Fe + h * st + g * it + x * Mt + Pe] = or; } } } return e.makeTensorInfo(m.shape, m.dtype, m.values); } -var x$ = { kernelName: sn, backendName: "cpu", kernelFunc: zY }; -var VY = Ie(an, (r) => Math.cos(r)); -var y$ = { kernelName: an, backendName: "cpu", kernelFunc: VY }; -var WY = Ie(un, (r) => Math.cosh(r)); -var b$ = { kernelName: un, backendName: "cpu", kernelFunc: WY }; -function UY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { image: n, boxes: s, boxInd: a } = t8, { cropSize: i, method: p, extrapolationValue: u } = o, [c, l, m, d] = n.shape, f = s.shape[0], [h, g] = i, x = me([f, h, g, d], "float32"), b = e.data.get(s.dataId).values, C = e.data.get(a.dataId).values, S = e.data.get(n.dataId).values, k = y.computeStrides(n.shape), _ = y.computeStrides(x.shape); +var y$ = { kernelName: nn, backendName: "cpu", kernelFunc: zY }; +var VY = Ie(sn, (r15) => Math.cos(r15)); +var b$ = { kernelName: sn, backendName: "cpu", kernelFunc: VY }; +var WY = Ie(an, (r15) => Math.cosh(r15)); +var C$ = { kernelName: an, backendName: "cpu", kernelFunc: WY }; +function UY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { image: n, boxes: s, boxInd: a } = t8, { cropSize: i, method: p, extrapolationValue: u } = o, [c, l, m, d] = n.shape, f = s.shape[0], [h, g] = i, x = me([f, h, g, d], "float32"), b = e.data.get(s.dataId).values, C = e.data.get(a.dataId).values, S = e.data.get(n.dataId).values, k = y.computeStrides(n.shape), _ = y.computeStrides(x.shape); for (let E = 0; E < f; E++) { let R = E * 4, D = b[R], P = b[R + 1], O = b[R + 2], M = b[R + 3], L = C[E]; if (L >= c) @@ -13903,8 +13904,8 @@ function UY(r) { be = le + ee * k[2] + Y * k[1] + L * k[0]; let Fe = S[be]; be = le + oe * k[2] + Y * k[1] + L * k[0]; - let Pe = S[be], st = _e + (ve - _e) * ie, lt = Fe + (Pe - Fe) * ie; - be = le + re * _[2] + U * _[1] + E * _[0], x.values[be] = st + (lt - st) * J; + let Pe = S[be], st = _e + (ve - _e) * ie, ct = Fe + (Pe - Fe) * ie; + be = le + re * _[2] + U * _[1] + E * _[0], x.values[be] = st + (ct - st) * J; } } } else @@ -13927,12 +13928,12 @@ function UY(r) { } return e.makeTensorInfo(x.shape, x.dtype, x.values); } -var C$ = { kernelName: ln, backendName: "cpu", kernelFunc: UY }; -function GY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o; +var w$ = { kernelName: cn, backendName: "cpu", kernelFunc: UY }; +function GY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o; Q(n, "cumprod"); let p = w.getAxesPermutation([s], n.shape.length), u = n; - p != null && (u = It({ inputs: { x: n }, backend: e, attrs: { perm: p } })); + p != null && (u = St({ inputs: { x: n }, backend: e, attrs: { perm: p } })); let c = w.getInnerMostAxes(1, n.shape.length)[0]; if (c !== u.shape.length - 1) throw new Error(`backend.cumprod in CPU expects an inner-most axis=${u.shape.length - 1} but got axis=${c}`); @@ -13949,17 +13950,17 @@ function GY(r) { } let g = e.makeTensorInfo(u.shape, l, m); if (p != null) { - let x = w.getUndoAxesPermutation(p), b = It({ inputs: { x: g }, backend: e, attrs: { perm: x } }); + let x = w.getUndoAxesPermutation(p), b = St({ inputs: { x: g }, backend: e, attrs: { perm: x } }); return e.disposeIntermediateTensorInfo(g), e.disposeIntermediateTensorInfo(u), b; } return g; } -var w$ = { kernelName: pn, backendName: "cpu", kernelFunc: GY }; -function HY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o; +var S$ = { kernelName: un, backendName: "cpu", kernelFunc: GY }; +function HY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o; Q(n, "cumsum"); let p = w.getAxesPermutation([s], n.shape.length), u = n; - p != null && (u = It({ inputs: { x: n }, backend: e, attrs: { perm: p } })); + p != null && (u = St({ inputs: { x: n }, backend: e, attrs: { perm: p } })); let c = w.getInnerMostAxes(1, n.shape.length)[0]; if (c !== u.shape.length - 1) throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length - 1} but got axis=${c}`); @@ -13976,26 +13977,26 @@ function HY(r) { } let g = e.makeTensorInfo(u.shape, l, m); if (p != null) { - let x = w.getUndoAxesPermutation(p), b = It({ inputs: { x: g }, backend: e, attrs: { perm: x } }); + let x = w.getUndoAxesPermutation(p), b = St({ inputs: { x: g }, backend: e, attrs: { perm: x } }); return e.disposeIntermediateTensorInfo(g), e.disposeIntermediateTensorInfo(u), b; } return g; } -var S$ = { kernelName: cn, backendName: "cpu", kernelFunc: HY }; -function KY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, weights: s } = t8, { size: a, binaryOutput: i } = o; +var I$ = { kernelName: pn, backendName: "cpu", kernelFunc: HY }; +function KY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, weights: s } = t8, { size: a, binaryOutput: i } = o; if (n.shape.length === 1) { - let p = e.data.get(n.dataId).values, u = e.data.get(s.dataId).values, c = Ic(p, u, s.dtype, s.shape, a); + let p = e.data.get(n.dataId).values, u = e.data.get(s.dataId).values, c = yc(p, u, s.dtype, s.shape, a); return e.makeTensorInfo([a], s.dtype, c); } else if (n.shape.length === 2) { - let p = e.bufferSync(n), u = e.bufferSync(s), c = Nf(p, u, a, i); + let p = e.bufferSync(n), u = e.bufferSync(s), c = kf(p, u, a, i); return e.makeTensorInfo(c.shape, s.dtype, c.values); } throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`); } -var I$ = { kernelName: ra, backendName: "cpu", kernelFunc: KY }; -function qY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { blockSize: s, dataFormat: a } = o; +var v$ = { kernelName: ra, backendName: "cpu", kernelFunc: KY }; +function qY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { blockSize: s, dataFormat: a } = o; y.assert(a === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`); let i = n.shape[0], p = n.shape[1], u = n.shape[2], c = n.shape[3], l = p * s, m = u * s, d = c / (s * s), f = e.data.get(n.dataId).values, h = new Float32Array(i * l * m * d), g = 0; for (let x = 0; x < i; ++x) @@ -14011,9 +14012,9 @@ function qY(r) { } return e.makeTensorInfo([i, l, m, d], n.dtype, h); } -var v$ = { kernelName: mn, backendName: "cpu", kernelFunc: qY }; -function bI(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o; +var k$ = { kernelName: ln, backendName: "cpu", kernelFunc: qY }; +function CI(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o; Q([n, s], "depthwiseConv2DNative"); let c = y.computeStrides(n.shape), l = y.computeStrides(s.shape), m = p; m == null && (m = [1, 1]), y.assert(w.eitherStridesOrDilationsAreOne(a, m), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`); @@ -14047,9 +14048,9 @@ function bI(r) { } return e.makeTensorInfo(_.shape, _.dtype, _.values); } -var k$ = { kernelName: dn, backendName: "cpu", kernelFunc: bI }; -function jY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, dy: s } = t8, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o; +var N$ = { kernelName: mn, backendName: "cpu", kernelFunc: CI }; +function jY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, dy: s } = t8, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o; Q([n, s], "depthwiseConv2dNativeBackpropFilter"); let l = w.computeConv2DInfo(n.shape, c, a, i, p, u, true), { strideHeight: m, strideWidth: d, filterHeight: f, filterWidth: h } = l, g = new tt(l.filterShape, "float32"), x = l.padInfo.left, b = l.padInfo.top, C = l.outChannels / l.inChannels, S = e.data.get(n.dataId).values, k = new tt(n.shape, n.dtype, S), _ = e.data.get(s.dataId).values, E = new tt(s.shape, s.dtype, _); for (let R = 0; R < f; ++R) { @@ -14072,9 +14073,9 @@ function jY(r) { } return e.makeTensorInfo(g.shape, g.dtype, g.values); } -var N$ = { kernelName: Mi, backendName: "cpu", kernelFunc: jY }; -function XY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, filter: s } = t8, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o; +var T$ = { kernelName: Fi, backendName: "cpu", kernelFunc: jY }; +function XY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, filter: s } = t8, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o; Q([n, s], "depthwiseConv2DNativeBackpropInput"); let l = y.computeStrides(n.shape), m = y.computeStrides(s.shape), d = w.computeConv2DInfo(c, s.shape, a, i, p, u, true), f = new tt(d.inShape, "float32"), h = f.values, [g, x, b] = f.strides, C = e.data.get(n.dataId).values, [S, k, _] = l, E = e.data.get(s.dataId).values, [R, D, P] = m, { batchSize: O, filterHeight: M, filterWidth: L, inChannels: B, inHeight: z, inWidth: U, outChannels: j, outHeight: q, outWidth: Y, strideHeight: J, strideWidth: re } = d, ne = M - 1 - d.padInfo.top, ee = L - 1 - d.padInfo.left, oe = j / B; for (let ie = 0; ie < O; ++ie) @@ -14082,33 +14083,33 @@ function XY(r) { for (let be = 0; be < z; ++be) { let _e = be - ne, ve = Math.max(0, Math.ceil(_e / J)), Fe = Math.min(q, (M + _e) / J); for (let Pe = 0; Pe < U; ++Pe) { - let st = Pe - ee, lt = Math.max(0, Math.ceil(st / re)), Ge = Math.min(Y, (L + st) / re), mt = 0; + let st = Pe - ee, ct = Math.max(0, Math.ceil(st / re)), Ge = Math.min(Y, (L + st) / re), lt = 0; for (let it = ve; it < Fe; ++it) { - let gt = it * J - _e; - for (let xt = lt; xt < Ge; ++xt) { - let Lr = xt * re - st, Lt = S * ie + k * it + _ * xt, to = R * (M - 1 - gt) + D * (L - 1 - Lr) + P * le; - for (let nr = 0; nr < oe; ++nr) { - let _t = le * oe + nr, sr = C[Lt + _t], ar = E[to + nr]; - mt += sr * ar; + let ht = it * J - _e; + for (let gt = ct; gt < Ge; ++gt) { + let Mr = gt * re - st, Mt = S * ie + k * it + _ * gt, eo = R * (M - 1 - ht) + D * (L - 1 - Mr) + P * le; + for (let rr = 0; rr < oe; ++rr) { + let Tt = le * oe + rr, or = C[Mt + Tt], nr = E[eo + rr]; + lt += or * nr; } } } - h[g * ie + x * be + b * Pe + le] = mt; + h[g * ie + x * be + b * Pe + le] = lt; } } return e.makeTensorInfo(f.shape, f.dtype, f.values); } -var T$ = { kernelName: Li, backendName: "cpu", kernelFunc: XY }; -function YY(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8, n = y.sizeFromShape(o.shape), s = e.data.get(o.dataId).values, a = me([n, n], o.dtype), i = a.values; +var _$ = { kernelName: Pi, backendName: "cpu", kernelFunc: XY }; +function YY(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8, n = y.sizeFromShape(o.shape), s = e.data.get(o.dataId).values, a = me([n, n], o.dtype), i = a.values; for (let u = 0; u < s.length; u++) i[u * n + u] = s[u]; let p = [...o.shape, ...o.shape]; return e.makeTensorInfo(p, a.dtype, a.values); } -var _$ = { kernelName: oa, backendName: "cpu", kernelFunc: YY }; -var $$ = { kernelName: fn, backendName: "cpu", kernelFunc: ({ inputs: r, backend: t8, attrs: e }) => { - let { x: o, filter: n } = r, { strides: s, pad: a, dilations: i } = e, p = t8, u = p.data.get(o.dataId).values, c = o.shape.length, l = p.data.get(n.dataId).values, m = n.shape.length, { batchSize: d, inHeight: f, inWidth: h, inChannels: g, outHeight: x, outWidth: b, padInfo: C, strideHeight: S, strideWidth: k, filterHeight: _, filterWidth: E, dilationHeight: R, dilationWidth: D, outShape: P } = w.computeDilation2DInfo(o.shape, n.shape, s, a, "NHWC", i), O = y.sizeFromShape(P), M = P.length, L = y.getArrayFromDType(o.dtype, O); +var $$ = { kernelName: oa, backendName: "cpu", kernelFunc: YY }; +var E$ = { kernelName: dn, backendName: "cpu", kernelFunc: ({ inputs: r15, backend: t8, attrs: e }) => { + let { x: o, filter: n } = r15, { strides: s, pad: a, dilations: i } = e, p = t8, u = p.data.get(o.dataId).values, c = o.shape.length, l = p.data.get(n.dataId).values, m = n.shape.length, { batchSize: d, inHeight: f, inWidth: h, inChannels: g, outHeight: x, outWidth: b, padInfo: C, strideHeight: S, strideWidth: k, filterHeight: _, filterWidth: E, dilationHeight: R, dilationWidth: D, outShape: P } = w.computeDilation2DInfo(o.shape, n.shape, s, a, "NHWC", i), O = y.sizeFromShape(P), M = P.length, L = y.getArrayFromDType(o.dtype, O); for (let z = 0; z < d; ++z) for (let U = 0; U < x; ++U) { let j = U * S - C.top; @@ -14134,9 +14135,9 @@ var $$ = { kernelName: fn, backendName: "cpu", kernelFunc: ({ inputs: r, backend } return { dataId: p.write(y.toTypedArray(L, o.dtype), P, o.dtype), shape: P, dtype: o.dtype }; } }; -var E$ = { kernelName: zi, backendName: "cpu", kernelFunc: ({ inputs: r, backend: t8, attrs: e }) => { - let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = e, u = t8, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: C, strideWidth: S, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: R, outShape: D } = w.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p); - y.assert(s.rank === D.length, () => `Error in ${zi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`); +var R$ = { kernelName: Mi, backendName: "cpu", kernelFunc: ({ inputs: r15, backend: t8, attrs: e }) => { + let { x: o, filter: n, dy: s } = r15, { strides: a, pad: i, dilations: p } = e, u = t8, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: C, strideWidth: S, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: R, outShape: D } = w.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p); + y.assert(s.rank === D.length, () => `Error in ${Mi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`); let P = y.toNestedArray(D, u.data.get(s.dataId).values), O = y.makeZerosNestedTypedArray(n.shape, n.dtype); for (let L = 0; L < m; ++L) for (let B = 0; B < g; ++B) { @@ -14162,9 +14163,9 @@ var E$ = { kernelName: zi, backendName: "cpu", kernelFunc: ({ inputs: r, backend } return { dataId: u.write(y.toTypedArray(O, o.dtype), n.shape, n.dtype), shape: n.shape, dtype: n.dtype }; } }; -var R$ = { kernelName: Bi, backendName: "cpu", kernelFunc: ({ inputs: r, backend: t8, attrs: e }) => { - let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = e, u = t8, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: C, strideWidth: S, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: R, outShape: D } = w.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p); - y.assert(s.rank === D.length, () => `Error in ${Bi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`); +var D$ = { kernelName: Oi, backendName: "cpu", kernelFunc: ({ inputs: r15, backend: t8, attrs: e }) => { + let { x: o, filter: n, dy: s } = r15, { strides: a, pad: i, dilations: p } = e, u = t8, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: C, strideWidth: S, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: R, outShape: D } = w.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p); + y.assert(s.rank === D.length, () => `Error in ${Oi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`); let P = y.toNestedArray(D, u.data.get(s.dataId).values), O = y.makeZerosNestedTypedArray(o.shape, o.dtype); for (let L = 0; L < m; ++L) for (let B = 0; B < g; ++B) { @@ -14190,8 +14191,8 @@ var R$ = { kernelName: Bi, backendName: "cpu", kernelFunc: ({ inputs: r, backend } return { dataId: u.write(y.toTypedArray(O, o.dtype), o.shape, o.dtype), shape: o.shape, dtype: o.dtype }; } }; -function QY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { image: n } = t8, { canvas: s, options: a } = o, { contextOptions: i, imageOptions: p } = a || {}, u = (p == null ? void 0 : p.alpha) || 1, c = (i == null ? void 0 : i.contextType) || "2d"; +function QY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { image: n } = t8, { canvas: s, options: a } = o, { contextOptions: i, imageOptions: p } = a || {}, u = (p == null ? void 0 : p.alpha) || 1, c = (i == null ? void 0 : i.contextType) || "2d"; if (c !== "2d") throw new Error(`Context type ${i.contextType} is not supported by the CPU backend.`); let l = s.getContext(c, (i == null ? void 0 : i.contextAttributes) || {}); @@ -14216,15 +14217,15 @@ function QY(r) { let b = new ImageData(x, d, m); return l.putImageData(b, 0, 0), n; } -var D$ = { kernelName: Pu, backendName: "cpu", kernelFunc: QY }; -function gi(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, keepDims: a } = o; +var A$ = { kernelName: _u, backendName: "cpu", kernelFunc: QY }; +function di(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, keepDims: a } = o; Q(n, "sum"); let i; - n.dtype === "bool" ? i = Do({ inputs: { x: n }, backend: e, attrs: { dtype: "int32" } }) : i = mr({ inputs: { x: n }, backend: e }); + n.dtype === "bool" ? i = Ro({ inputs: { x: n }, backend: e, attrs: { dtype: "int32" } }) : i = lr({ inputs: { x: n }, backend: e }); let p = i.shape.length, u = y.parseAxisParam(s, i.shape), c = w.getAxesPermutation(u, p), l = u, m = i; - c != null && (m = It({ inputs: { x: i }, backend: e, attrs: { perm: c } }), l = w.getInnerMostAxes(l.length, p)), w.assertAxesAreInnerMostDims("sum", l, m.shape.length); - let [d, f] = w.computeOutAndReduceShapes(m.shape, l), h = w.upcastType(m.dtype, "int32"), g = wc(e, d, h), x = y.sizeFromShape(f), b = e.data.get(g.dataId).values, C = e.data.get(m.dataId).values; + c != null && (m = St({ inputs: { x: i }, backend: e, attrs: { perm: c } }), l = w.getInnerMostAxes(l.length, p)), w.assertAxesAreInnerMostDims("sum", l, m.shape.length); + let [d, f] = w.computeOutAndReduceShapes(m.shape, l), h = w.upcastType(m.dtype, "int32"), g = gc(e, d, h), x = y.sizeFromShape(f), b = e.data.get(g.dataId).values, C = e.data.get(m.dataId).values; for (let S = 0; S < b.length; ++S) { let k = S * x, _ = 0; for (let E = 0; E < x; ++E) @@ -14237,29 +14238,29 @@ function gi(r) { } return e.disposeIntermediateTensorInfo(i), c != null && e.disposeIntermediateTensorInfo(m), g; } -var A$ = { kernelName: Is, backendName: "cpu", kernelFunc: gi }; -function ZY(r) { - let { inputs: t8, backend: e, attrs: o } = r, { equation: n } = o, s = t8, { allDims: a, summedDims: i, idDims: p } = w.decodeEinsumEquation(n, s.length); +var F$ = { kernelName: Ss, backendName: "cpu", kernelFunc: di }; +function ZY(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { equation: n } = o, s = t8, { allDims: a, summedDims: i, idDims: p } = w.decodeEinsumEquation(n, s.length); w.checkEinsumDimSizes(a.length, p, s); let { path: u, steps: c } = w.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = []; for (let h = 0; h < l; ++h) { for (let g of c[h]) { let { permutationIndices: x, expandDims: b } = w.getEinsumPermutation(d, p[g]), C; - w.isIdentityPermutation(x) ? C = s[g] : (C = It({ inputs: { x: s[g] }, backend: e, attrs: { perm: x } }), f.push(C)); + w.isIdentityPermutation(x) ? C = s[g] : (C = St({ inputs: { x: s[g] }, backend: e, attrs: { perm: x } }), f.push(C)); let S = C.shape.slice(); for (let k = 0; k < b.length; ++k) S.splice(b[k], 0, 1); - y.arraysEqual(C.shape, S) || (C = Ve({ inputs: { x: C }, backend: e, attrs: { shape: S } }), f.push(C)), m === null ? m = C : (m = dp({ inputs: { a: C, b: m }, backend: e }), f.push(m)); + y.arraysEqual(C.shape, S) || (C = Ve({ inputs: { x: C }, backend: e, attrs: { shape: S } }), f.push(C)), m === null ? m = C : (m = sp({ inputs: { a: C, b: m }, backend: e }), f.push(m)); } - h < l - 1 && (u[h] >= 0 && (m = gi({ inputs: { x: m }, backend: e, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); + h < l - 1 && (u[h] >= 0 && (m = di({ inputs: { x: m }, backend: e, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); } for (let h of f) h !== m && e.disposeIntermediateTensorInfo(h); return m; } -var F$ = { kernelName: Vi, backendName: "cpu", kernelFunc: ZY }; -function JY(r) { - let { inputs: t8, backend: e } = r, { dy: o, y: n } = t8; +var P$ = { kernelName: Li, backendName: "cpu", kernelFunc: ZY }; +function JY(r15) { + let { inputs: t8, backend: e } = r15, { dy: o, y: n } = t8; Q([o, n], "eluGrad"); let s = new Float32Array(y.sizeFromShape(n.shape)), a = e.data.get(n.dataId).values, i = e.data.get(o.dataId).values; for (let p = 0; p < a.length; ++p) { @@ -14268,45 +14269,45 @@ function JY(r) { } return e.makeTensorInfo(n.shape, "float32", s); } -var P$ = { kernelName: Ya, backendName: "cpu", kernelFunc: JY }; +var O$ = { kernelName: Xa, backendName: "cpu", kernelFunc: JY }; var eQ = w.ERF_P; var tQ = w.ERF_A1; var rQ = w.ERF_A2; var oQ = w.ERF_A3; var nQ = w.ERF_A4; var sQ = w.ERF_A5; -var aQ = Ie(xn, (r) => { - let t8 = Math.sign(r), e = Math.abs(r), o = 1 / (1 + eQ * e); +var aQ = Ie(gn, (r15) => { + let t8 = Math.sign(r15), e = Math.abs(r15), o = 1 / (1 + eQ * e); return t8 * (1 - ((((sQ * o + nQ) * o + oQ) * o + rQ) * o + tQ) * o * Math.exp(-e * e)); }); -var O$ = { kernelName: xn, backendName: "cpu", kernelFunc: aQ }; -function $c(r) { - let { inputs: t8, backend: e, attrs: o } = r, { input: n } = t8, { dim: s } = o, a = n.shape.length, i = n.shape.slice(), p = s; +var M$ = { kernelName: gn, backendName: "cpu", kernelFunc: aQ }; +function Ic(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { input: n } = t8, { dim: s } = o, a = n.shape.length, i = n.shape.slice(), p = s; return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), Ve({ inputs: { x: n }, backend: e, attrs: { shape: i } }); } -var M$ = { kernelName: na, backendName: "cpu", kernelFunc: $c }; -var iQ = ze((r, t8) => r / t8); -var Ul = Ye(hn, iQ); -var Gl = { kernelName: hn, backendName: "cpu", kernelFunc: Ul }; -function Vf(r, t8, e) { - let o = r.shape, n = o[0], s = o[1], a = e.data.get(r.dataId), i = a.complexTensorInfos.real, p = a.complexTensorInfos.imag, u = [n, s], c = y.sizeFromShape(u), l = y.getTypedArrayFromDType("float32", c), m = y.getTypedArrayFromDType("float32", c); +var L$ = { kernelName: na, backendName: "cpu", kernelFunc: Ic }; +var iQ = ze((r15, t8) => r15 / t8); +var Vl = Ye(fn, iQ); +var Wl = { kernelName: fn, backendName: "cpu", kernelFunc: Vl }; +function zf(r15, t8, e) { + let o = r15.shape, n = o[0], s = o[1], a = e.data.get(r15.dataId), i = a.complexTensorInfos.real, p = a.complexTensorInfos.imag, u = [n, s], c = y.sizeFromShape(u), l = y.getTypedArrayFromDType("float32", c), m = y.getTypedArrayFromDType("float32", c); for (let g = 0; g < n; g++) { - let x = Fo({ inputs: { x: i }, backend: e, attrs: { begin: [g, 0], size: [1, s] } }), b = Fo({ inputs: { x: p }, backend: e, attrs: { begin: [g, 0], size: [1, s] } }), C = Kt({ inputs: { real: x, imag: b }, backend: e }), { real: S, imag: k } = uQ(C, t8, e), _ = w.mergeRealAndImagArrays(S, k); + let x = Ao({ inputs: { x: i }, backend: e, attrs: { begin: [g, 0], size: [1, s] } }), b = Ao({ inputs: { x: p }, backend: e, attrs: { begin: [g, 0], size: [1, s] } }), C = Ht({ inputs: { real: x, imag: b }, backend: e }), { real: S, imag: k } = uQ(C, t8, e), _ = w.mergeRealAndImagArrays(S, k); for (let E = 0; E < s; E++) { let R = w.getComplexWithIndex(_, E); l[g * s + E] = R.real, m[g * s + E] = R.imag; } e.disposeIntermediateTensorInfo(x), e.disposeIntermediateTensorInfo(b), e.disposeIntermediateTensorInfo(C); } - let d = e.makeTensorInfo(u, "float32", l), f = e.makeTensorInfo(u, "float32", m), h = Kt({ inputs: { real: d, imag: f }, backend: e }); + let d = e.makeTensorInfo(u, "float32", l), f = e.makeTensorInfo(u, "float32", m), h = Ht({ inputs: { real: d, imag: f }, backend: e }); return e.disposeIntermediateTensorInfo(d), e.disposeIntermediateTensorInfo(f), h; } -function uQ(r, t8, e) { - let o = y.sizeFromShape(r.shape), n = e.data.get(r.dataId), s = e.data.get(n.complexTensorInfos.real.dataId).values, a = e.data.get(n.complexTensorInfos.imag.dataId).values; +function uQ(r15, t8, e) { + let o = y.sizeFromShape(r15.shape), n = e.data.get(r15.dataId), s = e.data.get(n.complexTensorInfos.real.dataId).values, a = e.data.get(n.complexTensorInfos.imag.dataId).values; if (pQ(o)) { - let i = CI(s, a, o, t8, e), p = [r.shape[0], r.shape[1]]; + let i = wI(s, a, o, t8, e), p = [r15.shape[0], r15.shape[1]]; if (t8) { - let u = e.makeTensorInfo(p, "float32", i.real), c = e.makeTensorInfo(p, "float32", i.imag), l = e.makeTensorInfo([], "float32", y.createScalarValue(o, "float32")), m = mr({ inputs: { x: l }, backend: e }), d = Gl.kernelFunc({ inputs: { a: u, b: l }, backend: e }), f = Gl.kernelFunc({ inputs: { a: c, b: m }, backend: e }), h = e.data.get(d.dataId).values, g = e.data.get(f.dataId).values; + let u = e.makeTensorInfo(p, "float32", i.real), c = e.makeTensorInfo(p, "float32", i.imag), l = e.makeTensorInfo([], "float32", y.createScalarValue(o, "float32")), m = lr({ inputs: { x: l }, backend: e }), d = Wl.kernelFunc({ inputs: { a: u, b: l }, backend: e }), f = Wl.kernelFunc({ inputs: { a: c, b: m }, backend: e }), h = e.data.get(d.dataId).values, g = e.data.get(f.dataId).values; return e.disposeIntermediateTensorInfo(u), e.disposeIntermediateTensorInfo(c), e.disposeIntermediateTensorInfo(l), e.disposeIntermediateTensorInfo(m), e.disposeIntermediateTensorInfo(d), e.disposeIntermediateTensorInfo(f), { real: h, imag: g }; } return i; @@ -14315,42 +14316,42 @@ function uQ(r, t8, e) { return w.splitRealAndImagArrays(p); } } -function pQ(r) { - return (r & r - 1) === 0; +function pQ(r15) { + return (r15 & r15 - 1) === 0; } -function CI(r, t8, e, o, n) { +function wI(r15, t8, e, o, n) { if (e === 1) - return { real: r, imag: t8 }; - let s = w.mergeRealAndImagArrays(r, t8), a = e / 2, i = w.complexWithEvenIndex(s), p = i.real, u = i.imag, c = [p.length], l = n.makeTensorInfo(c, "float32", p), m = n.makeTensorInfo(c, "float32", u), d = Kt({ inputs: { real: l, imag: m }, backend: n }), f = w.complexWithOddIndex(s), h = f.real, g = f.imag, x = [h.length], b = n.makeTensorInfo(x, "float32", h), C = n.makeTensorInfo(x, "float32", g), S = Kt({ inputs: { real: b, imag: C }, backend: n }), k = CI(p, u, a, o, n), _ = k.real, E = k.imag, R = [_.length], D = n.makeTensorInfo(R, "float32", _), P = n.makeTensorInfo(R, "float32", E), O = Kt({ inputs: { real: D, imag: P }, backend: n }), M = CI(h, g, a, o, n), L = M.real, B = M.imag, z = [L.length], U = n.makeTensorInfo(z, "float32", L), j = n.makeTensorInfo(z, "float32", B), q = Kt({ inputs: { real: U, imag: j }, backend: n }), Y = w.exponents(e, o), J = [Y.real.length], re = n.makeTensorInfo(J, "float32", Y.real), ne = n.makeTensorInfo(J, "float32", Y.imag), ee = Kt({ inputs: { real: re, imag: ne }, backend: n }), oe = dp({ inputs: { a: ee, b: q }, backend: n }), ie = Oa({ inputs: { a: O, b: oe }, backend: n }), le = Vl({ inputs: { a: O, b: oe }, backend: n }), be = Ro({ inputs: { input: ie }, backend: n }), _e = Ro({ inputs: { input: le }, backend: n }), ve = Ma({ inputs: { input: ie }, backend: n }), Fe = Ma({ inputs: { input: le }, backend: n }), Pe = yu({ inputs: [be, _e], backend: n, attrs: { axis: 0 } }), st = yu({ inputs: [ve, Fe], backend: n, attrs: { axis: 0 } }), lt = n.data.get(Pe.dataId).values, Ge = n.data.get(st.dataId).values; - return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(C), n.disposeIntermediateTensorInfo(S), n.disposeIntermediateTensorInfo(D), n.disposeIntermediateTensorInfo(P), n.disposeIntermediateTensorInfo(O), n.disposeIntermediateTensorInfo(U), n.disposeIntermediateTensorInfo(j), n.disposeIntermediateTensorInfo(q), n.disposeIntermediateTensorInfo(re), n.disposeIntermediateTensorInfo(ne), n.disposeIntermediateTensorInfo(ee), n.disposeIntermediateTensorInfo(oe), n.disposeIntermediateTensorInfo(ie), n.disposeIntermediateTensorInfo(le), n.disposeIntermediateTensorInfo(be), n.disposeIntermediateTensorInfo(ve), n.disposeIntermediateTensorInfo(_e), n.disposeIntermediateTensorInfo(Fe), n.disposeIntermediateTensorInfo(Pe), n.disposeIntermediateTensorInfo(st), { real: lt, imag: Ge }; + return { real: r15, imag: t8 }; + let s = w.mergeRealAndImagArrays(r15, t8), a = e / 2, i = w.complexWithEvenIndex(s), p = i.real, u = i.imag, c = [p.length], l = n.makeTensorInfo(c, "float32", p), m = n.makeTensorInfo(c, "float32", u), d = Ht({ inputs: { real: l, imag: m }, backend: n }), f = w.complexWithOddIndex(s), h = f.real, g = f.imag, x = [h.length], b = n.makeTensorInfo(x, "float32", h), C = n.makeTensorInfo(x, "float32", g), S = Ht({ inputs: { real: b, imag: C }, backend: n }), k = wI(p, u, a, o, n), _ = k.real, E = k.imag, R = [_.length], D = n.makeTensorInfo(R, "float32", _), P = n.makeTensorInfo(R, "float32", E), O = Ht({ inputs: { real: D, imag: P }, backend: n }), M = wI(h, g, a, o, n), L = M.real, B = M.imag, z = [L.length], U = n.makeTensorInfo(z, "float32", L), j = n.makeTensorInfo(z, "float32", B), q = Ht({ inputs: { real: U, imag: j }, backend: n }), Y = w.exponents(e, o), J = [Y.real.length], re = n.makeTensorInfo(J, "float32", Y.real), ne = n.makeTensorInfo(J, "float32", Y.imag), ee = Ht({ inputs: { real: re, imag: ne }, backend: n }), oe = sp({ inputs: { a: ee, b: q }, backend: n }), ie = Pa({ inputs: { a: O, b: oe }, backend: n }), le = Bl({ inputs: { a: O, b: oe }, backend: n }), be = Eo({ inputs: { input: ie }, backend: n }), _e = Eo({ inputs: { input: le }, backend: n }), ve = Oa({ inputs: { input: ie }, backend: n }), Fe = Oa({ inputs: { input: le }, backend: n }), Pe = du({ inputs: [be, _e], backend: n, attrs: { axis: 0 } }), st = du({ inputs: [ve, Fe], backend: n, attrs: { axis: 0 } }), ct = n.data.get(Pe.dataId).values, Ge = n.data.get(st.dataId).values; + return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(C), n.disposeIntermediateTensorInfo(S), n.disposeIntermediateTensorInfo(D), n.disposeIntermediateTensorInfo(P), n.disposeIntermediateTensorInfo(O), n.disposeIntermediateTensorInfo(U), n.disposeIntermediateTensorInfo(j), n.disposeIntermediateTensorInfo(q), n.disposeIntermediateTensorInfo(re), n.disposeIntermediateTensorInfo(ne), n.disposeIntermediateTensorInfo(ee), n.disposeIntermediateTensorInfo(oe), n.disposeIntermediateTensorInfo(ie), n.disposeIntermediateTensorInfo(le), n.disposeIntermediateTensorInfo(be), n.disposeIntermediateTensorInfo(ve), n.disposeIntermediateTensorInfo(_e), n.disposeIntermediateTensorInfo(Fe), n.disposeIntermediateTensorInfo(Pe), n.disposeIntermediateTensorInfo(st), { real: ct, imag: Ge }; } -function cQ(r, t8, e) { +function cQ(r15, t8, e) { let o = new Float32Array(t8 * 2); for (let n = 0; n < t8; n++) { let s = 0, a = 0; for (let i = 0; i < t8; i++) { - let p = w.exponent(n * i, t8, e), u = w.getComplexWithIndex(r, i); + let p = w.exponent(n * i, t8, e), u = w.getComplexWithIndex(r15, i); s += u.real * p.real - u.imag * p.imag, a += u.real * p.imag + u.imag * p.real; } e && (s /= t8, a /= t8), w.assignToTypedArray(o, s, a, n); } return o; } -function lQ(r) { - let { inputs: t8, backend: e } = r, { input: o } = t8, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Ve({ inputs: { x: o }, backend: e, attrs: { shape: [a, s] } }), p = Vf(i, false, e), u = Ve({ inputs: { x: p }, backend: e, attrs: { shape: o.shape } }); +function lQ(r15) { + let { inputs: t8, backend: e } = r15, { input: o } = t8, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Ve({ inputs: { x: o }, backend: e, attrs: { shape: [a, s] } }), p = zf(i, false, e), u = Ve({ inputs: { x: p }, backend: e, attrs: { shape: o.shape } }); return e.disposeIntermediateTensorInfo(i), e.disposeIntermediateTensorInfo(p), u; } -var L$ = { kernelName: Wi, backendName: "cpu", kernelFunc: lQ }; -function Hl(r) { - let { backend: t8, attrs: e } = r, { shape: o, value: n, dtype: s } = e, a = s || y.inferDtype(n), i = y.getArrayFromDType(a, y.sizeFromShape(o)); +var B$ = { kernelName: Bi, backendName: "cpu", kernelFunc: lQ }; +function Ul(r15) { + let { backend: t8, attrs: e } = r15, { shape: o, value: n, dtype: s } = e, a = s || y.inferDtype(n), i = y.getArrayFromDType(a, y.sizeFromShape(o)); return mQ(i, n, a), t8.makeTensorInfo(o, a, i); } -var B$ = { kernelName: sa, backendName: "cpu", kernelFunc: Hl }; -function mQ(r, t8, e) { - r.fill(t8); +var z$ = { kernelName: sa, backendName: "cpu", kernelFunc: Ul }; +function mQ(r15, t8, e) { + r15.fill(t8); } -var z$ = { kernelName: wn, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: t8, backend: e }) => { - let { image: o } = r, n = e, s = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [a, i, p, u] = o.shape, c = n.data.get(o.dataId).values; +var V$ = { kernelName: Cn, backendName: "cpu", kernelFunc: ({ inputs: r15, attrs: t8, backend: e }) => { + let { image: o } = r15, n = e, s = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [a, i, p, u] = o.shape, c = n.data.get(o.dataId).values; for (let m = 0; m < a; m++) { let d = m * p * i * u; for (let f = 0; f < i; f++) { @@ -14370,52 +14371,52 @@ var z$ = { kernelName: wn, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: } return { dataId: n.write(s, o.shape, o.dtype), shape: o.shape, dtype: o.dtype }; } }; -function dQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = yI({ inputs: { x: n, filter: s }, backend: e, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } }); +function dQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = bI({ inputs: { x: n, filter: s }, backend: e, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } }); if (a) { let g = h; if (c === "NCHW" && a.shape.length === 1 && a.shape[0] !== 1) { let x = Ve({ inputs: { x: a }, backend: e, attrs: { shape: [a.shape[0], 1, 1] } }); - h = Oa({ inputs: { a: h, b: x }, backend: e }), e.disposeIntermediateTensorInfo(x); + h = Pa({ inputs: { a: h, b: x }, backend: e }), e.disposeIntermediateTensorInfo(x); } else - h = Oa({ inputs: { a: h, b: a }, backend: e }); + h = Pa({ inputs: { a: h, b: a }, backend: e }); e.disposeIntermediateTensorInfo(g); } if (d) { let g = h; if (c === "NCHW" && d === "prelu" && i.shape.length === 1 && i.shape[0] !== 1) { let x = Ve({ inputs: { x: i }, backend: e, attrs: { shape: [i.shape[0], 1, 1] } }); - h = Cp(e, h, d, x, f), e.disposeIntermediateTensorInfo(x); + h = mp(e, h, d, x, f), e.disposeIntermediateTensorInfo(x); } else - h = Cp(e, h, d, i, f); + h = mp(e, h, d, i, f); e.disposeIntermediateTensorInfo(g); } return h; } -var V$ = { kernelName: vo, backendName: "cpu", kernelFunc: dQ }; -function fQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = bI({ inputs: { x: n, filter: s }, backend: e, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } }); +var W$ = { kernelName: Io, backendName: "cpu", kernelFunc: dQ }; +function fQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = CI({ inputs: { x: n, filter: s }, backend: e, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } }); if (a) { let g = h; - h = Oa({ inputs: { a: h, b: a }, backend: e }), e.disposeIntermediateTensorInfo(g); + h = Pa({ inputs: { a: h, b: a }, backend: e }), e.disposeIntermediateTensorInfo(g); } if (d) { let g = h; - h = Cp(e, h, d, i, f), e.disposeIntermediateTensorInfo(g); + h = mp(e, h, d, i, f), e.disposeIntermediateTensorInfo(g); } return h; } -var W$ = { kernelName: ko, backendName: "cpu", kernelFunc: fQ }; -function hQ(r) { - let { inputs: t8, backend: e } = r, { params: o, indices: n } = t8, s = y.sizeFromShape(o.shape), a = n.shape, i = a[a.length - 1], [p, u, c, l] = w.prepareAndValidate(o, n); +var U$ = { kernelName: vo, backendName: "cpu", kernelFunc: fQ }; +function hQ(r15) { + let { inputs: t8, backend: e } = r15, { params: o, indices: n } = t8, s = y.sizeFromShape(o.shape), a = n.shape, i = a[a.length - 1], [p, u, c, l] = w.prepareAndValidate(o, n); if (u === 0) return e.makeTensorInfo(p, o.dtype, []); - let m = e.data.get(n.dataId).values, d = e.bufferSync(o), f = Tf(m, d, o.dtype, u, i, c, l, o.shape, s); + let m = e.data.get(n.dataId).values, d = e.bufferSync(o), f = Nf(m, d, o.dtype, u, i, c, l, o.shape, s); return e.makeTensorInfo(p, o.dtype, f.values); } -var U$ = { kernelName: kn, backendName: "cpu", kernelFunc: hQ }; -function gQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, indices: s } = t8, { axis: a, batchDims: i } = o; +var G$ = { kernelName: vn, backendName: "cpu", kernelFunc: hQ }; +function gQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, indices: s } = t8, { axis: a, batchDims: i } = o; Q([n, s], "gatherV2"); let p = y.parseAxisParam(a, n.shape)[0], u = e.data.get(s.dataId).values, c = n.shape[p]; for (let S = 0; S < u.length; ++S) { @@ -14424,38 +14425,38 @@ function gQ(r) { } let l = i; i == null && (l = 0); - let m = y.sizeFromShape(s.shape), d = w.segment_util.collectGatherOpShapeInfo(n, s, p, l), f = Ve({ inputs: { x: n }, backend: e, attrs: { shape: [d.batchSize, d.outerSize, d.dimSize, d.sliceSize] } }), h = Ve({ inputs: { x: s }, backend: e, attrs: { shape: [d.batchSize, m / d.batchSize] } }), g = [d.batchSize, d.outerSize, m / d.batchSize, d.sliceSize], x = e.bufferSync(h), b = e.bufferSync(f), C = _f(b, x, g); + let m = y.sizeFromShape(s.shape), d = w.segment_util.collectGatherOpShapeInfo(n, s, p, l), f = Ve({ inputs: { x: n }, backend: e, attrs: { shape: [d.batchSize, d.outerSize, d.dimSize, d.sliceSize] } }), h = Ve({ inputs: { x: s }, backend: e, attrs: { shape: [d.batchSize, m / d.batchSize] } }), g = [d.batchSize, d.outerSize, m / d.batchSize, d.sliceSize], x = e.bufferSync(h), b = e.bufferSync(f), C = Tf(b, x, g); return e.disposeIntermediateTensorInfo(f), e.disposeIntermediateTensorInfo(h), e.makeTensorInfo(d.outputShape, C.dtype, C.values); } -var G$ = { kernelName: aa, backendName: "cpu", kernelFunc: gQ }; -function xQ(r) { - let { inputs: t8, backend: e } = r, { input: o } = t8, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Ve({ inputs: { x: o }, backend: e, attrs: { shape: [a, s] } }), p = Vf(i, true, e), u = Ve({ inputs: { x: p }, backend: e, attrs: { shape: o.shape } }); +var H$ = { kernelName: aa, backendName: "cpu", kernelFunc: gQ }; +function xQ(r15) { + let { inputs: t8, backend: e } = r15, { input: o } = t8, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Ve({ inputs: { x: o }, backend: e, attrs: { shape: [a, s] } }), p = zf(i, true, e), u = Ve({ inputs: { x: p }, backend: e, attrs: { shape: o.shape } }); return e.disposeIntermediateTensorInfo(i), e.disposeIntermediateTensorInfo(p), u; } -var H$ = { kernelName: Ui, backendName: "cpu", kernelFunc: xQ }; -var yQ = Ie(_n, (r) => Number.isFinite(r) ? 1 : 0, "bool"); -var K$ = { kernelName: _n, backendName: "cpu", kernelFunc: yQ }; -var bQ = Ie($n, (r) => Math.abs(r) === 1 / 0 ? 1 : 0, "bool"); -var q$ = { kernelName: $n, backendName: "cpu", kernelFunc: bQ }; -var CQ = Ie(En, (r) => Number.isNaN(r) ? 1 : 0, "bool"); -var j$ = { kernelName: En, backendName: "cpu", kernelFunc: CQ }; -function wQ(r) { - let { backend: t8, attrs: e } = r, { start: o, stop: n, num: s } = e, a = $f(o, n, s); +var K$ = { kernelName: zi, backendName: "cpu", kernelFunc: xQ }; +var yQ = Ie(Tn, (r15) => Number.isFinite(r15) ? 1 : 0, "bool"); +var q$ = { kernelName: Tn, backendName: "cpu", kernelFunc: yQ }; +var bQ = Ie(_n, (r15) => Math.abs(r15) === 1 / 0 ? 1 : 0, "bool"); +var j$ = { kernelName: _n, backendName: "cpu", kernelFunc: bQ }; +var CQ = Ie($n, (r15) => Number.isNaN(r15) ? 1 : 0, "bool"); +var X$ = { kernelName: $n, backendName: "cpu", kernelFunc: CQ }; +function wQ(r15) { + let { backend: t8, attrs: e } = r15, { start: o, stop: n, num: s } = e, a = _f(o, n, s); return t8.makeTensorInfo([a.length], "float32", a); } -var X$ = { kernelName: Fn, backendName: "cpu", kernelFunc: wQ }; -var SQ = Ie(On, (r) => Math.log1p(r)); -var Y$ = { kernelName: On, backendName: "cpu", kernelFunc: SQ }; -var IQ = ze((r, t8) => r && t8); -var vQ = Ye(Mn, IQ, null, "bool"); -var Q$ = { kernelName: Mn, backendName: "cpu", kernelFunc: vQ }; -var kQ = Ie(Ln, (r) => r ? 0 : 1, "bool"); -var Z$ = { kernelName: Ln, backendName: "cpu", kernelFunc: kQ }; -var NQ = ze((r, t8) => r || t8); -var TQ = Ye(Bn, NQ, null, "bool"); -var J$ = { kernelName: Bn, backendName: "cpu", kernelFunc: TQ }; -function _Q(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { depthRadius: s, bias: a, alpha: i, beta: p } = o; +var Y$ = { kernelName: An, backendName: "cpu", kernelFunc: wQ }; +var SQ = Ie(Pn, (r15) => Math.log1p(r15)); +var Q$ = { kernelName: Pn, backendName: "cpu", kernelFunc: SQ }; +var IQ = ze((r15, t8) => r15 && t8); +var vQ = Ye(On, IQ, null, "bool"); +var Z$ = { kernelName: On, backendName: "cpu", kernelFunc: vQ }; +var kQ = Ie(Mn, (r15) => r15 ? 0 : 1, "bool"); +var J$ = { kernelName: Mn, backendName: "cpu", kernelFunc: kQ }; +var NQ = ze((r15, t8) => r15 || t8); +var TQ = Ye(Ln, NQ, null, "bool"); +var eE = { kernelName: Ln, backendName: "cpu", kernelFunc: TQ }; +function _Q(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { depthRadius: s, bias: a, alpha: i, beta: p } = o; Q(n, "LRN"); let u = n.shape[3], c = u - 1, l = e.data.get(n.dataId).values, m = y.sizeFromShape(n.shape), d = new Float32Array(m); function f(h) { @@ -14472,9 +14473,9 @@ function _Q(r) { } return e.makeTensorInfo(n.shape, n.dtype, d); } -var eE = { kernelName: zn, backendName: "cpu", kernelFunc: _Q }; -function $Q(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, y: s, dy: a } = t8, { depthRadius: i, bias: p, alpha: u, beta: c } = o; +var tE = { kernelName: Bn, backendName: "cpu", kernelFunc: _Q }; +function $Q(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, y: s, dy: a } = t8, { depthRadius: i, bias: p, alpha: u, beta: c } = o; Q(a, "LRNGrad"); let l = y.sizeFromShape(a.shape), m = a.shape[3], d = e.data.get(a.dataId).values, f = e.data.get(n.dataId).values, h = e.data.get(s.dataId).values, g = new Float32Array(l), x = l; for (let b = 0; b < x; b++) { @@ -14489,46 +14490,46 @@ function $Q(r) { } return e.makeTensorInfo(a.shape, n.dtype, g); } -var tE = { kernelName: Qa, backendName: "cpu", kernelFunc: $Q }; -function wI(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { reductionIndices: s, keepDims: a } = o, i = e, p = n.shape, u = p.length, c = y.parseAxisParam(s, p), l = c, m = w.getAxesPermutation(l, u), d = i.data.get(n.dataId).values; +var rE = { kernelName: Ya, backendName: "cpu", kernelFunc: $Q }; +function SI(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { reductionIndices: s, keepDims: a } = o, i = e, p = n.shape, u = p.length, c = y.parseAxisParam(s, p), l = c, m = w.getAxesPermutation(l, u), d = i.data.get(n.dataId).values; if (m != null) { let S = new Array(u); for (let k = 0; k < S.length; k++) S[k] = p[m[k]]; - d = vc(d, p, n.dtype, m, S), l = w.getInnerMostAxes(l.length, u), p = S; + d = bc(d, p, n.dtype, m, S), l = w.getInnerMostAxes(l.length, u), p = S; } Q(n, "max"), w.assertAxesAreInnerMostDims("max", l, u); - let [f, h] = w.computeOutAndReduceShapes(p, l), g = y.sizeFromShape(h), x = Ef(d, g, f, n.dtype), b = i.write(x, f, n.dtype), C = f; + let [f, h] = w.computeOutAndReduceShapes(p, l), g = y.sizeFromShape(h), x = $f(d, g, f, n.dtype), b = i.write(x, f, n.dtype), C = f; return a && (C = w.expandShapeToKeepDim(f, c)), { dataId: b, shape: C, dtype: n.dtype }; } -var rE = { kernelName: Vn, backendName: "cpu", kernelFunc: wI }; -function EQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8; +var oE = { kernelName: zn, backendName: "cpu", kernelFunc: SI }; +function EQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8; Q(n, "maxPool"); let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1; y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); let c = w.computePool2DInfo(n.shape, s, a, u, i, p), l; if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape)) - l = mr({ inputs: { x: n }, backend: e }); + l = lr({ inputs: { x: n }, backend: e }); else { - let m = e.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = _c(m, n.shape, n.dtype, d, c, "max"); + let m = e.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = Sc(m, n.shape, n.dtype, d, c, "max"); l = e.makeTensorInfo(c.outShape, n.dtype, f.values); } return l; } -var oE = { kernelName: Un, backendName: "cpu", kernelFunc: EQ }; -function RQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o; +var nE = { kernelName: Wn, backendName: "cpu", kernelFunc: EQ }; +function RQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o; Q(n, "maxPool3d"); - let c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = e.data.get(n.dataId).values, m = zf(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "max"); + let c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = e.data.get(n.dataId).values, m = Bf(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "max"); return e.makeTensorInfo(m.shape, "float32", m.values); } -var nE = { kernelName: ia, backendName: "cpu", kernelFunc: RQ }; -function DQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s } = t8, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o; +var sE = { kernelName: ia, backendName: "cpu", kernelFunc: RQ }; +function DQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s } = t8, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o; Q([n, s], "maxPool3DGrad"); - let c = w.computePool3DInfo(s.shape, a, i, 1, p, u), l = e.bufferSync(s), m = J_(l, c), d = c.strideDepth, f = c.strideHeight, h = c.strideWidth, g = c.dilationDepth, x = c.dilationHeight, b = c.dilationWidth, C = c.effectiveFilterDepth, S = c.effectiveFilterHeight, k = c.effectiveFilterWidth, _ = C - 1 - c.padInfo.front, E = k - 1 - c.padInfo.left, R = S - 1 - c.padInfo.top, D = me(s.shape, "float32"), P = e.bufferSync(n); + let c = w.computePool3DInfo(s.shape, a, i, 1, p, u), l = e.bufferSync(s), m = e$(l, c), d = c.strideDepth, f = c.strideHeight, h = c.strideWidth, g = c.dilationDepth, x = c.dilationHeight, b = c.dilationWidth, C = c.effectiveFilterDepth, S = c.effectiveFilterHeight, k = c.effectiveFilterWidth, _ = C - 1 - c.padInfo.front, E = k - 1 - c.padInfo.left, R = S - 1 - c.padInfo.top, D = me(s.shape, "float32"), P = e.bufferSync(n); for (let O = 0; O < c.batchSize; ++O) for (let M = 0; M < c.inChannels; ++M) for (let L = 0; L < c.inDepth; ++L) @@ -14557,11 +14558,11 @@ function DQ(r) { } return e.makeTensorInfo(D.shape, D.dtype, D.values); } -var sE = { kernelName: Ki, backendName: "cpu", kernelFunc: DQ }; -function AQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s, output: a } = t8, i = s; +var aE = { kernelName: Ui, backendName: "cpu", kernelFunc: DQ }; +function AQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s, output: a } = t8, i = s; Q([s, a], "maxPoolGrad"); - let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = w.computePool2DInfo(i.shape, p, u, 1, c, l), d = e.data.get(i.dataId).values, f = me(m.outShape, i.dtype, Bf(d, i.shape, i.dtype, m).values), h = m.strideHeight, g = m.strideWidth, x = m.dilationHeight, b = m.dilationWidth, C = m.effectiveFilterHeight, S = m.effectiveFilterWidth, k = S - 1 - m.padInfo.left, _ = C - 1 - m.padInfo.top, E = me(i.shape, "float32"), R = e.data.get(n.dataId).values, D = me(n.shape, "float32", R); + let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = w.computePool2DInfo(i.shape, p, u, 1, c, l), d = e.data.get(i.dataId).values, f = me(m.outShape, i.dtype, Lf(d, i.shape, i.dtype, m).values), h = m.strideHeight, g = m.strideWidth, x = m.dilationHeight, b = m.dilationWidth, C = m.effectiveFilterHeight, S = m.effectiveFilterWidth, k = S - 1 - m.padInfo.left, _ = C - 1 - m.padInfo.top, E = me(i.shape, "float32"), R = e.data.get(n.dataId).values, D = me(n.shape, "float32", R); for (let P = 0; P < m.batchSize; ++P) for (let O = 0; O < m.inChannels; ++O) for (let M = 0; M < m.inHeight; ++M) @@ -14585,33 +14586,33 @@ function AQ(r) { } return e.makeTensorInfo(E.shape, E.dtype, E.values); } -var aE = { kernelName: Hi, backendName: "cpu", kernelFunc: AQ }; -function iE(r, t8, e, o, n) { - let s = y.computeStrides(t8), a = _c(r, t8, e, s, n, "max"), i = Bf(r, t8, e, n, true, o); +var iE = { kernelName: Wi, backendName: "cpu", kernelFunc: AQ }; +function uE(r15, t8, e, o, n) { + let s = y.computeStrides(t8), a = Sc(r15, t8, e, s, n, "max"), i = Lf(r15, t8, e, n, true, o); return [a.values, i.values]; } -var uE = { kernelName: ua, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: t8, backend: e }) => { - let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = t8, p = e; +var pE = { kernelName: ua, backendName: "cpu", kernelFunc: ({ inputs: r15, attrs: t8, backend: e }) => { + let { x: o } = r15, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = t8, p = e; Q(o, "MaxPoolWithArgmax"); - let u = p.data.get(o.dataId).values, c = w.computePool2DInfo(o.shape, n, s, [1, 1], a), [l, m] = iE(u, o.shape, o.dtype, i, c), d = p.write(l, c.outShape, o.dtype), f = p.write(m, c.outShape, o.dtype); + let u = p.data.get(o.dataId).values, c = w.computePool2DInfo(o.shape, n, s, [1, 1], a), [l, m] = uE(u, o.shape, o.dtype, i, c), d = p.write(l, c.outShape, o.dtype), f = p.write(m, c.outShape, o.dtype); return [{ dataId: d, shape: c.outShape, dtype: o.dtype }, { dataId: f, shape: c.outShape, dtype: "int32" }]; } }; -function FQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, keepDims: a } = o, i = y.parseAxisParam(s, n.shape), u = w.computeOutAndReduceShapes(n.shape, i)[1], c = y.sizeFromShape(u), l = [], m = e.makeTensorInfo([], "float32", new Float32Array([c])); +function FQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, keepDims: a } = o, i = y.parseAxisParam(s, n.shape), u = w.computeOutAndReduceShapes(n.shape, i)[1], c = y.sizeFromShape(u), l = [], m = e.makeTensorInfo([], "float32", new Float32Array([c])); l.push(m); - let d = Do({ inputs: { x: n }, backend: e, attrs: { dtype: "float32" } }); + let d = Ro({ inputs: { x: n }, backend: e, attrs: { dtype: "float32" } }); l.push(d); - let f = Ul({ inputs: { a: d, b: m }, backend: e }); + let f = Vl({ inputs: { a: d, b: m }, backend: e }); l.push(f); - let h = gi({ inputs: { x: f }, backend: e, attrs: { axis: s, keepDims: a } }); + let h = di({ inputs: { x: f }, backend: e, attrs: { axis: s, keepDims: a } }); return l.forEach((g) => e.disposeIntermediateTensorInfo(g)), h; } -var pE = { kernelName: Gn, backendName: "cpu", kernelFunc: FQ }; -function PQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, keepDims: a } = o; +var cE = { kernelName: Un, backendName: "cpu", kernelFunc: FQ }; +function PQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, keepDims: a } = o; Q(n, "min"); let i = y.parseAxisParam(s, n.shape), p = i, u = w.getAxesPermutation(p, n.shape.length), c = n; - u != null && (c = It({ inputs: { x: n }, backend: e, attrs: { perm: u } }), p = w.getInnerMostAxes(p.length, n.shape.length)), w.assertAxesAreInnerMostDims("min", p, c.shape.length); + u != null && (c = St({ inputs: { x: n }, backend: e, attrs: { perm: u } }), p = w.getInnerMostAxes(p.length, n.shape.length)), w.assertAxesAreInnerMostDims("min", p, c.shape.length); let [l, m] = w.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = e.data.get(c.dataId).values; for (let x = 0; x < f.length; ++x) { let b = x * d, C = h[b]; @@ -14629,9 +14630,9 @@ function PQ(r) { } return g; } -var cE = { kernelName: Hn, backendName: "cpu", kernelFunc: PQ }; -function OQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { paddings: s, mode: a } = o; +var lE = { kernelName: Gn, backendName: "cpu", kernelFunc: PQ }; +function OQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { paddings: s, mode: a } = o; Q(n, "mirrorPad"); let i = s.map((C, S) => C[0] + n.shape[S] + C[1]), p = s.map((C) => C[0]), u = s.map((C, S) => C[0] + n.shape[S]), c = a === "reflect" ? 0 : 1, l = e.data.get(n.dataId).values, m = n.shape.length, d = y.computeStrides(n.shape), f = y.sizeFromShape(i), h = i.length, g = y.computeStrides(i), x = y.getTypedArrayFromDType(n.dtype, f); for (let C = 0; C < f; C++) { @@ -14644,32 +14645,32 @@ function OQ(r) { } return { dataId: e.write(x, i, n.dtype), shape: i, dtype: n.dtype }; } -var lE = { kernelName: qn, backendName: "cpu", kernelFunc: OQ }; -var MQ = ze((r, t8) => { - let e = r % t8; - return r < 0 && t8 < 0 || r >= 0 && t8 >= 0 ? e : (e + t8) % t8; +var mE = { kernelName: Kn, backendName: "cpu", kernelFunc: OQ }; +var MQ = ze((r15, t8) => { + let e = r15 % t8; + return r15 < 0 && t8 < 0 || r15 >= 0 && t8 >= 0 ? e : (e + t8) % t8; }); -var LQ = Ye(jn, MQ); -var mE = { kernelName: jn, backendName: "cpu", kernelFunc: LQ }; -var fE = Kp(qw()); -function SI(r) { - let { inputs: t8, backend: e, attrs: o } = r, { logits: n } = t8, { dim: s } = o, a = n.shape.length, i = s; +var LQ = Ye(qn, MQ); +var dE = { kernelName: qn, backendName: "cpu", kernelFunc: LQ }; +var hE = Lp(qw()); +function II(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { logits: n } = t8, { dim: s } = o, a = n.shape.length, i = s; if (i === -1 && (i = a - 1), i !== a - 1) throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${a} and dim was ${i}`); - let p = y.parseAxisParam([i], n.shape), u = wI({ inputs: { x: n }, backend: e, attrs: { reductionIndices: p, keepDims: false } }), c = w.expandShapeToKeepDim(u.shape, p), l = Ve({ inputs: { x: u }, backend: e, attrs: { shape: c } }), m = Vl({ inputs: { a: n, b: l }, backend: e }), d = KS({ inputs: { x: m }, backend: e }), f = gi({ inputs: { x: d }, backend: e, attrs: { axis: p, keepDims: false } }), h = Ve({ inputs: { x: f }, backend: e, attrs: { shape: c } }), g = Ul({ inputs: { a: d, b: h }, backend: e }); + let p = y.parseAxisParam([i], n.shape), u = SI({ inputs: { x: n }, backend: e, attrs: { reductionIndices: p, keepDims: false } }), c = w.expandShapeToKeepDim(u.shape, p), l = Ve({ inputs: { x: u }, backend: e, attrs: { shape: c } }), m = Bl({ inputs: { a: n, b: l }, backend: e }), d = KS({ inputs: { x: m }, backend: e }), f = di({ inputs: { x: d }, backend: e, attrs: { axis: p, keepDims: false } }), h = Ve({ inputs: { x: f }, backend: e, attrs: { shape: c } }), g = Vl({ inputs: { a: d, b: h }, backend: e }); return e.disposeIntermediateTensorInfo(u), e.disposeIntermediateTensorInfo(l), e.disposeIntermediateTensorInfo(m), e.disposeIntermediateTensorInfo(d), e.disposeIntermediateTensorInfo(f), e.disposeIntermediateTensorInfo(h), g; } -var dE = { kernelName: vs, backendName: "cpu", kernelFunc: SI }; -function BQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { logits: n } = t8, { numSamples: s, seed: a, normalized: i } = o; +var fE = { kernelName: Is, backendName: "cpu", kernelFunc: II }; +function BQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { logits: n } = t8, { numSamples: s, seed: a, normalized: i } = o; Q(n, "multinomial"); - let p = i ? n : SI({ inputs: { logits: n }, backend: e, attrs: { dim: -1 } }), u = p.shape[0], c = p.shape[1], l = e.data.get(p.dataId).values, m = [u, s], d = y.makeZerosTypedArray(y.sizeFromShape(m), "int32"); + let p = i ? n : II({ inputs: { logits: n }, backend: e, attrs: { dim: -1 } }), u = p.shape[0], c = p.shape[1], l = e.data.get(p.dataId).values, m = [u, s], d = y.makeZerosTypedArray(y.sizeFromShape(m), "int32"); for (let f = 0; f < u; ++f) { let h = f * c, g = new Float32Array(c - 1); g[0] = l[h]; for (let C = 1; C < g.length; ++C) g[C] = g[C - 1] + l[h + C]; - let x = fE.alea(a.toString()), b = f * s; + let x = hE.alea(a.toString()), b = f * s; for (let C = 0; C < s; ++C) { let S = x(); d[b + C] = g.length; @@ -14682,33 +14683,33 @@ function BQ(r) { } return i || e.disposeIntermediateTensorInfo(p), e.makeTensorInfo(m, "int32", d); } -var hE = { kernelName: Xn, backendName: "cpu", kernelFunc: BQ }; -var zQ = Wt.nonMaxSuppressionV3Impl; -function VQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o; +var gE = { kernelName: jn, backendName: "cpu", kernelFunc: BQ }; +var zQ = Vt.nonMaxSuppressionV3Impl; +function VQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o; Q(n, "NonMaxSuppression"); let u = e.data.get(n.dataId).values, c = e.data.get(s.dataId).values, { selectedIndices: l } = zQ(u, c, a, i, p); return e.makeTensorInfo([l.length], "int32", new Int32Array(l)); } -var gE = { kernelName: Zn, backendName: "cpu", kernelFunc: VQ }; -var WQ = Wt.nonMaxSuppressionV4Impl; -function UQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o; +var xE = { kernelName: Qn, backendName: "cpu", kernelFunc: VQ }; +var WQ = Vt.nonMaxSuppressionV4Impl; +function UQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o; Q(n, "NonMaxSuppressionPadded"); let c = e.data.get(n.dataId).values, l = e.data.get(s.dataId).values, { selectedIndices: m, validOutputs: d } = WQ(c, l, a, i, p, u); return [e.makeTensorInfo([m.length], "int32", new Int32Array(m)), e.makeTensorInfo([], "int32", new Int32Array([d]))]; } -var xE = { kernelName: Za, backendName: "cpu", kernelFunc: UQ }; -var GQ = Wt.nonMaxSuppressionV5Impl; -function HQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o; +var yE = { kernelName: Qa, backendName: "cpu", kernelFunc: UQ }; +var GQ = Vt.nonMaxSuppressionV5Impl; +function HQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o; Q(n, "NonMaxSuppressionWithScore"); let c = e.data.get(n.dataId).values, l = e.data.get(s.dataId).values, m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = GQ(c, l, m, d, f, h); return [e.makeTensorInfo([g.length], "int32", new Int32Array(g)), e.makeTensorInfo([x.length], "float32", new Float32Array(x))]; } -var yE = { kernelName: Jn, backendName: "cpu", kernelFunc: HQ }; -function KQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { indices: n } = t8, { dtype: s, depth: a, onValue: i, offValue: p } = o; +var bE = { kernelName: Zn, backendName: "cpu", kernelFunc: HQ }; +function KQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { indices: n } = t8, { dtype: s, depth: a, onValue: i, offValue: p } = o; Q(n, "oneHot"); let u = y.sizeFromShape(n.shape), c = new Float32Array(u * a); c.fill(p); @@ -14717,46 +14718,46 @@ function KQ(r) { l[m] >= 0 && l[m] < a && (c[m * a + l[m]] = i); return e.makeTensorInfo([...n.shape, a], s, c); } -var bE = { kernelName: es, backendName: "cpu", kernelFunc: KQ }; -function Kl(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8; +var CE = { kernelName: Jn, backendName: "cpu", kernelFunc: KQ }; +function Gl(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8; if (o.dtype === "string") throw new Error("zerosLike is not supported for string tensors"); if (o.dtype === "complex64") { - let n = Ro({ inputs: { input: o }, backend: e }), s = Kl({ inputs: { x: n }, backend: e }), a = Ma({ inputs: { input: o }, backend: e }), i = Kl({ inputs: { x: a }, backend: e }), p = Kt({ inputs: { real: s, imag: i }, backend: e }); + let n = Eo({ inputs: { input: o }, backend: e }), s = Gl({ inputs: { x: n }, backend: e }), a = Oa({ inputs: { input: o }, backend: e }), i = Gl({ inputs: { x: a }, backend: e }), p = Ht({ inputs: { real: s, imag: i }, backend: e }); return e.disposeIntermediateTensorInfo(n), e.disposeIntermediateTensorInfo(s), e.disposeIntermediateTensorInfo(a), e.disposeIntermediateTensorInfo(i), p; } else - return Hl({ backend: e, attrs: { shape: o.shape, value: 0, dtype: o.dtype } }); + return Ul({ backend: e, attrs: { shape: o.shape, value: 0, dtype: o.dtype } }); } -var CE = { kernelName: Sa, backendName: "cpu", kernelFunc: Kl }; -function wE(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8; +var wE = { kernelName: Sa, backendName: "cpu", kernelFunc: Gl }; +function SE(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8; if (o.dtype === "string") throw new Error("onesLike is not supported for string tensors"); if (o.dtype === "complex64") { - let n = Ro({ inputs: { input: o }, backend: e }), s = wE({ inputs: { x: n }, backend: e }), a = Ma({ inputs: { input: o }, backend: e }), i = Kl({ inputs: { x: a }, backend: e }), p = Kt({ inputs: { real: s, imag: i }, backend: e }); + let n = Eo({ inputs: { input: o }, backend: e }), s = SE({ inputs: { x: n }, backend: e }), a = Oa({ inputs: { input: o }, backend: e }), i = Gl({ inputs: { x: a }, backend: e }), p = Ht({ inputs: { real: s, imag: i }, backend: e }); return e.disposeIntermediateTensorInfo(n), e.disposeIntermediateTensorInfo(s), e.disposeIntermediateTensorInfo(a), e.disposeIntermediateTensorInfo(i), p; } else - return Hl({ backend: e, attrs: { shape: o.shape, value: 1, dtype: o.dtype } }); + return Ul({ backend: e, attrs: { shape: o.shape, value: 1, dtype: o.dtype } }); } -var SE = { kernelName: ca, backendName: "cpu", kernelFunc: wE }; -function II(r) { - let { inputs: t8, backend: e, attrs: o } = r, { axis: n } = o; +var IE = { kernelName: ca, backendName: "cpu", kernelFunc: SE }; +function vI(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { axis: n } = o; if (t8.length === 1) - return $c({ inputs: { input: t8[0] }, backend: e, attrs: { dim: n } }); + return Ic({ inputs: { input: t8[0] }, backend: e, attrs: { dim: n } }); let s = t8[0].shape, a = t8[0].dtype; t8.forEach((c) => { y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes"); }); let i = [], p = t8.map((c) => { - let l = $c({ inputs: { input: c }, backend: e, attrs: { dim: n } }); + let l = Ic({ inputs: { input: c }, backend: e, attrs: { dim: n } }); return i.push(l), l; - }), u = yu({ inputs: p, backend: e, attrs: { axis: n } }); + }), u = du({ inputs: p, backend: e, attrs: { axis: n } }); return i.forEach((c) => e.disposeIntermediateTensorInfo(c)), u; } -var IE = { kernelName: la, backendName: "cpu", kernelFunc: II }; -function qQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { paddings: s, constantValue: a } = o; +var vE = { kernelName: la, backendName: "cpu", kernelFunc: vI }; +function qQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { paddings: s, constantValue: a } = o; Q(n, "pad"); let i = s.map((b, C) => b[0] + n.shape[C] + b[1]), p = s.map((b) => b[0]), u = e.data.get(n.dataId).values, c = y.sizeFromShape(n.shape), l = n.shape.length, m = y.computeStrides(n.shape), d = y.sizeFromShape(i), f = i.length, h = y.computeStrides(i), g = y.getTypedArrayFromDType(n.dtype, d); a !== 0 && g.fill(a); @@ -14766,34 +14767,34 @@ function qQ(r) { } return { dataId: e.write(g, i, n.dtype), shape: i, dtype: n.dtype }; } -var Wf = { kernelName: ts, backendName: "cpu", kernelFunc: qQ }; -var jQ = ze((r, t8) => Math.pow(r, t8)); -var XQ = Ye(rs, jQ); -var vE = { kernelName: rs, backendName: "cpu", kernelFunc: XQ }; -function YQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = t8, { outputRaggedRank: i } = o, p = n.map((x) => e.data.get(x.dataId).values), u = n.map((x) => x.shape), c = e.data.get(s.dataId).values, l = e.data.get(a.dataId).values, [m, d, f] = Rf(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => e.makeTensorInfo([x.length], "int32", x)), g = e.makeTensorInfo(f, s.dtype, d); +var Vf = { kernelName: es, backendName: "cpu", kernelFunc: qQ }; +var jQ = ze((r15, t8) => Math.pow(r15, t8)); +var XQ = Ye(ts, jQ); +var kE = { kernelName: ts, backendName: "cpu", kernelFunc: XQ }; +function YQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = t8, { outputRaggedRank: i } = o, p = n.map((x) => e.data.get(x.dataId).values), u = n.map((x) => x.shape), c = e.data.get(s.dataId).values, l = e.data.get(a.dataId).values, [m, d, f] = Ef(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => e.makeTensorInfo([x.length], "int32", x)), g = e.makeTensorInfo(f, s.dtype, d); return h.concat([g]); } -var kE = { kernelName: Qp, backendName: "cpu", kernelFunc: YQ }; -function QQ(r) { - let { inputs: t8, backend: e } = r, { starts: o, limits: n, deltas: s } = t8, a = e.data.get(o.dataId).values, i = e.data.get(n.dataId).values, p = e.data.get(s.dataId).values, [u, c] = Df(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = e.makeTensorInfo([u.length], "int32", u), m = e.makeTensorInfo([c.length], o.dtype, c); +var NE = { kernelName: Up, backendName: "cpu", kernelFunc: YQ }; +function QQ(r15) { + let { inputs: t8, backend: e } = r15, { starts: o, limits: n, deltas: s } = t8, a = e.data.get(o.dataId).values, i = e.data.get(n.dataId).values, p = e.data.get(s.dataId).values, [u, c] = Rf(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = e.makeTensorInfo([u.length], "int32", u), m = e.makeTensorInfo([c.length], o.dtype, c); return [l, m]; } -var NE = { kernelName: Zp, backendName: "cpu", kernelFunc: QQ }; -function ZQ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = t8, { rowPartitionTypes: p } = o, u = e.data.get(n.dataId).values, c = e.data.get(s.dataId).values, l = e.data.get(a.dataId).values, m = i.map((g) => e.data.get(g.dataId).values), d = i.map((g) => g.shape), [f, h] = Af(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p); +var TE = { kernelName: Gp, backendName: "cpu", kernelFunc: QQ }; +function ZQ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = t8, { rowPartitionTypes: p } = o, u = e.data.get(n.dataId).values, c = e.data.get(s.dataId).values, l = e.data.get(a.dataId).values, m = i.map((g) => e.data.get(g.dataId).values), d = i.map((g) => g.shape), [f, h] = Df(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p); return e.makeTensorInfo(f, s.dtype, h); } -var TE = { kernelName: Jp, backendName: "cpu", kernelFunc: ZQ }; -function JQ(r) { - let { backend: t8, attrs: e } = r, { start: o, stop: n, dtype: s, step: a } = e, i = fp(o, n, a, s); +var _E = { kernelName: Hp, backendName: "cpu", kernelFunc: ZQ }; +function JQ(r15) { + let { backend: t8, attrs: e } = r15, { start: o, stop: n, dtype: s, step: a } = e, i = ap(o, n, a, s); return t8.makeTensorInfo([i.length], s, i); } -var _E = { kernelName: ma, backendName: "cpu", kernelFunc: JQ }; -var e7 = Ie(ss, (r) => 1 / r); -var $E = { kernelName: ss, backendName: "cpu", kernelFunc: e7 }; -function t7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { images: n } = t8, { alignCorners: s, halfPixelCenters: a, size: i } = o; +var $E = { kernelName: ma, backendName: "cpu", kernelFunc: JQ }; +var e7 = Ie(ns, (r15) => 1 / r15); +var EE = { kernelName: ns, backendName: "cpu", kernelFunc: e7 }; +function t7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { images: n } = t8, { alignCorners: s, halfPixelCenters: a, size: i } = o; Q(n, "resizeBilinear"); let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = e.data.get(n.dataId).values, g = new Float32Array(y.sizeFromShape([l, u, c, f])), x = [s && u > 1 ? m - 1 : m, s && c > 1 ? d - 1 : d], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], C = 0, S = x[0] / b[0], k = x[1] / b[1]; for (let _ = 0; _ < l; _++) @@ -14813,9 +14814,9 @@ function t7(r) { } return e.makeTensorInfo([l, u, c, f], "float32", g); } -var EE = { kernelName: us, backendName: "cpu", kernelFunc: t7 }; -function r7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { images: n, dy: s } = t8, { alignCorners: a } = o; +var RE = { kernelName: is, backendName: "cpu", kernelFunc: t7 }; +function r72(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { images: n, dy: s } = t8, { alignCorners: a } = o; Q([s, n], "resizeBilinearGrad"); let i = y.computeStrides(n.shape), [p, u, c, l] = n.shape, [, m, d] = s.shape, f = new Float32Array(p * u * c * l), h = [a && m > 1 ? u - 1 : u, a && d > 1 ? c - 1 : c], g = [a && m > 1 ? m - 1 : m, a && d > 1 ? d - 1 : d], x = h[0] / g[0], b = h[1] / g[1], C = e.data.get(s.dataId).values, S = 0; for (let k = 0; k < p; k++) { @@ -14833,9 +14834,9 @@ function r7(r) { } return e.makeTensorInfo([p, c, u, l], "float32", f); } -var RE = { kernelName: ei, backendName: "cpu", kernelFunc: r7 }; -function o7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { images: n } = t8, { alignCorners: s, halfPixelCenters: a, size: i } = o; +var DE = { kernelName: Ja, backendName: "cpu", kernelFunc: r72 }; +function o7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { images: n } = t8, { alignCorners: s, halfPixelCenters: a, size: i } = o; Q(n, "resizeNearestNeighbor"); let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = e.data.get(n.dataId).values, g = new Float32Array(l * u * c * f), x = [s && u > 1 ? m - 1 : m, s && c > 1 ? d - 1 : d], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], C = x[0] / b[0], S = x[1] / b[1], k = 0; for (let _ = 0; _ < l; _++) { @@ -14857,9 +14858,9 @@ function o7(r) { } return e.makeTensorInfo([l, u, c, f], n.dtype, g); } -var DE = { kernelName: is, backendName: "cpu", kernelFunc: o7 }; -function n7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { images: n, dy: s } = t8, { alignCorners: a } = o; +var AE = { kernelName: as, backendName: "cpu", kernelFunc: o7 }; +function n7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { images: n, dy: s } = t8, { alignCorners: a } = o; Q([s, n], "resizeNearestNeighborGrad"); let i = y.computeStrides(n.shape), p = y.computeStrides(s.shape), [u, c, l, m] = n.shape, [, d, f] = s.shape, h = new Float32Array(u * c * l * m), g = e.data.get(s.dataId).values, x = [a && d > 1 ? c - 1 : c, a && f > 1 ? l - 1 : l], b = [a && d > 1 ? d - 1 : d, a && f > 1 ? f - 1 : f], C = x[0] / b[0], S = x[1] / b[1], k = 1 / C, _ = 1 / S, E = Math.ceil(k) * 2 + 2, R = Math.ceil(_) * 2 + 2; for (let D = 0; D < u; D++) { @@ -14891,13 +14892,13 @@ function n7(r) { } return e.makeTensorInfo(n.shape, n.dtype, h); } -var AE = { kernelName: Ja, backendName: "cpu", kernelFunc: n7 }; -function s7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { dims: s } = o; +var FE = { kernelName: Za, backendName: "cpu", kernelFunc: n7 }; +function s7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { dims: s } = o; Q(n, "reverse"); let a = n.shape.length, i = y.parseAxisParam(s, n.shape); if (a === 0) - return mr({ inputs: { x: n }, backend: e }); + return lr({ inputs: { x: n }, backend: e }); let p = new tt(n.shape, n.dtype), u = e.bufferSync(n); for (let c = 0; c < p.size; c++) { let l = p.indexToLoc(c), m = l.slice(); @@ -14905,9 +14906,9 @@ function s7(r) { } return e.makeTensorInfo(p.shape, p.dtype, p.values); } -var FE = { kernelName: cs, backendName: "cpu", kernelFunc: s7 }; -var PE = { kernelName: As, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: t8, backend: e }) => { - let { image: o } = r, { radians: n, fillValue: s, center: a } = t8, i = e, p = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [u, c, l, m] = o.shape, [d, f] = w.getImageCenter(a, c, l), h = 255, g = Math.sin(n), x = Math.cos(n), b = i.data.get(o.dataId).values; +var PE = { kernelName: ps, backendName: "cpu", kernelFunc: s7 }; +var OE = { kernelName: Ds, backendName: "cpu", kernelFunc: ({ inputs: r15, attrs: t8, backend: e }) => { + let { image: o } = r15, { radians: n, fillValue: s, center: a } = t8, i = e, p = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [u, c, l, m] = o.shape, [d, f] = w.getImageCenter(a, c, l), h = 255, g = Math.sin(n), x = Math.cos(n), b = i.data.get(o.dataId).values; for (let S = 0; S < u; S++) { let k = S * l * c * m; for (let _ = 0; _ < c; _++) { @@ -14930,44 +14931,44 @@ var PE = { kernelName: As, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: } return { dataId: i.write(p, o.shape, o.dtype), shape: o.shape, dtype: o.dtype }; } }; -var a7 = Ie(ls, (r) => { - let t8 = Math.floor(r); - return r - t8 < 0.5 ? Math.floor(r) : r - t8 > 0.5 ? Math.ceil(r) : t8 % 2 === 0 ? t8 : t8 + 1; +var a7 = Ie(cs, (r15) => { + let t8 = Math.floor(r15); + return r15 - t8 < 0.5 ? Math.floor(r15) : r15 - t8 > 0.5 ? Math.ceil(r15) : t8 % 2 === 0 ? t8 : t8 + 1; }); -var OE = { kernelName: ls, backendName: "cpu", kernelFunc: a7 }; -function i7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { indices: n, updates: s } = t8, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(s, n, a), m = true, d = e.bufferSync(n), f = e.bufferSync(s), h = zs(d, f, a, l, u, p, i, c, 0, m); +var ME = { kernelName: cs, backendName: "cpu", kernelFunc: a7 }; +function i7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { indices: n, updates: s } = t8, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(s, n, a), m = true, d = e.bufferSync(n), f = e.bufferSync(s), h = zs(d, f, a, l, u, p, i, c, 0, m); return e.makeTensorInfo(a, h.dtype, h.values); } -var ME = { kernelName: ds, backendName: "cpu", kernelFunc: i7 }; -function u7(r, t8) { - let e = 0, o = r.length, n = 0; +var LE = { kernelName: ms, backendName: "cpu", kernelFunc: i7 }; +function u7(r15, t8) { + let e = 0, o = r15.length, n = 0; for (; e < o; ) - n = Math.floor((e + o) / 2), r[n] < t8 ? e = n + 1 : o = n; + n = Math.floor((e + o) / 2), r15[n] < t8 ? e = n + 1 : o = n; return o; } -function p7(r, t8) { - let e = 0, o = r.length, n = 0; +function p7(r15, t8) { + let e = 0, o = r15.length, n = 0; for (; e < o; ) - n = Math.floor((e + o) / 2), r[n] <= t8 ? e = n + 1 : o = n; + n = Math.floor((e + o) / 2), r15[n] <= t8 ? e = n + 1 : o = n; return o; } -function LE(r, t8, e, o, n, s) { +function BE(r15, t8, e, o, n, s) { let a = y.getArrayFromDType("int32", e * n); for (let i = 0; i < e; ++i) { - let p = r.slice(i * o, (i + 1) * o), u = i * n; + let p = r15.slice(i * o, (i + 1) * o), u = i * n; for (let c = 0; c < n; ++c) a[u + c] = s === "left" ? u7(p, t8[c + u]) : p7(p, t8[c + u]); } return a; } -function c7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { sortedSequence: n, values: s } = t8, { side: a } = o, i = e.data.get(n.dataId).values, p = e.data.get(s.dataId).values, u = LE(i, p, n.shape[0], n.shape[1], s.shape[1], a); +function c7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { sortedSequence: n, values: s } = t8, { side: a } = o, i = e.data.get(n.dataId).values, p = e.data.get(s.dataId).values, u = BE(i, p, n.shape[0], n.shape[1], s.shape[1], a); return e.makeTensorInfo(s.shape, "int32", u); } -var BE = { kernelName: hs, backendName: "cpu", kernelFunc: c7 }; -function l7(r) { - let { inputs: t8, backend: e } = r, { condition: o, t: n, e: s } = t8; +var zE = { kernelName: fs, backendName: "cpu", kernelFunc: c7 }; +function l7(r15) { + let { inputs: t8, backend: e } = r15, { condition: o, t: n, e: s } = t8; Q([o, n, s], "select"); let a = o.shape.length, i = e.data.get(o.dataId).values, p = e.data.get(n.dataId).values, u = e.data.get(s.dataId).values, c = dt(n.dtype, s.dtype), l = y.makeZerosTypedArray(y.sizeFromShape(n.shape), c), m = 0, d = a === 0 || a > 1 || n.shape.length === 1 ? 1 : y.sizeFromShape(n.shape.slice(1)); for (let f = 0; f < i.length; f++) @@ -14975,37 +14976,37 @@ function l7(r) { i[f] === 1 ? l[m++] = p[f] : l[m++] = u[f]; return e.makeTensorInfo(n.shape, c, l); } -var zE = { kernelName: fa, backendName: "cpu", kernelFunc: l7 }; +var VE = { kernelName: fa, backendName: "cpu", kernelFunc: l7 }; var m7 = w.SELU_SCALEALPHA; var d7 = w.SELU_SCALE; -var f7 = Ie(gs, (r) => r >= 0 ? d7 * r : m7 * (Math.exp(r) - 1)); -var VE = { kernelName: gs, backendName: "cpu", kernelFunc: f7 }; -var h7 = Ie(bs, (r) => r < 0 ? -1 : r > 0 ? 1 : 0); -var WE = { kernelName: bs, backendName: "cpu", kernelFunc: h7 }; -var g7 = Ie(xs, (r) => Math.sin(r)); -var UE = { kernelName: xs, backendName: "cpu", kernelFunc: g7 }; -var x7 = Ie(ys, (r) => Math.sinh(r)); -var GE = { kernelName: ys, backendName: "cpu", kernelFunc: x7 }; +var f7 = Ie(hs, (r15) => r15 >= 0 ? d7 * r15 : m7 * (Math.exp(r15) - 1)); +var WE = { kernelName: hs, backendName: "cpu", kernelFunc: f7 }; +var h7 = Ie(ys, (r15) => r15 < 0 ? -1 : r15 > 0 ? 1 : 0); +var UE = { kernelName: ys, backendName: "cpu", kernelFunc: h7 }; +var g7 = Ie(gs, (r15) => Math.sin(r15)); +var GE = { kernelName: gs, backendName: "cpu", kernelFunc: g7 }; +var x7 = Ie(xs, (r15) => Math.sinh(r15)); +var HE = { kernelName: xs, backendName: "cpu", kernelFunc: x7 }; var y7 = 11920928955078125e-23; -var HE = Math.log(y7) + 2; -var b7 = Ie(ws, (r) => { - let t8 = r > -HE, e = r < HE, o = Math.exp(r), n; - return e ? n = o : t8 ? n = r : n = Math.log(1 + o), n; +var KE = Math.log(y7) + 2; +var b7 = Ie(Cs, (r15) => { + let t8 = r15 > -KE, e = r15 < KE, o = Math.exp(r15), n; + return e ? n = o : t8 ? n = r15 : n = Math.log(1 + o), n; }); -var KE = { kernelName: ws, backendName: "cpu", kernelFunc: b7 }; -function C7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { blockShape: s, paddings: a } = o; +var qE = { kernelName: Cs, backendName: "cpu", kernelFunc: b7 }; +function C7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { blockShape: s, paddings: a } = o; Q([n], "spaceToBatchND"); let i = y.sizeFromShape(s), p = [[0, 0]]; p.push(...a); for (let _ = 1 + s.length; _ < n.shape.length; ++_) p.push([0, 0]); - let u = Wf.kernelFunc({ inputs: { x: n }, backend: e, attrs: { paddings: p, constantValue: 0 } }), c = w.getReshaped(u.shape, s, i, false), l = w.getPermuted(c.length, s.length, false), m = w.getReshapedPermuted(u.shape, s, i, false), h = Ve({ inputs: { x: u }, backend: e, attrs: { shape: c } }), b = It({ inputs: { x: h }, backend: e, attrs: { perm: l } }), k = Ve({ inputs: { x: b }, backend: e, attrs: { shape: m } }); + let u = Vf.kernelFunc({ inputs: { x: n }, backend: e, attrs: { paddings: p, constantValue: 0 } }), c = w.getReshaped(u.shape, s, i, false), l = w.getPermuted(c.length, s.length, false), m = w.getReshapedPermuted(u.shape, s, i, false), h = Ve({ inputs: { x: u }, backend: e, attrs: { shape: c } }), b = St({ inputs: { x: h }, backend: e, attrs: { perm: l } }), k = Ve({ inputs: { x: b }, backend: e, attrs: { shape: m } }); return e.disposeIntermediateTensorInfo(u), e.disposeIntermediateTensorInfo(h), e.disposeIntermediateTensorInfo(b), k; } -var qE = { kernelName: ga, backendName: "cpu", kernelFunc: C7 }; -function w7(r) { - let { inputs: t8, backend: e } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = t8; +var jE = { kernelName: ga, backendName: "cpu", kernelFunc: C7 }; +function w7(r15) { + let { inputs: t8, backend: e } = r15, { indices: o, values: n, denseShape: s, defaultValue: a } = t8; if (s.shape.length !== 1) throw new Error(`Dense shape must be a vector, saw: ${s.shape}`); @@ -15018,12 +15019,12 @@ function w7(r) { if (a.shape.length !== 0) throw new Error(`Default value must be a scalar, saw: ${a.shape}`); - let i = e.data.get(o.dataId).values, p = e.data.get(n.dataId).values, u = e.data.get(s.dataId).values, c = e.data.get(a.dataId).values[0], [l, m, d, f, h] = Ff(i, o.shape, o.dtype, p, n.dtype, u, c); + let i = e.data.get(o.dataId).values, p = e.data.get(n.dataId).values, u = e.data.get(s.dataId).values, c = e.data.get(a.dataId).values[0], [l, m, d, f, h] = Af(i, o.shape, o.dtype, p, n.dtype, u, c); return [e.makeTensorInfo(m, o.dtype, l), e.makeTensorInfo([m[0]], n.dtype, d), e.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), e.makeTensorInfo([h.length], o.dtype, new Int32Array(h))]; } -var jE = { kernelName: ji, backendName: "cpu", kernelFunc: w7 }; -function S7(r) { - let { inputs: t8, backend: e } = r, { inputIndices: o, inputShape: n, newShape: s } = t8; +var XE = { kernelName: Hi, backendName: "cpu", kernelFunc: w7 }; +function S7(r15) { + let { inputs: t8, backend: e } = r15, { inputIndices: o, inputShape: n, newShape: s } = t8; if (o.shape.length !== 2) throw new Error(`Input indices should be a matrix but received shape ${o.shape}`); @@ -15032,12 +15033,12 @@ function S7(r) { ${n.shape}`); if (s.shape.length !== 1) throw new Error(`Target shape should be a vector but received shape ${s.shape}`); - let a = Array.from(e.data.get(n.dataId).values), i = e.data.get(o.dataId).values, p = Array.from(e.data.get(s.dataId).values), [u, c, l] = Pf(i, o.shape, o.dtype, a, p); + let a = Array.from(e.data.get(n.dataId).values), i = e.data.get(o.dataId).values, p = Array.from(e.data.get(s.dataId).values), [u, c, l] = Ff(i, o.shape, o.dtype, a, p); return [e.makeTensorInfo(c, o.dtype, u), e.makeTensorInfo([l.length], s.dtype, new Int32Array(l))]; } -var XE = { kernelName: ti, backendName: "cpu", kernelFunc: S7 }; -function I7(r) { - let { inputs: t8, backend: e } = r, { data: o, indices: n, segmentIds: s } = t8; +var YE = { kernelName: ei, backendName: "cpu", kernelFunc: S7 }; +function I7(r15) { + let { inputs: t8, backend: e } = r15, { data: o, indices: n, segmentIds: s } = t8; if (o.shape.length < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.shape.length !== 1) @@ -15048,12 +15049,12 @@ function I7(r) { ${s.shape}`); if (n.shape[0] !== s.shape[0]) throw new Error("segmentIds and indices should have same size."); - let a = e.data.get(o.dataId).values, i = e.data.get(n.dataId).values, p = e.data.get(s.dataId).values, [u, c] = Nc(a, o.shape, o.dtype, i, p, true); + let a = e.data.get(o.dataId).values, i = e.data.get(n.dataId).values, p = e.data.get(s.dataId).values, [u, c] = Cc(a, o.shape, o.dtype, i, p, true); return e.makeTensorInfo(c, o.dtype, u); } -var YE = { kernelName: ya, backendName: "cpu", kernelFunc: I7 }; -function v7(r) { - let { inputs: t8, backend: e } = r, { data: o, indices: n, segmentIds: s } = t8; +var QE = { kernelName: ya, backendName: "cpu", kernelFunc: I7 }; +function v7(r15) { + let { inputs: t8, backend: e } = r15, { data: o, indices: n, segmentIds: s } = t8; if (o.shape.length < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.shape.length !== 1) @@ -15064,12 +15065,12 @@ function v7(r) { ${s.shape}`); if (n.shape[0] !== s.shape[0]) throw new Error("segmentIds and indices should have same size."); - let a = e.data.get(o.dataId).values, i = e.data.get(n.dataId).values, p = e.data.get(s.dataId).values, [u, c] = Nc(a, o.shape, o.dtype, i, p); + let a = e.data.get(o.dataId).values, i = e.data.get(n.dataId).values, p = e.data.get(s.dataId).values, [u, c] = Cc(a, o.shape, o.dtype, i, p); return e.makeTensorInfo(c, o.dtype, u); } -var QE = { kernelName: ba, backendName: "cpu", kernelFunc: v7 }; -function k7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = t8, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = w.calculateShapes(s, n, i), d = false, f = e.bufferSync(n), h; +var ZE = { kernelName: ba, backendName: "cpu", kernelFunc: v7 }; +function k7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { sparseIndices: n, sparseValues: s, defaultValue: a } = t8, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = w.calculateShapes(s, n, i), d = false, f = e.bufferSync(n), h; switch (s.dtype) { case "bool": { let g = e.bufferSync(s), x = !!e.data.get(a.dataId).values[0]; @@ -15096,19 +15097,19 @@ function k7(r) { } return e.makeTensorInfo(i, h.dtype, h.values); } -var ZE = { kernelName: ks, backendName: "cpu", kernelFunc: k7 }; -function N7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = w.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice(); +var JE = { kernelName: vs, backendName: "cpu", kernelFunc: k7 }; +function N7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = w.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice(); return p.map((l) => { let m = [...c]; m[i] = l; - let d = Fo({ inputs: { x: n }, backend: e, attrs: { begin: u, size: m } }); + let d = Ao({ inputs: { x: n }, backend: e, attrs: { begin: u, size: m } }); return u[i] += l, d; }); } -var JE = { kernelName: xa, backendName: "cpu", kernelFunc: N7 }; -var eR = { kernelName: Xi, backendName: "cpu", kernelFunc: ({ inputs: r, backend: t8 }) => { - let { x: e } = r, o = t8; +var eR = { kernelName: xa, backendName: "cpu", kernelFunc: N7 }; +var tR = { kernelName: Ki, backendName: "cpu", kernelFunc: ({ inputs: r15, backend: t8 }) => { + let { x: e } = r15, o = t8; Q(e, "square"); let n = o.data.get(e.dataId).values, s = new Float32Array(n.length); for (let i = 0; i < n.length; ++i) { @@ -15117,80 +15118,80 @@ var eR = { kernelName: Xi, backendName: "cpu", kernelFunc: ({ inputs: r, backend } return { dataId: o.write(s, e.shape, e.dtype), shape: e.shape, dtype: e.dtype }; } }; -var T7 = Ie(So, (r, t8) => { +var T7 = Ie(wo, (r15, t8) => { let e = t8; - return isNaN(r) ? NaN : r > 0 ? 1 : e.alpha; + return isNaN(r15) ? NaN : r15 > 0 ? 1 : e.alpha; }); -var tR = { kernelName: So, backendName: "cpu", kernelFunc: T7 }; -function _7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o; +var rR = { kernelName: wo, backendName: "cpu", kernelFunc: T7 }; +function _7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o; Q(n, "stridedSlice"); - let { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; + let { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: S } = pt.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; if (h) k = Ve({ inputs: { x: n }, backend: e, attrs: { shape: f } }); else if (g || x) { y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`); - let _ = ct.computeOutShape(b, C, S), E = Fo({ inputs: { x: n }, backend: e, attrs: { begin: b, size: _ } }); + let _ = pt.computeOutShape(b, C, S), E = Ao({ inputs: { x: n }, backend: e, attrs: { begin: b, size: _ } }); k = Ve({ inputs: { x: E }, backend: e, attrs: { shape: f } }), e.disposeIntermediateTensorInfo(E); } else { - let _ = e.bufferSync(n), E = Of(d, _, S, b); + let _ = e.bufferSync(n), E = Pf(d, _, S, b); k = e.makeTensorInfo(f, E.dtype, E.values); } return k; } -var rR = { kernelName: Ts, backendName: "cpu", kernelFunc: _7 }; -function $7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = t8, m = e.data.get(c.dataId).values, d = e.data.get(l.dataId).values, [f, h] = gp(m, d, n, s, a, i, p, u); +var oR = { kernelName: Ns, backendName: "cpu", kernelFunc: _7 }; +function $7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = t8, m = e.data.get(c.dataId).values, d = e.data.get(l.dataId).values, [f, h] = up(m, d, n, s, a, i, p, u); return [e.makeTensorInfo([f.length], "string", f), e.makeTensorInfo(l.shape, "int32", h)]; } -var oR = { kernelName: Ca, backendName: "cpu", kernelFunc: $7 }; -function E7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { skipEmpty: n } = o, { input: s, delimiter: a } = t8; +var nR = { kernelName: Ca, backendName: "cpu", kernelFunc: $7 }; +function E7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { skipEmpty: n } = o, { input: s, delimiter: a } = t8; if (s.dtype !== "string") throw new Error("Input must be of datatype string"); if (s.shape.length !== 1) throw new Error(`Input must be a vector, got shape: ${s.shape}`); if (a.shape.length !== 0) throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`); - let i = e.data.get(s.dataId).values, p = e.data.get(a.dataId).values[0], [u, c, l] = xp(i, p, n), m = c.length; + let i = e.data.get(s.dataId).values, p = e.data.get(a.dataId).values[0], [u, c, l] = pp(i, p, n), m = c.length; return [e.makeTensorInfo([m, 2], "int32", u), e.makeTensorInfo([m], "string", c), e.makeTensorInfo([2], "int32", new Int32Array(l))]; } -var nR = { kernelName: Yi, backendName: "cpu", kernelFunc: E7 }; -function R7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { numBuckets: n } = o, { input: s } = t8; +var sR = { kernelName: qi, backendName: "cpu", kernelFunc: E7 }; +function R7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { numBuckets: n } = o, { input: s } = t8; if (s.dtype !== "string") throw new Error("Input must be of datatype string"); if (n <= 0) throw new Error("Number of buckets must be at least 1"); - let a = e.data.get(s.dataId).values, i = yp(a, n); + let a = e.data.get(s.dataId).values, i = cp(a, n); return e.makeTensorInfo(s.shape, "int32", i); } -var sR = { kernelName: Qi, backendName: "cpu", kernelFunc: R7 }; -var D7 = Ie($s, (r) => Math.tan(r)); -var aR = { kernelName: $s, backendName: "cpu", kernelFunc: D7 }; -var A7 = Ie(Es, (r) => Math.tanh(r)); -var iR = { kernelName: Es, backendName: "cpu", kernelFunc: A7 }; -function F7(r) { - let { inputs: t8, backend: e } = r, { tensor: o, indices: n, updates: s } = t8, { sliceRank: a, numUpdates: i, sliceSize: p, strides: u, outputSize: c } = w.calculateShapes(s, n, o.shape), l = false, m = e.bufferSync(n), d = e.bufferSync(s), f = e.bufferSync(o), h = zs(m, d, o.shape, c, p, i, a, u, f, l); +var aR = { kernelName: ji, backendName: "cpu", kernelFunc: R7 }; +var D7 = Ie(_s, (r15) => Math.tan(r15)); +var iR = { kernelName: _s, backendName: "cpu", kernelFunc: D7 }; +var A7 = Ie($s, (r15) => Math.tanh(r15)); +var uR = { kernelName: $s, backendName: "cpu", kernelFunc: A7 }; +function F7(r15) { + let { inputs: t8, backend: e } = r15, { tensor: o, indices: n, updates: s } = t8, { sliceRank: a, numUpdates: i, sliceSize: p, strides: u, outputSize: c } = w.calculateShapes(s, n, o.shape), l = false, m = e.bufferSync(n), d = e.bufferSync(s), f = e.bufferSync(o), h = zs(m, d, o.shape, c, p, i, a, u, f, l); return e.makeTensorInfo(o.shape, h.dtype, h.values); } -var uR = { kernelName: fs, backendName: "cpu", kernelFunc: F7 }; -function P7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { reps: s } = o; +var pR = { kernelName: ds, backendName: "cpu", kernelFunc: F7 }; +function P7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { reps: s } = o; Q(n, "tile"); - let a = Mf(e.bufferSync(n), s); + let a = Of(e.bufferSync(n), s); return e.makeTensorInfo(a.shape, a.dtype, a.values); } -var pR = { kernelName: po, backendName: "cpu", kernelFunc: P7 }; -function O7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { k: s, sorted: a } = o; +var cR = { kernelName: uo, backendName: "cpu", kernelFunc: P7 }; +function O7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { k: s, sorted: a } = o; Q(n, "topk"); - let i = e.data.get(n.dataId).values, [p, u] = Lf(i, n.shape, n.dtype, s, a); + let i = e.data.get(n.dataId).values, [p, u] = Mf(i, n.shape, n.dtype, s, a); return [e.makeTensorInfo(p.shape, p.dtype, p.values), e.makeTensorInfo(u.shape, u.dtype, u.values)]; } -var cR = { kernelName: Rs, backendName: "cpu", kernelFunc: O7 }; -function M7(r) { - let { inputs: t8, attrs: e, backend: o } = r, { image: n, transforms: s } = t8, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = e, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = y.computeStrides(n.shape), b = x[0], C = x[1], S = x[2], k = y.computeStrides(g), _ = k[0], E = k[1], R = k[2], D = y.getTypedArrayFromDType(n.dtype, y.sizeFromShape(g)); +var lR = { kernelName: Es, backendName: "cpu", kernelFunc: O7 }; +function M7(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, { image: n, transforms: s } = t8, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = e, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = y.computeStrides(n.shape), b = x[0], C = x[1], S = x[2], k = y.computeStrides(g), _ = k[0], E = k[1], R = k[2], D = y.getTypedArrayFromDType(n.dtype, y.sizeFromShape(g)); D.fill(p); let P = o.data.get(n.dataId).values, O = o.data.get(s.dataId).values; for (let L = 0; L < c; ++L) { @@ -15201,7 +15202,7 @@ function M7(r) { let q, Y = B[6] * U + B[7] * z + 1; if (Y === 0) continue; - let J = (B[0] * U + B[1] * z + B[2]) / Y, re = (B[3] * U + B[4] * z + B[5]) / Y, ne = lR(J, m, i), ee = lR(re, l, i); + let J = (B[0] * U + B[1] * z + B[2]) / Y, re = (B[3] * U + B[4] * z + B[5]) / Y, ne = mR(J, m, i), ee = mR(re, l, i); switch (a) { case "nearest": q = W7(P, l, m, b, C, S, L, ee, ne, j, p); @@ -15219,22 +15220,22 @@ function M7(r) { } return { dataId: o.write(D, g, n.dtype), shape: n.shape, dtype: n.dtype }; } -var mR = { kernelName: Ds, backendName: "cpu", kernelFunc: M7 }; -function lR(r, t8, e) { +var dR = { kernelName: Rs, backendName: "cpu", kernelFunc: M7 }; +function mR(r15, t8, e) { switch (e) { case "reflect": - return L7(r, t8); + return L7(r15, t8); case "wrap": - return B7(r, t8); + return B7(r15, t8); case "nearest": - return V7(r, t8); + return V7(r15, t8); case "constant": default: - return z7(r, t8); + return z7(r15, t8); } } -function L7(r, t8) { - let e = r; +function L7(r15, t8) { + let e = r15; if (e < 0) if (t8 <= 1) e = 0; @@ -15251,8 +15252,8 @@ function L7(r, t8) { } return y.clamp(0, e, t8 - 1); } -function B7(r, t8) { - let e = r; +function B7(r15, t8) { + let e = r15; if (e < 0) if (t8 <= 1) e = 0; @@ -15269,33 +15270,33 @@ function B7(r, t8) { } return y.clamp(0, e, t8 - 1); } -function z7(r, t8) { - return r; +function z7(r15, t8) { + return r15; } -function V7(r, t8) { - return y.clamp(0, r, t8 - 1); +function V7(r15, t8) { + return y.clamp(0, r15, t8 - 1); } -function ql(r, t8, e, o, n, s, a, i, p, u, c) { +function Hl(r15, t8, e, o, n, s, a, i, p, u, c) { let l = a * o + i * n + p * s + u; - return 0 <= i && i < t8 && 0 <= p && p < e ? r[l] : c; + return 0 <= i && i < t8 && 0 <= p && p < e ? r15[l] : c; } -function W7(r, t8, e, o, n, s, a, i, p, u, c) { +function W7(r15, t8, e, o, n, s, a, i, p, u, c) { let l = Math.round(i), m = Math.round(p); - return ql(r, t8, e, o, n, s, a, l, m, u, c); + return Hl(r15, t8, e, o, n, s, a, l, m, u, c); } -function U7(r, t8, e, o, n, s, a, i, p, u, c) { - let l = Math.floor(i), m = Math.floor(p), d = l + 1, f = m + 1, h = (f - p) * ql(r, t8, e, o, n, s, a, l, m, u, c) + (p - m) * ql(r, t8, e, o, n, s, a, l, f, u, c), g = (f - p) * ql(r, t8, e, o, n, s, a, d, m, u, c) + (p - m) * ql(r, t8, e, o, n, s, a, d, f, u, c); +function U7(r15, t8, e, o, n, s, a, i, p, u, c) { + let l = Math.floor(i), m = Math.floor(p), d = l + 1, f = m + 1, h = (f - p) * Hl(r15, t8, e, o, n, s, a, l, m, u, c) + (p - m) * Hl(r15, t8, e, o, n, s, a, l, f, u, c), g = (f - p) * Hl(r15, t8, e, o, n, s, a, d, m, u, c) + (p - m) * Hl(r15, t8, e, o, n, s, a, d, f, u, c); return (d - i) * h + (i - l) * g; } -function G7(r) { - let { inputs: t8, attrs: e, backend: o } = r, { axis: n } = e, { x: s } = t8; +function G7(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, { axis: n } = e, { x: s } = t8; Q(s, "unique"); - let a = o.data.get(s.dataId).values, { outputValues: i, outputShape: p, indices: u } = bp(a, n, s.shape, s.dtype); + let a = o.data.get(s.dataId).values, { outputValues: i, outputShape: p, indices: u } = lp(a, n, s.shape, s.dtype); return [o.makeTensorInfo(p, s.dtype, i), o.makeTensorInfo([u.length], "int32", u)]; } -var dR = { kernelName: Zi, backendName: "cpu", kernelFunc: G7 }; -function H7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { value: n } = t8, { axis: s } = o; +var fR = { kernelName: Xi, backendName: "cpu", kernelFunc: G7 }; +function H7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { value: n } = t8, { axis: s } = o; s < 0 && (s += n.shape.length); let a = n.shape.length, i = n.shape[s], p = new Array(a - 1), u = 0; for (let d = 0; d < a; d++) @@ -15305,156 +15306,156 @@ function H7(r) { let m = new Array(i); for (let d = 0; d < m.length; d++) { c[s] = d; - let f = Fo({ inputs: { x: n }, backend: e, attrs: { begin: c, size: l } }); + let f = Ao({ inputs: { x: n }, backend: e, attrs: { begin: c, size: l } }); m[d] = Ve({ inputs: { x: f }, backend: e, attrs: { shape: p } }), e.disposeIntermediateTensorInfo(f); } return m; } -var fR = { kernelName: wa, backendName: "cpu", kernelFunc: H7 }; -function K7(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, segmentIds: s } = t8, { numSegments: a } = o; +var hR = { kernelName: wa, backendName: "cpu", kernelFunc: H7 }; +function K7(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, segmentIds: s } = t8, { numSegments: a } = o; Q(n, "unsortedSegmentSum"); let i = n.shape.length, p = s.shape.length, u = [], c = [], l = i - p, m = s; for (let f = 0; f < l; ++f) { - let h = $c({ inputs: { input: m }, backend: e, attrs: { dim: f + 1 } }); + let h = Ic({ inputs: { input: m }, backend: e, attrs: { dim: f + 1 } }); m = h, c.push(h); } for (let f = 0; f < a; ++f) { - let h = y.createScalarValue(f, "int32"), g = e.makeTensorInfo([], "int32", h), x = GS({ inputs: { a: g, b: m }, backend: e }), b = Do({ inputs: { x }, backend: e, attrs: { dtype: "float32" } }), C = dp({ inputs: { a: b, b: n }, backend: e }), S = gi({ inputs: { x: C }, backend: e, attrs: { axis: 0, keepDims: false } }); + let h = y.createScalarValue(f, "int32"), g = e.makeTensorInfo([], "int32", h), x = GS({ inputs: { a: g, b: m }, backend: e }), b = Ro({ inputs: { x }, backend: e, attrs: { dtype: "float32" } }), C = sp({ inputs: { a: b, b: n }, backend: e }), S = di({ inputs: { x: C }, backend: e, attrs: { axis: 0, keepDims: false } }); u.push(S), c.push(g), c.push(x), c.push(b), c.push(C), c.push(S); } - let d = II({ inputs: u, backend: e, attrs: { axis: 0 } }); + let d = vI({ inputs: u, backend: e, attrs: { axis: 0 } }); return c.forEach((f) => e.disposeIntermediateTensorInfo(f)), d; } -var hR = { kernelName: Ji, backendName: "cpu", kernelFunc: K7 }; -var q7 = [z_, jT, V_, W_, JT, U_, G_, H_, K_, q_, j_, X_, Y_, Q_, Z_, e$, t$, r$, o$, B_, n$, s$, a$, e_, i$, ZT, t_, u$, XT, p$, l$, m$, d$, f$, h$, g$, x$, y$, b$, C$, w$, S$, I$, v$, k$, N$, T$, _$, $$, E$, R$, D$, F$, A_, P$, r_, O$, o_, M$, n_, L$, B$, z$, s_, a_, V$, W$, U$, G$, i_, u_, YT, H$, c$, K$, q$, j$, F_, p_, c_, X$, l_, Y$, Q$, Z$, J$, eE, tE, rE, m_, oE, nE, sE, aE, uE, pE, cE, d_, lE, mE, hE, f_, h_, gE, xE, yE, g_, bE, SE, IE, Wf, vE, P_, y_, kE, NE, TE, _E, QT, Gl, $E, O_, M_, L_, EE, RE, DE, AE, FE, PE, OE, I_, ME, BE, zE, VE, k_, WE, UE, GE, N_, dE, KE, qE, jE, XE, YE, QE, ZE, JE, __, eR, $_, E_, tR, rR, oR, nR, sR, R_, A$, aR, iR, uR, pR, cR, mR, x_, dR, fR, hR, CE]; -for (let r of q7) - ri(r); -var Ac = {}; -qe(Ac, { assertNotComplex: () => Vs, bindCanvasToFramebuffer: () => rZ, bindColorTextureToFramebuffer: () => Ql, bindTextureToProgramUniformSampler: () => BI, bindTextureUnit: () => bR, bindVertexBufferToProgramAttribute: () => jf, callAndCheck: () => ce, canBeRepresented: () => TI, createFragmentShader: () => $I, createFramebuffer: () => OI, createProgram: () => EI, createStaticIndexBuffer: () => AI, createStaticVertexBuffer: () => DI, createTexture: () => FI, createVertexShader: () => _I, getBatchDim: () => yi, getExtensionOrThrow: () => Ec, getFramebufferErrorMessage: () => CR, getMaxTexturesInShader: () => WI, getNumChannels: () => eZ, getProgramUniformLocation: () => LI, getProgramUniformLocationOrThrow: () => MI, getRowsCols: () => bi, getShapeAs3D: () => Dc, getTextureShapeFromLogicalShape: () => zI, getWebGLDisjointQueryTimerVersion: () => UI, getWebGLErrorMessage: () => yR, getWebGLMaxTextureSize: () => VI, hasExtension: () => qr, isCapableOfRenderingToFloatTexture: () => GI, isDownloadFloatTextureEnabled: () => HI, isReshapeFree: () => Cu, isWebGLFenceEnabled: () => KI, isWebGLVersionEnabled: () => Yf, linkProgram: () => RI, logShaderSourceAndInfoLog: () => qf, resetMaxTextureSize: () => oZ, resetMaxTexturesInShader: () => nZ, unbindColorTextureFromFramebuffer: () => Xf, unbindTextureUnit: () => tZ, validateFramebuffer: () => Rc, validateProgram: () => Yl, validateTextureSize: () => PI }); -var wp = {}; -var Uf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true }; -function vI(r, t8) { - wp[r] = t8; +var gR = { kernelName: Yi, backendName: "cpu", kernelFunc: K7 }; +var q7 = [V_, XT, W_, U_, e_, G_, H_, K_, q_, j_, X_, Y_, Q_, Z_, J_, t$, r$, o$, n$, z_, s$, a$, i$, t_, u$, JT, r_, p$, YT, c$, m$, d$, f$, h$, g$, x$, y$, b$, C$, w$, S$, I$, v$, k$, N$, T$, _$, $$, E$, R$, D$, A$, P$, F_, O$, o_, M$, n_, L$, s_, B$, z$, V$, a_, i_, W$, U$, G$, H$, u_, p_, QT, K$, l$, q$, j$, X$, P_, c_, l_, Y$, m_, Q$, Z$, J$, eE, tE, rE, oE, d_, nE, sE, aE, iE, pE, cE, lE, f_, mE, dE, gE, h_, g_, xE, yE, bE, x_, CE, IE, vE, Vf, kE, O_, b_, NE, TE, _E, $E, ZT, Wl, EE, M_, L_, B_, RE, DE, AE, FE, PE, OE, ME, v_, LE, zE, VE, WE, N_, UE, GE, HE, T_, fE, qE, jE, XE, YE, QE, ZE, JE, eR, $_, tR, E_, R_, rR, oR, nR, sR, aR, D_, F$, iR, uR, pR, cR, lR, dR, y_, fR, hR, gR, wE]; +for (let r15 of q7) + ti(r15); +var Tc = {}; +qe(Tc, { assertNotComplex: () => Vs, bindCanvasToFramebuffer: () => rZ, bindColorTextureToFramebuffer: () => Xl, bindTextureToProgramUniformSampler: () => zI, bindTextureUnit: () => CR, bindVertexBufferToProgramAttribute: () => qf, callAndCheck: () => ce, canBeRepresented: () => _I, createFragmentShader: () => EI, createFramebuffer: () => MI, createProgram: () => RI, createStaticIndexBuffer: () => FI, createStaticVertexBuffer: () => AI, createTexture: () => PI, createVertexShader: () => $I, getBatchDim: () => hi, getExtensionOrThrow: () => vc, getFramebufferErrorMessage: () => wR, getMaxTexturesInShader: () => UI, getNumChannels: () => eZ, getProgramUniformLocation: () => BI, getProgramUniformLocationOrThrow: () => LI, getRowsCols: () => gi, getShapeAs3D: () => Nc, getTextureShapeFromLogicalShape: () => VI, getWebGLDisjointQueryTimerVersion: () => GI, getWebGLErrorMessage: () => bR, getWebGLMaxTextureSize: () => WI, hasExtension: () => Kr, isCapableOfRenderingToFloatTexture: () => HI, isDownloadFloatTextureEnabled: () => KI, isReshapeFree: () => hu, isWebGLFenceEnabled: () => qI, isWebGLVersionEnabled: () => Xf, linkProgram: () => DI, logShaderSourceAndInfoLog: () => Kf, resetMaxTextureSize: () => oZ, resetMaxTexturesInShader: () => nZ, unbindColorTextureFromFramebuffer: () => jf, unbindTextureUnit: () => tZ, validateFramebuffer: () => kc, validateProgram: () => jl, validateTextureSize: () => OI }); +var dp = {}; +var Wf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true }; +function kI(r15, t8) { + dp[r15] = t8; } -function Kr(r, t8) { - if (!(r in wp) || t8 != null) { - let o = X7(r, t8); +function Hr(r15, t8) { + if (!(r15 in dp) || t8 != null) { + let o = X7(r15, t8); if (o !== null) - wp[r] = o; + dp[r15] = o; else - return console.log("Could not get context for WebGL version", r), null; + return console.log("Could not get context for WebGL version", r15), null; } - let e = wp[r]; - return e == null || e.isContextLost() ? (delete wp[r], Kr(r)) : (e.disable(e.DEPTH_TEST), e.disable(e.STENCIL_TEST), e.disable(e.BLEND), e.disable(e.DITHER), e.disable(e.POLYGON_OFFSET_FILL), e.disable(e.SAMPLE_COVERAGE), e.enable(e.SCISSOR_TEST), e.enable(e.CULL_FACE), e.cullFace(e.BACK), wp[r]); + let e = dp[r15]; + return e == null || e.isContextLost() ? (delete dp[r15], Hr(r15)) : (e.disable(e.DEPTH_TEST), e.disable(e.STENCIL_TEST), e.disable(e.BLEND), e.disable(e.DITHER), e.disable(e.POLYGON_OFFSET_FILL), e.disable(e.SAMPLE_COVERAGE), e.enable(e.SCISSOR_TEST), e.enable(e.CULL_FACE), e.cullFace(e.BACK), dp[r15]); } -function j7(r) { - if (!A().getBool("IS_SAFARI") && typeof OffscreenCanvas != "undefined" && r === 2) +function j7(r15) { + if (!A().getBool("IS_SAFARI") && typeof OffscreenCanvas != "undefined" && r15 === 2) return new OffscreenCanvas(300, 150); if (typeof document != "undefined") return document.createElement("canvas"); throw new Error("Cannot create a canvas in this context"); } -function X7(r, t8) { - if (r !== 1 && r !== 2) +function X7(r15, t8) { + if (r15 !== 1 && r15 !== 2) throw new Error("Cannot get WebGL rendering context, WebGL is disabled."); - let e = t8 == null ? j7(r) : t8; + let e = t8 == null ? j7(r15) : t8; return e.addEventListener("webglcontextlost", (o) => { - o.preventDefault(), delete wp[r]; - }, false), A().getBool("SOFTWARE_WEBGL_ENABLED") && (Uf.failIfMajorPerformanceCaveat = false), r === 1 ? e.getContext("webgl", Uf) || e.getContext("experimental-webgl", Uf) : e.getContext("webgl2", Uf); + o.preventDefault(), delete dp[r15]; + }, false), A().getBool("SOFTWARE_WEBGL_ENABLED") && (Wf.failIfMajorPerformanceCaveat = false), r15 === 1 ? e.getContext("webgl", Wf) || e.getContext("experimental-webgl", Wf) : e.getContext("webgl2", Wf); } -var bu; -(function(r) { - r[r.DENSE = 0] = "DENSE", r[r.SHARED_BATCH = 1] = "SHARED_BATCH"; -})(bu || (bu = {})); -var dr; -(function(r) { - r[r.RENDER = 0] = "RENDER", r[r.UPLOAD = 1] = "UPLOAD", r[r.PIXELS = 2] = "PIXELS", r[r.DOWNLOAD = 3] = "DOWNLOAD"; -})(dr || (dr = {})); -var rr; -(function(r) { - r[r.UNPACKED_FLOAT16 = 0] = "UNPACKED_FLOAT16", r[r.UNPACKED_FLOAT32 = 1] = "UNPACKED_FLOAT32", r[r.PACKED_4X1_UNSIGNED_BYTE = 2] = "PACKED_4X1_UNSIGNED_BYTE", r[r.PACKED_2X2_FLOAT32 = 3] = "PACKED_2X2_FLOAT32", r[r.PACKED_2X2_FLOAT16 = 4] = "PACKED_2X2_FLOAT16"; -})(rr || (rr = {})); -function Sp(r, t8) { - return [t8, r]; +var fu; +(function(r15) { + r15[r15.DENSE = 0] = "DENSE", r15[r15.SHARED_BATCH = 1] = "SHARED_BATCH"; +})(fu || (fu = {})); +var mr; +(function(r15) { + r15[r15.RENDER = 0] = "RENDER", r15[r15.UPLOAD = 1] = "UPLOAD", r15[r15.PIXELS = 2] = "PIXELS", r15[r15.DOWNLOAD = 3] = "DOWNLOAD"; +})(mr || (mr = {})); +var er; +(function(r15) { + r15[r15.UNPACKED_FLOAT16 = 0] = "UNPACKED_FLOAT16", r15[r15.UNPACKED_FLOAT32 = 1] = "UNPACKED_FLOAT32", r15[r15.PACKED_4X1_UNSIGNED_BYTE = 2] = "PACKED_4X1_UNSIGNED_BYTE", r15[r15.PACKED_2X2_FLOAT32 = 3] = "PACKED_2X2_FLOAT32", r15[r15.PACKED_2X2_FLOAT16 = 4] = "PACKED_2X2_FLOAT16"; +})(er || (er = {})); +function fp(r15, t8) { + return [t8, r15]; } -function gR(r, t8) { - return r * t8; +function xR(r15, t8) { + return r15 * t8; } -function jl(r) { - let t8 = y.sizeFromShape(r), e = Math.ceil(t8 / 4); +function Kl(r15) { + let t8 = y.sizeFromShape(r15), e = Math.ceil(t8 / 4); return y.sizeToSquarishShape(e); } -function La(r, t8) { - return [Math.max(1, Math.ceil(t8 / 2)), Math.max(1, Math.ceil(r / 2))]; +function Ma(r15, t8) { + return [Math.max(1, Math.ceil(t8 / 2)), Math.max(1, Math.ceil(r15 / 2))]; } -function xR(r, t8) { - let [e, o] = La(r, t8); +function yR(r15, t8) { + let [e, o] = Ma(r15, t8); return e * o * 4; } -function Xl(r, t8) { - let e = r, o, n, s, a, i, p, u, c, l, m; - return A().getNumber("WEBGL_VERSION") === 2 ? (o = e.R32F, n = e.R16F, s = e.RGBA16F, a = e.RGBA32F, i = e.RED, u = 4, c = 1, l = e.HALF_FLOAT, m = e.FLOAT, p = e.RGBA8) : (o = r.RGBA, n = r.RGBA, s = r.RGBA, a = e.RGBA, i = r.RGBA, u = 4, c = 4, l = t8 != null ? t8.HALF_FLOAT_OES : null, m = r.FLOAT, p = r.RGBA), { internalFormatFloat: o, internalFormatHalfFloat: n, internalFormatPackedHalfFloat: s, internalFormatPackedFloat: a, textureFormatFloat: i, downloadTextureFormat: p, downloadUnpackNumChannels: u, defaultNumChannels: c, textureTypeHalfFloat: l, textureTypeFloat: m }; +function ql(r15, t8) { + let e = r15, o, n, s, a, i, p, u, c, l, m; + return A().getNumber("WEBGL_VERSION") === 2 ? (o = e.R32F, n = e.R16F, s = e.RGBA16F, a = e.RGBA32F, i = e.RED, u = 4, c = 1, l = e.HALF_FLOAT, m = e.FLOAT, p = e.RGBA8) : (o = r15.RGBA, n = r15.RGBA, s = r15.RGBA, a = e.RGBA, i = r15.RGBA, u = 4, c = 4, l = t8 != null ? t8.HALF_FLOAT_OES : null, m = r15.FLOAT, p = r15.RGBA), { internalFormatFloat: o, internalFormatHalfFloat: n, internalFormatPackedHalfFloat: s, internalFormatPackedFloat: a, textureFormatFloat: i, downloadTextureFormat: p, downloadUnpackNumChannels: u, defaultNumChannels: c, textureTypeHalfFloat: l, textureTypeFloat: m }; } -function ce(r, t8) { +function ce(r15, t8) { let e = t8(); - return A().getBool("DEBUG") && Y7(r), e; + return A().getBool("DEBUG") && Y7(r15), e; } -function Y7(r) { - let t8 = r.getError(); - if (t8 !== r.NO_ERROR) - throw new Error("WebGL Error: " + yR(r, t8)); +function Y7(r15) { + let t8 = r15.getError(); + if (t8 !== r15.NO_ERROR) + throw new Error("WebGL Error: " + bR(r15, t8)); } var Q7 = 596e-10; var Z7 = 65504; -function TI(r) { - return !!(A().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || r === 0 || Q7 < Math.abs(r) && Math.abs(r) < Z7); +function _I(r15) { + return !!(A().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || r15 === 0 || Q7 < Math.abs(r15) && Math.abs(r15) < Z7); } -function yR(r, t8) { +function bR(r15, t8) { switch (t8) { - case r.NO_ERROR: + case r15.NO_ERROR: return "NO_ERROR"; - case r.INVALID_ENUM: + case r15.INVALID_ENUM: return "INVALID_ENUM"; - case r.INVALID_VALUE: + case r15.INVALID_VALUE: return "INVALID_VALUE"; - case r.INVALID_OPERATION: + case r15.INVALID_OPERATION: return "INVALID_OPERATION"; - case r.INVALID_FRAMEBUFFER_OPERATION: + case r15.INVALID_FRAMEBUFFER_OPERATION: return "INVALID_FRAMEBUFFER_OPERATION"; - case r.OUT_OF_MEMORY: + case r15.OUT_OF_MEMORY: return "OUT_OF_MEMORY"; - case r.CONTEXT_LOST_WEBGL: + case r15.CONTEXT_LOST_WEBGL: return "CONTEXT_LOST_WEBGL"; default: return `Unknown error code ${t8}`; } } -function Ec(r, t8) { - return xi(r, () => r.getExtension(t8), 'Extension "' + t8 + '" not supported on this browser.'); +function vc(r15, t8) { + return fi(r15, () => r15.getExtension(t8), 'Extension "' + t8 + '" not supported on this browser.'); } -function _I(r, t8) { - let e = xi(r, () => r.createShader(r.VERTEX_SHADER), "Unable to create vertex WebGLShader."); - if (ce(r, () => r.shaderSource(e, t8)), ce(r, () => r.compileShader(e)), r.getShaderParameter(e, r.COMPILE_STATUS) === false) - throw console.log(r.getShaderInfoLog(e)), new Error("Failed to compile vertex shader."); +function $I(r15, t8) { + let e = fi(r15, () => r15.createShader(r15.VERTEX_SHADER), "Unable to create vertex WebGLShader."); + if (ce(r15, () => r15.shaderSource(e, t8)), ce(r15, () => r15.compileShader(e)), r15.getShaderParameter(e, r15.COMPILE_STATUS) === false) + throw console.log(r15.getShaderInfoLog(e)), new Error("Failed to compile vertex shader."); return e; } -function $I(r, t8) { - let e = xi(r, () => r.createShader(r.FRAGMENT_SHADER), "Unable to create fragment WebGLShader."); - if (ce(r, () => r.shaderSource(e, t8)), ce(r, () => r.compileShader(e)), A().get("ENGINE_COMPILE_ONLY")) +function EI(r15, t8) { + let e = fi(r15, () => r15.createShader(r15.FRAGMENT_SHADER), "Unable to create fragment WebGLShader."); + if (ce(r15, () => r15.shaderSource(e, t8)), ce(r15, () => r15.compileShader(e)), A().get("ENGINE_COMPILE_ONLY")) return e; - if (r.getShaderParameter(e, r.COMPILE_STATUS) === false) - throw qf(t8, r.getShaderInfoLog(e)), new Error("Failed to compile fragment shader."); + if (r15.getShaderParameter(e, r15.COMPILE_STATUS) === false) + throw Kf(t8, r15.getShaderInfoLog(e)), new Error("Failed to compile fragment shader."); return e; } var J7 = /ERROR: [0-9]+:([0-9]+):/g; -function qf(r, t8) { +function Kf(r15, t8) { let e = J7.exec(t8); if (e == null) { - console.log(`Couldn't parse line number in error: ${t8}`), console.log(r); + console.log(`Couldn't parse line number in error: ${t8}`), console.log(r15); return; } - let o = +e[1], n = r.split(` + let o = +e[1], n = r15.split(` `), s = n.length.toString().length + 2, a = n.map((l, m) => y.rightPad((m + 1).toString(), s) + l), i = 0; for (let l = 0; l < a.length; l++) i = Math.max(a[l].length, i); @@ -15464,245 +15465,245 @@ function qf(r, t8) { `)[0]), console.log(`%c ${y.rightPad(u[0], i)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"), console.log(c.join(` `)); } -function EI(r) { - return xi(r, () => r.createProgram(), "Unable to create WebGLProgram."); +function RI(r15) { + return fi(r15, () => r15.createProgram(), "Unable to create WebGLProgram."); } -function RI(r, t8) { - if (ce(r, () => r.linkProgram(t8)), !A().get("ENGINE_COMPILE_ONLY") && r.getProgramParameter(t8, r.LINK_STATUS) === false) - throw console.log(r.getProgramInfoLog(t8)), new Error("Failed to link vertex and fragment shaders."); +function DI(r15, t8) { + if (ce(r15, () => r15.linkProgram(t8)), !A().get("ENGINE_COMPILE_ONLY") && r15.getProgramParameter(t8, r15.LINK_STATUS) === false) + throw console.log(r15.getProgramInfoLog(t8)), new Error("Failed to link vertex and fragment shaders."); } -function Yl(r, t8) { - if (ce(r, () => r.validateProgram(t8)), r.getProgramParameter(t8, r.VALIDATE_STATUS) === false) - throw console.log(r.getProgramInfoLog(t8)), new Error("Shader program validation failed."); +function jl(r15, t8) { + if (ce(r15, () => r15.validateProgram(t8)), r15.getProgramParameter(t8, r15.VALIDATE_STATUS) === false) + throw console.log(r15.getProgramInfoLog(t8)), new Error("Shader program validation failed."); } -function DI(r, t8) { - let e = xi(r, () => r.createBuffer(), "Unable to create WebGLBuffer"); - return ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, e)), ce(r, () => r.bufferData(r.ARRAY_BUFFER, t8, r.STATIC_DRAW)), e; +function AI(r15, t8) { + let e = fi(r15, () => r15.createBuffer(), "Unable to create WebGLBuffer"); + return ce(r15, () => r15.bindBuffer(r15.ARRAY_BUFFER, e)), ce(r15, () => r15.bufferData(r15.ARRAY_BUFFER, t8, r15.STATIC_DRAW)), e; } -function AI(r, t8) { - let e = xi(r, () => r.createBuffer(), "Unable to create WebGLBuffer"); - return ce(r, () => r.bindBuffer(r.ELEMENT_ARRAY_BUFFER, e)), ce(r, () => r.bufferData(r.ELEMENT_ARRAY_BUFFER, t8, r.STATIC_DRAW)), e; +function FI(r15, t8) { + let e = fi(r15, () => r15.createBuffer(), "Unable to create WebGLBuffer"); + return ce(r15, () => r15.bindBuffer(r15.ELEMENT_ARRAY_BUFFER, e)), ce(r15, () => r15.bufferData(r15.ELEMENT_ARRAY_BUFFER, t8, r15.STATIC_DRAW)), e; } function eZ() { return A().getNumber("WEBGL_VERSION") === 2 ? 1 : 4; } -function FI(r) { - return xi(r, () => r.createTexture(), "Unable to create WebGLTexture."); +function PI(r15) { + return fi(r15, () => r15.createTexture(), "Unable to create WebGLTexture."); } -function PI(r, t8) { +function OI(r15, t8) { let e = A().getNumber("WEBGL_MAX_TEXTURE_SIZE"); - if (r <= 0 || t8 <= 0) { - let o = `[${r}x${t8}]`; + if (r15 <= 0 || t8 <= 0) { + let o = `[${r15}x${t8}]`; throw new Error("Requested texture size " + o + " is invalid."); } - if (r > e || t8 > e) { - let o = `[${r}x${t8}]`, n = `[${e}x${e}]`; + if (r15 > e || t8 > e) { + let o = `[${r15}x${t8}]`, n = `[${e}x${e}]`; throw new Error("Requested texture size " + o + " greater than WebGL maximum on this browser / GPU " + n + "."); } } -function OI(r) { - return xi(r, () => r.createFramebuffer(), "Unable to create WebGLFramebuffer."); +function MI(r15) { + return fi(r15, () => r15.createFramebuffer(), "Unable to create WebGLFramebuffer."); } -function jf(r, t8, e, o, n, s, a) { - let i = r.getAttribLocation(t8, e); - return i === -1 ? false : (ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, o)), ce(r, () => r.vertexAttribPointer(i, n, r.FLOAT, false, s, a)), ce(r, () => r.enableVertexAttribArray(i)), true); +function qf(r15, t8, e, o, n, s, a) { + let i = r15.getAttribLocation(t8, e); + return i === -1 ? false : (ce(r15, () => r15.bindBuffer(r15.ARRAY_BUFFER, o)), ce(r15, () => r15.vertexAttribPointer(i, n, r15.FLOAT, false, s, a)), ce(r15, () => r15.enableVertexAttribArray(i)), true); } -function bR(r, t8, e) { - wR(r, e), ce(r, () => r.activeTexture(r.TEXTURE0 + e)), ce(r, () => r.bindTexture(r.TEXTURE_2D, t8)); +function CR(r15, t8, e) { + SR(r15, e), ce(r15, () => r15.activeTexture(r15.TEXTURE0 + e)), ce(r15, () => r15.bindTexture(r15.TEXTURE_2D, t8)); } -function tZ(r, t8) { - wR(r, t8), ce(r, () => r.activeTexture(r.TEXTURE0 + t8)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)); +function tZ(r15, t8) { + SR(r15, t8), ce(r15, () => r15.activeTexture(r15.TEXTURE0 + t8)), ce(r15, () => r15.bindTexture(r15.TEXTURE_2D, null)); } -function MI(r, t8, e) { - return xi(r, () => r.getUniformLocation(t8, e), 'uniform "' + e + '" not present in program.'); +function LI(r15, t8, e) { + return fi(r15, () => r15.getUniformLocation(t8, e), 'uniform "' + e + '" not present in program.'); } -function LI(r, t8, e) { - return r.getUniformLocation(t8, e); +function BI(r15, t8, e) { + return r15.getUniformLocation(t8, e); } -function BI(r, t8, e, o) { - ce(r, () => bR(r, t8, o)), ce(r, () => r.uniform1i(e, o)); +function zI(r15, t8, e, o) { + ce(r15, () => CR(r15, t8, o)), ce(r15, () => r15.uniform1i(e, o)); } -function rZ(r) { - ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, null)), ce(r, () => r.viewport(0, 0, r.canvas.width, r.canvas.height)), ce(r, () => r.scissor(0, 0, r.canvas.width, r.canvas.height)); +function rZ(r15) { + ce(r15, () => r15.bindFramebuffer(r15.FRAMEBUFFER, null)), ce(r15, () => r15.viewport(0, 0, r15.canvas.width, r15.canvas.height)), ce(r15, () => r15.scissor(0, 0, r15.canvas.width, r15.canvas.height)); } -function Ql(r, t8, e) { - ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, e)), ce(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, t8, 0)); +function Xl(r15, t8, e) { + ce(r15, () => r15.bindFramebuffer(r15.FRAMEBUFFER, e)), ce(r15, () => r15.framebufferTexture2D(r15.FRAMEBUFFER, r15.COLOR_ATTACHMENT0, r15.TEXTURE_2D, t8, 0)); } -function Xf(r, t8) { - ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, t8)), ce(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, null, 0)); +function jf(r15, t8) { + ce(r15, () => r15.bindFramebuffer(r15.FRAMEBUFFER, t8)), ce(r15, () => r15.framebufferTexture2D(r15.FRAMEBUFFER, r15.COLOR_ATTACHMENT0, r15.TEXTURE_2D, null, 0)); } -function Rc(r) { - let t8 = r.checkFramebufferStatus(r.FRAMEBUFFER); - if (t8 !== r.FRAMEBUFFER_COMPLETE) - throw new Error("Error binding framebuffer: " + CR(r, t8)); +function kc(r15) { + let t8 = r15.checkFramebufferStatus(r15.FRAMEBUFFER); + if (t8 !== r15.FRAMEBUFFER_COMPLETE) + throw new Error("Error binding framebuffer: " + wR(r15, t8)); } -function CR(r, t8) { +function wR(r15, t8) { switch (t8) { - case r.FRAMEBUFFER_INCOMPLETE_ATTACHMENT: + case r15.FRAMEBUFFER_INCOMPLETE_ATTACHMENT: return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT"; - case r.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT: + case r15.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT: return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT"; - case r.FRAMEBUFFER_INCOMPLETE_DIMENSIONS: + case r15.FRAMEBUFFER_INCOMPLETE_DIMENSIONS: return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS"; - case r.FRAMEBUFFER_UNSUPPORTED: + case r15.FRAMEBUFFER_UNSUPPORTED: return "FRAMEBUFFER_UNSUPPORTED"; default: return `unknown error ${t8}`; } } -function xi(r, t8, e) { - let o = ce(r, () => t8()); +function fi(r15, t8, e) { + let o = ce(r15, () => t8()); if (o == null) throw new Error(e); return o; } -function wR(r, t8) { - let e = r.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, o = t8 + r.TEXTURE0; - if (o < r.TEXTURE0 || o > e) { +function SR(r15, t8) { + let e = r15.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, o = t8 + r15.TEXTURE0; + if (o < r15.TEXTURE0 || o > e) { let n = `[gl.TEXTURE0, gl.TEXTURE${e}]`; throw new Error(`textureUnit must be in ${n}.`); } } -function yi(r, t8 = 2) { - return y.sizeFromShape(r.slice(0, r.length - t8)); +function hi(r15, t8 = 2) { + return y.sizeFromShape(r15.slice(0, r15.length - t8)); } -function bi(r) { - if (r.length === 0) +function gi(r15) { + if (r15.length === 0) throw Error("Cannot get rows and columns of an empty shape array."); - return [r.length > 1 ? r[r.length - 2] : 1, r[r.length - 1]]; + return [r15.length > 1 ? r15[r15.length - 2] : 1, r15[r15.length - 1]]; } -function Dc(r) { +function Nc(r15) { let t8 = [1, 1, 1]; - return r.length === 0 || r.length === 1 && r[0] === 1 || (t8 = [yi(r), ...bi(r)]), t8; + return r15.length === 0 || r15.length === 1 && r15[0] === 1 || (t8 = [hi(r15), ...gi(r15)]), t8; } -function zI(r, t8 = false) { +function VI(r15, t8 = false) { let e = A().getNumber("WEBGL_MAX_TEXTURE_SIZE"), o = A().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE"); - o === 1 / 0 && A().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE") && (o = e / 2), t8 && (e = e * 2, o = o * 2, r = r.map((i, p) => p >= r.length - 2 ? y.nearestLargerEven(r[p]) : r[p]), r.length === 1 && (r = [2, r[0]])), r.length !== 2 && (r = y.squeezeShape(r).newShape); - let n = y.sizeFromShape(r), s = null; - r.length <= 1 && n <= e ? s = [1, n] : r.length === 2 && r[0] <= e && r[1] <= e ? s = r : r.length === 3 && r[0] * r[1] <= e && r[2] <= e ? s = [r[0] * r[1], r[2]] : r.length === 3 && r[0] <= e && r[1] * r[2] <= e ? s = [r[0], r[1] * r[2]] : r.length === 4 && r[0] * r[1] * r[2] <= e && r[3] <= e ? s = [r[0] * r[1] * r[2], r[3]] : r.length === 4 && r[0] <= e && r[1] * r[2] * r[3] <= e && (s = [r[0], r[1] * r[2] * r[3]]); + o === 1 / 0 && A().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE") && (o = e / 2), t8 && (e = e * 2, o = o * 2, r15 = r15.map((i, p) => p >= r15.length - 2 ? y.nearestLargerEven(r15[p]) : r15[p]), r15.length === 1 && (r15 = [2, r15[0]])), r15.length !== 2 && (r15 = y.squeezeShape(r15).newShape); + let n = y.sizeFromShape(r15), s = null; + r15.length <= 1 && n <= e ? s = [1, n] : r15.length === 2 && r15[0] <= e && r15[1] <= e ? s = r15 : r15.length === 3 && r15[0] * r15[1] <= e && r15[2] <= e ? s = [r15[0] * r15[1], r15[2]] : r15.length === 3 && r15[0] <= e && r15[1] * r15[2] <= e ? s = [r15[0], r15[1] * r15[2]] : r15.length === 4 && r15[0] * r15[1] * r15[2] <= e && r15[3] <= e ? s = [r15[0] * r15[1] * r15[2], r15[3]] : r15.length === 4 && r15[0] <= e && r15[1] * r15[2] * r15[3] <= e && (s = [r15[0], r15[1] * r15[2] * r15[3]]); let a = s != null && Math.max(...s) > o && Math.min(...s) <= (t8 ? 2 : 1) && Math.min(...s) > 0; if (s == null || a) if (t8) { - let i = yi(r), p = 2, u = 2; - r.length && ([p, u] = bi(r)), n = i * (p / 2) * (u / 2), s = y.sizeToSquarishShape(n).map((c) => c * 2); + let i = hi(r15), p = 2, u = 2; + r15.length && ([p, u] = gi(r15)), n = i * (p / 2) * (u / 2), s = y.sizeToSquarishShape(n).map((c) => c * 2); } else s = y.sizeToSquarishShape(n); return s; } -function Gf(r) { - return r % 2 === 0; +function Uf(r15) { + return r15 % 2 === 0; } -function Cu(r, t8) { - if (r = r.slice(-2), t8 = t8.slice(-2), y.arraysEqual(r, t8) || !r.length || !t8.length || r[0] === 0 || r[1] === 0 || t8[0] === 0 || t8[1] === 0) +function hu(r15, t8) { + if (r15 = r15.slice(-2), t8 = t8.slice(-2), y.arraysEqual(r15, t8) || !r15.length || !t8.length || r15[0] === 0 || r15[1] === 0 || t8[0] === 0 || t8[1] === 0) return true; - if (r.length !== t8.length) { - let e = r[r.length - 1], o = t8[t8.length - 1]; - if (e === o || Gf(e) && Gf(o) && (r[0] === 1 || t8[0] === 1)) + if (r15.length !== t8.length) { + let e = r15[r15.length - 1], o = t8[t8.length - 1]; + if (e === o || Uf(e) && Uf(o) && (r15[0] === 1 || t8[0] === 1)) return true; } - return r[1] === t8[1] && Gf(r[0]) && Gf(t8[0]); + return r15[1] === t8[1] && Uf(r15[0]) && Uf(t8[0]); } +var Gf; var Hf; -var Kf; -function VI(r) { - if (Hf == null) { - let t8 = Kr(r); - Hf = t8.getParameter(t8.MAX_TEXTURE_SIZE); +function WI(r15) { + if (Gf == null) { + let t8 = Hr(r15); + Gf = t8.getParameter(t8.MAX_TEXTURE_SIZE); } - return Hf; + return Gf; } function oZ() { - Hf = null; + Gf = null; } function nZ() { - Kf = null; + Hf = null; } -function WI(r) { - if (Kf == null) { - let t8 = Kr(r); - Kf = t8.getParameter(t8.MAX_TEXTURE_IMAGE_UNITS); +function UI(r15) { + if (Hf == null) { + let t8 = Hr(r15); + Hf = t8.getParameter(t8.MAX_TEXTURE_IMAGE_UNITS); } - return Math.min(16, Kf); + return Math.min(16, Hf); } -function UI(r) { - if (r === 0) +function GI(r15) { + if (r15 === 0) return 0; - let t8, e = Kr(r); - return qr(e, "EXT_disjoint_timer_query_webgl2") && r === 2 ? t8 = 2 : qr(e, "EXT_disjoint_timer_query") ? t8 = 1 : t8 = 0, t8; + let t8, e = Hr(r15); + return Kr(e, "EXT_disjoint_timer_query_webgl2") && r15 === 2 ? t8 = 2 : Kr(e, "EXT_disjoint_timer_query") ? t8 = 1 : t8 = 0, t8; } -function qr(r, t8) { - return r.getExtension(t8) != null; +function Kr(r15, t8) { + return r15.getExtension(t8) != null; } -function Yf(r) { +function Xf(r15) { try { - if (Kr(r) != null) + if (Hr(r15) != null) return true; } catch (t8) { return console.log("Error when getting WebGL context: ", t8), false; } return false; } -function GI(r) { - if (r === 0) +function HI(r15) { + if (r15 === 0) return false; - let t8 = Kr(r); - if (r === 1) { - if (!qr(t8, "OES_texture_float")) + let t8 = Hr(r15); + if (r15 === 1) { + if (!Kr(t8, "OES_texture_float")) return false; - } else if (!qr(t8, "EXT_color_buffer_float")) + } else if (!Kr(t8, "EXT_color_buffer_float")) return false; - return NI(t8); + return TI(t8); } -function HI(r) { - if (r === 0) +function KI(r15) { + if (r15 === 0) return false; - let t8 = Kr(r); - if (r === 1) { - if (!qr(t8, "OES_texture_float") || !qr(t8, "WEBGL_color_buffer_float")) + let t8 = Hr(r15); + if (r15 === 1) { + if (!Kr(t8, "OES_texture_float") || !Kr(t8, "WEBGL_color_buffer_float")) return false; } else { - if (qr(t8, "EXT_color_buffer_float")) - return NI(t8); + if (Kr(t8, "EXT_color_buffer_float")) + return TI(t8); let o = "EXT_color_buffer_half_float"; - if (qr(t8, o)) { + if (Kr(t8, o)) { let n = t8.getExtension(o); return sZ(t8, n); } return false; } - return NI(t8); + return TI(t8); } -function NI(r) { - let t8 = Xl(r), e = r.createTexture(); - r.bindTexture(r.TEXTURE_2D, e); +function TI(r15) { + let t8 = ql(r15), e = r15.createTexture(); + r15.bindTexture(r15.TEXTURE_2D, e); let o = 1, n = 1; - r.texImage2D(r.TEXTURE_2D, 0, t8.internalFormatFloat, o, n, 0, t8.textureFormatFloat, t8.textureTypeFloat, null); - let s = r.createFramebuffer(); - r.bindFramebuffer(r.FRAMEBUFFER, s), r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, e, 0); - let a = r.checkFramebufferStatus(r.FRAMEBUFFER) === r.FRAMEBUFFER_COMPLETE; - return r.bindTexture(r.TEXTURE_2D, null), r.bindFramebuffer(r.FRAMEBUFFER, null), r.deleteTexture(e), r.deleteFramebuffer(s), a; + r15.texImage2D(r15.TEXTURE_2D, 0, t8.internalFormatFloat, o, n, 0, t8.textureFormatFloat, t8.textureTypeFloat, null); + let s = r15.createFramebuffer(); + r15.bindFramebuffer(r15.FRAMEBUFFER, s), r15.framebufferTexture2D(r15.FRAMEBUFFER, r15.COLOR_ATTACHMENT0, r15.TEXTURE_2D, e, 0); + let a = r15.checkFramebufferStatus(r15.FRAMEBUFFER) === r15.FRAMEBUFFER_COMPLETE; + return r15.bindTexture(r15.TEXTURE_2D, null), r15.bindFramebuffer(r15.FRAMEBUFFER, null), r15.deleteTexture(e), r15.deleteFramebuffer(s), a; } -function sZ(r, t8) { - let e = Xl(r, t8), o = r.createTexture(); - r.bindTexture(r.TEXTURE_2D, o); +function sZ(r15, t8) { + let e = ql(r15, t8), o = r15.createTexture(); + r15.bindTexture(r15.TEXTURE_2D, o); let n = 1, s = 1; - r.texImage2D(r.TEXTURE_2D, 0, e.internalFormatHalfFloat, n, s, 0, e.textureFormatFloat, e.textureTypeHalfFloat, null); - let a = r.createFramebuffer(); - r.bindFramebuffer(r.FRAMEBUFFER, a), r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, o, 0); - let i = r.checkFramebufferStatus(r.FRAMEBUFFER) === r.FRAMEBUFFER_COMPLETE; - return r.bindTexture(r.TEXTURE_2D, null), r.bindFramebuffer(r.FRAMEBUFFER, null), r.deleteTexture(o), r.deleteFramebuffer(a), i; + r15.texImage2D(r15.TEXTURE_2D, 0, e.internalFormatHalfFloat, n, s, 0, e.textureFormatFloat, e.textureTypeHalfFloat, null); + let a = r15.createFramebuffer(); + r15.bindFramebuffer(r15.FRAMEBUFFER, a), r15.framebufferTexture2D(r15.FRAMEBUFFER, r15.COLOR_ATTACHMENT0, r15.TEXTURE_2D, o, 0); + let i = r15.checkFramebufferStatus(r15.FRAMEBUFFER) === r15.FRAMEBUFFER_COMPLETE; + return r15.bindTexture(r15.TEXTURE_2D, null), r15.bindFramebuffer(r15.FRAMEBUFFER, null), r15.deleteTexture(o), r15.deleteFramebuffer(a), i; } -function KI(r) { - return r !== 2 ? false : Kr(r).fenceSync != null; +function qI(r15) { + return r15 !== 2 ? false : Hr(r15).fenceSync != null; } -function Vs(r, t8) { - Array.isArray(r) || (r = [r]), r.forEach((e) => { +function Vs(r15, t8) { + Array.isArray(r15) || (r15 = [r15]), r15.forEach((e) => { e != null && y.assert(e.dtype !== "complex64", () => `${t8} does not support complex64 tensors in the WebGL backend.`); }); } var Se = A(); Se.registerFlag("HAS_WEBGL", () => Se.getNumber("WEBGL_VERSION") > 0); -Se.registerFlag("WEBGL_VERSION", () => Yf(2) ? 2 : Yf(1) ? 1 : 0); +Se.registerFlag("WEBGL_VERSION", () => Xf(2) ? 2 : Xf(1) ? 1 : 0); Se.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false); Se.registerFlag("WEBGL_BUFFER_SUPPORTED", () => Se.get("WEBGL_VERSION") === 2); Se.registerFlag("WEBGL_CPU_FORWARD", () => true); @@ -15719,29 +15720,29 @@ Se.registerFlag("WEBGL_PACK_REDUCE", () => Se.getBool("WEBGL_PACK")); Se.registerFlag("WEBGL_LAZILY_UNPACK", () => Se.getBool("WEBGL_PACK")); Se.registerFlag("WEBGL_CONV_IM2COL", () => Se.getBool("WEBGL_PACK")); Se.registerFlag("WEBGL_PACK_CONV2DTRANSPOSE", () => Se.getBool("WEBGL_PACK")); -Se.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => VI(Se.getNumber("WEBGL_VERSION"))); -Se.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => WI(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => WI(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => UI(Se.getNumber("WEBGL_VERSION"))); Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => { - let r = Se.getNumber("WEBGL_VERSION"); - return r === 0 ? 0 : UI(r); + let r15 = Se.getNumber("WEBGL_VERSION"); + return r15 === 0 ? 0 : GI(r15); }); -Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Se.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !ou.isMobile()); -Se.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => GI(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Se.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !Ji.isMobile()); +Se.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => HI(Se.getNumber("WEBGL_VERSION"))); Se.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => Se.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : Se.getBool("WEBGL_RENDER_FLOAT32_CAPABLE")); -Se.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => HI(Se.getNumber("WEBGL_VERSION"))); -Se.registerFlag("WEBGL_FENCE_API_ENABLED", () => KI(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => KI(Se.getNumber("WEBGL_VERSION"))); +Se.registerFlag("WEBGL_FENCE_API_ENABLED", () => qI(Se.getNumber("WEBGL_VERSION"))); Se.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => Se.getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? 4 : 0); -Se.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => -1, (r) => { - if (typeof r != "number") - throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${r}.`); - if (r < 0 && r !== -1) - throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`); +Se.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => -1, (r15) => { + if (typeof r15 != "number") + throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${r15}.`); + if (r15 < 0 && r15 !== -1) + throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r15}.`); }); -Se.registerFlag("WEBGL_FLUSH_THRESHOLD", () => ou.isMobile() ? 1 : -1, (r) => { - if (typeof r != "number") - throw new Error(`WEBGL_FLUSH_THRESHOLD must be a number but got ${r}.`); - if (r < 0 && r !== -1) - throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`); +Se.registerFlag("WEBGL_FLUSH_THRESHOLD", () => Ji.isMobile() ? 1 : -1, (r15) => { + if (typeof r15 != "number") + throw new Error(`WEBGL_FLUSH_THRESHOLD must be a number but got ${r15}.`); + if (r15 < 0 && r15 !== -1) + throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r15}.`); }); Se.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128); Se.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false); @@ -15753,9 +15754,9 @@ Se.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE", () => 1 / 0); Se.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE", () => false); Se.registerFlag("WEBGL2_ISNAN_CUSTOM", () => false); Se.registerFlag("ENGINE_COMPILE_ONLY", () => false); -function vt() { - let r, t8, e, o, n, s, a, i, p, u; - return A().getNumber("WEBGL_VERSION") === 2 ? (r = "#version 300 es", t8 = "in", e = "out", o = "in", n = "texture", s = "outputColor", a = "out vec4 outputColor;", i = A().getBool("WEBGL2_ISNAN_CUSTOM") ? ` +function It() { + let r15, t8, e, o, n, s, a, i, p, u; + return A().getNumber("WEBGL_VERSION") === 2 ? (r15 = "#version 300 es", t8 = "in", e = "out", o = "in", n = "texture", s = "outputColor", a = "out vec4 outputColor;", i = A().getBool("WEBGL2_ISNAN_CUSTOM") ? ` bool isnan_custom(float val) { uint floatToUint = floatBitsToUint(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; @@ -15776,7 +15777,7 @@ function vt() { ivec4 newRound(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `) : (r = "", t8 = "attribute", e = "varying", o = "varying", n = "texture2D", s = "gl_FragColor", a = "", i = ` + `) : (r15 = "", t8 = "attribute", e = "varying", o = "varying", n = "texture2D", s = "gl_FragColor", a = "", i = ` #define isnan(value) isnan_custom(value) bool isnan_custom(float val) { return (val > 0. || val < 1. || val == 0.) ? false : true; @@ -15801,52 +15802,52 @@ function vt() { ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `), { version: r, attribute: t8, varyingVs: e, varyingFs: o, texture2D: n, output: s, defineOutput: a, defineSpecialNaN: i, defineSpecialInf: p, defineRound: u }; + `), { version: r15, attribute: t8, varyingVs: e, varyingFs: o, texture2D: n, output: s, defineOutput: a, defineSpecialNaN: i, defineSpecialInf: p, defineRound: u }; } -function Ws(r, t8, e = "index") { +function Ws(r15, t8, e = "index") { let o = y.computeStrides(t8); return o.map((n, s) => { - let a = `int ${r[s]} = ${e} / ${n}`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${e} - ${r[s]} * ${n}` : `index -= ${r[s]} * ${n}`; + let a = `int ${r15[s]} = ${e} / ${n}`, i = s === o.length - 1 ? `int ${r15[s + 1]} = ${e} - ${r15[s]} * ${n}` : `index -= ${r15[s]} * ${n}`; return `${a}; ${i};`; }).join(""); } -function Ip(r, t8, e = "index") { +function hp(r15, t8, e = "index") { let o = y.computeStrides(t8); return o.map((n, s) => { - let a = `int ${r[s]} = ${e} / outShapeStrides[${s}]`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${e} - ${r[s]} * outShapeStrides[${s}]` : `index -= ${r[s]} * outShapeStrides[${s}]`; + let a = `int ${r15[s]} = ${e} / outShapeStrides[${s}]`, i = s === o.length - 1 ? `int ${r15[s + 1]} = ${e} - ${r15[s]} * outShapeStrides[${s}]` : `index -= ${r15[s]} * outShapeStrides[${s}]`; return `${a}; ${i};`; }).join(""); } -function aZ(r, t8) { - let e = r.length, o = r.map((s) => `${t8}[${s}]`), n = new Array(e - 1); +function aZ(r15, t8) { + let e = r15.length, o = r15.map((s) => `${t8}[${s}]`), n = new Array(e - 1); n[e - 2] = o[e - 1]; for (let s = e - 3; s >= 0; --s) n[s] = `(${n[s + 1]} * ${o[s + 1]})`; return n; } -function SR(r, t8, e = "index") { - let o = r.map((s, a) => a), n = aZ(o, t8); +function IR(r15, t8, e = "index") { + let o = r15.map((s, a) => a), n = aZ(o, t8); return n.map((s, a) => { - let i = `int ${r[a]} = ${e} / ${n[a]}`, p = a === n.length - 1 ? `int ${r[a + 1]} = ${e} - ${r[a]} * ${n[a]}` : `index -= ${r[a]} * ${n[a]}`; + let i = `int ${r15[a]} = ${e} / ${n[a]}`, p = a === n.length - 1 ? `int ${r15[a + 1]} = ${e} - ${r15[a]} * ${n[a]}` : `index -= ${r15[a]} * ${n[a]}`; return `${i}; ${p};`; }).join(""); } -function Fc(r) { - let t8 = y.computeStrides(r).map((e) => e.toString()); +function _c(r15) { + let t8 = y.computeStrides(r15).map((e) => e.toString()); return ` int getFlatIndex(ivec3 coords) { return coords.x * ${t8[0]} + coords.y * ${t8[1]} + coords.z; } `; } -function Pc() { +function $c() { return ` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } `; } -var Qf = ` +var Yf = ` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -15886,13 +15887,13 @@ var Qf = ` return c / 255.0; } `; -var { getBroadcastDims: IR } = w; -function vR(r, t8, e) { +var { getBroadcastDims: vR } = w; +function kR(r15, t8, e) { let o = []; - if (r.forEach((d) => { + if (r15.forEach((d) => { let f = y.sizeFromShape(d.shapeInfo.logicalShape); if (d.shapeInfo.isUniform ? o.push(`uniform float ${d.name}${f > 1 ? `[${f}]` : ""};`) : (o.push(`uniform sampler2D ${d.name};`), o.push(`uniform int offset${d.name};`)), e.enableShapeUniforms) { - let { uniformShape: h } = Zf(e.packedInputs, d.shapeInfo.logicalShape, d.shapeInfo.texShape); + let { uniformShape: h } = Qf(e.packedInputs, d.shapeInfo.logicalShape, d.shapeInfo.texShape); switch (h.length) { case 1: o.push(`uniform int ${d.name}Shape;`); @@ -15934,114 +15935,114 @@ function vR(r, t8, e) { o.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : ""};`); }); let n = o.join(` -`), s = r.map((d) => iZ(d, t8, e.packedInputs, e.enableShapeUniforms)).join(` -`), a = t8.texShape, i = vt(), p = cZ(i), u, c, l = dZ(i); +`), s = r15.map((d) => iZ(d, t8, e.packedInputs, e.enableShapeUniforms)).join(` +`), a = t8.texShape, i = It(), p = cZ(i), u, c, l = dZ(i); return t8.isPacked ? (u = uZ(t8.logicalShape, a, e.enableShapeUniforms), c = mZ(i)) : (u = pZ(t8.logicalShape, a, e.enableShapeUniforms), c = lZ(i)), e.packedInputs && (l += xZ), [l, p, c, n, u, s, e.userCode].join(` `); } -function Mc(r, t8 = false) { - let e = r.shapeInfo.logicalShape; +function Rc(r15, t8 = false) { + let e = r15.shapeInfo.logicalShape; switch (e.length) { case 0: - return $Z(r, t8); + return $Z(r15, t8); case 1: - return RZ(r, t8); + return RZ(r15, t8); case 2: - return AZ(r, t8); + return AZ(r15, t8); case 3: - return PZ(r, t8); + return PZ(r15, t8); case 4: - return MZ(r, t8); + return MZ(r15, t8); case 5: - return LZ(r); + return LZ(r15); case 6: - return BZ(r); + return BZ(r15); default: throw new Error(`${e.length}-D input sampling is not yet supported`); } } -function kR(r, t8) { - switch (r.shapeInfo.logicalShape.length) { +function NR(r15, t8) { + switch (r15.shapeInfo.logicalShape.length) { case 0: - return _Z(r); + return _Z(r15); case 1: - return EZ(r, t8); + return EZ(r15, t8); case 2: - return DZ(r, t8); + return DZ(r15, t8); case 3: - return FZ(r, t8); + return FZ(r15, t8); default: - return OZ(r, t8); + return OZ(r15, t8); } } -function iZ(r, t8, e = false, o) { +function iZ(r15, t8, e = false, o) { let n = ""; - e ? n += kR(r, o) : n += Mc(r, o); - let s = r.shapeInfo.logicalShape, a = t8.logicalShape; - return s.length <= a.length && (e ? n += zZ(r, t8) : n += VZ(r, t8)), n; + e ? n += NR(r15, o) : n += Rc(r15, o); + let s = r15.shapeInfo.logicalShape, a = t8.logicalShape; + return s.length <= a.length && (e ? n += zZ(r15, t8) : n += VZ(r15, t8)), n; } -function uZ(r, t8, e) { - switch (r.length) { +function uZ(r15, t8, e) { + switch (r15.length) { case 0: - return NR(); + return TR(); case 1: - return yZ(r, t8, e); + return yZ(r15, t8, e); case 2: - return NZ(r, t8, e); + return NZ(r15, t8, e); case 3: - return CZ(r, t8, e); + return CZ(r15, t8, e); default: - return SZ(r, t8, e); + return SZ(r15, t8, e); } } -function pZ(r, t8, e) { - switch (r.length) { +function pZ(r15, t8, e) { + switch (r15.length) { case 0: - return NR(); + return TR(); case 1: - return bZ(r, t8, e); + return bZ(r15, t8, e); case 2: - return TZ(r, t8, e); + return TZ(r15, t8, e); case 3: - return wZ(r, t8, e); + return wZ(r15, t8, e); case 4: - return IZ(r, t8, e); + return IZ(r15, t8, e); case 5: - return vZ(r, t8); + return vZ(r15, t8); case 6: - return kZ(r, t8); + return kZ(r15, t8); default: - throw new Error(`${r.length}-D output sampling is not yet supported`); + throw new Error(`${r15.length}-D output sampling is not yet supported`); } } -function cZ(r) { +function cZ(r15) { return ` float sampleTexture(sampler2D textureSampler, vec2 uv) { - return ${r.texture2D}(textureSampler, uv).r; + return ${r15.texture2D}(textureSampler, uv).r; } `; } -function lZ(r) { +function lZ(r15) { return ` void setOutput(float val) { - ${r.output} = vec4(val, 0, 0, 0); + ${r15.output} = vec4(val, 0, 0, 0); } `; } -function mZ(r) { +function mZ(r15) { return ` void setOutput(vec4 val) { - ${r.output} = val; + ${r15.output} = val; } `; } -function dZ(r) { - return `${r.version} +function dZ(r15) { + return `${r15.version} precision highp float; precision highp int; precision highp sampler2D; - ${r.varyingFs} vec2 resultUV; - ${r.defineOutput} + ${r15.varyingFs} vec2 resultUV; + ${r15.defineOutput} const vec2 halfCR = vec2(0.5, 0.5); struct ivec5 @@ -16064,9 +16065,9 @@ function dZ(r) { }; uniform float NAN; - ${r.defineSpecialNaN} - ${r.defineSpecialInf} - ${r.defineRound} + ${r15.defineSpecialNaN} + ${r15.defineSpecialInf} + ${r15.defineRound} int imod(int x, int y) { return x - y * (x / y); @@ -16140,14 +16141,14 @@ var xZ = ` return modCoord == 0. ? frag.r : frag.g; } `; -function NR() { +function TR() { return ` int getOutputCoords() { return 0; } `; } -function yZ(r, t8, e) { +function yZ(r15, t8, e) { let o = [Math.ceil(t8[0] / 2), Math.ceil(t8[1] / 2)]; return o[0] === 1 ? e ? ` int getOutputCoords() { @@ -16180,7 +16181,7 @@ function yZ(r, t8, e) { } `; } -function bZ(r, t8, e) { +function bZ(r15, t8, e) { return t8[0] === 1 ? e ? ` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); @@ -16211,7 +16212,7 @@ function bZ(r, t8, e) { } `; } -function CZ(r, t8, e) { +function CZ(r15, t8, e) { if (e) return ` ivec3 getOutputCoords() { @@ -16231,7 +16232,7 @@ function CZ(r, t8, e) { return ivec3(b, r, c); } `; - let o = [Math.ceil(t8[0] / 2), Math.ceil(t8[1] / 2)], n = Math.ceil(r[2] / 2), s = n * Math.ceil(r[1] / 2); + let o = [Math.ceil(t8[0] / 2), Math.ceil(t8[1] / 2)], n = Math.ceil(r15[2] / 2), s = n * Math.ceil(r15[1] / 2); return ` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * @@ -16248,18 +16249,18 @@ function CZ(r, t8, e) { } `; } -function wZ(r, t8, e) { +function wZ(r15, t8, e) { if (e) return ` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Ip(["r", "c", "d"], r)} + ${hp(["r", "c", "d"], r15)} return ivec3(r, c, d); } `; - let o = Ws(["r", "c", "d"], r); + let o = Ws(["r", "c", "d"], r15); return ` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * @@ -16270,7 +16271,7 @@ function wZ(r, t8, e) { } `; } -function SZ(r, t8, e) { +function SZ(r15, t8, e) { if (e) return ` ivec4 getOutputCoords() { @@ -16295,14 +16296,14 @@ function SZ(r, t8, e) { return ivec4(b2, b, r, c); } `; - let o = [Math.ceil(t8[0] / 2), Math.ceil(t8[1] / 2)], n = Math.ceil(r[r.length - 1] / 2), s = n * Math.ceil(r[r.length - 2] / 2), a = s, i = "", p = "b, r, c"; - for (let u = 2; u < r.length - 1; u++) - a *= r[r.length - u - 1], i = ` + let o = [Math.ceil(t8[0] / 2), Math.ceil(t8[1] / 2)], n = Math.ceil(r15[r15.length - 1] / 2), s = n * Math.ceil(r15[r15.length - 2] / 2), a = s, i = "", p = "b, r, c"; + for (let u = 2; u < r15.length - 1; u++) + a *= r15[r15.length - u - 1], i = ` int b${u} = index / ${a}; index -= b${u} * ${a}; ` + i, p = `b${u}, ` + p; return ` - ivec${r.length} getOutputCoords() { + ivec${r15.length} getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${o[0]}, ${o[1]})); int index = resTexRC.x * ${o[1]} + resTexRC.y; @@ -16315,22 +16316,22 @@ function SZ(r, t8, e) { int r = 2 * (index / ${n}); int c = imod(index, ${n}) * 2; - return ivec${r.length}(${p}); + return ivec${r15.length}(${p}); } `; } -function IZ(r, t8, e) { +function IZ(r15, t8, e) { if (e) return ` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Ip(["r", "c", "d", "d2"], r)} + ${hp(["r", "c", "d", "d2"], r15)} return ivec4(r, c, d, d2); } `; - let o = Ws(["r", "c", "d", "d2"], r); + let o = Ws(["r", "c", "d", "d2"], r15); return ` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * @@ -16341,8 +16342,8 @@ function IZ(r, t8, e) { } `; } -function vZ(r, t8) { - let e = Ws(["r", "c", "d", "d2", "d3"], r); +function vZ(r15, t8) { + let e = Ws(["r", "c", "d", "d2", "d3"], r15); return ` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t8[0]}, @@ -16357,8 +16358,8 @@ function vZ(r, t8) { } `; } -function kZ(r, t8) { - let e = Ws(["r", "c", "d", "d2", "d3", "d4"], r); +function kZ(r15, t8) { + let e = Ws(["r", "c", "d", "d2", "d3", "d4"], r15); return ` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * @@ -16372,9 +16373,9 @@ function kZ(r, t8) { } `; } -function NZ(r, t8, e) { +function NZ(r15, t8, e) { let o = [Math.ceil(t8[0] / 2), Math.ceil(t8[1] / 2)]; - if (y.arraysEqual(r, t8)) + if (y.arraysEqual(r15, t8)) return e ? ` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); @@ -16385,7 +16386,7 @@ function NZ(r, t8, e) { return 2 * ivec2(resultUV.yx * vec2(${o[0]}, ${o[1]})); } `; - let n = Math.ceil(r[1] / 2); + let n = Math.ceil(r15[1] / 2); return e ? ` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); @@ -16412,8 +16413,8 @@ function NZ(r, t8, e) { } `; } -function TZ(r, t8, e) { - return y.arraysEqual(r, t8) ? e ? ` +function TZ(r15, t8, e) { + return y.arraysEqual(r15, t8) ? e ? ` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -16421,7 +16422,7 @@ function TZ(r, t8, e) { ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(${t8[0]}, ${t8[1]})); } - ` : r[1] === 1 ? e ? ` + ` : r15[1] === 1 ? e ? ` ivec2 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -16435,7 +16436,7 @@ function TZ(r, t8, e) { int index = resTexRC.x * ${t8[1]} + resTexRC.y; return ivec2(index, 0); } - ` : r[0] === 1 ? e ? ` + ` : r15[0] === 1 ? e ? ` ivec2 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -16463,35 +16464,35 @@ function TZ(r, t8, e) { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t8[0]}, ${t8[1]})); int index = resTexRC.x * ${t8[1]} + resTexRC.y; - int r = index / ${r[1]}; - int c = index - r * ${r[1]}; + int r = index / ${r15[1]}; + int c = index - r * ${r15[1]}; return ivec2(r, c); } `; } -function vp(r) { - return `offset${r}`; +function gp(r15) { + return `offset${r15}`; } -function _Z(r) { - let t8 = r.name, e = "get" + t8.charAt(0).toUpperCase() + t8.slice(1), o = vt(); +function _Z(r15) { + let t8 = r15.name, e = "get" + t8.charAt(0).toUpperCase() + t8.slice(1), o = It(); return ` vec4 ${e}() { return ${o.texture2D}(${t8}, halfCR); } `; } -function $Z(r, t8) { - let e = r.name, o = "get" + e.charAt(0).toUpperCase() + e.slice(1); - if (r.shapeInfo.isUniform) +function $Z(r15, t8) { + let e = r15.name, o = "get" + e.charAt(0).toUpperCase() + e.slice(1); + if (r15.shapeInfo.isUniform) return `float ${o}() {return ${e};}`; - let [n, s] = r.shapeInfo.texShape; + let [n, s] = r15.shapeInfo.texShape; if (n === 1 && s === 1) return ` float ${o}() { return sampleTexture(${e}, halfCR); } `; - let a = vp(e); + let a = gp(e); if (t8) return ` float ${o}() { @@ -16499,7 +16500,7 @@ function $Z(r, t8) { return sampleTexture(${e}, uv); } `; - let [i, p] = r.shapeInfo.texShape; + let [i, p] = r15.shapeInfo.texShape; return ` float ${o}() { vec2 uv = uvFromFlat(${i}, ${p}, ${a}); @@ -16507,8 +16508,8 @@ function $Z(r, t8) { } `; } -function EZ(r, t8) { - let e = r.name, o = "get" + e.charAt(0).toUpperCase() + e.slice(1), n = r.shapeInfo.texShape, s = vt(); +function EZ(r15, t8) { + let e = r15.name, o = "get" + e.charAt(0).toUpperCase() + e.slice(1), n = r15.shapeInfo.texShape, s = It(); if (t8) return ` vec4 ${o}(int index) { @@ -16527,22 +16528,22 @@ function EZ(r, t8) { } `; } -function RZ(r, t8) { - let e = r.name, o = "get" + e.charAt(0).toUpperCase() + e.slice(1); - if (r.shapeInfo.isUniform) +function RZ(r15, t8) { + let e = r15.name, o = "get" + e.charAt(0).toUpperCase() + e.slice(1); + if (r15.shapeInfo.isUniform) return ` float ${o}(int index) { - ${Lc(r)} + ${Dc(r15)} } `; - let n = r.shapeInfo.texShape, s = n[0], a = n[1]; + let n = r15.shapeInfo.texShape, s = n[0], a = n[1]; if (a === 1 && s === 1) return ` float ${o}(int index) { return sampleTexture(${e}, halfCR); } `; - let i = vp(e); + let i = gp(e); return a === 1 ? t8 ? ` float ${o}(int index) { vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${e}TexShape[0])); @@ -16575,8 +16576,8 @@ function RZ(r, t8) { } `; } -function DZ(r, t8) { - let e = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape, a = s[0], i = s[1], p = vt(); +function DZ(r15, t8) { + let e = r15.shapeInfo.logicalShape, o = r15.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r15.shapeInfo.texShape, a = s[0], i = s[1], p = It(); if (s != null && y.arraysEqual(e, s)) return t8 ? ` vec4 ${n}(int row, int col) { @@ -16608,8 +16609,8 @@ function DZ(r, t8) { } `; } -function AZ(r, t8) { - let e = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape; +function AZ(r15, t8) { + let e = r15.shapeInfo.logicalShape, o = r15.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r15.shapeInfo.texShape; if (s != null && y.arraysEqual(e, s)) { if (t8) return ` @@ -16628,22 +16629,22 @@ function AZ(r, t8) { } let { newShape: a, keptDims: i } = y.squeezeShape(e), p = a; if (p.length < e.length) { - let m = Bc(r, p), d = ["row", "col"]; + let m = Ac(r15, p), d = ["row", "col"]; return ` - ${Mc(m, t8)} + ${Rc(m, t8)} float ${n}(int row, int col) { - return ${n}(${zc(d, i)}); + return ${n}(${Fc(d, i)}); } `; } - if (r.shapeInfo.isUniform) + if (r15.shapeInfo.isUniform) return ` float ${n}(int row, int col) { int index = round(dot(vec2(row, col), vec2(${e[1]}, 1))); - ${Lc(r)} + ${Dc(r15)} } `; - let u = s[0], c = s[1], l = vp(o); + let u = s[0], c = s[1], l = gp(o); return c === 1 ? t8 ? ` float ${n}(int row, int col) { float index = dot(vec3(row, col, ${l}), vec3(${o}Shape[1], 1, 1)); @@ -16684,18 +16685,18 @@ function AZ(r, t8) { } `; } -function FZ(r, t8) { - let e = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape, a = [Math.ceil(s[0] / 2), Math.ceil(s[1] / 2)]; +function FZ(r15, t8) { + let e = r15.shapeInfo.logicalShape, o = r15.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r15.shapeInfo.texShape, a = [Math.ceil(s[0] / 2), Math.ceil(s[1] / 2)]; if (e[0] === 1) { - let m = e.slice(1), d = [1, 2], f = Bc(r, m), h = ["b", "row", "col"]; + let m = e.slice(1), d = [1, 2], f = Ac(r15, m), h = ["b", "row", "col"]; return ` - ${kR(f, t8)} + ${NR(f, t8)} vec4 ${n}(int b, int row, int col) { - return ${n}(${zc(h, d)}); + return ${n}(${Fc(h, d)}); } `; } - let i = vt(); + let i = It(); if (t8) return ` vec4 ${n}(int b, int row, int col) { @@ -16716,26 +16717,26 @@ function FZ(r, t8) { } `; } -function PZ(r, t8) { - let e = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = e[1] * e[2], a = e[2], { newShape: i, keptDims: p } = y.squeezeShape(e), u = i; +function PZ(r15, t8) { + let e = r15.shapeInfo.logicalShape, o = r15.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = e[1] * e[2], a = e[2], { newShape: i, keptDims: p } = y.squeezeShape(e), u = i; if (u.length < e.length) { - let h = Bc(r, u), g = ["row", "col", "depth"]; + let h = Ac(r15, u), g = ["row", "col", "depth"]; return ` - ${Mc(h, t8)} + ${Rc(h, t8)} float ${n}(int row, int col, int depth) { - return ${n}(${zc(g, p)}); + return ${n}(${Fc(g, p)}); } `; } - if (r.shapeInfo.isUniform) + if (r15.shapeInfo.isUniform) return ` float ${n}(int row, int col, int depth) { int index = round(dot(vec3(row, col, depth), vec3(${s}, ${a}, 1))); - ${Lc(r)} + ${Dc(r15)} } `; - let c = r.shapeInfo.texShape, l = c[0], m = c[1], d = r.shapeInfo.flatOffset; + let c = r15.shapeInfo.texShape, l = c[0], m = c[1], d = r15.shapeInfo.flatOffset; if (m === s && d == null) return t8 ? ` float ${n}(int row, int col, int depth) { @@ -16771,7 +16772,7 @@ function PZ(r, t8) { return sampleTexture(${o}, uv); } `; - let f = vp(o); + let f = gp(o); return t8 ? ` float ${n}(int row, int col, int depth) { // Explicitly use integer operations as dot() only works on floats. @@ -16790,8 +16791,8 @@ function PZ(r, t8) { } `; } -function OZ(r, t8) { - let e = r.name, o = "get" + e.charAt(0).toUpperCase() + e.slice(1), n = vt(); +function OZ(r15, t8) { + let e = r15.name, o = "get" + e.charAt(0).toUpperCase() + e.slice(1), n = It(); if (t8) return ` vec4 ${o}(int b2, int b, int row, int col) { @@ -16806,7 +16807,7 @@ function OZ(r, t8) { vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${n.texture2D}(${e}, uv); } `; - let s = r.shapeInfo.logicalShape, a = s.length, i = r.shapeInfo.texShape, p = [Math.ceil(i[0] / 2), Math.ceil(i[1] / 2)], u = p[0], c = p[1], l = Math.ceil(s[a - 1] / 2), m = l * Math.ceil(s[a - 2] / 2), d = "int b, int row, int col", f = `b * ${m} + (row / 2) * ${l} + (col / 2)`; + let s = r15.shapeInfo.logicalShape, a = s.length, i = r15.shapeInfo.texShape, p = [Math.ceil(i[0] / 2), Math.ceil(i[1] / 2)], u = p[0], c = p[1], l = Math.ceil(s[a - 1] / 2), m = l * Math.ceil(s[a - 2] / 2), d = "int b, int row, int col", f = `b * ${m} + (row / 2) * ${l} + (col / 2)`; for (let h = 2; h < a - 1; h++) d = `int b${h}, ` + d, m *= s[a - h - 1], f = `b${h} * ${m} + ` + f; return ` @@ -16819,26 +16820,26 @@ function OZ(r, t8) { } `; } -function MZ(r, t8) { - let e = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = e[3], a = e[2] * s, i = e[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(e); +function MZ(r15, t8) { + let e = r15.shapeInfo.logicalShape, o = r15.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = e[3], a = e[2] * s, i = e[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(e); if (p.length < e.length) { - let b = Bc(r, p), C = ["row", "col", "depth", "depth2"]; + let b = Ac(r15, p), C = ["row", "col", "depth", "depth2"]; return ` - ${Mc(b, t8)} + ${Rc(b, t8)} float ${n}(int row, int col, int depth, int depth2) { - return ${n}(${zc(C, u)}); + return ${n}(${Fc(C, u)}); } `; } - if (r.shapeInfo.isUniform) + if (r15.shapeInfo.isUniform) return ` float ${n}(int row, int col, int depth, int depth2) { int index = round(dot(vec4(row, col, depth, depth2), vec4(${i}, ${a}, ${s}, 1))); - ${Lc(r)} + ${Dc(r15)} } `; - let c = r.shapeInfo.flatOffset, l = r.shapeInfo.texShape, m = l[0], d = l[1], f = `int stride2 = ${o}Shape[3];`, h = `int stride1 = ${o}Shape[2] * stride2;`, g = `int stride0 = ${o}Shape[1] * stride1;`; + let c = r15.shapeInfo.flatOffset, l = r15.shapeInfo.texShape, m = l[0], d = l[1], f = `int stride2 = ${o}Shape[3];`, h = `int stride1 = ${o}Shape[2] * stride2;`, g = `int stride0 = ${o}Shape[1] * stride1;`; if (d === i && c == null) return t8 ? ` float ${n}(int row, int col, int depth, int depth2) { @@ -16883,7 +16884,7 @@ function MZ(r, t8) { return sampleTexture(${o}, uv); } `; - let x = vp(o); + let x = gp(o); return t8 ? ` float ${n}(int row, int col, int depth, int depth2) { // Explicitly use integer operations as dot() only works on floats. @@ -16905,28 +16906,28 @@ function MZ(r, t8) { } `; } -function LZ(r) { - let t8 = r.shapeInfo.logicalShape, e = r.name, o = "get" + e.charAt(0).toUpperCase() + e.slice(1), n = t8[4], s = t8[3] * n, a = t8[2] * s, i = t8[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(t8); +function LZ(r15) { + let t8 = r15.shapeInfo.logicalShape, e = r15.name, o = "get" + e.charAt(0).toUpperCase() + e.slice(1), n = t8[4], s = t8[3] * n, a = t8[2] * s, i = t8[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(t8); if (p.length < t8.length) { - let h = Bc(r, p), g = ["row", "col", "depth", "depth2", "depth3"]; + let h = Ac(r15, p), g = ["row", "col", "depth", "depth2", "depth3"]; return ` - ${Mc(h)} + ${Rc(h)} float ${o}(int row, int col, int depth, int depth2, int depth3) { - return ${o}(${zc(g, u)}); + return ${o}(${Fc(g, u)}); } `; } - if (r.shapeInfo.isUniform) + if (r15.shapeInfo.isUniform) return ` float ${o}(int row, int col, int depth, int depth2, int depth3) { float index = dot( vec4(row, col, depth, depth2), vec4(${i}, ${a}, ${s}, ${n})) + depth3; - ${Lc(r)} + ${Dc(r15)} } `; - let c = r.shapeInfo.flatOffset, l = r.shapeInfo.texShape, m = l[0], d = l[1]; + let c = r15.shapeInfo.flatOffset, l = r15.shapeInfo.texShape, m = l[0], d = l[1]; if (d === i && c == null) return ` float ${o}(int row, int col, int depth, int depth2, int depth3) { @@ -16951,7 +16952,7 @@ function LZ(r) { return sampleTexture(${e}, uv); } `; - let f = vp(e); + let f = gp(e); return ` float ${o}(int row, int col, int depth, int depth2, int depth3) { // Explicitly use integer operations as dot() only works on floats. @@ -16962,20 +16963,20 @@ function LZ(r) { } `; } -function BZ(r) { - let t8 = r.shapeInfo.logicalShape, e = r.name, o = "get" + e.charAt(0).toUpperCase() + e.slice(1), { newShape: n, keptDims: s } = y.squeezeShape(t8); +function BZ(r15) { + let t8 = r15.shapeInfo.logicalShape, e = r15.name, o = "get" + e.charAt(0).toUpperCase() + e.slice(1), { newShape: n, keptDims: s } = y.squeezeShape(t8); if (n.length < t8.length) { - let g = Bc(r, n), x = ["row", "col", "depth", "depth2", "depth3", "depth4"]; + let g = Ac(r15, n), x = ["row", "col", "depth", "depth2", "depth3", "depth4"]; return ` - ${Mc(g)} + ${Rc(g)} float ${o}(int row, int col, int depth, int depth2, int depth3, int depth4) { - return ${o}(${zc(x, s)}); + return ${o}(${Fc(x, s)}); } `; } let a = t8[5], i = t8[4] * a, p = t8[3] * i, u = t8[2] * p, c = t8[1] * u; - if (r.shapeInfo.isUniform) + if (r15.shapeInfo.isUniform) return ` float ${o}(int row, int col, int depth, int depth2, int depth3, int depth4) { @@ -16985,10 +16986,10 @@ function BZ(r) { dot( vec2(depth3, depth4), vec2(${a}, 1))); - ${Lc(r)} + ${Dc(r15)} } `; - let l = r.shapeInfo.flatOffset, m = r.shapeInfo.texShape, d = m[0], f = m[1]; + let l = r15.shapeInfo.flatOffset, m = r15.shapeInfo.texShape, d = m[0], f = m[1]; if (f === c && l == null) return ` float ${o}(int row, int col, int depth, @@ -17017,7 +17018,7 @@ function BZ(r) { return sampleTexture(${e}, uv); } `; - let h = vp(e); + let h = gp(e); return ` float ${o}(int row, int col, int depth, int depth2, int depth3, int depth4) { @@ -17029,8 +17030,8 @@ function BZ(r) { } `; } -function Lc(r) { - let t8 = r.name, e = y.sizeFromShape(r.shapeInfo.logicalShape); +function Dc(r15) { + let t8 = r15.name, e = y.sizeFromShape(r15.shapeInfo.logicalShape); return e < 2 ? `return ${t8};` : ` for (int i = 0; i < ${e}; i++) { if (i == index) { @@ -17039,13 +17040,13 @@ function Lc(r) { } `; } -function zZ(r, t8) { - let e = r.name, o = e.charAt(0).toUpperCase() + e.slice(1), n = "get" + o + "AtOutCoords", s = r.shapeInfo.logicalShape.length, a = t8.logicalShape.length, i = IR(r.shapeInfo.logicalShape, t8.logicalShape), p = Re(a), u = a - s, c, l = ["x", "y", "z", "w", "u", "v"]; +function zZ(r15, t8) { + let e = r15.name, o = e.charAt(0).toUpperCase() + e.slice(1), n = "get" + o + "AtOutCoords", s = r15.shapeInfo.logicalShape.length, a = t8.logicalShape.length, i = vR(r15.shapeInfo.logicalShape, t8.logicalShape), p = Re(a), u = a - s, c, l = ["x", "y", "z", "w", "u", "v"]; s === 0 ? c = "" : a < 2 && i.length >= 1 ? c = "coords = 0;" : c = i.map((b) => `coords.${l[b + u]} = 0;`).join(` `); let m = ""; - a < 2 && s > 0 ? m = "coords" : m = r.shapeInfo.logicalShape.map((b, C) => `coords.${l[C + u]}`).join(", "); - let d = "return outputValue;", h = y.sizeFromShape(r.shapeInfo.logicalShape) === 1, x = y.sizeFromShape(t8.logicalShape) === 1; + a < 2 && s > 0 ? m = "coords" : m = r15.shapeInfo.logicalShape.map((b, C) => `coords.${l[C + u]}`).join(", "); + let d = "return outputValue;", h = y.sizeFromShape(r15.shapeInfo.logicalShape) === 1, x = y.sizeFromShape(t8.logicalShape) === 1; if (s === 1 && !h && !x) d = ` return vec4(outputValue.xy, outputValue.xy); @@ -17069,19 +17070,19 @@ function zZ(r, t8) { } `; } -function VZ(r, t8) { - let e = r.name, o = e.charAt(0).toUpperCase() + e.slice(1), n = "get" + o + "AtOutCoords", s = t8.texShape, a = r.shapeInfo.texShape, i = r.shapeInfo.logicalShape.length, p = t8.logicalShape.length; - if (!r.shapeInfo.isUniform && i === p && r.shapeInfo.flatOffset == null && y.arraysEqual(a, s)) +function VZ(r15, t8) { + let e = r15.name, o = e.charAt(0).toUpperCase() + e.slice(1), n = "get" + o + "AtOutCoords", s = t8.texShape, a = r15.shapeInfo.texShape, i = r15.shapeInfo.logicalShape.length, p = t8.logicalShape.length; + if (!r15.shapeInfo.isUniform && i === p && r15.shapeInfo.flatOffset == null && y.arraysEqual(a, s)) return ` float ${n}() { return sampleTexture(${e}, resultUV); } `; - let u = Re(p), c = IR(r.shapeInfo.logicalShape, t8.logicalShape), l = p - i, m, d = ["x", "y", "z", "w", "u", "v"]; + let u = Re(p), c = vR(r15.shapeInfo.logicalShape, t8.logicalShape), l = p - i, m, d = ["x", "y", "z", "w", "u", "v"]; i === 0 ? m = "" : p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((h) => `coords.${d[h + l]} = 0;`).join(` `); let f = ""; - return p < 2 && i > 0 ? f = "coords" : f = r.shapeInfo.logicalShape.map((h, g) => `coords.${d[g + l]}`).join(", "), ` + return p < 2 && i > 0 ? f = "coords" : f = r15.shapeInfo.logicalShape.map((h, g) => `coords.${d[g + l]}`).join(", "), ` float ${n}() { ${u} coords = getOutputCoords(); ${m} @@ -17089,56 +17090,56 @@ function VZ(r, t8) { } `; } -function Re(r) { - if (r <= 1) +function Re(r15) { + if (r15 <= 1) return "int"; - if (r === 2) + if (r15 === 2) return "ivec2"; - if (r === 3) + if (r15 === 3) return "ivec3"; - if (r === 4) + if (r15 === 4) return "ivec4"; - if (r === 5) + if (r15 === 5) return "ivec5"; - if (r === 6) + if (r15 === 6) return "ivec6"; - throw Error(`GPU for rank ${r} is not yet supported`); + throw Error(`GPU for rank ${r15} is not yet supported`); } -function Zf(r, t8, e) { - let { newShape: o, keptDims: n } = y.squeezeShape(t8), s = t8.length, a = r && s === 3 && t8[0] === 1, i = a ? t8.slice(1) : o, p = !r && s > 1 && !y.arraysEqual(t8, e) && o.length < s || a; +function Qf(r15, t8, e) { + let { newShape: o, keptDims: n } = y.squeezeShape(t8), s = t8.length, a = r15 && s === 3 && t8[0] === 1, i = a ? t8.slice(1) : o, p = !r15 && s > 1 && !y.arraysEqual(t8, e) && o.length < s || a; return { useSqueezeShape: p, uniformShape: p ? i : t8, keptDims: n }; } -function Bc(r, t8) { - let e = JSON.parse(JSON.stringify(r)); +function Ac(r15, t8) { + let e = JSON.parse(JSON.stringify(r15)); return e.shapeInfo.logicalShape = t8, e; } -function zc(r, t8) { - return t8.map((e) => r[e]).join(", "); +function Fc(r15, t8) { + return t8.map((e) => r15[e]).join(", "); } -function _R(r, t8, e, o) { +function $R(r15, t8, e, o) { let n = e.map((c, l) => { let m = { logicalShape: c.shape, texShape: c.isUniform ? null : c.texData.texShape, isUniform: c.isUniform, isPacked: c.isUniform ? false : c.texData.isPacked, flatOffset: null }; return c.texData != null && c.texData.slice != null && c.texData.slice.flatOffset > 0 && (m.flatOffset = c.texData.slice.flatOffset), { name: t8.variableNames[l], shapeInfo: m }; - }), s = n.map((c) => c.shapeInfo), a = { logicalShape: o.shape, texShape: o.texData.texShape, isUniform: false, isPacked: o.texData.isPacked, flatOffset: null }, i = vR(n, a, t8), p = $I(r.gl, i), u = r.createProgram(p); - return A().get("ENGINE_COMPILE_ONLY") ? { program: t8, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a, variablesLocations: null, customUniformLocations: null, infLoc: null, nanLoc: null, outShapeLocation: null, outShapeStridesLocation: null, outTexShapeLocation: null } : (r.buildVao(u), Object.assign({ program: t8, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a }, qI(r, t8, u))); + }), s = n.map((c) => c.shapeInfo), a = { logicalShape: o.shape, texShape: o.texData.texShape, isUniform: false, isPacked: o.texData.isPacked, flatOffset: null }, i = kR(n, a, t8), p = EI(r15.gl, i), u = r15.createProgram(p); + return A().get("ENGINE_COMPILE_ONLY") ? { program: t8, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a, variablesLocations: null, customUniformLocations: null, infLoc: null, nanLoc: null, outShapeLocation: null, outShapeStridesLocation: null, outTexShapeLocation: null } : (r15.buildVao(u), Object.assign({ program: t8, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a }, jI(r15, t8, u))); } -function qI(r, t8, e) { +function jI(r15, t8, e) { let o = [], n = [], s, a, i, p = null, u = null; - u = r.getUniformLocation(e, "NAN", false), A().getNumber("WEBGL_VERSION") === 1 && (p = r.getUniformLocation(e, "INFINITY", false)); + u = r15.getUniformLocation(e, "NAN", false), A().getNumber("WEBGL_VERSION") === 1 && (p = r15.getUniformLocation(e, "INFINITY", false)); let c = false; for (let l of t8.variableNames) { - let m = { name: l, uniform: r.getUniformLocation(e, l, c), offset: r.getUniformLocation(e, `offset${l}`, c) }; - t8.enableShapeUniforms && (m.shape = r.getUniformLocation(e, `${l}Shape`, c), m.texShape = r.getUniformLocation(e, `${l}TexShape`, c)), o.push(m); + let m = { name: l, uniform: r15.getUniformLocation(e, l, c), offset: r15.getUniformLocation(e, `offset${l}`, c) }; + t8.enableShapeUniforms && (m.shape = r15.getUniformLocation(e, `${l}Shape`, c), m.texShape = r15.getUniformLocation(e, `${l}TexShape`, c)), o.push(m); } - if (t8.enableShapeUniforms && (s = r.getUniformLocation(e, "outShape", c), i = r.getUniformLocation(e, "outShapeStrides", c), a = r.getUniformLocation(e, "outTexShape", c)), t8.customUniforms) + if (t8.enableShapeUniforms && (s = r15.getUniformLocation(e, "outShape", c), i = r15.getUniformLocation(e, "outShapeStrides", c), a = r15.getUniformLocation(e, "outTexShape", c)), t8.customUniforms) for (let l of t8.customUniforms) - n.push(r.getUniformLocation(e, l.name, c)); + n.push(r15.getUniformLocation(e, l.name, c)); return { variablesLocations: o, customUniformLocations: n, infLoc: p, nanLoc: u, outShapeLocation: s, outShapeStridesLocation: i, outTexShapeLocation: a }; } -function TR(r, t8) { - if (r.length !== t8.length) - throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t8.length} inputs`); - r.forEach((e, o) => { +function _R(r15, t8) { + if (r15.length !== t8.length) + throw Error(`Binary was compiled with ${r15.length} inputs, but was executed with ${t8.length} inputs`); + r15.forEach((e, o) => { let n = e.logicalShape, s = t8[o], a = s.shape; if (!y.arraysEqual(n, a)) throw Error(`Binary was compiled with different shapes than the current args. Shapes ${n} and ${a} must match`); @@ -17149,58 +17150,58 @@ function TR(r, t8) { throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${p} must match`); }); } -function $R(r, t8, e, o, n) { - t8.program.enableShapeUniforms || (TR(t8.inShapeInfos, e), TR([t8.outShapeInfo], [o])); +function ER(r15, t8, e, o, n) { + t8.program.enableShapeUniforms || (_R(t8.inShapeInfos, e), _R([t8.outShapeInfo], [o])); let s = o.texData.texture, a = o.texData.texShape; - o.texData.isPacked ? r.setOutputPackedMatrixTexture(s.texture, a[0], a[1]) : r.setOutputMatrixTexture(s.texture, a[0], a[1]), r.setProgram(t8.webGLProgram), r.bindVertexArray(t8.webGLProgram.vao), A().getNumber("WEBGL_VERSION") === 1 && t8.infLoc !== null && r.gl.uniform1f(t8.infLoc, 1 / 0), t8.nanLoc !== null && r.gl.uniform1f(t8.nanLoc, NaN); + o.texData.isPacked ? r15.setOutputPackedMatrixTexture(s.texture, a[0], a[1]) : r15.setOutputMatrixTexture(s.texture, a[0], a[1]), r15.setProgram(t8.webGLProgram), r15.bindVertexArray(t8.webGLProgram.vao), A().getNumber("WEBGL_VERSION") === 1 && t8.infLoc !== null && r15.gl.uniform1f(t8.infLoc, 1 / 0), t8.nanLoc !== null && r15.gl.uniform1f(t8.nanLoc, NaN); for (let p = 0; p < e.length; ++p) { let u = e[p], { uniform: c, offset: l, shape: m, texShape: d } = t8.variablesLocations[p]; if (m) { - let { uniformShape: f } = Zf(t8.program.packedInputs, u.shape, u.texData.texShape); + let { uniformShape: f } = Qf(t8.program.packedInputs, u.shape, u.texData.texShape); switch (f.length) { case 1: - r.gl.uniform1iv(m, new Int32Array(f)); + r15.gl.uniform1iv(m, new Int32Array(f)); break; case 2: - r.gl.uniform2iv(m, new Int32Array(f)); + r15.gl.uniform2iv(m, new Int32Array(f)); break; case 3: - r.gl.uniform3iv(m, new Int32Array(f)); + r15.gl.uniform3iv(m, new Int32Array(f)); break; case 4: - r.gl.uniform4iv(m, new Int32Array(f)); + r15.gl.uniform4iv(m, new Int32Array(f)); break; default: break; } } - if (d && r.gl.uniform2i(d, u.texData.texShape[0], u.texData.texShape[1]), c != null) { + if (d && r15.gl.uniform2i(d, u.texData.texShape[0], u.texData.texShape[1]), c != null) { if (u.isUniform) { if (y.sizeFromShape(u.shape) < 2) - r.gl.uniform1f(c, u.uniformValues[0]); + r15.gl.uniform1f(c, u.uniformValues[0]); else { let f = u.uniformValues; - f instanceof Float32Array || (f = new Float32Array(f)), r.gl.uniform1fv(c, f); + f instanceof Float32Array || (f = new Float32Array(f)), r15.gl.uniform1fv(c, f); } continue; } - u.texData.slice != null && l != null && r.gl.uniform1i(l, u.texData.slice.flatOffset), r.setInputMatrixTexture(u.texData.texture.texture, c, p); + u.texData.slice != null && l != null && r15.gl.uniform1i(l, u.texData.slice.flatOffset), r15.setInputMatrixTexture(u.texData.texture.texture, c, p); } } let i = t8.outShapeLocation; if (i) switch (o.shape.length) { case 1: - r.gl.uniform1iv(i, new Int32Array(o.shape)); + r15.gl.uniform1iv(i, new Int32Array(o.shape)); break; case 2: - r.gl.uniform2iv(i, new Int32Array(o.shape)); + r15.gl.uniform2iv(i, new Int32Array(o.shape)); break; case 3: - r.gl.uniform3iv(i, new Int32Array(o.shape)); + r15.gl.uniform3iv(i, new Int32Array(o.shape)); break; case 4: - r.gl.uniform4iv(i, new Int32Array(o.shape)); + r15.gl.uniform4iv(i, new Int32Array(o.shape)); break; default: break; @@ -17209,77 +17210,77 @@ function $R(r, t8, e, o, n) { let p = y.computeStrides(o.shape); switch (o.shape.length) { case 2: - r.gl.uniform1iv(t8.outShapeStridesLocation, new Int32Array(p)); + r15.gl.uniform1iv(t8.outShapeStridesLocation, new Int32Array(p)); break; case 3: - r.gl.uniform2iv(t8.outShapeStridesLocation, new Int32Array(p)); + r15.gl.uniform2iv(t8.outShapeStridesLocation, new Int32Array(p)); break; case 4: - r.gl.uniform3iv(t8.outShapeStridesLocation, new Int32Array(p)); + r15.gl.uniform3iv(t8.outShapeStridesLocation, new Int32Array(p)); break; default: break; } } - if (t8.outTexShapeLocation && r.gl.uniform2i(t8.outTexShapeLocation, o.texData.texShape[0], o.texData.texShape[1]), t8.program.customUniforms && n) + if (t8.outTexShapeLocation && r15.gl.uniform2i(t8.outTexShapeLocation, o.texData.texShape[0], o.texData.texShape[1]), t8.program.customUniforms && n) for (let p = 0; p < t8.program.customUniforms.length; ++p) { let u = t8.program.customUniforms[p], c = t8.customUniformLocations[p], l = n[p]; if (u.type === "float") - r.gl.uniform1fv(c, l); + r15.gl.uniform1fv(c, l); else if (u.type === "vec2") - r.gl.uniform2fv(c, l); + r15.gl.uniform2fv(c, l); else if (u.type === "vec3") - r.gl.uniform3fv(c, l); + r15.gl.uniform3fv(c, l); else if (u.type === "vec4") - r.gl.uniform4fv(c, l); + r15.gl.uniform4fv(c, l); else if (u.type === "int") - r.gl.uniform1iv(c, l); + r15.gl.uniform1iv(c, l); else if (u.type === "ivec2") - r.gl.uniform2iv(c, l); + r15.gl.uniform2iv(c, l); else if (u.type === "ivec3") - r.gl.uniform3iv(c, l); + r15.gl.uniform3iv(c, l); else if (u.type === "ivec4") - r.gl.uniform4iv(c, l); + r15.gl.uniform4iv(c, l); else throw Error(`uniform type ${u.type} is not supported yet.`); } - r.executeProgram(); + r15.executeProgram(); } -function ER(r, t8, e) { +function RR(r15, t8, e) { let o = ""; t8.concat(e).forEach((a) => { let i = a.texData != null && a.texData.slice != null && a.texData.slice.flatOffset > 0; - if (r.enableShapeUniforms && !a.isUniform) { - let p = a.texData.texShape, { useSqueezeShape: u, uniformShape: c, keptDims: l } = Zf(r.packedInputs, a.shape, p), m = "", d = "", f = ""; - if (c.length === 1 && r.packedInputs) { + if (r15.enableShapeUniforms && !a.isUniform) { + let p = a.texData.texShape, { useSqueezeShape: u, uniformShape: c, keptDims: l } = Qf(r15.packedInputs, a.shape, p), m = "", d = "", f = ""; + if (c.length === 1 && r15.packedInputs) { let k = [Math.ceil(p[0] / 2), Math.ceil(p[1] / 2)]; m = `${k[0] > 1}_${k[1] > 1}`; - } else if (c.length === 2 && !r.packedInputs) + } else if (c.length === 2 && !r15.packedInputs) d = `${c[0] > 1}_${c[1] > 1}`; - else if (c.length > 2 && !r.packedInputs) { + else if (c.length > 2 && !r15.packedInputs) { let k = y.computeStrides(c); f = `${k[0] === p[1]}_${k[k.length - 1] === p[1]}`; } - let h = a.shape.length, g = c.length === 2 && y.arraysEqual(a.shape, p), x = y.sizeFromShape(a.shape) === 1, b = w.getBroadcastDims(a.shape, e.shape), C = !r.packedInputs && h === e.shape.length && y.arraysEqual(p, e.texData.texShape), S = r.packedInputs || c.length > 2 ? "" : `${p[0] > 1}_${p[1] > 1}`; + let h = a.shape.length, g = c.length === 2 && y.arraysEqual(a.shape, p), x = y.sizeFromShape(a.shape) === 1, b = w.getBroadcastDims(a.shape, e.shape), C = !r15.packedInputs && h === e.shape.length && y.arraysEqual(p, e.texData.texShape), S = r15.packedInputs || c.length > 2 ? "" : `${p[0] > 1}_${p[1] > 1}`; o += `${h}_${C}_${u ? l : ""}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${S}_${i}`; } else { let p = a.isUniform ? "uniform" : a.texData.texShape; o += `${a.shape}_${p}_${i}`; } }); - let n = r.userCode, s = r.constructor.name; + let n = r15.userCode, s = r15.constructor.name; return s += "_" + o + "_" + n + `${A().getNumber("WEBGL_VERSION")}`, s; } -function pt(r) { - return A().getBool("WEBGL_USE_SHAPES_UNIFORMS") && r <= 4; +function ut(r15) { + return A().getBool("WEBGL_USE_SHAPES_UNIFORMS") && r15 <= 4; } -var Jf = class { +var Zf = class { constructor(t8) { - this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outPackingScheme = bu.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; - let e = vt(); - this.outputShape = t8, this.enableShapeUniforms = pt(this.outputShape.length), this.userCode = ` + this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outPackingScheme = fu.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; + let e = It(); + this.outputShape = t8, this.enableShapeUniforms = ut(this.outputShape.length), this.userCode = ` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms ? Ip(["r", "c", "d"], t8) : Ws(["r", "c", "d"], t8)} + ${this.enableShapeUniforms ? hp(["r", "c", "d"], t8) : Ws(["r", "c", "d"], t8)} return ivec3(r, c, d); } @@ -17300,13 +17301,13 @@ var Jf = class { `; } }; -var eh = class { +var Jf = class { constructor(t8) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outPackingScheme = bu.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; - let e = vt(); - this.outputShape = t8, this.enableShapeUniforms = pt(this.outputShape.length), this.userCode = ` + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outPackingScheme = fu.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; + let e = It(); + this.outputShape = t8, this.enableShapeUniforms = ut(this.outputShape.length), this.userCode = ` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms ? Ip(["r", "c", "d"], t8) : Ws(["r", "c", "d"], t8)} + ${this.enableShapeUniforms ? hp(["r", "c", "d"], t8) : Ws(["r", "c", "d"], t8)} return ivec3(r, c, d); } @@ -17327,12 +17328,12 @@ var eh = class { `; } }; -var th = class { +var eh = class { constructor(t8) { - this.variableNames = ["A"], this.outTexUsage = dr.DOWNLOAD; - let e = vt(); + this.variableNames = ["A"], this.outTexUsage = mr.DOWNLOAD; + let e = It(); this.outputShape = t8, this.userCode = ` - ${Qf} + ${Yf} void main() { float x = getAAtOutCoords(); @@ -17341,12 +17342,12 @@ var th = class { `; } }; -var rh = class { +var th = class { constructor(t8) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outTexUsage = dr.DOWNLOAD; - let e = vt(); + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outTexUsage = mr.DOWNLOAD; + let e = It(); this.outputShape = t8, this.userCode = ` - ${Qf} + ${Yf} void main() { ivec3 coords = getOutputCoords(); @@ -17357,11 +17358,11 @@ var rh = class { } }; var GZ = { R: 0, G: 1, B: 2, A: 3 }; -var Zl = class { +var Yl = class { constructor(t8, e = false, o = "RGBA") { this.variableNames = ["A"], this.customUniforms = [{ name: "texShape", type: "ivec2" }]; - let n = vt(); - this.outputShape = t8, this.enableShapeUniforms = pt(this.outputShape.length); + let n = It(); + this.outputShape = t8, this.enableShapeUniforms = ut(this.outputShape.length); let s = "result"; e && (s = "floor(result * 255. + 0.5)"); let a = ""; @@ -17373,7 +17374,7 @@ var Zl = class { }`; } this.userCode = ` - ${this.enableShapeUniforms ? Pc() : Fc(t8)} + ${this.enableShapeUniforms ? $c() : _c(t8)} void main() { ivec3 coords = getOutputCoords(); @@ -17395,11 +17396,11 @@ var Zl = class { `; } }; -var oh = class { +var rh = class { constructor(t8, e = false) { this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.customUniforms = [{ name: "texShape", type: "ivec2" }]; - let o = vt(); - this.outputShape = t8, this.enableShapeUniforms = pt(this.outputShape.length); + let o = It(); + this.outputShape = t8, this.enableShapeUniforms = ut(this.outputShape.length); let n = "", s = "result"; e && (s = "floor(result * 255. + 0.5)"); for (let a = 0; a <= 1; a++) @@ -17436,7 +17437,7 @@ var oh = class { `; } this.userCode = ` - ${this.enableShapeUniforms ? Pc() : Fc(t8)} + ${this.enableShapeUniforms ? $c() : _c(t8)} void main() { ivec3 coords = getOutputCoords(); @@ -17454,10 +17455,10 @@ var oh = class { `; } }; -var cv = {}; -qe(cv, { bindVertexProgramAttributeStreams: () => rv, createBufferFromOutputTexture: () => sv, createFloat16MatrixTexture: () => ZI, createFloat16PackedMatrixTexture: () => tv, createFloat32MatrixTexture: () => QI, createIndexBuffer: () => YI, createPackedMatrixTexture: () => ev, createUnsignedBytesMatrixTexture: () => JI, createVertexBuffer: () => XI, createVertexShader: () => jI, downloadByteEncodedFloatMatrixFromOutputTexture: () => iv, downloadFloat32MatrixFromBuffer: () => av, downloadMatrixFromPackedOutputTexture: () => pv, downloadPackedMatrixFromBuffer: () => uv, getInternalFormatForFloat16MatrixTexture: () => sh, getInternalFormatForFloat16PackedMatrixTexture: () => uh, getInternalFormatForFloat32MatrixTexture: () => nh, getInternalFormatForPackedMatrixTexture: () => ih, getInternalFormatForUnsignedBytesMatrixTexture: () => ah, uploadDenseMatrixToTexture: () => ov, uploadPixelDataToTexture: () => nv }); -function jI(r) { - let t8 = vt(), e = `${t8.version} +var lv = {}; +qe(lv, { bindVertexProgramAttributeStreams: () => ov, createBufferFromOutputTexture: () => av, createFloat16MatrixTexture: () => JI, createFloat16PackedMatrixTexture: () => rv, createFloat32MatrixTexture: () => ZI, createIndexBuffer: () => QI, createPackedMatrixTexture: () => tv, createUnsignedBytesMatrixTexture: () => ev, createVertexBuffer: () => YI, createVertexShader: () => XI, downloadByteEncodedFloatMatrixFromOutputTexture: () => uv, downloadFloat32MatrixFromBuffer: () => iv, downloadMatrixFromPackedOutputTexture: () => cv, downloadPackedMatrixFromBuffer: () => pv, getInternalFormatForFloat16MatrixTexture: () => nh, getInternalFormatForFloat16PackedMatrixTexture: () => ih, getInternalFormatForFloat32MatrixTexture: () => oh, getInternalFormatForPackedMatrixTexture: () => ah, getInternalFormatForUnsignedBytesMatrixTexture: () => sh, uploadDenseMatrixToTexture: () => nv, uploadPixelDataToTexture: () => sv }); +function XI(r15) { + let t8 = It(), e = `${t8.version} precision highp float; ${t8.attribute} vec3 clipSpacePos; ${t8.attribute} vec2 uv; @@ -17467,94 +17468,94 @@ function jI(r) { gl_Position = vec4(clipSpacePos, 1); resultUV = uv; }`; - return _I(r, e); + return $I(r15, e); } -function XI(r) { +function YI(r15) { let t8 = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]); - return DI(r, t8); + return AI(r15, t8); } -function YI(r) { +function QI(r15) { let t8 = new Uint16Array([0, 1, 2, 2, 1, 3]); - return AI(r, t8); + return FI(r15, t8); } -function Jl(r, t8, e, o, n, s) { - PI(t8, e); - let a = FI(r), i = r.TEXTURE_2D; - return ce(r, () => r.bindTexture(i, a)), ce(r, () => r.texParameteri(i, r.TEXTURE_WRAP_S, r.CLAMP_TO_EDGE)), ce(r, () => r.texParameteri(i, r.TEXTURE_WRAP_T, r.CLAMP_TO_EDGE)), ce(r, () => r.texParameteri(i, r.TEXTURE_MIN_FILTER, r.NEAREST)), ce(r, () => r.texParameteri(i, r.TEXTURE_MAG_FILTER, r.NEAREST)), A().getNumber("WEBGL_VERSION") === 1 ? ce(r, () => r.texImage2D(i, 0, o, t8, e, 0, n, s, null)) : ce(r, () => r.texStorage2D(i, 1, o, t8, e)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)), { texture: a, texShape: [e, t8] }; +function Ql(r15, t8, e, o, n, s) { + OI(t8, e); + let a = PI(r15), i = r15.TEXTURE_2D; + return ce(r15, () => r15.bindTexture(i, a)), ce(r15, () => r15.texParameteri(i, r15.TEXTURE_WRAP_S, r15.CLAMP_TO_EDGE)), ce(r15, () => r15.texParameteri(i, r15.TEXTURE_WRAP_T, r15.CLAMP_TO_EDGE)), ce(r15, () => r15.texParameteri(i, r15.TEXTURE_MIN_FILTER, r15.NEAREST)), ce(r15, () => r15.texParameteri(i, r15.TEXTURE_MAG_FILTER, r15.NEAREST)), A().getNumber("WEBGL_VERSION") === 1 ? ce(r15, () => r15.texImage2D(i, 0, o, t8, e, 0, n, s, null)) : ce(r15, () => r15.texStorage2D(i, 1, o, t8, e)), ce(r15, () => r15.bindTexture(r15.TEXTURE_2D, null)), { texture: a, texShape: [e, t8] }; } -function nh(r) { - return r.internalFormatFloat; +function oh(r15) { + return r15.internalFormatFloat; } -function QI(r, t8, e, o) { - let [n, s] = Sp(t8, e); - return Jl(r, n, s, nh(o), o.textureFormatFloat, r.FLOAT); +function ZI(r15, t8, e, o) { + let [n, s] = fp(t8, e); + return Ql(r15, n, s, oh(o), o.textureFormatFloat, r15.FLOAT); } -function sh(r) { - return r.internalFormatHalfFloat; +function nh(r15) { + return r15.internalFormatHalfFloat; } -function ZI(r, t8, e, o) { - let [n, s] = Sp(t8, e); - return Jl(r, n, s, sh(o), o.textureFormatFloat, o.textureTypeHalfFloat); +function JI(r15, t8, e, o) { + let [n, s] = fp(t8, e); + return Ql(r15, n, s, nh(o), o.textureFormatFloat, o.textureTypeHalfFloat); } -function ah(r) { - return r.downloadTextureFormat; +function sh(r15) { + return r15.downloadTextureFormat; } -function JI(r, t8, e, o) { - let [n, s] = Sp(t8, e); - return Jl(r, n, s, ah(o), r.RGBA, r.UNSIGNED_BYTE); +function ev(r15, t8, e, o) { + let [n, s] = fp(t8, e); + return Ql(r15, n, s, sh(o), r15.RGBA, r15.UNSIGNED_BYTE); } -function ih(r) { - return r.internalFormatPackedFloat; +function ah(r15) { + return r15.internalFormatPackedFloat; } -function ev(r, t8, e, o) { - let [n, s] = La(t8, e); - return Jl(r, n, s, ih(o), r.RGBA, r.FLOAT); +function tv(r15, t8, e, o) { + let [n, s] = Ma(t8, e); + return Ql(r15, n, s, ah(o), r15.RGBA, r15.FLOAT); } -function uh(r) { - return r.internalFormatPackedHalfFloat; +function ih(r15) { + return r15.internalFormatPackedHalfFloat; } -function tv(r, t8, e, o) { - let [n, s] = La(t8, e); - return Jl(r, n, s, uh(o), r.RGBA, o.textureTypeHalfFloat); +function rv(r15, t8, e, o) { + let [n, s] = Ma(t8, e); + return Ql(r15, n, s, ih(o), r15.RGBA, o.textureTypeHalfFloat); } -function rv(r, t8, e) { - return ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, e)), jf(r, t8, "clipSpacePos", e, 3, 20, 0) && jf(r, t8, "uv", e, 2, 20, 12); +function ov(r15, t8, e) { + return ce(r15, () => r15.bindBuffer(r15.ARRAY_BUFFER, e)), qf(r15, t8, "clipSpacePos", e, 3, 20, 0) && qf(r15, t8, "uv", e, 2, 20, 12); } -function ov(r, t8, e, o, n, s) { - ce(r, () => r.bindTexture(r.TEXTURE_2D, t8)); +function nv(r15, t8, e, o, n, s) { + ce(r15, () => r15.bindTexture(r15.TEXTURE_2D, t8)); let a, i, p; - n instanceof Uint8Array ? (a = new Uint8Array(e * o * 4), i = r.UNSIGNED_BYTE, p = r.RGBA) : (a = new Float32Array(e * o * 4), i = r.FLOAT, p = s.internalFormatPackedFloat), a.set(n), A().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, e, o, r.RGBA, i, a)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, p, e, o, 0, r.RGBA, i, a)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)); + n instanceof Uint8Array ? (a = new Uint8Array(e * o * 4), i = r15.UNSIGNED_BYTE, p = r15.RGBA) : (a = new Float32Array(e * o * 4), i = r15.FLOAT, p = s.internalFormatPackedFloat), a.set(n), A().getNumber("WEBGL_VERSION") === 2 ? ce(r15, () => r15.texSubImage2D(r15.TEXTURE_2D, 0, 0, 0, e, o, r15.RGBA, i, a)) : ce(r15, () => r15.texImage2D(r15.TEXTURE_2D, 0, p, e, o, 0, r15.RGBA, i, a)), ce(r15, () => r15.bindTexture(r15.TEXTURE_2D, null)); } -function nv(r, t8, e) { - ce(r, () => r.bindTexture(r.TEXTURE_2D, t8)), e.data instanceof Uint8Array ? A().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, e.width, e.height, r.RGBA, r.UNSIGNED_BYTE, e.data)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, e.width, e.height, 0, r.RGBA, r.UNSIGNED_BYTE, e.data)) : A().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, r.RGBA, r.UNSIGNED_BYTE, e)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, r.RGBA, r.UNSIGNED_BYTE, e)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)); +function sv(r15, t8, e) { + ce(r15, () => r15.bindTexture(r15.TEXTURE_2D, t8)), e.data instanceof Uint8Array ? A().getNumber("WEBGL_VERSION") === 2 ? ce(r15, () => r15.texSubImage2D(r15.TEXTURE_2D, 0, 0, 0, e.width, e.height, r15.RGBA, r15.UNSIGNED_BYTE, e.data)) : ce(r15, () => r15.texImage2D(r15.TEXTURE_2D, 0, r15.RGBA, e.width, e.height, 0, r15.RGBA, r15.UNSIGNED_BYTE, e.data)) : A().getNumber("WEBGL_VERSION") === 2 ? ce(r15, () => r15.texSubImage2D(r15.TEXTURE_2D, 0, 0, 0, r15.RGBA, r15.UNSIGNED_BYTE, e)) : ce(r15, () => r15.texImage2D(r15.TEXTURE_2D, 0, r15.RGBA, r15.RGBA, r15.UNSIGNED_BYTE, e)), ce(r15, () => r15.bindTexture(r15.TEXTURE_2D, null)); } -function sv(r, t8, e, o) { - let n = r.createBuffer(); - ce(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, n)); +function av(r15, t8, e, o) { + let n = r15.createBuffer(); + ce(r15, () => r15.bindBuffer(r15.PIXEL_PACK_BUFFER, n)); let i = 4 * 4 * t8 * e; - return ce(r, () => r.bufferData(r.PIXEL_PACK_BUFFER, i, r.STREAM_READ)), ce(r, () => r.readPixels(0, 0, e, t8, r.RGBA, r.FLOAT, 0)), ce(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, null)), n; + return ce(r15, () => r15.bufferData(r15.PIXEL_PACK_BUFFER, i, r15.STREAM_READ)), ce(r15, () => r15.readPixels(0, 0, e, t8, r15.RGBA, r15.FLOAT, 0)), ce(r15, () => r15.bindBuffer(r15.PIXEL_PACK_BUFFER, null)), n; } -function av(r, t8, e) { - let o = r, n = new Float32Array(e); +function iv(r15, t8, e) { + let o = r15, n = new Float32Array(e); return o.bindBuffer(o.PIXEL_PACK_BUFFER, t8), o.getBufferSubData(o.PIXEL_PACK_BUFFER, 0, n), o.bindBuffer(o.PIXEL_PACK_BUFFER, null), n; } -function iv(r, t8, e, o) { - let [n, s] = Sp(t8, e), a = 4, i = new Uint8Array(gR(t8 * e, a)); - return ce(r, () => r.readPixels(0, 0, n, s, o.downloadTextureFormat, r.UNSIGNED_BYTE, i)), new Float32Array(i.buffer); +function uv(r15, t8, e, o) { + let [n, s] = fp(t8, e), a = 4, i = new Uint8Array(xR(t8 * e, a)); + return ce(r15, () => r15.readPixels(0, 0, n, s, o.downloadTextureFormat, r15.UNSIGNED_BYTE, i)), new Float32Array(i.buffer); } -function uv(r, t8, e, o, n, s, a, i) { - let p = r, u = new Float32Array(xR(s, a)); +function pv(r15, t8, e, o, n, s, a, i) { + let p = r15, u = new Float32Array(yR(s, a)); return p.bindBuffer(p.PIXEL_PACK_BUFFER, t8), p.getBufferSubData(p.PIXEL_PACK_BUFFER, 0, u), p.bindBuffer(p.PIXEL_PACK_BUFFER, null), u; } -function pv(r, t8, e) { +function cv(r15, t8, e) { let o = new Float32Array(t8 * e * 4); - return ce(r, () => r.readPixels(0, 0, e, t8, r.RGBA, r.FLOAT, o)), o; + return ce(r15, () => r15.readPixels(0, 0, e, t8, r15.RGBA, r15.FLOAT, o)), o; } -var kp = class { +var xp = class { constructor(t8) { this.outputTexture = null, this.program = null, this.disposed = false, this.itemsToPoll = []; let e = A().getNumber("WEBGL_VERSION"); - if (t8 != null ? (this.gl = t8, vI(e, t8)) : this.gl = Kr(e), t8 = this.gl, A().getNumber("WEBGL_VERSION") === 2) { + if (t8 != null ? (this.gl = t8, kI(e, t8)) : this.gl = Hr(e), t8 = this.gl, A().getNumber("WEBGL_VERSION") === 2) { let s = t8; this.createVertexArray = () => ce(s, () => s.createVertexArray()), this.bindVertexArray = (a) => ce(s, () => s.bindVertexArray(a)), this.deleteVertexArray = (a) => ce(s, () => s.deleteVertexArray(a)), this.getVertexArray = () => ce(s, () => s.getParameter(s.VERTEX_ARRAY_BINDING)); } else if (t8 != null) { @@ -17566,21 +17567,21 @@ var kp = class { let o = "WEBGL_color_buffer_float", n = "EXT_color_buffer_half_float"; if (this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile"), A().getNumber("WEBGL_VERSION") === 1) { let s = "OES_texture_float", a = "OES_texture_half_float"; - if (this.textureFloatExtension = Ec(this.gl, s), qr(this.gl, a)) - this.textureHalfFloatExtension = Ec(this.gl, a); + if (this.textureFloatExtension = vc(this.gl, s), Kr(this.gl, a)) + this.textureHalfFloatExtension = vc(this.gl, a); else if (A().get("WEBGL_FORCE_F16_TEXTURES")) throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true."); - if (this.colorBufferFloatExtension = this.gl.getExtension(o), qr(this.gl, n)) - this.colorBufferHalfFloatExtension = Ec(this.gl, n); + if (this.colorBufferFloatExtension = this.gl.getExtension(o), Kr(this.gl, n)) + this.colorBufferHalfFloatExtension = vc(this.gl, n); else if (A().get("WEBGL_FORCE_F16_TEXTURES")) throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true."); - } else if (o = "EXT_color_buffer_float", qr(this.gl, o)) + } else if (o = "EXT_color_buffer_float", Kr(this.gl, o)) this.colorBufferFloatExtension = this.gl.getExtension(o); - else if (qr(this.gl, n)) + else if (Kr(this.gl, n)) this.colorBufferHalfFloatExtension = this.gl.getExtension(n); else throw new Error("GL context does not support color renderable floats"); - this.vertexBuffer = XI(this.gl), this.indexBuffer = YI(this.gl), this.framebuffer = OI(this.gl), this.textureConfig = Xl(this.gl, this.textureHalfFloatExtension); + this.vertexBuffer = YI(this.gl), this.indexBuffer = QI(this.gl), this.framebuffer = MI(this.gl), this.textureConfig = ql(this.gl, this.textureHalfFloatExtension); } get debug() { return A().getBool("DEBUG"); @@ -17593,41 +17594,41 @@ var kp = class { ce(t8, () => t8.finish()), ce(t8, () => t8.bindFramebuffer(t8.FRAMEBUFFER, null)), ce(t8, () => t8.deleteFramebuffer(this.framebuffer)), ce(t8, () => t8.bindBuffer(t8.ARRAY_BUFFER, null)), ce(t8, () => t8.bindBuffer(t8.ELEMENT_ARRAY_BUFFER, null)), ce(t8, () => t8.deleteBuffer(this.indexBuffer)), this.disposed = true; } createFloat32MatrixTexture(t8, e) { - return this.throwIfDisposed(), QI(this.gl, t8, e, this.textureConfig); - } - createFloat16MatrixTexture(t8, e) { return this.throwIfDisposed(), ZI(this.gl, t8, e, this.textureConfig); } - createUnsignedBytesMatrixTexture(t8, e) { + createFloat16MatrixTexture(t8, e) { return this.throwIfDisposed(), JI(this.gl, t8, e, this.textureConfig); } - uploadPixelDataToTexture(t8, e) { - this.throwIfDisposed(), nv(this.gl, t8, e); - } - uploadDenseMatrixToTexture(t8, e, o, n) { - this.throwIfDisposed(), ov(this.gl, t8, e, o, n, this.textureConfig); - } - createFloat16PackedMatrixTexture(t8, e) { - return this.throwIfDisposed(), tv(this.gl, t8, e, this.textureConfig); - } - createPackedMatrixTexture(t8, e) { + createUnsignedBytesMatrixTexture(t8, e) { return this.throwIfDisposed(), ev(this.gl, t8, e, this.textureConfig); } + uploadPixelDataToTexture(t8, e) { + this.throwIfDisposed(), sv(this.gl, t8, e); + } + uploadDenseMatrixToTexture(t8, e, o, n) { + this.throwIfDisposed(), nv(this.gl, t8, e, o, n, this.textureConfig); + } + createFloat16PackedMatrixTexture(t8, e) { + return this.throwIfDisposed(), rv(this.gl, t8, e, this.textureConfig); + } + createPackedMatrixTexture(t8, e) { + return this.throwIfDisposed(), tv(this.gl, t8, e, this.textureConfig); + } deleteMatrixTexture(t8) { - this.throwIfDisposed(), this.outputTexture === t8 && (Xf(this.gl, this.framebuffer), this.outputTexture = null), ce(this.gl, () => this.gl.deleteTexture(t8)); + this.throwIfDisposed(), this.outputTexture === t8 && (jf(this.gl, this.framebuffer), this.outputTexture = null), ce(this.gl, () => this.gl.deleteTexture(t8)); } downloadByteEncodedFloatMatrixFromOutputTexture(t8, e, o) { - return this.downloadMatrixDriver(t8, () => iv(this.gl, e, o, this.textureConfig)); + return this.downloadMatrixDriver(t8, () => uv(this.gl, e, o, this.textureConfig)); } downloadPackedMatrixFromBuffer(t8, e, o, n, s, a) { - return uv(this.gl, t8, e, o, n, s, a, this.textureConfig); + return pv(this.gl, t8, e, o, n, s, a, this.textureConfig); } downloadFloat32MatrixFromBuffer(t8, e) { - return av(this.gl, t8, e); + return iv(this.gl, t8, e); } createBufferFromTexture(t8, e, o) { this.bindTextureToFrameBuffer(t8); - let n = sv(this.gl, e, o, this.textureConfig); + let n = av(this.gl, e, o, this.textureConfig); return this.unbindTextureToFrameBuffer(), n; } createAndWaitForFence() { @@ -17647,30 +17648,30 @@ var kp = class { return { query: e, isFencePassed: o }; } downloadMatrixFromPackedTexture(t8, e, o) { - return this.downloadMatrixDriver(t8, () => pv(this.gl, e, o)); + return this.downloadMatrixDriver(t8, () => cv(this.gl, e, o)); } createProgram(t8) { this.throwIfDisposed(); let e = this.gl; - this.vertexShader == null && (this.vertexShader = jI(e)); - let o = EI(e); - ce(e, () => e.attachShader(o, this.vertexShader)), ce(e, () => e.attachShader(o, t8)), RI(e, o); + this.vertexShader == null && (this.vertexShader = XI(e)); + let o = RI(e); + ce(e, () => e.attachShader(o, this.vertexShader)), ce(e, () => e.attachShader(o, t8)), DI(e, o); let n = Object.assign(o, { vao: this.createVertexArray() }); - return this.debug && Yl(e, n), n; + return this.debug && jl(e, n), n; } buildVao(t8) { this.setProgram(t8), this.bindVertexArray(t8.vao); let e = this.gl; - ce(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, this.indexBuffer)), rv(e, t8, this.vertexBuffer); + ce(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, this.indexBuffer)), ov(e, t8, this.vertexBuffer); } deleteProgram(t8) { this.throwIfDisposed(), t8 === this.program && (this.program = null), t8 != null && (ce(this.gl, () => this.gl.deleteProgram(t8)), this.deleteVertexArray(t8.vao)); } setProgram(t8) { - this.throwIfDisposed(), this.program = t8, this.program != null && this.debug && Yl(this.gl, this.program), ce(this.gl, () => this.gl.useProgram(t8)); + this.throwIfDisposed(), this.program = t8, this.program != null && this.debug && jl(this.gl, this.program), ce(this.gl, () => this.gl.useProgram(t8)); } getUniformLocation(t8, e, o = true) { - return this.throwIfDisposed(), o ? MI(this.gl, t8, e) : LI(this.gl, t8, e); + return this.throwIfDisposed(), o ? LI(this.gl, t8, e) : BI(this.gl, t8, e); } getAttributeLocation(t8, e) { return this.throwIfDisposed(), ce(this.gl, () => this.gl.getAttribLocation(t8, e)); @@ -17679,14 +17680,14 @@ var kp = class { return this.throwIfDisposed(), this.gl.getUniformLocation(t8, e); } setInputMatrixTexture(t8, e, o) { - this.throwIfDisposed(), this.throwIfNoProgram(), BI(this.gl, t8, e, o); + this.throwIfDisposed(), this.throwIfNoProgram(), zI(this.gl, t8, e, o); } setOutputMatrixTexture(t8, e, o) { this.setOutputMatrixTextureDriver(t8, o, e); } setOutputPackedMatrixTexture(t8, e, o) { this.throwIfDisposed(); - let [n, s] = La(e, o); + let [n, s] = Ma(e, o); this.setOutputMatrixTextureDriver(t8, n, s); } setOutputMatrixWriteRegion(t8, e, o, n) { @@ -17696,7 +17697,7 @@ var kp = class { throw new Error("setOutputPackedMatrixWriteRegion not implemented."); } debugValidate() { - this.program != null && Yl(this.gl, this.program), Rc(this.gl); + this.program != null && jl(this.gl, this.program), kc(this.gl); } executeProgram() { this.throwIfDisposed(), this.throwIfNoProgram(); @@ -17711,7 +17712,7 @@ var kp = class { this.throwIfDisposed(), ce(this.gl, () => this.gl.finish()); } getQueryTimerExtension() { - return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = Ec(this.gl, A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query")), this.disjointQueryTimerExtension; + return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = vc(this.gl, A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query")), this.disjointQueryTimerExtension; } getQueryTimerExtensionWebGL2() { return this.getQueryTimerExtension(); @@ -17781,10 +17782,10 @@ var kp = class { "setTimeoutCustom" in A().platform && (o = A().platform.setTimeoutCustom.bind(A().platform)), y.repeatedTry(() => (this.pollItems(), this.itemsToPoll.length === 0), () => 0, null, o); } bindTextureToFrameBuffer(t8) { - this.throwIfDisposed(), Ql(this.gl, t8, this.framebuffer), this.debug && Rc(this.gl); + this.throwIfDisposed(), Xl(this.gl, t8, this.framebuffer), this.debug && kc(this.gl); } unbindTextureToFrameBuffer() { - this.outputTexture != null ? (Ql(this.gl, this.outputTexture, this.framebuffer), this.debug && Rc(this.gl)) : Xf(this.gl, this.framebuffer); + this.outputTexture != null ? (Xl(this.gl, this.outputTexture, this.framebuffer), this.debug && kc(this.gl)) : jf(this.gl, this.framebuffer); } downloadMatrixDriver(t8, e) { this.bindTextureToFrameBuffer(t8); @@ -17794,7 +17795,7 @@ var kp = class { setOutputMatrixTextureDriver(t8, e, o) { this.throwIfDisposed(); let n = this.gl; - Ql(n, t8, this.framebuffer), this.debug && Rc(n), this.outputTexture = t8, ce(n, () => n.viewport(0, 0, e, o)), ce(n, () => n.scissor(0, 0, e, o)); + Xl(n, t8, this.framebuffer), this.debug && kc(n), this.outputTexture = t8, ce(n, () => n.viewport(0, 0, e, o)), ce(n, () => n.scissor(0, 0, e, o)); } setOutputMatrixWriteRegionDriver(t8, e, o, n) { this.throwIfDisposed(), ce(this.gl, () => this.gl.scissor(t8, e, o, n)); @@ -17808,37 +17809,37 @@ var kp = class { throw new Error("No GPU program is currently set."); } }; -function HZ(r) { +function HZ(r15) { let t8 = 0; - for (; t8 < r.length && r[t8](); ++t8) + for (; t8 < r15.length && r15[t8](); ++t8) ; return t8 - 1; } -var { addImpl: RR, bincountImpl: ph, bincountReduceImpl: DR, bitwiseAndImpl: AR, castImpl: FR, ceilImpl: PR, concatImpl: OR, equalImpl: MR, expImpl: LR, expm1Impl: BR, floorImpl: zR, gatherNdImpl: VR, gatherV2Impl: WR, greaterImpl: UR, greaterEqualImpl: GR, lessImpl: HR, lessEqualImpl: KR, linSpaceImpl: qR, logImpl: jR, maxImpl: XR, maximumImpl: YR, minimumImpl: QR, multiplyImpl: ZR, negImpl: JR, notEqualImpl: eD, prodImpl: tD, raggedGatherImpl: rD, raggedRangeImpl: oD, raggedTensorToTensorImpl: nD, rangeImpl: sD, rsqrtImpl: aD, scatterImpl: iD, sigmoidImpl: uD, simpleAbsImpl: ch, sliceImpl: pD, sparseFillEmptyRowsImpl: cD, sparseReshapeImpl: lD, sparseSegmentReductionImpl: lh, sqrtImpl: mD, staticRegexReplaceImpl: dD, stridedSliceImpl: fD, stringNGramsImpl: hD, stringSplitImpl: gD, stringToHashBucketFastImpl: xD, subImpl: yD, tileImpl: bD, topKImpl: CD, transposeImpl: Np, uniqueImpl: wD } = Tc; -function lv(r, t8) { - return ["x", "y", "z", "w", "u", "v"].slice(0, t8).map((e) => `${r}.${e}`); +var { addImpl: DR, bincountImpl: uh, bincountReduceImpl: AR, bitwiseAndImpl: FR, castImpl: PR, ceilImpl: OR, concatImpl: MR, equalImpl: LR, expImpl: BR, expm1Impl: zR, floorImpl: VR, gatherNdImpl: WR, gatherV2Impl: UR, greaterImpl: GR, greaterEqualImpl: HR, lessImpl: KR, lessEqualImpl: qR, linSpaceImpl: jR, logImpl: XR, maxImpl: YR, maximumImpl: QR, minimumImpl: ZR, multiplyImpl: JR, negImpl: eD, notEqualImpl: tD, prodImpl: rD, raggedGatherImpl: oD, raggedRangeImpl: nD, raggedTensorToTensorImpl: sD, rangeImpl: aD, rsqrtImpl: iD, scatterImpl: uD, sigmoidImpl: pD, simpleAbsImpl: ph, sliceImpl: cD, sparseFillEmptyRowsImpl: lD, sparseReshapeImpl: mD, sparseSegmentReductionImpl: ch, sqrtImpl: dD, staticRegexReplaceImpl: fD, stridedSliceImpl: hD, stringNGramsImpl: gD, stringSplitImpl: xD, stringToHashBucketFastImpl: yD, subImpl: bD, tileImpl: CD, topKImpl: wD, transposeImpl: yp, uniqueImpl: SD } = wc; +function mv(r15, t8) { + return ["x", "y", "z", "w", "u", "v"].slice(0, t8).map((e) => `${r15}.${e}`); } -function Dt(r, t8) { - return t8 === 1 ? [r] : lv(r, t8); +function Rt(r15, t8) { + return t8 === 1 ? [r15] : mv(r15, t8); } -function SD(r, t8) { - if (r === 1) +function ID(r15, t8) { + if (r15 === 1) return "rc"; let e = ""; - for (let o = 0; o < r; o++) - e += t8[o], o < r - 1 && (e += ","); + for (let o = 0; o < r15; o++) + e += t8[o], o < r15 - 1 && (e += ","); return e; } -var mh = class { +var lh = class { constructor(t8) { - if (this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outputShape = t8, this.rank = t8.length, this.enableShapeUniforms = pt(this.outputShape.length), this.rank === 0) + if (this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outputShape = t8, this.rank = t8.length, this.enableShapeUniforms = ut(this.outputShape.length), this.rank === 0) this.userCode = ` void main() { setOutput(vec4(getA(), 0., 0., 0.)); } `; else { - let e = Dt("rc", this.rank), o = Re(this.rank), n = this.getOutOfBoundsCondition(e), s = this.getSetup(e), a = this.getOutput(e); + let e = Rt("rc", this.rank), o = Re(this.rank), n = this.getOutOfBoundsCondition(e), s = this.getSetup(e), a = this.getOutput(e); this.userCode = ` void main() { ${o} rc = getOutputCoords(); @@ -17895,9 +17896,9 @@ var mh = class { rEdge || cEdge ? 0. : getA(${e[3]})`; } }; -var Vc = class { +var Pc = class { constructor(t8, e) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }], this.outputShape = t8, this.enableShapeUniforms = pt(this.outputShape.length); + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }], this.outputShape = t8, this.enableShapeUniforms = ut(this.outputShape.length); let o = ""; for (let n = 0; n < 4; n++) { let s = "thisRC = rc;"; @@ -17916,7 +17917,7 @@ var Vc = class { } this.userCode = ` ${KZ(e, this.enableShapeUniforms)} - ${this.enableShapeUniforms ? Pc() : Fc(t8)} + ${this.enableShapeUniforms ? $c() : _c(t8)} void main() { ivec3 rc = getOutputCoords(); @@ -17934,36 +17935,36 @@ var Vc = class { `; } }; -function KZ(r, t8) { +function KZ(r15, t8) { return ` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t8 ? SR(["r", "c", "d"], "inputShape") : Ws(["r", "c", "d"], r)} + ${t8 ? IR(["r", "c", "d"], "inputShape") : Ws(["r", "c", "d"], r15)} return ivec3(r, c, d); } `; } -var dh = class { +var mh = class { constructor(t8) { this.gpgpu = t8, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0, this.freeTextures = {}, this.usedTextures = {}, this.logEnabled = false; } acquireTexture(t8, e, o) { - let n = vD(e, o), s = kD(t8, n, o); + let n = kD(e, o), s = ND(t8, n, o); s in this.freeTextures || (this.freeTextures[s] = []), s in this.usedTextures || (this.usedTextures[s] = []); - let a = ID(t8, n, this.gpgpu.gl, this.gpgpu.textureConfig, o); + let a = vD(t8, n, this.gpgpu.gl, this.gpgpu.textureConfig, o); if (this.freeTextures[s].length > 0) { this.numFreeTextures--, this.numUsedTextures++, this._numBytesFree -= a, this.log(); let p = this.freeTextures[s].pop(); return this.usedTextures[s].push(p), p; } let i; - return n === rr.PACKED_2X2_FLOAT32 ? i = this.gpgpu.createPackedMatrixTexture(t8[0], t8[1]) : n === rr.PACKED_2X2_FLOAT16 ? i = this.gpgpu.createFloat16PackedMatrixTexture(t8[0], t8[1]) : n === rr.UNPACKED_FLOAT32 ? i = this.gpgpu.createFloat32MatrixTexture(t8[0], t8[1]) : n === rr.UNPACKED_FLOAT16 ? i = this.gpgpu.createFloat16MatrixTexture(t8[0], t8[1]) : n === rr.PACKED_4X1_UNSIGNED_BYTE && (i = this.gpgpu.createUnsignedBytesMatrixTexture(t8[0], t8[1])), this.usedTextures[s].push(i), this.numUsedTextures++, this._numBytesAllocated += a, this.log(), i; + return n === er.PACKED_2X2_FLOAT32 ? i = this.gpgpu.createPackedMatrixTexture(t8[0], t8[1]) : n === er.PACKED_2X2_FLOAT16 ? i = this.gpgpu.createFloat16PackedMatrixTexture(t8[0], t8[1]) : n === er.UNPACKED_FLOAT32 ? i = this.gpgpu.createFloat32MatrixTexture(t8[0], t8[1]) : n === er.UNPACKED_FLOAT16 ? i = this.gpgpu.createFloat16MatrixTexture(t8[0], t8[1]) : n === er.PACKED_4X1_UNSIGNED_BYTE && (i = this.gpgpu.createUnsignedBytesMatrixTexture(t8[0], t8[1])), this.usedTextures[s].push(i), this.numUsedTextures++, this._numBytesAllocated += a, this.log(), i; } releaseTexture(t8, e, o, n) { if (this.freeTextures == null) return; - let s = vD(o, n), a = kD(e, s, n); + let s = kD(o, n), a = ND(e, s, n); a in this.freeTextures || (this.freeTextures[a] = []); - let i = ID(e, s, this.gpgpu.gl, this.gpgpu.textureConfig, n), p = A().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD"); + let i = vD(e, s, this.gpgpu.gl, this.gpgpu.textureConfig, n), p = A().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD"); p !== -1 && this._numBytesAllocated > p ? (this.gpgpu.deleteMatrixTexture(t8.texture), this._numBytesAllocated -= i) : (this.freeTextures[a].push(t8), this.numFreeTextures++, this._numBytesFree += i), this.numUsedTextures--; let u = this.usedTextures[a], c = u && u.indexOf(t8); if (c == null || c < 0) @@ -18004,15 +18005,15 @@ var dh = class { } } }; -function qZ(r, t8) { - let e = r; +function qZ(r15, t8) { + let e = r15; if (t8 === e.R32F) return 4; if (t8 === e.R16F) return 2; if (t8 === e.RGBA32F) return 16; - if (t8 === r.RGBA) + if (t8 === r15.RGBA) return 16; if (t8 === e.RGBA16F) return 8; @@ -18020,52 +18021,52 @@ function qZ(r, t8) { return 4; throw new Error(`Unknown internal format ${t8}`); } -function ID(r, t8, e, o, n) { +function vD(r15, t8, e, o, n) { let s = jZ(t8, o), a; if (n) { - let [p, u] = La(r[0], r[1]); + let [p, u] = Ma(r15[0], r15[1]); a = p * u; } else { - let [p, u] = Sp(r[0], r[1]); + let [p, u] = fp(r15[0], r15[1]); a = p * u; } let i = qZ(e, s); return a * i; } -function jZ(r, t8) { - switch (r) { - case rr.PACKED_2X2_FLOAT32: - return ih(t8); - case rr.PACKED_2X2_FLOAT16: - return uh(t8); - case rr.UNPACKED_FLOAT32: - return nh(t8); - case rr.UNPACKED_FLOAT16: - return sh(t8); - case rr.PACKED_4X1_UNSIGNED_BYTE: +function jZ(r15, t8) { + switch (r15) { + case er.PACKED_2X2_FLOAT32: return ah(t8); + case er.PACKED_2X2_FLOAT16: + return ih(t8); + case er.UNPACKED_FLOAT32: + return oh(t8); + case er.UNPACKED_FLOAT16: + return nh(t8); + case er.PACKED_4X1_UNSIGNED_BYTE: + return sh(t8); default: - throw new Error(`Unknown physical texture type ${r}`); + throw new Error(`Unknown physical texture type ${r15}`); } } -function XZ(r) { - return A().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? r ? rr.PACKED_2X2_FLOAT32 : rr.UNPACKED_FLOAT32 : r ? rr.PACKED_2X2_FLOAT16 : rr.UNPACKED_FLOAT16; +function XZ(r15) { + return A().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? r15 ? er.PACKED_2X2_FLOAT32 : er.UNPACKED_FLOAT32 : r15 ? er.PACKED_2X2_FLOAT16 : er.UNPACKED_FLOAT16; } -function vD(r, t8) { - if (r === dr.UPLOAD) - return rr.PACKED_2X2_FLOAT32; - if (r === dr.RENDER || r == null) +function kD(r15, t8) { + if (r15 === mr.UPLOAD) + return er.PACKED_2X2_FLOAT32; + if (r15 === mr.RENDER || r15 == null) return XZ(t8); - if (r === dr.DOWNLOAD || r === dr.PIXELS) - return rr.PACKED_4X1_UNSIGNED_BYTE; - throw new Error(`Unknown logical texture type ${r}`); + if (r15 === mr.DOWNLOAD || r15 === mr.PIXELS) + return er.PACKED_4X1_UNSIGNED_BYTE; + throw new Error(`Unknown logical texture type ${r15}`); } -function kD(r, t8, e) { - return `${r[0]}_${r[1]}_${t8}_${e}`; +function ND(r15, t8, e) { + return `${r15[0]}_${r15[1]}_${t8}_${e}`; } -var or = class { +var tr = class { constructor(t8, e) { - this.variableNames = ["A"], this.outputShape = t8, this.enableShapeUniforms = pt(this.outputShape.length), this.userCode = ` + this.variableNames = ["A"], this.outputShape = t8, this.enableShapeUniforms = ut(this.outputShape.length), this.userCode = ` float unaryOperation(float x) { ${e} } @@ -18079,20 +18080,20 @@ var or = class { `; } }; -var Ut = "if (isnan(x)) return x;"; -var ND = "return x;"; -var mv = "return abs(x);"; -var TD = "return (x >= 0.0) ? x : (exp(x) - 1.0);"; -var _D = Ut + ` +var Wt = "if (isnan(x)) return x;"; +var TD = "return x;"; +var dv = "return abs(x);"; +var _D = "return (x >= 0.0) ? x : (exp(x) - 1.0);"; +var $D = Wt + ` return (x < 0.0) ? 0.0 : x; `; -var $D = Ut + ` +var ED = Wt + ` return (x < 0.0) ? 0.0 : min(6.0, x); `; -var Ba = "return x;"; -var ED = "return 1.0 / (1.0 + exp(-1.0 * x));"; -var DD = "return x;"; -var AD = ` +var La = "return x;"; +var RD = "return 1.0 / (1.0 + exp(-1.0 * x));"; +var AD = "return x;"; +var FD = ` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -18102,7 +18103,7 @@ var AD = ` return result; `; -var FD = ` +var PD = ` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -18113,7 +18114,7 @@ var FD = ` return result; `; -var PD = ` +var OD = ` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -18124,10 +18125,10 @@ var PD = ` return result; `; -var OD = "return 1.0 / (1.0 + exp(-1.0 * x));"; -var Fr = class { +var MD = "return 1.0 / (1.0 + exp(-1.0 * x));"; +var Ar = class { constructor(t8, e) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t8, this.enableShapeUniforms = pt(this.outputShape.length), this.userCode = ` + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t8, this.enableShapeUniforms = ut(this.outputShape.length), this.userCode = ` vec4 unaryOperation(vec4 x) { ${e} } @@ -18141,10 +18142,10 @@ var Fr = class { `; } }; -var fh = class { +var dh = class { constructor(t8) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outputShape = t8, this.enableShapeUniforms = pt(this.outputShape.length); - let e = t8.length, o = Dt("rc", e), n = Re(e), s = SD(e, o), a = o.slice(-2), i = e <= 1 ? "rc" : `vec2(${a.join(",")})`; + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outputShape = t8, this.enableShapeUniforms = ut(this.outputShape.length); + let e = t8.length, o = Rt("rc", e), n = Re(e), s = ID(e, o), a = o.slice(-2), i = e <= 1 ? "rc" : `vec2(${a.join(",")})`; this.userCode = ` void main() { ${n} rc = getOutputCoords(); @@ -18155,39 +18156,39 @@ var fh = class { `; } }; -var QZ = Wt.whereImpl; +var QZ = Vt.whereImpl; var ZZ = 1e-7; var JZ = 1e-4; -var hh = {}; -function e9(r) { - return r in hh || (hh[r] = {}), hh[r]; +var fh = {}; +function e9(r15) { + return r15 in fh || (fh[r15] = {}), fh[r15]; } var t9 = A().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"); -var r9 = 600; +var r92 = 600; function o9() { - return A().global.screen == null ? 1024 : A().global.screen.height * A().global.screen.width * window.devicePixelRatio * r9 / 1024 / 1024; + return A().global.screen == null ? 1024 : A().global.screen.height * A().global.screen.width * window.devicePixelRatio * r92 / 1024 / 1024; } -var wu = class extends ao { +var Oc = class r13 extends so { nextDataId() { - return wu.nextDataId++; + return r13.nextDataId++; } constructor(t8) { if (super(), this.pendingRead = /* @__PURE__ */ new WeakMap(), this.pendingDisposal = /* @__PURE__ */ new WeakSet(), this.dataRefCount = /* @__PURE__ */ new WeakMap(), this.numBytesInGPU = 0, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.lastGlFlushTime = 0, this.warnedAboutMemory = false, this.pendingDeletes = 0, this.disposed = false, !A().getBool("HAS_WEBGL")) throw new Error("WebGL is not supported on this device"); let e; if (t8 != null) { - if (t8 instanceof kp) + if (t8 instanceof xp) e = t8; else { - let o = Kr(A().getNumber("WEBGL_VERSION"), t8); - e = new kp(o); + let o = Hr(A().getNumber("WEBGL_VERSION"), t8); + e = new xp(o); } this.binaryCache = {}, this.gpgpuCreatedLocally = false; } else { - let o = Kr(A().getNumber("WEBGL_VERSION")); - e = new kp(o), this.binaryCache = e9(A().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true; + let o = Hr(A().getNumber("WEBGL_VERSION")); + e = new xp(o), this.binaryCache = e9(A().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true; } - this.gpgpu = e, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new dh(this.gpgpu), this.numMBBeforeWarning = o9(), this.texData = new zo(this, pr()); + this.gpgpu = e, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new mh(this.gpgpu), this.numMBBeforeWarning = o9(), this.texData = new Bo(this, ur()); } numDataIds() { return this.texData.numDataIds() - this.pendingDeletes; @@ -18195,14 +18196,14 @@ var wu = class extends ao { writeTexture(t8, e, o, n, s, a) { let i = this.makeTensorInfo(e, o), p = this.texData.get(i.dataId); p.isPacked = false, p.texture = { texture: t8, texShape: [n, s] }, p.texShape = [n, s]; - let u = Dc(e), c = new Zl(u, false, a), l = this.runWebGLProgram(c, [i], o, [[n, s]]); + let u = Nc(e), c = new Yl(u, false, a), l = this.runWebGLProgram(c, [i], o, [[n, s]]); return l.shape = e, p.texture = null, this.disposeIntermediateTensorInfo(i), l.dataId; } write(t8, e, o) { if ((A().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || A().getBool("DEBUG")) && this.checkNumericalProblems(t8), o === "complex64" && t8 != null) throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag)."); let n = { id: this.nextDataId() }; - return this.texData.set(n, { shape: e, dtype: o, values: t8, usage: dr.UPLOAD, refCount: 1 }), n; + return this.texData.set(n, { shape: e, dtype: o, values: t8, usage: mr.UPLOAD, refCount: 1 }), n; } refCount(t8) { return this.texData.has(t8) ? this.texData.get(t8).refCount : 0; @@ -18220,7 +18221,7 @@ var wu = class extends ao { move(t8, e, o, n, s) { if (A().getBool("DEBUG") && this.checkNumericalProblems(e), n === "complex64") throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag)."); - this.texData.set(t8, { shape: o, dtype: n, values: e, usage: dr.UPLOAD, refCount: s }); + this.texData.set(t8, { shape: o, dtype: n, values: e, usage: mr.UPLOAD, refCount: s }); } disposeIntermediateTensorInfo(t8) { this.disposeData(t8.dataId); @@ -18229,7 +18230,7 @@ var wu = class extends ao { let e = this.texData.get(t8), { values: o, dtype: n, complexTensorInfos: s, slice: a, shape: i, isPacked: p } = e; if (a != null) { let m; - p ? m = new Fr(i, Ba) : m = new or(i, Ba); + p ? m = new Ar(i, La) : m = new tr(i, La); let d = this.runWebGLProgram(m, [{ dataId: t8, shape: i, dtype: n }], n), f = this.readSync(d.dataId); return this.disposeIntermediateTensorInfo(d), f; } @@ -18255,7 +18256,7 @@ var wu = class extends ao { let e = this.texData.get(t8), { values: o, shape: n, slice: s, dtype: a, complexTensorInfos: i, isPacked: p } = e; if (s != null) { let f; - p ? f = new Fr(n, Ba) : f = new or(n, Ba); + p ? f = new Ar(n, La) : f = new tr(n, La); let h = this.runWebGLProgram(f, [{ dataId: t8, shape: n, dtype: a }], a), g = this.read(h.dataId); return this.disposeIntermediateTensorInfo(h), g; } @@ -18267,7 +18268,7 @@ var wu = class extends ao { if (a !== "complex64" && A().get("WEBGL_BUFFER_SUPPORTED")) { c = this.decode(t8); let f = this.texData.get(c.dataId); - u = this.gpgpu.createBufferFromTexture(f.texture.texture, ...jl(n)); + u = this.gpgpu.createBufferFromTexture(f.texture.texture, ...Kl(n)); } this.pendingRead.set(t8, []), a !== "complex64" && await this.gpgpu.createAndWaitForFence(); let l; @@ -18285,7 +18286,7 @@ var wu = class extends ao { ce(f, () => f.deleteBuffer(u)); } let m = this.convertAndCacheOnCPU(t8, l), d = this.pendingRead.get(t8); - return this.pendingRead.delete(t8), d.forEach((f) => f(m)), this.pendingDisposal.has(t8) && (this.pendingDisposal.delete(t8), this.disposeData(t8) && pr().removeDataId(t8, this), this.pendingDeletes--), m; + return this.pendingRead.delete(t8), d.forEach((f) => f(m)), this.pendingDisposal.has(t8) && (this.pendingDisposal.delete(t8), this.disposeData(t8) && ur().removeDataId(t8, this), this.pendingDeletes--), m; } readToGPU(t8, e = {}) { let o = this.texData.get(t8), { values: n, shape: s, slice: a, dtype: i, isPacked: p, texture: u } = o; @@ -18293,13 +18294,13 @@ var wu = class extends ao { throw new Error("Does not support reading texture for complex64 dtype."); if (a != null) { let d; - p ? d = new Fr(s, Ba) : d = new or(s, Ba); + p ? d = new Ar(s, La) : d = new tr(s, La); let f = this.runWebGLProgram(d, [{ dataId: t8, shape: s, dtype: i }], i), h = this.readToGPU(f, e); return this.disposeIntermediateTensorInfo(f), h; } if (u == null) throw n != null ? new Error("Data is not on GPU but on CPU.") : new Error("There is no data on GPU or CPU."); - let c = this.decode(t8, e.customTexShape), l = pr().makeTensorFromTensorInfo(c), m = this.texData.get(c.dataId); + let c = this.decode(t8, e.customTexShape), l = ur().makeTensorFromTensorInfo(c), m = this.texData.get(c.dataId); return Object.assign({ tensorRef: l }, m.texture); } bufferSync(t8) { @@ -18317,17 +18318,17 @@ var wu = class extends ao { if (t8 != null) for (let e = 0; e < t8.length; e++) { let o = t8[e]; - if (!TI(o)) + if (!_I(o)) throw A().getBool("WEBGL_RENDER_FLOAT32_CAPABLE") ? Error(`The value ${o} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`) : Error(`The value ${o} cannot be represented on this device.`); } } getValuesFromTexture(t8) { let { shape: e, dtype: o, isPacked: n } = this.texData.get(t8), s = y.sizeFromShape(e); if (A().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) { - let m = this.decode(t8), d = this.texData.get(m.dataId), f = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...jl(e)).subarray(0, s); + let m = this.decode(t8), d = this.texData.get(m.dataId), f = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...Kl(e)).subarray(0, s); return this.disposeIntermediateTensorInfo(m), f; } - let a = A().getBool("WEBGL_PACK") && n === true, i = a ? Dc(e) : e, p = a ? new rh(i) : new th(i), u = this.runWebGLProgram(p, [{ shape: i, dtype: o, dataId: t8 }], "float32"), c = this.texData.get(u.dataId), l = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture, c.texShape[0], c.texShape[1]).subarray(0, s); + let a = A().getBool("WEBGL_PACK") && n === true, i = a ? Nc(e) : e, p = a ? new th(i) : new eh(i), u = this.runWebGLProgram(p, [{ shape: i, dtype: o, dataId: t8 }], "float32"), c = this.texData.get(u.dataId), l = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture, c.texShape[0], c.texShape[1]).subarray(0, s); return this.disposeIntermediateTensorInfo(u), l; } timerAvailable() { @@ -18400,18 +18401,18 @@ var wu = class extends ao { return QZ(t8.shape, e); } packedUnaryOp(t8, e, o) { - let n = new Fr(t8.shape, e), s = this.compileAndRun(n, [t8], o); - return pr().makeTensorFromTensorInfo(s); + let n = new Ar(t8.shape, e), s = this.compileAndRun(n, [t8], o); + return ur().makeTensorFromTensorInfo(s); } abs(t8) { if (this.shouldExecuteOnCPU([t8]) && t8.dtype !== "complex64") { - let n = ch(this.texData.get(t8.dataId).values); + let n = ph(this.texData.get(t8.dataId).values); return this.makeOutput(t8.shape, t8.dtype, n); } if (A().getBool("WEBGL_PACK_UNARY_OPERATIONS")) - return this.packedUnaryOp(t8, mv, t8.dtype); - let e = new or(t8.shape, mv), o = this.compileAndRun(e, [t8]); - return pr().makeTensorFromTensorInfo(o); + return this.packedUnaryOp(t8, dv, t8.dtype); + let e = new tr(t8.shape, dv), o = this.compileAndRun(e, [t8]); + return ur().makeTensorFromTensorInfo(o); } makeTensorInfo(t8, e, o) { let n; @@ -18423,18 +18424,18 @@ var wu = class extends ao { return this.texData.get(n).usage = null, { dataId: n, shape: t8, dtype: e }; } makeOutput(t8, e, o) { - return pr().makeTensorFromTensorInfo(this.makeTensorInfo(t8, e, o), this); + return ur().makeTensorFromTensorInfo(this.makeTensorInfo(t8, e, o), this); } unpackTensor(t8) { - let e = new fh(t8.shape); + let e = new dh(t8.shape); return this.runWebGLProgram(e, [t8], t8.dtype); } packTensor(t8) { - let e = new mh(t8.shape), o = true; + let e = new lh(t8.shape), o = true; return this.runWebGLProgram(e, [t8], t8.dtype, null, o); } packedReshape(t8, e) { - let o = [yi(t8.shape), ...bi(t8.shape)], n = { dtype: t8.dtype, shape: o, dataId: t8.dataId }, s = [yi(e), ...bi(e)], a = new Vc(s, o), i = true, p = [o], u = this.runWebGLProgram(a, [n], t8.dtype, p, i); + let o = [hi(t8.shape), ...gi(t8.shape)], n = { dtype: t8.dtype, shape: o, dataId: t8.dataId }, s = [hi(e), ...gi(e)], a = new Pc(s, o), i = true, p = [o], u = this.runWebGLProgram(a, [n], t8.dtype, p, i); return { dataId: u.dataId, shape: e, dtype: u.dtype }; } decode(t8, e) { @@ -18443,15 +18444,15 @@ var wu = class extends ao { let m = y.sizeFromShape(s), d = e[0] * e[1] * 4; y.assert(m <= d, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data."); } - let i = Dc(s), p; - n ? p = new eh(i) : p = new Jf(i); - let u = true, c = [e != null ? e : jl(i)], l = this.runWebGLProgram(p, [{ shape: i, dtype: a, dataId: t8 }], a, c, u, e); + let i = Nc(s), p; + n ? p = new Jf(i) : p = new Zf(i); + let u = true, c = [e != null ? e : Kl(i)], l = this.runWebGLProgram(p, [{ shape: i, dtype: a, dataId: t8 }], a, c, u, e); return { dtype: a, shape: s, dataId: l.dataId }; } runWebGLProgram(t8, e, o, n, s = false, a) { let i = this.makeTensorInfo(t8.outputShape, o), p = this.texData.get(i.dataId); - if (t8.packedOutput && (p.isPacked = true), t8.outPackingScheme === bu.DENSE) { - let x = a != null ? a : jl(t8.outputShape); + if (t8.packedOutput && (p.isPacked = true), t8.outPackingScheme === fu.DENSE) { + let x = a != null ? a : Kl(t8.outputShape); p.texShape = x.map((b) => b * 2); } if (t8.outTexUsage != null && (p.usage = t8.outTexUsage), y.sizeFromShape(i.shape) === 0) @@ -18467,15 +18468,15 @@ var wu = class extends ao { } if (this.uploadToGPU(x.dataId), !!b.isPacked != !!t8.packedInputs) x = b.isPacked ? this.unpackTensor(x) : this.packTensor(x), u.push(x), b = this.texData.get(x.dataId); - else if (b.isPacked && !Cu(b.shape, x.shape)) { + else if (b.isPacked && !hu(b.shape, x.shape)) { let C = x, S = x.shape; x.shape = b.shape, x = this.packedReshape(x, S), u.push(x), b = this.texData.get(x.dataId), C.shape = S; } return { shape: x.shape, texData: b, isUniform: false }; }); this.uploadToGPU(i.dataId); - let l = { shape: i.shape, texData: p, isUniform: false }, m = ER(t8, c, l), d = this.getAndSaveBinary(m, () => _R(this.gpgpu, t8, c, l)), f = this.activeTimers != null, h; - f && (h = this.startTimer()), A().get("ENGINE_COMPILE_ONLY") || $R(this.gpgpu, d, c, l, n), u.forEach((x) => this.disposeIntermediateTensorInfo(x)), f && (h = this.endTimer(h), this.activeTimers.push({ name: t8.constructor.name, query: this.getQueryTime(h) })); + let l = { shape: i.shape, texData: p, isUniform: false }, m = RR(t8, c, l), d = this.getAndSaveBinary(m, () => $R(this.gpgpu, t8, c, l)), f = this.activeTimers != null, h; + f && (h = this.startTimer()), A().get("ENGINE_COMPILE_ONLY") || ER(this.gpgpu, d, c, l, n), u.forEach((x) => this.disposeIntermediateTensorInfo(x)), f && (h = this.endTimer(h), this.activeTimers.push({ name: t8.constructor.name, query: this.getQueryTime(h) })); let g = A().getNumber("WEBGL_FLUSH_THRESHOLD"); if (g > 0) { let x = y.now(); @@ -18523,11 +18524,11 @@ var wu = class extends ao { let u = this.activeTimers != null, c; u && (c = y.now()); let l = e.texShape; - if (l == null && (l = zI(o, p), e.texShape = l), s != null) { - let m = Dc(o), d, f = l[1], h = l[0], g = s instanceof Uint8Array || s instanceof Uint8ClampedArray; - (p || !g) && ([f, h] = La(l[0], l[1])), p ? d = new oh(m, g) : d = new Zl(m, g); + if (l == null && (l = VI(o, p), e.texShape = l), s != null) { + let m = Nc(o), d, f = l[1], h = l[0], g = s instanceof Uint8Array || s instanceof Uint8ClampedArray; + (p || !g) && ([f, h] = Ma(l[0], l[1])), p ? d = new rh(m, g) : d = new Yl(m, g); let x = g ? [h, f] : l, b = this.makeTensorInfo(x, n), C = this.texData.get(b.dataId); - g ? C.usage = dr.PIXELS : C.usage = dr.UPLOAD, C.texShape = x, this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId), f, h, s); + g ? C.usage = mr.PIXELS : C.usage = mr.UPLOAD, C.texShape = x, this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId), f, h, s); let S = [[h, f]], k = true, _ = this.runWebGLProgram(d, [b], n, S, k), E = this.texData.get(_.dataId); e.texShape = E.texShape, e.isPacked = E.isPacked, e.usage = E.usage, A().get("ENGINE_COMPILE_ONLY") ? this.disposeData(_.dataId) : (e.texture = E.texture, e.values = null, this.texData.delete(_.dataId)), this.disposeIntermediateTensorInfo(b), u && (this.uploadWaitMs += y.now() - c); } else { @@ -18578,50 +18579,50 @@ var wu = class extends ao { } checkCompletion_(t8) { if (this.gpgpu.gl.getProgramParameter(t8.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false) - throw console.log(this.gpgpu.gl.getProgramInfoLog(t8.webGLProgram)), this.gpgpu.gl.getShaderParameter(t8.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false ? (qf(t8.source, this.gpgpu.gl.getShaderInfoLog(t8.fragmentShader)), new Error("Failed to compile fragment shader.")) : new Error("Failed to link vertex and fragment shaders."); + throw console.log(this.gpgpu.gl.getProgramInfoLog(t8.webGLProgram)), this.gpgpu.gl.getShaderParameter(t8.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false ? (Kf(t8.source, this.gpgpu.gl.getShaderInfoLog(t8.fragmentShader)), new Error("Failed to compile fragment shader.")) : new Error("Failed to link vertex and fragment shaders."); return true; } getUniformLocations() { for (let t8 of Object.values(this.binaryCache)) { this.gpgpu.buildVao(t8.webGLProgram); - let { variablesLocations: e, customUniformLocations: o, infLoc: n, nanLoc: s, outShapeLocation: a, outShapeStridesLocation: i, outTexShapeLocation: p } = qI(this.gpgpu, t8.program, t8.webGLProgram); + let { variablesLocations: e, customUniformLocations: o, infLoc: n, nanLoc: s, outShapeLocation: a, outShapeStridesLocation: i, outTexShapeLocation: p } = jI(this.gpgpu, t8.program, t8.webGLProgram); t8.variablesLocations = e, t8.customUniformLocations = o, t8.infLoc = n, t8.nanLoc = s, t8.outShapeLocation = a, t8.outShapeStridesLocation = i, t8.outTexShapeLocation = p; } } createTensorFromGPUData(t8, e, o) { t8.channels = t8.channels || "RGBA"; - let { texture: n, height: s, width: a, channels: i } = t8, p = pr().backend; + let { texture: n, height: s, width: a, channels: i } = t8, p = ur().backend; if (!p.gpgpu.gl.isTexture(n)) throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'."); let u = p.writeTexture(n, e, o, s, a, i); - return pr().makeTensorFromDataId(u, e, o, p); + return ur().makeTensorFromDataId(u, e, o, p); } }; -wu.nextDataId = 0; -function n9(r, t8) { +Oc.nextDataId = 0; +function n9(r15, t8) { if (t8 === "float32" || t8 === "complex64") - return r; + return r15; if (t8 === "int32" || t8 === "bool") { - let e = t8 === "int32" ? new Int32Array(r.length) : new Uint8Array(r.length); + let e = t8 === "int32" ? new Int32Array(r15.length) : new Uint8Array(r15.length); for (let o = 0; o < e.length; ++o) - e[o] = Math.round(r[o]); + e[o] = Math.round(r15[o]); return e; } else throw new Error(`Unknown dtype ${t8}`); } -var s9 = "4.11.0"; -function MD() { +var s9 = "4.14.0"; +function LD() { A().set("WEBGL_FORCE_F16_TEXTURES", true); } -ou.isBrowser() && su("webgl", () => new wu(), 2); -var Cat = { forceHalfFloat: MD }; -var Wc = ` +Ji.isBrowser() && tu("webgl", () => new Oc(), 2); +var Cat = { forceHalfFloat: LD }; +var Mc = ` if (isnan(a)) return a; if (isnan(b)) return b; `; -var Pr = class { +var Fr = class { constructor(t8, e, o) { - this.variableNames = ["A", "B"], this.outputShape = w.assertAndGetBroadcastShape(e, o), this.enableShapeUniforms = pt(this.outputShape.length), this.userCode = ` + this.variableNames = ["A", "B"], this.outputShape = w.assertAndGetBroadcastShape(e, o), this.enableShapeUniforms = ut(this.outputShape.length), this.userCode = ` float binaryOperation(float a, float b) { ${t8} } @@ -18634,17 +18635,17 @@ var Pr = class { `; } }; -var Xr = ` +var jr = ` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; `; -var jr = class { +var qr = class { constructor(t8, e, o, n = false) { this.variableNames = ["A", "B"], this.supportsBroadcasting = true, this.packedInputs = true, this.packedOutput = true, this.outputShape = w.assertAndGetBroadcastShape(e, o); let s = this.outputShape.length; - this.enableShapeUniforms = pt(s); + this.enableShapeUniforms = ut(s); let a = ""; if (n) if (s === 0 || y.sizeFromShape(this.outputShape) === 1) @@ -18666,7 +18667,7 @@ var jr = class { result.w = 0.; `; else { - let p = Dt("coords", s); + let p = Rt("coords", s); this.enableShapeUniforms ? a += ` bool nextRowOutOfBounds = (${p[s - 2]} + 1) >= outShape[${s} - 2]; @@ -18702,38 +18703,38 @@ var jr = class { `; } }; -function At(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8; +function Dt(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8; return e.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; } -var LD = { kernelName: wo, backendName: "webgl", kernelFunc: At }; -function Or(r) { - let { inputs: t8, backend: e } = r, { real: o, imag: n } = t8, s = e.makeTensorInfo(o.shape, "complex64"), a = e.texData.get(s.dataId), i = At({ inputs: { x: o }, backend: e }), p = At({ inputs: { x: n }, backend: e }); +var BD = { kernelName: Co, backendName: "webgl", kernelFunc: Dt }; +function Pr(r15) { + let { inputs: t8, backend: e } = r15, { real: o, imag: n } = t8, s = e.makeTensorInfo(o.shape, "complex64"), a = e.texData.get(s.dataId), i = Dt({ inputs: { x: o }, backend: e }), p = Dt({ inputs: { x: n }, backend: e }); return a.complexTensorInfos = { real: i, imag: p }, s; } -var BD = { kernelName: Fi, backendName: "webgl", kernelFunc: Or }; -var dv = "return (a < 0.) ? b * a : a;"; -var fv = ` +var zD = { kernelName: Ri, backendName: "webgl", kernelFunc: Pr }; +var fv = "return (a < 0.) ? b * a : a;"; +var hv = ` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `; -function a9(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { alpha: s } = o, a = e.makeTensorInfo([], "float32", y.createScalarValue(s, "float32")), i = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new jr(fv, n.shape, a.shape) : new Pr(dv, n.shape, a.shape), p = e.runWebGLProgram(i, [n, a], "float32"); +function a9(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { alpha: s } = o, a = e.makeTensorInfo([], "float32", y.createScalarValue(s, "float32")), i = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new qr(hv, n.shape, a.shape) : new Fr(fv, n.shape, a.shape), p = e.runWebGLProgram(i, [n, a], "float32"); return e.disposeIntermediateTensorInfo(a), p; } -var zD = { kernelName: Rn, backendName: "webgl", kernelFunc: a9 }; -var hv = "return (a < 0.) ? b * a : a;"; -var gv = ` +var VD = { kernelName: En, backendName: "webgl", kernelFunc: a9 }; +var gv = "return (a < 0.) ? b * a : a;"; +var xv = ` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `; -function i9(r) { - let { inputs: t8, backend: e } = r, { x: o, alpha: n } = t8, s = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new jr(gv, o.shape, n.shape) : new Pr(hv, o.shape, n.shape); +function i9(r15) { + let { inputs: t8, backend: e } = r15, { x: o, alpha: n } = t8, s = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new qr(xv, o.shape, n.shape) : new Fr(gv, o.shape, n.shape); return e.runWebGLProgram(s, [o, n], "float32"); } -var VD = { kernelName: os, backendName: "webgl", kernelFunc: i9 }; -var Po = "if (isnan(x)) return x;"; -function xe({ opSnippet: r, packedOpSnippet: t8, cpuKernelImpl: e, dtype: o }) { +var WD = { kernelName: rs, backendName: "webgl", kernelFunc: i9 }; +var Fo = "if (isnan(x)) return x;"; +function xe({ opSnippet: r15, packedOpSnippet: t8, cpuKernelImpl: e, dtype: o }) { return ({ inputs: n, backend: s }) => { let { x: a } = n, i = s, p = o || a.dtype; if (i.shouldExecuteOnCPU([a]) && e != null) { @@ -18741,17 +18742,17 @@ function xe({ opSnippet: r, packedOpSnippet: t8, cpuKernelImpl: e, dtype: o }) { return i.makeTensorInfo(a.shape, p, m); } let u = A().getBool("WEBGL_PACK_UNARY_OPERATIONS") && t8 != null, c; - return u ? c = new Fr(a.shape, t8) : c = new or(a.shape, r), i.runWebGLProgram(c, [a], p); + return u ? c = new Ar(a.shape, t8) : c = new tr(a.shape, r15), i.runWebGLProgram(c, [a], p); }; } -function nt({ opSnippet: r, packedOpSnippet: t8, checkOutOfBounds: e = false, supportsComplex: o = false, cpuKernelImpl: n, dtype: s }) { +function nt({ opSnippet: r15, packedOpSnippet: t8, checkOutOfBounds: e = false, supportsComplex: o = false, cpuKernelImpl: n, dtype: s }) { return ({ inputs: a, backend: i }) => { let { a: p, b: u } = a, c = i; if (o && p.dtype === "complex64") { let f = c.texData.get(p.dataId), h = c.texData.get(u.dataId), [g, x] = [[f.complexTensorInfos.real, h.complexTensorInfos.real], [f.complexTensorInfos.imag, h.complexTensorInfos.imag]].map((C) => { - let [S, k] = C, _ = { dataId: S.dataId, dtype: S.dtype, shape: p.shape }, E = { dataId: k.dataId, dtype: k.dtype, shape: u.shape }, R = new Pr(r, p.shape, u.shape); + let [S, k] = C, _ = { dataId: S.dataId, dtype: S.dtype, shape: p.shape }, E = { dataId: k.dataId, dtype: k.dtype, shape: u.shape }, R = new Fr(r15, p.shape, u.shape); return c.runWebGLProgram(R, [_, E], dt(S.dtype, k.dtype)); - }), b = Or({ inputs: { real: g, imag: x }, backend: c }); + }), b = Pr({ inputs: { real: g, imag: x }, backend: c }); return c.disposeIntermediateTensorInfo(g), c.disposeIntermediateTensorInfo(x), b; } let l = s || dt(p.dtype, u.dtype); @@ -18760,29 +18761,29 @@ function nt({ opSnippet: r, packedOpSnippet: t8, checkOutOfBounds: e = false, su return k.values = b, S; } let m = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") && t8 != null, d; - return m ? d = new jr(t8, p.shape, u.shape, e) : d = new Pr(r, p.shape, u.shape), c.runWebGLProgram(d, [p, u], l); + return m ? d = new qr(t8, p.shape, u.shape, e) : d = new Fr(r15, p.shape, u.shape), c.runWebGLProgram(d, [p, u], l); }; } -function Ci(r, t8 = false) { - if (r === "linear") - return t8 ? DD : ND; - if (r === "relu") - return t8 ? FD : _D; - if (r === "elu") +function xi(r15, t8 = false) { + if (r15 === "linear") return t8 ? AD : TD; - if (r === "relu6") + if (r15 === "relu") return t8 ? PD : $D; - if (r === "prelu") - return t8 ? gv : hv; - if (r === "leakyrelu") - return t8 ? fv : dv; - if (r === "sigmoid") + if (r15 === "elu") + return t8 ? FD : _D; + if (r15 === "relu6") return t8 ? OD : ED; - throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`); + if (r15 === "prelu") + return t8 ? xv : gv; + if (r15 === "leakyrelu") + return t8 ? hv : fv; + if (r15 === "sigmoid") + return t8 ? MD : RD; + throw new Error(`Activation ${r15} has not been implemented for the WebGL backend.`); } -var Uc = class { +var Lc = class { constructor(t8, e, o, n = false, s = false, a = false, i = null, p = false, u = false) { - this.variableNames = ["matrixA", "matrixB"], this.packedInputs = true, this.packedOutput = true, this.outputShape = o, this.enableShapeUniforms = pt(this.outputShape.length); + this.variableNames = ["matrixA", "matrixB"], this.packedInputs = true, this.packedOutput = true, this.outputShape = o, this.enableShapeUniforms = ut(this.outputShape.length); let c = n ? t8[1] : t8[2], l = Math.ceil(c / 2), m = n ? "i * 2, rc.y" : "rc.y, i * 2", d = s ? "rc.z, i * 2" : "i * 2, rc.z", f = n ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"], h = s ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"], g = "", x = ""; i && (p ? g = `vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); @@ -18830,8 +18831,8 @@ var Uc = class { `; } }; -var xv = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" }; -var em = class { +var yv = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" }; +var Zl = class { constructor(t8, e, o) { this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.outputShape = w.assertAndGetBroadcastShape(e, o), this.userCode = ` float binaryOpComplex( @@ -18849,33 +18850,33 @@ var em = class { `; } }; -var WD = "return a * b;"; -function tm(r) { - let { inputs: t8, backend: e } = r, { a: o, b: n } = t8, s = w.upcastType(o.dtype, n.dtype); +var UD = "return a * b;"; +function Jl(r15) { + let { inputs: t8, backend: e } = r15, { a: o, b: n } = t8, s = w.upcastType(o.dtype, n.dtype); if (o.dtype === "complex64") { - let i = e.texData.get(o.dataId), p = e.texData.get(n.dataId), u = new em(xv.REAL, o.shape, n.shape), c = new em(xv.IMAG, o.shape, n.shape), l = [{ dataId: i.complexTensorInfos.real.dataId, dtype: i.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: i.complexTensorInfos.imag.dataId, dtype: i.complexTensorInfos.imag.dtype, shape: o.shape }, { dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: n.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: n.shape }], m = e.runWebGLProgram(u, l, "float32"), d = e.runWebGLProgram(c, l, "float32"), f = Or({ inputs: { real: m, imag: d }, backend: e }); + let i = e.texData.get(o.dataId), p = e.texData.get(n.dataId), u = new Zl(yv.REAL, o.shape, n.shape), c = new Zl(yv.IMAG, o.shape, n.shape), l = [{ dataId: i.complexTensorInfos.real.dataId, dtype: i.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: i.complexTensorInfos.imag.dataId, dtype: i.complexTensorInfos.imag.dtype, shape: o.shape }, { dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: n.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: n.shape }], m = e.runWebGLProgram(u, l, "float32"), d = e.runWebGLProgram(c, l, "float32"), f = Pr({ inputs: { real: m, imag: d }, backend: e }); return e.disposeIntermediateTensorInfo(m), e.disposeIntermediateTensorInfo(d), f; } if (e.shouldExecuteOnCPU([o, n])) { - let i = e.texData.get(o.dataId), p = e.texData.get(n.dataId), [u, c] = ZR(o.shape, n.shape, i.values, p.values, s), l = e.makeTensorInfo(c, s), m = e.texData.get(l.dataId); + let i = e.texData.get(o.dataId), p = e.texData.get(n.dataId), [u, c] = JR(o.shape, n.shape, i.values, p.values, s), l = e.makeTensorInfo(c, s), m = e.texData.get(l.dataId); return m.values = u, l; } let a; - return A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? a = new jr(WD, o.shape, n.shape) : a = new Pr(WD, o.shape, n.shape), e.runWebGLProgram(a, [o, n], s); + return A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? a = new qr(UD, o.shape, n.shape) : a = new Fr(UD, o.shape, n.shape), e.runWebGLProgram(a, [o, n], s); } -var UD = { kernelName: Yn, backendName: "webgl", kernelFunc: tm }; -function GD(r, t8, e) { - let o = [yi(r.shape), ...bi(r.shape)], n = { dtype: r.dtype, shape: o, dataId: r.dataId }, s = [yi(t8), ...bi(t8)], a = new Vc(s, o), i = true, p = [o], u = e.runWebGLProgram(a, [n], r.dtype, p, i); +var GD = { kernelName: Xn, backendName: "webgl", kernelFunc: Jl }; +function HD(r15, t8, e) { + let o = [hi(r15.shape), ...gi(r15.shape)], n = { dtype: r15.dtype, shape: o, dataId: r15.dataId }, s = [hi(t8), ...gi(t8)], a = new Pc(s, o), i = true, p = [o], u = e.runWebGLProgram(a, [n], r15.dtype, p, i); return { dataId: u.dataId, shape: t8, dtype: u.dtype }; } -function te(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { shape: s } = o, a = e, i = y.sizeFromShape(n.shape), p = y.inferFromImplicitShape(s, i), u = y.sizeFromShape(p); +function te(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { shape: s } = o, a = e, i = y.sizeFromShape(n.shape), p = y.inferFromImplicitShape(s, i), u = y.sizeFromShape(p); y.assert(i === u, () => `The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`); let c = a.texData.get(n.dataId); - return c.isPacked && !Cu(n.shape, p) && !(c.texture !== null && Cu(c.shape, p)) ? GD(n, p, a) : (a.incRef(n.dataId), { dataId: n.dataId, shape: p, dtype: n.dtype }); + return c.isPacked && !hu(n.shape, p) && !(c.texture !== null && hu(c.shape, p)) ? HD(n, p, a) : (a.incRef(n.dataId), { dataId: n.dataId, shape: p, dtype: n.dtype }); } -var HD = { kernelName: da, backendName: "webgl", kernelFunc: te }; -var rm = class { +var KD = { kernelName: da, backendName: "webgl", kernelFunc: te }; +var em = class { constructor(t8, e) { this.variableNames = ["x"]; let { windowSize: o, batchSize: n, inSize: s, outSize: a } = t8; @@ -18942,7 +18943,7 @@ var rm = class { `; } }; -var gh = class { +var hh = class { constructor(t8, e) { this.variableNames = ["x"]; let { windowSize: o, batchSize: n, inSize: s, outSize: a } = t8; @@ -19049,23 +19050,23 @@ var gh = class { `; } }; -function p9(r) { +function p9(r15) { let t8 = []; for (; t8.length === 0 || t8[t8.length - 1].outSize !== 1; ) { - let e = t8.length ? t8[t8.length - 1].outSize : r[1], o = w.computeOptimalWindowSize(e); + let e = t8.length ? t8[t8.length - 1].outSize : r15[1], o = w.computeOptimalWindowSize(e); t8.push({ inSize: e, windowSize: o, outSize: Math.ceil(e / o) }); } return t8; } -function Yr(r, t8, e, o) { - let n = p9(r.shape), s = r; +function Xr(r15, t8, e, o) { + let n = p9(r15.shape), s = r15; for (let a = 0; a < n.length; a++) { let { inSize: i, windowSize: p, outSize: u } = n[a], c, l; - e === "mean" ? c = a === 0 ? new rm({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, i) : new rm({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }) : c = new gh({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, e), l = s, s = o.runWebGLProgram(c, [s], t8), l.dataId !== r.dataId && o.disposeIntermediateTensorInfo(l); + e === "mean" ? c = a === 0 ? new em({ windowSize: p, inSize: i, batchSize: r15.shape[0], outSize: u }, i) : new em({ windowSize: p, inSize: i, batchSize: r15.shape[0], outSize: u }) : c = new hh({ windowSize: p, inSize: i, batchSize: r15.shape[0], outSize: u }, e), l = s, s = o.runWebGLProgram(c, [s], t8), l.dataId !== r15.dataId && o.disposeIntermediateTensorInfo(l); } return s; } -var xh = class { +var gh = class { constructor(t8, e) { this.variableNames = ["A"]; let o = new Array(t8.length); @@ -19081,16 +19082,16 @@ var xh = class { `; } }; -function c9(r) { - let t8 = r.length; +function c9(r15) { + let t8 = r15.length; if (t8 > 6) throw Error(`Transpose for rank ${t8} is not yet supported`); let e = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"], o = new Array(t8); - for (let n = 0; n < r.length; n++) - o[r[n]] = e[n]; + for (let n = 0; n < r15.length; n++) + o[r15[n]] = e[n]; return o.join(); } -var yh = class { +var xh = class { constructor(t8, e) { this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true; let o = new Array(t8.length); @@ -19098,7 +19099,7 @@ var yh = class { o[c] = t8[e[c]]; if (this.outputShape = o, this.rank = o.length, this.rank > 6) throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`); - let n = Re(this.rank), s = lv("rc", this.rank), a = new Array(this.rank); + let n = Re(this.rank), s = mv("rc", this.rank), a = new Array(this.rank); for (let c = 0; c < e.length; c++) a[e[c]] = s[c]; let i = `vec2(${a.slice(-2).join()})`, p = `++${s[this.rank - 1]} < ${o[this.rank - 1]}`, u = `getChannel(getA(${a.join()}), ${i})`; @@ -19122,54 +19123,54 @@ var yh = class { `; } }; -function Su(r, t8, e) { - let o = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new yh(r.shape, t8) : new xh(r.shape, t8); - return e.runWebGLProgram(o, [r], r.dtype); +function gu(r15, t8, e) { + let o = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new xh(r15.shape, t8) : new gh(r15.shape, t8); + return e.runWebGLProgram(o, [r15], r15.dtype); } -function KD(r, t8, e, o) { - let n = t8, s = r.shape.length, a = y.parseAxisParam(n, r.shape), i = a, p = w.getAxesPermutation(i, s), u = p != null, c = r; - u && (c = Su(r, p, o), i = w.getInnerMostAxes(i.length, s)), w.assertAxesAreInnerMostDims("sum", i, s); +function qD(r15, t8, e, o) { + let n = t8, s = r15.shape.length, a = y.parseAxisParam(n, r15.shape), i = a, p = w.getAxesPermutation(i, s), u = p != null, c = r15; + u && (c = gu(r15, p, o), i = w.getInnerMostAxes(i.length, s)), w.assertAxesAreInnerMostDims("sum", i, s); let [l, m] = w.computeOutAndReduceShapes(c.shape, i), d = l; e && (d = w.expandShapeToKeepDim(l, a)); - let f = y.sizeFromShape(m), g = y.sizeFromShape(r.shape) / f, x = te({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: o }), b = ni(r.dtype), C = Yr(x, b, "sum", o), S = te({ inputs: { x: C }, attrs: { shape: d }, backend: o }); + let f = y.sizeFromShape(m), g = y.sizeFromShape(r15.shape) / f, x = te({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: o }), b = oi(r15.dtype), C = Xr(x, b, "sum", o), S = te({ inputs: { x: C }, attrs: { shape: d }, backend: o }); return o.disposeIntermediateTensorInfo(x), o.disposeIntermediateTensorInfo(C), u && o.disposeIntermediateTensorInfo(c), S; } -function Tp(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, keepDims: a } = o; - return KD(n, s, a, e); +function bp(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, keepDims: a } = o; + return qD(n, s, a, e); } -var qD = { kernelName: Is, backendName: "webgl", kernelFunc: Tp }; -function Ct(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { perm: s } = o, a = e, i = n.shape.length, p = new Array(i); +var jD = { kernelName: Ss, backendName: "webgl", kernelFunc: bp }; +function bt(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { perm: s } = o, a = e, i = n.shape.length, p = new Array(i); for (let c = 0; c < p.length; c++) p[c] = n.shape[s[c]]; let u; if (a.shouldExecuteOnCPU([n])) { - let l = a.texData.get(n.dataId).values, m = Np(l, n.shape, n.dtype, s, p); + let l = a.texData.get(n.dataId).values, m = yp(l, n.shape, n.dtype, s, p); u = a.makeTensorInfo(p, n.dtype); let d = a.texData.get(u.dataId); d.values = m; } else - u = Su(n, s, a); + u = gu(n, s, a); return u; } -var jD = { kernelName: co, backendName: "webgl", kernelFunc: Ct }; -var yv = 1e3; -function _p({ a: r, b: t8, transposeA: e, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) { - let u = r.shape.length, c = t8.shape.length, l = e ? r.shape[u - 2] : r.shape[u - 1], m = o ? t8.shape[c - 1] : t8.shape[c - 2], d = e ? r.shape[u - 1] : r.shape[u - 2], f = o ? t8.shape[c - 2] : t8.shape[c - 1], h = r.shape.slice(0, -2), g = t8.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), S = Ir.assertAndGetBroadcastShape(r.shape.slice(0, -2), t8.shape.slice(0, -2)).concat([d, f]); - y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${t8.shape} and transposeA=${e} and transposeB=${o} must match.`); - let k = e ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], E = te({ inputs: { x: r }, backend: n, attrs: { shape: k } }), R = te({ inputs: { x: t8 }, backend: n, attrs: { shape: _ } }), D = [E, R], P = Math.max(x, b), O = e ? E.shape[1] : E.shape[2], M = s != null, L = a != null, B = p === "leakyrelu", z = p != null ? Ci(p, true) : null, U = M || L || B || z != null, j; - if ((d === 1 || f === 1) && O > yv && U === false) { +var XD = { kernelName: po, backendName: "webgl", kernelFunc: bt }; +var bv = 1e3; +function Cp({ a: r15, b: t8, transposeA: e, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) { + let u = r15.shape.length, c = t8.shape.length, l = e ? r15.shape[u - 2] : r15.shape[u - 1], m = o ? t8.shape[c - 1] : t8.shape[c - 2], d = e ? r15.shape[u - 1] : r15.shape[u - 2], f = o ? t8.shape[c - 2] : t8.shape[c - 1], h = r15.shape.slice(0, -2), g = t8.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), S = Sr.assertAndGetBroadcastShape(r15.shape.slice(0, -2), t8.shape.slice(0, -2)).concat([d, f]); + y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r15.shape} and ${t8.shape} and transposeA=${e} and transposeB=${o} must match.`); + let k = e ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], E = te({ inputs: { x: r15 }, backend: n, attrs: { shape: k } }), R = te({ inputs: { x: t8 }, backend: n, attrs: { shape: _ } }), D = [E, R], P = Math.max(x, b), O = e ? E.shape[1] : E.shape[2], M = s != null, L = a != null, B = p === "leakyrelu", z = p != null ? xi(p, true) : null, U = M || L || B || z != null, j; + if ((d === 1 || f === 1) && O > bv && U === false) { let Y = E, J = R; - e && (Y = Ct({ inputs: { x: E }, backend: n, attrs: { perm: [0, 2, 1] } }), D.push(Y)), o && (J = Ct({ inputs: { x: R }, backend: n, attrs: { perm: [0, 2, 1] } }), D.push(J)); + e && (Y = bt({ inputs: { x: E }, backend: n, attrs: { perm: [0, 2, 1] } }), D.push(Y)), o && (J = bt({ inputs: { x: R }, backend: n, attrs: { perm: [0, 2, 1] } }), D.push(J)); let re = f !== 1, ne = f === 1, ee = Y; re && (ee = te({ inputs: { x: Y }, backend: n, attrs: { shape: [P, O, 1] } }), D.push(ee)); let oe = f === 1 ? 2 : 1, ie = J; ne && (ie = te({ inputs: { x: J }, backend: n, attrs: { shape: [P, 1, O] } }), D.push(ie)); - let le = tm({ inputs: { a: ee, b: ie }, backend: n }); - j = Tp({ inputs: { x: le }, backend: n, attrs: { axis: oe, keepDims: true } }), D.push(le); + let le = Jl({ inputs: { a: ee, b: ie }, backend: n }); + j = bp({ inputs: { x: le }, backend: n, attrs: { axis: oe, keepDims: true } }), D.push(le); } else { - let Y = dt(r.dtype, t8.dtype), J = new Uc(k, _, [P, d, f], e, o, M, z, L, B), re = [E, R]; + let Y = dt(r15.dtype, t8.dtype), J = new Lc(k, _, [P, d, f], e, o, M, z, L, B), re = [E, R]; if (s != null && re.push(s), L && re.push(a), B) { let ne = n.makeTensorInfo([], "float32", y.createScalarValue(i, "float32")); re.push(ne), D.push(ne); @@ -19182,39 +19183,39 @@ function _p({ a: r, b: t8, transposeA: e, transposeB: o, backend: n, bias: s = n n.disposeIntermediateTensorInfo(Y); return q; } -function l9(r) { - let { inputs: t8, backend: e, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = t8, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o; - return _p({ a: n, b: s, transposeA: p, transposeB: u, backend: e, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c }); +function l9(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { a: n, b: s, bias: a, preluActivationWeights: i } = t8, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o; + return Cp({ a: n, b: s, transposeA: p, transposeB: u, backend: e, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c }); } -var XD = { kernelName: Io, backendName: "webgl", kernelFunc: l9 }; -var YD = "return abs(x);"; -function m9(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8; +var YD = { kernelName: So, backendName: "webgl", kernelFunc: l9 }; +var QD = "return abs(x);"; +function m9(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8; if (e.shouldExecuteOnCPU([o]) && o.dtype !== "complex64") { - let s = e.texData.get(o.dataId), a = ch(s.values); + let s = e.texData.get(o.dataId), a = ph(s.values); return e.makeTensorInfo(o.shape, o.dtype, a); } let n; - return A().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Fr(o.shape, YD) : n = new or(o.shape, YD), e.runWebGLProgram(n, [o], o.dtype); + return A().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Ar(o.shape, QD) : n = new tr(o.shape, QD), e.runWebGLProgram(n, [o], o.dtype); } -var QD = { kernelName: Xs, backendName: "webgl", kernelFunc: m9 }; -var d9 = Ut + ` +var ZD = { kernelName: Xs, backendName: "webgl", kernelFunc: m9 }; +var d9 = Wt + ` if (abs(x) > 1.) { return NAN; } return acos(x); `; var f9 = xe({ opSnippet: d9 }); -var ZD = { kernelName: Wo, backendName: "webgl", kernelFunc: f9 }; -var h9 = Ut + ` +var JD = { kernelName: Vo, backendName: "webgl", kernelFunc: f9 }; +var h9 = Wt + ` if (x < 1.0) return NAN; return log(x + sqrt(x * x - 1.0));`; var g9 = xe({ opSnippet: h9 }); -var JD = { kernelName: Uo, backendName: "webgl", kernelFunc: g9 }; -var eA = "return a + b;"; -var x9 = nt({ opSnippet: eA, packedOpSnippet: eA, supportsComplex: true, cpuKernelImpl: RR }); -var tA = { kernelName: uo, backendName: "webgl", kernelFunc: x9 }; -var bh = class { +var eA = { kernelName: Wo, backendName: "webgl", kernelFunc: g9 }; +var tA = "return a + b;"; +var x9 = nt({ opSnippet: tA, packedOpSnippet: tA, supportsComplex: true, cpuKernelImpl: DR }); +var rA = { kernelName: io, backendName: "webgl", kernelFunc: x9 }; +var yh = class { constructor(t8, e) { this.outputShape = [], this.outputShape = t8, this.variableNames = e.map((s, a) => `T${a}`); let o = []; @@ -19233,7 +19234,7 @@ var bh = class { `; } }; -var Ch = class { +var bh = class { constructor(t8, e) { this.outputShape = [], this.packedInputs = true, this.packedOutput = true, this.outputShape = t8, this.variableNames = e.map((s, a) => `T${a}`); let o = []; @@ -19252,22 +19253,22 @@ var Ch = class { `; } }; -function wh(r) { - let { inputs: t8, backend: e } = r, o = t8; +function Ch(r15) { + let { inputs: t8, backend: e } = r15, o = t8; if (o.length === 1) - return At({ inputs: { x: o[0] }, backend: e }); + return Dt({ inputs: { x: o[0] }, backend: e }); if (o.length > A().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")) { - let p = Math.floor(o.length / 2), u = wh({ inputs: o.slice(0, p), backend: e }), c = wh({ inputs: o.slice(p), backend: e }); - return wh({ inputs: [u, c], backend: e }); + let p = Math.floor(o.length / 2), u = Ch({ inputs: o.slice(0, p), backend: e }), c = Ch({ inputs: o.slice(p), backend: e }); + return Ch({ inputs: [u, c], backend: e }); } - let n = o.map((p) => p.dtype).reduce((p, u) => dt(p, u)), s = o.map((p) => p.shape), i = A().getBool("WEBGL_PACK") ? new Ch(o[0].shape, s) : new bh(o[0].shape, s); + let n = o.map((p) => p.dtype).reduce((p, u) => dt(p, u)), s = o.map((p) => p.shape), i = A().getBool("WEBGL_PACK") ? new bh(o[0].shape, s) : new yh(o[0].shape, s); return e.runWebGLProgram(i, o, n); } -var rA = { kernelName: Go, backendName: "webgl", kernelFunc: wh }; -function y9(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = n; - c != null && (l = Ct({ inputs: { x: n }, backend: e, attrs: { perm: c } }), u = w.getInnerMostAxes(u.length, i)), w.assertAxesAreInnerMostDims("all", u, i); - let [m, d] = w.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: e, attrs: { shape: [-1, f] } }), g = Yr(h, h.dtype, "all", e), x; +var oA = { kernelName: Uo, backendName: "webgl", kernelFunc: Ch }; +function y9(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = n; + c != null && (l = bt({ inputs: { x: n }, backend: e, attrs: { perm: c } }), u = w.getInnerMostAxes(u.length, i)), w.assertAxesAreInnerMostDims("all", u, i); + let [m, d] = w.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: e, attrs: { shape: [-1, f] } }), g = Xr(h, h.dtype, "all", e), x; if (a) { let b = w.expandShapeToKeepDim(m, p); x = te({ inputs: { x: g }, backend: e, attrs: { shape: b } }); @@ -19275,11 +19276,11 @@ function y9(r) { x = te({ inputs: { x: g }, backend: e, attrs: { shape: m } }); return e.disposeIntermediateTensorInfo(h), e.disposeIntermediateTensorInfo(g), c != null && e.disposeIntermediateTensorInfo(l), x; } -var oA = { kernelName: Ho, backendName: "webgl", kernelFunc: y9 }; -function b9(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = n; - c != null && (l = Ct({ inputs: { x: n }, backend: e, attrs: { perm: c } }), u = w.getInnerMostAxes(u.length, i)), w.assertAxesAreInnerMostDims("any", u, i); - let [m, d] = w.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: e, attrs: { shape: [-1, f] } }), g = Yr(h, h.dtype, "any", e), x; +var nA = { kernelName: Go, backendName: "webgl", kernelFunc: y9 }; +function b9(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = n; + c != null && (l = bt({ inputs: { x: n }, backend: e, attrs: { perm: c } }), u = w.getInnerMostAxes(u.length, i)), w.assertAxesAreInnerMostDims("any", u, i); + let [m, d] = w.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: e, attrs: { shape: [-1, f] } }), g = Xr(h, h.dtype, "any", e), x; if (a) { let b = w.expandShapeToKeepDim(m, p); x = te({ inputs: { x: g }, backend: e, attrs: { shape: b } }); @@ -19287,8 +19288,8 @@ function b9(r) { x = te({ inputs: { x: g }, backend: e, attrs: { shape: m } }); return e.disposeIntermediateTensorInfo(h), e.disposeIntermediateTensorInfo(g), c != null && e.disposeIntermediateTensorInfo(l), x; } -var nA = { kernelName: Ko, backendName: "webgl", kernelFunc: b9 }; -var Sh = class { +var sA = { kernelName: Ho, backendName: "webgl", kernelFunc: b9 }; +var wh = class { constructor(t8, e, o) { this.variableNames = ["A"]; let { windowSize: n, batchSize: s, outSize: a } = t8; @@ -19317,12 +19318,12 @@ var Sh = class { `; } }; -var Ih = class { +var Sh = class { constructor(t8, e, o, n) { this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, y.assert(t8.length > 2, () => `Packed arg${o.charAt(0).toUpperCase() + o.slice(1)} supports only inputs with rank above 2.`); let s = t8[t8.length - 1], a = Math.ceil(s / e); this.outputShape = t8.slice(0, -1), a > 1 && this.outputShape.push(a), n || this.variableNames.push("bestIndicesA"); - let i = this.outputShape, p = i.length, u = Re(p), c = Dt("coords", p), l, m; + let i = this.outputShape, p = i.length, u = Re(p), c = Rt("coords", p), l, m; if (a === 1) { m = p + 1; let R = Re(m); @@ -19345,7 +19346,7 @@ var Ih = class { --${c[p - 1]}; ${u} sourceLocB = coords; --${c[p - 2]};`; - let d = ["x", "y", "z", "w", "u", "v"].slice(0, m), f = "." + d[m - 1], h = d.map((R) => "int " + R), g = Dt("sourceLocR", m - 1).concat("inIdx.r"), x = Dt("sourceLocG", m - 1).concat("inIdx.g"), b = Dt("sourceLocB", m - 1).concat("inIdx.b"), C = Dt("sourceLocA", m - 1).concat("inIdx.a"), S = o === "max" ? "greaterThan" : "lessThan", k = n ? "" : ` + let d = ["x", "y", "z", "w", "u", "v"].slice(0, m), f = "." + d[m - 1], h = d.map((R) => "int " + R), g = Rt("sourceLocR", m - 1).concat("inIdx.r"), x = Rt("sourceLocG", m - 1).concat("inIdx.g"), b = Rt("sourceLocB", m - 1).concat("inIdx.b"), C = Rt("sourceLocA", m - 1).concat("inIdx.a"), S = o === "max" ? "greaterThan" : "lessThan", k = n ? "" : ` inIdx = round(vec4(getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${x.join()}), getBestIndicesAChannel(${b.join()}), @@ -19395,70 +19396,70 @@ var Ih = class { `; } }; -function sA(r, t8, e, o = null) { +function aA(r15, t8, e, o = null) { let n = t8.shape[0], s = t8.shape[1]; o != null && (n = o.shape[0], s = o.shape[1]); - let a = w.computeOptimalWindowSize(s), i = { windowSize: a, inSize: s, batchSize: n, outSize: Math.ceil(s / a) }, p = new Sh(i, e, o == null), u = [t8]; + let a = w.computeOptimalWindowSize(s), i = { windowSize: a, inSize: s, batchSize: n, outSize: Math.ceil(s / a) }, p = new wh(i, e, o == null), u = [t8]; o != null && u.push(o); - let c = r.runWebGLProgram(p, u, "int32"); + let c = r15.runWebGLProgram(p, u, "int32"); if (c.shape[1] === 1) return c; - let l = sA(r, t8, e, c); - return r.disposeIntermediateTensorInfo(c), l; + let l = aA(r15, t8, e, c); + return r15.disposeIntermediateTensorInfo(c), l; } -function aA(r, t8, e, o = null) { - let n = o != null ? o.shape : t8.shape, s = n[n.length - 1], a = w.computeOptimalWindowSize(s), i = new Ih(n, a, e, o == null), p = o == null ? [t8] : [t8, o], u = r.runWebGLProgram(i, p, "int32"); +function iA(r15, t8, e, o = null) { + let n = o != null ? o.shape : t8.shape, s = n[n.length - 1], a = w.computeOptimalWindowSize(s), i = new Sh(n, a, e, o == null), p = o == null ? [t8] : [t8, o], u = r15.runWebGLProgram(i, p, "int32"); if (u.shape.length === t8.shape.length) { - let c = aA(r, t8, e, u); - return r.disposeIntermediateTensorInfo(u), c; + let c = iA(r15, t8, e, u); + return r15.disposeIntermediateTensorInfo(u), c; } return u; } -function vh(r, t8, e, o) { +function Ih(r15, t8, e, o) { let n = [e]; if (w.assertAxesAreInnerMostDims("arg" + o.charAt(0).toUpperCase() + o.slice(1), n, t8.shape.length), !A().getBool("WEBGL_PACK_REDUCE") || t8.shape.length <= 2) { - let s = [], a = r.texData.get(t8.dataId), i = a !== null && a.isPacked, p = t8; - i && (p = r.unpackTensor(t8), s.push(p)); - let [u, c] = w.computeOutAndReduceShapes(p.shape, n), l = y.sizeFromShape(c), m = te({ inputs: { x: p }, backend: r, attrs: { shape: [-1, l] } }); + let s = [], a = r15.texData.get(t8.dataId), i = a !== null && a.isPacked, p = t8; + i && (p = r15.unpackTensor(t8), s.push(p)); + let [u, c] = w.computeOutAndReduceShapes(p.shape, n), l = y.sizeFromShape(c), m = te({ inputs: { x: p }, backend: r15, attrs: { shape: [-1, l] } }); s.push(m); - let d = sA(r, m, o); + let d = aA(r15, m, o); s.push(d); - let f = te({ inputs: { x: d }, backend: r, attrs: { shape: u } }); - return s.forEach((h) => r.disposeIntermediateTensorInfo(h)), f; + let f = te({ inputs: { x: d }, backend: r15, attrs: { shape: u } }); + return s.forEach((h) => r15.disposeIntermediateTensorInfo(h)), f; } - return aA(r, t8, o); + return iA(r15, t8, o); } -function C9(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = Ct({ inputs: { x: n }, backend: e, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length); - let c = vh(e, p, a[0], "max"); +function C9(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; + i != null && (p = bt({ inputs: { x: n }, backend: e, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length); + let c = Ih(e, p, a[0], "max"); return u.forEach((l) => e.disposeIntermediateTensorInfo(l)), c; } -var iA = { kernelName: Ys, backendName: "webgl", kernelFunc: C9 }; -function w9(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = Ct({ inputs: { x: n }, backend: e, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length); - let c = vh(e, p, a[0], "min"); +var uA = { kernelName: Ys, backendName: "webgl", kernelFunc: C9 }; +function w9(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; + i != null && (p = bt({ inputs: { x: n }, backend: e, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length); + let c = Ih(e, p, a[0], "min"); return u.forEach((l) => e.disposeIntermediateTensorInfo(l)), c; } -var uA = { kernelName: Qs, backendName: "webgl", kernelFunc: w9 }; -var S9 = Ut + ` +var pA = { kernelName: Qs, backendName: "webgl", kernelFunc: w9 }; +var S9 = Wt + ` if (abs(x) > 1.) { return NAN; } return asin(x); `; var I9 = xe({ opSnippet: S9 }); -var pA = { kernelName: qo, backendName: "webgl", kernelFunc: I9 }; -var v9 = Ut + "return log(x + sqrt(x * x + 1.0));"; +var cA = { kernelName: Ko, backendName: "webgl", kernelFunc: I9 }; +var v9 = Wt + "return log(x + sqrt(x * x + 1.0));"; var k9 = xe({ opSnippet: v9 }); -var cA = { kernelName: jo, backendName: "webgl", kernelFunc: k9 }; -var N9 = Ut + ` +var lA = { kernelName: qo, backendName: "webgl", kernelFunc: k9 }; +var N9 = Wt + ` return atan(x); `; var T9 = xe({ opSnippet: N9 }); -var lA = { kernelName: Xo, backendName: "webgl", kernelFunc: T9 }; -var _9 = Wc + ` +var mA = { kernelName: jo, backendName: "webgl", kernelFunc: T9 }; +var _9 = Mc + ` return atan(a, b); `; var $9 = ` @@ -19466,16 +19467,16 @@ var $9 = ` bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - ` + Xr + ` + ` + jr + ` return result; `; var E9 = nt({ opSnippet: _9, packedOpSnippet: $9 }); -var mA = { kernelName: Qo, backendName: "webgl", kernelFunc: E9 }; -var R9 = Ut + ` +var dA = { kernelName: Yo, backendName: "webgl", kernelFunc: E9 }; +var R9 = Wt + ` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`; var D9 = xe({ opSnippet: R9 }); -var dA = { kernelName: Yo, backendName: "webgl", kernelFunc: D9 }; +var fA = { kernelName: Xo, backendName: "webgl", kernelFunc: D9 }; var Us = class { constructor(t8, e, o, n = false, s = false) { if (this.variableNames = ["x"], e === "avg" && o) @@ -19635,7 +19636,7 @@ var Us = class { `; } }; -var Iu = class { +var xu = class { constructor(t8, e, o, n = false, s = false) { if (this.variableNames = ["x"], e === "avg" && o) throw new Error("Cannot compute positions for average pool."); @@ -19816,24 +19817,24 @@ var Iu = class { `; } }; -function A9(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8; +function A9(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8; Vs(n, "avgPool"); let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1; y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); let c = w.computePool2DInfo(n.shape, s, a, u, i, p); if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape)) - return At({ inputs: { x: n }, backend: e }); + return Dt({ inputs: { x: n }, backend: e }); let l = new Us(c, "avg", false); return e.runWebGLProgram(l, [n], "float32"); } -var fA = { kernelName: Zo, backendName: "webgl", kernelFunc: A9 }; -function F9(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, p, u), m = new Iu(l, "avg", false); +var hA = { kernelName: Qo, backendName: "webgl", kernelFunc: A9 }; +function F9(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, p, u), m = new xu(l, "avg", false); return e.runWebGLProgram(m, [n], "float32"); } -var hA = { kernelName: Zs, backendName: "webgl", kernelFunc: F9 }; -var kh = class { +var gA = { kernelName: Zs, backendName: "webgl", kernelFunc: F9 }; +var vh = class { constructor(t8) { this.variableNames = ["dy"], this.outputShape = t8.inShape; let e = t8.filterHeight, o = t8.filterWidth, n = t8.strideHeight, s = t8.strideWidth, a = t8.dilationHeight, i = t8.dilationWidth, p = t8.effectiveFilterHeight, u = t8.effectiveFilterWidth, c = p - 1 - t8.padInfo.top, l = u - 1 - t8.padInfo.left, m = 1 / (e * o); @@ -19882,7 +19883,7 @@ var kh = class { `; } }; -var Nh = class { +var kh = class { constructor(t8) { this.variableNames = ["dy"], this.outputShape = t8.inShape; let e = t8.filterDepth, o = t8.filterHeight, n = t8.filterWidth, s = t8.strideDepth, a = t8.strideHeight, i = t8.strideWidth, p = t8.dilationDepth, u = t8.dilationHeight, c = t8.dilationWidth, l = t8.effectiveFilterDepth, m = t8.effectiveFilterHeight, d = t8.effectiveFilterWidth, f = l - 1 - t8.padInfo.front, h = m - 1 - t8.padInfo.top, g = d - 1 - t8.padInfo.left, x = 1 / (e * o * n); @@ -19945,24 +19946,24 @@ var Nh = class { `; } }; -function P9(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s } = t8, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = w.computePool3DInfo(a.shape, i, p, l, u, c), d = new Nh(m); +function P9(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s } = t8, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = w.computePool3DInfo(a.shape, i, p, l, u, c), d = new kh(m); return e.runWebGLProgram(d, [n], a.dtype); } -var gA = { kernelName: Ai, backendName: "webgl", kernelFunc: P9 }; -function O9(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s } = t8, a = s; +var xA = { kernelName: Ei, backendName: "webgl", kernelFunc: P9 }; +function O9(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s } = t8, a = s; Vs([n, s], "avgPoolGrad"); - let { filterSize: i, strides: p, pad: u } = o, c = w.computePool2DInfo(a.shape, i, p, 1, u), l = new kh(c); + let { filterSize: i, strides: p, pad: u } = o, c = w.computePool2DInfo(a.shape, i, p, 1, u), l = new vh(c); return e.runWebGLProgram(l, [n], a.dtype); } -var xA = { kernelName: Di, backendName: "webgl", kernelFunc: O9 }; -function M9(r) { - let { inputs: t8, backend: e, attrs: o } = r, { a: n, b: s } = t8, { transposeA: a, transposeB: i } = o; - return _p({ a: n, b: s, transposeA: a, transposeB: i, backend: e }); +var yA = { kernelName: $i, backendName: "webgl", kernelFunc: O9 }; +function M9(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { a: n, b: s } = t8, { transposeA: a, transposeB: i } = o; + return Cp({ a: n, b: s, transposeA: a, transposeB: i, backend: e }); } -var yA = { kernelName: Jo, backendName: "webgl", kernelFunc: M9 }; -var Th = class { +var bA = { kernelName: Zo, backendName: "webgl", kernelFunc: M9 }; +var Nh = class { constructor(t8, e, o, n, s, a) { this.outputShape = [], this.variableNames = ["x", "mean", "variance"], w.assertAndGetBroadcastShape(t8, e), w.assertAndGetBroadcastShape(t8, o); let i = "0.0"; @@ -19981,7 +19982,7 @@ var Th = class { `; } }; -var _h = class { +var Th = class { constructor(t8, e, o, n, s, a) { this.packedInputs = true, this.packedOutput = true, this.variableNames = ["x", "mean", "variance"], w.assertAndGetBroadcastShape(t8, e), w.assertAndGetBroadcastShape(t8, o); let i = "vec4(0.0)"; @@ -20003,8 +20004,8 @@ var _h = class { `; } }; -var L9 = ({ inputs: r, backend: t8, attrs: e }) => { - let { x: o, mean: n, variance: s, offset: a, scale: i } = r; +var L9 = ({ inputs: r15, backend: t8, attrs: e }) => { + let { x: o, mean: n, variance: s, offset: a, scale: i } = r15; y.assert(n.shape.length === s.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), y.assert(a == null || n.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), y.assert(i == null || n.shape.length === i.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."); let { varianceEpsilon: p } = e; p == null && (p = 1e-3); @@ -20012,16 +20013,16 @@ var L9 = ({ inputs: r, backend: t8, attrs: e }) => { a != null && (c = a.shape, u.push(a)); let l = null; i != null && (l = i.shape, u.push(i)); - let m = A().getBool("WEBGL_PACK_NORMALIZATION") ? new _h(o.shape, n.shape, s.shape, c, l, p) : new Th(o.shape, n.shape, s.shape, c, l, p); + let m = A().getBool("WEBGL_PACK_NORMALIZATION") ? new Th(o.shape, n.shape, s.shape, c, l, p) : new Nh(o.shape, n.shape, s.shape, c, l, p); return t8.runWebGLProgram(m, u, u[0].dtype); }; -var bA = { kernelName: vn, backendName: "webgl", kernelFunc: L9 }; -var $h = class { +var CA = { kernelName: In, backendName: "webgl", kernelFunc: L9 }; +var _h = class { constructor(t8) { this.variableNames = ["source"], this.outputShape = t8, this.rank = t8.length; let e = Re(this.rank); this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }]; - let o = B9(this.rank), n, s = t8.map((a, i) => `sourceLoc.${bv[i]} = start[${i}] + coords.${bv[i]};`); + let o = B9(this.rank), n, s = t8.map((a, i) => `sourceLoc.${Cv[i]} = start[${i}] + coords.${Cv[i]};`); n = ` ${e} sourceLoc; ${e} coords = getOutputCoords(); @@ -20035,18 +20036,18 @@ var $h = class { `; } }; -var bv = ["x", "y", "z", "w", "u", "v"]; -function B9(r) { - if (r === 1) +var Cv = ["x", "y", "z", "w", "u", "v"]; +function B9(r15) { + if (r15 === 1) return "sourceLoc"; - if (r <= 6) - return bv.slice(0, r).map((t8) => "sourceLoc." + t8).join(","); - throw Error(`Slicing for rank ${r} is not yet supported`); + if (r15 <= 6) + return Cv.slice(0, r15).map((t8) => "sourceLoc." + t8).join(","); + throw Error(`Slicing for rank ${r15} is not yet supported`); } -var Eh = class { +var $h = class { constructor(t8) { this.variableNames = ["source"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t8, this.rank = t8.length, this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }]; - let e = Re(this.rank), o = Dt("coords", this.rank), n = Dt("sourceLoc", this.rank), s = this.rank === 1 ? "sourceLoc" : `vec2(${n.slice(-2).join()})`, a = `getChannel(getSource(${n.join()}), ${s})`, i = ` + let e = Re(this.rank), o = Rt("coords", this.rank), n = Rt("sourceLoc", this.rank), s = this.rank === 1 ? "sourceLoc" : `vec2(${n.slice(-2).join()})`, a = `getChannel(getSource(${n.join()}), ${s})`, i = ` result.x = ${a}; if (++${o[this.rank - 1]} < ${t8[this.rank - 1]}) { ++${n[this.rank - 1]}; @@ -20079,42 +20080,42 @@ var Eh = class { `; } }; -function z9(r, t8, e, o) { - let n = o.texData.get(r.dataId), s = o.makeTensorInfo(e, r.dtype), a = o.texData.get(s.dataId); - Object.assign(a, n), a.refCount = 1, a.shape = e, a.dtype = r.dtype; - let i = ct.computeFlatOffset(t8, y.computeStrides(r.shape)); - n.slice && (i += n.slice.flatOffset), a.slice = { flatOffset: i, origDataId: n.slice && n.slice.origDataId || r.dataId }; +function z9(r15, t8, e, o) { + let n = o.texData.get(r15.dataId), s = o.makeTensorInfo(e, r15.dtype), a = o.texData.get(s.dataId); + Object.assign(a, n), a.refCount = 1, a.shape = e, a.dtype = r15.dtype; + let i = pt.computeFlatOffset(t8, y.computeStrides(r15.shape)); + n.slice && (i += n.slice.flatOffset), a.slice = { flatOffset: i, origDataId: n.slice && n.slice.origDataId || r15.dataId }; let p = o.dataRefCount.get(a.slice.origDataId) || 1; return o.dataRefCount.set(a.slice.origDataId, p + 1), s; } -function Gs(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { begin: s, size: a } = o, [i, p] = ct.parseSliceParams(n, s, a); - if (ct.assertParamsValid(n, i, p), y.sizeFromShape(p) === 0) +function Gs(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { begin: s, size: a } = o, [i, p] = pt.parseSliceParams(n, s, a); + if (pt.assertParamsValid(n, i, p), y.sizeFromShape(p) === 0) return e.makeTensorInfo(p, n.dtype, []); if (e.shouldExecuteOnCPU([n]) || n.dtype === "string") { - let l = e.texData.get(n.dataId), m = pD(l.values, i, p, n.shape, n.dtype); + let l = e.texData.get(n.dataId), m = cD(l.values, i, p, n.shape, n.dtype); return e.makeTensorInfo(p, n.dtype, m); } - let { isPacked: u } = e.texData.get(n.dataId), c = ct.isSliceContinous(n.shape, i, p); + let { isPacked: u } = e.texData.get(n.dataId), c = pt.isSliceContinous(n.shape, i, p); if (u || !c) { - let l = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new Eh(p) : new $h(p), m = [i]; + let l = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new $h(p) : new _h(p), m = [i]; return e.runWebGLProgram(l, [n], n.dtype, m); } return e.uploadToGPU(n.dataId), z9(n, i, p, e); } -var CA = { kernelName: ha, backendName: "webgl", kernelFunc: Gs }; -var V9 = (r) => { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { blockShape: s, crops: a } = o; +var wA = { kernelName: ha, backendName: "webgl", kernelFunc: Gs }; +var V9 = (r15) => { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { blockShape: s, crops: a } = o; y.assert(n.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"); - let i = s.reduce((b, C) => b * C), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = [], f = te({ inputs: { x: n }, backend: e, attrs: { shape: p } }), h = Ct({ inputs: { x: f }, backend: e, attrs: { perm: u } }), g = te({ inputs: { x: h }, backend: e, attrs: { shape: c } }), x = Gs({ inputs: { x: g }, backend: e, attrs: { begin: l, size: m } }); + let i = s.reduce((b, C) => b * C), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = [], f = te({ inputs: { x: n }, backend: e, attrs: { shape: p } }), h = bt({ inputs: { x: f }, backend: e, attrs: { perm: u } }), g = te({ inputs: { x: h }, backend: e, attrs: { shape: c } }), x = Gs({ inputs: { x: g }, backend: e, attrs: { begin: l, size: m } }); return d.push(f), d.push(h), d.push(g), d.forEach((b) => e.disposeIntermediateTensorInfo(b)), x; }; -var wA = { kernelName: Js, backendName: "webgl", kernelFunc: V9 }; -function W9(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, weights: s } = t8, { size: a } = o, i = e.readSync(n.dataId), p = e.readSync(s.dataId), u = ph(i, p, s.dtype, s.shape, a); +var SA = { kernelName: Js, backendName: "webgl", kernelFunc: V9 }; +function W9(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, weights: s } = t8, { size: a } = o, i = e.readSync(n.dataId), p = e.readSync(s.dataId), u = uh(i, p, s.dtype, s.shape, a); return e.makeTensorInfo([a], s.dtype, u); } -var SA = { kernelName: en, backendName: "webgl", kernelFunc: W9 }; +var IA = { kernelName: Jo, backendName: "webgl", kernelFunc: W9 }; var U9 = ` int r = int(a.r) & int(b.r); int g = int(a.g) & int(b.g); @@ -20125,67 +20126,67 @@ var U9 = ` var G9 = ` return float(int(a.r) & int(b.r)); `; -function H9(r) { - let { inputs: t8, backend: e } = r, { a: o, b: n } = t8, s = A().getBool("WEBGL_PACK_BINARY_OPERATIONS"), a = A().getNumber("WEBGL_VERSION"); +function H9(r15) { + let { inputs: t8, backend: e } = r15, { a: o, b: n } = t8, s = A().getBool("WEBGL_PACK_BINARY_OPERATIONS"), a = A().getNumber("WEBGL_VERSION"); if (e.shouldExecuteOnCPU([o, n]) || a === 1) { - let p = e.texData.get(o.dataId).values, u = e.texData.get(n.dataId).values, [c, l] = AR(o.shape, n.shape, p, u, o.dtype), m = e.makeTensorInfo(l, o.dtype), d = e.texData.get(m.dataId); + let p = e.texData.get(o.dataId).values, u = e.texData.get(n.dataId).values, [c, l] = FR(o.shape, n.shape, p, u, o.dtype), m = e.makeTensorInfo(l, o.dtype), d = e.texData.get(m.dataId); return d.values = c, m; } let i; - return s ? i = new jr(U9, o.shape, n.shape, false) : i = new Pr(G9, o.shape, n.shape), e.runWebGLProgram(i, [o, n], o.dtype); + return s ? i = new qr(U9, o.shape, n.shape, false) : i = new Fr(G9, o.shape, n.shape), e.runWebGLProgram(i, [o, n], o.dtype); } -var IA = { kernelName: ja, backendName: "webgl", kernelFunc: H9 }; -function K9(r) { - let { inputs: t8, backend: e } = r, { s0: o, s1: n } = t8, s = e.readSync(o.dataId), a = e.readSync(n.dataId), i = w.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); +var vA = { kernelName: qa, backendName: "webgl", kernelFunc: H9 }; +function K9(r15) { + let { inputs: t8, backend: e } = r15, { s0: o, s1: n } = t8, s = e.readSync(o.dataId), a = e.readSync(n.dataId), i = w.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); return e.makeTensorInfo([i.length], "int32", Int32Array.from(i)); } -var vA = { kernelName: ea, backendName: "webgl", kernelFunc: K9 }; +var kA = { kernelName: ea, backendName: "webgl", kernelFunc: K9 }; var q9 = "return float(a != b);"; -var Cv = nt({ opSnippet: q9, cpuKernelImpl: eD, dtype: "bool" }); -var kA = { kernelName: Qn, backendName: "webgl", kernelFunc: Cv }; -function wi(r) { - let { inputs: t8, backend: e } = r, { input: o } = t8, n = e.texData.get(o.dataId); - return At({ inputs: { x: n.complexTensorInfos.real }, backend: e }); +var wv = nt({ opSnippet: q9, cpuKernelImpl: tD, dtype: "bool" }); +var NA = { kernelName: Yn, backendName: "webgl", kernelFunc: wv }; +function yi(r15) { + let { inputs: t8, backend: e } = r15, { input: o } = t8, n = e.texData.get(o.dataId); + return Dt({ inputs: { x: n.complexTensorInfos.real }, backend: e }); } -var NA = { kernelName: qi, backendName: "webgl", kernelFunc: wi }; +var TA = { kernelName: Gi, backendName: "webgl", kernelFunc: yi }; var j9 = "return float(int(x));"; -function TA(r, t8) { - let e = new or(r.shape, j9), o = t8.runWebGLProgram(e, [r], "int32"); +function _A(r15, t8) { + let e = new tr(r15.shape, j9), o = t8.runWebGLProgram(e, [r15], "int32"); return { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; } -function wv(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { dtype: s } = o; +function Sv(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { dtype: s } = o; if (s === "complex64") { if (n.dtype === "complex64") - return At({ inputs: { x: n }, backend: e }); - let a = Gr(n.shape), i = wv({ inputs: { x: n }, backend: e, attrs: { dtype: "float32" } }), p = Or({ inputs: { real: i, imag: a }, backend: e }); + return Dt({ inputs: { x: n }, backend: e }); + let a = Ur(n.shape), i = Sv({ inputs: { x: n }, backend: e, attrs: { dtype: "float32" } }), p = Pr({ inputs: { real: i, imag: a }, backend: e }); return a.dispose(), e.disposeIntermediateTensorInfo(i), p; } if (n.dtype === "complex64") { - let a = wi({ inputs: { input: n }, backend: e }), i = wv({ inputs: { x: a }, backend: e, attrs: { dtype: s } }); + let a = yi({ inputs: { input: n }, backend: e }), i = Sv({ inputs: { x: a }, backend: e, attrs: { dtype: s } }); return e.disposeIntermediateTensorInfo(a), i; } if (!y.hasEncodingLoss(n.dtype, s)) { - let a = At({ inputs: { x: n }, backend: e }); + let a = Dt({ inputs: { x: n }, backend: e }); return { dataId: a.dataId, shape: a.shape, dtype: s }; } if (e.shouldExecuteOnCPU([n])) { - let a = e.texData.get(n.dataId).values, [i, p, u] = FR(a, n.shape, n.dtype, s); + let a = e.texData.get(n.dataId).values, [i, p, u] = PR(a, n.shape, n.dtype, s); return e.makeTensorInfo(i, p, u); } if (s === "int32") - return TA(n, e); + return _A(n, e); if (s === "bool") { - let a = e.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = Cv({ inputs: { a: n, b: a }, backend: e }); + let a = e.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = wv({ inputs: { a: n, b: a }, backend: e }); return e.disposeIntermediateTensorInfo(a), p; } throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`); } -var _A = { kernelName: bo, backendName: "webgl", kernelFunc: wv }; -var $A = "return ceil(x);"; -var X9 = xe({ opSnippet: $A, packedOpSnippet: $A, cpuKernelImpl: PR }); -var EA = { kernelName: tn, backendName: "webgl", kernelFunc: X9 }; -var Rh = class { +var $A = { kernelName: yo, backendName: "webgl", kernelFunc: Sv }; +var EA = "return ceil(x);"; +var X9 = xe({ opSnippet: EA, packedOpSnippet: EA, cpuKernelImpl: OR }); +var RA = { kernelName: en, backendName: "webgl", kernelFunc: X9 }; +var Eh = class { constructor(t8) { this.variableNames = ["A"], this.customUniforms = [{ name: "minVal", type: "float" }, { name: "maxVal", type: "float" }], this.outputShape = t8, this.userCode = ` @@ -20201,7 +20202,7 @@ var Rh = class { `; } }; -var Dh = class { +var Rh = class { constructor(t8) { this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "minVal", type: "float" }, { name: "maxVal", type: "float" }], this.outputShape = t8, this.userCode = ` void main() { @@ -20217,14 +20218,14 @@ var Dh = class { `; } }; -function Y9(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { clipValueMin: s, clipValueMax: a } = o, i; - A().getBool("WEBGL_PACK_CLIP") ? i = new Dh(n.shape) : i = new Rh(n.shape); +function Y9(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { clipValueMin: s, clipValueMax: a } = o, i; + A().getBool("WEBGL_PACK_CLIP") ? i = new Rh(n.shape) : i = new Eh(n.shape); let p = [[s], [a]]; return e.runWebGLProgram(i, [n], n.dtype, p); } -var RA = { kernelName: Co, backendName: "webgl", kernelFunc: Y9 }; -var Ah = class { +var DA = { kernelName: bo, backendName: "webgl", kernelFunc: Y9 }; +var Dh = class { constructor(t8) { this.variableNames = ["real", "imag"], this.outputShape = t8, this.userCode = ` void main() { @@ -20242,15 +20243,15 @@ var Ah = class { `; } }; -function DA(r, t8) { - return { dataId: t8.dataId, dtype: t8.dtype, shape: r.shape }; +function AA(r15, t8) { + return { dataId: t8.dataId, dtype: t8.dtype, shape: r15.shape }; } -function Q9(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8, n = e.texData.get(o.dataId), s = new Ah(o.shape), a = [DA(o, n.complexTensorInfos.real), DA(o, n.complexTensorInfos.imag)]; +function Q9(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8, n = e.texData.get(o.dataId), s = new Dh(o.shape), a = [AA(o, n.complexTensorInfos.real), AA(o, n.complexTensorInfos.imag)]; return e.runWebGLProgram(s, a, a[0].dtype); } -var AA = { kernelName: Pi, backendName: "webgl", kernelFunc: Q9 }; -var Fh = class { +var FA = { kernelName: Di, backendName: "webgl", kernelFunc: Q9 }; +var Ah = class { constructor(t8) { this.outputShape = [], this.outputShape = w.computeOutShape(t8, 1), this.variableNames = t8.map((a, i) => `T${i}`); let e = new Array(t8.length - 1); @@ -20275,10 +20276,10 @@ var Fh = class { `; } }; -var Oh = class { +var Ph = class { constructor(t8, e) { this.packedInputs = true, this.packedOutput = true, this.outputShape = [], this.outputShape = w.computeOutShape(t8, e); - let o = this.outputShape, n = o.length, s = Re(n), a = Dt("coords", n), i = ["x", "y", "z", "w", "u", "v"].slice(0, n); + let o = this.outputShape, n = o.length, s = Re(n), a = Rt("coords", n), i = ["x", "y", "z", "w", "u", "v"].slice(0, n); this.variableNames = t8.map((h, g) => `T${g}`); let p = new Array(t8.length - 1); p[0] = t8[0][e]; @@ -20293,15 +20294,15 @@ var Oh = class { m += ` if (${u} < ${p[h]} && ${u} >= ${p[h - 1]}) { return getChannel( - getT${h}(${Ph(i, u, g)}), - vec2(${Ph(c, u, g)})); + getT${h}(${Fh(i, u, g)}), + vec2(${Fh(c, u, g)})); }`; } let d = p.length, f = p[p.length - 1]; m += ` return getChannel( - getT${d}(${Ph(i, u, f)}), - vec2(${Ph(c, u, f)}));`, this.userCode = ` + getT${d}(${Fh(i, u, f)}), + vec2(${Fh(c, u, f)}));`, this.userCode = ` float getValue(${i.map((h) => "int " + h)}) { ${m} } @@ -20330,70 +20331,70 @@ var Oh = class { `; } }; -function Ph(r, t8, e) { - let o = r.indexOf(t8); - return r.map((s, a) => a === o ? `${s} - ${e}` : s).join(); +function Fh(r15, t8, e) { + let o = r15.indexOf(t8); + return r15.map((s, a) => a === o ? `${s} - ${e}` : s).join(); } -function $p(r) { - let { inputs: t8, backend: e } = r, { input: o } = t8, n = e.texData.get(o.dataId); - return At({ inputs: { x: n.complexTensorInfos.imag }, backend: e }); +function wp(r15) { + let { inputs: t8, backend: e } = r15, { input: o } = t8, n = e.texData.get(o.dataId); + return Dt({ inputs: { x: n.complexTensorInfos.imag }, backend: e }); } -var FA = { kernelName: Gi, backendName: "webgl", kernelFunc: $p }; -function Gc(r, t8, e) { - let o = r[0].dtype; +var PA = { kernelName: Vi, backendName: "webgl", kernelFunc: wp }; +function Bc(r15, t8, e) { + let o = r15[0].dtype; if (o === "complex64") { - let d = r.map((b) => wi({ inputs: { input: b }, backend: e })), f = r.map((b) => $p({ inputs: { input: b }, backend: e })), h = Gc(d, t8, e), g = Gc(f, t8, e), x = Or({ inputs: { real: h, imag: g }, backend: e }); + let d = r15.map((b) => yi({ inputs: { input: b }, backend: e })), f = r15.map((b) => wp({ inputs: { input: b }, backend: e })), h = Bc(d, t8, e), g = Bc(f, t8, e), x = Pr({ inputs: { real: h, imag: g }, backend: e }); return d.forEach((b) => e.disposeIntermediateTensorInfo(b)), f.forEach((b) => e.disposeIntermediateTensorInfo(b)), e.disposeIntermediateTensorInfo(h), e.disposeIntermediateTensorInfo(g), x; } - let n = e.shouldExecuteOnCPU(r); + let n = e.shouldExecuteOnCPU(r15); if (o === "string" && (n = true), n) { - let d = r.map((S) => { + let d = r15.map((S) => { let _ = [-1, y.sizeFromShape(S.shape.slice(t8))]; return te({ inputs: { x: S }, backend: e, attrs: { shape: _ } }); - }), f = d.map((S) => ({ vals: e.readSync(S.dataId), shape: S.shape })), h = w.computeOutShape(d.map((S) => S.shape), 1), g = d[0].shape[0] === 1, x = OR(f, h, o, g), b = w.computeOutShape(r.map((S) => S.shape), t8), C = e.makeTensorInfo(b, o, x); + }), f = d.map((S) => ({ vals: e.readSync(S.dataId), shape: S.shape })), h = w.computeOutShape(d.map((S) => S.shape), 1), g = d[0].shape[0] === 1, x = MR(f, h, o, g), b = w.computeOutShape(r15.map((S) => S.shape), t8), C = e.makeTensorInfo(b, o, x); return d.forEach((S) => e.disposeIntermediateTensorInfo(S)), C; } - let s = r.filter((d) => y.sizeFromShape(d.shape) > 0), a = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && s[0].shape.length > 1; + let s = r15.filter((d) => y.sizeFromShape(d.shape) > 0), a = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && s[0].shape.length > 1; if (s.length === 1) { - let d = a ? new or(r[0].shape, Ba) : new Fr(r[0].shape, Ba); - return e.runWebGLProgram(d, r, o); + let d = a ? new tr(r15[0].shape, La) : new Ar(r15[0].shape, La); + return e.runWebGLProgram(d, r15, o); } let i = A().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER"); if (s.length > i) { let d = []; for (let h = 0; h < s.length; h += i) { let g = s.slice(h, h + i); - d.push(Gc(g, t8, e)); + d.push(Bc(g, t8, e)); } - let f = Gc(d, t8, e); + let f = Bc(d, t8, e); for (let h of d) e.disposeIntermediateTensorInfo(h); return f; } if (a) { - let d = new Oh(s.map((f) => f.shape), t8); + let d = new Ph(s.map((f) => f.shape), t8); return e.runWebGLProgram(d, s, o); } - let { tensors2D: p, outShape: u } = Z9(s, t8, e), c = new Fh(p.map((d) => d.shape)), l = e.runWebGLProgram(c, p, o); + let { tensors2D: p, outShape: u } = Z9(s, t8, e), c = new Ah(p.map((d) => d.shape)), l = e.runWebGLProgram(c, p, o); p.forEach((d) => e.disposeIntermediateTensorInfo(d)); let m = te({ inputs: { x: l }, attrs: { shape: u }, backend: e }); return e.disposeIntermediateTensorInfo(l), m; } -function Z9(r, t8, e) { - let o = w.computeOutShape(r.map((s) => s.shape), t8); - return { tensors2D: r.map((s) => te({ inputs: { x: s }, attrs: { shape: [-1, y.sizeFromShape(s.shape.slice(t8))] }, backend: e })), outShape: o }; +function Z9(r15, t8, e) { + let o = w.computeOutShape(r15.map((s) => s.shape), t8); + return { tensors2D: r15.map((s) => te({ inputs: { x: s }, attrs: { shape: [-1, y.sizeFromShape(s.shape.slice(t8))] }, backend: e })), outShape: o }; } -function Sv(r) { - let { inputs: t8, backend: e, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, t8[0].shape)[0], a = t8.map((u) => u.shape); +function Iv(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { axis: n } = o, s = y.parseAxisParam(n, t8[0].shape)[0], a = t8.map((u) => u.shape); w.assertParamsConsistent(a, s); let i = w.computeOutShape(t8.map((u) => u.shape), s); if (y.sizeFromShape(i) === 0) return e.makeTensorInfo(i, t8[0].dtype, []); let p = t8.filter((u) => y.sizeFromShape(u.shape) > 0); - return p.length === 1 ? At({ inputs: { x: p[0] }, backend: e }) : Gc(p, s, e); + return p.length === 1 ? Dt({ inputs: { x: p[0] }, backend: e }) : Bc(p, s, e); } -var PA = { kernelName: ta, backendName: "webgl", kernelFunc: Sv }; -var Hc = class { +var OA = { kernelName: ta, backendName: "webgl", kernelFunc: Iv }; +var zc = class { constructor(t8, e = false, o = null, n = false, s = false) { this.variableNames = ["x", "W"], this.outputShape = t8.outShape; let a = t8.padInfo.top, i = t8.padInfo.left, p = t8.strideHeight, u = t8.strideWidth, c = t8.dilationHeight, l = t8.dilationWidth, m = t8.filterHeight, d = t8.filterWidth, f = Math.floor(t8.inChannels / 4) * 4, h = t8.inChannels % 4, g = t8.dataFormat === "channelsLast", x = g ? 1 : 2, b = g ? 2 : 3, C = g ? 3 : 1, S = "", k = ""; @@ -20536,7 +20537,7 @@ var Hc = class { `; } }; -var Mh = class { +var Oh = class { constructor(t8) { this.variableNames = ["x", "W"], this.outputShape = t8.outShape; let e = t8.padInfo.front, o = t8.padInfo.top, n = t8.padInfo.left, s = t8.strideDepth, a = t8.strideHeight, i = t8.strideWidth, p = t8.dilationDepth, u = t8.dilationHeight, c = t8.dilationWidth, l = t8.filterDepth, m = t8.filterHeight, d = t8.filterWidth, f = Math.floor(t8.inChannels / 4) * 4, h = t8.inChannels % 4; @@ -20631,9 +20632,9 @@ var Mh = class { `; } }; -var Kc = class { +var Vc = class { constructor(t8, e = false, o = null, n = false, s = false) { - this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = t8.outShape, this.enableShapeUniforms = pt(this.outputShape.length); + this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = t8.outShape, this.enableShapeUniforms = ut(this.outputShape.length); let a = t8.padInfo.left, i = t8.strideWidth, p = t8.dilationWidth, u = t8.filterHeight, c = t8.filterWidth, l = c, m = ` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`; @@ -20855,10 +20856,10 @@ var Kc = class { `; } }; -var Lh = class { +var Mh = class { constructor(t8, e) { - this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec4" }, { name: "pad", type: "ivec2" }, { name: "stride", type: "ivec2" }, { name: "dilation", type: "ivec2" }, { name: "inChannels", type: "int" }, { name: "itemsPerBlockRow", type: "int" }, { name: "outWidth", type: "int" }], this.outputShape = t8, this.enableShapeUniforms = pt(this.outputShape.length); - let { dataFormat: o } = e, n = vt(), s = o === "channelsLast", a = s ? 1 : 2, i = s ? 2 : 3, p = this.enableShapeUniforms ? "if(blockIndex < outShape[2] && pos < outShape[1]) {" : `if(blockIndex < ${t8[2]} && pos < ${t8[1]}) {`, u = ""; + this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec4" }, { name: "pad", type: "ivec2" }, { name: "stride", type: "ivec2" }, { name: "dilation", type: "ivec2" }, { name: "inChannels", type: "int" }, { name: "itemsPerBlockRow", type: "int" }, { name: "outWidth", type: "int" }], this.outputShape = t8, this.enableShapeUniforms = ut(this.outputShape.length); + let { dataFormat: o } = e, n = It(), s = o === "channelsLast", a = s ? 1 : 2, i = s ? 2 : 3, p = this.enableShapeUniforms ? "if(blockIndex < outShape[2] && pos < outShape[1]) {" : `if(blockIndex < ${t8[2]} && pos < ${t8[1]}) {`, u = ""; for (let c = 0; c <= 1; c++) for (let l = 0; l <= 1; l++) u += ` @@ -20912,50 +20913,50 @@ var Lh = class { `; } }; -function Bh(r, t8) { - let e = r.length; - return e >= 3 ? t8 ? [...r.slice(0, -3), r[e - 3] * r[e - 2], r[e - 1]] : [...r.slice(0, -3), r[e - 3], r[e - 2] * r[e - 1]] : !t8 && e === 1 && r[0] > 1 ? [r[0], 1] : null; +function Lh(r15, t8) { + let e = r15.length; + return e >= 3 ? t8 ? [...r15.slice(0, -3), r15[e - 3] * r15[e - 2], r15[e - 1]] : [...r15.slice(0, -3), r15[e - 3], r15[e - 2] * r15[e - 1]] : !t8 && e === 1 && r15[0] > 1 ? [r15[0], 1] : null; } -function zh({ x: r, filter: t8, convInfo: e, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { - let p = r.shape, u = o.texData.get(r.dataId), c = e.inChannels, l = p[0] * p[1] * p[2], m = e.outChannels, d = e.dataFormat === "channelsLast", f = false, h = false, g, x = []; +function Bh({ x: r15, filter: t8, convInfo: e, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { + let p = r15.shape, u = o.texData.get(r15.dataId), c = e.inChannels, l = p[0] * p[1] * p[2], m = e.outChannels, d = e.dataFormat === "channelsLast", f = false, h = false, g, x = []; if (s != null) { - let S = Bh(s.shape, d); + let S = Lh(s.shape, d); S != null && (s = te({ inputs: { x: s }, backend: o, attrs: { shape: S } }), x.push(s)); } if (n != null) { - let S = Bh(n.shape, d); + let S = Lh(n.shape, d); S != null && (n = te({ inputs: { x: n }, backend: o, attrs: { shape: S } }), x.push(n)); } - if (!((l === 1 || m === 1) && c > yv) && u.isPacked && d && u.texture != null && p[2] % 2 !== 0 && y.arraysEqual(u.shape.slice(-3), p.slice(-3))) { - let S = p[0] * p[1] * (p[2] + 1), k = { dataId: r.dataId, shape: [1, S, e.inChannels], dtype: r.dtype }, _ = u.shape; - u.shape = u.shape.slice(), u.shape[u.shape.length - 2]++, y.assert(Cu(u.shape, k.shape), () => `packed reshape ${u.shape} to ${k.shape} isn't free`); + if (!((l === 1 || m === 1) && c > bv) && u.isPacked && d && u.texture != null && p[2] % 2 !== 0 && y.arraysEqual(u.shape.slice(-3), p.slice(-3))) { + let S = p[0] * p[1] * (p[2] + 1), k = { dataId: r15.dataId, shape: [1, S, e.inChannels], dtype: r15.dtype }, _ = u.shape; + u.shape = u.shape.slice(), u.shape[u.shape.length - 2]++, y.assert(hu(u.shape, k.shape), () => `packed reshape ${u.shape} to ${k.shape} isn't free`); let E = te({ inputs: { x: t8 }, backend: o, attrs: { shape: [1, e.inChannels, e.outChannels] } }); x.push(E); - let R = _p({ a: k, b: E, backend: o, transposeA: f, transposeB: h, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), D = o.texData.get(R.dataId); - y.assert(D.isPacked, () => "batchMatMul result is expected to be packed"), u.shape = _, D.shape = e.outShape, g = At({ inputs: { x: R }, backend: o }), g.shape = e.outShape, x.push(R); + let R = Cp({ a: k, b: E, backend: o, transposeA: f, transposeB: h, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), D = o.texData.get(R.dataId); + y.assert(D.isPacked, () => "batchMatMul result is expected to be packed"), u.shape = _, D.shape = e.outShape, g = Dt({ inputs: { x: R }, backend: o }), g.shape = e.outShape, x.push(R); } else { - let S = e.outHeight * e.outWidth, k = te({ inputs: { x: r }, backend: o, attrs: { shape: d ? [e.batchSize, S, e.inChannels] : [e.batchSize, e.inChannels, S] } }), _ = te({ inputs: { x: t8 }, backend: o, attrs: { shape: [1, e.inChannels, e.outChannels] } }), E = _p({ a: d ? k : _, b: d ? _ : k, transposeA: !d, transposeB: h, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }); + let S = e.outHeight * e.outWidth, k = te({ inputs: { x: r15 }, backend: o, attrs: { shape: d ? [e.batchSize, S, e.inChannels] : [e.batchSize, e.inChannels, S] } }), _ = te({ inputs: { x: t8 }, backend: o, attrs: { shape: [1, e.inChannels, e.outChannels] } }), E = Cp({ a: d ? k : _, b: d ? _ : k, transposeA: !d, transposeB: h, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }); g = te({ inputs: { x: E }, backend: o, attrs: { shape: e.outShape } }), x.push(k), x.push(_), x.push(E); } for (let S of x) o.disposeIntermediateTensorInfo(S); return g; } -function Vh({ x: r, filter: t8, convInfo: e, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { +function zh({ x: r15, filter: t8, convInfo: e, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { let { filterWidth: p, filterHeight: u, inChannels: c, outWidth: l, outHeight: m, dataFormat: d } = e, f = d === "channelsLast", h = p * u * c, g = m * l, x = [e.batchSize, h, g], b = true, C = false, S = []; if (s != null) { - let q = Bh(s.shape, f); + let q = Lh(s.shape, f); q != null && (s = te({ inputs: { x: s }, backend: o, attrs: { shape: q } }), S.push(s)); } if (n != null) { - let q = Bh(n.shape, f); + let q = Lh(n.shape, f); q != null && (n = te({ inputs: { x: n }, backend: o, attrs: { shape: q } }), S.push(n)); } let k = te({ inputs: { x: t8 }, backend: o, attrs: { shape: [1, h, y.sizeFromShape(t8.shape) / h] } }); S.push(k); - let _ = new Lh(x, e), E = [r.shape, [e.padInfo.top, e.padInfo.left], [e.strideHeight, e.strideWidth], [e.dilationHeight, e.dilationWidth], [e.inChannels], [e.filterWidth * e.inChannels], [e.outWidth]], R = o.runWebGLProgram(_, [r], "float32", E), D = te({ inputs: { x: R }, backend: o, attrs: { shape: x } }); + let _ = new Mh(x, e), E = [r15.shape, [e.padInfo.top, e.padInfo.left], [e.strideHeight, e.strideWidth], [e.dilationHeight, e.dilationWidth], [e.inChannels], [e.filterWidth * e.inChannels], [e.outWidth]], R = o.runWebGLProgram(_, [r15], "float32", E), D = te({ inputs: { x: R }, backend: o, attrs: { shape: x } }); S.push(R), S.push(D); - let P = n != null, O = s != null, M = i === "leakyrelu", L = i ? Ci(i, true) : null, B = new Uc(f ? D.shape : k.shape, f ? k.shape : D.shape, f ? [e.batchSize, g, e.outChannels] : [e.batchSize, e.outChannels, g], b, C, P, L, O, M), z = f ? [D, k] : [k, D]; + let P = n != null, O = s != null, M = i === "leakyrelu", L = i ? xi(i, true) : null, B = new Lc(f ? D.shape : k.shape, f ? k.shape : D.shape, f ? [e.batchSize, g, e.outChannels] : [e.batchSize, e.outChannels, g], b, C, P, L, O, M), z = f ? [D, k] : [k, D]; if (n && z.push(n), O && z.push(s), M) { let q = o.makeTensorInfo([], "float32", y.createScalarValue(a, "float32")); z.push(q), S.push(q); @@ -20966,24 +20967,24 @@ function Vh({ x: r, filter: t8, convInfo: e, backend: o, bias: n = null, preluAc o.disposeIntermediateTensorInfo(q); return j; } -function J9(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s } = t8, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d; +function J9(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s } = t8, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d; if (m.filterHeight === 1 && m.filterWidth === 1 && m.dilationHeight === 1 && m.dilationWidth === 1 && m.strideHeight === 1 && m.strideWidth === 1 && (m.padInfo.type === "SAME" || m.padInfo.type === "VALID")) - d = zh({ x: n, filter: s, convInfo: m, backend: e }); + d = Bh({ x: n, filter: s, convInfo: m, backend: e }); else if (m.strideWidth <= 2 && l === "channelsLast" && A().getBool("WEBGL_EXP_CONV")) { - let h = new Kc(m), g = [[m.padInfo.top, m.padInfo.left], [m.strideHeight, m.strideWidth], [m.dilationHeight, m.dilationWidth], [m.inHeight, m.inWidth]]; + let h = new Vc(m), g = [[m.padInfo.top, m.padInfo.left], [m.strideHeight, m.strideWidth], [m.dilationHeight, m.dilationWidth], [m.inHeight, m.inWidth]]; d = e.runWebGLProgram(h, [n, s], "float32", g); } else if (A().getBool("WEBGL_CONV_IM2COL")) - d = Vh({ x: n, filter: s, convInfo: m, backend: e }); + d = zh({ x: n, filter: s, convInfo: m, backend: e }); else { - let h = new Hc(m); + let h = new zc(m); d = e.runWebGLProgram(h, [n, s], "float32"); } let f = te({ inputs: { x: d }, backend: e, attrs: { shape: m.outShape } }); return e.disposeIntermediateTensorInfo(d), f; } -var OA = { kernelName: rn, backendName: "webgl", kernelFunc: J9 }; -var Wh = class { +var MA = { kernelName: tn, backendName: "webgl", kernelFunc: J9 }; +var Vh = class { constructor(t8) { this.variableNames = ["x", "dy"], this.outputShape = t8.filterShape; let e = t8.strideHeight, o = t8.strideWidth, n = t8.padInfo.top, s = t8.padInfo.left, a = t8.dataFormat === "channelsLast"; @@ -21027,7 +21028,7 @@ var Wh = class { `; } }; -var Uh = class { +var Wh = class { constructor(t8) { this.variableNames = ["dy", "W"], this.outputShape = t8.inShape; let e = t8.filterHeight, o = t8.filterWidth, n = t8.strideHeight, s = t8.strideWidth, a = t8.dataFormat === "channelsLast", i = e - 1 - t8.padInfo.top, p = o - 1 - t8.padInfo.left, u = a ? 1 : 2, c = a ? 2 : 3, l = a ? 3 : 1; @@ -21087,7 +21088,7 @@ var Uh = class { `; } }; -var Gh = class { +var Uh = class { constructor(t8) { this.variableNames = ["x", "dy"], this.outputShape = t8.filterShape; let e = t8.strideDepth, o = t8.strideHeight, n = t8.strideWidth, s = t8.padInfo.front, a = t8.padInfo.top, i = t8.padInfo.left; @@ -21136,7 +21137,7 @@ var Gh = class { `; } }; -var Hh = class { +var Gh = class { constructor(t8) { this.variableNames = ["dy", "W"], this.outputShape = t8.inShape; let e = t8.filterDepth, o = t8.filterHeight, n = t8.filterWidth, s = t8.strideDepth, a = t8.strideHeight, i = t8.strideWidth, p = e - 1 - t8.padInfo.front, u = o - 1 - t8.padInfo.top, c = n - 1 - t8.padInfo.left; @@ -21200,14 +21201,14 @@ var Hh = class { `; } }; -function eJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, dy: s } = t8, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new Wh(m); +function eJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, dy: s } = t8, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new Vh(m); return e.runWebGLProgram(d, [n, s], "float32"); } -var MA = { kernelName: Oi, backendName: "webgl", kernelFunc: eJ }; -var Kh = class { +var LA = { kernelName: Ai, backendName: "webgl", kernelFunc: eJ }; +var Hh = class { constructor(t8) { - this.variableNames = ["dy", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "strides", type: "vec2" }], this.outputShape = t8.inShape, this.enableShapeUniforms = pt(this.outputShape.length); + this.variableNames = ["dy", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "strides", type: "vec2" }], this.outputShape = t8.inShape, this.enableShapeUniforms = ut(this.outputShape.length); let e = t8.filterHeight, o = t8.filterWidth, n = e - 1 - t8.padInfo.top, s = o - 1 - t8.padInfo.left; this.userCode = ` const ivec2 pads = ivec2(${n}, ${s}); @@ -21286,50 +21287,50 @@ var Kh = class { `; } }; -function tJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, filter: s } = t8, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(u), m = w.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l); +function tJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, filter: s } = t8, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(u), m = w.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l); if (A().getBool("WEBGL_PACK_CONV2DTRANSPOSE") && l === "channelsLast") { - let d = [[m.strideHeight, m.strideWidth]], f = new Kh(m); + let d = [[m.strideHeight, m.strideWidth]], f = new Hh(m); return e.runWebGLProgram(f, [n, s], "float32", d); } else { - let d = new Uh(m); + let d = new Wh(m); return e.runWebGLProgram(d, [n, s], "float32"); } } -var LA = { kernelName: on, backendName: "webgl", kernelFunc: tJ }; -function rJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p } = o, u = w.computeConv3DInfo(n.shape, s.shape, a, p, i), c = new Mh(u); +var BA = { kernelName: rn, backendName: "webgl", kernelFunc: tJ }; +function rJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p } = o, u = w.computeConv3DInfo(n.shape, s.shape, a, p, i), c = new Oh(u); return e.runWebGLProgram(c, [n, s], "float32"); } -var BA = { kernelName: nn, backendName: "webgl", kernelFunc: rJ }; -function oJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, dy: s } = t8, { strides: a, pad: i, filterShape: p } = o, u = w.computeConv3DInfo(n.shape, p, a, 1, i), c = new Gh(u); +var zA = { kernelName: on, backendName: "webgl", kernelFunc: rJ }; +function oJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, dy: s } = t8, { strides: a, pad: i, filterShape: p } = o, u = w.computeConv3DInfo(n.shape, p, a, 1, i), c = new Uh(u); return e.runWebGLProgram(c, [n, s], "float32"); } -var zA = { kernelName: Xa, backendName: "webgl", kernelFunc: oJ }; -function nJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, filter: s } = t8, { pad: a, strides: i, inputShape: p } = o, u = w.computeConv3DInfo(p, s.shape, i, 1, a), c = new Hh(u); +var VA = { kernelName: ja, backendName: "webgl", kernelFunc: oJ }; +function nJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, filter: s } = t8, { pad: a, strides: i, inputShape: p } = o, u = w.computeConv3DInfo(p, s.shape, i, 1, a), c = new Gh(u); return e.runWebGLProgram(c, [n, s], "float32"); } -var VA = { kernelName: sn, backendName: "webgl", kernelFunc: nJ }; -var sJ = Po + ` +var WA = { kernelName: nn, backendName: "webgl", kernelFunc: nJ }; +var sJ = Fo + ` return cos(x); `; var aJ = ` vec4 result = cos(x); bvec4 isNaN = isnan(x); - ${Xr} + ${jr} return result; `; var iJ = xe({ opSnippet: sJ, packedOpSnippet: aJ }); -var WA = { kernelName: an, backendName: "webgl", kernelFunc: iJ }; +var UA = { kernelName: sn, backendName: "webgl", kernelFunc: iJ }; var uJ = ` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `; var pJ = xe({ opSnippet: uJ }); -var UA = { kernelName: un, backendName: "webgl", kernelFunc: pJ }; -var qh = class { +var GA = { kernelName: an, backendName: "webgl", kernelFunc: pJ }; +var Kh = class { constructor(t8, e, o, n, s) { this.variableNames = ["Image", "Boxes", "BoxInd"], this.outputShape = []; let [a, i, p, u] = t8, [c] = e, [l, m] = o; @@ -21399,101 +21400,101 @@ var qh = class { `; } }; -var cJ = (r) => { - let { inputs: t8, backend: e, attrs: o } = r, { image: n, boxes: s, boxInd: a } = t8, { cropSize: i, method: p, extrapolationValue: u } = o, c = new qh(n.shape, s.shape, i, p, u); +var cJ = (r15) => { + let { inputs: t8, backend: e, attrs: o } = r15, { image: n, boxes: s, boxInd: a } = t8, { cropSize: i, method: p, extrapolationValue: u } = o, c = new Kh(n.shape, s.shape, i, p, u); return e.runWebGLProgram(c, [n, s, a], "float32"); }; -var GA = { kernelName: ln, backendName: "webgl", kernelFunc: cJ }; -var Ep; -(function(r) { - r.Prod = "*", r.Sum = "+"; -})(Ep || (Ep = {})); -var om = class { +var HA = { kernelName: cn, backendName: "webgl", kernelFunc: cJ }; +var Sp; +(function(r15) { + r15.Prod = "*", r15.Sum = "+"; +})(Sp || (Sp = {})); +var tm = class { constructor(t8, e, o, n) { this.op = t8, this.outputShape = e, this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }]; - let s = this.outputShape.length, a = this.op === Ep.Prod ? "1.0" : "0.0", i = o ? a : `getX(${HA(s, "coords", this.op)})`, p = this.outputShape[this.outputShape.length - 1], u = "", c = ""; + let s = this.outputShape.length, a = this.op === Sp.Prod ? "1.0" : "0.0", i = o ? a : `getX(${KA(s, "coords", this.op)})`, p = this.outputShape[this.outputShape.length - 1], u = "", c = ""; o ? (u = n ? `end != ${p - 1}` : "end != 0", c = n ? "end + 1" : "end - 1") : (u = n ? `end + pow2 < ${p}` : "end >= pow2", c = n ? "end + pow2" : "end - pow2"), this.userCode = ` void main() { ${Re(s)} coords = getOutputCoords(); - int end = ${KA(s, "coords", this.op)}; + int end = ${qA(s, "coords", this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${u}) { int idx = ${c}; - ${KA(s, "coords", this.op)} = idx; - val ${this.op}= getX(${HA(s, "coords", this.op)}); + ${qA(s, "coords", this.op)} = idx; + val ${this.op}= getX(${KA(s, "coords", this.op)}); } setOutput(val); } `; } }; -function HA(r, t8, e) { - if (r === 1) +function KA(r15, t8, e) { + if (r15 === 1) return `${t8}`; - if (r === 2) + if (r15 === 2) return `${t8}.x, ${t8}.y`; - if (r === 3) + if (r15 === 3) return `${t8}.x, ${t8}.y, ${t8}.z`; - if (r === 4) + if (r15 === 4) return `${t8}.x, ${t8}.y, ${t8}.z, ${t8}.w`; - throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`); + throw new Error(`Cumulative ${e} for rank ${r15} is not yet supported`); } -function KA(r, t8, e) { - if (r === 1) +function qA(r15, t8, e) { + if (r15 === 1) return `${t8}`; - if (r === 2) + if (r15 === 2) return `${t8}.y`; - if (r === 3) + if (r15 === 3) return `${t8}.z`; - if (r === 4) + if (r15 === 4) return `${t8}.w`; - throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`); + throw new Error(`Cumulative ${e} for rank ${r15} is not yet supported`); } -function jh(r, t8, e, o, n, s) { +function qh(r15, t8, e, o, n, s) { let a = t8.shape.length, i = w.getAxesPermutation([o], a), p = t8; - i != null && (p = Ct({ inputs: { x: t8 }, backend: e, attrs: { perm: i } })); + i != null && (p = bt({ inputs: { x: t8 }, backend: e, attrs: { perm: i } })); let u = w.getInnerMostAxes(1, a)[0]; if (u !== a - 1) throw new Error(`WebGL cumprod shader expects an inner-most axis=${t8.shape.length - 1} but got axis=${o}`); - let c = p.shape[u], l = At({ inputs: { x: p }, backend: e }); + let c = p.shape[u], l = Dt({ inputs: { x: p }, backend: e }); for (let m = 0; m <= Math.ceil(Math.log2(c)) - 1; m++) { - let d = new om(r, p.shape, false, s), f = [[m]], h = l; + let d = new tm(r15, p.shape, false, s), f = [[m]], h = l; l = e.runWebGLProgram(d, [l], l.dtype, f), e.disposeIntermediateTensorInfo(h); } if (n) { - let m = new om(r, p.shape, n, s), d = l; + let m = new tm(r15, p.shape, n, s), d = l; l = e.runWebGLProgram(m, [l], l.dtype), e.disposeIntermediateTensorInfo(d); } if (i != null) { - let m = w.getUndoAxesPermutation(i), d = Ct({ inputs: { x: l }, backend: e, attrs: { perm: m } }); + let m = w.getUndoAxesPermutation(i), d = bt({ inputs: { x: l }, backend: e, attrs: { perm: m } }); return e.disposeIntermediateTensorInfo(l), e.disposeIntermediateTensorInfo(p), d; } return l; } -function lJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o; - return jh(Ep.Prod, n, e, s, a, i); +function lJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o; + return qh(Sp.Prod, n, e, s, a, i); } -var qA = { kernelName: pn, backendName: "webgl", kernelFunc: lJ }; -function mJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o; - return jh(Ep.Sum, n, e, s, a, i); +var jA = { kernelName: un, backendName: "webgl", kernelFunc: lJ }; +function mJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o; + return qh(Sp.Sum, n, e, s, a, i); } -var jA = { kernelName: cn, backendName: "webgl", kernelFunc: mJ }; -function dJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, weights: s } = t8, { size: a, binaryOutput: i } = o; +var XA = { kernelName: pn, backendName: "webgl", kernelFunc: mJ }; +function dJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, weights: s } = t8, { size: a, binaryOutput: i } = o; if (n.shape.length === 1) { - let p = e.readSync(n.dataId), u = e.readSync(s.dataId), c = ph(p, u, s.dtype, s.shape, a); + let p = e.readSync(n.dataId), u = e.readSync(s.dataId), c = uh(p, u, s.dtype, s.shape, a); return e.makeTensorInfo([a], s.dtype, c); } else if (n.shape.length === 2) { - let p = e.bufferSync(n), u = e.bufferSync(s), c = DR(p, u, a, i); + let p = e.bufferSync(n), u = e.bufferSync(s), c = AR(p, u, a, i); return e.makeTensorInfo(c.shape, s.dtype, c.values); } throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`); } -var XA = { kernelName: ra, backendName: "webgl", kernelFunc: dJ }; -var Xh = class { +var YA = { kernelName: ra, backendName: "webgl", kernelFunc: dJ }; +var jh = class { constructor(t8, e, o) { this.variableNames = ["x"], this.outputShape = [], this.outputShape = t8, this.blockSize = e, this.dataFormat = o, this.userCode = ` void main() { @@ -21532,14 +21533,14 @@ var Xh = class { return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)"; } }; -function fJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = new Xh(f, s, a); +function fJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = new jh(f, s, a); return e.runWebGLProgram(h, [n], n.dtype); } -var YA = { kernelName: mn, backendName: "webgl", kernelFunc: fJ }; -var qc = class { +var QA = { kernelName: ln, backendName: "webgl", kernelFunc: fJ }; +var Wc = class { constructor(t8, e = false, o = null, n = false, s = false) { - this.variableNames = ["x", "W"], this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = t8.outShape, this.enableShapeUniforms = pt(this.outputShape.length); + this.variableNames = ["x", "W"], this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = t8.outShape, this.enableShapeUniforms = ut(this.outputShape.length); let a = t8.filterHeight, i = t8.filterWidth, p = t8.outChannels / t8.inChannels, u = "", c = ""; o && (n ? u = `float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); @@ -21599,9 +21600,9 @@ var qc = class { `; } }; -var jc = class { +var Uc = class { constructor(t8, e = false, o = null, n = false, s = false) { - this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = t8.outShape, this.enableShapeUniforms = pt(this.outputShape.length); + this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = t8.outShape, this.enableShapeUniforms = ut(this.outputShape.length); let a = t8.outChannels / t8.inChannels, i = t8.padInfo.left, p = t8.strideWidth, u = t8.dilationWidth, c = t8.filterHeight, l = t8.filterWidth, m = l, d = ` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`; @@ -21816,16 +21817,16 @@ var jc = class { `; } }; -function hJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o, c = p; +function hJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o, c = p; c == null && (c = [1, 1]), y.assert(w.eitherStridesOrDilationsAreOne(a, c), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`); let l = w.computeConv2DInfo(n.shape, s.shape, a, c, i, u, true), m; - A().getBool("WEBGL_PACK_DEPTHWISECONV") && l.strideWidth <= 2 && l.outChannels / l.inChannels === 1 ? m = new jc(l) : m = new qc(l); + A().getBool("WEBGL_PACK_DEPTHWISECONV") && l.strideWidth <= 2 && l.outChannels / l.inChannels === 1 ? m = new Uc(l) : m = new Wc(l); let d = [[l.padInfo.top, l.padInfo.left], [l.strideHeight, l.strideWidth], [l.dilationHeight, l.dilationWidth], [l.inHeight, l.inWidth]]; return e.runWebGLProgram(m, [n, s], "float32", d); } -var QA = { kernelName: dn, backendName: "webgl", kernelFunc: hJ }; -var Yh = class { +var ZA = { kernelName: mn, backendName: "webgl", kernelFunc: hJ }; +var Xh = class { constructor(t8) { this.variableNames = ["x", "dy"], this.outputShape = t8.filterShape; let e = t8.strideHeight, o = t8.strideWidth, n = t8.padInfo.top, s = t8.padInfo.left, a = t8.outChannels / t8.inChannels; @@ -21867,7 +21868,7 @@ var Yh = class { `; } }; -var Qh = class { +var Yh = class { constructor(t8) { this.variableNames = ["dy", "W"], this.outputShape = t8.inShape; let e = t8.filterHeight, o = t8.filterWidth, n = t8.strideHeight, s = t8.strideWidth, a = e - 1 - t8.padInfo.top, i = o - 1 - t8.padInfo.left, p = t8.outChannels / t8.inChannels; @@ -21919,17 +21920,17 @@ var Qh = class { `; } }; -function gJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, dy: s } = t8, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = w.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new Yh(l); +function gJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, dy: s } = t8, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = w.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new Xh(l); return e.runWebGLProgram(m, [n, s], "float32"); } -var ZA = { kernelName: Mi, backendName: "webgl", kernelFunc: gJ }; -function xJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, filter: s } = t8, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = w.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new Qh(l); +var JA = { kernelName: Fi, backendName: "webgl", kernelFunc: gJ }; +function xJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, filter: s } = t8, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = w.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new Yh(l); return e.runWebGLProgram(m, [n, s], "float32"); } -var JA = { kernelName: Li, backendName: "webgl", kernelFunc: xJ }; -var Zh = class { +var eF = { kernelName: Pi, backendName: "webgl", kernelFunc: xJ }; +var Qh = class { constructor(t8) { this.variableNames = ["X"], this.outputShape = [t8, t8], this.userCode = ` void main() { @@ -21940,12 +21941,12 @@ var Zh = class { `; } }; -function yJ(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = te({ inputs: { x: o }, backend: e, attrs: { shape: [s] } }), i = new Zh(s), p = e.runWebGLProgram(i, [a], a.dtype), u = te({ inputs: { x: p }, backend: e, attrs: { shape: n } }); +function yJ(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = te({ inputs: { x: o }, backend: e, attrs: { shape: [s] } }), i = new Qh(s), p = e.runWebGLProgram(i, [a], a.dtype), u = te({ inputs: { x: p }, backend: e, attrs: { shape: n } }); return e.disposeIntermediateTensorInfo(a), e.disposeIntermediateTensorInfo(p), u; } -var eF = { kernelName: oa, backendName: "webgl", kernelFunc: yJ }; -var Jh = class { +var tF = { kernelName: oa, backendName: "webgl", kernelFunc: yJ }; +var Zh = class { constructor(t8) { this.variableNames = ["x", "W"], this.outputShape = t8.outShape; let { inHeight: e, inWidth: o, padInfo: n, strideHeight: s, strideWidth: a, filterHeight: i, filterWidth: p, dilationHeight: u, dilationWidth: c } = t8, { top: l, left: m } = n; @@ -21990,33 +21991,33 @@ var Jh = class { `; } }; -function bJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p } = o, u = w.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c, l = new Jh(u); +function bJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p } = o, u = w.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c, l = new Zh(u); c = e.runWebGLProgram(l, [n, s], "float32"); let m = te({ inputs: { x: c }, backend: e, attrs: { shape: u.outShape } }); return e.disposeIntermediateTensorInfo(c), m; } -var tF = { kernelName: fn, backendName: "webgl", kernelFunc: bJ }; -function CJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { equation: n } = o, s = t8, { allDims: a, summedDims: i, idDims: p } = w.decodeEinsumEquation(n, s.length); +var rF = { kernelName: dn, backendName: "webgl", kernelFunc: bJ }; +function CJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { equation: n } = o, s = t8, { allDims: a, summedDims: i, idDims: p } = w.decodeEinsumEquation(n, s.length); w.checkEinsumDimSizes(a.length, p, s); let { path: u, steps: c } = w.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = []; for (let h = 0; h < l; ++h) { for (let g of c[h]) { let { permutationIndices: x, expandDims: b } = w.getEinsumPermutation(d, p[g]), C; - w.isIdentityPermutation(x) ? C = s[g] : (C = Ct({ inputs: { x: s[g] }, backend: e, attrs: { perm: x } }), f.push(C)); + w.isIdentityPermutation(x) ? C = s[g] : (C = bt({ inputs: { x: s[g] }, backend: e, attrs: { perm: x } }), f.push(C)); let S = C.shape.slice(); for (let k = 0; k < b.length; ++k) S.splice(b[k], 0, 1); - y.arraysEqual(C.shape, S) || (C = te({ inputs: { x: C }, backend: e, attrs: { shape: S } }), f.push(C)), m === null ? m = C : (m = tm({ inputs: { a: C, b: m }, backend: e }), f.push(m)); + y.arraysEqual(C.shape, S) || (C = te({ inputs: { x: C }, backend: e, attrs: { shape: S } }), f.push(C)), m === null ? m = C : (m = Jl({ inputs: { a: C, b: m }, backend: e }), f.push(m)); } - h < l - 1 && (u[h] >= 0 && (m = Tp({ inputs: { x: m }, backend: e, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); + h < l - 1 && (u[h] >= 0 && (m = bp({ inputs: { x: m }, backend: e, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); } for (let h of f) h !== m && e.disposeIntermediateTensorInfo(h); return m; } -var rF = { kernelName: Vi, backendName: "webgl", kernelFunc: CJ }; +var oF = { kernelName: Li, backendName: "webgl", kernelFunc: CJ }; var wJ = "return (x >= 0.0) ? x : (exp(x) - 1.0);"; var SJ = ` vec4 result; @@ -22029,23 +22030,23 @@ var SJ = ` return result; `; var IJ = xe({ opSnippet: wJ, packedOpSnippet: SJ }); -var oF = { kernelName: gn, backendName: "webgl", kernelFunc: IJ }; +var nF = { kernelName: hn, backendName: "webgl", kernelFunc: IJ }; var vJ = "return (b >= 0.0) ? a : a * (b + 1.0);"; var kJ = ` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `; -var NJ = (r) => { - let { inputs: t8, backend: e } = r, { dy: o, y: n } = t8, s = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new jr(kJ, o.shape, n.shape) : new Pr(vJ, o.shape, n.shape); +var NJ = (r15) => { + let { inputs: t8, backend: e } = r15, { dy: o, y: n } = t8, s = A().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new qr(kJ, o.shape, n.shape) : new Fr(vJ, o.shape, n.shape); return e.runWebGLProgram(s, [o, n], o.dtype); }; -var nF = { kernelName: Ya, backendName: "webgl", kernelFunc: NJ }; +var sF = { kernelName: Xa, backendName: "webgl", kernelFunc: NJ }; var TJ = ` return vec4(equal(a, b)); `; var _J = "return float(a == b);"; -var $J = nt({ opSnippet: _J, packedOpSnippet: TJ, dtype: "bool", cpuKernelImpl: MR }); -var sF = { kernelName: yn, backendName: "webgl", kernelFunc: $J }; +var $J = nt({ opSnippet: _J, packedOpSnippet: TJ, dtype: "bool", cpuKernelImpl: LR }); +var aF = { kernelName: xn, backendName: "webgl", kernelFunc: $J }; var EJ = ` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, @@ -22063,8 +22064,8 @@ var EJ = ` return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `; var RJ = xe({ opSnippet: EJ }); -var aF = { kernelName: xn, backendName: "webgl", kernelFunc: RJ }; -var DJ = Po + ` +var iF = { kernelName: gn, backendName: "webgl", kernelFunc: RJ }; +var DJ = Fo + ` return exp(x); `; var AJ = ` @@ -22077,17 +22078,17 @@ var AJ = ` return result; `; -var Iv = xe({ opSnippet: DJ, packedOpSnippet: AJ, cpuKernelImpl: LR, dtype: "float32" }); -var iF = { kernelName: bn, backendName: "webgl", kernelFunc: Iv }; -function eg(r) { - let { inputs: t8, attrs: e, backend: o } = r, { dim: n } = e, { input: s } = t8, a = s.shape.length, i = s.shape.slice(), p = n; +var vv = xe({ opSnippet: DJ, packedOpSnippet: AJ, cpuKernelImpl: BR, dtype: "float32" }); +var uF = { kernelName: yn, backendName: "webgl", kernelFunc: vv }; +function Jh(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, { dim: n } = e, { input: s } = t8, a = s.shape.length, i = s.shape.slice(), p = n; return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), te({ inputs: { x: s }, backend: o, attrs: { shape: i } }); } -var uF = { kernelName: na, backendName: "webgl", kernelFunc: eg }; -var pF = "return exp(x) - 1.0;"; -var FJ = xe({ opSnippet: pF, packedOpSnippet: pF, cpuKernelImpl: BR }); -var cF = { kernelName: Cn, backendName: "webgl", kernelFunc: FJ }; -var nm = class { +var pF = { kernelName: na, backendName: "webgl", kernelFunc: Jh }; +var cF = "return exp(x) - 1.0;"; +var FJ = xe({ opSnippet: cF, packedOpSnippet: cF, cpuKernelImpl: zR }); +var lF = { kernelName: bn, backendName: "webgl", kernelFunc: FJ }; +var rm = class { constructor(t8, e, o) { this.variableNames = ["real", "imag"]; let n = e[1]; @@ -22135,18 +22136,18 @@ var nm = class { `; } }; -function tg(r, t8, e) { - let o = e.texData.get(r.dataId), n = y.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = te({ inputs: { x: r }, backend: e, attrs: { shape: [a, s] } }), p = i.shape, u = new nm("real", p, t8), c = new nm("imag", p, t8), l = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: p }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: p }], m = e.runWebGLProgram(u, l, "float32"), d = e.runWebGLProgram(c, l, "float32"), f = Or({ inputs: { real: m, imag: d }, backend: e }); +function eg(r15, t8, e) { + let o = e.texData.get(r15.dataId), n = y.sizeFromShape(r15.shape), s = r15.shape[r15.shape.length - 1], a = n / s, i = te({ inputs: { x: r15 }, backend: e, attrs: { shape: [a, s] } }), p = i.shape, u = new rm("real", p, t8), c = new rm("imag", p, t8), l = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: p }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: p }], m = e.runWebGLProgram(u, l, "float32"), d = e.runWebGLProgram(c, l, "float32"), f = Pr({ inputs: { real: m, imag: d }, backend: e }); e.disposeIntermediateTensorInfo(m), e.disposeIntermediateTensorInfo(d); - let h = te({ inputs: { x: f }, backend: e, attrs: { shape: r.shape } }); + let h = te({ inputs: { x: f }, backend: e, attrs: { shape: r15.shape } }); return e.disposeIntermediateTensorInfo(i), e.disposeIntermediateTensorInfo(f), h; } -function PJ(r) { - let { inputs: t8, backend: e } = r, { input: o } = t8; - return tg(o, false, e); +function PJ(r15) { + let { inputs: t8, backend: e } = r15, { input: o } = t8; + return eg(o, false, e); } -var lF = { kernelName: Wi, backendName: "webgl", kernelFunc: PJ }; -var rg = class { +var mF = { kernelName: Bi, backendName: "webgl", kernelFunc: PJ }; +var tg = class { constructor(t8, e) { this.outputShape = [], this.customUniforms = [{ name: "value", type: "float" }], this.variableNames = ["x"], this.outputShape = t8, this.userCode = ` void main() { @@ -22156,18 +22157,18 @@ var rg = class { `; } }; -function Si(r) { - let { backend: t8, attrs: e } = r, { shape: o, value: n } = e, { dtype: s } = e; +function bi(r15) { + let { backend: t8, attrs: e } = r15, { shape: o, value: n } = e, { dtype: s } = e; if (s = s || y.inferDtype(n), s === "string") { let a = y.getArrayFromDType(s, y.sizeFromShape(o)); return a.fill(n), t8.makeTensorInfo(o, s, a); } else { - let a = new rg(o, n), i = [[n]]; + let a = new tg(o, n), i = [[n]]; return t8.runWebGLProgram(a, [], s, i); } } -var mF = { kernelName: sa, backendName: "webgl", kernelFunc: Si }; -var og = class { +var dF = { kernelName: sa, backendName: "webgl", kernelFunc: bi }; +var rg = class { constructor(t8) { this.variableNames = ["Image"], this.outputShape = []; let e = t8[2]; @@ -22188,13 +22189,13 @@ var og = class { `; } }; -var dF = { kernelName: wn, backendName: "webgl", kernelFunc: ({ inputs: r, backend: t8 }) => { - let { image: e } = r, o = t8, n = new og(e.shape); +var fF = { kernelName: Cn, backendName: "webgl", kernelFunc: ({ inputs: r15, backend: t8 }) => { + let { image: e } = r15, o = t8, n = new rg(e.shape); return o.runWebGLProgram(n, [e], e.dtype); } }; -var fF = "return floor(x);"; -var OJ = xe({ opSnippet: fF, packedOpSnippet: fF, cpuKernelImpl: zR }); -var hF = { kernelName: Sn, backendName: "webgl", kernelFunc: OJ }; +var hF = "return floor(x);"; +var OJ = xe({ opSnippet: hF, packedOpSnippet: hF, cpuKernelImpl: VR }); +var gF = { kernelName: wn, backendName: "webgl", kernelFunc: OJ }; var MJ = ` float s = sign(a) * sign(b); int ia = round(a); @@ -22229,11 +22230,11 @@ var LJ = ` return vec4(result); `; var BJ = nt({ opSnippet: MJ, packedOpSnippet: LJ, dtype: "int32" }); -var gF = { kernelName: In, backendName: "webgl", kernelFunc: BJ }; -var ng = class { +var xF = { kernelName: Sn, backendName: "webgl", kernelFunc: BJ }; +var og = class { constructor(t8) { this.variableNames = ["A"]; - let e = vt(), [o, n] = t8; + let e = It(), [o, n] = t8; this.outputShape = t8, this.userCode = ` void main() { ivec3 coords = getOutputCoords(); @@ -22259,10 +22260,10 @@ var ng = class { `; } }; -var sg = class { +var ng = class { constructor(t8) { this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true; - let e = vt(), [o, n] = t8; + let e = It(), [o, n] = t8; this.outputShape = t8, this.userCode = ` void main() { ivec3 coords = getOutputCoords(); @@ -22300,22 +22301,22 @@ var sg = class { `; } }; -var xF = { kernelName: Mu, backendName: "webgl", kernelFunc: zJ }; -var Xc; -var vv = A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); -function zJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { pixels: n } = t8, { numChannels: s } = o, a = typeof HTMLVideoElement != "undefined" && n instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && n instanceof HTMLImageElement, [p, u] = a ? [n.videoWidth, n.videoHeight] : [n.width, n.height], c = [u, p], l = [u, p, s]; +var yF = { kernelName: Eu, backendName: "webgl", kernelFunc: zJ }; +var Gc; +var kv = A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); +function zJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { pixels: n } = t8, { numChannels: s } = o, a = typeof HTMLVideoElement != "undefined" && n instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && n instanceof HTMLImageElement, [p, u] = a ? [n.videoWidth, n.videoHeight] : [n.width, n.height], c = [u, p], l = [u, p, s]; if (i || a) { let h = A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); - (Xc == null || h !== vv) && (vv = h, Xc = document.createElement("canvas").getContext("2d", { willReadFrequently: vv })), Xc.canvas.width = p, Xc.canvas.height = u, Xc.drawImage(n, 0, 0, p, u), n = Xc.canvas; + (Gc == null || h !== kv) && (kv = h, Gc = document.createElement("canvas").getContext("2d", { willReadFrequently: kv })), Gc.canvas.width = p, Gc.canvas.height = u, Gc.drawImage(n, 0, 0, p, u), n = Gc.canvas; } let m = e.makeTensorInfo(c, "int32"); - e.texData.get(m.dataId).usage = dr.PIXELS, e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId), n); - let d = A().getBool("WEBGL_PACK") ? new sg(l) : new ng(l), f = e.runWebGLProgram(d, [m], "int32"); + e.texData.get(m.dataId).usage = mr.PIXELS, e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId), n); + let d = A().getBool("WEBGL_PACK") ? new ng(l) : new og(l), f = e.runWebGLProgram(d, [m], "int32"); return e.disposeData(m.dataId), f; } -function VJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = w.convertConv2DDataFormat(c), g = w.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h), x, b = [], C = a != null, S = i != null, k = d === "leakyrelu", _ = () => { +function VJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = w.convertConv2DDataFormat(c), g = w.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h), x, b = [], C = a != null, S = i != null, k = d === "leakyrelu", _ = () => { let R = [n, s], D = (P, O) => { if (O === "NCHW" && P.shape.length === 1 && P.shape[0] !== 1) { let M = te({ inputs: { x: P }, backend: e, attrs: { shape: [P.shape[0], 1, 1] } }); @@ -22330,35 +22331,35 @@ function VJ(r) { return R; }; if (g.filterHeight === 1 && g.filterWidth === 1 && g.dilationHeight === 1 && g.dilationWidth === 1 && g.strideHeight === 1 && g.strideWidth === 1 && (g.padInfo.type === "SAME" || g.padInfo.type === "VALID")) - x = zh({ x: n, filter: s, convInfo: g, backend: e, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f }); + x = Bh({ x: n, filter: s, convInfo: g, backend: e, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f }); else if (g.strideWidth <= 2 && h === "channelsLast" && A().getBool("WEBGL_EXP_CONV")) { - let R = d ? Ci(d, true) : null, D = new Kc(g, C, R, S, k), P = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], O = _(); + let R = d ? xi(d, true) : null, D = new Vc(g, C, R, S, k), P = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], O = _(); x = e.runWebGLProgram(D, O, "float32", P); } else if (A().getBool("WEBGL_CONV_IM2COL")) - x = Vh({ x: n, filter: s, convInfo: g, backend: e, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f }); + x = zh({ x: n, filter: s, convInfo: g, backend: e, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f }); else { - let R = d ? Ci(d, false) : null, D = new Hc(g, C, R, S, k), P = _(); + let R = d ? xi(d, false) : null, D = new zc(g, C, R, S, k), P = _(); x = e.runWebGLProgram(D, P, "float32"); } let E = te({ inputs: { x }, backend: e, attrs: { shape: g.outShape } }); return b.push(x), b.forEach((R) => e.disposeIntermediateTensorInfo(R)), E; } -var yF = { kernelName: vo, backendName: "webgl", kernelFunc: VJ }; -function WJ(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = [], h = c; +var bF = { kernelName: Io, backendName: "webgl", kernelFunc: VJ }; +function WJ(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = [], h = c; h == null && (h = [1, 1]), y.assert(w.eitherStridesOrDilationsAreOne(p, h), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${h}'`); - let g = w.computeConv2DInfo(n.shape, s.shape, p, h, u, l, true), x = A().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, b = m ? Ci(m, x) : null, C = [n, s], S = a != null, k = i != null, _ = m === "leakyrelu"; + let g = w.computeConv2DInfo(n.shape, s.shape, p, h, u, l, true), x = A().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, b = m ? xi(m, x) : null, C = [n, s], S = a != null, k = i != null, _ = m === "leakyrelu"; if (S && C.push(a), k && C.push(i), _) { let P = e.makeTensorInfo([], "float32", y.createScalarValue(d, "float32")); C.push(P), f.push(P); } let E; - x ? E = new jc(g, S, b, k, _) : E = new qc(g, S, b, k, _); + x ? E = new Uc(g, S, b, k, _) : E = new Wc(g, S, b, k, _); let R = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], D = e.runWebGLProgram(E, C, "float32", R); return f.forEach((P) => e.disposeIntermediateTensorInfo(P)), D; } -var bF = { kernelName: ko, backendName: "webgl", kernelFunc: WJ }; -var ag = class { +var CF = { kernelName: vo, backendName: "webgl", kernelFunc: WJ }; +var sg = class { constructor(t8, e, o, n) { this.sliceDim = t8, this.strides = e, this.paramsShape = n, this.variableNames = ["x", "indices"], this.outputShape = o; let s = Re(o.length), a = ` @@ -22382,17 +22383,17 @@ var ag = class { `; } }; -function UJ(r) { - let { inputs: t8, backend: e } = r, { params: o, indices: n } = t8, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = w.prepareAndValidate(o, n), m = te({ inputs: { x: n }, backend: e, attrs: { shape: [u, a] } }), d = te({ inputs: { x: o }, backend: e, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } }); +function UJ(r15) { + let { inputs: t8, backend: e } = r15, { params: o, indices: n } = t8, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = w.prepareAndValidate(o, n), m = te({ inputs: { x: n }, backend: e, attrs: { shape: [u, a] } }), d = te({ inputs: { x: o }, backend: e, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } }); if (e.shouldExecuteOnCPU([o, n]) || o.dtype === "string") { - let x = e.readSync(n.dataId), b = e.bufferSync(o), C = VR(x, b, o.dtype, u, a, c, l, o.shape, i); + let x = e.readSync(n.dataId), b = e.bufferSync(o), C = WR(x, b, o.dtype, u, a, c, l, o.shape, i); return e.makeTensorInfo(p, o.dtype, C.values); } - let f = new ag(a, l, [u, c], o.shape), h = e.runWebGLProgram(f, [d, m], d.dtype), g = te({ inputs: { x: h }, backend: e, attrs: { shape: p } }); + let f = new sg(a, l, [u, c], o.shape), h = e.runWebGLProgram(f, [d, m], d.dtype), g = te({ inputs: { x: h }, backend: e, attrs: { shape: p } }); return e.disposeIntermediateTensorInfo(m), e.disposeIntermediateTensorInfo(d), e.disposeIntermediateTensorInfo(h), g; } -var CF = { kernelName: kn, backendName: "webgl", kernelFunc: UJ }; -var ig = class { +var wF = { kernelName: vn, backendName: "webgl", kernelFunc: UJ }; +var ag = class { constructor(t8, e) { this.variableNames = ["A", "indices"], this.outputShape = e, this.rank = e.length; let o = Re(this.rank), n = GJ(t8, 2); @@ -22406,14 +22407,14 @@ var ig = class { `; } }; -function GJ(r, t8) { +function GJ(r15, t8) { let e = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = []; - for (let n = 0; n < r.length; n++) + for (let n = 0; n < r15.length; n++) n === 2 ? o.push("index") : o.push(`${e[n]}`); return o.join(); } -function kv(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, indices: s } = t8, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0]; +function Nv(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, indices: s } = t8, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0]; if (A().get("DEBUG")) { let b = e.readSync(s.dataId), C = n.shape[p]; for (let S = 0; S < b.length; ++S) { @@ -22425,59 +22426,59 @@ function kv(r) { l.push(m), l.push(d); let f = [u.batchSize, u.outerSize, c / u.batchSize, u.sliceSize]; if (e.shouldExecuteOnCPU([n, s]) || n.dtype === "string") { - let b = e.bufferSync(d), C = e.bufferSync(m), S = WR(C, b, f); + let b = e.bufferSync(d), C = e.bufferSync(m), S = UR(C, b, f); return l.forEach((k) => e.disposeIntermediateTensorInfo(k)), e.makeTensorInfo(u.outputShape, S.dtype, S.values); } - let h = new ig(m.shape, f), g = e.runWebGLProgram(h, [m, d], m.dtype); + let h = new ag(m.shape, f), g = e.runWebGLProgram(h, [m, d], m.dtype); l.push(g); let x = te({ inputs: { x: g }, backend: e, attrs: { shape: u.outputShape } }); return l.forEach((b) => e.disposeIntermediateTensorInfo(b)), x; } -var wF = { kernelName: aa, backendName: "webgl", kernelFunc: kv }; +var SF = { kernelName: aa, backendName: "webgl", kernelFunc: Nv }; var HJ = "return float(a > b);"; var KJ = ` return vec4(greaterThan(a, b)); `; -var qJ = nt({ opSnippet: HJ, packedOpSnippet: KJ, cpuKernelImpl: UR, dtype: "bool" }); -var SF = { kernelName: Nn, backendName: "webgl", kernelFunc: qJ }; +var qJ = nt({ opSnippet: HJ, packedOpSnippet: KJ, cpuKernelImpl: GR, dtype: "bool" }); +var IF = { kernelName: kn, backendName: "webgl", kernelFunc: qJ }; var jJ = "return float(a >= b);"; var XJ = ` return vec4(greaterThanEqual(a, b)); `; -var YJ = nt({ opSnippet: jJ, packedOpSnippet: XJ, dtype: "bool", cpuKernelImpl: GR }); -var IF = { kernelName: Tn, backendName: "webgl", kernelFunc: YJ }; -function QJ(r) { - let { inputs: t8, backend: e } = r, { input: o } = t8; - return tg(o, true, e); +var YJ = nt({ opSnippet: jJ, packedOpSnippet: XJ, dtype: "bool", cpuKernelImpl: HR }); +var vF = { kernelName: Nn, backendName: "webgl", kernelFunc: YJ }; +function QJ(r15) { + let { inputs: t8, backend: e } = r15, { input: o } = t8; + return eg(o, true, e); } -var vF = { kernelName: Ui, backendName: "webgl", kernelFunc: QJ }; +var kF = { kernelName: zi, backendName: "webgl", kernelFunc: QJ }; var ZJ = "return float(!isnan(x) && !isinf(x));"; var JJ = xe({ opSnippet: ZJ, dtype: "bool" }); -var kF = { kernelName: _n, backendName: "webgl", kernelFunc: JJ }; +var NF = { kernelName: Tn, backendName: "webgl", kernelFunc: JJ }; var eee = "return float(isinf(x));"; var tee = xe({ opSnippet: eee, dtype: "bool" }); -var NF = { kernelName: $n, backendName: "webgl", kernelFunc: tee }; +var TF = { kernelName: _n, backendName: "webgl", kernelFunc: tee }; var ree = "return float(isnan(x));"; var oee = xe({ opSnippet: ree, dtype: "bool" }); -var TF = { kernelName: En, backendName: "webgl", kernelFunc: oee }; +var _F = { kernelName: $n, backendName: "webgl", kernelFunc: oee }; var nee = "return float(a < b);"; var see = ` return vec4(lessThan(a, b)); `; -var aee = nt({ opSnippet: nee, packedOpSnippet: see, cpuKernelImpl: HR, dtype: "bool" }); -var _F = { kernelName: Dn, backendName: "webgl", kernelFunc: aee }; +var aee = nt({ opSnippet: nee, packedOpSnippet: see, cpuKernelImpl: KR, dtype: "bool" }); +var $F = { kernelName: Rn, backendName: "webgl", kernelFunc: aee }; var iee = "return float(a <= b);"; var uee = ` return vec4(lessThanEqual(a, b)); `; -var pee = nt({ opSnippet: iee, packedOpSnippet: uee, cpuKernelImpl: KR, dtype: "bool" }); -var $F = { kernelName: An, backendName: "webgl", kernelFunc: pee }; -function cee(r) { - let { backend: t8, attrs: e } = r, { start: o, stop: n, num: s } = e, a = qR(o, n, s); +var pee = nt({ opSnippet: iee, packedOpSnippet: uee, cpuKernelImpl: qR, dtype: "bool" }); +var EF = { kernelName: Dn, backendName: "webgl", kernelFunc: pee }; +function cee(r15) { + let { backend: t8, attrs: e } = r15, { start: o, stop: n, num: s } = e, a = jR(o, n, s); return t8.makeTensorInfo([a.length], "float32", a); } -var EF = { kernelName: Fn, backendName: "webgl", kernelFunc: cee }; -var lee = Po + ` +var RF = { kernelName: An, backendName: "webgl", kernelFunc: cee }; +var lee = Fo + ` return x < 0.0 ? 0./0. : log(x); `; var mee = ` @@ -22489,13 +22490,13 @@ var mee = ` result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; `; -var dee = xe({ opSnippet: lee, packedOpSnippet: mee, cpuKernelImpl: jR }); -var RF = { kernelName: Pn, backendName: "webgl", kernelFunc: dee }; -var fee = Po + ` +var dee = xe({ opSnippet: lee, packedOpSnippet: mee, cpuKernelImpl: XR }); +var DF = { kernelName: Fn, backendName: "webgl", kernelFunc: dee }; +var fee = Fo + ` return log(1.0 + x); `; var hee = xe({ opSnippet: fee }); -var DF = { kernelName: On, backendName: "webgl", kernelFunc: hee }; +var AF = { kernelName: Pn, backendName: "webgl", kernelFunc: hee }; var gee = "return float(a >= 1.0 && b >= 1.0);"; var xee = ` return vec4( @@ -22503,10 +22504,10 @@ var xee = ` vec4(greaterThanEqual(b, vec4(1.0)))); `; var yee = nt({ opSnippet: gee, packedOpSnippet: xee, dtype: "bool" }); -var AF = { kernelName: Mn, backendName: "webgl", kernelFunc: yee }; +var FF = { kernelName: On, backendName: "webgl", kernelFunc: yee }; var bee = "return float(!(x >= 1.0));"; var Cee = xe({ opSnippet: bee }); -var FF = { kernelName: Ln, backendName: "webgl", kernelFunc: Cee }; +var PF = { kernelName: Mn, backendName: "webgl", kernelFunc: Cee }; var wee = "return float(a >= 1.0 || b >= 1.0);"; var See = ` return min( @@ -22515,8 +22516,8 @@ var See = ` vec4(1.0)); `; var Iee = nt({ opSnippet: wee, packedOpSnippet: See, dtype: "bool" }); -var PF = { kernelName: Bn, backendName: "webgl", kernelFunc: Iee }; -var ug = class { +var OF = { kernelName: Ln, backendName: "webgl", kernelFunc: Iee }; +var ig = class { constructor(t8, e, o, n, s) { this.variableNames = ["x"], this.outputShape = []; let a = e, i = t8[3] - 1; @@ -22544,7 +22545,7 @@ var ug = class { `; } }; -var pg = class { +var ug = class { constructor(t8, e, o, n, s) { this.variableNames = ["x"], this.outputShape = [], this.packedInputs = true, this.packedOutput = true; let a = e, i = t8[3] - 1; @@ -22615,12 +22616,12 @@ var pg = class { `; } }; -var vee = (r) => { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u = A().getBool("WEBGL_PACK_NORMALIZATION") ? new pg(n.shape, s, a, i, p) : new ug(n.shape, s, a, i, p); +var vee = (r15) => { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u = A().getBool("WEBGL_PACK_NORMALIZATION") ? new ug(n.shape, s, a, i, p) : new ig(n.shape, s, a, i, p); return e.runWebGLProgram(u, [n], n.dtype); }; -var OF = { kernelName: zn, backendName: "webgl", kernelFunc: vee }; -var cg = class { +var MF = { kernelName: Bn, backendName: "webgl", kernelFunc: vee }; +var pg = class { constructor(t8, e, o, n, s) { this.variableNames = ["inputImage", "outputImage", "dy"], this.outputShape = [], this.outputShape = t8, this.depth = t8[3], this.depthRadius = e, this.bias = o, this.alpha = n, this.beta = s, this.userCode = ` void main() { @@ -22680,28 +22681,28 @@ var cg = class { `; } }; -var kee = (r) => { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, y: s, dy: a } = t8, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new cg(n.shape, i, p, u, c); +var kee = (r15) => { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, y: s, dy: a } = t8, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new pg(n.shape, i, p, u, c); return e.runWebGLProgram(l, [n, s, a], n.dtype); }; -var MF = { kernelName: Qa, backendName: "webgl", kernelFunc: kee }; -function LF(r, t8, e, o) { - let n = y.sizeFromShape(t8), a = y.sizeFromShape(r.shape) / n, i = te({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = Yr(i, r.dtype, "max", o), u = te({ inputs: { x: p }, attrs: { shape: e }, backend: o }); +var LF = { kernelName: Ya, backendName: "webgl", kernelFunc: kee }; +function BF(r15, t8, e, o) { + let n = y.sizeFromShape(t8), a = y.sizeFromShape(r15.shape) / n, i = te({ inputs: { x: r15 }, attrs: { shape: [a, n] }, backend: o }), p = Xr(i, r15.dtype, "max", o), u = te({ inputs: { x: p }, attrs: { shape: e }, backend: o }); return o.disposeIntermediateTensorInfo(i), o.disposeIntermediateTensorInfo(p), u; } -function Nv(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { reductionIndices: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = c != null, m = e.shouldExecuteOnCPU([n]), d = n; +function Tv(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { reductionIndices: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = c != null, m = e.shouldExecuteOnCPU([n]), d = n; if (l) { if (m) { let C = e.texData.get(d.dataId).values, S = new Array(i); for (let E = 0; E < S.length; E++) S[E] = n.shape[c[E]]; - let k = Np(C, n.shape, n.dtype, c, S); + let k = yp(C, n.shape, n.dtype, c, S); d = e.makeTensorInfo(S, n.dtype); let _ = e.texData.get(d.dataId); _.values = k; } else - d = Su(n, c, e); + d = gu(n, c, e); u = w.getInnerMostAxes(u.length, i); } w.assertAxesAreInnerMostDims("max", u, i); @@ -22709,16 +22710,16 @@ function Nv(r) { a && (g = w.expandShapeToKeepDim(f, p)); let x; if (m) { - let C = e.texData.get(d.dataId).values, S = XR(C, y.sizeFromShape(h), g, n.dtype); + let C = e.texData.get(d.dataId).values, S = YR(C, y.sizeFromShape(h), g, n.dtype); x = e.makeTensorInfo(g, n.dtype); let k = e.texData.get(x.dataId); k.values = S; } else - x = LF(d, h, g, e); + x = BF(d, h, g, e); return l && e.disposeIntermediateTensorInfo(d), x; } -var BF = { kernelName: Vn, backendName: "webgl", kernelFunc: Nv }; -var Nee = Wc + ` +var zF = { kernelName: zn, backendName: "webgl", kernelFunc: Tv }; +var Nee = Mc + ` return max(a, b); `; var Tee = ` @@ -22726,29 +22727,29 @@ var Tee = ` bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - ` + Xr + ` + ` + jr + ` return result; `; -var _ee = nt({ opSnippet: Nee, packedOpSnippet: Tee, cpuKernelImpl: YR }); -var zF = { kernelName: Wn, backendName: "webgl", kernelFunc: _ee }; -function $ee(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8; +var _ee = nt({ opSnippet: Nee, packedOpSnippet: Tee, cpuKernelImpl: QR }); +var VF = { kernelName: Vn, backendName: "webgl", kernelFunc: _ee }; +function $ee(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8; Vs(n, "maxPool"); let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1; y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); let c = w.computePool2DInfo(n.shape, s, a, u, i, p); if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape)) - return At({ inputs: { x: n }, backend: e }); + return Dt({ inputs: { x: n }, backend: e }); let l = new Us(c, "max", false); return e.runWebGLProgram(l, [n], n.dtype); } -var VF = { kernelName: Un, backendName: "webgl", kernelFunc: $ee }; -function Eee(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new Iu(l, "max", false); +var WF = { kernelName: Wn, backendName: "webgl", kernelFunc: $ee }; +function Eee(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new xu(l, "max", false); return e.runWebGLProgram(m, [n], n.dtype); } -var WF = { kernelName: ia, backendName: "webgl", kernelFunc: Eee }; -var lg = class { +var UF = { kernelName: ia, backendName: "webgl", kernelFunc: Eee }; +var cg = class { constructor(t8) { this.variableNames = ["dy", "maxPos"], this.outputShape = t8.inShape; let e = t8.strideHeight, o = t8.strideWidth, n = t8.dilationHeight, s = t8.effectiveFilterHeight, a = t8.effectiveFilterWidth, i = s - 1 - t8.padInfo.top, p = a - 1 - t8.padInfo.left, u = s * a - 1; @@ -22801,7 +22802,7 @@ var lg = class { `; } }; -var mg = class { +var lg = class { constructor(t8) { this.variableNames = ["dy", "maxPos"], this.outputShape = t8.inShape; let e = t8.strideDepth, o = t8.strideHeight, n = t8.strideWidth, s = t8.dilationDepth, a = t8.dilationHeight, i = t8.dilationWidth, p = t8.effectiveFilterDepth, u = t8.effectiveFilterHeight, c = t8.effectiveFilterWidth, l = p - 1 - t8.padInfo.front, m = u - 1 - t8.padInfo.top, d = c - 1 - t8.padInfo.left, f = p * u * c - 1; @@ -22872,63 +22873,63 @@ var mg = class { `; } }; -function Ree(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s } = t8, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = w.computePool3DInfo(a.shape, i, p, l, u, c), d = new Iu(m, "max", true), f = e.runWebGLProgram(d, [a], a.dtype), h = new mg(m), g = e.runWebGLProgram(h, [n, f], a.dtype); +function Ree(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s } = t8, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = w.computePool3DInfo(a.shape, i, p, l, u, c), d = new xu(m, "max", true), f = e.runWebGLProgram(d, [a], a.dtype), h = new lg(m), g = e.runWebGLProgram(h, [n, f], a.dtype); return e.disposeIntermediateTensorInfo(f), g; } -var UF = { kernelName: Ki, backendName: "webgl", kernelFunc: Ree }; -function Dee(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s, output: a } = t8, i = s; +var GF = { kernelName: Ui, backendName: "webgl", kernelFunc: Ree }; +function Dee(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s, output: a } = t8, i = s; Vs([s, a], "maxPoolGrad"); - let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = w.computePool2DInfo(i.shape, p, u, 1, c, l), d = true, f = new Us(m, "max", d), h = e.runWebGLProgram(f, [i], i.dtype), g = new lg(m), x = e.runWebGLProgram(g, [n, h], i.dtype); + let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = w.computePool2DInfo(i.shape, p, u, 1, c, l), d = true, f = new Us(m, "max", d), h = e.runWebGLProgram(f, [i], i.dtype), g = new cg(m), x = e.runWebGLProgram(g, [n, h], i.dtype); return e.disposeIntermediateTensorInfo(h), x; } -var GF = { kernelName: Hi, backendName: "webgl", kernelFunc: Dee }; -function HF(r, t8, e, o) { - let n = new Us(e, "max", false), s = o.runWebGLProgram(n, [r], "float32"); +var HF = { kernelName: Wi, backendName: "webgl", kernelFunc: Dee }; +function KF(r15, t8, e, o) { + let n = new Us(e, "max", false), s = o.runWebGLProgram(n, [r15], "float32"); n = new Us(e, "max", true, true, t8); - let a = o.runWebGLProgram(n, [r], "float32"); + let a = o.runWebGLProgram(n, [r15], "float32"); return [s, a]; } -var KF = { kernelName: ua, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: t8, backend: e }) => { - let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = t8, p = e; +var qF = { kernelName: ua, backendName: "webgl", kernelFunc: ({ inputs: r15, attrs: t8, backend: e }) => { + let { x: o } = r15, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = t8, p = e; y.assert(o.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`); let u = [1, 1]; y.assert(w.eitherStridesOrDilationsAreOne(s, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`); - let c = w.computePool2DInfo(o.shape, n, s, u, a), [l, m] = HF(o, i, c, p); + let c = w.computePool2DInfo(o.shape, n, s, u, a), [l, m] = KF(o, i, c, p); return [l, m]; } }; -function qF(r, t8, e, o) { - let n = y.sizeFromShape(t8), a = y.sizeFromShape(r.shape) / n, i = te({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = Yr(i, "float32", "mean", o), u = te({ inputs: { x: p }, attrs: { shape: e }, backend: o }); +function jF(r15, t8, e, o) { + let n = y.sizeFromShape(t8), a = y.sizeFromShape(r15.shape) / n, i = te({ inputs: { x: r15 }, attrs: { shape: [a, n] }, backend: o }), p = Xr(i, "float32", "mean", o), u = te({ inputs: { x: p }, attrs: { shape: e }, backend: o }); return o.disposeIntermediateTensorInfo(i), o.disposeIntermediateTensorInfo(p), u; } -var jF = { kernelName: Gn, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: t8, backend: e }) => { - let { x: o } = r, { keepDims: n, axis: s } = t8, a = e, i = o.shape.length, p = y.parseAxisParam(s, o.shape), u = p, c = w.getAxesPermutation(u, i), l = c != null, m = a.shouldExecuteOnCPU([o]), d = [], f = o; +var XF = { kernelName: Un, backendName: "webgl", kernelFunc: ({ inputs: r15, attrs: t8, backend: e }) => { + let { x: o } = r15, { keepDims: n, axis: s } = t8, a = e, i = o.shape.length, p = y.parseAxisParam(s, o.shape), u = p, c = w.getAxesPermutation(u, i), l = c != null, m = a.shouldExecuteOnCPU([o]), d = [], f = o; if (l) { if (m) { let S = a.texData.get(f.dataId).values, k = new Array(i); for (let R = 0; R < k.length; R++) k[R] = o.shape[c[R]]; - let _ = Np(S, o.shape, o.dtype, c, k); + let _ = yp(S, o.shape, o.dtype, c, k); f = a.makeTensorInfo(k, o.dtype); let E = a.texData.get(f.dataId); E.values = _; } else - f = Su(o, c, a); + f = gu(o, c, a); d.push(f), u = w.getInnerMostAxes(u.length, i); } w.assertAxesAreInnerMostDims("sum", u, i); let [h, g] = w.computeOutAndReduceShapes(f.shape, u), x = h; n && (x = w.expandShapeToKeepDim(h, p)); - let b = qF(f, g, x, a); + let b = jF(f, g, x, a); for (let C of d) a.disposeIntermediateTensorInfo(C); return b; } }; -function Aee(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = n; - c != null && (l = Ct({ inputs: { x: n }, backend: e, attrs: { perm: c } }), u = w.getInnerMostAxes(u.length, n.shape.length)), w.assertAxesAreInnerMostDims("min", u, i); - let [m, d] = w.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: e, attrs: { shape: [-1, f] } }), g = Yr(h, h.dtype, "min", e), x; +function Aee(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = n; + c != null && (l = bt({ inputs: { x: n }, backend: e, attrs: { perm: c } }), u = w.getInnerMostAxes(u.length, n.shape.length)), w.assertAxesAreInnerMostDims("min", u, i); + let [m, d] = w.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: e, attrs: { shape: [-1, f] } }), g = Xr(h, h.dtype, "min", e), x; if (a) { let b = w.expandShapeToKeepDim(m, p); x = te({ inputs: { x: g }, backend: e, attrs: { shape: b } }); @@ -22936,8 +22937,8 @@ function Aee(r) { x = te({ inputs: { x: g }, backend: e, attrs: { shape: m } }); return e.disposeIntermediateTensorInfo(h), e.disposeIntermediateTensorInfo(g), c != null && e.disposeIntermediateTensorInfo(l), x; } -var XF = { kernelName: Hn, backendName: "webgl", kernelFunc: Aee }; -var Fee = Wc + ` +var YF = { kernelName: Gn, backendName: "webgl", kernelFunc: Aee }; +var Fee = Mc + ` return min(a, b); `; var Pee = ` @@ -22945,12 +22946,12 @@ var Pee = ` bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - ` + Xr + ` + ` + jr + ` return result; `; -var Oee = nt({ opSnippet: Fee, packedOpSnippet: Pee, cpuKernelImpl: QR }); -var YF = { kernelName: Kn, backendName: "webgl", kernelFunc: Oee }; -var dg = class { +var Oee = nt({ opSnippet: Fee, packedOpSnippet: Pee, cpuKernelImpl: ZR }); +var QF = { kernelName: Hn, backendName: "webgl", kernelFunc: Oee }; +var mg = class { constructor(t8, e, o) { this.variableNames = ["x"], this.outputShape = e.map((c, l) => c[0] + t8[l] + c[1]); let n = t8.length, s = Re(n), a = e.map((c) => c[0]).join(","), i = e.map((c, l) => c[0] + t8[l]).join(","), p = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, n), u = o === "reflect" ? 0 : 1; @@ -22990,10 +22991,10 @@ var dg = class { `; } }; -var fg = class { +var dg = class { constructor(t8, e, o) { this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e.map((f, h) => f[0] + t8[h] + f[1]); - let n = t8.length, s = Re(n), a = e.map((f) => f[0]).join(","), i = e.map((f, h) => f[0] + t8[h]).join(","), p = Dt("rc", n), u = Dt("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = o === "reflect" ? 0 : 1, d = ""; + let n = t8.length, s = Re(n), a = e.map((f) => f[0]).join(","), i = e.map((f, h) => f[0] + t8[h]).join(","), p = Rt("rc", n), u = Rt("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = o === "reflect" ? 0 : 1, d = ""; if (n === 1) { let f = ` ${s} source = rc; @@ -23060,22 +23061,22 @@ var fg = class { `; } }; -var Mee = ({ inputs: r, backend: t8, attrs: e }) => { - let { x: o } = r, { paddings: n, mode: s } = e, a = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new fg(o.shape, n, s) : new dg(o.shape, n, s); +var Mee = ({ inputs: r15, backend: t8, attrs: e }) => { + let { x: o } = r15, { paddings: n, mode: s } = e, a = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new dg(o.shape, n, s) : new mg(o.shape, n, s); return t8.runWebGLProgram(a, [o], o.dtype); }; -var QF = { kernelName: qn, backendName: "webgl", kernelFunc: Mee }; +var ZF = { kernelName: Kn, backendName: "webgl", kernelFunc: Mee }; var Lee = `if (b == 0.0) return NAN; return mod(a, b);`; var Bee = ` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - ` + Xr + ` + ` + jr + ` return result; `; var zee = nt({ opSnippet: Lee, packedOpSnippet: Bee }); -var ZF = { kernelName: jn, backendName: "webgl", kernelFunc: zee }; -var hg = class { +var JF = { kernelName: qn, backendName: "webgl", kernelFunc: zee }; +var fg = class { constructor(t8, e, o) { this.variableNames = ["probs"], this.customUniforms = [{ name: "seed", type: "float" }], this.outputShape = [t8, o], this.userCode = ` void main() { @@ -23124,22 +23125,22 @@ var Wee = ` return result; `; -var Tv = nt({ opSnippet: Vee, packedOpSnippet: Wee, checkOutOfBounds: true }); -var JF = { kernelName: hn, backendName: "webgl", kernelFunc: Tv }; -var e3 = "return a - b;"; -var _v = nt({ opSnippet: e3, packedOpSnippet: e3, supportsComplex: true, cpuKernelImpl: yD }); -var t3 = { kernelName: _s, backendName: "webgl", kernelFunc: _v }; -function $v(r) { - let { inputs: t8, backend: e, attrs: o } = r, { logits: n } = t8, { dim: s } = o, a = y.parseAxisParam([s], n.shape), i = Nv({ inputs: { x: n }, backend: e, attrs: { reductionIndices: a, keepDims: false } }), p = w.expandShapeToKeepDim(i.shape, a), u = te({ inputs: { x: i }, backend: e, attrs: { shape: p } }), c = _v({ inputs: { a: n, b: u }, backend: e }), l = Iv({ inputs: { x: c }, backend: e }), m = Tp({ inputs: { x: l }, backend: e, attrs: { axis: a, keepDims: false } }), d = te({ inputs: { x: m }, backend: e, attrs: { shape: p } }), f = Tv({ inputs: { a: l, b: d }, backend: e }); +var _v = nt({ opSnippet: Vee, packedOpSnippet: Wee, checkOutOfBounds: true }); +var e3 = { kernelName: fn, backendName: "webgl", kernelFunc: _v }; +var t3 = "return a - b;"; +var $v = nt({ opSnippet: t3, packedOpSnippet: t3, supportsComplex: true, cpuKernelImpl: bD }); +var r32 = { kernelName: Ts, backendName: "webgl", kernelFunc: $v }; +function Ev(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { logits: n } = t8, { dim: s } = o, a = y.parseAxisParam([s], n.shape), i = Tv({ inputs: { x: n }, backend: e, attrs: { reductionIndices: a, keepDims: false } }), p = w.expandShapeToKeepDim(i.shape, a), u = te({ inputs: { x: i }, backend: e, attrs: { shape: p } }), c = $v({ inputs: { a: n, b: u }, backend: e }), l = vv({ inputs: { x: c }, backend: e }), m = bp({ inputs: { x: l }, backend: e, attrs: { axis: a, keepDims: false } }), d = te({ inputs: { x: m }, backend: e, attrs: { shape: p } }), f = _v({ inputs: { a: l, b: d }, backend: e }); return e.disposeIntermediateTensorInfo(i), e.disposeIntermediateTensorInfo(u), e.disposeIntermediateTensorInfo(c), e.disposeIntermediateTensorInfo(l), e.disposeIntermediateTensorInfo(m), e.disposeIntermediateTensorInfo(d), f; } -var r3 = { kernelName: vs, backendName: "webgl", kernelFunc: $v }; -function Uee(r) { - let { inputs: t8, backend: e, attrs: o } = r, { logits: n } = t8, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : $v({ inputs: { logits: n }, backend: e, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new hg(u, c, s), m = [[a]], d = e.runWebGLProgram(l, [p], "int32", m); +var o3 = { kernelName: Is, backendName: "webgl", kernelFunc: Ev }; +function Uee(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { logits: n } = t8, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : Ev({ inputs: { logits: n }, backend: e, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new fg(u, c, s), m = [[a]], d = e.runWebGLProgram(l, [p], "int32", m); return i || e.disposeIntermediateTensorInfo(p), d; } -var o3 = { kernelName: Xn, backendName: "webgl", kernelFunc: Uee }; -var Gee = Ut + ` +var n3 = { kernelName: jn, backendName: "webgl", kernelFunc: Uee }; +var Gee = Wt + ` return -x; `; var Hee = ` @@ -23153,38 +23154,38 @@ var Hee = ` return result; `; -function Kee(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8; +function Kee(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8; if (e.shouldExecuteOnCPU([o])) { - let s = e.texData.get(o.dataId), [a, i] = JR(s.values, o.shape, o.dtype); + let s = e.texData.get(o.dataId), [a, i] = eD(s.values, o.shape, o.dtype); return e.makeTensorInfo(i, o.dtype, a); } let n; - return A().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Fr(o.shape, Hee) : n = new or(o.shape, Gee), e.runWebGLProgram(n, [o], o.dtype); + return A().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Ar(o.shape, Hee) : n = new tr(o.shape, Gee), e.runWebGLProgram(n, [o], o.dtype); } -var n3 = { kernelName: pa, backendName: "webgl", kernelFunc: Kee }; -var qee = Wt.nonMaxSuppressionV3Impl; -function jee(r) { +var s3 = { kernelName: pa, backendName: "webgl", kernelFunc: Kee }; +var qee = Vt.nonMaxSuppressionV3Impl; +function jee(r15) { w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: t8, backend: e, attrs: o } = r, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = e.readSync(n.dataId), c = e.readSync(s.dataId), { selectedIndices: l } = qee(u, c, a, i, p); + let { inputs: t8, backend: e, attrs: o } = r15, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = e.readSync(n.dataId), c = e.readSync(s.dataId), { selectedIndices: l } = qee(u, c, a, i, p); return e.makeTensorInfo([l.length], "int32", new Int32Array(l)); } -var s3 = { kernelName: Zn, backendName: "webgl", kernelFunc: jee }; -var Xee = Wt.nonMaxSuppressionV4Impl; -function Yee(r) { +var a3 = { kernelName: Qn, backendName: "webgl", kernelFunc: jee }; +var Xee = Vt.nonMaxSuppressionV4Impl; +function Yee(r15) { w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: t8, backend: e, attrs: o } = r, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o, c = e.readSync(n.dataId), l = e.readSync(s.dataId), { selectedIndices: m, validOutputs: d } = Xee(c, l, a, i, p, u); + let { inputs: t8, backend: e, attrs: o } = r15, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o, c = e.readSync(n.dataId), l = e.readSync(s.dataId), { selectedIndices: m, validOutputs: d } = Xee(c, l, a, i, p, u); return [e.makeTensorInfo([m.length], "int32", new Int32Array(m)), e.makeTensorInfo([], "int32", new Int32Array([d]))]; } -var a3 = { kernelName: Za, backendName: "webgl", kernelFunc: Yee }; -var Qee = Wt.nonMaxSuppressionV5Impl; -function Zee(r) { +var i3 = { kernelName: Qa, backendName: "webgl", kernelFunc: Yee }; +var Qee = Vt.nonMaxSuppressionV5Impl; +function Zee(r15) { w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: t8, backend: e, attrs: o } = r, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = e.readSync(n.dataId), l = e.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = Qee(c, l, m, d, f, h); + let { inputs: t8, backend: e, attrs: o } = r15, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = e.readSync(n.dataId), l = e.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = Qee(c, l, m, d, f, h); return [e.makeTensorInfo([g.length], "int32", new Int32Array(g)), e.makeTensorInfo([x.length], "float32", new Float32Array(x))]; } -var i3 = { kernelName: Jn, backendName: "webgl", kernelFunc: Zee }; -var gg = class { +var u3 = { kernelName: Zn, backendName: "webgl", kernelFunc: Zee }; +var hg = class { constructor(t8, e, o, n) { this.variableNames = ["indices"], this.outputShape = [t8, e], this.userCode = ` void main() { @@ -23196,49 +23197,49 @@ var gg = class { `; } }; -var Jee = (r) => { - let { inputs: t8, backend: e, attrs: o } = r, { indices: n } = t8, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new gg(u, a, i, p), l = te({ inputs: { x: n }, backend: e, attrs: { shape: [u] } }), m = e.runWebGLProgram(c, [l], s); +var Jee = (r15) => { + let { inputs: t8, backend: e, attrs: o } = r15, { indices: n } = t8, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new hg(u, a, i, p), l = te({ inputs: { x: n }, backend: e, attrs: { shape: [u] } }), m = e.runWebGLProgram(c, [l], s); e.disposeIntermediateTensorInfo(l); let d = [...n.shape, a], f = te({ inputs: { x: m }, backend: e, attrs: { shape: d } }); return e.disposeIntermediateTensorInfo(m), f; }; -var u3 = { kernelName: es, backendName: "webgl", kernelFunc: Jee }; -function sm(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8; +var p3 = { kernelName: Jn, backendName: "webgl", kernelFunc: Jee }; +function om(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8; if (o.dtype === "complex64") { - let n = wi({ inputs: { input: o }, backend: e }), s = sm({ inputs: { x: n }, backend: e }), a = $p({ inputs: { input: o }, backend: e }), i = sm({ inputs: { x: a }, backend: e }), p = Or({ inputs: { real: s, imag: i }, backend: e }); + let n = yi({ inputs: { input: o }, backend: e }), s = om({ inputs: { x: n }, backend: e }), a = wp({ inputs: { input: o }, backend: e }), i = om({ inputs: { x: a }, backend: e }), p = Pr({ inputs: { real: s, imag: i }, backend: e }); return e.disposeIntermediateTensorInfo(n), e.disposeIntermediateTensorInfo(s), e.disposeIntermediateTensorInfo(a), e.disposeIntermediateTensorInfo(i), p; } else - return Si({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: e }); + return bi({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: e }); } -var p3 = { kernelName: Sa, backendName: "webgl", kernelFunc: sm }; -function c3(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8; +var c3 = { kernelName: Sa, backendName: "webgl", kernelFunc: om }; +function l3(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8; if (o.dtype === "string") throw new Error("onesLike is not supported under string dtype"); if (o.dtype === "complex64") { - let n = wi({ inputs: { input: o }, backend: e }), s = c3({ inputs: { x: n }, backend: e }), a = $p({ inputs: { input: o }, backend: e }), i = sm({ inputs: { x: a }, backend: e }), p = Or({ inputs: { real: s, imag: i }, backend: e }); + let n = yi({ inputs: { input: o }, backend: e }), s = l3({ inputs: { x: n }, backend: e }), a = wp({ inputs: { input: o }, backend: e }), i = om({ inputs: { x: a }, backend: e }), p = Pr({ inputs: { real: s, imag: i }, backend: e }); return e.disposeIntermediateTensorInfo(n), e.disposeIntermediateTensorInfo(s), e.disposeIntermediateTensorInfo(a), e.disposeIntermediateTensorInfo(i), p; } else - return Si({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: e }); + return bi({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: e }); } -var l3 = { kernelName: ca, backendName: "webgl", kernelFunc: c3 }; -function ete(r) { - let { inputs: t8, backend: e, attrs: o } = r, { axis: n } = o; +var m3 = { kernelName: ca, backendName: "webgl", kernelFunc: l3 }; +function ete(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { axis: n } = o; if (t8.length === 1) - return eg({ inputs: { input: t8[0] }, backend: e, attrs: { dim: n } }); + return Jh({ inputs: { input: t8[0] }, backend: e, attrs: { dim: n } }); let s = t8[0].shape, a = t8[0].dtype; t8.forEach((c) => { y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes"); }); let i = [], p = t8.map((c) => { - let l = eg({ inputs: { input: c }, backend: e, attrs: { dim: n } }); + let l = Jh({ inputs: { input: c }, backend: e, attrs: { dim: n } }); return i.push(l), l; - }), u = Sv({ inputs: p, backend: e, attrs: { axis: n } }); + }), u = Iv({ inputs: p, backend: e, attrs: { axis: n } }); return i.forEach((c) => e.disposeIntermediateTensorInfo(c)), u; } -var m3 = { kernelName: la, backendName: "webgl", kernelFunc: ete }; -var xg = class { +var d3 = { kernelName: la, backendName: "webgl", kernelFunc: ete }; +var gg = class { constructor(t8, e, o) { this.variableNames = ["x"], this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = e.map((u, c) => u[0] + t8[c] + u[1]); let n = t8.length, s = Re(n), a = e.map((u) => u[0]).join(","), i = e.map((u, c) => u[0] + t8[c]).join(","), p = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, n); @@ -23274,10 +23275,10 @@ var xg = class { `; } }; -var yg = class { +var xg = class { constructor(t8, e, o) { this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = e.map((h, g) => h[0] + t8[g] + h[1]); - let n = t8.length, s = Re(n), a = e.map((h) => h[0]).join(","), i = e.map((h, g) => h[0] + t8[g]).join(","), p = Dt("rc", n), u = Dt("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = [`${s} rc = outputLoc;`, `${p[n - 1]} += 1; + let n = t8.length, s = Re(n), a = e.map((h) => h[0]).join(","), i = e.map((h, g) => h[0] + t8[g]).join(","), p = Rt("rc", n), u = Rt("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = [`${s} rc = outputLoc;`, `${p[n - 1]} += 1; if(${c}) { `, n === 1 ? "" : `} rc = outputLoc; @@ -23307,16 +23308,16 @@ var yg = class { `; } }; -var Ev = (r) => { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { paddings: s, constantValue: a } = o; +var Rv = (r15) => { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { paddings: s, constantValue: a } = o; if (y.sizeFromShape(n.shape) === 0) { let u = s.map((c, l) => c[0] + n.shape[l] + c[1]); - return Si({ backend: e, attrs: { shape: u, value: a, dtype: n.dtype } }); + return bi({ backend: e, attrs: { shape: u, value: a, dtype: n.dtype } }); } - let i = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new yg(n.shape, s, a) : new xg(n.shape, s, a), p = [[a]]; + let i = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new xg(n.shape, s, a) : new gg(n.shape, s, a), p = [[a]]; return e.runWebGLProgram(i, [n], n.dtype, p); }; -var d3 = { kernelName: ts, backendName: "webgl", kernelFunc: Ev }; +var f3 = { kernelName: es, backendName: "webgl", kernelFunc: Rv }; var tte = ` if(a < 0.0 && floor(b) < b){ return NAN; @@ -23343,20 +23344,20 @@ var rte = ` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - ` + Xr + ` + ` + jr + ` return result; `; var ote = nt({ opSnippet: tte, packedOpSnippet: rte }); -var f3 = { kernelName: rs, backendName: "webgl", kernelFunc: ote }; -function nte(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, keepDims: a } = o, i = n.shape.length, p = [], u = y.parseAxisParam(s, n.shape), c = u, l = w.getAxesPermutation(c, i), m = n; - l != null && (m = Ct({ inputs: { x: n }, backend: e, attrs: { perm: l } }), c = w.getInnerMostAxes(c.length, i), p.push(m)), w.assertAxesAreInnerMostDims("prod", c, i); +var h3 = { kernelName: ts, backendName: "webgl", kernelFunc: ote }; +function nte(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, keepDims: a } = o, i = n.shape.length, p = [], u = y.parseAxisParam(s, n.shape), c = u, l = w.getAxesPermutation(c, i), m = n; + l != null && (m = bt({ inputs: { x: n }, backend: e, attrs: { perm: l } }), c = w.getInnerMostAxes(c.length, i), p.push(m)), w.assertAxesAreInnerMostDims("prod", c, i); let d; if (e.shouldExecuteOnCPU([m])) { - let f = e.texData.get(m.dataId).values, { outVals: h, outShape: g, outDtype: x } = tD(m.shape, m.dtype, f, c); + let f = e.texData.get(m.dataId).values, { outVals: h, outShape: g, outDtype: x } = rD(m.shape, m.dtype, f, c); d = e.makeTensorInfo(g, x, h); } else { - let [f, h] = w.computeOutAndReduceShapes(m.shape, c), g = y.sizeFromShape(h), x = te({ inputs: { x: m }, backend: e, attrs: { shape: [-1, g] } }), b = ni(n.dtype), C = Yr(x, b, "prod", e); + let [f, h] = w.computeOutAndReduceShapes(m.shape, c), g = y.sizeFromShape(h), x = te({ inputs: { x: m }, backend: e, attrs: { shape: [-1, g] } }), b = oi(n.dtype), C = Xr(x, b, "prod", e); d = te({ inputs: { x: C }, backend: e, attrs: { shape: f } }), p.push(x), p.push(C); } if (a) { @@ -23366,31 +23367,31 @@ function nte(r) { } return p.forEach((f) => e.disposeIntermediateTensorInfo(f)), d; } -var h3 = { kernelName: ns, backendName: "webgl", kernelFunc: nte }; -function ste(r) { - let { inputs: t8, backend: e, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = t8, { outputRaggedRank: i } = o, p = n.map((x) => e.readSync(x.dataId)), u = n.map((x) => x.shape), c = e.readSync(s.dataId), l = e.readSync(a.dataId), [m, d, f] = rD(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => e.makeTensorInfo([x.length], "int32", x)), g = e.makeTensorInfo(f, s.dtype, d); +var g3 = { kernelName: os, backendName: "webgl", kernelFunc: nte }; +function ste(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = t8, { outputRaggedRank: i } = o, p = n.map((x) => e.readSync(x.dataId)), u = n.map((x) => x.shape), c = e.readSync(s.dataId), l = e.readSync(a.dataId), [m, d, f] = oD(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => e.makeTensorInfo([x.length], "int32", x)), g = e.makeTensorInfo(f, s.dtype, d); return h.concat([g]); } -var g3 = { kernelName: Qp, backendName: "webgl", kernelFunc: ste }; -function ate(r) { - let { inputs: t8, backend: e } = r, { starts: o, limits: n, deltas: s } = t8, a = e.readSync(o.dataId), i = e.readSync(n.dataId), p = e.readSync(s.dataId), [u, c] = oD(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = e.makeTensorInfo([u.length], "int32", u), m = e.makeTensorInfo([c.length], o.dtype, c); +var x3 = { kernelName: Up, backendName: "webgl", kernelFunc: ste }; +function ate(r15) { + let { inputs: t8, backend: e } = r15, { starts: o, limits: n, deltas: s } = t8, a = e.readSync(o.dataId), i = e.readSync(n.dataId), p = e.readSync(s.dataId), [u, c] = nD(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = e.makeTensorInfo([u.length], "int32", u), m = e.makeTensorInfo([c.length], o.dtype, c); return [l, m]; } -var x3 = { kernelName: Zp, backendName: "webgl", kernelFunc: ate }; -function ite(r) { - let { inputs: t8, backend: e, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = t8, { rowPartitionTypes: p } = o, u = e.readSync(n.dataId), c = e.readSync(s.dataId), l = e.readSync(a.dataId), m = i.map((g) => e.readSync(g.dataId)), d = i.map((g) => g.shape), [f, h] = nD(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p); +var y3 = { kernelName: Gp, backendName: "webgl", kernelFunc: ate }; +function ite(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = t8, { rowPartitionTypes: p } = o, u = e.readSync(n.dataId), c = e.readSync(s.dataId), l = e.readSync(a.dataId), m = i.map((g) => e.readSync(g.dataId)), d = i.map((g) => g.shape), [f, h] = sD(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p); return e.makeTensorInfo(f, s.dtype, h); } -var y3 = { kernelName: Jp, backendName: "webgl", kernelFunc: ite }; -var Rv = (r) => { - let { backend: t8, attrs: e } = r, { start: o, stop: n, step: s, dtype: a } = e, i = sD(o, n, s, a); +var b3 = { kernelName: Hp, backendName: "webgl", kernelFunc: ite }; +var Dv = (r15) => { + let { backend: t8, attrs: e } = r15, { start: o, stop: n, step: s, dtype: a } = e, i = aD(o, n, s, a); return t8.makeTensorInfo([i.length], a, i); }; -var b3 = { kernelName: ma, backendName: "webgl", kernelFunc: Rv }; +var C3 = { kernelName: ma, backendName: "webgl", kernelFunc: Dv }; var ute = "return 1.0 / x;"; var pte = xe({ opSnippet: ute }); -var C3 = { kernelName: ss, backendName: "webgl", kernelFunc: pte }; -var cte = Ut + ` +var w3 = { kernelName: ns, backendName: "webgl", kernelFunc: pte }; +var cte = Wt + ` return (x < 0.0) ? 0.0 : x; `; var lte = ` @@ -23405,8 +23406,8 @@ var lte = ` return result; `; var mte = xe({ opSnippet: cte, packedOpSnippet: lte }); -var w3 = { kernelName: as, backendName: "webgl", kernelFunc: mte }; -var dte = Ut + ` +var S3 = { kernelName: ss, backendName: "webgl", kernelFunc: mte }; +var dte = Wt + ` return (x < 0.0) ? 0.0 : min(6.0, x); `; var fte = ` @@ -23421,8 +23422,8 @@ var fte = ` return result; `; var hte = xe({ opSnippet: dte, packedOpSnippet: fte }); -var S3 = { kernelName: ps, backendName: "webgl", kernelFunc: hte }; -var bg = class { +var I3 = { kernelName: us, backendName: "webgl", kernelFunc: hte }; +var yg = class { constructor(t8, e, o, n, s) { this.variableNames = ["A"], this.outputShape = []; let [a, i, p, u] = t8; @@ -23464,7 +23465,7 @@ var bg = class { `; } }; -var Cg = class { +var bg = class { constructor(t8, e, o, n, s) { this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = []; let [a, i, p, u] = t8; @@ -23550,12 +23551,12 @@ var Cg = class { `; } }; -function gte(r) { - let { inputs: t8, backend: e, attrs: o } = r, { images: n } = t8, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = A().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new Cg(n.shape, p, u, s, a) : new bg(n.shape, p, u, s, a); +function gte(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { images: n } = t8, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = A().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new bg(n.shape, p, u, s, a) : new yg(n.shape, p, u, s, a); return e.runWebGLProgram(c, [n], "float32"); } -var I3 = { kernelName: us, backendName: "webgl", kernelFunc: gte }; -var wg = class { +var v3 = { kernelName: is, backendName: "webgl", kernelFunc: gte }; +var Cg = class { constructor(t8, e, o) { this.variableNames = ["dy"], this.outputShape = [], this.outputShape = e; let [, n, s] = e, [, a, i] = t8, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2; @@ -23643,12 +23644,12 @@ var wg = class { `; } }; -function xte(r) { - let { inputs: t8, backend: e, attrs: o } = r, { images: n, dy: s } = t8, { alignCorners: a } = o, i = new wg(s.shape, n.shape, a); +function xte(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { images: n, dy: s } = t8, { alignCorners: a } = o, i = new Cg(s.shape, n.shape, a); return e.runWebGLProgram(i, [s], s.dtype); } -var v3 = { kernelName: ei, backendName: "webgl", kernelFunc: xte }; -var Sg = class { +var k3 = { kernelName: Ja, backendName: "webgl", kernelFunc: xte }; +var wg = class { constructor(t8, e, o, n, s) { this.variableNames = ["A"], this.outputShape = []; let [a, i, p, u] = t8; @@ -23679,7 +23680,7 @@ var Sg = class { `; } }; -var Ig = class { +var Sg = class { constructor(t8, e, o, n, s) { this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = []; let [a, i, p, u] = t8; @@ -23729,12 +23730,12 @@ var Ig = class { `; } }; -function yte(r) { - let { inputs: t8, backend: e, attrs: o } = r, { images: n } = t8, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = A().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new Ig(n.shape, p, u, s, a) : new Sg(n.shape, p, u, s, a); +function yte(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { images: n } = t8, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = A().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new Sg(n.shape, p, u, s, a) : new wg(n.shape, p, u, s, a); return e.runWebGLProgram(c, [n], n.dtype); } -var k3 = { kernelName: is, backendName: "webgl", kernelFunc: yte }; -var vg = class { +var N3 = { kernelName: as, backendName: "webgl", kernelFunc: yte }; +var Ig = class { constructor(t8, e, o) { this.variableNames = ["dy"], this.outputShape = [], this.outputShape = e; let [, n, s] = e, [, a, i] = t8, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2; @@ -23811,12 +23812,12 @@ var vg = class { `; } }; -function bte(r) { - let { inputs: t8, backend: e, attrs: o } = r, { images: n, dy: s } = t8, { alignCorners: a } = o, i = new vg(s.shape, n.shape, a); +function bte(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { images: n, dy: s } = t8, { alignCorners: a } = o, i = new Ig(s.shape, n.shape, a); return e.runWebGLProgram(i, [s], s.dtype); } -var N3 = { kernelName: Ja, backendName: "webgl", kernelFunc: bte }; -var kg = class { +var T3 = { kernelName: Za, backendName: "webgl", kernelFunc: bte }; +var vg = class { constructor(t8, e) { this.variableNames = ["x"]; let o = t8.length; @@ -23840,14 +23841,14 @@ var kg = class { `; } }; -var Ng = class { +var kg = class { constructor(t8, e) { this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true; let o = t8.length; if (o > 4) throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`); this.outputShape = t8; - let n = Dt("rc", o), s = `${n[o - 1]} + 1 < ${this.outputShape[o - 1]}`, a = `${n[o - 2]} + 1 < ${this.outputShape[o - 2]}`, i = Re(o); + let n = Rt("rc", o), s = `${n[o - 1]} + 1 < ${this.outputShape[o - 1]}`, a = `${n[o - 2]} + 1 < ${this.outputShape[o - 2]}`, i = Re(o); o === 1 ? this.userCode = ` void main(){ int rc = getOutputCoords(); @@ -23898,15 +23899,15 @@ var Ng = class { } } }; -function Cte(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { dims: s } = o, a = n.shape.length, i = y.parseAxisParam(s, n.shape); +function Cte(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { dims: s } = o, a = n.shape.length, i = y.parseAxisParam(s, n.shape); if (a === 0) - return At({ inputs: { x: n }, backend: e }); - let p = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new Ng(n.shape, i) : new kg(n.shape, i); + return Dt({ inputs: { x: n }, backend: e }); + let p = A().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new kg(n.shape, i) : new vg(n.shape, i); return e.runWebGLProgram(p, [n], n.dtype); } -var T3 = { kernelName: cs, backendName: "webgl", kernelFunc: Cte }; -var Tg = class { +var _3 = { kernelName: ps, backendName: "webgl", kernelFunc: Cte }; +var Ng = class { constructor(t8, e) { this.variableNames = ["Image"], this.outputShape = [], this.customUniforms = [{ name: "params", type: "vec4" }]; let o = t8[1], n = t8[2]; @@ -23934,8 +23935,8 @@ var Tg = class { `; } }; -var _3 = { kernelName: As, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: t8, backend: e }) => { - let { image: o } = r, { radians: n, fillValue: s, center: a } = t8, i = e, p = new Tg(o.shape, s), [u, c] = w.getImageCenter(a, o.shape[1], o.shape[2]), l = [[u, c, Math.sin(n), Math.cos(n)]]; +var $3 = { kernelName: Ds, backendName: "webgl", kernelFunc: ({ inputs: r15, attrs: t8, backend: e }) => { + let { image: o } = r15, { radians: n, fillValue: s, center: a } = t8, i = e, p = new Ng(o.shape, s), [u, c] = w.getImageCenter(a, o.shape[1], o.shape[2]), l = [[u, c, Math.sin(n), Math.cos(n)]]; return i.runWebGLProgram(p, [o], o.dtype, l); } }; var wte = ` @@ -23955,11 +23956,11 @@ var wte = ` } `; var Ste = xe({ opSnippet: wte }); -var $3 = { kernelName: ls, backendName: "webgl", kernelFunc: Ste }; +var E3 = { kernelName: cs, backendName: "webgl", kernelFunc: Ste }; var Ite = "return inversesqrt(x);"; -var vte = xe({ opSnippet: Ite, cpuKernelImpl: aD }); -var E3 = { kernelName: ms, backendName: "webgl", kernelFunc: vte }; -var vu = class { +var vte = xe({ opSnippet: Ite, cpuKernelImpl: iD }); +var R3 = { kernelName: ls, backendName: "webgl", kernelFunc: vte }; +var yu = class { constructor(t8, e, o, n, s, a, i = true, p = false) { this.variableNames = ["updates", "indices", "defaultValue"], this.outputShape = a; let u = Re(s.length), c = Re(a.length), l = ""; @@ -23992,7 +23993,7 @@ var vu = class { `; } }; -var _g = class { +var Tg = class { constructor(t8, e, o, n, s, a, i = true, p = false) { this.variableNames = ["updates", "indices", "defaultValue"], this.packedInputs = true, this.packedOutput = true, this.outputShape = a; let u = Re(s.length), c = Re(a.length), l = ""; @@ -24042,17 +24043,17 @@ var _g = class { `; } }; -function kte(r) { - let { inputs: t8, backend: e, attrs: o } = r, { indices: n, updates: s } = t8, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(s, n, a), m = [l / u, u]; +function kte(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { indices: n, updates: s } = t8, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(s, n, a), m = [l / u, u]; if (l === 0) return e.makeTensorInfo(a, n.dtype); let d = te({ inputs: { x: n }, backend: e, attrs: { shape: [p, i] } }), f = te({ inputs: { x: s }, backend: e, attrs: { shape: [p, u] } }), h = e.makeTensorInfo([], "float32", new Float32Array([0])), g; - A().getBool("WEBGL_PACK") ? g = new _g(p, i, d.shape.length, f.shape.length, c, m) : g = new vu(p, i, d.shape.length, f.shape.length, c, m); + A().getBool("WEBGL_PACK") ? g = new Tg(p, i, d.shape.length, f.shape.length, c, m) : g = new yu(p, i, d.shape.length, f.shape.length, c, m); let x = e.runWebGLProgram(g, [f, d, h], f.dtype), b = te({ inputs: { x }, backend: e, attrs: { shape: a } }); return e.disposeIntermediateTensorInfo(d), e.disposeIntermediateTensorInfo(f), e.disposeIntermediateTensorInfo(x), e.disposeIntermediateTensorInfo(h), b; } -var R3 = { kernelName: ds, backendName: "webgl", kernelFunc: kte }; -var $g = class { +var D3 = { kernelName: ms, backendName: "webgl", kernelFunc: kte }; +var _g = class { constructor(t8, e, o, n) { this.variableNames = ["sortedSequence", "values"], this.customUniforms = [{ name: "numInputs", type: "int" }], this.outputShape = [t8, o]; let s = "while (left < right) {", a = `for (int i = 0; i < ${Math.ceil(Math.log2(e + 1))}; ++i) { if (left >= right) break;`, i = A().getNumber("WEBGL_VERSION") === 2 ? s : a, p = n === "left" ? "<" : "<="; @@ -24084,12 +24085,12 @@ var $g = class { `; } }; -function Nte(r) { - let { inputs: t8, backend: e, attrs: o } = r, { sortedSequence: n, values: s } = t8, { side: a } = o, i = new $g(n.shape[0], n.shape[1], s.shape[1], a), p = [[n.shape[1]]]; +function Nte(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { sortedSequence: n, values: s } = t8, { side: a } = o, i = new _g(n.shape[0], n.shape[1], s.shape[1], a), p = [[n.shape[1]]]; return e.runWebGLProgram(i, [n, s], "int32", p); } -var D3 = { kernelName: hs, backendName: "webgl", kernelFunc: Nte }; -var Eg = class { +var A3 = { kernelName: fs, backendName: "webgl", kernelFunc: Nte }; +var $g = class { constructor(t8, e, o) { this.variableNames = ["c", "a", "b"], this.outputShape = e; let n, s; @@ -24117,11 +24118,11 @@ var Eg = class { `; } }; -function Tte(r) { - let { inputs: t8, backend: e } = r, { condition: o, t: n, e: s } = t8, a = new Eg(o.shape.length, n.shape, n.shape.length); +function Tte(r15) { + let { inputs: t8, backend: e } = r15, { condition: o, t: n, e: s } = t8, a = new $g(o.shape.length, n.shape, n.shape.length); return e.runWebGLProgram(a, [o, n, s], dt(n.dtype, s.dtype)); } -var A3 = { kernelName: fa, backendName: "webgl", kernelFunc: Tte }; +var F3 = { kernelName: fa, backendName: "webgl", kernelFunc: Tte }; var _te = ` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 @@ -24130,8 +24131,8 @@ var _te = ` return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `; var $te = xe({ opSnippet: _te }); -var F3 = { kernelName: gs, backendName: "webgl", kernelFunc: $te }; -var Ete = Po + ` +var P3 = { kernelName: hs, backendName: "webgl", kernelFunc: $te }; +var Ete = Fo + ` return 1.0 / (1.0 + exp(-1.0 * x)); `; var Rte = ` @@ -24145,31 +24146,31 @@ var Rte = ` return result; `; -var Dte = xe({ opSnippet: Ete, packedOpSnippet: Rte, cpuKernelImpl: uD }); -var P3 = { kernelName: Cs, backendName: "webgl", kernelFunc: Dte }; +var Dte = xe({ opSnippet: Ete, packedOpSnippet: Rte, cpuKernelImpl: pD }); +var O3 = { kernelName: bs, backendName: "webgl", kernelFunc: Dte }; var Ate = ` if (isnan(x)) { return 0.0; } return sign(x); `; var Fte = xe({ opSnippet: Ate }); -var O3 = { kernelName: bs, backendName: "webgl", kernelFunc: Fte }; -var Pte = Po + ` +var M3 = { kernelName: ys, backendName: "webgl", kernelFunc: Fte }; +var Pte = Fo + ` return sin(x); `; var Ote = ` vec4 result = sin(x); bvec4 isNaN = isnan(x); - ${Xr} + ${jr} return result; `; var Mte = xe({ opSnippet: Pte, packedOpSnippet: Ote }); -var M3 = { kernelName: xs, backendName: "webgl", kernelFunc: Mte }; +var L3 = { kernelName: gs, backendName: "webgl", kernelFunc: Mte }; var Lte = ` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `; var Bte = xe({ opSnippet: Lte }); -var L3 = { kernelName: ys, backendName: "webgl", kernelFunc: Bte }; +var B3 = { kernelName: xs, backendName: "webgl", kernelFunc: Bte }; var zte = ` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -24192,20 +24193,20 @@ var zte = ` return result; `; var Vte = xe({ opSnippet: zte }); -var B3 = { kernelName: ws, backendName: "webgl", kernelFunc: Vte }; -var Wte = (r) => { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { blockShape: s, paddings: a } = o; +var z3 = { kernelName: Cs, backendName: "webgl", kernelFunc: Vte }; +var Wte = (r15) => { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { blockShape: s, paddings: a } = o; y.assert(n.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"); let i = s.reduce((x, b) => x * b), p = [[0, 0]]; p.push(...a); for (let x = 1 + s.length; x < n.shape.length; ++x) p.push([0, 0]); - let u = [], c = Ev({ inputs: { x: n }, backend: e, attrs: { paddings: p, constantValue: 0 } }), l = w.getReshaped(c.shape, s, i, false), m = w.getPermuted(l.length, s.length, false), d = w.getReshapedPermuted(c.shape, s, i, false), f = te({ inputs: { x: c }, backend: e, attrs: { shape: l } }), h = Ct({ inputs: { x: f }, backend: e, attrs: { perm: m } }), g = te({ inputs: { x: h }, backend: e, attrs: { shape: d } }); + let u = [], c = Rv({ inputs: { x: n }, backend: e, attrs: { paddings: p, constantValue: 0 } }), l = w.getReshaped(c.shape, s, i, false), m = w.getPermuted(l.length, s.length, false), d = w.getReshapedPermuted(c.shape, s, i, false), f = te({ inputs: { x: c }, backend: e, attrs: { shape: l } }), h = bt({ inputs: { x: f }, backend: e, attrs: { perm: m } }), g = te({ inputs: { x: h }, backend: e, attrs: { shape: d } }); return u.push(c), u.push(f), u.push(h), u.forEach((x) => e.disposeIntermediateTensorInfo(x)), g; }; -var z3 = { kernelName: ga, backendName: "webgl", kernelFunc: Wte }; -function Ute(r) { - let { inputs: t8, backend: e } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = t8; +var V3 = { kernelName: ga, backendName: "webgl", kernelFunc: Wte }; +function Ute(r15) { + let { inputs: t8, backend: e } = r15, { indices: o, values: n, denseShape: s, defaultValue: a } = t8; if (s.shape.length !== 1) throw new Error(`Dense shape must be a vector, saw: ${s.shape}`); @@ -24218,24 +24219,24 @@ function Ute(r) { if (a.shape.length !== 0) throw new Error(`Default value must be a scalar, saw: ${a.shape}`); - let i = e.readSync(o.dataId), p = e.readSync(n.dataId), u = e.readSync(s.dataId), c = e.readSync(a.dataId)[0], [l, m, d, f, h] = cD(i, o.shape, o.dtype, p, n.dtype, u, c); + let i = e.readSync(o.dataId), p = e.readSync(n.dataId), u = e.readSync(s.dataId), c = e.readSync(a.dataId)[0], [l, m, d, f, h] = lD(i, o.shape, o.dtype, p, n.dtype, u, c); return [e.makeTensorInfo(m, o.dtype, l), e.makeTensorInfo([m[0]], n.dtype, d), e.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), e.makeTensorInfo([h.length], o.dtype, new Int32Array(h))]; } -var V3 = { kernelName: ji, backendName: "webgl", kernelFunc: Ute }; -function Gte(r) { - let { inputs: t8, backend: e } = r, { inputIndices: o, inputShape: n, newShape: s } = t8; +var W3 = { kernelName: Hi, backendName: "webgl", kernelFunc: Ute }; +function Gte(r15) { + let { inputs: t8, backend: e } = r15, { inputIndices: o, inputShape: n, newShape: s } = t8; if (o.shape.length !== 2) throw new Error(`Input indices should be a matrix but received shape ${o.shape}`); if (n.shape.length !== 1) throw new Error(`Input shape should be a vector but received shape ${n.shape}`); if (s.shape.length !== 1) throw new Error(`Target shape should be a vector but received shape ${s.shape}`); - let a = Array.from(e.readSync(n.dataId)), i = e.readSync(o.dataId), p = Array.from(e.readSync(s.dataId)), [u, c, l] = lD(i, o.shape, o.dtype, a, p); + let a = Array.from(e.readSync(n.dataId)), i = e.readSync(o.dataId), p = Array.from(e.readSync(s.dataId)), [u, c, l] = mD(i, o.shape, o.dtype, a, p); return [e.makeTensorInfo(c, o.dtype, u), e.makeTensorInfo([l.length], s.dtype, new Int32Array(l))]; } -var W3 = { kernelName: ti, backendName: "webgl", kernelFunc: Gte }; -function Hte(r) { - let { inputs: t8, backend: e } = r, { data: o, indices: n, segmentIds: s } = t8; +var U3 = { kernelName: ei, backendName: "webgl", kernelFunc: Gte }; +function Hte(r15) { + let { inputs: t8, backend: e } = r15, { data: o, indices: n, segmentIds: s } = t8; if (o.shape.length < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.shape.length !== 1) @@ -24244,12 +24245,12 @@ function Hte(r) { if (s.shape.length !== 1) throw new Error(`Segment ids should be a vector but received shape ${s.shape}`); - let a = e.readSync(o.dataId), i = e.readSync(n.dataId), p = e.readSync(s.dataId), [u, c] = lh(a, o.shape, o.dtype, i, p, true); + let a = e.readSync(o.dataId), i = e.readSync(n.dataId), p = e.readSync(s.dataId), [u, c] = ch(a, o.shape, o.dtype, i, p, true); return e.makeTensorInfo(c, o.dtype, u); } -var U3 = { kernelName: ya, backendName: "webgl", kernelFunc: Hte }; -function Kte(r) { - let { inputs: t8, backend: e } = r, { data: o, indices: n, segmentIds: s } = t8; +var G3 = { kernelName: ya, backendName: "webgl", kernelFunc: Hte }; +function Kte(r15) { + let { inputs: t8, backend: e } = r15, { data: o, indices: n, segmentIds: s } = t8; if (o.shape.length < 1) throw new Error("Data should be at least 1 dimensional but received scalar"); if (n.shape.length !== 1) @@ -24258,22 +24259,22 @@ function Kte(r) { if (s.shape.length !== 1) throw new Error(`Segment ids should be a vector but received shape ${s.shape}`); - let a = e.readSync(o.dataId), i = e.readSync(n.dataId), p = e.readSync(s.dataId), [u, c] = lh(a, o.shape, o.dtype, i, p); + let a = e.readSync(o.dataId), i = e.readSync(n.dataId), p = e.readSync(s.dataId), [u, c] = ch(a, o.shape, o.dtype, i, p); return e.makeTensorInfo(c, o.dtype, u); } -var G3 = { kernelName: ba, backendName: "webgl", kernelFunc: Kte }; -function qte(r) { - let { inputs: t8, backend: e, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = t8, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = w.calculateShapes(s, n, i), d = false; +var H3 = { kernelName: ba, backendName: "webgl", kernelFunc: Kte }; +function qte(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { sparseIndices: n, sparseValues: s, defaultValue: a } = t8, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = w.calculateShapes(s, n, i), d = false; if (s.dtype === "string") { - let x = e.bufferSync(n), b = e.bufferSync(s), C = y.decodeString(e.readSync(a.dataId)[0]), S = iD(x, b, i, m, c, u, p, l, C, d); + let x = e.bufferSync(n), b = e.bufferSync(s), C = y.decodeString(e.readSync(a.dataId)[0]), S = uD(x, b, i, m, c, u, p, l, C, d); return e.makeTensorInfo(i, S.dtype, S.values); } - let f = new vu(u, p, n.shape.length, s.shape.length, l, [m, 1], d), h = e.runWebGLProgram(f, [s, n, a], s.dtype), g = te({ inputs: { x: h }, backend: e, attrs: { shape: i } }); + let f = new yu(u, p, n.shape.length, s.shape.length, l, [m, 1], d), h = e.runWebGLProgram(f, [s, n, a], s.dtype), g = te({ inputs: { x: h }, backend: e, attrs: { shape: i } }); return e.disposeIntermediateTensorInfo(h), g; } -var H3 = { kernelName: ks, backendName: "webgl", kernelFunc: qte }; -function jte(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = w.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice(); +var K3 = { kernelName: vs, backendName: "webgl", kernelFunc: qte }; +function jte(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = w.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice(); return p.map((m) => { let d = [...l]; d[i] = m; @@ -24281,32 +24282,32 @@ function jte(r) { return c[i] += m, f; }); } -var K3 = { kernelName: xa, backendName: "webgl", kernelFunc: jte }; -var q3 = "return sqrt(x);"; -var Xte = xe({ opSnippet: q3, packedOpSnippet: q3, cpuKernelImpl: mD }); -var j3 = { kernelName: Ss, backendName: "webgl", kernelFunc: Xte }; +var q3 = { kernelName: xa, backendName: "webgl", kernelFunc: jte }; +var j3 = "return sqrt(x);"; +var Xte = xe({ opSnippet: j3, packedOpSnippet: j3, cpuKernelImpl: dD }); +var X3 = { kernelName: ws, backendName: "webgl", kernelFunc: Xte }; var Yte = "return x * x;"; var Qte = xe({ opSnippet: Yte }); -var X3 = { kernelName: Xi, backendName: "webgl", kernelFunc: Qte }; -var Y3 = "return (a - b) * (a - b);"; -var Zte = nt({ opSnippet: Y3, packedOpSnippet: Y3 }); -var Q3 = { kernelName: Ns, backendName: "webgl", kernelFunc: Zte }; -function Jte(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8; +var Y3 = { kernelName: Ki, backendName: "webgl", kernelFunc: Qte }; +var Q3 = "return (a - b) * (a - b);"; +var Zte = nt({ opSnippet: Q3, packedOpSnippet: Q3 }); +var Z3 = { kernelName: ks, backendName: "webgl", kernelFunc: Zte }; +function Jte(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8; if (n.dtype !== "string") throw new Error("Input must be of datatype string"); - let s = e.readSync(n.dataId), a = w.fromUint8ToStringArray(s), i = dD(a, "string", o); + let s = e.readSync(n.dataId), a = w.fromUint8ToStringArray(s), i = fD(a, "string", o); return e.makeTensorInfo(n.shape, "string", i); } -var Z3 = { kernelName: Ou, backendName: "webgl", kernelFunc: Jte }; -function ere({ inputs: r, attrs: t8, backend: e }) { - let { x: o } = r, n = Ut + ` +var J3 = { kernelName: $u, backendName: "webgl", kernelFunc: Jte }; +function ere({ inputs: r15, attrs: t8, backend: e }) { + let { x: o } = r15, n = Wt + ` return x > 0.0 ? 1.0 : float(${t8.alpha}); - `, s = new or(o.shape, n); + `, s = new tr(o.shape, n); return e.runWebGLProgram(s, [o], o.dtype); } -var J3 = { kernelName: So, backendName: "webgl", kernelFunc: ere }; -var Rg = class { +var eP = { kernelName: wo, backendName: "webgl", kernelFunc: ere }; +var Eg = class { constructor(t8, e, o) { this.variableNames = ["x"], this.outputShape = o; let n = o.length, s = Re(o.length), a = Re(o.length), i = ""; @@ -24327,70 +24328,70 @@ var Rg = class { `; } }; -function tre(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; +function tre(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: S } = pt.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; if (h) k = te({ inputs: { x: n }, backend: e, attrs: { shape: f } }); else if (g || x) { y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`); - let E = ct.computeOutShape(b, C, S), R = Gs({ inputs: { x: n }, backend: e, attrs: { begin: b, size: E } }); + let E = pt.computeOutShape(b, C, S), R = Gs({ inputs: { x: n }, backend: e, attrs: { begin: b, size: E } }); k = te({ inputs: { x: R }, backend: e, attrs: { shape: f } }), e.disposeIntermediateTensorInfo(R); } else if (e.shouldExecuteOnCPU([n])) { - let R = e.readSync(n.dataId), D = me(n.shape, n.dtype, R), P = fD(d, D, S, b); + let R = e.readSync(n.dataId), D = me(n.shape, n.dtype, R), P = hD(d, D, S, b); k = e.makeTensorInfo(f, n.dtype, P.values); } else { - let R = new Rg(b, S, d); + let R = new Eg(b, S, d); k = e.runWebGLProgram(R, [n], n.dtype); } let _ = te({ inputs: { x: k }, backend: e, attrs: { shape: f } }); return e.disposeIntermediateTensorInfo(k), _; } -var eP = { kernelName: Ts, backendName: "webgl", kernelFunc: tre }; -function rre(r) { - let { inputs: t8, backend: e, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = t8, m = e.readSync(c.dataId), d = e.readSync(l.dataId), [f, h] = hD(m, d, n, s, a, i, p, u); +var tP = { kernelName: Ns, backendName: "webgl", kernelFunc: tre }; +function rre(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = t8, m = e.readSync(c.dataId), d = e.readSync(l.dataId), [f, h] = gD(m, d, n, s, a, i, p, u); return [e.makeTensorInfo([f.length], "string", f), e.makeTensorInfo(l.shape, "int32", h)]; } -var tP = { kernelName: Ca, backendName: "webgl", kernelFunc: rre }; -function ore(r) { - let { inputs: t8, backend: e, attrs: o } = r, { skipEmpty: n } = o, { input: s, delimiter: a } = t8; +var rP = { kernelName: Ca, backendName: "webgl", kernelFunc: rre }; +function ore(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { skipEmpty: n } = o, { input: s, delimiter: a } = t8; if (s.dtype !== "string") throw new Error("Input must be of datatype string"); if (s.shape.length !== 1) throw new Error(`Input must be a vector, got shape: ${s.shape}`); if (a.shape.length !== 0) throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`); - let i = e.readSync(s.dataId), p = e.readSync(a.dataId)[0], [u, c, l] = gD(i, p, n), m = c.length; + let i = e.readSync(s.dataId), p = e.readSync(a.dataId)[0], [u, c, l] = xD(i, p, n), m = c.length; return [e.makeTensorInfo([m, 2], "int32", u), e.makeTensorInfo([m], "string", c), e.makeTensorInfo([2], "int32", new Int32Array(l))]; } -var rP = { kernelName: Yi, backendName: "webgl", kernelFunc: ore }; -function nre(r) { - let { inputs: t8, backend: e, attrs: o } = r, { numBuckets: n } = o, { input: s } = t8; +var oP = { kernelName: qi, backendName: "webgl", kernelFunc: ore }; +function nre(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { numBuckets: n } = o, { input: s } = t8; if (s.dtype !== "string") throw new Error("Input must be of datatype string"); if (n <= 0) throw new Error("Number of buckets must be at least 1"); - let a = e.readSync(s.dataId), i = xD(a, n); + let a = e.readSync(s.dataId), i = yD(a, n); return e.makeTensorInfo(s.shape, "int32", i); } -var oP = { kernelName: Qi, backendName: "webgl", kernelFunc: nre }; +var nP = { kernelName: ji, backendName: "webgl", kernelFunc: nre }; var sre = "return tan(x);"; var are = xe({ opSnippet: sre }); -var nP = { kernelName: $s, backendName: "webgl", kernelFunc: are }; +var sP = { kernelName: _s, backendName: "webgl", kernelFunc: are }; var ire = ` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `; var ure = xe({ opSnippet: ire }); -var sP = { kernelName: Es, backendName: "webgl", kernelFunc: ure }; -function pre(r) { - let { inputs: t8, backend: e, attrs: o } = r, { tensor: n, indices: s, updates: a } = t8, {} = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(a, s, n.shape), m = [l / u, u]; +var aP = { kernelName: $s, backendName: "webgl", kernelFunc: ure }; +function pre(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { tensor: n, indices: s, updates: a } = t8, {} = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(a, s, n.shape), m = [l / u, u]; if (l === 0) return e.makeTensorInfo(n.shape, s.dtype); - let d = te({ inputs: { x: s }, backend: e, attrs: { shape: [p, i] } }), f = te({ inputs: { x: a }, backend: e, attrs: { shape: [p, u] } }), h = te({ inputs: { x: n }, backend: e, attrs: { shape: m } }), g = new vu(p, i, d.shape.length, f.shape.length, c, m, false, true), x = e.runWebGLProgram(g, [f, d, h], h.dtype), b = te({ inputs: { x }, backend: e, attrs: { shape: n.shape } }); + let d = te({ inputs: { x: s }, backend: e, attrs: { shape: [p, i] } }), f = te({ inputs: { x: a }, backend: e, attrs: { shape: [p, u] } }), h = te({ inputs: { x: n }, backend: e, attrs: { shape: m } }), g = new yu(p, i, d.shape.length, f.shape.length, c, m, false, true), x = e.runWebGLProgram(g, [f, d, h], h.dtype), b = te({ inputs: { x }, backend: e, attrs: { shape: n.shape } }); return e.disposeIntermediateTensorInfo(d), e.disposeIntermediateTensorInfo(f), e.disposeIntermediateTensorInfo(h), e.disposeIntermediateTensorInfo(x), b; } -var aP = { kernelName: fs, backendName: "webgl", kernelFunc: pre }; -var Dg = class { +var iP = { kernelName: ds, backendName: "webgl", kernelFunc: pre }; +var Rg = class { constructor(t8, e) { this.variableNames = ["A"]; let o = new Array(t8.length); @@ -24406,28 +24407,28 @@ var Dg = class { `; } }; -function cre(r) { - let t8 = r.length; +function cre(r15) { + let t8 = r15.length; if (t8 > 5) throw Error(`Tile for rank ${t8} is not yet supported`); if (t8 === 1) - return `imod(resRC, ${r[0]})`; + return `imod(resRC, ${r15[0]})`; let e = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"], o = []; - for (let n = 0; n < r.length; n++) - o.push(`imod(${e[n]}, ${r[n]})`); + for (let n = 0; n < r15.length; n++) + o.push(`imod(${e[n]}, ${r15[n]})`); return o.join(); } -function Dv(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { reps: s } = o; +function Av(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { reps: s } = o; if (n.dtype === "string" || n.shape.length > 5) { - let p = e.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = me(n.shape, n.dtype, u), l = bD(c, s); + let p = e.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = me(n.shape, n.dtype, u), l = CD(c, s); return e.makeTensorInfo(l.shape, l.dtype, l.values); } - let a = new Dg(n.shape, s); + let a = new Rg(n.shape, s); return e.runWebGLProgram(a, [n], n.dtype); } -var iP = { kernelName: po, backendName: "webgl", kernelFunc: Dv }; -var Ag = class { +var uP = { kernelName: uo, backendName: "webgl", kernelFunc: Av }; +var Dg = class { constructor(t8) { this.variableNames = ["x", "indices"], this.customUniforms = [{ name: "n", type: "int" }, { name: "firstPass", type: "int" }, { name: "negativeInf", type: "float" }, { name: "dir", type: "int" }, { name: "inc", type: "int" }], this.outputShape = t8, this.userCode = ` void main() { @@ -24472,7 +24473,7 @@ var Ag = class { `; } }; -var Fg = class { +var Ag = class { constructor(t8) { this.variableNames = ["x", "indices"], this.customUniforms = [{ name: "n", type: "int" }, { name: "firstPass", type: "int" }, { name: "k", type: "int" }], this.outputShape = t8, this.userCode = ` void main() { @@ -24511,30 +24512,30 @@ var Fg = class { `; } }; -function Rp(r, t8) { - t8 !== null && r.disposeIntermediateTensorInfo(t8); +function Ip(r15, t8) { + t8 !== null && r15.disposeIntermediateTensorInfo(t8); } -function uP(r) { +function pP(r15) { let t8 = 1; - for (; t8 < r; ) + for (; t8 < r15; ) t8 *= 2; return t8; } -function lre(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { k: s, sorted: a } = o, i = A().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), p = A().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"), u = n.shape, c = u[u.length - 1]; +function lre(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { k: s, sorted: a } = o, i = A().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), p = A().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"), u = n.shape, c = u[u.length - 1]; if (e.shouldExecuteOnCPU([n]) || c < i || s > p) { - let P = e.readSync(n.dataId), [O, M] = CD(P, u, n.dtype, s, a); + let P = e.readSync(n.dataId), [O, M] = wD(P, u, n.dtype, s, a); return [e.makeTensorInfo(O.shape, O.dtype, O.values), e.makeTensorInfo(M.shape, M.dtype, M.values)]; } if (s === 0) return u[u.length - 1] = 0, [e.makeTensorInfo(u, n.dtype, []), e.makeTensorInfo(u, "int32", [])]; if (c === 1) - return [n, Si({ attrs: { shape: u, dtype: "int32", value: 0 }, backend: e })]; + return [n, bi({ attrs: { shape: u, dtype: "int32", value: 0 }, backend: e })]; let l = e.texData.get(n.dataId), m = l !== null && l.isPacked, d = m ? e.unpackTensor(n) : n, h = y.sizeFromShape(u) / c, g = te({ inputs: { x: d }, attrs: { shape: [h, c] }, backend: e }); - m && Rp(e, d); - let x = uP(s), b = uP(c), C = null, S = () => C === null ? [g, g] : [g, C], k = (P, O, M) => { - let L = S(), B = new Ag(M), U = [[c], [C === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [P], [O]], j = C; - C = e.runWebGLProgram(B, L, "int32", U), Rp(e, j); + m && Ip(e, d); + let x = pP(s), b = pP(c), C = null, S = () => C === null ? [g, g] : [g, C], k = (P, O, M) => { + let L = S(), B = new Dg(M), U = [[c], [C === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [P], [O]], j = C; + C = e.runWebGLProgram(B, L, "int32", U), Ip(e, j); }; for (let P = 1; P < x; P *= 2) { let O = P * 2; @@ -24542,23 +24543,23 @@ function lre(r) { k(O, M, [h, b]); } for (let P = b; P > x; P /= 2) { - let O = S(), M = new Fg([h, P / 2]), B = [[c], [C === null ? 1 : 0], [x]], z = C; - C = e.runWebGLProgram(M, O, "int32", B), Rp(e, z); + let O = S(), M = new Ag([h, P / 2]), B = [[c], [C === null ? 1 : 0], [x]], z = C; + C = e.runWebGLProgram(M, O, "int32", B), Ip(e, z); let U = x / 2, j = U * 2; for (let q = U; q >= 1; q /= 2) k(j, q, C.shape); } let _ = C; - C = Gs({ inputs: { x: C }, backend: e, attrs: { begin: 0, size: [h, s] } }), Rp(e, _); - let E = kv({ inputs: { x: g, indices: C }, backend: e, attrs: { axis: 1, batchDims: 1 } }); - Rp(e, g); + C = Gs({ inputs: { x: C }, backend: e, attrs: { begin: 0, size: [h, s] } }), Ip(e, _); + let E = Nv({ inputs: { x: g, indices: C }, backend: e, attrs: { axis: 1, batchDims: 1 } }); + Ip(e, g); let R = u.slice(0, -1); - R.push(s), _ = C, C = te({ inputs: { x: C }, attrs: { shape: R }, backend: e }), Rp(e, _); + R.push(s), _ = C, C = te({ inputs: { x: C }, attrs: { shape: R }, backend: e }), Ip(e, _); let D = E; - return E = te({ inputs: { x: E }, attrs: { shape: R }, backend: e }), Rp(e, D), [E, C]; + return E = te({ inputs: { x: E }, attrs: { shape: R }, backend: e }), Ip(e, D), [E, C]; } -var pP = { kernelName: Rs, backendName: "webgl", kernelFunc: lre }; -var Pg = class { +var cP = { kernelName: Es, backendName: "webgl", kernelFunc: lre }; +var Fg = class { constructor(t8, e, o, n, s, a) { this.variableNames = ["Image", "Transforms"], this.outputShape = a; let i = o === "nearest" ? 1 : 2, p; @@ -24694,20 +24695,20 @@ var Pg = class { `; } }; -function mre(r) { - let { inputs: t8, backend: e, attrs: o } = r, { image: n, transforms: s } = t8, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Pg(l, m, a, i, p, g); +function mre(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { image: n, transforms: s } = t8, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Fg(l, m, a, i, p, g); return e.runWebGLProgram(x, [n, s], "float32"); } -var cP = { kernelName: Ds, backendName: "webgl", kernelFunc: mre }; -function dre(r) { - let { inputs: t8, attrs: e, backend: o } = r, { axis: n } = e, { x: s } = t8; +var lP = { kernelName: Rs, backendName: "webgl", kernelFunc: mre }; +function dre(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, { axis: n } = e, { x: s } = t8; Vs(s, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded"); - let a = o.readSync(s.dataId), { outputValues: i, outputShape: p, indices: u } = wD(a, n, s.shape, s.dtype); + let a = o.readSync(s.dataId), { outputValues: i, outputShape: p, indices: u } = SD(a, n, s.shape, s.dtype); return [o.makeTensorInfo(p, s.dtype, i), o.makeTensorInfo([u.length], "int32", u)]; } -var lP = { kernelName: Zi, backendName: "webgl", kernelFunc: dre }; -function fre(r) { - let { inputs: t8, backend: e, attrs: o } = r, { value: n } = t8, { axis: s } = o; +var mP = { kernelName: Xi, backendName: "webgl", kernelFunc: dre }; +function fre(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { value: n } = t8, { axis: s } = o; s < 0 && (s += n.shape.length); let a = n, i = a.shape.length, p = n.shape[s], u = new Array(i - 1), c = 0; for (let h = 0; h < i; h++) @@ -24722,8 +24723,8 @@ function fre(r) { } return l.forEach((h) => e.disposeIntermediateTensorInfo(h)), f; } -var mP = { kernelName: wa, backendName: "webgl", kernelFunc: fre }; -var Og = class { +var dP = { kernelName: wa, backendName: "webgl", kernelFunc: fre }; +var Pg = class { constructor(t8, e) { this.variableNames = ["x", "segmentIds"]; let o = t8.windowSize, n = t8.batchSize, s = t8.inSize, a = t8.numSegments, i = a * Math.ceil(s / o); @@ -24840,43 +24841,43 @@ var Og = class { `; } }; -function hre(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, segmentIds: s } = t8, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = w.getAxesPermutation([u], i), l = n; - c != null && (l = Ct({ inputs: { x: n }, backend: e, attrs: { perm: c } }), p.push(l), u = w.getInnerMostAxes(1, i)[0]); +function hre(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, segmentIds: s } = t8, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = w.getAxesPermutation([u], i), l = n; + c != null && (l = bt({ inputs: { x: n }, backend: e, attrs: { perm: c } }), p.push(l), u = w.getInnerMostAxes(1, i)[0]); let m = w.segment_util.computeOutShape(l.shape, u, a), d = y.sizeFromShape([l.shape[u]]), f = te({ inputs: { x: l }, backend: e, attrs: { shape: [-1, d] } }); p.push(f); - let h = ni(n.dtype), g = (S, k, _, E, R) => { - let D = S.shape[0], P = S.shape[1], O = w.segment_util.segOpComputeOptimalWindowSize(P, R), M = { windowSize: O, inSize: P, batchSize: D, numSegments: R }, L = new Og(M, k), B = e.compileAndRun(L, [S, _], E); + let h = oi(n.dtype), g = (S, k, _, E, R) => { + let D = S.shape[0], P = S.shape[1], O = w.segment_util.segOpComputeOptimalWindowSize(P, R), M = { windowSize: O, inSize: P, batchSize: D, numSegments: R }, L = new Pg(M, k), B = e.compileAndRun(L, [S, _], E); if (p.push(B), B.shape[1] === R) return B; - let z = Rv({ backend: e, attrs: { start: 0, stop: R, step: 1, dtype: "float32" } }), U = Dv({ inputs: { x: z }, backend: e, attrs: { reps: [P / O] } }); + let z = Dv({ backend: e, attrs: { start: 0, stop: R, step: 1, dtype: "float32" } }), U = Av({ inputs: { x: z }, backend: e, attrs: { reps: [P / O] } }); return p.push(z), p.push(U), g(B, k, U, E, R); }, x = g(f, "unsortedSegmentSum", s, h, a), b = te({ inputs: { x }, backend: e, attrs: { shape: m } }), C = b; if (c != null) { p.push(b); let S = w.getUndoAxesPermutation(c); - C = Ct({ inputs: { x: C }, backend: e, attrs: { perm: S } }); + C = bt({ inputs: { x: C }, backend: e, attrs: { perm: S } }); } return p.forEach((S) => e.disposeIntermediateTensorInfo(S)), C; } -var dP = { kernelName: Ji, backendName: "webgl", kernelFunc: hre }; -var gre = [XD, QD, ZD, JD, tA, rA, oA, nA, iA, uA, pA, cA, lA, mA, dA, fA, hA, gA, xA, yA, bA, wA, SA, IA, vA, _A, EA, RA, BD, AA, PA, OA, MA, LA, BA, zA, VA, WA, UA, GA, qA, jA, XA, YA, QA, ZA, JA, eF, tF, rF, oF, nF, sF, aF, iF, uF, cF, lF, mF, dF, hF, gF, xF, yF, bF, CF, wF, SF, IF, LD, vF, FA, kF, NF, TF, zD, _F, $F, EF, RF, DF, AF, FF, PF, OF, MF, BF, zF, VF, WF, UF, GF, KF, jF, XF, YF, QF, ZF, o3, UD, n3, s3, a3, i3, kA, u3, l3, m3, d3, f3, VD, h3, g3, x3, y3, b3, NA, JF, C3, w3, S3, HD, I3, v3, k3, N3, T3, _3, $3, E3, R3, D3, A3, F3, P3, O3, M3, L3, CA, r3, B3, z3, V3, W3, U3, G3, H3, K3, j3, X3, Q3, Z3, J3, eP, tP, rP, oP, t3, qD, nP, sP, aP, iP, pP, cP, jD, lP, mP, dP, p3]; -for (let r of gre) - ri(r); +var fP = { kernelName: Yi, backendName: "webgl", kernelFunc: hre }; +var gre = [YD, ZD, JD, eA, rA, oA, nA, sA, uA, pA, cA, lA, mA, dA, fA, hA, gA, xA, yA, bA, CA, SA, IA, vA, kA, $A, RA, DA, zD, FA, OA, MA, LA, BA, zA, VA, WA, UA, GA, HA, jA, XA, YA, QA, ZA, JA, eF, tF, rF, oF, nF, sF, aF, iF, uF, pF, lF, mF, dF, fF, gF, xF, yF, bF, CF, wF, SF, IF, vF, BD, kF, PA, NF, TF, _F, VD, $F, EF, RF, DF, AF, FF, PF, OF, MF, LF, zF, VF, WF, UF, GF, HF, qF, XF, YF, QF, ZF, JF, n3, GD, s3, a3, i3, u3, NA, p3, m3, d3, f3, h3, WD, g3, x3, y3, b3, C3, TA, e3, w3, S3, I3, KD, v3, k3, N3, T3, _3, $3, E3, R3, D3, A3, F3, P3, O3, M3, L3, B3, wA, o3, z3, V3, W3, U3, G3, H3, K3, q3, X3, Y3, Z3, J3, eP, tP, rP, oP, nP, r32, jD, sP, aP, iP, uP, cP, lP, XD, mP, dP, fP, c3]; +for (let r15 of gre) + ti(r15); var we; -(function(r) { - r[r.float32 = 0] = "float32", r[r.int32 = 1] = "int32", r[r.bool = 2] = "bool", r[r.string = 3] = "string", r[r.complex64 = 4] = "complex64"; +(function(r15) { + r15[r15.float32 = 0] = "float32", r15[r15.int32 = 1] = "int32", r15[r15.bool = 2] = "bool", r15[r15.string = 3] = "string", r15[r15.complex64 = 4] = "complex64"; })(we || (we = {})); -var ku; -(function(r) { - r[r.linear = 0] = "linear", r[r.relu = 1] = "relu", r[r.relu6 = 2] = "relu6", r[r.prelu = 3] = "prelu", r[r.leakyrelu = 4] = "leakyrelu", r[r.sigmoid = 5] = "sigmoid", r[r.elu = 6] = "elu"; -})(ku || (ku = {})); -var fP; -function xre(r) { - fP = r.wasm.cwrap(Io, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]); +var bu; +(function(r15) { + r15[r15.linear = 0] = "linear", r15[r15.relu = 1] = "relu", r15[r15.relu6 = 2] = "relu6", r15[r15.prelu = 3] = "prelu", r15[r15.leakyrelu = 4] = "leakyrelu", r15[r15.sigmoid = 5] = "sigmoid", r15[r15.elu = 6] = "elu"; +})(bu || (bu = {})); +var hP; +function xre(r15) { + hP = r15.wasm.cwrap(So, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function yre(r) { - let { inputs: t8, backend: e, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = t8; +function yre(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { a: n, b: s, bias: a, preluActivationWeights: i } = t8; if (n.dtype !== "float32" || s.dtype !== "float32") throw new Error("_FusedMatMul for non non-float32 tensors not yet supported."); let { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m = e.dataIdMap.get(n.dataId).id, d = e.dataIdMap.get(s.dataId).id, f = 0; @@ -24886,89 +24887,89 @@ function yre(r) { throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`); f = R.id; } - let h = i == null ? 0 : e.dataIdMap.get(i.dataId).id, g = ku[c]; + let h = i == null ? 0 : e.dataIdMap.get(i.dataId).id, g = bu[c]; if (g == null) throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`); - let x = p ? n.shape[2] : n.shape[1], b = u ? s.shape[1] : s.shape[2], C = Ir.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)), S = e.makeOutput([...C, x, b], n.dtype), k = e.dataIdMap.get(S.dataId).id, _ = new Uint8Array(new Int32Array(n.shape).buffer), E = new Uint8Array(new Int32Array(s.shape).buffer); - return fP(m, _, n.shape.length, d, E, s.shape.length, p, u, g, f, h, l || 0, k), S; + let x = p ? n.shape[2] : n.shape[1], b = u ? s.shape[1] : s.shape[2], C = Sr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)), S = e.makeOutput([...C, x, b], n.dtype), k = e.dataIdMap.get(S.dataId).id, _ = new Uint8Array(new Int32Array(n.shape).buffer), E = new Uint8Array(new Int32Array(s.shape).buffer); + return hP(m, _, n.shape.length, d, E, s.shape.length, p, u, g, f, h, l || 0, k), S; } -var hP = { kernelName: Io, backendName: "wasm", setupFunc: xre, kernelFunc: yre }; -function he(r, t8) { +var gP = { kernelName: So, backendName: "wasm", setupFunc: xre, kernelFunc: yre }; +function he(r15, t8) { let e; function o(s) { - e = s.wasm.cwrap(r, null, ["number", "number", "number"]); + e = s.wasm.cwrap(r15, null, ["number", "number", "number"]); } function n(s) { let { backend: a, inputs: { x: i } } = s, p = a.dataIdMap.get(i.dataId).id, u = a.makeOutput(i.shape, t8 || i.dtype), c = a.dataIdMap.get(u.dataId).id; return y.sizeFromShape(u.shape) === 0 || e(p, we[i.dtype], c), u; } - return { kernelName: r, backendName: "wasm", setupFunc: o, kernelFunc: n }; + return { kernelName: r15, backendName: "wasm", setupFunc: o, kernelFunc: n }; } -var gP = he(Xs); -var xP = he(Wo); -var yP = he(Uo); -function Ue(r, t8, e) { +var xP = he(Xs); +var yP = he(Vo); +var bP = he(Wo); +function Ue(r15, t8, e) { let o; function n(a) { - o = a.wasm.cwrap(r, null, ["number", "array", "number", "number", "array", "number", "number", "number"]); + o = a.wasm.cwrap(r15, null, ["number", "array", "number", "number", "array", "number", "number", "number"]); } function s(a) { let { backend: i, inputs: p } = a, { a: u, b: c } = p, l = i.dataIdMap.get(u.dataId).id, m = i.dataIdMap.get(c.dataId).id, d = e != null ? e : u.dtype, f = w.assertAndGetBroadcastShape(u.shape, c.shape), h = i.makeOutput(f, d); if (y.sizeFromShape(f) === 0) return h; let g = new Uint8Array(new Int32Array(u.shape).buffer), x = new Uint8Array(new Int32Array(c.shape).buffer), b = i.dataIdMap.get(h.dataId).id; - return (() => o(l, g, u.shape.length, m, x, c.shape.length, we[u.dtype], b))(), h; + return o(l, g, u.shape.length, m, x, c.shape.length, we[u.dtype], b), h; } - return { kernelName: r, backendName: "wasm", setupFunc: n, kernelFunc: s }; + return { kernelName: r15, backendName: "wasm", setupFunc: n, kernelFunc: s }; } var bre = true; -var bP = Ue(uo, bre); -var CP; -function Cre(r) { - CP = r.wasm.cwrap(Go, null, ["array", "number", "number", "number"]); +var CP = Ue(io, bre); +var wP; +function Cre(r15) { + wP = r15.wasm.cwrap(Uo, null, ["array", "number", "number", "number"]); } -function wre(r) { - let { inputs: t8, backend: e } = r, o = e.makeOutput(t8[0].shape, t8[0].dtype); +function wre(r15) { + let { inputs: t8, backend: e } = r15, o = e.makeOutput(t8[0].shape, t8[0].dtype); if (y.sizeFromShape(o.shape) === 0) return o; let n = t8.map((i) => e.dataIdMap.get(i.dataId).id), s = new Uint8Array(new Int32Array(n).buffer), a = e.dataIdMap.get(o.dataId).id; - return CP(s, n.length, we[o.dtype], a), o; + return wP(s, n.length, we[o.dtype], a), o; } -var wP = { kernelName: Go, backendName: "wasm", setupFunc: Cre, kernelFunc: wre }; -function Dp(r) { - let { inputs: { x: t8 }, backend: e } = r; +var SP = { kernelName: Uo, backendName: "wasm", setupFunc: Cre, kernelFunc: wre }; +function vp(r15) { + let { inputs: { x: t8 }, backend: e } = r15; if (t8.dtype === "string") - return ur(e.readSync(t8.dataId), t8.shape, t8.dtype); + return ar(e.readSync(t8.dataId), t8.shape, t8.dtype); let o = e.makeOutput(t8.shape, t8.dtype), n = e.typedArrayFromHeap(t8); return e.typedArrayFromHeap(o).set(n), o; } -var SP = { kernelName: wo, backendName: "wasm", kernelFunc: Dp }; -var IP; -function Sre(r) { - IP = r.wasm.cwrap(co, null, ["number", "array", "number", "number", "number", "array", "number"]); +var IP = { kernelName: Co, backendName: "wasm", kernelFunc: vp }; +var vP; +function Sre(r15) { + vP = r15.wasm.cwrap(po, null, ["number", "array", "number", "number", "number", "array", "number"]); } -function go(r) { - let { inputs: t8, backend: e, attrs: o } = r, [n, s] = vre(t8.x.shape, o.perm), a = true; +function ho(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, [n, s] = vre(t8.x.shape, o.perm), a = true; for (let f = 0; f < s.length; f++) s[f] !== f && (a = false); let i = Ire(t8.x.shape, o.perm), p = { dataId: t8.x.dataId, shape: n, dtype: t8.x.dtype }; if (a) { - let f = Dp({ inputs: t8, backend: e }); + let f = vp({ inputs: t8, backend: e }); return f.shape = i, f; } let u = e.makeOutput(i, p.dtype), c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = new Uint8Array(new Int32Array(s).buffer), d = new Uint8Array(new Int32Array(p.shape).buffer); - return IP(c, d, p.shape.length, we[p.dtype], l, m, s.length), u; + return vP(c, d, p.shape.length, we[p.dtype], l, m, s.length), u; } -function Ire(r, t8) { - let e = new Array(r.length); +function Ire(r15, t8) { + let e = new Array(r15.length); for (let o = 0; o < e.length; o++) - e[o] = r[t8[o]]; + e[o] = r15[t8[o]]; return e; } -function vre(r, t8) { +function vre(r15, t8) { let e = [], o = []; - for (let n = 0; n < r.length; ++n) - r[n] !== 1 && e.push(r[n]), r[t8[n]] !== 1 && o.push(t8[n]); + for (let n = 0; n < r15.length; ++n) + r15[n] !== 1 && e.push(r15[n]), r15[t8[n]] !== 1 && o.push(t8[n]); for (let n = 0; n < o.length; ++n) { let s = -1; for (let a = 0; a < o.length; ++a) @@ -24977,25 +24978,25 @@ function vre(r, t8) { } return [e, o]; } -var vP = { kernelName: co, backendName: "wasm", kernelFunc: go, setupFunc: Sre }; -function _r(r, t8, e) { - let o = r.shape, n = r.shape.length, s = y.parseAxisParam(t8, o), a = s, i = w.getAxesPermutation(a, n), p = null, u = false; +var kP = { kernelName: po, backendName: "wasm", kernelFunc: ho, setupFunc: Sre }; +function Tr(r15, t8, e) { + let o = r15.shape, n = r15.shape.length, s = y.parseAxisParam(t8, o), a = s, i = w.getAxesPermutation(a, n), p = null, u = false; if (i != null) { let c = new Array(n); for (let d = 0; d < c.length; d++) c[d] = o[i[d]]; - a = w.getInnerMostAxes(a.length, n), p = go({ inputs: { x: r }, attrs: { perm: i }, backend: e }); - let l = e.dataIdMap.get(r.dataId).id; + a = w.getInnerMostAxes(a.length, n), p = ho({ inputs: { x: r15 }, attrs: { perm: i }, backend: e }); + let l = e.dataIdMap.get(r15.dataId).id; e.dataIdMap.get(p.dataId).id !== l && (u = true); } return { transposed: p, originalAxes: s, axes: a, inputWasTransposed: u }; } -var kP; -function kre(r) { - kP = r.wasm.cwrap(Ho, null, ["number, number, number"]); +var NP; +function kre(r15) { + NP = r15.wasm.cwrap(Go, null, ["number, number, number"]); } -function Nre(r) { - let { backend: t8, inputs: e, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = e, p = t8.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, t8); +function Nre(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { axis: n, keepDims: s } = o, { x: a } = e, p = t8.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, t8); if (d) { let C = t8.dataIdMap.get(c.dataId).id; u = c, p = C; @@ -25005,7 +25006,7 @@ function Nre(r) { let [h, g] = w.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = t8.makeOutput(h, a.dtype); if (y.sizeFromShape(u.shape) !== 0) { let C = t8.dataIdMap.get(b.dataId).id; - kP(p, x, C); + NP(p, x, C); } if (d && t8.disposeData(c.dataId), s) { let C = w.expandShapeToKeepDim(b.shape, m); @@ -25013,13 +25014,13 @@ function Nre(r) { } return b; } -var NP = { kernelName: Ho, backendName: "wasm", setupFunc: kre, kernelFunc: Nre }; -var TP; -function Tre(r) { - TP = r.wasm.cwrap(Ko, null, ["number, number, number"]); +var TP = { kernelName: Go, backendName: "wasm", setupFunc: kre, kernelFunc: Nre }; +var _P; +function Tre(r15) { + _P = r15.wasm.cwrap(Ho, null, ["number, number, number"]); } -function _re(r) { - let { backend: t8, inputs: e, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = e, p = t8.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, t8); +function _re(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { axis: n, keepDims: s } = o, { x: a } = e, p = t8.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, t8); if (d) { let C = t8.dataIdMap.get(c.dataId).id; u = c, p = C; @@ -25029,7 +25030,7 @@ function _re(r) { let [h, g] = w.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = t8.makeOutput(h, a.dtype); if (y.sizeFromShape(u.shape) !== 0) { let C = t8.dataIdMap.get(b.dataId).id; - TP(p, x, C); + _P(p, x, C); } if (d && t8.disposeData(c.dataId), s) { let C = w.expandShapeToKeepDim(b.shape, m); @@ -25037,14 +25038,14 @@ function _re(r) { } return b; } -var _P = { kernelName: Ko, backendName: "wasm", setupFunc: Tre, kernelFunc: _re }; -function Mg(r) { +var $P = { kernelName: Ho, backendName: "wasm", setupFunc: Tre, kernelFunc: _re }; +function Og(r15) { let t8; function e(n) { - t8 = n.wasm.cwrap(r, null, ["number", "number", "number", "number", "number"]); + t8 = n.wasm.cwrap(r15, null, ["number", "number", "number", "number", "number"]); } function o(n) { - let { backend: s, inputs: a, attrs: i } = n, { axis: p } = i, { x: u } = a, c = s.dataIdMap.get(u.dataId).id, l = c, m = u, { transposed: d, axes: f, inputWasTransposed: h } = _r(u, p, s); + let { backend: s, inputs: a, attrs: i } = n, { axis: p } = i, { x: u } = a, c = s.dataIdMap.get(u.dataId).id, l = c, m = u, { transposed: d, axes: f, inputWasTransposed: h } = Tr(u, p, s); if (h) { let k = s.dataIdMap.get(d.dataId).id; k !== c && (m = d, l = k); @@ -25052,83 +25053,83 @@ function Mg(r) { let g = m.shape.slice(0, -1), x = s.makeOutput(g, "int32"), b = s.dataIdMap.get(x.dataId).id, C = y.sizeFromShape(x.shape), S = m.shape[f[0]]; return t8(l, we[m.dtype], C, S, b), h && s.disposeData(d.dataId), x; } - return { kernelName: r, backendName: "wasm", setupFunc: e, kernelFunc: o }; + return { kernelName: r15, backendName: "wasm", setupFunc: e, kernelFunc: o }; } -var $P = Mg(Ys); -var EP = Mg(Qs); -var RP = he(qo); -var DP = he(jo); -var AP = he(Xo); -var FP = Ue(Qo, false); -var PP = he(Yo); -var OP; -function $re(r) { - OP = r.wasm.cwrap(Zo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var EP = Og(Ys); +var RP = Og(Qs); +var DP = he(Ko); +var AP = he(qo); +var FP = he(jo); +var PP = Ue(Yo, false); +var OP = he(Xo); +var MP; +function $re(r15) { + MP = r15.wasm.cwrap(Qo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Ere(r) { - let { inputs: t8, attrs: e, backend: o } = r, n = t8.x, s = o.dataIdMap.get(n.dataId).id, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = e, c = w.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.strideHeight, b = c.strideWidth, C = c.inChannels; +function Ere(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, n = t8.x, s = o.dataIdMap.get(n.dataId).id, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = e, c = w.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.strideHeight, b = c.strideWidth, C = c.inChannels; if (c.dataFormat !== "channelsLast") throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`); if (c.dilationWidth !== 1 || c.dilationHeight !== 1) throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`); let S = o.makeOutput(c.outShape, "float32"), k = o.dataIdMap.get(S.dataId).id; - return OP(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, C, k), S; + return MP(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, C, k), S; } -var MP = { kernelName: Zo, backendName: "wasm", setupFunc: $re, kernelFunc: Ere }; -var LP; -function Rre(r) { - LP = r.wasm.cwrap("AvgPool3D", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var LP = { kernelName: Qo, backendName: "wasm", setupFunc: $re, kernelFunc: Ere }; +var BP; +function Rre(r15) { + BP = r15.wasm.cwrap("AvgPool3D", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Dre(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = e.makeOutput(c.outShape, n.dtype); - return LP(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l; +function Dre(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = e.makeOutput(c.outShape, n.dtype); + return BP(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l; } -var BP = { kernelName: Zs, backendName: "wasm", setupFunc: Rre, kernelFunc: Dre }; -var zP; -function Are(r) { - zP = r.wasm.cwrap("AvgPool3DGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var zP = { kernelName: Zs, backendName: "wasm", setupFunc: Rre, kernelFunc: Dre }; +var VP; +function Are(r15) { + VP = r15.wasm.cwrap("AvgPool3DGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Fre(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s } = t8, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = w.computePool3DInfo(s.shape, a, i, 1, p, u), l = e.makeOutput(s.shape, s.dtype); - return zP(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left, c.filterDepth, c.filterHeight, c.filterWidth), l; +function Fre(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s } = t8, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = w.computePool3DInfo(s.shape, a, i, 1, p, u), l = e.makeOutput(s.shape, s.dtype); + return VP(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left, c.filterDepth, c.filterHeight, c.filterWidth), l; } -var VP = { kernelName: Ai, backendName: "wasm", setupFunc: Are, kernelFunc: Fre }; -var WP; -function Pre(r) { - WP = r.wasm.cwrap("AvgPoolGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var WP = { kernelName: Ei, backendName: "wasm", setupFunc: Are, kernelFunc: Fre }; +var UP; +function Pre(r15) { + UP = r15.wasm.cwrap("AvgPoolGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Ore(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s } = t8, { filterSize: a, strides: i, pad: p } = o, u = w.computePool2DInfo(s.shape, a, i, 1, p), c = e.makeOutput(s.shape, s.dtype); - return WP(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(c.dataId).id, u.batchSize, u.inChannels, u.inHeight, u.inWidth, u.outHeight, u.outWidth, u.strideHeight, u.strideWidth, u.dilationHeight, u.dilationWidth, u.effectiveFilterHeight, u.effectiveFilterWidth, u.padInfo.top, u.padInfo.left, u.filterHeight, u.filterWidth), c; +function Ore(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s } = t8, { filterSize: a, strides: i, pad: p } = o, u = w.computePool2DInfo(s.shape, a, i, 1, p), c = e.makeOutput(s.shape, s.dtype); + return UP(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(c.dataId).id, u.batchSize, u.inChannels, u.inHeight, u.inWidth, u.outHeight, u.outWidth, u.strideHeight, u.strideWidth, u.dilationHeight, u.dilationWidth, u.effectiveFilterHeight, u.effectiveFilterWidth, u.padInfo.top, u.padInfo.left, u.filterHeight, u.filterWidth), c; } -var UP = { kernelName: Di, backendName: "wasm", setupFunc: Pre, kernelFunc: Ore }; -function Vt(r) { - let { inputs: t8, attrs: e } = r, { x: o } = t8, { shape: n } = e, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s); - return y.assert(s === y.sizeFromShape(a), () => `new shape: ${a}, old shape: ${o.shape}. New shape and old shape must have the same number of elements.`), r.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype }; +var GP = { kernelName: $i, backendName: "wasm", setupFunc: Pre, kernelFunc: Ore }; +function zt(r15) { + let { inputs: t8, attrs: e } = r15, { x: o } = t8, { shape: n } = e, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s); + return y.assert(s === y.sizeFromShape(a), () => `new shape: ${a}, old shape: ${o.shape}. New shape and old shape must have the same number of elements.`), r15.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype }; } -var GP = { kernelName: da, backendName: "wasm", kernelFunc: Vt }; -var HP; -function Mre(r) { - HP = r.wasm.cwrap(Jo, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]); +var HP = { kernelName: da, backendName: "wasm", kernelFunc: zt }; +var KP; +function Mre(r15) { + KP = r15.wasm.cwrap(Zo, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]); } -function Lre(r) { - let { inputs: t8, backend: e, attrs: o } = r, { a: n, b: s } = t8, { transposeA: a, transposeB: i } = o; +function Lre(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { a: n, b: s } = t8, { transposeA: a, transposeB: i } = o; if (n.dtype !== "float32" || s.dtype !== "float32") throw new Error("BatchMatMul for non non-float32 tensors not yet supported."); - let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), C = Ir.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]); + let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), C = Sr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]); y.assert(c === l, () => `Error in matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`); - let S = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = Vt({ inputs: { x: n }, backend: e, attrs: { shape: S } }), E = Vt({ inputs: { x: s }, backend: e, attrs: { shape: k } }), R = e.dataIdMap.get(_.dataId).id, D = e.dataIdMap.get(E.dataId).id, P = a ? _.shape[2] : _.shape[1], O = i ? E.shape[1] : E.shape[2], M = Math.max(g, x), L = e.makeOutput([M, P, O], _.dtype), B = e.dataIdMap.get(L.dataId).id, z = new Uint8Array(new Int32Array(_.shape).buffer), U = new Uint8Array(new Int32Array(E.shape).buffer); - return HP(R, z, _.shape.length, D, U, E.shape.length, a, i, B), e.disposeData(_.dataId), e.disposeData(E.dataId), L.shape = C, L; + let S = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = zt({ inputs: { x: n }, backend: e, attrs: { shape: S } }), E = zt({ inputs: { x: s }, backend: e, attrs: { shape: k } }), R = e.dataIdMap.get(_.dataId).id, D = e.dataIdMap.get(E.dataId).id, P = a ? _.shape[2] : _.shape[1], O = i ? E.shape[1] : E.shape[2], M = Math.max(g, x), L = e.makeOutput([M, P, O], _.dtype), B = e.dataIdMap.get(L.dataId).id, z = new Uint8Array(new Int32Array(_.shape).buffer), U = new Uint8Array(new Int32Array(E.shape).buffer); + return KP(R, z, _.shape.length, D, U, E.shape.length, a, i, B), e.disposeData(_.dataId), e.disposeData(E.dataId), L.shape = C, L; } -var KP = { kernelName: Jo, backendName: "wasm", setupFunc: Mre, kernelFunc: Lre }; -function Oo(r) { - let { inputs: { x: t8 }, attrs: { begin: e, size: o }, backend: n } = r, [s, a] = ct.parseSliceParams(t8, e, o), i = ct.isSliceContinous(t8.shape, s, a), p = n.readSync(t8.dataId), u = n.makeOutput(a, t8.dtype), c = y.computeStrides(t8.shape), l = n.dataIdMap.get(u.dataId); +var qP = { kernelName: Zo, backendName: "wasm", setupFunc: Mre, kernelFunc: Lre }; +function Po(r15) { + let { inputs: { x: t8 }, attrs: { begin: e, size: o }, backend: n } = r15, [s, a] = pt.parseSliceParams(t8, e, o), i = pt.isSliceContinous(t8.shape, s, a), p = n.readSync(t8.dataId), u = n.makeOutput(a, t8.dtype), c = y.computeStrides(t8.shape), l = n.dataIdMap.get(u.dataId); if (i) { - let f = ct.computeFlatOffset(s, c); + let f = pt.computeFlatOffset(s, c); return t8.dtype === "string" ? l.stringBytes = p.slice(f, f + y.sizeFromShape(a)) : n.typedArrayFromHeap(u).set(p.subarray(f, f + y.sizeFromShape(a))), u; } if (t8.dtype === "string") { - let f = hp(p, s, a, t8.shape, t8.dtype); + let f = ip(p, s, a, t8.shape, t8.dtype); return l.stringBytes = f, u; } let m = n.typedArrayFromHeap(u), d = t8.shape.length; @@ -25139,91 +25140,91 @@ function Oo(r) { else if (d === 4) Vre(p, c[0], c[1], c[2], m, s, a); else { - let f = hp(p, s, a, t8.shape, t8.dtype); + let f = ip(p, s, a, t8.shape, t8.dtype); m.set(f); } return u; } -function Bre(r, t8, e, o, n) { +function Bre(r15, t8, e, o, n) { let s = 0, a = o[0], i = o[1], p = a + n[0]; for (let u = a; u < p; u++) { let c = u * t8 + i; - e.set(r.subarray(c, c + n[1]), s), s += n[1]; + e.set(r15.subarray(c, c + n[1]), s), s += n[1]; } } -function zre(r, t8, e, o, n, s) { +function zre(r15, t8, e, o, n, s) { let a = 0, i = n[0], p = n[1], u = n[2], c = i + s[0], l = p + s[1]; for (let m = i; m < c; m++) for (let d = p; d < l; d++) { let f = m * t8 + d * e + u; - o.set(r.subarray(f, f + s[2]), a), a += s[2]; + o.set(r15.subarray(f, f + s[2]), a), a += s[2]; } } -function Vre(r, t8, e, o, n, s, a) { +function Vre(r15, t8, e, o, n, s, a) { let i = 0, p = s[0], u = s[1], c = s[2], l = p + a[0], m = u + a[1], d = c + a[2], f = s[3]; for (let h = p; h < l; h++) for (let g = u; g < m; g++) for (let x = c; x < d; x++) { let b = h * t8 + g * e + x * o + f; - n.set(r.subarray(b, b + a[3]), i), i += a[3]; + n.set(r15.subarray(b, b + a[3]), i), i += a[3]; } } -var qP = { kernelName: ha, backendName: "wasm", kernelFunc: Oo }; -function Wre(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { blockShape: s, crops: a } = o, i = s.reduce((x, b) => x * b), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = Vt({ inputs: { x: n }, backend: e, attrs: { shape: p } }), f = go({ inputs: { x: d }, backend: e, attrs: { perm: u } }), h = Vt({ inputs: { x: f }, backend: e, attrs: { shape: c } }), g = Oo({ inputs: { x: h }, backend: e, attrs: { begin: l, size: m } }); +var jP = { kernelName: ha, backendName: "wasm", kernelFunc: Po }; +function Wre(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { blockShape: s, crops: a } = o, i = s.reduce((x, b) => x * b), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = zt({ inputs: { x: n }, backend: e, attrs: { shape: p } }), f = ho({ inputs: { x: d }, backend: e, attrs: { perm: u } }), h = zt({ inputs: { x: f }, backend: e, attrs: { shape: c } }), g = Po({ inputs: { x: h }, backend: e, attrs: { begin: l, size: m } }); return e.disposeData(d.dataId), e.disposeData(f.dataId), e.disposeData(h.dataId), g; } -var jP = { kernelName: Js, backendName: "wasm", kernelFunc: Wre }; -var XP; -function Ure(r) { - XP = r.wasm.cwrap(en, null, ["number", "number", "boolean", "number", "number", "number"]); +var XP = { kernelName: Js, backendName: "wasm", kernelFunc: Wre }; +var YP; +function Ure(r15) { + YP = r15.wasm.cwrap(Jo, null, ["number", "number", "boolean", "number", "number", "number"]); } -function Gre(r) { - let { backend: t8, inputs: e, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = s.shape.reduce((l, m) => l * m, 1) !== 0, p = n.shape.length === 1 ? [a] : [n.shape[0], a], u = t8.makeOutput(p, s.dtype); +function Gre(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { x: n, weights: s } = e, { size: a } = o, i = s.shape.reduce((l, m) => l * m, 1) !== 0, p = n.shape.length === 1 ? [a] : [n.shape[0], a], u = t8.makeOutput(p, s.dtype); function c(l) { return t8.dataIdMap.get(l.dataId).id; } - return XP(c(n), a, i, c(s), we[s.dtype], c(u)), u; + return YP(c(n), a, i, c(s), we[s.dtype], c(u)), u; } -var YP = { kernelName: en, backendName: "wasm", setupFunc: Ure, kernelFunc: Gre }; +var QP = { kernelName: Jo, backendName: "wasm", setupFunc: Ure, kernelFunc: Gre }; var Hre = true; -var QP = Ue(ja, Hre); -function Kre(r) { - let { inputs: t8, backend: e } = r, { s0: o, s1: n } = t8, s = e.typedArrayFromHeap(o), a = e.typedArrayFromHeap(n), i = w.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); +var ZP = Ue(qa, Hre); +function Kre(r15) { + let { inputs: t8, backend: e } = r15, { s0: o, s1: n } = t8, s = e.typedArrayFromHeap(o), a = e.typedArrayFromHeap(n), i = w.assertAndGetBroadcastShape(Array.from(s), Array.from(a)); return e.makeOutput([i.length], "int32", void 0, new Int32Array(i)); } -var ZP = { kernelName: ea, backendName: "wasm", kernelFunc: Kre }; -function Mr(r) { - let { inputs: { x: t8 }, attrs: { dtype: e }, backend: o } = r, n = o.makeOutput(t8.shape, e), s = o.typedArrayFromHeap(t8); +var JP = { kernelName: ea, backendName: "wasm", kernelFunc: Kre }; +function Or(r15) { + let { inputs: { x: t8 }, attrs: { dtype: e }, backend: o } = r15, n = o.makeOutput(t8.shape, e), s = o.typedArrayFromHeap(t8); return o.typedArrayFromHeap(n).set(s), n; } -var JP = { kernelName: bo, backendName: "wasm", kernelFunc: Mr }; -var eO = he(tn); -var tO; -function qre(r) { - tO = r.wasm.cwrap(Co, null, ["number", "number", "number", "number"]); +var eO = { kernelName: yo, backendName: "wasm", kernelFunc: Or }; +var tO = he(en); +var rO; +function qre(r15) { + rO = r15.wasm.cwrap(bo, null, ["number", "number", "number", "number"]); } -function jre(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { clipValueMin: s, clipValueMax: a } = o, i = e.dataIdMap.get(n.dataId).id, p = e.makeOutput(n.shape, n.dtype), u = e.dataIdMap.get(p.dataId).id; - return tO(i, s, a, u), p; +function jre(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { clipValueMin: s, clipValueMax: a } = o, i = e.dataIdMap.get(n.dataId).id, p = e.makeOutput(n.shape, n.dtype), u = e.dataIdMap.get(p.dataId).id; + return rO(i, s, a, u), p; } -var rO = { kernelName: Co, backendName: "wasm", setupFunc: qre, kernelFunc: jre }; -function Av(r) { - let { inputs: t8, backend: e } = r, o = y.parseAxisParam(r.attrs.axis, t8[0].shape)[0], n = t8.map((d) => d.shape); +var oO = { kernelName: bo, backendName: "wasm", setupFunc: qre, kernelFunc: jre }; +function Fv(r15) { + let { inputs: t8, backend: e } = r15, o = y.parseAxisParam(r15.attrs.axis, t8[0].shape)[0], n = t8.map((d) => d.shape); w.assertParamsConsistent(n, o); let s = w.computeOutShape(t8.map((d) => d.shape), o), a = t8.filter((d) => y.sizeFromShape(d.shape) > 0); if (a.length === 1) - return Dp({ inputs: { x: a[0] }, backend: e }); + return vp({ inputs: { x: a[0] }, backend: e }); let i = e.makeOutput(s, t8[0].dtype); if (y.sizeFromShape(s) === 0) return i; if (a[0].dtype === "string") { let d = a.map((C) => { let k = [-1, y.sizeFromShape(C.shape.slice(o))]; - return Vt({ inputs: { x: C }, backend: e, attrs: { shape: k } }); + return zt({ inputs: { x: C }, backend: e, attrs: { shape: k } }); }), f = d.map((C) => ({ vals: e.readSync(C.dataId), shape: C.shape })); s = w.computeOutShape(d.map((C) => C.shape), 1); - let h = d[0].shape[0] === 1, g = mp(f, s, t8[0].dtype, h), x = w.computeOutShape(a.map((C) => C.shape), o); + let h = d[0].shape[0] === 1, g = np(f, s, t8[0].dtype, h), x = w.computeOutShape(a.map((C) => C.shape), o); i.shape = x; let b = e.dataIdMap.get(i.dataId); return b.stringBytes = w.fromStringArrayToUint8(g), d.forEach((C) => e.disposeData(C.dataId)), i; @@ -25241,262 +25242,264 @@ function Av(r) { } return i; } -var oO = { kernelName: ta, backendName: "wasm", kernelFunc: Av }; -var nO; -function Xre(r) { - nO = r.wasm.cwrap(rn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var nO = { kernelName: ta, backendName: "wasm", kernelFunc: Fv }; +var sO; +function Xre(r15) { + sO = r15.wasm.cwrap(tn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Yre(r) { - let { inputs: t8, attrs: e, backend: o } = r, { x: n, filter: s } = t8, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l, dataFormat: m } = e, d = w.convertConv2DDataFormat(m), f = w.computeConv2DInfo(n.shape, s.shape, p, u, c, l, false, d), h = f.filterHeight, g = f.filterWidth, x = f.padInfo.top, b = f.padInfo.right, C = f.padInfo.bottom, S = f.padInfo.left, k = f.dilationHeight, _ = f.dilationWidth, E = f.strideHeight, R = f.strideWidth, D = f.inChannels, P = f.outChannels, O = f.padInfo.type === "SAME" ? 1 : 0; +function Yre(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, { x: n, filter: s } = t8, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l, dataFormat: m } = e, d = w.convertConv2DDataFormat(m), f = w.computeConv2DInfo(n.shape, s.shape, p, u, c, l, false, d), h = f.filterHeight, g = f.filterWidth, x = f.padInfo.top, b = f.padInfo.right, C = f.padInfo.bottom, S = f.padInfo.left, k = f.dilationHeight, _ = f.dilationWidth, E = f.strideHeight, R = f.strideWidth, D = f.inChannels, P = f.outChannels, O = f.padInfo.type === "SAME" ? 1 : 0; if (f.dataFormat !== "channelsLast") throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`); let M = o.makeOutput(f.outShape, "float32"), L = o.dataIdMap.get(M.dataId).id; - return nO(a, n.shape[0], n.shape[1], n.shape[2], i, h, g, x, b, C, S, O, k, _, E, R, D, P, L), M; + return sO(a, n.shape[0], n.shape[1], n.shape[2], i, h, g, x, b, C, S, O, k, _, E, R, D, P, L), M; } -var sO = { kernelName: rn, backendName: "wasm", setupFunc: Xre, kernelFunc: Yre }; -var aO; -function Qre(r) { - aO = r.wasm.cwrap(on, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var aO = { kernelName: tn, backendName: "wasm", setupFunc: Xre, kernelFunc: Yre }; +var iO; +function Qre(r15) { + iO = r15.wasm.cwrap(rn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Zre(r) { - let { backend: t8, inputs: e, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, inputShape: c } = o, l = 1, m = w.convertConv2DDataFormat(p), d = w.computeConv2DInfo(c, s.shape, a, l, i, u, false, m), { batchSize: f, filterHeight: h, filterWidth: g, inChannels: x, inHeight: b, inWidth: C, outChannels: S, outHeight: k, outWidth: _, strideHeight: E, strideWidth: R } = d, D = h - 1 - d.padInfo.top, P = g - 1 - d.padInfo.left, O = d.dataFormat === "channelsLast", M = y.computeStrides(d.inShape), L = y.computeStrides(n.shape), [B, z, U] = y.computeStrides(s.shape), j = M[0], q = O ? M[1] : M[2], Y = O ? M[2] : 1, J = O ? 1 : M[1], re = L[0], ne = O ? L[1] : L[2], ee = O ? L[2] : 1, oe = O ? 1 : L[1], ie = t8.makeOutput(d.inShape, "float32"), le = t8.dataIdMap.get(ie.dataId).id, be = t8.dataIdMap.get(n.dataId).id, _e = t8.dataIdMap.get(s.dataId).id; - return aO(be, _e, f, h, g, b, C, x, k, _, S, E, R, D, P, B, z, U, j, q, Y, J, re, ne, ee, oe, le), ie; +function Zre(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { dy: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, inputShape: c } = o, l = 1, m = w.convertConv2DDataFormat(p), d = w.computeConv2DInfo(c, s.shape, a, l, i, u, false, m), { batchSize: f, filterHeight: h, filterWidth: g, inChannels: x, inHeight: b, inWidth: C, outChannels: S, outHeight: k, outWidth: _, strideHeight: E, strideWidth: R } = d, D = h - 1 - d.padInfo.top, P = g - 1 - d.padInfo.left, O = d.dataFormat === "channelsLast", M = y.computeStrides(d.inShape), L = y.computeStrides(n.shape), [B, z, U] = y.computeStrides(s.shape), j = M[0], q = O ? M[1] : M[2], Y = O ? M[2] : 1, J = O ? 1 : M[1], re = L[0], ne = O ? L[1] : L[2], ee = O ? L[2] : 1, oe = O ? 1 : L[1], ie = t8.makeOutput(d.inShape, "float32"), le = t8.dataIdMap.get(ie.dataId).id, be = t8.dataIdMap.get(n.dataId).id, _e = t8.dataIdMap.get(s.dataId).id; + return iO(be, _e, f, h, g, b, C, x, k, _, S, E, R, D, P, B, z, U, j, q, Y, J, re, ne, ee, oe, le), ie; } -var iO = { kernelName: on, backendName: "wasm", setupFunc: Qre, kernelFunc: Zre }; -var uO; -function Jre(r) { - uO = r.wasm.cwrap(nn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var uO = { kernelName: rn, backendName: "wasm", setupFunc: Qre, kernelFunc: Zre }; +var pO; +function Jre(r15) { + pO = r15.wasm.cwrap(on, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function eoe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p } = o; +function eoe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p } = o; if (n.dtype !== "float32") throw new Error(`Tensor x must have dtype float32, got ${n.dtype}`); if (s.dtype !== "float32") throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`); let u = w.computeConv3DInfo(n.shape, s.shape, a, p, i), c = e.makeOutput(u.outShape, n.dtype); - return uO(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c; + return pO(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c; } -var pO = { kernelName: nn, backendName: "wasm", setupFunc: Jre, kernelFunc: eoe }; -var cO; -function toe(r) { - cO = r.wasm.cwrap(Xa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var cO = { kernelName: on, backendName: "wasm", setupFunc: Jre, kernelFunc: eoe }; +var lO; +function toe(r15) { + lO = r15.wasm.cwrap(ja, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function roe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, dy: s } = t8, { strides: a, pad: i, filterShape: p } = o; +function roe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, dy: s } = t8, { strides: a, pad: i, filterShape: p } = o; if (n.dtype !== "float32") throw new Error(`Tensor dy must have dtype float32, got ${n.dtype}`); if (s.dtype !== "float32") throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`); let u = w.computeConv3DInfo(n.shape, p, a, 1, i), c = e.makeOutput(u.filterShape, s.dtype); - return cO(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c; + return lO(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c; } -var lO = { kernelName: Xa, backendName: "wasm", setupFunc: toe, kernelFunc: roe }; -var mO; -function ooe(r) { - mO = r.wasm.cwrap(sn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var mO = { kernelName: ja, backendName: "wasm", setupFunc: toe, kernelFunc: roe }; +var dO; +function ooe(r15) { + dO = r15.wasm.cwrap(nn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function noe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, filter: s } = t8, { pad: a, strides: i, inputShape: p } = o; +function noe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, filter: s } = t8, { pad: a, strides: i, inputShape: p } = o; if (n.dtype !== "float32") throw new Error(`Tensor dy must have dtype float32, got ${n.dtype}`); if (s.dtype !== "float32") throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`); let u = w.computeConv3DInfo(p, s.shape, i, 1, a), c = e.makeOutput(u.inShape, n.dtype); - return mO(e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c; + return dO(e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c; } -var dO = { kernelName: sn, backendName: "wasm", setupFunc: ooe, kernelFunc: noe }; -var fO = he(an); -var hO = he(un); -var Fv; -(function(r) { - r[r.bilinear = 0] = "bilinear", r[r.nearest = 1] = "nearest"; -})(Fv || (Fv = {})); -var gO; -function soe(r) { - gO = r.wasm.cwrap(ln, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]); +var fO = { kernelName: nn, backendName: "wasm", setupFunc: ooe, kernelFunc: noe }; +var hO = he(sn); +var gO = he(an); +var Pv; +(function(r15) { + r15[r15.bilinear = 0] = "bilinear", r15[r15.nearest = 1] = "nearest"; +})(Pv || (Pv = {})); +var xO; +function soe(r15) { + xO = r15.wasm.cwrap(cn, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]); } -function aoe(r) { - let { backend: t8, inputs: e, attrs: o } = r, { method: n, extrapolationValue: s, cropSize: a } = o, { image: i, boxes: p, boxInd: u } = e, c = p.shape[0], [l, m] = a, d = [c, l, m, i.shape[3]], f = t8.dataIdMap.get(i.dataId), h; - i.dtype !== "float32" && (h = Mr({ backend: t8, inputs: { x: i }, attrs: { dtype: "float32" } }), f = t8.dataIdMap.get(h.dataId)); +function aoe(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { method: n, extrapolationValue: s, cropSize: a } = o, { image: i, boxes: p, boxInd: u } = e, c = p.shape[0], [l, m] = a, d = [c, l, m, i.shape[3]], f = t8.dataIdMap.get(i.dataId), h; + i.dtype !== "float32" && (h = Or({ backend: t8, inputs: { x: i }, attrs: { dtype: "float32" } }), f = t8.dataIdMap.get(h.dataId)); let g = f.id, x = t8.dataIdMap.get(p.dataId).id, b = t8.dataIdMap.get(u.dataId).id, C = t8.makeOutput(d, "float32"), S = t8.dataIdMap.get(C.dataId).id, k = new Uint8Array(new Int32Array(i.shape).buffer); - return gO(g, x, b, c, k, l, m, Fv[n], s, S), h != null && t8.disposeData(h.dataId), C; + return xO(g, x, b, c, k, l, m, Pv[n], s, S), h != null && t8.disposeData(h.dataId), C; } -var xO = { kernelName: ln, backendName: "wasm", setupFunc: soe, kernelFunc: aoe }; -var yO; -function ioe(r) { - yO = r.wasm.cwrap(pn, null, ["number", "number", "number", "number", "number", "number"]); +var yO = { kernelName: cn, backendName: "wasm", setupFunc: soe, kernelFunc: aoe }; +var bO; +function ioe(r15) { + bO = r15.wasm.cwrap(un, null, ["number", "number", "number", "number", "number", "number"]); } -function uoe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length; +function uoe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length; y.assert(n.dtype === "float32" || n.dtype === "int32", () => `cumprod does not support ${n.dtype} tensors in the WASM backend`); let u = w.getAxesPermutation([s], p), c = n; - u !== null && (c = go({ inputs: { x: n }, attrs: { perm: u }, backend: e })); + u !== null && (c = ho({ inputs: { x: n }, attrs: { perm: u }, backend: e })); let l = w.getInnerMostAxes(1, p)[0]; w.assertAxesAreInnerMostDims("cumprod", [l], p); let m = e.makeOutput(c.shape, c.dtype), d = c.shape[l], f = e.dataIdMap.get(c.dataId).id, h = e.dataIdMap.get(m.dataId).id; - yO(f, a ? 1 : 0, i ? 1 : 0, d, h, we[n.dtype]); + bO(f, a ? 1 : 0, i ? 1 : 0, d, h, we[n.dtype]); let g = m; if (u !== null) { let x = w.getUndoAxesPermutation(u); - g = go({ inputs: { x: m }, attrs: { perm: x }, backend: e }), e.disposeData(c.dataId), e.disposeData(m.dataId); + g = ho({ inputs: { x: m }, attrs: { perm: x }, backend: e }), e.disposeData(c.dataId), e.disposeData(m.dataId); } return g; } -var bO = { kernelName: pn, backendName: "wasm", setupFunc: ioe, kernelFunc: uoe }; -var CO; -function poe(r) { - CO = r.wasm.cwrap(cn, null, ["number", "number", "number", "number", "number", "number"]); +var CO = { kernelName: un, backendName: "wasm", setupFunc: ioe, kernelFunc: uoe }; +var wO; +function poe(r15) { + wO = r15.wasm.cwrap(pn, null, ["number", "number", "number", "number", "number", "number"]); } -function coe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length; +function coe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length; y.assert(n.dtype === "float32" || n.dtype === "int32", () => `cumsum does not support ${n.dtype} tensors in the WASM backend`); let u = w.getAxesPermutation([s], p), c = n; - u !== null && (c = go({ inputs: { x: n }, attrs: { perm: u }, backend: e })); + u !== null && (c = ho({ inputs: { x: n }, attrs: { perm: u }, backend: e })); let l = w.getInnerMostAxes(1, p)[0]; w.assertAxesAreInnerMostDims("cumsum", [l], p); let m = e.makeOutput(c.shape, c.dtype), d = c.shape[l], f = e.dataIdMap.get(c.dataId).id, h = e.dataIdMap.get(m.dataId).id; - CO(f, a ? 1 : 0, i ? 1 : 0, d, h, we[n.dtype]); + wO(f, a ? 1 : 0, i ? 1 : 0, d, h, we[n.dtype]); let g = m; if (u !== null) { let x = w.getUndoAxesPermutation(u); - g = go({ inputs: { x: m }, attrs: { perm: x }, backend: e }), e.disposeData(c.dataId), e.disposeData(m.dataId); + g = ho({ inputs: { x: m }, attrs: { perm: x }, backend: e }), e.disposeData(c.dataId), e.disposeData(m.dataId); } return g; } -var wO = { kernelName: cn, backendName: "wasm", setupFunc: poe, kernelFunc: coe }; -var SO; -function loe(r) { - SO = r.wasm.cwrap("DenseBincount", null, ["number", "array", "number", "number", "boolean", "number", "number", "boolean", "number"]); +var SO = { kernelName: pn, backendName: "wasm", setupFunc: poe, kernelFunc: coe }; +var IO; +function loe(r15) { + IO = r15.wasm.cwrap("DenseBincount", null, ["number", "array", "number", "number", "boolean", "number", "number", "boolean", "number"]); } -function moe(r) { - let { backend: t8, inputs: e, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o, p = s.shape.reduce((m, d) => m * d, 1) !== 0, u = n.shape.length === 1 ? [a] : [n.shape[0], a], c = t8.makeOutput(u, s.dtype); +function moe(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o, p = s.shape.reduce((m, d) => m * d, 1) !== 0, u = n.shape.length === 1 ? [a] : [n.shape[0], a], c = t8.makeOutput(u, s.dtype); function l(m) { return t8.dataIdMap.get(m.dataId).id; } - return SO(l(n), new Uint8Array(new Int32Array(n.shape).buffer), n.shape.length, a, p, l(s), we[s.dtype], i, l(c)), c; + return IO(l(n), new Uint8Array(new Int32Array(n.shape).buffer), n.shape.length, a, p, l(s), we[s.dtype], i, l(c)), c; } -var IO = { kernelName: ra, backendName: "wasm", setupFunc: loe, kernelFunc: moe }; -var vO; -function doe(r) { - vO = r.wasm.cwrap(mn, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]); +var vO = { kernelName: ra, backendName: "wasm", setupFunc: loe, kernelFunc: moe }; +var kO; +function doe(r15) { + kO = r15.wasm.cwrap(ln, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]); } -function foe(r) { - let { backend: t8, inputs: e, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = t8.makeOutput(f, "float32"), x = t8.dataIdMap.get(n.dataId).id, b = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), C = new Uint8Array(new Int32Array(f).buffer), S = new Uint8Array(new Int32Array(y.computeStrides(f)).buffer), k = t8.dataIdMap.get(h.dataId).id; - return vO(x, s, a === "NHWC" ? 1 : 0, b, n.shape.length - 1, C, S, f.length, k), h; +function foe(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = t8.makeOutput(f, "float32"), x = t8.dataIdMap.get(n.dataId).id, b = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), C = new Uint8Array(new Int32Array(f).buffer), S = new Uint8Array(new Int32Array(y.computeStrides(f)).buffer), k = t8.dataIdMap.get(h.dataId).id; + return kO(x, s, a === "NHWC" ? 1 : 0, b, n.shape.length - 1, C, S, f.length, k), h; } -var kO = { kernelName: mn, backendName: "wasm", setupFunc: doe, kernelFunc: foe }; -var NO; -function hoe(r) { - NO = r.wasm.cwrap(dn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var NO = { kernelName: ln, backendName: "wasm", setupFunc: doe, kernelFunc: foe }; +var TO; +function hoe(r15) { + TO = r15.wasm.cwrap(mn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function goe(r) { - let { inputs: t8, attrs: e, backend: o } = r, { x: n, filter: s } = t8, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l } = e, m = u == null ? [1, 1] : u, d = w.computeConv2DInfo(n.shape, s.shape, p, m, c, l, true), f = d.filterHeight, h = d.filterWidth, g = d.padInfo.top, x = d.padInfo.right, b = d.padInfo.bottom, C = d.padInfo.left, S = d.dilationHeight, k = d.dilationWidth, _ = d.strideHeight, E = d.strideWidth, R = d.inChannels, D = d.outChannels, P = d.padInfo.type === "SAME" ? 1 : 0; +function goe(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, { x: n, filter: s } = t8, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l } = e, m = u == null ? [1, 1] : u, d = w.computeConv2DInfo(n.shape, s.shape, p, m, c, l, true), f = d.filterHeight, h = d.filterWidth, g = d.padInfo.top, x = d.padInfo.right, b = d.padInfo.bottom, C = d.padInfo.left, S = d.dilationHeight, k = d.dilationWidth, _ = d.strideHeight, E = d.strideWidth, R = d.inChannels, D = d.outChannels, P = d.padInfo.type === "SAME" ? 1 : 0; if (d.dataFormat !== "channelsLast") throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`); let O = o.makeOutput(d.outShape, "float32"), M = o.dataIdMap.get(O.dataId).id; - return NO(a, n.shape[0], n.shape[1], n.shape[2], i, f, h, g, x, b, C, P, S, k, _, E, R, D, M), O; + return TO(a, n.shape[0], n.shape[1], n.shape[2], i, f, h, g, x, b, C, P, S, k, _, E, R, D, M), O; } -var TO = { kernelName: dn, backendName: "wasm", setupFunc: hoe, kernelFunc: goe }; -var _O; -function xoe(r) { - _O = r.wasm.cwrap("Diag", null, ["number", "number", "number", "number"]); +var _O = { kernelName: mn, backendName: "wasm", setupFunc: hoe, kernelFunc: goe }; +var $O; +function xoe(r15) { + $O = r15.wasm.cwrap("Diag", null, ["number", "number", "number", "number"]); } -function yoe(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8, n = y.sizeFromShape(o.shape), s = e.makeOutput([...o.shape, ...o.shape], o.dtype); - return _O(e.dataIdMap.get(o.dataId).id, we[o.dtype], n, e.dataIdMap.get(s.dataId).id), s; +function yoe(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8, n = y.sizeFromShape(o.shape), s = e.makeOutput([...o.shape, ...o.shape], o.dtype); + return $O(e.dataIdMap.get(o.dataId).id, we[o.dtype], n, e.dataIdMap.get(s.dataId).id), s; } -var $O = { kernelName: oa, backendName: "wasm", setupFunc: xoe, kernelFunc: yoe }; -var EO; -function boe(r) { - EO = r.wasm.cwrap(fn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var EO = { kernelName: oa, backendName: "wasm", setupFunc: xoe, kernelFunc: yoe }; +var RO; +function boe(r15) { + RO = r15.wasm.cwrap(dn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Coe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p } = o; +function Coe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p } = o; if (n.dtype !== s.dtype) throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${n.dtype} and ${s.dtype}`); let u = w.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c = e.makeOutput(u.outShape, n.dtype); - return EO(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(c.dataId).id, we[n.dtype], u.batchSize, u.inChannels, u.inHeight, u.inWidth, u.outHeight, u.outWidth, u.strideHeight, u.strideWidth, u.dilationHeight, u.dilationWidth, u.filterHeight, u.filterWidth, u.padInfo.top, u.padInfo.left), c; + return RO(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(c.dataId).id, we[n.dtype], u.batchSize, u.inChannels, u.inHeight, u.inWidth, u.outHeight, u.outWidth, u.strideHeight, u.strideWidth, u.dilationHeight, u.dilationWidth, u.filterHeight, u.filterWidth, u.padInfo.top, u.padInfo.left), c; } -var RO = { kernelName: fn, backendName: "wasm", setupFunc: boe, kernelFunc: Coe }; -var DO; -function woe(r) { - DO = r.wasm.cwrap(zi, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var DO = { kernelName: dn, backendName: "wasm", setupFunc: boe, kernelFunc: Coe }; +var AO; +function woe(r15) { + AO = r15.wasm.cwrap(Mi, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Soe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s, dy: a } = t8, { strides: i, pad: p, dilations: u } = o; +function Soe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s, dy: a } = t8, { strides: i, pad: p, dilations: u } = o; if (n.dtype !== s.dtype || n.dtype !== a.dtype) throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`); let c = w.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = e.makeOutput(s.shape, s.dtype); - return DO(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(a.dataId).id, e.dataIdMap.get(l.dataId).id, we[n.dtype], c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.filterHeight, c.filterWidth, c.padInfo.top, c.padInfo.left), l; + return AO(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(a.dataId).id, e.dataIdMap.get(l.dataId).id, we[n.dtype], c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.filterHeight, c.filterWidth, c.padInfo.top, c.padInfo.left), l; } -var AO = { kernelName: zi, backendName: "wasm", setupFunc: woe, kernelFunc: Soe }; -var FO; -function Ioe(r) { - FO = r.wasm.cwrap(Bi, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var FO = { kernelName: Mi, backendName: "wasm", setupFunc: woe, kernelFunc: Soe }; +var PO; +function Ioe(r15) { + PO = r15.wasm.cwrap(Oi, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function voe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s, dy: a } = t8, { strides: i, pad: p, dilations: u } = o; +function voe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s, dy: a } = t8, { strides: i, pad: p, dilations: u } = o; if (n.dtype !== s.dtype || n.dtype !== a.dtype) throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`); let c = w.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = e.makeOutput(n.shape, n.dtype); - return FO(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(a.dataId).id, e.dataIdMap.get(l.dataId).id, we[n.dtype], c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.filterHeight, c.filterWidth, c.padInfo.top, c.padInfo.left), l; + return PO(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(a.dataId).id, e.dataIdMap.get(l.dataId).id, we[n.dtype], c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.filterHeight, c.filterWidth, c.padInfo.top, c.padInfo.left), l; } -var PO = { kernelName: Bi, backendName: "wasm", setupFunc: Ioe, kernelFunc: voe }; -var OO = he(gn); -var MO; -function koe(r) { - MO = r.wasm.cwrap(Ya, null, ["number", "number", "number"]); +var OO = { kernelName: Oi, backendName: "wasm", setupFunc: Ioe, kernelFunc: voe }; +var MO = he(hn); +var LO; +function koe(r15) { + LO = r15.wasm.cwrap(Xa, null, ["number", "number", "number"]); } -function Noe(r) { - let { inputs: t8, backend: e } = r, { dy: o, y: n } = t8, s = e.makeOutput(n.shape, "float32"), a = (i) => e.dataIdMap.get(i.dataId).id; - return MO(a(n), a(o), a(s)), s; +function Noe(r15) { + let { inputs: t8, backend: e } = r15, { dy: o, y: n } = t8, s = e.makeOutput(n.shape, "float32"), a = (i) => e.dataIdMap.get(i.dataId).id; + return LO(a(n), a(o), a(s)), s; } -var LO = { kernelName: Ya, backendName: "wasm", setupFunc: koe, kernelFunc: Noe }; +var BO = { kernelName: Xa, backendName: "wasm", setupFunc: koe, kernelFunc: Noe }; var Toe = false; -var BO = Ue(yn, Toe, "bool"); -var zO = he(xn); -var VO = he(bn, "float32"); -function Lg(r) { - let { inputs: t8, attrs: e, backend: o } = r, { input: n } = t8, { dim: s } = e, a = n.shape.length, i = n.shape.slice(), p = s; - return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), Vt({ inputs: { x: n }, backend: o, attrs: { shape: i } }); +var zO = Ue(xn, Toe, "bool"); +var VO = he(gn); +var WO = he(yn, "float32"); +function Mg(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, { input: n } = t8, { dim: s } = e, a = n.shape.length, i = n.shape.slice(), p = s; + return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), zt({ inputs: { x: n }, backend: o, attrs: { shape: i } }); } -var WO = { kernelName: na, backendName: "wasm", kernelFunc: Lg }; -var UO = he(Cn, "float32"); -function Pv(r) { - let { attrs: { shape: t8, value: e, dtype: o }, backend: n } = r, s = n.makeOutput(t8, o); - return n.typedArrayFromHeap(s).fill(e), s; +var UO = { kernelName: na, backendName: "wasm", kernelFunc: Mg }; +var GO = he(bn, "float32"); +function Ov(r15) { + let { attrs: { shape: t8, value: e }, backend: o } = r15, { attrs: { dtype: n } } = r15; + n = n || y.inferDtype(e); + let s = o.makeOutput(t8, n); + return o.typedArrayFromHeap(s).fill(e), s; } -var GO = { kernelName: sa, backendName: "wasm", kernelFunc: Pv }; -var HO; -function _oe(r) { - HO = r.wasm.cwrap(wn, null, ["number", "number", "number", "number", "number", "number"]); +var HO = { kernelName: sa, backendName: "wasm", kernelFunc: Ov }; +var KO; +function _oe(r15) { + KO = r15.wasm.cwrap(Cn, null, ["number", "number", "number", "number", "number", "number"]); } -function $oe(r) { - let { inputs: t8, backend: e } = r, { image: o } = t8, n = e.makeOutput(o.shape, o.dtype), s = e.dataIdMap.get(o.dataId).id, a = e.dataIdMap.get(n.dataId).id, [i, p, u, c] = o.shape; - return HO(s, i, p, u, c, a), n; +function $oe(r15) { + let { inputs: t8, backend: e } = r15, { image: o } = t8, n = e.makeOutput(o.shape, o.dtype), s = e.dataIdMap.get(o.dataId).id, a = e.dataIdMap.get(n.dataId).id, [i, p, u, c] = o.shape; + return KO(s, i, p, u, c, a), n; } -var KO = { kernelName: wn, backendName: "wasm", kernelFunc: $oe, setupFunc: _oe }; -var qO = he(Sn); +var qO = { kernelName: Cn, backendName: "wasm", kernelFunc: $oe, setupFunc: _oe }; +var jO = he(wn); var Eoe = false; -var jO = Ue(In, Eoe); -var XO; -function Roe(r) { - XO = r.wasm.cwrap(vn, null, ["number", "number", "number", "number", "number", "number", "number"]); +var XO = Ue(Sn, Eoe); +var YO; +function Roe(r15) { + YO = r15.wasm.cwrap(In, null, ["number", "number", "number", "number", "number", "number", "number"]); } -function Doe(r) { - let { backend: t8, inputs: e, attrs: o } = r, { varianceEpsilon: n } = o, { x: s, mean: a, variance: i, offset: p, scale: u } = e, c = t8.dataIdMap.get(s.dataId).id, l = t8.dataIdMap.get(a.dataId).id, m = t8.dataIdMap.get(i.dataId).id, d = p != null ? t8.dataIdMap.get(p.dataId).id : 0, f = u != null ? t8.dataIdMap.get(u.dataId).id : 0, h = t8.makeOutput(s.shape, s.dtype); +function Doe(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { varianceEpsilon: n } = o, { x: s, mean: a, variance: i, offset: p, scale: u } = e, c = t8.dataIdMap.get(s.dataId).id, l = t8.dataIdMap.get(a.dataId).id, m = t8.dataIdMap.get(i.dataId).id, d = p != null ? t8.dataIdMap.get(p.dataId).id : 0, f = u != null ? t8.dataIdMap.get(u.dataId).id : 0, h = t8.makeOutput(s.shape, s.dtype); if (y.sizeFromShape(s.shape) === 0) return h; let g = t8.dataIdMap.get(h.dataId).id; - return XO(c, l, m, d, f, n, g), h; + return YO(c, l, m, d, f, n, g), h; } -var YO = { kernelName: vn, backendName: "wasm", setupFunc: Roe, kernelFunc: Doe }; -var QO; -function Aoe(r) { - QO = r.wasm.cwrap(vo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var QO = { kernelName: In, backendName: "wasm", setupFunc: Roe, kernelFunc: Doe }; +var ZO; +function Aoe(r15) { + ZO = r15.wasm.cwrap(Io, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Foe(r) { - let { inputs: t8, attrs: e, backend: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = e, h = w.computeConv2DInfo(n.shape, s.shape, p, c, u, m), g = ku[d]; +function Foe(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = e, h = w.computeConv2DInfo(n.shape, s.shape, p, c, u, m), g = bu[d]; if (g == null) throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`); let x = o.dataIdMap.get(n.dataId).id, b = o.dataIdMap.get(s.dataId).id, C = h.outChannels, S = 0; @@ -25512,15 +25515,15 @@ function Foe(r) { if (l !== "NHWC") throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`); let J = o.makeOutput(h.outShape, "float32"), re = o.dataIdMap.get(J.dataId).id, ne = i == null ? 0 : o.dataIdMap.get(i.dataId).id; - return QO(x, j, q, Y, b, k, _, S, E, R, D, P, U, O, M, L, B, z, C, g, ne, f || 0, re), J; + return ZO(x, j, q, Y, b, k, _, S, E, R, D, P, U, O, M, L, B, z, C, g, ne, f || 0, re), J; } -var ZO = { kernelName: vo, backendName: "wasm", setupFunc: Aoe, kernelFunc: Foe }; -var JO; -function Poe(r) { - JO = r.wasm.cwrap(ko, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var JO = { kernelName: Io, backendName: "wasm", setupFunc: Aoe, kernelFunc: Foe }; +var eM; +function Poe(r15) { + eM = r15.wasm.cwrap(vo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Ooe(r) { - let { inputs: t8, attrs: e, backend: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = e, h = w.computeConv2DInfo(n.shape, s.shape, p, c, u, m, true), g = ku[d]; +function Ooe(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = e, h = w.computeConv2DInfo(n.shape, s.shape, p, c, u, m, true), g = bu[d]; if (g == null) throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`); let x = o.dataIdMap.get(n.dataId).id, b = o.dataIdMap.get(s.dataId).id, C = h.outChannels, S = 0; @@ -25536,110 +25539,110 @@ function Ooe(r) { if (l !== "NHWC") throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`); let J = o.makeOutput(h.outShape, "float32"), re = o.dataIdMap.get(J.dataId).id, ne = i == null ? 0 : o.dataIdMap.get(i.dataId).id; - return JO(x, j, q, Y, b, k, _, S, E, R, D, P, U, O, M, L, B, z, C, g, ne, f || 0, re), J; + return eM(x, j, q, Y, b, k, _, S, E, R, D, P, U, O, M, L, B, z, C, g, ne, f || 0, re), J; } -var eM = { kernelName: ko, backendName: "wasm", setupFunc: Poe, kernelFunc: Ooe }; -var tM; -function Moe(r) { - tM = r.wasm.cwrap(kn, null, ["number", "number", "number", "number", "number", "number", "array", "number"]); +var tM = { kernelName: vo, backendName: "wasm", setupFunc: Poe, kernelFunc: Ooe }; +var rM; +function Moe(r15) { + rM = r15.wasm.cwrap(vn, null, ["number", "number", "number", "number", "number", "number", "array", "number"]); } -function Loe(r) { - let { backend: t8, inputs: e } = r, { params: o, indices: n } = e, [s, a, i, p] = af.prepareAndValidate(o, n), u = t8.makeOutput(s, o.dtype); +function Loe(r15) { + let { backend: t8, inputs: e } = r15, { params: o, indices: n } = e, [s, a, i, p] = sf.prepareAndValidate(o, n), u = t8.makeOutput(s, o.dtype); if (a === 0) return u; let c = n.shape, l = c[c.length - 1], d = t8.dataIdMap.get(o.dataId).id, h = t8.dataIdMap.get(n.dataId).id, g = new Uint8Array(new Int32Array(p).buffer), x = t8.dataIdMap.get(u.dataId).id; - return tM(d, we[o.dtype], h, a, l, i, g, x), u; + return rM(d, we[o.dtype], h, a, l, i, g, x), u; } -var rM = { kernelName: kn, backendName: "wasm", setupFunc: Moe, kernelFunc: Loe }; -var oM; -function Boe(r) { - oM = r.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]); +var oM = { kernelName: vn, backendName: "wasm", setupFunc: Moe, kernelFunc: Loe }; +var nM; +function Boe(r15) { + nM = r15.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]); } -function zoe(r) { - let { backend: t8, inputs: e, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = t8.readSync(s.dataId), c = n.shape[p]; +function zoe(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = t8.readSync(s.dataId), c = n.shape[p]; for (let D = 0; D < u.length; ++D) { let P = u[D]; y.assert(P <= c - 1 && P >= 0, () => `GatherV2: the index value ${P} is not in [0, ${c - 1}]`); } - let l = w.segment_util.collectGatherOpShapeInfo(n, s, p, i), m = Vt({ inputs: { x: n }, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] }, backend: t8 }), d = y.sizeFromShape(s.shape), f = Vt({ inputs: { x: s }, attrs: { shape: [l.batchSize, d / l.batchSize] }, backend: t8 }), h = [l.batchSize, l.outerSize, d / l.batchSize, l.sliceSize], g = t8.makeOutput(h, n.dtype); + let l = w.segment_util.collectGatherOpShapeInfo(n, s, p, i), m = zt({ inputs: { x: n }, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] }, backend: t8 }), d = y.sizeFromShape(s.shape), f = zt({ inputs: { x: s }, attrs: { shape: [l.batchSize, d / l.batchSize] }, backend: t8 }), h = [l.batchSize, l.outerSize, d / l.batchSize, l.sliceSize], g = t8.makeOutput(h, n.dtype); if (y.sizeFromShape(n.shape) === 0) return g; let x = m.shape.length - 1, C = t8.dataIdMap.get(m.dataId).id, k = t8.dataIdMap.get(f.dataId).id, _ = t8.dataIdMap.get(g.dataId).id, E = new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer), R = new Uint8Array(new Int32Array(y.computeStrides(h)).buffer); - return oM(C, we[n.dtype], E, x, k, l.batchSize, R, _), t8.disposeData(m.dataId), t8.disposeData(f.dataId), g.shape = l.outputShape, g; + return nM(C, we[n.dtype], E, x, k, l.batchSize, R, _), t8.disposeData(m.dataId), t8.disposeData(f.dataId), g.shape = l.outputShape, g; } -var nM = { kernelName: aa, backendName: "wasm", setupFunc: Boe, kernelFunc: zoe }; +var sM = { kernelName: aa, backendName: "wasm", setupFunc: Boe, kernelFunc: zoe }; var Voe = false; -var sM = Ue(Nn, Voe, "bool"); +var aM = Ue(kn, Voe, "bool"); var Woe = false; -var aM = Ue(Tn, Woe, "bool"); -var iM = he(_n, "bool"); -var uM = he($n, "bool"); -var pM = he(En, "bool"); -var cM; -function Uoe(r) { - cM = r.wasm.cwrap(Rn, null, ["number", "number", "number", "number"]); +var iM = Ue(Nn, Woe, "bool"); +var uM = he(Tn, "bool"); +var pM = he(_n, "bool"); +var cM = he($n, "bool"); +var lM; +function Uoe(r15) { + lM = r15.wasm.cwrap(En, null, ["number", "number", "number", "number"]); } -function Goe(r) { - let { inputs: { x: t8 }, attrs: { alpha: e }, backend: o } = r, n = o.dataIdMap.get(t8.dataId).id, s = o.makeOutput(t8.shape, "float32"); +function Goe(r15) { + let { inputs: { x: t8 }, attrs: { alpha: e }, backend: o } = r15, n = o.dataIdMap.get(t8.dataId).id, s = o.makeOutput(t8.shape, "float32"); if (y.sizeFromShape(t8.shape) !== 0) { let a = o.dataIdMap.get(s.dataId).id; - cM(n, we[t8.dtype], e, a); + lM(n, we[t8.dtype], e, a); } return s; } -var lM = { kernelName: Rn, backendName: "wasm", setupFunc: Uoe, kernelFunc: Goe }; +var mM = { kernelName: En, backendName: "wasm", setupFunc: Uoe, kernelFunc: Goe }; var Hoe = false; -var mM = Ue(Dn, Hoe, "bool"); +var dM = Ue(Rn, Hoe, "bool"); var Koe = false; -var dM = Ue(An, Koe, "bool"); -var fM; -function qoe(r) { - fM = r.wasm.cwrap(Fn, null, ["number", "number", "number", "number"]); +var fM = Ue(Dn, Koe, "bool"); +var hM; +function qoe(r15) { + hM = r15.wasm.cwrap(An, null, ["number", "number", "number", "number"]); } -function joe(r) { - let { attrs: t8, backend: e } = r, { start: o, stop: n, num: s } = t8, a = Math.floor(s), i = e.makeOutput([a], "float32"); - return fM(e.dataIdMap.get(i.dataId).id, o, n, a), i; +function joe(r15) { + let { attrs: t8, backend: e } = r15, { start: o, stop: n, num: s } = t8, a = Math.floor(s), i = e.makeOutput([a], "float32"); + return hM(e.dataIdMap.get(i.dataId).id, o, n, a), i; } -var hM = { kernelName: Fn, backendName: "wasm", setupFunc: qoe, kernelFunc: joe }; -var gM = he(Pn); -var xM = he(On); +var gM = { kernelName: An, backendName: "wasm", setupFunc: qoe, kernelFunc: joe }; +var xM = he(Fn); +var yM = he(Pn); var Xoe = false; -var yM = Ue(Mn, Xoe, "bool"); -var bM = he(Ln); +var bM = Ue(On, Xoe, "bool"); +var CM = he(Mn); var Yoe = false; -var CM = Ue(Bn, Yoe, "bool"); +var wM = Ue(Ln, Yoe, "bool"); var Qoe = false; -var wM = Ue($0, Qoe, "bool"); -var SM; -function Zoe(r) { - SM = r.wasm.cwrap(zn, null, ["number", "number", "number", "number", "number", "number", "number"]); +var SM = Ue(E0, Qoe, "bool"); +var IM; +function Zoe(r15) { + IM = r15.wasm.cwrap(Bn, null, ["number", "number", "number", "number", "number", "number", "number"]); } -function Joe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { depthRadius: s, bias: a, alpha: i, beta: p } = o; +function Joe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { depthRadius: s, bias: a, alpha: i, beta: p } = o; if (n.dtype !== "float32") throw new Error("LRN error: x must have dtype float32"); let u = e.makeOutput(n.shape, n.dtype); - return SM(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(u.dataId).id, n.shape[3], s, a, i, p), u; + return IM(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(u.dataId).id, n.shape[3], s, a, i, p), u; } -var IM = { kernelName: zn, backendName: "wasm", setupFunc: Zoe, kernelFunc: Joe }; -var vM; -function ene(r) { - vM = r.wasm.cwrap(Qa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var vM = { kernelName: Bn, backendName: "wasm", setupFunc: Zoe, kernelFunc: Joe }; +var kM; +function ene(r15) { + kM = r15.wasm.cwrap(Ya, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function tne(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, y: s, dy: a } = t8, { depthRadius: i, bias: p, alpha: u, beta: c } = o; +function tne(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, y: s, dy: a } = t8, { depthRadius: i, bias: p, alpha: u, beta: c } = o; if (n.dtype !== "float32" || s.dtype !== "float32" || a.dtype !== "float32") throw new Error("LRNGrad error: x, y, and dy must have dtype float32"); let l = e.makeOutput(n.shape, n.dtype); - return vM(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(a.dataId).id, e.dataIdMap.get(l.dataId).id, a.shape[3], i, p, u, c), l; + return kM(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(a.dataId).id, e.dataIdMap.get(l.dataId).id, a.shape[3], i, p, u, c), l; } -var kM = { kernelName: Qa, backendName: "wasm", setupFunc: ene, kernelFunc: tne }; -var NM; -function rne(r) { - NM = r.wasm.cwrap(Vn, null, ["number", "number", "number", "number"]); +var NM = { kernelName: Ya, backendName: "wasm", setupFunc: ene, kernelFunc: tne }; +var TM; +function rne(r15) { + TM = r15.wasm.cwrap(zn, null, ["number", "number", "number", "number"]); } -function one(r) { - let { backend: t8, inputs: e, attrs: o } = r, { reductionIndices: n, keepDims: s } = o, { x: a } = e, p = t8.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, t8); +function one(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { reductionIndices: n, keepDims: s } = o, { x: a } = e, p = t8.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, t8); if (d) { let C = t8.dataIdMap.get(c.dataId).id; u = c, p = C; @@ -25649,7 +25652,7 @@ function one(r) { let [h, g] = w.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = t8.makeOutput(h, a.dtype); if (y.sizeFromShape(u.shape) !== 0) { let C = t8.dataIdMap.get(b.dataId).id; - NM(p, we[a.dtype], x, C); + TM(p, we[a.dtype], x, C); } if (d && t8.disposeData(c.dataId), s) { let C = w.expandShapeToKeepDim(b.shape, m); @@ -25657,80 +25660,80 @@ function one(r) { } return b; } -var TM = { kernelName: Vn, backendName: "wasm", setupFunc: rne, kernelFunc: one }; +var _M = { kernelName: zn, backendName: "wasm", setupFunc: rne, kernelFunc: one }; var nne = false; -var _M = Ue(Wn, nne); -var $M; -function sne(r) { - $M = r.wasm.cwrap(Un, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var $M = Ue(Vn, nne); +var EM; +function sne(r15) { + EM = r15.wasm.cwrap(Wn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function ane(r) { - let { inputs: t8, attrs: e, backend: o } = r, n = t8.x, s = o.dataIdMap.get(n.dataId).id; +function ane(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, n = t8.x, s = o.dataIdMap.get(n.dataId).id; y.assert(n.dtype === "float32", () => `Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`); let { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = e, c = w.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.dilationHeight, b = c.dilationWidth, C = c.strideHeight, S = c.strideWidth, k = c.inChannels, _ = c.outChannels; if (c.dataFormat !== "channelsLast") throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`); let E = o.makeOutput(c.outShape, "float32"), R = o.dataIdMap.get(E.dataId).id; - return $M(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, C, S, k, _, R), E; + return EM(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, C, S, k, _, R), E; } -var EM = { kernelName: Un, backendName: "wasm", setupFunc: sne, kernelFunc: ane }; -var RM; -function ine(r) { - RM = r.wasm.cwrap("MaxPool3D", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var RM = { kernelName: Wn, backendName: "wasm", setupFunc: sne, kernelFunc: ane }; +var DM; +function ine(r15) { + DM = r15.wasm.cwrap("MaxPool3D", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function une(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = e.makeOutput(c.outShape, n.dtype); - return RM(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l; +function une(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = w.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = e.makeOutput(c.outShape, n.dtype); + return DM(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l; } -var DM = { kernelName: ia, backendName: "wasm", setupFunc: ine, kernelFunc: une }; -var AM; -function pne(r) { - AM = r.wasm.cwrap("MaxPool3DGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var AM = { kernelName: ia, backendName: "wasm", setupFunc: ine, kernelFunc: une }; +var FM; +function pne(r15) { + FM = r15.wasm.cwrap("MaxPool3DGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function cne(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s } = t8, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = w.computePool3DInfo(s.shape, a, i, 1, p, u), l = e.makeOutput(s.shape, s.dtype); - return AM(e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l; +function cne(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s } = t8, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = w.computePool3DInfo(s.shape, a, i, 1, p, u), l = e.makeOutput(s.shape, s.dtype); + return FM(e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l; } -var FM = { kernelName: Ki, backendName: "wasm", setupFunc: pne, kernelFunc: cne }; -var PM; -function lne(r) { - PM = r.wasm.cwrap("MaxPoolGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var PM = { kernelName: Ui, backendName: "wasm", setupFunc: pne, kernelFunc: cne }; +var OM; +function lne(r15) { + OM = r15.wasm.cwrap("MaxPoolGrad", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function mne(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s } = t8, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = w.computePool2DInfo(s.shape, a, i, 1, p, u), l = e.makeOutput(s.shape, s.dtype); - return PM(e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.top, c.padInfo.left), l; +function mne(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s } = t8, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = w.computePool2DInfo(s.shape, a, i, 1, p, u), l = e.makeOutput(s.shape, s.dtype); + return OM(e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.top, c.padInfo.left), l; } -var OM = { kernelName: Hi, backendName: "wasm", setupFunc: lne, kernelFunc: mne }; -var MM; -function dne(r) { - MM = r.wasm.cwrap("MaxPoolWithArgmax", null, ["number", "number", "number", "number", "boolean", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var MM = { kernelName: Wi, backendName: "wasm", setupFunc: lne, kernelFunc: mne }; +var LM; +function dne(r15) { + LM = r15.wasm.cwrap("MaxPoolWithArgmax", null, ["number", "number", "number", "number", "boolean", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function fne(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { filterSize: s, strides: a, pad: i, includeBatchInIndex: p } = o; +function fne(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { filterSize: s, strides: a, pad: i, includeBatchInIndex: p } = o; y.assert(n.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`); let u = [1, 1]; y.assert(w.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`); let c = w.computePool2DInfo(n.shape, s, a, [1, 1], i), l = e.makeOutput(c.outShape, n.dtype), m = e.makeOutput(c.outShape, "int32"); - return MM(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(l.dataId).id, e.dataIdMap.get(m.dataId).id, we[n.dtype], p, c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.top, c.padInfo.left), [l, m]; + return LM(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(l.dataId).id, e.dataIdMap.get(m.dataId).id, we[n.dtype], p, c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.top, c.padInfo.left), [l, m]; } -var LM = { kernelName: ua, backendName: "wasm", setupFunc: dne, kernelFunc: fne }; -var BM; -function hne(r) { - BM = r.wasm.cwrap(Gn, null, ["number, number, number"]); +var BM = { kernelName: ua, backendName: "wasm", setupFunc: dne, kernelFunc: fne }; +var zM; +function hne(r15) { + zM = r15.wasm.cwrap(Un, null, ["number, number, number"]); } -function gne(r) { - let { backend: t8, inputs: e, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = e, i = t8.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, t8), f = l; +function gne(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { axis: n, keepDims: s } = o, { x: a } = e, i = t8.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, t8), f = l; if (d) { let S = t8.dataIdMap.get(c.dataId).id; S !== i && (u = c, p = S, f = w.getInnerMostAxes(f.length, u.shape.length)); } w.assertAxesAreInnerMostDims("mean", f, u.shape.length); let [h, g] = w.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = u; - u.dtype !== "float32" && (b = Mr({ backend: t8, inputs: { x: u }, attrs: { dtype: "float32" } }), p = t8.dataIdMap.get(b.dataId).id); + u.dtype !== "float32" && (b = Or({ backend: t8, inputs: { x: u }, attrs: { dtype: "float32" } }), p = t8.dataIdMap.get(b.dataId).id); let C = t8.makeOutput(h, "float32"); if (y.sizeFromShape(u.shape) !== 0) { let S = t8.dataIdMap.get(C.dataId).id; - BM(p, x, S); + zM(p, x, S); } if (d && t8.disposeData(c.dataId), s) { let S = w.expandShapeToKeepDim(C.shape, m); @@ -25738,13 +25741,13 @@ function gne(r) { } return u.dtype !== "float32" && t8.disposeData(b.dataId), C; } -var zM = { kernelName: Gn, backendName: "wasm", setupFunc: hne, kernelFunc: gne }; -var VM; -function xne(r) { - VM = r.wasm.cwrap(Hn, null, ["number", "number", "number", "number"]); +var VM = { kernelName: Un, backendName: "wasm", setupFunc: hne, kernelFunc: gne }; +var WM; +function xne(r15) { + WM = r15.wasm.cwrap(Gn, null, ["number", "number", "number", "number"]); } -function yne(r) { - let { backend: t8, inputs: e, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = e, i = t8.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, t8); +function yne(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { axis: n, keepDims: s } = o, { x: a } = e, i = t8.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, t8); if (d) { let C = t8.dataIdMap.get(c.dataId).id; C !== i && (u = c, p = C); @@ -25754,7 +25757,7 @@ function yne(r) { let [h, g] = w.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = t8.makeOutput(h, u.dtype); if (y.sizeFromShape(u.shape) !== 0) { let C = t8.dataIdMap.get(b.dataId).id; - VM(p, we[a.dtype], x, C); + WM(p, we[a.dtype], x, C); } if (d && t8.disposeData(c.dataId), s) { let C = w.expandShapeToKeepDim(b.shape, m); @@ -25762,144 +25765,144 @@ function yne(r) { } return b; } -var WM = { kernelName: Hn, backendName: "wasm", setupFunc: xne, kernelFunc: yne }; +var UM = { kernelName: Gn, backendName: "wasm", setupFunc: xne, kernelFunc: yne }; var bne = false; -var UM = Ue(Kn, bne); -var Ov; -(function(r) { - r[r.reflect = 0] = "reflect", r[r.symmetric = 1] = "symmetric"; -})(Ov || (Ov = {})); -var GM; -function Cne(r) { - GM = r.wasm.cwrap(qn, null, ["number", "array", "number", "number", "array", "array", "number", "number"]); +var GM = Ue(Hn, bne); +var Mv; +(function(r15) { + r15[r15.reflect = 0] = "reflect", r15[r15.symmetric = 1] = "symmetric"; +})(Mv || (Mv = {})); +var HM; +function Cne(r15) { + HM = r15.wasm.cwrap(Kn, null, ["number", "array", "number", "number", "array", "array", "number", "number"]); } -function wne(r) { - let { inputs: { x: t8 }, backend: e, attrs: { paddings: o, mode: n } } = r, s = o.map((f, h) => f[0] + t8.shape[h] + f[1]), a = e.dataIdMap.get(t8.dataId).id, i = e.makeOutput(s, t8.dtype), p = e.dataIdMap.get(i.dataId).id, u = new Uint8Array(new Int32Array(t8.shape).buffer), c = o.map((f) => f[0]), l = o.map((f) => f[1]), m = new Uint8Array(new Int32Array(c).buffer), d = new Uint8Array(new Int32Array(l).buffer); - return GM(a, u, t8.shape.length, we[t8.dtype], m, d, Ov[n], p), i; +function wne(r15) { + let { inputs: { x: t8 }, backend: e, attrs: { paddings: o, mode: n } } = r15, s = o.map((f, h) => f[0] + t8.shape[h] + f[1]), a = e.dataIdMap.get(t8.dataId).id, i = e.makeOutput(s, t8.dtype), p = e.dataIdMap.get(i.dataId).id, u = new Uint8Array(new Int32Array(t8.shape).buffer), c = o.map((f) => f[0]), l = o.map((f) => f[1]), m = new Uint8Array(new Int32Array(c).buffer), d = new Uint8Array(new Int32Array(l).buffer); + return HM(a, u, t8.shape.length, we[t8.dtype], m, d, Mv[n], p), i; } -var HM = { kernelName: qn, backendName: "wasm", kernelFunc: wne, setupFunc: Cne }; -var KM; -function Sne(r) { - KM = r.wasm.cwrap(vs, null, ["number", "number", "number", "number"]); +var KM = { kernelName: Kn, backendName: "wasm", kernelFunc: wne, setupFunc: Cne }; +var qM; +function Sne(r15) { + qM = r15.wasm.cwrap(Is, null, ["number", "number", "number", "number"]); } -function Mv(r) { - let { backend: t8, inputs: { logits: e }, attrs: { dim: o } } = r, n = t8.dataIdMap.get(e.dataId).id, s = t8.makeOutput(e.shape, e.dtype), a = t8.dataIdMap.get(s.dataId).id, i = e.shape[o], p = y.sizeFromShape(e.shape) / i; - return y.sizeFromShape(s.shape) === 0 || KM(n, a, i, p), s; +function Lv(r15) { + let { backend: t8, inputs: { logits: e }, attrs: { dim: o } } = r15, n = t8.dataIdMap.get(e.dataId).id, s = t8.makeOutput(e.shape, e.dtype), a = t8.dataIdMap.get(s.dataId).id, i = e.shape[o], p = y.sizeFromShape(e.shape) / i; + return y.sizeFromShape(s.shape) === 0 || qM(n, a, i, p), s; } -var qM = { kernelName: vs, backendName: "wasm", setupFunc: Sne, kernelFunc: Mv }; -var jM; -function Ine(r) { - jM = r.wasm.cwrap(Xn, null, ["number", "number", "number", "number", "number", "number"]); +var jM = { kernelName: Is, backendName: "wasm", setupFunc: Sne, kernelFunc: Lv }; +var XM; +function Ine(r15) { + XM = r15.wasm.cwrap(jn, null, ["number", "number", "number", "number", "number", "number"]); } -function vne(r) { - let { inputs: t8, backend: e, attrs: o } = r, { logits: n } = t8, { numSamples: s, seed: a, normalized: i } = o; +function vne(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { logits: n } = t8, { numSamples: s, seed: a, normalized: i } = o; if (n.dtype !== "float32") throw new Error(`Tensor logits must have dtype float32, got ${n.dtype}`); - let p = i ? n : Mv({ inputs: { logits: n }, backend: e, attrs: { dim: n.shape.length - 1 } }), [u, c] = p.shape, l = e.makeOutput([u, s], "int32"); - return jM(e.dataIdMap.get(p.dataId).id, u, c, s, a, e.dataIdMap.get(l.dataId).id), i || e.disposeData(p.dataId), l; + let p = i ? n : Lv({ inputs: { logits: n }, backend: e, attrs: { dim: n.shape.length - 1 } }), [u, c] = p.shape, l = e.makeOutput([u, s], "int32"); + return XM(e.dataIdMap.get(p.dataId).id, u, c, s, a, e.dataIdMap.get(l.dataId).id), i || e.disposeData(p.dataId), l; } -var XM = { kernelName: Xn, backendName: "wasm", setupFunc: Ine, kernelFunc: vne }; -var YM = Ue(jn, true); +var YM = { kernelName: jn, backendName: "wasm", setupFunc: Ine, kernelFunc: vne }; +var QM = Ue(qn, true); var kne = true; -var QM = Ue(Yn, kne); -var ZM = he(pa); -function Yc(r, t8) { - let e = new Int32Array(r.wasm.HEAPU8.buffer, t8, 4), o = e[0], n = e[1], s = e[2], a = e[3]; - return r.wasm._free(t8), { pSelectedIndices: o, selectedSize: n, pSelectedScores: s, pValidOutputs: a }; +var ZM = Ue(Xn, kne); +var JM = he(pa); +function Hc(r15, t8) { + let e = new Int32Array(r15.wasm.HEAPU8.buffer, t8, 4), o = e[0], n = e[1], s = e[2], a = e[3]; + return r15.wasm._free(t8), { pSelectedIndices: o, selectedSize: n, pSelectedScores: s, pValidOutputs: a }; } -var JM; -function Nne(r) { - JM = r.wasm.cwrap(Zn, "number", ["number", "number", "number", "number", "number"]); +var eL; +function Nne(r15) { + eL = r15.wasm.cwrap(Qn, "number", ["number", "number", "number", "number", "number"]); } -function Tne(r) { - let { backend: t8, inputs: e, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a } = o, { boxes: i, scores: p } = e, u = t8.dataIdMap.get(i.dataId).id, c = t8.dataIdMap.get(p.dataId).id, l = JM(u, c, s, n, a), { pSelectedIndices: m, selectedSize: d, pSelectedScores: f, pValidOutputs: h } = Yc(t8, l); +function Tne(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a } = o, { boxes: i, scores: p } = e, u = t8.dataIdMap.get(i.dataId).id, c = t8.dataIdMap.get(p.dataId).id, l = eL(u, c, s, n, a), { pSelectedIndices: m, selectedSize: d, pSelectedScores: f, pValidOutputs: h } = Hc(t8, l); return t8.wasm._free(f), t8.wasm._free(h), t8.makeOutput([d], "int32", m); } -var eL = { kernelName: Zn, backendName: "wasm", setupFunc: Nne, kernelFunc: Tne }; -var tL; -function _ne(r) { - tL = r.wasm.cwrap(Za, "number", ["number", "number", "number", "number", "number", "bool"]); +var tL = { kernelName: Qn, backendName: "wasm", setupFunc: Nne, kernelFunc: Tne }; +var rL; +function _ne(r15) { + rL = r15.wasm.cwrap(Qa, "number", ["number", "number", "number", "number", "number", "bool"]); } -function $ne(r) { - let { backend: t8, inputs: e, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, padToMaxOutputSize: i } = o, { boxes: p, scores: u } = e, c = t8.dataIdMap.get(p.dataId).id, l = t8.dataIdMap.get(u.dataId).id, m = tL(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = Yc(t8, m); +function $ne(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, padToMaxOutputSize: i } = o, { boxes: p, scores: u } = e, c = t8.dataIdMap.get(p.dataId).id, l = t8.dataIdMap.get(u.dataId).id, m = rL(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = Hc(t8, m); t8.wasm._free(h); let x = t8.makeOutput([f], "int32", d), b = t8.makeOutput([], "int32", g); return [x, b]; } -var rL = { kernelName: Za, backendName: "wasm", setupFunc: _ne, kernelFunc: $ne }; -var oL; -function Ene(r) { - oL = r.wasm.cwrap(Jn, "number", ["number", "number", "number", "number", "number", "number"]); +var oL = { kernelName: Qa, backendName: "wasm", setupFunc: _ne, kernelFunc: $ne }; +var nL; +function Ene(r15) { + nL = r15.wasm.cwrap(Zn, "number", ["number", "number", "number", "number", "number", "number"]); } -function Rne(r) { - let { backend: t8, inputs: e, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, softNmsSigma: i } = o, { boxes: p, scores: u } = e, c = t8.dataIdMap.get(p.dataId).id, l = t8.dataIdMap.get(u.dataId).id, m = oL(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = Yc(t8, m); +function Rne(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, softNmsSigma: i } = o, { boxes: p, scores: u } = e, c = t8.dataIdMap.get(p.dataId).id, l = t8.dataIdMap.get(u.dataId).id, m = nL(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = Hc(t8, m); t8.wasm._free(g); let x = t8.makeOutput([f], "int32", d), b = t8.makeOutput([f], "float32", h); return [x, b]; } -var nL = { kernelName: Jn, backendName: "wasm", setupFunc: Ene, kernelFunc: Rne }; +var sL = { kernelName: Zn, backendName: "wasm", setupFunc: Ene, kernelFunc: Rne }; var Dne = false; -var sL = Ue(Qn, Dne, "bool"); -var aL; -function Ane(r) { - aL = r.wasm.cwrap(es, null, ["number", "number", "number", "number", "number"]); +var aL = Ue(Yn, Dne, "bool"); +var iL; +function Ane(r15) { + iL = r15.wasm.cwrap(Jn, null, ["number", "number", "number", "number", "number"]); } -function Fne(r) { - let { inputs: t8, backend: e, attrs: o } = r, { indices: n } = t8, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = e.makeOutput([...n.shape, a], s), c = e.dataIdMap.get(u.dataId).id, m = e.dataIdMap.get(n.dataId).id; - return aL(m, a, i, p, c), u; +function Fne(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { indices: n } = t8, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = e.makeOutput([...n.shape, a], s), c = e.dataIdMap.get(u.dataId).id, m = e.dataIdMap.get(n.dataId).id; + return iL(m, a, i, p, c), u; } -var iL = { kernelName: es, backendName: "wasm", setupFunc: Ane, kernelFunc: Fne }; -function Pne(r) { - let { inputs: { x: t8 }, backend: e } = r, o = e.makeOutput(t8.shape, t8.dtype); +var uL = { kernelName: Jn, backendName: "wasm", setupFunc: Ane, kernelFunc: Fne }; +function Pne(r15) { + let { inputs: { x: t8 }, backend: e } = r15, o = e.makeOutput(t8.shape, t8.dtype); return e.typedArrayFromHeap(o).fill(1), o; } -var uL = { kernelName: ca, backendName: "wasm", kernelFunc: Pne }; -function One(r) { - let { inputs: t8, backend: e, attrs: o } = r, { axis: n } = o; +var pL = { kernelName: ca, backendName: "wasm", kernelFunc: Pne }; +function One(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { axis: n } = o; if (t8.length === 1) - return Lg({ inputs: { input: t8[0] }, backend: e, attrs: { dim: n } }); + return Mg({ inputs: { input: t8[0] }, backend: e, attrs: { dim: n } }); let s = t8[0].shape, a = t8[0].dtype; t8.forEach((c) => { y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes"); }); let i = [], p = t8.map((c) => { - let l = Lg({ inputs: { input: c }, backend: e, attrs: { dim: n } }); + let l = Mg({ inputs: { input: c }, backend: e, attrs: { dim: n } }); return i.push(l), l; - }), u = Av({ inputs: p, backend: e, attrs: { axis: n } }); + }), u = Fv({ inputs: p, backend: e, attrs: { axis: n } }); return i.forEach((c) => e.disposeData(c.dataId)), u; } -var pL = { kernelName: la, backendName: "wasm", kernelFunc: One }; -var cL; -function Mne(r) { - cL = r.wasm.cwrap(ts, null, ["number", "array", "number", "number", "array", "array", "number", "number"]); +var cL = { kernelName: la, backendName: "wasm", kernelFunc: One }; +var lL; +function Mne(r15) { + lL = r15.wasm.cwrap(es, null, ["number", "array", "number", "number", "array", "array", "number", "number"]); } -function Lne(r) { - let { inputs: { x: t8 }, backend: e, attrs: { paddings: o, constantValue: n } } = r, s = o.map((h, g) => h[0] + t8.shape[g] + h[1]); +function Lne(r15) { + let { inputs: { x: t8 }, backend: e, attrs: { paddings: o, constantValue: n } } = r15, s = o.map((h, g) => h[0] + t8.shape[g] + h[1]); if (y.sizeFromShape(t8.shape) === 0) - return Pv({ backend: e, attrs: { shape: s, value: n, dtype: t8.dtype } }); + return Ov({ backend: e, attrs: { shape: s, value: n, dtype: t8.dtype } }); let a = e.dataIdMap.get(t8.dataId).id, i = e.makeOutput(s, t8.dtype), u = e.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(t8.shape).buffer), l = o.map((h) => h[0]), m = o.map((h) => h[1]), d = new Uint8Array(new Int32Array(l).buffer), f = new Uint8Array(new Int32Array(m).buffer); - return cL(a, c, t8.shape.length, we[t8.dtype], d, f, n, u), i; + return lL(a, c, t8.shape.length, we[t8.dtype], d, f, n, u), i; } -var Bg = { kernelName: ts, backendName: "wasm", kernelFunc: Lne, setupFunc: Mne }; +var Lg = { kernelName: es, backendName: "wasm", kernelFunc: Lne, setupFunc: Mne }; var Bne = false; -var lL = Ue(rs, Bne); -var mL; -function zne(r) { - mL = r.wasm.cwrap(os, null, ["number", "number", "number"]); +var mL = Ue(ts, Bne); +var dL; +function zne(r15) { + dL = r15.wasm.cwrap(rs, null, ["number", "number", "number"]); } -function Vne(r) { - let { inputs: t8, backend: e } = r, { x: o, alpha: n } = t8, s = e.dataIdMap.get(o.dataId).id, a = e.dataIdMap.get(n.dataId).id, i = s, p = o, u = p; - p.dtype !== "float32" && (u = Mr({ backend: e, inputs: { x: o }, attrs: { dtype: "float32" } }), i = e.dataIdMap.get(u.dataId).id); +function Vne(r15) { + let { inputs: t8, backend: e } = r15, { x: o, alpha: n } = t8, s = e.dataIdMap.get(o.dataId).id, a = e.dataIdMap.get(n.dataId).id, i = s, p = o, u = p; + p.dtype !== "float32" && (u = Or({ backend: e, inputs: { x: o }, attrs: { dtype: "float32" } }), i = e.dataIdMap.get(u.dataId).id); let c = e.makeOutput(o.shape, "float32"), l = e.dataIdMap.get(c.dataId).id; - return mL(i, a, l), p.dtype !== "float32" && e.disposeData(u.dataId), c; + return dL(i, a, l), p.dtype !== "float32" && e.disposeData(u.dataId), c; } -var dL = { kernelName: os, backendName: "wasm", setupFunc: zne, kernelFunc: Vne }; -var fL; -function Wne(r) { - fL = r.wasm.cwrap(ns, null, ["number", "number", "number", "number"]); +var fL = { kernelName: rs, backendName: "wasm", setupFunc: zne, kernelFunc: Vne }; +var hL; +function Wne(r15) { + hL = r15.wasm.cwrap(os, null, ["number", "number", "number", "number"]); } -function Une(r) { - let { backend: t8, inputs: e, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = e, i = t8.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, t8), f = l; +function Une(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { axis: n, keepDims: s } = o, { x: a } = e, i = t8.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, t8), f = l; if (d) { let C = t8.dataIdMap.get(c.dataId).id; C !== i && (u = c, p = C, f = w.getInnerMostAxes(f.length, u.shape.length)); @@ -25908,7 +25911,7 @@ function Une(r) { let [h, g] = w.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = t8.makeOutput(h, u.dtype); if (y.sizeFromShape(u.shape) !== 0) { let C = t8.dataIdMap.get(b.dataId).id; - fL(p, x, we[b.dtype], C); + hL(p, x, we[b.dtype], C); } if (d && t8.disposeData(c.dataId), s) { let C = w.expandShapeToKeepDim(b.shape, m); @@ -25916,153 +25919,153 @@ function Une(r) { } return b; } -var hL = { kernelName: ns, backendName: "wasm", setupFunc: Wne, kernelFunc: Une }; -var Gne = (r) => { - let { backend: t8, attrs: e } = r, { start: o, stop: n, step: s, dtype: a } = e, i = fp(o, n, s, a), p = t8.makeOutput([i.length], a); +var gL = { kernelName: os, backendName: "wasm", setupFunc: Wne, kernelFunc: Une }; +var Gne = (r15) => { + let { backend: t8, attrs: e } = r15, { start: o, stop: n, step: s, dtype: a } = e, i = ap(o, n, s, a), p = t8.makeOutput([i.length], a); return t8.typedArrayFromHeap(p).set(i), p; }; -var gL = { kernelName: ma, backendName: "wasm", kernelFunc: Gne }; +var xL = { kernelName: ma, backendName: "wasm", kernelFunc: Gne }; var Hne = true; -var xL = Ue(hn, Hne); -var yL = he(ss); -var bL = he(as); -var CL = he(ps); -var wL; -function Kne(r) { - wL = r.wasm.cwrap(us, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var yL = Ue(fn, Hne); +var bL = he(ns); +var CL = he(ss); +var wL = he(us); +var SL; +function Kne(r15) { + SL = r15.wasm.cwrap(is, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function qne(r) { - let { backend: t8, inputs: e, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = t8.dataIdMap.get(n.dataId), g; - h.dtype !== "float32" && (g = Mr({ backend: t8, inputs: { x: n }, attrs: { dtype: "float32" } }), h = t8.dataIdMap.get(g.dataId)); +function qne(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = t8.dataIdMap.get(n.dataId), g; + h.dtype !== "float32" && (g = Or({ backend: t8, inputs: { x: n }, attrs: { dtype: "float32" } }), h = t8.dataIdMap.get(g.dataId)); let x = h.id, b = t8.makeOutput(f, "float32"); if (y.sizeFromShape(n.shape) === 0) return b; let C = t8.dataIdMap.get(b.dataId).id; - return wL(x, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, C), g != null && t8.disposeData(g.dataId), b; + return SL(x, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, C), g != null && t8.disposeData(g.dataId), b; } -var SL = { kernelName: us, backendName: "wasm", setupFunc: Kne, kernelFunc: qne }; -var IL; -function jne(r) { - IL = r.wasm.cwrap(ei, null, ["number", "number", "number", "array", "array", "boolean"]); +var IL = { kernelName: is, backendName: "wasm", setupFunc: Kne, kernelFunc: qne }; +var vL; +function jne(r15) { + vL = r15.wasm.cwrap(Ja, null, ["number", "number", "number", "array", "array", "boolean"]); } -function Xne(r) { - let { inputs: t8, backend: e, attrs: o } = r, { images: n, dy: s } = t8, { alignCorners: a } = o, i = e.makeOutput(n.shape, "float32"), p = e.dataIdMap.get(n.dataId), u; - return p.dtype !== "float32" && (u = Mr({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), p = e.dataIdMap.get(u.dataId)), IL(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(i.dataId).id, new Uint8Array(new Int32Array(n.shape).buffer), new Uint8Array(new Int32Array(s.shape).buffer), a), u != null && e.disposeData(u.dataId), i; +function Xne(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { images: n, dy: s } = t8, { alignCorners: a } = o, i = e.makeOutput(n.shape, "float32"), p = e.dataIdMap.get(n.dataId), u; + return p.dtype !== "float32" && (u = Or({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), p = e.dataIdMap.get(u.dataId)), vL(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(i.dataId).id, new Uint8Array(new Int32Array(n.shape).buffer), new Uint8Array(new Int32Array(s.shape).buffer), a), u != null && e.disposeData(u.dataId), i; } -var vL = { kernelName: ei, backendName: "wasm", setupFunc: jne, kernelFunc: Xne }; -var kL; -function Yne(r) { - kL = r.wasm.cwrap(is, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var kL = { kernelName: Ja, backendName: "wasm", setupFunc: jne, kernelFunc: Xne }; +var NL; +function Yne(r15) { + NL = r15.wasm.cwrap(as, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Qne(r) { - let { backend: t8, inputs: e, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = t8.makeOutput(f, "float32"); +function Qne(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = t8.makeOutput(f, "float32"); if (y.sizeFromShape(n.shape) === 0) return h; let g = t8.dataIdMap.get(n.dataId), x; - g.dtype !== "float32" && (x = Mr({ backend: t8, inputs: { x: n }, attrs: { dtype: "float32" } }), g = t8.dataIdMap.get(x.dataId)); + g.dtype !== "float32" && (x = Or({ backend: t8, inputs: { x: n }, attrs: { dtype: "float32" } }), g = t8.dataIdMap.get(x.dataId)); let b = g.id, C = t8.dataIdMap.get(h.dataId).id; - return kL(b, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, C), x != null && t8.disposeData(x.dataId), h; + return NL(b, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, C), x != null && t8.disposeData(x.dataId), h; } -var NL = { kernelName: is, backendName: "wasm", setupFunc: Yne, kernelFunc: Qne }; -var TL; -function Zne(r) { - TL = r.wasm.cwrap(Ja, null, ["number", "number", "number", "array", "array", "boolean"]); +var TL = { kernelName: as, backendName: "wasm", setupFunc: Yne, kernelFunc: Qne }; +var _L; +function Zne(r15) { + _L = r15.wasm.cwrap(Za, null, ["number", "number", "number", "array", "array", "boolean"]); } -function Jne(r) { - let { inputs: t8, backend: e, attrs: o } = r, { images: n, dy: s } = t8, { alignCorners: a } = o, i = e.makeOutput(n.shape, "float32"), p = e.dataIdMap.get(n.dataId), u; - return p.dtype !== "float32" && (u = Mr({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), p = e.dataIdMap.get(u.dataId)), TL(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(i.dataId).id, new Uint8Array(new Int32Array(n.shape).buffer), new Uint8Array(new Int32Array(s.shape).buffer), a), u != null && e.disposeData(u.dataId), i; +function Jne(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { images: n, dy: s } = t8, { alignCorners: a } = o, i = e.makeOutput(n.shape, "float32"), p = e.dataIdMap.get(n.dataId), u; + return p.dtype !== "float32" && (u = Or({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), p = e.dataIdMap.get(u.dataId)), _L(e.dataIdMap.get(n.dataId).id, e.dataIdMap.get(s.dataId).id, e.dataIdMap.get(i.dataId).id, new Uint8Array(new Int32Array(n.shape).buffer), new Uint8Array(new Int32Array(s.shape).buffer), a), u != null && e.disposeData(u.dataId), i; } -var _L = { kernelName: Ja, backendName: "wasm", setupFunc: Zne, kernelFunc: Jne }; -var $L; -function ese(r) { - $L = r.wasm.cwrap(cs, null, ["number", "array", "number", "array", "number", "number"]); +var $L = { kernelName: Za, backendName: "wasm", setupFunc: Zne, kernelFunc: Jne }; +var EL; +function ese(r15) { + EL = r15.wasm.cwrap(ps, null, ["number", "array", "number", "array", "number", "number"]); } -function tse(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { dims: s } = o, a = y.parseAxisParam(s, n.shape); +function tse(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { dims: s } = o, a = y.parseAxisParam(s, n.shape); if (n.shape.length === 0) - return Dp({ inputs: { x: n }, backend: e }); + return vp({ inputs: { x: n }, backend: e }); let i = e.makeOutput(n.shape, n.dtype), p = e.dataIdMap.get(n.dataId).id, u = e.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(a).buffer), l = new Uint8Array(new Int32Array(n.shape).buffer); - $L(p, c, a.length, l, n.shape.length, u); - let m = Vt({ inputs: { x: i }, attrs: { shape: n.shape }, backend: e }); + EL(p, c, a.length, l, n.shape.length, u); + let m = zt({ inputs: { x: i }, attrs: { shape: n.shape }, backend: e }); return e.disposeData(i.dataId), m; } -var EL = { kernelName: cs, backendName: "wasm", kernelFunc: tse, setupFunc: ese }; -var RL; -function rse(r) { - RL = r.wasm.cwrap(As, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]); +var RL = { kernelName: ps, backendName: "wasm", kernelFunc: tse, setupFunc: ese }; +var DL; +function rse(r15) { + DL = r15.wasm.cwrap(Ds, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]); } -function ose(r) { - let { inputs: t8, backend: e, attrs: o } = r, { image: n } = t8, { radians: s, fillValue: a, center: i } = o, p = e.makeOutput(n.shape, n.dtype), u = e.dataIdMap.get(n.dataId).id, c = e.dataIdMap.get(p.dataId).id, [l, m, d, f] = n.shape, [h, g] = w.getImageCenter(i, m, d), x = a === 0, b = 255, C = typeof a == "number" ? [a, a, a, x ? 0 : b] : [...a, b], S = new Uint8Array(new Int32Array(C).buffer); - return RL(u, l, m, d, f, s, h, g, S, C.length, c), p; +function ose(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { image: n } = t8, { radians: s, fillValue: a, center: i } = o, p = e.makeOutput(n.shape, n.dtype), u = e.dataIdMap.get(n.dataId).id, c = e.dataIdMap.get(p.dataId).id, [l, m, d, f] = n.shape, [h, g] = w.getImageCenter(i, m, d), x = a === 0, b = 255, C = typeof a == "number" ? [a, a, a, x ? 0 : b] : [...a, b], S = new Uint8Array(new Int32Array(C).buffer); + return DL(u, l, m, d, f, s, h, g, S, C.length, c), p; } -var DL = { kernelName: As, backendName: "wasm", kernelFunc: ose, setupFunc: rse }; -var AL = he(ls); -var FL = he(ms); -var PL; -function nse(r) { - PL = r.wasm.cwrap(ds, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]); +var AL = { kernelName: Ds, backendName: "wasm", kernelFunc: ose, setupFunc: rse }; +var FL = he(cs); +var PL = he(ls); +var OL; +function nse(r15) { + OL = r15.wasm.cwrap(ms, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]); } -function sse(r) { - let { backend: t8, inputs: e, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, i = t8.makeOutput(a, s.dtype); +function sse(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { indices: n, updates: s } = e, { shape: a } = o, i = t8.makeOutput(a, s.dtype); if (y.sizeFromShape(a) === 0) return i; - let { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = hu.calculateShapes(s, n, a), f = t8.dataIdMap.get(n.dataId).id, g = t8.dataIdMap.get(s.dataId).id, x = new Uint8Array(new Int32Array(l).buffer), b = t8.dataIdMap.get(i.dataId).id; - return PL(f, g, we[s.dtype], p, u, c, x, m, b), i; + let { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = lu.calculateShapes(s, n, a), f = t8.dataIdMap.get(n.dataId).id, g = t8.dataIdMap.get(s.dataId).id, x = new Uint8Array(new Int32Array(l).buffer), b = t8.dataIdMap.get(i.dataId).id; + return OL(f, g, we[s.dtype], p, u, c, x, m, b), i; } -var OL = { kernelName: ds, backendName: "wasm", setupFunc: nse, kernelFunc: sse }; -var ML; -function ase(r) { - ML = r.wasm.cwrap(hs, null, ["number", "number", "number", "number", "number", "number", "bool", "number"]); +var ML = { kernelName: ms, backendName: "wasm", setupFunc: nse, kernelFunc: sse }; +var LL; +function ase(r15) { + LL = r15.wasm.cwrap(fs, null, ["number", "number", "number", "number", "number", "number", "bool", "number"]); } -function ise(r) { - let { inputs: t8, backend: e, attrs: o } = r, { sortedSequence: n, values: s } = t8, { side: a } = o; +function ise(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { sortedSequence: n, values: s } = t8, { side: a } = o; if (n.dtype !== s.dtype) throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. Got ${n.dtype} and ${s.dtype}`); let i = e.makeOutput(s.shape, "int32"); function p(u) { return e.dataIdMap.get(u.dataId).id; } - return ML(p(n), p(s), n.shape[0], n.shape[1], s.shape[1], we[n.dtype], a === "left", p(i)), i; + return LL(p(n), p(s), n.shape[0], n.shape[1], s.shape[1], we[n.dtype], a === "left", p(i)), i; } -var LL = { kernelName: hs, backendName: "wasm", setupFunc: ase, kernelFunc: ise }; -var BL; -function use(r) { - BL = r.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]); +var BL = { kernelName: fs, backendName: "wasm", setupFunc: ase, kernelFunc: ise }; +var zL; +function use(r15) { + zL = r15.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]); } -function pse(r) { - let { inputs: t8, backend: e } = r, { condition: o, t: n, e: s } = t8, a = e.dataIdMap.get(o.dataId).id, i = e.dataIdMap.get(n.dataId).id, p = e.dataIdMap.get(s.dataId).id, u = e.makeOutput(n.shape, n.dtype), c = e.dataIdMap.get(u.dataId).id, l = o.shape.length, m = n.shape.length, d = l === 0 || l > 1 || m === 1 ? 1 : y.sizeFromShape(n.shape.slice(1)); - return BL(a, i, p, d, c), u; +function pse(r15) { + let { inputs: t8, backend: e } = r15, { condition: o, t: n, e: s } = t8, a = e.dataIdMap.get(o.dataId).id, i = e.dataIdMap.get(n.dataId).id, p = e.dataIdMap.get(s.dataId).id, u = e.makeOutput(n.shape, n.dtype), c = e.dataIdMap.get(u.dataId).id, l = o.shape.length, m = n.shape.length, d = l === 0 || l > 1 || m === 1 ? 1 : y.sizeFromShape(n.shape.slice(1)); + return zL(a, i, p, d, c), u; } -var zL = { kernelName: fa, backendName: "wasm", kernelFunc: pse, setupFunc: use }; -var VL = he(gs); -var WL; -function cse(r) { - WL = r.wasm.cwrap(Cs, null, ["number", "number"]); +var VL = { kernelName: fa, backendName: "wasm", kernelFunc: pse, setupFunc: use }; +var WL = he(hs); +var UL; +function cse(r15) { + UL = r15.wasm.cwrap(bs, null, ["number", "number"]); } -function lse(r) { - let { backend: t8, inputs: { x: e } } = r, o = t8.dataIdMap.get(e.dataId).id, n = t8.makeOutput(e.shape, e.dtype), s = t8.dataIdMap.get(n.dataId).id; - return y.sizeFromShape(n.shape) === 0 || WL(o, s), n; +function lse(r15) { + let { backend: t8, inputs: { x: e } } = r15, o = t8.dataIdMap.get(e.dataId).id, n = t8.makeOutput(e.shape, e.dtype), s = t8.dataIdMap.get(n.dataId).id; + return y.sizeFromShape(n.shape) === 0 || UL(o, s), n; } -var UL = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: cse, kernelFunc: lse }; -var GL = he(bs); -var HL = he(xs); -var KL = he(ys); -var qL = he(ws); -function mse(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { blockShape: s, paddings: a } = o, i = y.sizeFromShape(s), p = [[0, 0]]; +var GL = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: cse, kernelFunc: lse }; +var HL = he(ys); +var KL = he(gs); +var qL = he(xs); +var jL = he(Cs); +function mse(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { blockShape: s, paddings: a } = o, i = y.sizeFromShape(s), p = [[0, 0]]; p.push(...a); for (let _ = 1 + s.length; _ < n.shape.length; ++_) p.push([0, 0]); - let u = Bg.kernelFunc({ inputs: { x: n }, backend: e, attrs: { paddings: p, constantValue: 0 } }), c = w.getReshaped(u.shape, s, i, false), l = w.getPermuted(c.length, s.length, false), m = w.getReshapedPermuted(u.shape, s, i, false), h = Vt({ inputs: { x: u }, backend: e, attrs: { shape: c } }), b = go({ inputs: { x: h }, backend: e, attrs: { perm: l } }), k = Vt({ inputs: { x: b }, backend: e, attrs: { shape: m } }); + let u = Lg.kernelFunc({ inputs: { x: n }, backend: e, attrs: { paddings: p, constantValue: 0 } }), c = w.getReshaped(u.shape, s, i, false), l = w.getPermuted(c.length, s.length, false), m = w.getReshapedPermuted(u.shape, s, i, false), h = zt({ inputs: { x: u }, backend: e, attrs: { shape: c } }), b = ho({ inputs: { x: h }, backend: e, attrs: { perm: l } }), k = zt({ inputs: { x: b }, backend: e, attrs: { shape: m } }); return e.disposeData(u.dataId), e.disposeData(h.dataId), e.disposeData(b.dataId), k; } -var jL = { kernelName: ga, backendName: "wasm", kernelFunc: mse }; -var XL; -function dse(r) { - XL = r.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var XL = { kernelName: ga, backendName: "wasm", kernelFunc: mse }; +var YL; +function dse(r15) { + YL = r15.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function fse(r) { - let { backend: t8, inputs: e } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = e, i = o.shape[0], p = o.shape[1], u = t8.readSync(s.dataId)[0], c = [i + u, p], l = t8.dataIdMap.get(o.dataId).id, m = t8.dataIdMap.get(n.dataId).id, d = t8.dataIdMap.get(a.dataId).id, f = t8.makeOutput(c, o.dtype), h = t8.dataIdMap.get(f.dataId).id, g = t8.makeOutput(c.slice(0, 1), n.dtype), x = t8.dataIdMap.get(g.dataId).id, b = t8.makeOutput([u], "bool"), C = t8.dataIdMap.get(b.dataId).id, S = t8.makeOutput([i], o.dtype), k = t8.dataIdMap.get(S.dataId).id, _ = t8.makeOutput([4], "int32"), E = t8.dataIdMap.get(_.dataId).id, R = XL(l, m, we[n.dtype], i, u, p, d, h, x, C, k, E), D = t8.readSync(_.dataId), P; +function fse(r15) { + let { backend: t8, inputs: e } = r15, { indices: o, values: n, denseShape: s, defaultValue: a } = e, i = o.shape[0], p = o.shape[1], u = t8.readSync(s.dataId)[0], c = [i + u, p], l = t8.dataIdMap.get(o.dataId).id, m = t8.dataIdMap.get(n.dataId).id, d = t8.dataIdMap.get(a.dataId).id, f = t8.makeOutput(c, o.dtype), h = t8.dataIdMap.get(f.dataId).id, g = t8.makeOutput(c.slice(0, 1), n.dtype), x = t8.dataIdMap.get(g.dataId).id, b = t8.makeOutput([u], "bool"), C = t8.dataIdMap.get(b.dataId).id, S = t8.makeOutput([i], o.dtype), k = t8.dataIdMap.get(S.dataId).id, _ = t8.makeOutput([4], "int32"), E = t8.dataIdMap.get(_.dataId).id, R = YL(l, m, we[n.dtype], i, u, p, d, h, x, C, k, E), D = t8.readSync(_.dataId), P; switch (D[0]) { case 1: { P = w.getSparseFillEmptyRowsIndicesDenseShapeMismatch(D[1]); @@ -26081,15 +26084,15 @@ function fse(r) { if (t8.disposeData(_.dataId), P) throw t8.disposeData(f.dataId), t8.disposeData(g.dataId), t8.disposeData(b.dataId), t8.disposeData(S.dataId), new Error(P); let O = f, M = g; - return R !== c[0] && (O = Oo({ inputs: { x: f }, attrs: { begin: 0, size: [R, p] }, backend: t8 }), M = Oo({ inputs: { x: g }, attrs: { begin: 0, size: R }, backend: t8 }), t8.disposeData(f.dataId), t8.disposeData(g.dataId)), [O, M, b, S]; + return R !== c[0] && (O = Po({ inputs: { x: f }, attrs: { begin: 0, size: [R, p] }, backend: t8 }), M = Po({ inputs: { x: g }, attrs: { begin: 0, size: R }, backend: t8 }), t8.disposeData(f.dataId), t8.disposeData(g.dataId)), [O, M, b, S]; } -var YL = { kernelName: ji, backendName: "wasm", setupFunc: dse, kernelFunc: fse }; -var QL; -function hse(r) { - QL = r.wasm.cwrap(ti, null, ["number", "number", "number", "number", "number", "number", "number"]); +var QL = { kernelName: Hi, backendName: "wasm", setupFunc: dse, kernelFunc: fse }; +var ZL; +function hse(r15) { + ZL = r15.wasm.cwrap(ei, null, ["number", "number", "number", "number", "number", "number", "number"]); } -function gse(r) { - let { backend: t8, inputs: e } = r, { inputIndices: o, inputShape: n, newShape: s } = e; +function gse(r15) { + let { backend: t8, inputs: e } = r15, { inputIndices: o, inputShape: n, newShape: s } = e; if (o.shape.length !== 2) throw new Error(`Input indices should be a matrix but received shape ${o.shape}`); @@ -26099,7 +26102,7 @@ function gse(r) { if (s.shape.length !== 1) throw new Error(`Target shape should be a vector but received shape ${s.shape}`); let a = t8.dataIdMap.get(o.dataId).id, i = t8.dataIdMap.get(n.dataId).id, p = t8.dataIdMap.get(s.dataId).id, u = o.shape[0], c = y.sizeFromShape(s.shape), l = t8.makeOutput([u, c], o.dtype), m = t8.dataIdMap.get(l.dataId).id, d = t8.makeOutput([c], s.dtype), f = t8.dataIdMap.get(d.dataId).id, h = t8.makeOutput([3], "int32"), g = t8.dataIdMap.get(h.dataId).id; - QL(a, i, p, u, m, f, g); + ZL(a, i, p, u, m, f, g); let x = t8.readSync(h.dataId), b; switch (x[0]) { case 0: { @@ -26130,19 +26133,19 @@ function gse(r) { throw t8.disposeData(l.dataId), t8.disposeData(d.dataId), new Error(b); return [l, d]; } -var ZL = { kernelName: ti, backendName: "wasm", setupFunc: hse, kernelFunc: gse }; -var JL; -function zg(r) { - JL = r.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]); +var JL = { kernelName: ei, backendName: "wasm", setupFunc: hse, kernelFunc: gse }; +var eB; +function Bg(r15) { + eB = r15.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]); } -function Vg(r, t8) { - let { backend: e, inputs: o } = r, { data: n, indices: s, segmentIds: a } = o, i = s.shape[0], p = e.readSync(a.dataId, i - 1, i)[0], c = i > 0 ? p + 1 : 0; +function zg(r15, t8) { + let { backend: e, inputs: o } = r15, { data: n, indices: s, segmentIds: a } = o, i = s.shape[0], p = e.readSync(a.dataId, i - 1, i)[0], c = i > 0 ? p + 1 : 0; if (c < 0) throw new Error(w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); let l = n.shape.slice(); l[0] = c; let m = e.dataIdMap.get(n.dataId).id, d = e.dataIdMap.get(s.dataId).id, f = e.dataIdMap.get(a.dataId).id, h = e.makeOutput(l, n.dtype), g = e.dataIdMap.get(h.dataId).id, x = e.makeOutput([4], "int32"), b = e.dataIdMap.get(x.dataId).id; - JL(m, we[n.dtype], n.shape[0], d, f, g, b, t8, 0); + eB(m, we[n.dtype], n.shape[0], d, f, g, b, t8, 0); let C = e.readSync(x.dataId), S; switch (C[0]) { case 0: { @@ -26166,97 +26169,97 @@ function Vg(r, t8) { throw e.disposeData(h.dataId), new Error(S); return h; } -function xse(r) { - return Vg(r, true); +function xse(r15) { + return zg(r15, true); } -var eB = { kernelName: ya, backendName: "wasm", setupFunc: zg, kernelFunc: xse }; -function yse(r) { - return Vg(r, false); +var tB = { kernelName: ya, backendName: "wasm", setupFunc: Bg, kernelFunc: xse }; +function yse(r15) { + return zg(r15, false); } -var tB = { kernelName: ba, backendName: "wasm", setupFunc: zg, kernelFunc: yse }; -var rB; -function bse(r) { - rB = r.wasm.cwrap(ks, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]); +var rB = { kernelName: ba, backendName: "wasm", setupFunc: Bg, kernelFunc: yse }; +var oB; +function bse(r15) { + oB = r15.wasm.cwrap(vs, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]); } -function Cse(r) { - let { backend: t8, inputs: e, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, p = t8.makeOutput(i, a.dtype); +function Cse(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, p = t8.makeOutput(i, a.dtype); if (y.sizeFromShape(i) === 0) return p; let { sliceRank: u, numUpdates: c, sliceSize: l, strides: m, outputSize: d } = w.calculateShapes(s, n, i), f = t8.dataIdMap.get(n.dataId).id, h = t8.dataIdMap.get(s.dataId).id, g = t8.dataIdMap.get(a.dataId).id, x = new Uint8Array(new Int32Array(m).buffer), b = t8.dataIdMap.get(p.dataId).id; - return rB(f, h, s.shape.length, g, we[a.dtype], u, c, l, x, d, b), p; + return oB(f, h, s.shape.length, g, we[a.dtype], u, c, l, x, d, b), p; } -var oB = { kernelName: ks, backendName: "wasm", setupFunc: bse, kernelFunc: Cse }; -function wse(r) { - let { inputs: t8, attrs: e, backend: o } = r, { x: n } = t8, { numOrSizeSplits: s, axis: a } = e, i = y.parseAxisParam(a, n.shape)[0], p = w.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice(); +var nB = { kernelName: vs, backendName: "wasm", setupFunc: bse, kernelFunc: Cse }; +function wse(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, { x: n } = t8, { numOrSizeSplits: s, axis: a } = e, i = y.parseAxisParam(a, n.shape)[0], p = w.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice(); return p.map((l) => { let m = [...c]; m[i] = l; - let d = Oo({ inputs: { x: n }, attrs: { begin: u, size: m }, backend: o }); + let d = Po({ inputs: { x: n }, attrs: { begin: u, size: m }, backend: o }); return u[i] += l, d; }); } -var nB = { kernelName: xa, backendName: "wasm", kernelFunc: wse }; -var sB = he(Ss); -var aB = he(Xi); +var sB = { kernelName: xa, backendName: "wasm", kernelFunc: wse }; +var aB = he(ws); +var iB = he(Ki); var Sse = true; -var iB = Ue(Ns, Sse); -var uB; -function Ise(r) { - uB = r.wasm.cwrap(So, null, ["number", "number", "number", "number"]); +var uB = Ue(ks, Sse); +var pB; +function Ise(r15) { + pB = r15.wasm.cwrap(wo, null, ["number", "number", "number", "number"]); } -function vse(r) { - let { backend: t8, inputs: e, attrs: o } = r, { alpha: n } = o, { x: s } = e, a = t8.dataIdMap.get(s.dataId).id, i = t8.makeOutput(s.shape, s.dtype), p = t8.dataIdMap.get(i.dataId).id; - return uB(a, n, we[s.dtype], p), i; +function vse(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { alpha: n } = o, { x: s } = e, a = t8.dataIdMap.get(s.dataId).id, i = t8.makeOutput(s.shape, s.dtype), p = t8.dataIdMap.get(i.dataId).id; + return pB(a, n, we[s.dtype], p), i; } -var pB = { kernelName: So, backendName: "wasm", setupFunc: Ise, kernelFunc: vse }; -var cB; -function kse(r) { - cB = r.wasm.cwrap(Ts, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]); +var cB = { kernelName: wo, backendName: "wasm", setupFunc: Ise, kernelFunc: vse }; +var lB; +function kse(r15) { + lB = r15.wasm.cwrap(Ns, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]); } -function Nse(r) { - let { backend: t8, inputs: e, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; +function Nse(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: S } = pt.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; if (h) - k = Vt({ inputs: { x: n }, backend: t8, attrs: { shape: f } }); + k = zt({ inputs: { x: n }, backend: t8, attrs: { shape: f } }); else if (g || x) { y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`); - let _ = ct.computeOutShape(b, C, S), E = Oo({ inputs: { x: n }, backend: t8, attrs: { begin: b, size: _ } }); - k = Vt({ inputs: { x: E }, backend: t8, attrs: { shape: f } }), t8.disposeData(E.dataId); + let _ = pt.computeOutShape(b, C, S), E = Po({ inputs: { x: n }, backend: t8, attrs: { begin: b, size: _ } }); + k = zt({ inputs: { x: E }, backend: t8, attrs: { shape: f } }), t8.disposeData(E.dataId); } else { let _ = t8.makeOutput(d, "float32"), E = t8.dataIdMap.get(n.dataId).id, R = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), D = new Uint8Array(new Int32Array(b).buffer), P = new Uint8Array(new Int32Array(C).buffer), O = new Uint8Array(new Int32Array(S).buffer), M = new Uint8Array(new Int32Array(d).buffer), L = new Uint8Array(new Int32Array(y.computeStrides(d)).buffer), B = t8.dataIdMap.get(_.dataId).id; - cB(E, R, n.shape.length, D, P, O, M, L, d.length, B), k = Vt({ inputs: { x: _ }, backend: t8, attrs: { shape: f } }), t8.disposeData(_.dataId); + lB(E, R, n.shape.length, D, P, O, M, L, d.length, B), k = zt({ inputs: { x: _ }, backend: t8, attrs: { shape: f } }), t8.disposeData(_.dataId); } return k; } -var lB = { kernelName: Ts, backendName: "wasm", setupFunc: kse, kernelFunc: Nse }; -function Tse(r) { - let { backend: t8, inputs: e, attrs: o } = r, { data: n, dataSplits: s } = e, { separator: a, nGramWidths: i, leftPad: p, rightPad: u, padWidth: c, preserveShortSequences: l } = o, m = t8.readSync(n.dataId), d = t8.readSync(s.dataId), [f, h] = gp(m, d, a, i, p, u, c, l), g = t8.makeOutput([f.length], "string"), x = t8.dataIdMap.get(g.dataId); +var mB = { kernelName: Ns, backendName: "wasm", setupFunc: kse, kernelFunc: Nse }; +function Tse(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { data: n, dataSplits: s } = e, { separator: a, nGramWidths: i, leftPad: p, rightPad: u, padWidth: c, preserveShortSequences: l } = o, m = t8.readSync(n.dataId), d = t8.readSync(s.dataId), [f, h] = up(m, d, a, i, p, u, c, l), g = t8.makeOutput([f.length], "string"), x = t8.dataIdMap.get(g.dataId); x.stringBytes = f; let b = t8.makeOutput(s.shape, "int32"); return t8.typedArrayFromHeap(b).set(h), [g, b]; } -var mB = { kernelName: Ca, backendName: "wasm", kernelFunc: Tse }; -function _se(r) { - let { backend: t8, inputs: e, attrs: o } = r, { input: n, delimiter: s } = e, { skipEmpty: a } = o, i = t8.readSync(n.dataId), p = t8.readSync(s.dataId), [u, c, l] = xp(i, p[0], a), m = c.length, d = t8.makeOutput([m, 2], "int32"); +var dB = { kernelName: Ca, backendName: "wasm", kernelFunc: Tse }; +function _se(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { input: n, delimiter: s } = e, { skipEmpty: a } = o, i = t8.readSync(n.dataId), p = t8.readSync(s.dataId), [u, c, l] = pp(i, p[0], a), m = c.length, d = t8.makeOutput([m, 2], "int32"); t8.typedArrayFromHeap(d).set(u); let h = t8.makeOutput([m], "string"), g = t8.dataIdMap.get(h.dataId); g.stringBytes = c; let x = t8.makeOutput([2], "int32"); return t8.typedArrayFromHeap(x).set(l), [d, h, x]; } -var dB = { kernelName: Yi, backendName: "wasm", kernelFunc: _se }; -function $se(r) { - let { backend: t8, inputs: e, attrs: o } = r, { input: n } = e, { numBuckets: s } = o, a = t8.readSync(n.dataId), i = yp(a, s), p = t8.makeOutput(n.shape, "int32"); +var fB = { kernelName: qi, backendName: "wasm", kernelFunc: _se }; +function $se(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { input: n } = e, { numBuckets: s } = o, a = t8.readSync(n.dataId), i = cp(a, s), p = t8.makeOutput(n.shape, "int32"); return t8.typedArrayFromHeap(p).set(i), p; } -var fB = { kernelName: Qi, backendName: "wasm", kernelFunc: $se }; +var hB = { kernelName: ji, backendName: "wasm", kernelFunc: $se }; var Ese = true; -var hB = Ue(_s, Ese); -var gB; -function Rse(r) { - gB = r.wasm.cwrap(Is, null, ["number", "number", "number", "number"]); +var gB = Ue(Ts, Ese); +var xB; +function Rse(r15) { + xB = r15.wasm.cwrap(Ss, null, ["number", "number", "number", "number"]); } -function Dse(r) { - let { backend: t8, inputs: e, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = e, i = t8.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = _r(a, n, t8), f = l; +function Dse(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { axis: n, keepDims: s } = o, { x: a } = e, i = t8.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, t8), f = l; if (d) { let C = t8.dataIdMap.get(c.dataId).id; C !== i && (u = c, p = C, f = w.getInnerMostAxes(f.length, u.shape.length)); @@ -26265,7 +26268,7 @@ function Dse(r) { let [h, g] = w.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = t8.makeOutput(h, u.dtype); if (y.sizeFromShape(u.shape) !== 0) { let C = t8.dataIdMap.get(b.dataId).id; - gB(p, x, we[b.dtype], C); + xB(p, x, we[b.dtype], C); } if (d && t8.disposeData(c.dataId), s) { let C = w.expandShapeToKeepDim(b.shape, m); @@ -26273,50 +26276,50 @@ function Dse(r) { } return b; } -var xB = { kernelName: Is, backendName: "wasm", setupFunc: Rse, kernelFunc: Dse }; -var yB = he($s); -var bB = he(Es); -var CB; -function Ase(r) { - CB = r.wasm.cwrap(fs, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number", "number"]); +var yB = { kernelName: Ss, backendName: "wasm", setupFunc: Rse, kernelFunc: Dse }; +var bB = he(_s); +var CB = he($s); +var wB; +function Ase(r15) { + wB = r15.wasm.cwrap(ds, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number", "number"]); } -function Fse(r) { - let { backend: t8, inputs: e, attrs: o } = r, { tensor: n, indices: s, updates: a } = e, {} = o, i = t8.makeOutput(n.shape, n.dtype); +function Fse(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { tensor: n, indices: s, updates: a } = e, {} = o, i = t8.makeOutput(n.shape, n.dtype); if (y.sizeFromShape(n.shape) === 0) return i; - let { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = hu.calculateShapes(a, s, n.shape), f = t8.dataIdMap.get(s.dataId).id, g = t8.dataIdMap.get(a.dataId).id, b = t8.dataIdMap.get(n.dataId).id, C = new Uint8Array(new Int32Array(l).buffer), S = t8.dataIdMap.get(i.dataId).id; - return CB(f, g, we[a.dtype], p, u, c, C, m, S, b), i; + let { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = lu.calculateShapes(a, s, n.shape), f = t8.dataIdMap.get(s.dataId).id, g = t8.dataIdMap.get(a.dataId).id, b = t8.dataIdMap.get(n.dataId).id, C = new Uint8Array(new Int32Array(l).buffer), S = t8.dataIdMap.get(i.dataId).id; + return wB(f, g, we[a.dtype], p, u, c, C, m, S, b), i; } -var wB = { kernelName: fs, backendName: "wasm", setupFunc: Ase, kernelFunc: Fse }; -var SB; -function Pse(r) { - SB = r.wasm.cwrap(po, null, ["number", "array", "number", "array", "number", "number"]); +var SB = { kernelName: ds, backendName: "wasm", setupFunc: Ase, kernelFunc: Fse }; +var IB; +function Pse(r15) { + IB = r15.wasm.cwrap(uo, null, ["number", "array", "number", "array", "number", "number"]); } -function Ose(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, s = e.dataIdMap.get(n.dataId).id, { reps: a } = o, i = new Array(n.shape.length); +function Ose(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, s = e.dataIdMap.get(n.dataId).id, { reps: a } = o, i = new Array(n.shape.length); for (let m = 0; m < i.length; m++) i[m] = n.shape[m] * a[m]; let p = new Uint8Array(new Int32Array(n.shape).buffer), u = new Uint8Array(new Int32Array(i).buffer), c = e.makeOutput(i, n.dtype), l = e.dataIdMap.get(c.dataId).id; - return SB(s, p, n.shape.length, u, i.length, we[c.dtype], l), c; + return IB(s, p, n.shape.length, u, i.length, we[c.dtype], l), c; } -var IB = { kernelName: po, backendName: "wasm", setupFunc: Pse, kernelFunc: Ose }; -var vB; -function Mse(r) { - vB = r.wasm.cwrap(Rs, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]); +var vB = { kernelName: uo, backendName: "wasm", setupFunc: Pse, kernelFunc: Ose }; +var kB; +function Mse(r15) { + kB = r15.wasm.cwrap(Es, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]); } -var Lse = ({ inputs: r, backend: t8, attrs: e }) => { - let { x: o } = r, { k: n, sorted: s } = e, a = t8.dataIdMap.get(o.dataId).id, i = new Uint8Array(new Int32Array(o.shape).buffer), p = o.shape.slice(); +var Lse = ({ inputs: r15, backend: t8, attrs: e }) => { + let { x: o } = r15, { k: n, sorted: s } = e, a = t8.dataIdMap.get(o.dataId).id, i = new Uint8Array(new Int32Array(o.shape).buffer), p = o.shape.slice(); p[p.length - 1] = n; let u = t8.makeOutput(p, o.dtype), c = t8.dataIdMap.get(u.dataId).id, l = t8.makeOutput(p, "int32"), m = t8.dataIdMap.get(l.dataId).id; - return vB(a, i, o.shape.length, we[o.dtype], n, s, c, m), [u, l]; + return kB(a, i, o.shape.length, we[o.dtype], n, s, c, m), [u, l]; }; -var kB = { kernelName: Rs, backendName: "wasm", setupFunc: Mse, kernelFunc: Lse }; -var NB; -function Bse(r) { - NB = r.wasm.cwrap(Ds, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "array", "number", "number", "number", "number", "number"]); +var NB = { kernelName: Es, backendName: "wasm", setupFunc: Mse, kernelFunc: Lse }; +var TB; +function Bse(r15) { + TB = r15.wasm.cwrap(Rs, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "array", "number", "number", "number", "number", "number"]); } -function zse(r) { - let { backend: t8, inputs: e, attrs: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), b = new Uint8Array(new Int32Array(y.computeStrides(g)).buffer), C = t8.makeOutput(g, n.dtype), S = t8.dataIdMap.get(C.dataId).id, _ = t8.dataIdMap.get(n.dataId).id, R = t8.dataIdMap.get(s.dataId).id, D = a === "nearest" ? 1 : 2, P; +function zse(r15) { + let { backend: t8, inputs: e, attrs: o } = r15, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), b = new Uint8Array(new Int32Array(y.computeStrides(g)).buffer), C = t8.makeOutput(g, n.dtype), S = t8.dataIdMap.get(C.dataId).id, _ = t8.dataIdMap.get(n.dataId).id, R = t8.dataIdMap.get(s.dataId).id, D = a === "nearest" ? 1 : 2, P; switch (i) { case "constant": P = 1; @@ -26334,16 +26337,16 @@ function zse(r) { P = 1; break; } - return NB(_, R, s.shape[0] > 1, c, f, h, d, m, l, x, n.shape.length - 1, b, g.length - 1, D, P, p, S), C; + return TB(_, R, s.shape[0] > 1, c, f, h, d, m, l, x, n.shape.length - 1, b, g.length - 1, D, P, p, S), C; } -var TB = { kernelName: Ds, backendName: "wasm", setupFunc: Bse, kernelFunc: zse }; -function Vse(r) { - let { inputs: t8, attrs: e, backend: o } = r, { axis: n } = e, { x: s } = t8, { outputValues: a, outputShape: i, indices: p } = bp(o.readSync(s.dataId), n, s.shape, s.dtype); +var _B = { kernelName: Rs, backendName: "wasm", setupFunc: Bse, kernelFunc: zse }; +function Vse(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, { axis: n } = e, { x: s } = t8, { outputValues: a, outputShape: i, indices: p } = lp(o.readSync(s.dataId), n, s.shape, s.dtype); return [o.makeOutput(i, s.dtype, void 0, a), o.makeOutput([p.length], "int32", void 0, p)]; } -var _B = { kernelName: Zi, backendName: "wasm", kernelFunc: Vse }; -function Wse(r) { - let { inputs: t8, backend: e, attrs: o } = r, { value: n } = t8, { axis: s } = o; +var $B = { kernelName: Xi, backendName: "wasm", kernelFunc: Vse }; +function Wse(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { value: n } = t8, { axis: s } = o; s < 0 && (s += n.shape.length); let a = n.shape[s], i = n.shape.length, p = new Array(i - 1), u = 0; for (let d = 0; d < i; d++) @@ -26351,43 +26354,43 @@ function Wse(r) { let c = new Array(a), l = new Array(i).fill(0), m = n.shape.slice(); m[s] = 1; for (let d = 0; d < c.length; d++) - l[s] = d, c[d] = Oo({ inputs: { x: n }, attrs: { begin: l, size: m }, backend: e }); + l[s] = d, c[d] = Po({ inputs: { x: n }, attrs: { begin: l, size: m }, backend: e }); return c.map(({ dataId: d, dtype: f }) => ({ dataId: d, dtype: f, shape: p })); } -var $B = { kernelName: wa, backendName: "wasm", kernelFunc: Wse }; -function Use(r) { - let { inputs: { x: t8 }, backend: e } = r, o = e.makeOutput(t8.shape, t8.dtype); +var EB = { kernelName: wa, backendName: "wasm", kernelFunc: Wse }; +function Use(r15) { + let { inputs: { x: t8 }, backend: e } = r15, o = e.makeOutput(t8.shape, t8.dtype); return e.typedArrayFromHeap(o).fill(0), o; } -var EB = { kernelName: Sa, backendName: "wasm", kernelFunc: Use }; -var Gse = [hP, gP, xP, yP, bP, wP, NP, _P, $P, EP, RP, DP, AP, FP, PP, MP, UP, BP, VP, KP, jP, YP, QP, ZP, JP, eO, rO, oO, sO, iO, pO, lO, dO, fO, hO, xO, bO, wO, IO, kO, TO, $O, RO, AO, PO, OO, LO, BO, zO, VO, WO, UO, GO, KO, qO, jO, YO, ZO, eM, rM, nM, sM, aM, SP, iM, uM, pM, lM, mM, dM, hM, xM, gM, yM, bM, CM, wM, IM, kM, TM, _M, EM, DM, FM, OM, LM, zM, WM, UM, HM, XM, YM, QM, ZM, eL, rL, nL, sL, iL, uL, pL, Bg, lL, dL, hL, gL, xL, yL, bL, CL, GP, SL, vL, NL, _L, EL, DL, AL, FL, OL, LL, zL, VL, UL, GL, HL, KL, qP, qM, qL, jL, YL, ZL, eB, tB, oB, nB, sB, aB, iB, pB, lB, mB, dB, fB, hB, xB, yB, bB, wB, IB, kB, TB, vP, _B, $B, EB]; -for (let r of Gse) - ri(r); -var Lv = A(); -Lv.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => { +var RB = { kernelName: Sa, backendName: "wasm", kernelFunc: Use }; +var Gse = [gP, xP, yP, bP, CP, SP, TP, $P, EP, RP, DP, AP, FP, PP, OP, LP, GP, zP, WP, qP, XP, QP, ZP, JP, eO, tO, oO, nO, aO, uO, cO, mO, fO, hO, gO, yO, CO, SO, vO, NO, _O, EO, DO, FO, OO, MO, BO, zO, VO, WO, UO, GO, HO, qO, jO, XO, QO, JO, tM, oM, sM, aM, iM, IP, uM, pM, cM, mM, dM, fM, gM, yM, xM, bM, CM, wM, SM, vM, NM, _M, $M, RM, AM, PM, MM, BM, VM, UM, GM, KM, YM, QM, ZM, JM, tL, oL, sL, aL, uL, pL, cL, Lg, mL, fL, gL, xL, yL, bL, CL, wL, HP, IL, kL, TL, $L, RL, AL, FL, PL, ML, BL, VL, WL, GL, HL, KL, qL, jP, jM, jL, XL, QL, JL, tB, rB, nB, sB, aB, iB, uB, cB, mB, dB, fB, hB, gB, yB, bB, CB, SB, vB, NB, _B, kP, $B, EB, RB]; +for (let r15 of Gse) + ti(r15); +var Bv = A(); +Bv.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => { try { return WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 9, 1, 7, 0, 65, 0, 253, 15, 26, 11])); - } catch (r) { + } catch (r15) { return false; } }); -Lv.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => { - if (Lv.get("IS_NODE")) +Bv.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => { + if (Bv.get("IS_NODE")) return false; try { return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)), WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 5, 4, 1, 3, 1, 1, 10, 11, 1, 9, 0, 65, 0, 254, 16, 2, 0, 26, 11])); - } catch (r) { + } catch (r15) { return false; } }); -var Kv = Kp(FB()); -var zB = Kp(OB()); -var qv = Kp(MB()); -var LB = Kv.default || Kv; -var Hse = qv.default || qv; -var pm = class extends ao { +var qv = Lp(PB()); +var VB = Lp(MB()); +var jv = Lp(LB()); +var BB = qv.default || qv; +var Hse = jv.default || jv; +var im = class extends so { constructor(t8) { - super(), this.wasm = t8, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(WB), Hv = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new zo(this, pr()); + super(), this.wasm = t8, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(UB), Kv = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Bo(this, ur()); } write(t8, e, o) { let n = { id: this.dataIdNextNumber++ }; @@ -26475,113 +26478,113 @@ var pm = class extends ao { } } }; -function Kse(r) { - return (t8, e) => (y.fetch(r, { credentials: "same-origin" }).then((o) => { - o.ok || t8.env.a(`failed to load wasm binary file at '${r}'`), o.arrayBuffer().then((n) => { +function Kse(r15) { + return (t8, e) => (y.fetch(r15, { credentials: "same-origin" }).then((o) => { + o.ok || t8.env.a(`failed to load wasm binary file at '${r15}'`), o.arrayBuffer().then((n) => { WebAssembly.instantiate(n, t8).then((s) => { e(s.instance, s.module); }); }); }), {}); } -function BB(r, t8, e) { - if (Gg != null) - return Gg; +function zB(r15, t8, e) { + if (Ug != null) + return Ug; let o = "tfjs-backend-wasm.wasm"; - return r && t8 ? o = "tfjs-backend-wasm-threaded-simd.wasm" : r && (o = "tfjs-backend-wasm-simd.wasm"), im != null && im[o] != null ? im[o] : e + o; + return r15 && t8 ? o = "tfjs-backend-wasm-threaded-simd.wasm" : r15 && (o = "tfjs-backend-wasm-simd.wasm"), sm != null && sm[o] != null ? sm[o] : e + o; } -async function VB() { - let [r, t8] = await Promise.all([A().getAsync("WASM_HAS_SIMD_SUPPORT"), A().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]); +async function WB() { + let [r15, t8] = await Promise.all([A().getAsync("WASM_HAS_SIMD_SUPPORT"), A().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]); return new Promise((e, o) => { let n = {}; n.locateFile = (i, p) => { if (i.endsWith(".worker.js")) { - let u = zB.wasmWorkerContents.replace(/\n/g, "\\n"), c = new Blob([u], { type: "application/javascript" }); + let u = VB.wasmWorkerContents.replace(/\n/g, "\\n"), c = new Blob([u], { type: "application/javascript" }); return URL.createObjectURL(c); } - return i.endsWith(".wasm") ? BB(r, t8, am != null ? am : p) : p + i; - }, jv && (n.instantiateWasm = Kse(BB(r, t8, am != null ? am : ""))); + return i.endsWith(".wasm") ? zB(r15, t8, nm != null ? nm : p) : p + i; + }, Xv && (n.instantiateWasm = Kse(zB(r15, t8, nm != null ? nm : ""))); let s = false; n.onAbort = () => { - if (s || um) + if (s || am) return; - um = true, o({ message: "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers" }); + am = true, o({ message: "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers" }); }; let a; - t8 && r && Gg == null ? (n.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + LB.toString()], { type: "text/javascript" }), a = LB(n)) : a = Hse(n), a.then((i) => { - s = true, um = false; + t8 && r15 && Ug == null ? (n.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + BB.toString()], { type: "text/javascript" }), a = BB(n)) : a = Hse(n), a.then((i) => { + s = true, am = false; let p = null; i.tfjs = { init: i.cwrap("init", null, []), initWithThreadsCount: i.cwrap("init_with_threads_count", null, ["number"]), getThreadsCount: i.cwrap("get_threads_count", "number", []), registerTensor: i.cwrap("register_tensor", null, ["number", "number", "number"]), disposeData: i.cwrap("dispose_data", p, ["number"]), dispose: i.cwrap("dispose", p, []) }, e({ wasm: i }); }).catch(o); }); } -function qse(r, t8) { +function qse(r15, t8) { switch (t8) { case "float32": - return new Float32Array(r); + return new Float32Array(r15); case "int32": - return new Int32Array(r); + return new Int32Array(r15); case "bool": - return new Uint8Array(r); + return new Uint8Array(r15); default: throw new Error(`Unknown dtype ${t8}`); } } var jse = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"]; -var Gg = null; -var am = null; -var im = {}; -var um = false; -var jv = false; -function Xse(r, t8 = false) { - if (Pw("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), um) +var Ug = null; +var nm = null; +var sm = {}; +var am = false; +var Xv = false; +function Xse(r15, t8 = false) { + if (Pw("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), am) throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`"); - Gg = r, jv = t8; + Ug = r15, Xv = t8; } -function Yse(r, t8 = false) { - if (um) +function Yse(r15, t8 = false) { + if (am) throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`"); - if (typeof r == "string") - am = r; + if (typeof r15 == "string") + nm = r15; else { - im = r; - let e = jse.filter((o) => im[o] == null); + sm = r15; + let e = jse.filter((o) => sm[o] == null); if (e.length > 0) throw new Error(`There were no entries found for the following binaries: ${e.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`); } - jv = t8; + Xv = t8; } -var WB = -1; -var Hv = -1; -function Qse(r) { - WB = r; +var UB = -1; +var Kv = -1; +function Qse(r15) { + UB = r15; } function Zse() { - if (Hv === -1) + if (Kv === -1) throw new Error("WASM backend not initialized."); - return Hv; + return Kv; } -var Jse = "4.11.0"; +var Jse = "4.14.0"; var eae = 2; -su("wasm", async () => { - let { wasm: r } = await VB(); - return new pm(r); +tu("wasm", async () => { + let { wasm: r15 } = await WB(); + return new im(r15); }, eae); -var xo = A(); -xo.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15); -xo.registerFlag("WEBGPU_CPU_FORWARD", () => true); -xo.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE", () => -1); -xo.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => true); -xo.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false); -xo.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3); -xo.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false); -xo.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE", () => true); -xo.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG", () => false); -xo.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL", () => -1); -xo.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER", () => false); -xo.registerFlag("WEBGPU_PRINT_SHADER", () => ""); -xo.registerFlag("WEBGPU_ENGINE_COMPILE_ONLY", () => false); -var Hg = class { +var go = A(); +go.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15); +go.registerFlag("WEBGPU_CPU_FORWARD", () => true); +go.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE", () => -1); +go.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => true); +go.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false); +go.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3); +go.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false); +go.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE", () => true); +go.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG", () => false); +go.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL", () => -1); +go.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER", () => false); +go.registerFlag("WEBGPU_PRINT_SHADER", () => ""); +go.registerFlag("WEBGPU_ENGINE_COMPILE_ONLY", () => false); +var Gg = class { constructor(t8) { t8 && (this.vendor = t8.vendor, this.architecture = t8.architecture, this.intelGPUGeneration = this.getIntelGPUGeneration()); } @@ -26598,18 +26601,18 @@ var Hg = class { return this.vendor === "intel"; } }; -var Kg = class { +var Hg = class { constructor(t8) { this.device = t8, this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0; } acquireBuffer(t8, e, o = false, n = true) { - let s, a = UB(t8, e); + let s, a = GB(t8, e); return n ? (this.freeBuffers.has(a) || this.freeBuffers.set(a, []), this.freeBuffers.get(a).length > 0 ? (s = this.freeBuffers.get(a).pop(), this.numFreeBuffers--) : (s = this.device.createBuffer({ size: t8, usage: e, mappedAtCreation: o }), this.numBytesAllocated += t8)) : (s = this.device.createBuffer({ size: t8, usage: e, mappedAtCreation: o }), this.numBytesAllocated += t8), this.usedBuffers.has(a) || this.usedBuffers.set(a, []), this.usedBuffers.get(a).push(s), this.numUsedBuffers++, this.numBytesUsed += t8, s; } releaseBuffer(t8, e = true) { if (this.freeBuffers.size === 0) return; - let o = t8.size, n = t8.usage, s = UB(o, n), a = this.usedBuffers.get(s), i = a.indexOf(t8); + let o = t8.size, n = t8.usage, s = GB(o, n), a = this.usedBuffers.get(s), i = a.indexOf(t8); if (i < 0) throw new Error("Cannot find the buffer in buffer manager"); a[i] = a[a.length - 1], a.pop(), this.numUsedBuffers--, this.numBytesUsed -= o, e ? (this.freeBuffers.get(s).push(t8), this.numFreeBuffers++) : (t8.destroy(), this.numBytesAllocated -= o); @@ -26632,15 +26635,15 @@ var Kg = class { }), this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0; } }; -function UB(r, t8) { - return `${r}_${t8}`; +function GB(r15, t8) { + return `${r15}_${t8}`; } -var qg = class { +var Kg = class { constructor(t8) { this.device = t8, this.numUsedTextures = 0, this.numFreeTextures = 0, this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0; } acquireTexture(t8, e, o, n) { - let s = HB(o), a = t8 * e * s, i = GB(t8, e, o, n); + let s = KB(o), a = t8 * e * s, i = HB(t8, e, o, n); if (this.freeTextures.has(i) || this.freeTextures.set(i, []), this.usedTextures.has(i) || this.usedTextures.set(i, []), this.numBytesUsed += a, this.numUsedTextures++, this.freeTextures.get(i).length > 0) { this.numFreeTextures--; let u = this.freeTextures.get(i).shift(); @@ -26653,13 +26656,13 @@ var qg = class { releaseTexture(t8) { if (this.freeTextures.size === 0) return; - let e = t8.width, o = t8.height, n = t8.format, s = t8.usage, a = GB(e, o, n, s); + let e = t8.width, o = t8.height, n = t8.format, s = t8.usage, a = HB(e, o, n, s); this.freeTextures.has(a) || this.freeTextures.set(a, []), this.freeTextures.get(a).push(t8), this.numFreeTextures++, this.numUsedTextures--; let i = this.usedTextures.get(a), p = i.indexOf(t8); if (p < 0) throw new Error("Cannot release a texture that was never provided by this texture manager"); i.splice(p, 1); - let u = HB(n), c = e * o * u; + let u = KB(n), c = e * o * u; this.numBytesUsed -= c; } getNumUsedTextures() { @@ -26680,51 +26683,51 @@ var qg = class { }), this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numUsedTextures = 0, this.numFreeTextures = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0; } }; -function GB(r, t8, e, o) { - return `${r}_${t8}_${e}_${o}`; +function HB(r15, t8, e, o) { + return `${r15}_${t8}_${e}_${o}`; } -function HB(r) { - if (r === "rgba8unorm") +function KB(r15) { + if (r15 === "rgba8unorm") return 16; - throw new Error(`${r} is not supported!`); + throw new Error(`${r15} is not supported!`); } -function KB(r, t8) { - if (Math.max(...r) > 5) +function qB(r15, t8) { + if (Math.max(...r15) > 5) throw new Error("Cannot symbolically compute strides for rank > 6 tensor."); - let e = r.length, o = "xyzwuv", n = r.map((a) => `${t8}.${o[a]}`), s = new Array(e - 1); + let e = r15.length, o = "xyzwuv", n = r15.map((a) => `${t8}.${o[a]}`), s = new Array(e - 1); s[e - 2] = n[e - 1]; for (let a = e - 3; a >= 0; --a) s[a] = `(${s[a + 1]} * ${n[a + 1]})`; return s; } -var Qr = (r, t8, e) => e === "int32" ? `atomicAdd(${r}, bitcast(${t8}));` : ` +var Yr = (r15, t8, e) => e === "int32" ? `atomicAdd(${r15}, bitcast(${t8}));` : ` { var oldValue = 0; loop { let newValueF32 = bitcast(oldValue) + (${t8}); let newValue = bitcast(newValueF32); - let res = atomicCompareExchangeWeak(${r}, oldValue, newValue); + let res = atomicCompareExchangeWeak(${r15}, oldValue, newValue); if res.exchanged { break; } oldValue = res.old_value; } }`; -var Ii; -(function(r) { - r[r.FROM_PIXELS = 0] = "FROM_PIXELS", r[r.DRAW = 1] = "DRAW"; -})(Ii || (Ii = {})); -var YB = (r, t8, e, o, n) => { - let s = { dtype: o.dtype, shape: o.shape }, a = rae(e, s, t8), i = r.createShaderModule({ code: a, label: t8.constructor.name }), p = A().get("WEBGPU_PRINT_SHADER"); +var Ci; +(function(r15) { + r15[r15.FROM_PIXELS = 0] = "FROM_PIXELS", r15[r15.DRAW = 1] = "DRAW"; +})(Ci || (Ci = {})); +var QB = (r15, t8, e, o, n) => { + let s = { dtype: o.dtype, shape: o.shape }, a = rae(e, s, t8), i = r15.createShaderModule({ code: a, label: t8.constructor.name }), p = A().get("WEBGPU_PRINT_SHADER"); if (p !== "") { p = p.toLowerCase(); let u = p.split(","); (p === "all" || u.some((c) => t8.shaderKey.toLowerCase().includes(c))) && (console.group(t8.shaderKey), console.debug(a), console.groupEnd()); } - return n ? r.createComputePipelineAsync({ compute: { module: i, entryPoint: "_start" }, label: t8.constructor.name, layout: "auto" }) : r.createComputePipeline({ compute: { module: i, entryPoint: "_start" }, label: t8.constructor.name, layout: "auto" }); + return n ? r15.createComputePipelineAsync({ compute: { module: i, entryPoint: "_start" }, label: t8.constructor.name, layout: "auto" }) : r15.createComputePipeline({ compute: { module: i, entryPoint: "_start" }, label: t8.constructor.name, layout: "auto" }); }; -var Ae = (r, t8 = "f32") => { - switch (r) { +var Ae = (r15, t8 = "f32") => { + switch (r15) { case 1: return `${t8}`; case 2: @@ -26734,42 +26737,42 @@ var Ae = (r, t8 = "f32") => { case 4: return `vec4<${t8}>`; default: - throw new Error(`${r}-component ${t8} is not supported.`); + throw new Error(`${r15}-component ${t8} is not supported.`); } }; -function ht(r) { - if (r <= 1) +function ft(r15) { + if (r15 <= 1) return "i32"; - if (r === 2) + if (r15 === 2) return "vec2"; - if (r === 3) + if (r15 === 3) return "vec3"; - if (r === 4) + if (r15 === 4) return "vec4"; - if (r === 5) + if (r15 === 5) return "vec5"; - if (r === 6) + if (r15 === 6) return "vec6"; - throw Error(`GPU for rank ${r} is not yet supported`); + throw Error(`GPU for rank ${r15} is not yet supported`); } -function Mo(r) { - if (r === 0) +function Oo(r15) { + if (r15 === 0) return "x"; - if (r === 1) + if (r15 === 1) return "y"; - if (r === 2) + if (r15 === 2) return "z"; - if (r === 3) + if (r15 === 3) return "w"; - if (r === 4) + if (r15 === 4) return "u"; - if (r === 5) + if (r15 === 5) return "v"; - throw Error(`Index ${r} is not yet supported`); + throw Error(`Index ${r15} is not yet supported`); } -function G(...r) { +function G(...r15) { let t8; - switch (r.length) { + switch (r15.length) { case 0: t8 = ` fn main() @@ -26777,7 +26780,7 @@ function G(...r) { break; case 1: t8 = ` - fn main(${r[0]} : i32) + fn main(${r15[0]} : i32) `; break; default: @@ -26785,7 +26788,7 @@ function G(...r) { } return t8; } -function qB(r, t8) { +function jB(r15, t8) { let e; return e = ` ${tae(t8)} @@ -26799,16 +26802,16 @@ function qB(r, t8) { globalId = GlobalId; numWorkgroups = NumWorkgroups; workgroupId = WorkgroupId; - ${r ? "main(getGlobalIndex());" : "main();"}; + ${r15 ? "main(getGlobalIndex());" : "main();"}; } `, e; } -function tae(r) { +function tae(r15) { return ` - @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]}) + @compute @workgroup_size(${r15.workgroupSize[0]}, ${r15.workgroupSize[1]}, ${r15.workgroupSize[2]}) `; } -function rae(r, t8, e) { +function rae(r15, t8, e) { let o = [], n = e.workgroupSize[0] * e.workgroupSize[1] * e.workgroupSize[2]; if (e.outputComponent = e.outputComponent ? e.outputComponent : 1, o.push(` @@ -26820,13 +26823,13 @@ function rae(r, t8, e) { // Only used when the y/z dimension of workgroup size is 1. fn getGlobalIndex() -> i32 { - ${ZB(e) ? " return i32(globalId.x);" : ` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + + ${JB(e) ? " return i32(globalId.x);" : ` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u + localIndex); `} } `), e.pixelsOpType != null) { - let f = e.pixelsOpType === Ii.FROM_PIXELS ? `@group(0) @binding(0) var result: array<${Nu(t8.dtype, e.outputComponent)}>;` : `@group(0) @binding(1) var inBuf : array<${Nu(r[0].dtype, e.outputComponent)}>;`, h = t8.shape.length === 3 ? "vec2" : "i32"; + let f = e.pixelsOpType === Ci.FROM_PIXELS ? `@group(0) @binding(0) var result: array<${Cu(t8.dtype, e.outputComponent)}>;` : `@group(0) @binding(1) var inBuf : array<${Cu(r15[0].dtype, e.outputComponent)}>;`, h = t8.shape.length === 3 ? "vec2" : "i32"; o.push(` struct Uniform { outShapeStrides : ${h}, @@ -26838,53 +26841,53 @@ function rae(r, t8, e) { ${f} @group(0) @binding(2) var uniforms: Uniform; `); - let g = XB(e); - return [jB, o.join(` -`), cm(t8.shape), e.getUserCode(), qB(g, e)].join(` + let g = YB(e); + return [XB, o.join(` +`), um(t8.shape), e.getUserCode(), jB(g, e)].join(` `); } let s, a, i = "struct Uniforms { NAN : f32, INFINITY : f32, "; e.variableNames.forEach((f, h) => { - let g = ht(r[h].shape.length); - i += `${f.charAt(0).toLowerCase() + f.slice(1)}Shape : ${g}, `, s = r[h].shape.length - 1, a = ht(s), i += `${f.charAt(0).toLowerCase() + f.slice(1)}ShapeStrides: ${a}, `; + let g = ft(r15[h].shape.length); + i += `${f.charAt(0).toLowerCase() + f.slice(1)}Shape : ${g}, `, s = r15[h].shape.length - 1, a = ft(s), i += `${f.charAt(0).toLowerCase() + f.slice(1)}ShapeStrides: ${a}, `; }); - let p = ht(t8.shape.length); - i += `outShape : ${p}, `, s = t8.shape.length - 1, a = ht(s), i += ` + let p = ft(t8.shape.length); + i += `outShape : ${p}, `, s = t8.shape.length - 1, a = ft(s), i += ` outShapeStrides: ${a}, `, e.size && (i += "size : i32, "), e.uniforms && (i += e.uniforms), i += "};", i = cae(i), o.push(i), e.atomic ? o.push(` @group(0) @binding(0) var result: array>; `) : o.push(` - @group(0) @binding(0) var result: array<${Nu(t8.dtype, e.outputComponent)}>; + @group(0) @binding(0) var result: array<${Cu(t8.dtype, e.outputComponent)}>; `), e.variableNames.forEach((f, h) => { o.push(` - @group(0) @binding(${1 + h}) var ${f}: array<${e.variableComponents ? Nu(r[h].dtype, e.variableComponents[h]) : Nu(r[h].dtype, e.outputComponent)}>; + @group(0) @binding(${1 + h}) var ${f}: array<${e.variableComponents ? Cu(r15[h].dtype, e.variableComponents[h]) : Cu(r15[h].dtype, e.outputComponent)}>; `); }), i !== "" && o.push(` @group(0) @binding(${1 + e.variableNames.length}) var uniforms: Uniforms; `); - let u = iae(t8.shape, e.dispatchLayout), c = [jB, o.join(` -`) + oae, cm(t8.shape), u, uae(t8.shape.length)]; + let u = iae(t8.shape, e.dispatchLayout), c = [XB, o.join(` +`) + oae, um(t8.shape), u, uae(t8.shape.length)]; e.atomic || c.push(pae(t8.shape, t8.dtype, e.outputComponent)), e.variableNames.forEach((f, h) => { - c.push(`${cm(r[h].shape, f)}`); + c.push(`${um(r15[h].shape, f)}`); }); - let l = r.map((f, h) => aae(f, t8.shape, e.variableComponents ? e.variableComponents[h] : e.outputComponent, e.dispatchLayout.x.length === t8.shape.length)).join(` + let l = r15.map((f, h) => aae(f, t8.shape, e.variableComponents ? e.variableComponents[h] : e.outputComponent, e.dispatchLayout.x.length === t8.shape.length)).join(` `); c.push(l), c.push(e.getUserCode()); - let m = XB(e); - return c.push(qB(m, e)), c.join(` + let m = YB(e); + return c.push(jB(m, e)), c.join(` `); } -function QB(r, t8, e) { - let o = r.shaderKey; - if (r.pixelsOpType != null) +function ZB(r15, t8, e) { + let o = r15.shaderKey; + if (r15.pixelsOpType != null) return o; let n = [], s = []; t8.forEach((c) => { n.push(c.shape), s.push(c.dtype); }), n.push(e.shape), s.push(e.dtype); - let a = t8.map((c) => w.getBroadcastDims(c.shape, e.shape)), i = t8.map((c) => y.arraysEqual(c.shape, e.shape)).join("_"), p = a.map((c) => c.join("_")).join(";"), u = ZB(r) ? "flatDispatch" : ""; - return o += "_" + (r.workgroupSize ? r.workgroupSize.join(",") : "") + n.map((c) => c.length).join(",") + s.join(",") + r.variableNames.join(",") + p + i + u, o; + let a = t8.map((c) => w.getBroadcastDims(c.shape, e.shape)), i = t8.map((c) => y.arraysEqual(c.shape, e.shape)).join("_"), p = a.map((c) => c.join("_")).join(";"), u = JB(r15) ? "flatDispatch" : ""; + return o += "_" + (r15.workgroupSize ? r15.workgroupSize.join(",") : "") + n.map((c) => c.length).join(",") + s.join(",") + r15.variableNames.join(",") + p + i + u, o; } -var jB = ` +var XB = ` struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32}; struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32}; @@ -26940,11 +26943,11 @@ var oae = ` return abs(val) == uniforms.INFINITY; } `; -function cm(r, t8 = "") { - let e = r.length, o = t8 !== "" ? `get${t8.charAt(0).toUpperCase() + t8.slice(1)}CoordsFromIndex` : "getCoordsFromIndex", n = t8 !== "" ? `${t8.charAt(0).toLowerCase() + t8.slice(1)}ShapeStrides` : "outShapeStrides"; +function um(r15, t8 = "") { + let e = r15.length, o = t8 !== "" ? `get${t8.charAt(0).toUpperCase() + t8.slice(1)}CoordsFromIndex` : "getCoordsFromIndex", n = t8 !== "" ? `${t8.charAt(0).toLowerCase() + t8.slice(1)}ShapeStrides` : "outShapeStrides"; if (e <= 1) return `fn ${o}(index : i32) -> i32 { return index; }`; - let s = y.computeStrides(r), a = ht(e), i = []; + let s = y.computeStrides(r15), a = ft(e), i = []; for (let u = 0; u < e; u++) i.push(`d${u}`); if (s.length === 1) @@ -26954,7 +26957,7 @@ function cm(r, t8 = "") { }`; let p; return p = "var index2 = index;" + s.map((u, c) => { - let l = `let ${i[c]} = index2 / uniforms.${n}.${Mo(c)}`, m = c === s.length - 1 ? `let ${i[c + 1]} = index2 - ${i[c]} * uniforms.${n}.${Mo(c)}` : `index2 = index2 - ${i[c]} * uniforms.${n}.${Mo(c)}`; + let l = `let ${i[c]} = index2 / uniforms.${n}.${Oo(c)}`, m = c === s.length - 1 ? `let ${i[c + 1]} = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}` : `index2 = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`; return `${l}; ${m};`; }).join(""), ` fn ${o}(index : i32) -> ${a} { @@ -26963,8 +26966,8 @@ function cm(r, t8 = "") { } `; } -function nae(r, t8) { - let e = r.name, o = r.shape.length, n = ht(o), s = "get" + e.charAt(0).toUpperCase() + e.slice(1), a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = a.map((c) => `${c} : i32`).join(", "); +function nae(r15, t8) { + let e = r15.name, o = r15.shape.length, n = ft(o), s = "get" + e.charAt(0).toUpperCase() + e.slice(1), a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = a.map((c) => `${c} : i32`).join(", "); if (o < 1) return ` fn ${s}() -> ${Ae(t8)} { @@ -26979,9 +26982,9 @@ function nae(r, t8) { } `; } -function sae(r, t8, e, o) { - let n = r.name, s = n.charAt(0).toUpperCase() + n.slice(1), a = "get" + s + "ByOutput", i = r.shape.length, p = t8.length, u = ht(p); - if (y.arraysEqual(r.shape, t8) && o) +function sae(r15, t8, e, o) { + let n = r15.name, s = n.charAt(0).toUpperCase() + n.slice(1), a = "get" + s + "ByOutput", i = r15.shape.length, p = t8.length, u = ft(p); + if (y.arraysEqual(r15.shape, t8) && o) return ` fn ${a}Index(globalIndex : i32) -> ${Ae(e)} { return ${Ae(e)}(${n}[globalIndex]); @@ -26991,7 +26994,7 @@ function sae(r, t8, e, o) { return ${Ae(e)}(${n}[${p > 1 ? "getOutputIndexFromCoords(coords)" : "coords"}${e === 1 ? "" : ` / ${e}`}]); } `; - let c = w.getBroadcastDims(r.shape, t8), l = p - i, m = ""; + let c = w.getBroadcastDims(r15.shape, t8), l = p - i, m = ""; if (i === 0) return ` fn ${a}Index(globalIndex : i32) -> ${Ae(e)}{ @@ -27002,13 +27005,13 @@ function sae(r, t8, e, o) { return get${s}(); } `; - p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((g) => `coords.${Mo(g + l)} = 0;`).join(` + p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((g) => `coords.${Oo(g + l)} = 0;`).join(` `); let d = ""; if (p < 2 && i > 0) d = "coords"; else if (p > 1) { - let g = ht(i), x = r.shape.map((b, C) => `coords.${Mo(C + l)}`).join(", "); + let g = ft(i), x = r15.shape.map((b, C) => `coords.${Oo(C + l)}`).join(", "); d = `${g}(${x})`; } else d = "coords"; @@ -27027,16 +27030,16 @@ function sae(r, t8, e, o) { } `; } -function aae(r, t8, e, o) { - let n = nae(r, e); - return r.shape.length <= t8.length && (n += sae(r, t8, e, o)), n; +function aae(r15, t8, e, o) { + let n = nae(r15, e); + return r15.shape.length <= t8.length && (n += sae(r15, t8, e, o)), n; } -function iae(r, t8) { - let { x: e, y: o = [], z: n = [] } = t8, s = r.length, a = e.length + o.length + n.length; +function iae(r15, t8) { + let { x: e, y: o = [], z: n = [] } = t8, s = r15.length, a = e.length + o.length + n.length; if (a !== s) return ""; if (e.length === s) - return `fn getOutputCoords() -> ${ht(s)}{ + return `fn getOutputCoords() -> ${ft(s)}{ let globalIndex = getGlobalIndex(); return getCoordsFromIndex(globalIndex); } @@ -27048,7 +27051,7 @@ function iae(r, t8) { if (d.length === 1) i += `let d${d[0]} = i32(globalId[${m}]);`; else { - let f = KB(d, "uniforms.outShape"); + let f = qB(d, "uniforms.outShape"); i += `var index${m} = i32(globalId[${m}]);`; for (let h = 0; h < f.length; h++) i += `let d${d[h]} = index${m} / ${f[h]};`, h === f.length - 1 ? i += `let d${d[h + 1]} = index${m} - d${d[h]} * ${f[h]};` : i += `index${m} = index${m} - d${d[h]} * ${f[h]};`; @@ -27057,14 +27060,14 @@ function iae(r, t8) { let u = []; for (let m = 0; m < a; m++) u.push(`d${m}`); - let c = ht(a), l = `fn getOutputCoords() -> ${c} { + let c = ft(a), l = `fn getOutputCoords() -> ${c} { ${i} `; return u.length === 0 ? l += `return ${c}(0); }` : l += `return ${c}(${u.join(",")}); }`, l; } -function uae(r) { +function uae(r15) { let t8 = ""; - switch (r) { + switch (r15) { case 0: case 1: t8 += ` @@ -27119,23 +27122,23 @@ function uae(r) { `; break; default: - y.assert(false, () => `Unsupported ${r}D shape`); + y.assert(false, () => `Unsupported ${r15}D shape`); break; } return t8; } -function ZB(r) { - return r.dispatch[1] === 1 && r.dispatch[2] === 1; +function JB(r15) { + return r15.dispatch[1] === 1 && r15.dispatch[2] === 1; } -function Nu(r, t8 = 1) { - if (r === "float32") +function Cu(r15, t8 = 1) { + if (r15 === "float32") return Ae(t8, "f32"); - if (r === "int32" || r === "bool") + if (r15 === "int32" || r15 === "bool") return Ae(t8, "i32"); - throw new Error(`type ${r} is not supported.`); + throw new Error(`type ${r15} is not supported.`); } -function pae(r, t8, e) { - let o = r.length, n = Nu(t8, e), s = `fn setOutputAtIndex(flatIndex : i32, value : ${Ae(e)}) { +function pae(r15, t8, e) { + let o = r15.length, n = Cu(t8, e), s = `fn setOutputAtIndex(flatIndex : i32, value : ${Ae(e)}) { result[flatIndex] = ${n}(value); } @@ -27144,7 +27147,7 @@ function pae(r, t8, e) { } `; if (o >= 2) { - let a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = ht(o); + let a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = ft(o); s += ` fn setOutputAtCoords(${a.map((p) => `${p} : i32`).join(", ")}, value : ${Ae(e)}) { let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); @@ -27158,87 +27161,87 @@ function pae(r, t8, e) { } return s; } -function cae(r) { +function cae(r15) { let t8 = /(\w+)\s*:\s*vec(5|6)/g; - r = r.replace(t8, (o) => "@align(16) " + o); + r15 = r15.replace(t8, (o) => "@align(16) " + o); let e = /vec(5|6)\s*,\s*(\w+)/g; - return r = r.replace(e, (o, n, s) => `vec${n}, @align(16) ${s}`), r; + return r15 = r15.replace(e, (o, n, s) => `vec${n}, @align(16) ${s}`), r15; } -function XB(r) { - return !(r.dispatchLayout.hasOwnProperty("y") && r.dispatchLayout.y.length !== 0 || r.dispatchLayout.hasOwnProperty("z") && r.dispatchLayout.z.length !== 0); +function YB(r15) { + return !(r15.dispatchLayout.hasOwnProperty("y") && r15.dispatchLayout.y.length !== 0 || r15.dispatchLayout.hasOwnProperty("z") && r15.dispatchLayout.z.length !== 0); } -var Yv = {}; -qe(Yv, { GPUBytesPerElement: () => jg, MatMulProgramType: () => Lo, assertNotComplex: () => fm, computeDispatch: () => H, computeWorkPerThreadForConv2d: () => mm, computeWorkgroupInfoForMatMul: () => Xv, computeWorkgroupSizeForConv2d: () => lm, flatDispatchLayout: () => X, isWebGPUSupported: () => dm, tilesFitEvenlyIntoShape: () => mae }); -var Ap = (r) => { +var Qv = {}; +qe(Qv, { GPUBytesPerElement: () => qg, MatMulProgramType: () => Mo, assertNotComplex: () => mm, computeDispatch: () => H, computeWorkPerThreadForConv2d: () => cm, computeWorkgroupInfoForMatMul: () => Yv, computeWorkgroupSizeForConv2d: () => pm, flatDispatchLayout: () => X, isWebGPUSupported: () => lm, tilesFitEvenlyIntoShape: () => mae }); +var kp = (r15) => { let t8 = 1; - for (let e = 0; e < r.length; e++) - t8 *= r[e]; + for (let e = 0; e < r15.length; e++) + t8 *= r15[e]; return t8; }; -function mae(r, t8) { - if (r.length !== t8.length) - throw new Error(`Cannot compute whether rank ${r.length} tiles fit evenly into rank ${t8.length} shape - ranks must match.`); - return t8.every((e, o) => e % r[o] === 0); +function mae(r15, t8) { + if (r15.length !== t8.length) + throw new Error(`Cannot compute whether rank ${r15.length} tiles fit evenly into rank ${t8.length} shape - ranks must match.`); + return t8.every((e, o) => e % r15[o] === 0); } -function H(r, t8, e = [1, 1, 1], o = [1, 1, 1]) { - let [n, s, a] = [Math.ceil(Ap(r.x.map((i) => t8[i])) / (e[0] * o[0])), r.y ? Math.ceil(Ap(r.y.map((i) => t8[i])) / (e[1] * o[1])) : 1, r.z ? Math.ceil(Ap(r.z.map((i) => t8[i])) / (e[2] * o[2])) : 1]; +function H(r15, t8, e = [1, 1, 1], o = [1, 1, 1]) { + let [n, s, a] = [Math.ceil(kp(r15.x.map((i) => t8[i])) / (e[0] * o[0])), r15.y ? Math.ceil(kp(r15.y.map((i) => t8[i])) / (e[1] * o[1])) : 1, r15.z ? Math.ceil(kp(r15.z.map((i) => t8[i])) / (e[2] * o[2])) : 1]; return [n, s, a]; } -function Xv(r, t8, e, o = false) { +function Yv(r15, t8, e, o = false) { let n = [8, 8, 1], s = [4, 4, 1]; - return o || (r <= 8 && (s[1] = 1), t8 <= 16 && e <= 16 && (n[0] = 4)), { workgroupSize: n, elementsPerThread: s }; + return o || (r15 <= 8 && (s[1] = 1), t8 <= 16 && e <= 16 && (n[0] = 4)), { workgroupSize: n, elementsPerThread: s }; } -function lm(r, t8, e = false) { +function pm(r15, t8, e = false) { if (e) return [8, 8, 1]; - let o = Ap(r.x.map((s) => t8[s])), n = Ap(r.y.map((s) => t8[s])); + let o = kp(r15.x.map((s) => t8[s])), n = kp(r15.y.map((s) => t8[s])); return o <= 4 ? [4, 16, 1] : n <= 4 ? [16, 4, 1] : [16, 16, 1]; } -function mm(r, t8, e = false) { +function cm(r15, t8, e = false) { if (e) return [4, 4, 1]; - let o = Ap(r.x.map((s) => t8[s])), n = Ap(r.y.map((s) => t8[s])); + let o = kp(r15.x.map((s) => t8[s])), n = kp(r15.y.map((s) => t8[s])); return o <= 4 ? [1, 2, 1] : n <= 4 ? [2, 1, 1] : [2, 2, 1]; } -function X(r) { - return { x: r.map((t8, e) => e) }; +function X(r15) { + return { x: r15.map((t8, e) => e) }; } -function jg(r) { - if (r === "float32" || r === "int32" || r === "bool" || r === "string") +function qg(r15) { + if (r15 === "float32" || r15 === "int32" || r15 === "bool" || r15 === "string") return 4; - if (r === "complex64") + if (r15 === "complex64") return 8; - throw new Error(`Unknown dtype ${r}`); + throw new Error(`Unknown dtype ${r15}`); } -function dm() { - return (typeof window != "undefined" || typeof WorkerGlobalScope != "undefined") && !!navigator.gpu; +function lm() { + return !!(globalThis && globalThis.navigator && globalThis.navigator.gpu); } -function fm(r, t8) { - Array.isArray(r) || (r = [r]), r.forEach((e) => { +function mm(r15, t8) { + Array.isArray(r15) || (r15 = [r15]), r15.forEach((e) => { e != null && y.assert(e.dtype !== "complex64", () => `${t8} does not support complex64 tensors in the WebGPU backend.`); }); } -var Lo; -(function(r) { - r[r.MatMulReduceProgram = 0] = "MatMulReduceProgram", r[r.MatMulSplitKProgram = 1] = "MatMulSplitKProgram", r[r.MatMulSmallOutputSizeProgram = 2] = "MatMulSmallOutputSizeProgram", r[r.MatMulPackedProgram = 3] = "MatMulPackedProgram", r[r.MatMulMax = 4] = "MatMulMax"; -})(Lo || (Lo = {})); +var Mo; +(function(r15) { + r15[r15.MatMulReduceProgram = 0] = "MatMulReduceProgram", r15[r15.MatMulSplitKProgram = 1] = "MatMulSplitKProgram", r15[r15.MatMulSmallOutputSizeProgram = 2] = "MatMulSmallOutputSizeProgram", r15[r15.MatMulPackedProgram = 3] = "MatMulPackedProgram", r15[r15.MatMulMax = 4] = "MatMulMax"; +})(Mo || (Mo = {})); var dae = A().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"); -var fae = (r, t8) => { - let e = r.limits.maxComputeWorkgroupsPerDimension, o = t8.dispatchLayout, n = t8.dispatch; +var fae = (r15, t8) => { + let e = r15.limits.maxComputeWorkgroupsPerDimension, o = t8.dispatchLayout, n = t8.dispatch; if (n.every((a) => a <= e)) return n; y.assert(n[0] > e && o.y === void 0 && o.z === void 0, () => "Dispatch size exceeds WebGPU limits in Y or Z dimension."); let s = Math.ceil(Math.sqrt(n[0])); return s > e ? (s = Math.ceil(Math.cbrt(n[0])), y.assert(s <= e, () => "Total dispatch size exceeds WebGPU maximum."), [s, s, s]) : [s, s, 1]; }; -var Tu = class extends ao { +var Kc = class r14 extends so { nextDataId() { - return Tu.nextDataId++; + return r14.nextDataId++; } constructor(t8, e) { - if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.dispatchCountInPass = 0, this.disposed = false, this.downloadWaitMs = 0, this.tensorDataPendingDisposal = [], this.queryResolveBuffer = null, this.querySet = null, this.querySetCount = 2, this.stagingPendingDisposal = [], this.uniformPendingDisposal = [], this.uploadWaitMs = 0, this.hasReadSyncWarned = false, this.hasTimestampQueryWarned = false, !dm()) + if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.dispatchCountInPass = 0, this.disposed = false, this.downloadWaitMs = 0, this.tensorDataPendingDisposal = [], this.queryResolveBuffer = null, this.querySet = null, this.querySetCount = 2, this.stagingPendingDisposal = [], this.uniformPendingDisposal = [], this.uploadWaitMs = 0, this.hasReadSyncWarned = false, this.hasTimestampQueryWarned = false, !lm()) throw new Error("WebGPU is not supported on this device"); - this.pipelineCache = {}, this.device = t8, this.queue = t8.queue, this.commandEncoder = null, this.computePassEncoder = null, this.adapterInfo = new Hg(e), this.supportTimestampQuery = this.device.features.has("timestamp-query"), this.thresholdToIncreaseWorkgroups = this.adapterInfo.intelGPUGeneration >= 12 ? 16 : 8, this.bufferManager = new Kg(this.device), this.textureManager = new qg(this.device), this.tensorMap = new zo(this, pr()), A().getBool("WEBGPU_USE_PROFILE_TOOL") && (this.dummyCanvas = document.createElement("canvas"), this.dummyCanvas.width = 1, this.dummyCanvas.height = 1, this.dummyContext = this.dummyCanvas.getContext("webgpu"), this.dummyContext.configure({ device: t8, format: "bgra8unorm" }), document.body.appendChild(this.dummyCanvas)); + this.pipelineCache = {}, this.device = t8, this.queue = t8.queue, this.commandEncoder = null, this.computePassEncoder = null, this.adapterInfo = new Gg(e), this.supportTimestampQuery = this.device.features.has("timestamp-query"), this.thresholdToIncreaseWorkgroups = this.adapterInfo.intelGPUGeneration >= 12 ? 16 : 8, this.bufferManager = new Hg(this.device), this.textureManager = new Kg(this.device), this.tensorMap = new Bo(this, ur()), A().getBool("WEBGPU_USE_PROFILE_TOOL") && (this.dummyCanvas = document.createElement("canvas"), this.dummyCanvas.width = 1, this.dummyCanvas.height = 1, this.dummyContext = this.dummyCanvas.getContext("webgpu"), this.dummyContext.configure({ device: t8, format: "bgra8unorm" }), document.body.appendChild(this.dummyCanvas)); } floatPrecision() { return 32; @@ -27383,12 +27386,12 @@ var Tu = class extends ao { throw new Error("Cannot write to a complex64 dtype. "); let s = { id: this.nextDataId() }; this.tensorMap.set(s, { dtype: o, shape: e, values: null, refCount: 1, external: t8.zeroCopy }); - let a = this.tensorMap.get(s), i = jg(a.dtype) * y.sizeFromShape(a.shape); + let a = this.tensorMap.get(s), i = qg(a.dtype) * y.sizeFromShape(a.shape); if (t8.buffer.size < i) throw new Error(`GPUBuffer size(${t8.buffer.size}) is smaller than tensor size(${i})!`); if ((t8.buffer.usage & (GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC)) !== (GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC)) throw new Error("GPUBuffer.usage should include GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC!"); - return t8.zeroCopy !== true && (n = this.copyBuffer(n)), a.resource = n, pr().makeTensorFromDataId(s, e, o, this); + return t8.zeroCopy !== true && (n = this.copyBuffer(n)), a.resource = n, ur().makeTensorFromDataId(s, e, o, this); } readToGPU(t8) { let e = this.tensorMap.get(t8), { values: o, dtype: n, shape: s, resource: a } = e; @@ -27398,7 +27401,7 @@ var Tu = class extends ao { throw o != null ? new Error("Data is not on GPU but on CPU.") : new Error("There is no data on GPU or CPU."); let i = a, p = i.size, u = i.usage, c = this.bufferManager.acquireBuffer(p, u); this.ensureCommandEncoderReady(), this.endComputePassEncoder(), this.commandEncoder.copyBufferToBuffer(a, 0, c, 0, p), this.submitQueue(); - let l = this.makeTensorInfo(s, n), m = pr().makeTensorFromTensorInfo(l), d = this.tensorMap.get(l.dataId); + let l = this.makeTensorInfo(s, n), m = ur().makeTensorFromTensorInfo(l), d = this.tensorMap.get(l.dataId); return d.resource = c, { tensorRef: m, buffer: c }; } bufferSync(t8) { @@ -27413,7 +27416,7 @@ var Tu = class extends ao { return me(t8.shape, t8.dtype, e); } async time(t8) { - !this.supportTimestampQuery && !this.hasTimestampQueryWarned && (console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled."), this.hasTimestampQueryWarned = true); + !this.supportTimestampQuery && !this.hasTimestampQueryWarned && (console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --enable-dawn-features=allow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled."), this.hasTimestampQueryWarned = true); let e = this.activeTimers, o = [], n = false; this.programTimersStack == null ? (this.programTimersStack = o, n = true) : this.activeTimers.push(o), this.activeTimers = o, t8(); let s = y.flatten(this.activeTimers.map((u) => u.query)).filter((u) => u != null), a = y.flatten(this.activeTimers.map((u) => u.name)).filter((u) => u != null); @@ -27434,7 +27437,7 @@ var Tu = class extends ao { let e = this.tensorMap.get(t8); if (e.resource != null) return; - let o = jg(e.dtype) * y.sizeFromShape(e.shape), n, s = GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST; + let o = qg(e.dtype) * y.sizeFromShape(e.shape), n, s = GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST; if (e.values) { if (n = this.bufferManager.acquireBuffer(o, s, true), n.mapState === "unmapped") { let a = this.bufferManager.acquireBuffer(o, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC, true, false), i = a.getMappedRange(); @@ -27494,9 +27497,9 @@ var Tu = class extends ao { throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts."); return this.uploadToGPU(p.dataId), { dtype: this.tensorMap.get(p.dataId).dtype, shape: p.shape, name: t8.variableNames[u] }; }); - t8.shaderKey = QB(t8, a, s); + t8.shaderKey = ZB(t8, a, s); let i = A().getBool("WEBGPU_ENGINE_COMPILE_ONLY"); - return t8.shaderKey in this.pipelineCache || (this.pipelineCache[t8.shaderKey] = YB(this.device, t8, a, s, i)), t8.pipeline = this.pipelineCache[t8.shaderKey], i || this.recordAndSubmit(t8, s, e, n), s; + return t8.shaderKey in this.pipelineCache || (this.pipelineCache[t8.shaderKey] = QB(this.device, t8, a, s, i)), t8.pipeline = this.pipelineCache[t8.shaderKey], i || this.recordAndSubmit(t8, s, e, n), s; } recordAndSubmit(t8, e, o, n) { if (t8.pipeline instanceof Promise) @@ -27526,7 +27529,7 @@ var Tu = class extends ao { let u = this.device.createBindGroup({ layout: t8.pipeline.getBindGroupLayout(0), entries: p.map((m, d) => ({ binding: d, resource: m })) }), c = this.activeTimers != null; this.ensureCommandEncoderReady(); let l = {}; - c && this.supportTimestampQuery ? (this.endComputePassEncoder(), this.querySet == null && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: this.querySetCount })), l.timestampWrites = [{ querySet: this.querySet, queryIndex: 0, location: "beginning" }, { querySet: this.querySet, queryIndex: 1, location: "end" }], this.computePassEncoder = this.commandEncoder.beginComputePass(l)) : this.computePassEncoder || (this.computePassEncoder = this.commandEncoder.beginComputePass(l)), this.computePassEncoder.setPipeline(t8.pipeline), this.computePassEncoder.setBindGroup(0, u), this.computePassEncoder.dispatchWorkgroups(t8.dispatch[0], t8.dispatch[1], t8.dispatch[2]), this.dispatchCountInPass++, (c || A().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchCountInPass || t8.pixelsOpType === Ii.DRAW) && (this.endComputePassEncoder(), c ? this.activeTimers.push({ name: t8.constructor.name, query: this.getQueryTime() }) : this.submitQueue()); + c && this.supportTimestampQuery ? (this.endComputePassEncoder(), this.querySet == null && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: this.querySetCount })), l.timestampWrites = { querySet: this.querySet, beginningOfPassWriteIndex: 0, endOfPassWriteIndex: 1 }, this.computePassEncoder = this.commandEncoder.beginComputePass(l)) : this.computePassEncoder || (this.computePassEncoder = this.commandEncoder.beginComputePass(l)), this.computePassEncoder.setPipeline(t8.pipeline), this.computePassEncoder.setBindGroup(0, u), this.computePassEncoder.dispatchWorkgroups(t8.dispatch[0], t8.dispatch[1], t8.dispatch[2]), this.dispatchCountInPass++, (c || A().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchCountInPass || t8.pixelsOpType === Ci.DRAW) && (this.endComputePassEncoder(), c ? this.activeTimers.push({ name: t8.constructor.name, query: this.getQueryTime() }) : this.submitQueue()); } async getQueryTime() { if (!this.supportTimestampQuery) @@ -27547,18 +27550,18 @@ var Tu = class extends ao { this.disposed || (this.querySet != null && this.querySet.destroy(), this.bufferManager.dispose(), this.textureManager.dispose(), this.disposed = true); } }; -Tu.nextDataId = 0; -dm() && su("webgpu", async () => { - let r = { powerPreference: A().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, t8 = await navigator.gpu.requestAdapter(r), e = {}, o = []; +Kc.nextDataId = 0; +lm() && tu("webgpu", async () => { + let r15 = { powerPreference: A().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, t8 = await navigator.gpu.requestAdapter(r15), e = {}, o = []; t8.features.has("timestamp-query") && o.push("timestamp-query"), t8.features.has("bgra8unorm-storage") && o.push(["bgra8unorm-storage"]), e.requiredFeatures = o; let n = t8.limits; e.requiredLimits = { maxComputeWorkgroupStorageSize: n.maxComputeWorkgroupStorageSize, maxComputeWorkgroupsPerDimension: n.maxComputeWorkgroupsPerDimension, maxStorageBufferBindingSize: n.maxStorageBufferBindingSize, maxBufferSize: n.maxBufferSize, maxComputeWorkgroupSizeX: n.maxComputeWorkgroupSizeX, maxComputeInvocationsPerWorkgroup: n.maxComputeInvocationsPerWorkgroup }; let s = await t8.requestDevice(e), a = await t8.requestAdapterInfo(); - return new Tu(s, a); + return new Kc(s, a); }, 3); var fe; -(function(r) { - r[r.ADD = 0] = "ADD", r[r.ATAN2 = 1] = "ATAN2", r[r.COMPLEX_MULTIPLY_IMAG = 2] = "COMPLEX_MULTIPLY_IMAG", r[r.COMPLEX_MULTIPLY_REAL = 3] = "COMPLEX_MULTIPLY_REAL", r[r.DIV = 4] = "DIV", r[r.ELU_DER = 5] = "ELU_DER", r[r.EQUAL = 6] = "EQUAL", r[r.FLOOR_DIV = 7] = "FLOOR_DIV", r[r.GREATER = 8] = "GREATER", r[r.GREATER_EQUAL = 9] = "GREATER_EQUAL", r[r.LESS = 10] = "LESS", r[r.LESS_EQUAL = 11] = "LESS_EQUAL", r[r.LOGICAL_AND = 12] = "LOGICAL_AND", r[r.LOGICAL_OR = 13] = "LOGICAL_OR", r[r.MAX = 14] = "MAX", r[r.MIN = 15] = "MIN", r[r.MOD = 16] = "MOD", r[r.MUL = 17] = "MUL", r[r.NOT_EQUAL = 18] = "NOT_EQUAL", r[r.POW = 19] = "POW", r[r.PRELU = 20] = "PRELU", r[r.SQUARED_DIFFERENCE = 21] = "SQUARED_DIFFERENCE", r[r.SUB = 22] = "SUB"; +(function(r15) { + r15[r15.ADD = 0] = "ADD", r15[r15.ATAN2 = 1] = "ATAN2", r15[r15.COMPLEX_MULTIPLY_IMAG = 2] = "COMPLEX_MULTIPLY_IMAG", r15[r15.COMPLEX_MULTIPLY_REAL = 3] = "COMPLEX_MULTIPLY_REAL", r15[r15.DIV = 4] = "DIV", r15[r15.ELU_DER = 5] = "ELU_DER", r15[r15.EQUAL = 6] = "EQUAL", r15[r15.FLOOR_DIV = 7] = "FLOOR_DIV", r15[r15.GREATER = 8] = "GREATER", r15[r15.GREATER_EQUAL = 9] = "GREATER_EQUAL", r15[r15.LESS = 10] = "LESS", r15[r15.LESS_EQUAL = 11] = "LESS_EQUAL", r15[r15.LOGICAL_AND = 12] = "LOGICAL_AND", r15[r15.LOGICAL_OR = 13] = "LOGICAL_OR", r15[r15.MAX = 14] = "MAX", r15[r15.MIN = 15] = "MIN", r15[r15.MOD = 16] = "MOD", r15[r15.MUL = 17] = "MUL", r15[r15.NOT_EQUAL = 18] = "NOT_EQUAL", r15[r15.POW = 19] = "POW", r15[r15.PRELU = 20] = "PRELU", r15[r15.SQUARED_DIFFERENCE = 21] = "SQUARED_DIFFERENCE", r15[r15.SUB = 22] = "SUB"; })(fe || (fe = {})); var hae = "let resultTemp = a + b;"; var gae = "let resultTemp = atan2(a, b);"; @@ -27674,10 +27677,10 @@ var Vae = ` `; var Wae = "let resultTemp = (a - b) * (a - b);"; var Uae = "let resultTemp = a - b;"; -function Qc(r, t8) { +function qc(r15, t8) { let e; do { - switch (r) { + switch (r15) { case fe.ATAN2: e = gae; break; @@ -27717,7 +27720,7 @@ function Qc(r, t8) { } `; } while (false); - switch (r) { + switch (r15) { case fe.ADD: e = hae; break; @@ -27774,8 +27777,8 @@ function Qc(r, t8) { `; } var Z; -(function(r) { - r[r.ABS = 0] = "ABS", r[r.ACOS = 1] = "ACOS", r[r.ACOSH = 2] = "ACOSH", r[r.ASIN = 3] = "ASIN", r[r.ASINH = 4] = "ASINH", r[r.ATAN = 5] = "ATAN", r[r.ATANH = 6] = "ATANH", r[r.CEIL = 7] = "CEIL", r[r.COS = 8] = "COS", r[r.COSH = 9] = "COSH", r[r.ELU = 10] = "ELU", r[r.ERF = 11] = "ERF", r[r.EXP = 12] = "EXP", r[r.EXPM1 = 13] = "EXPM1", r[r.FLOOR = 14] = "FLOOR", r[r.IS_FINITE = 15] = "IS_FINITE", r[r.IS_INF = 16] = "IS_INF", r[r.IS_NAN = 17] = "IS_NAN", r[r.LINEAR = 18] = "LINEAR", r[r.LOG = 19] = "LOG", r[r.LOG1P = 20] = "LOG1P", r[r.LOGICAL_NOT = 21] = "LOGICAL_NOT", r[r.NEG = 22] = "NEG", r[r.RELU = 23] = "RELU", r[r.RELU6 = 24] = "RELU6", r[r.LEAKYRELU = 25] = "LEAKYRELU", r[r.RECIPROCAL = 26] = "RECIPROCAL", r[r.ROUND = 27] = "ROUND", r[r.RSQRT = 28] = "RSQRT", r[r.SELU = 29] = "SELU", r[r.SIGMOID = 30] = "SIGMOID", r[r.SIGN = 31] = "SIGN", r[r.SIN = 32] = "SIN", r[r.SINH = 33] = "SINH", r[r.SOFTPLUS = 34] = "SOFTPLUS", r[r.SQRT = 35] = "SQRT", r[r.SQUARE = 36] = "SQUARE", r[r.STEP = 37] = "STEP", r[r.TAN = 38] = "TAN", r[r.TANH = 39] = "TANH", r[r.TO_INT = 40] = "TO_INT"; +(function(r15) { + r15[r15.ABS = 0] = "ABS", r15[r15.ACOS = 1] = "ACOS", r15[r15.ACOSH = 2] = "ACOSH", r15[r15.ASIN = 3] = "ASIN", r15[r15.ASINH = 4] = "ASINH", r15[r15.ATAN = 5] = "ATAN", r15[r15.ATANH = 6] = "ATANH", r15[r15.CEIL = 7] = "CEIL", r15[r15.COS = 8] = "COS", r15[r15.COSH = 9] = "COSH", r15[r15.ELU = 10] = "ELU", r15[r15.ERF = 11] = "ERF", r15[r15.EXP = 12] = "EXP", r15[r15.EXPM1 = 13] = "EXPM1", r15[r15.FLOOR = 14] = "FLOOR", r15[r15.IS_FINITE = 15] = "IS_FINITE", r15[r15.IS_INF = 16] = "IS_INF", r15[r15.IS_NAN = 17] = "IS_NAN", r15[r15.LINEAR = 18] = "LINEAR", r15[r15.LOG = 19] = "LOG", r15[r15.LOG1P = 20] = "LOG1P", r15[r15.LOGICAL_NOT = 21] = "LOGICAL_NOT", r15[r15.NEG = 22] = "NEG", r15[r15.RELU = 23] = "RELU", r15[r15.RELU6 = 24] = "RELU6", r15[r15.LEAKYRELU = 25] = "LEAKYRELU", r15[r15.RECIPROCAL = 26] = "RECIPROCAL", r15[r15.ROUND = 27] = "ROUND", r15[r15.RSQRT = 28] = "RSQRT", r15[r15.SELU = 29] = "SELU", r15[r15.SIGMOID = 30] = "SIGMOID", r15[r15.SIGN = 31] = "SIGN", r15[r15.SIN = 32] = "SIN", r15[r15.SINH = 33] = "SINH", r15[r15.SOFTPLUS = 34] = "SOFTPLUS", r15[r15.SQRT = 35] = "SQRT", r15[r15.SQUARE = 36] = "SQUARE", r15[r15.STEP = 37] = "STEP", r15[r15.TAN = 38] = "TAN", r15[r15.TANH = 39] = "TANH", r15[r15.TO_INT = 40] = "TO_INT"; })(Z || (Z = {})); var Gae = "return abs(a);"; var Hae = ` @@ -27928,8 +27931,8 @@ var Aie = ` return sign(a) * (1.0 - e2x) / (1.0 + e2x); `; var Fie = "return f32(i32((a)));"; -function vi(r, t8) { - switch (r) { +function wi(r15, t8) { + switch (r15) { case Z.ABS: return Gae; case Z.ACOS: @@ -28013,29 +28016,29 @@ function vi(r, t8) { case Z.TO_INT: return Fie; default: - throw new Error(`BinaryType ${r} is not implemented!`); + throw new Error(`BinaryType ${r15} is not implemented!`); } } -function fr(r, t8 = false, e = false, o = 3) { - if (r === null) +function dr(r15, t8 = false, e = false, o = 3) { + if (r15 === null) return ""; let n = ""; - if (r === "linear") - n = vi(Z.LINEAR); - else if (r === "relu") - n = vi(Z.RELU, e); - else if (r === "elu") - n = vi(Z.ELU, e); - else if (r === "relu6") - n = vi(Z.RELU6, e); - else if (r === "prelu") - n = Qc(fe.PRELU, e); - else if (r === "sigmoid") - n = vi(Z.SIGMOID, e); - else if (r === "leakyrelu") - n = vi(Z.LEAKYRELU, e); + if (r15 === "linear") + n = wi(Z.LINEAR); + else if (r15 === "relu") + n = wi(Z.RELU, e); + else if (r15 === "elu") + n = wi(Z.ELU, e); + else if (r15 === "relu6") + n = wi(Z.RELU6, e); + else if (r15 === "prelu") + n = qc(fe.PRELU, e); + else if (r15 === "sigmoid") + n = wi(Z.SIGMOID, e); + else if (r15 === "leakyrelu") + n = wi(Z.LEAKYRELU, e); else - throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`); + throw new Error(`Activation ${r15} has not been implemented for the WebGPU backend.`); let a = Ae(e ? 4 : 1), i = ""; return t8 ? i = ` fn activation(a : ${a}, coords : vec${o}) -> ${a} { @@ -28046,23 +28049,23 @@ function fr(r, t8 = false, e = false, o = 3) { ${n} }`, i; } -function Zr(r, t8) { +function Qr(r15, t8) { return ` - ${r ? "value = value + getBiasByOutputCoords(coords);" : ""} + ${r15 ? "value = value + getBiasByOutputCoords(coords);" : ""} ${t8 ? "value = activation(value, coords);" : ""} `; } -function Qv(r, t8, e = false, o = false, n = false, s = 1) { - y.assert(r && s === 1 || !r, () => `transposeA ${r} is not compatible with component size ${s}`); +function Zv(r15, t8, e = false, o = false, n = false, s = 1) { + y.assert(r15 && s === 1 || !r15, () => `transposeA ${r15} is not compatible with component size ${s}`); let a = ` - ${r ? "value = getA(batch, col, row);" : "value = getA(batch, row, col);"} + ${r15 ? "value = getA(batch, col, row);" : "value = getA(batch, row, col);"} `, i = t8 ? "value = getB(batch, col, row);" : "value = getB(batch, row, col);"; return ` fn mm_readA(batch: i32, row: i32, col: i32) -> ${Ae(s)} { var value = ${Ae(s)}(0.0); ${e && n ? a : ` - ${r ? "if(row < uniforms.dimAOuter && col < uniforms.dimInner)" : "if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"} + ${r15 ? "if(row < uniforms.dimAOuter && col < uniforms.dimInner)" : "if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"} { ${a} } @@ -28077,21 +28080,21 @@ function Qv(r, t8, e = false, o = false, n = false, s = 1) { } `; } -function hm(r, t8, e, o, n = false, s = false, a = false, i = 1) { +function dm(r15, t8, e, o, n = false, s = false, a = false, i = 1) { return ` - ${Qv(e, o, n, s, a, i)} + ${Zv(e, o, n, s, a, i)} fn mm_write(batch: i32, row: i32, col: i32, valueIn: ${Ae(i)}) { ${n && s ? "" : "if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} { var value = valueIn; let coords = vec3(batch, row, col); - ${Zr(r, t8)} + ${Qr(r15, t8)} setOutputAtCoords(coords[0], coords[1], coords[2], value); } } `; } -var Pie = (r, t8) => r ? ` +var Pie = (r15, t8) => r15 ? ` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol * ${t8}); @@ -28100,8 +28103,8 @@ var Pie = (r, t8) => r ? ` globalRow + innerRow, kStart + inputCol * ${t8}); `; -var Oie = (r, t8, e, o) => { - if (r) +var Oie = (r15, t8, e, o) => { + if (r15) return ` for (var k = 0; k < ${o}; k++) { let BCached0 = mm_Bsub[k][tileCol]; @@ -28124,13 +28127,13 @@ var Oie = (r, t8, e, o) => { }`; } }; -function Fp(r, t8, e = false, o = 32, n = false, s = 32, a = false) { - let i = t8[1] * r[1], p = t8[0] * r[0], u = e ? i : o, c = e ? o : i, l = u / t8[0], m = o / t8[1], d = r[1], f = r[0]; - return y.assert((e && l === 4 && r[1] === 4 || !e && (l === 3 || l === 4)) && u % t8[0] === 0 && o % t8[1] === 0 && r[0] === 4, () => `If transposeA ${e} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4. +function Np(r15, t8, e = false, o = 32, n = false, s = 32, a = false) { + let i = t8[1] * r15[1], p = t8[0] * r15[0], u = e ? i : o, c = e ? o : i, l = u / t8[0], m = o / t8[1], d = r15[1], f = r15[0]; + return y.assert((e && l === 4 && r15[1] === 4 || !e && (l === 3 || l === 4)) && u % t8[0] === 0 && o % t8[1] === 0 && r15[0] === 4, () => `If transposeA ${e} is true, innerElementSize ${l} and workPerThread[1] ${r15[1]} must be 4. Otherwise, innerElementSize ${l} must be 3 or 4. - tileAWidth ${u} must be divisible by workgroupSize[0]${t8[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${t8[1]}. colPerThread ${r[0]} must be 4.`), ` + tileAWidth ${u} must be divisible by workgroupSize[0]${t8[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${t8[1]}. colPerThread ${r15[0]} must be 4.`), ` var mm_Asub : array, ${u / l}>, ${c}>; - var mm_Bsub : array, ${p / r[0]}>, ${o}>; + var mm_Bsub : array, ${p / r15[0]}>, ${o}>; ${G()} { let localRow = i32(localId.y); @@ -28178,7 +28181,7 @@ function Fp(r, t8, e = false, o = 32, n = false, s = 32, a = false) { } }`; } -var JB = (r) => r ? ` +var ez = (r15) => r15 ? ` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol); @@ -28187,11 +28190,11 @@ var JB = (r) => r ? ` globalRowStart + inputRow, kStart + inputCol); `; -var Mie = (r) => r ? "let ACached = mm_Asub[k][tileRow + innerRow];" : "let ACached = mm_Asub[tileRow + innerRow][k];"; -function Pp(r, t8, e = false, o = 32, n = false, s = 32, a = false, i = false) { - let p = r[1] * t8[1], u = r[0] * t8[0], c = e ? p : o, l = e ? o : p; +var Mie = (r15) => r15 ? "let ACached = mm_Asub[k][tileRow + innerRow];" : "let ACached = mm_Asub[tileRow + innerRow][k];"; +function Tp(r15, t8, e = false, o = 32, n = false, s = 32, a = false, i = false) { + let p = r15[1] * t8[1], u = r15[0] * t8[0], c = e ? p : o, l = e ? o : p; y.assert(l % t8[1] === 0 && c % t8[0] === 0 && o % t8[1] === 0, () => `tileAHight ${l} must be divisible by workgroupSize[1]${t8[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${t8[0]}, tileInner ${o} must be divisible by workgroupSize[1]${t8[1]}`); - let m = l / t8[1], d = c / t8[0], f = o / t8[1], h = r[1], g = r[0], x = a ? ` + let m = l / t8[1], d = c / t8[0], f = o / t8[1], h = r15[1], g = r15[0], x = a ? ` let localRow = i32(localId.y); let localCol = i32(localId.x); let globalRowStart = i32(workgroupId.y) * ${p}; @@ -28202,7 +28205,7 @@ function Pp(r, t8, e = false, o = 32, n = false, s = 32, a = false, i = false) { // Load one tile of A into local memory. for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${t8[1]}) { for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${t8[0]}) { - ${JB(e)} + ${ez(e)} } } // Load one tile of B into local memory. @@ -28257,7 +28260,7 @@ function Pp(r, t8, e = false, o = 32, n = false, s = 32, a = false, i = false) { for (var innerCol = 0; innerCol < ${d}; innerCol++) { let inputRow = tileRowA + innerRow; let inputCol = tileColA + innerCol; - ${JB(e)} + ${ez(e)} } } @@ -28323,7 +28326,7 @@ function Pp(r, t8, e = false, o = 32, n = false, s = 32, a = false, i = false) { } `; } -var Lie = (r) => r ? ` +var Lie = (r15) => r15 ? ` mm_readA(batchA, colA, globalRow), mm_readA(batchA, colA + 1, globalRow), mm_readA(batchA, colA + 2, globalRow), @@ -28334,11 +28337,11 @@ var Lie = (r) => r ? ` mm_readA(batchA, globalRow, colA + 2), mm_readA(batchA, globalRow, colA + 3) `; -function Bie(r, t8 = false) { - y.assert(r[1] === 1 && r[2] === 1, () => `A linear work group size is required. But got ${r}.`); - let e = r[0] * 4; +function Bie(r15, t8 = false) { + y.assert(r15[1] === 1 && r15[2] === 1, () => `A linear work group size is required. But got ${r15}.`); + let e = r15[0] * 4; return ` - var mm_Asub : array, ${r[0]}>; + var mm_Asub : array, ${r15[0]}>; ${G()} { let tileCol = i32(localId.x); @@ -28378,14 +28381,14 @@ function Bie(r, t8 = false) { } `; } -var Xg = class { +var jg = class { constructor(t8, e, o = false, n = false, s = null, a = null, i = null, p = false) { this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e, this.dispatchLayout = { x: [2], y: [1], z: [0] }; let u = o ? t8[1] : t8[2]; if (this.isVec4 = (u % 4 === 0 && !o || e[1] % 4 === 0 && o) && e[2] % 4 === 0 && !n, this.outputComponent = this.isVec4 ? 4 : 1, this.isVectorA = e[1] === 1 && !o, !this.isVec4 && this.isVectorA) this.elementsPerThread = [1, 1, 1], this.workgroupSize = [32, 1, 1]; else { - let m = Xv(e[1], u, e[2], o); + let m = Yv(e[1], u, e[2], o); this.workgroupSize = m.workgroupSize, this.elementsPerThread = m.elementsPerThread; } this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread); @@ -28400,15 +28403,15 @@ var Xg = class { } getUserCode() { return ` - ${fr(this.activation, this.hasPreluActivationWeights, this.isVec4)} - ${hm(this.addBias, this.activation, false, this.transposeB, this.fitAOuter, this.fitBOuter, this.fitInner, this.isVec4 ? 4 : 1)} - ${this.isVec4 ? Fp(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, true) : this.isVectorA ? Bie(this.workgroupSize, this.transposeA) : Pp(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, this.sequentialAccessByThreads, true)} + ${dr(this.activation, this.hasPreluActivationWeights, this.isVec4)} + ${dm(this.addBias, this.activation, false, this.transposeB, this.fitAOuter, this.fitBOuter, this.fitInner, this.isVec4 ? 4 : 1)} + ${this.isVec4 ? Np(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, true) : this.isVectorA ? Bie(this.workgroupSize, this.transposeA) : Tp(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, this.sequentialAccessByThreads, true)} `; } }; -function zie(r) { +function zie(r15) { return ` - var sumValues : array; + var sumValues : array; ${G()} { let coords = getOutputCoords(); let batch = coords[0]; @@ -28418,7 +28421,7 @@ function zie(r) { let col = coords[2]; var sum = 0.0; let Length = uniforms.dimInner; - for (var k = i32(localId.x); k < Length; k = k + ${r}) { + for (var k = i32(localId.x); k < Length; k = k + ${r15}) { let dataA = mm_readA(batchA, row, k); let dataB = mm_readB(batchB, k, col); sum = sum + dataA * dataB; @@ -28426,7 +28429,7 @@ function zie(r) { sumValues[localId.x] = sum; workgroupBarrier(); - for(var currentSize = ${r / 2}u; currentSize > 1u; + for(var currentSize = ${r15 / 2}u; currentSize > 1u; currentSize = currentSize / 2u) { if (localId.x < currentSize) { @@ -28442,7 +28445,7 @@ function zie(r) { } `; } -var Yg = class { +var Xg = class { constructor(t8, e = false, o = false, n = null, s = null, a = null) { this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [256, 1, 1], this.outputShape = t8, this.dispatchLayout = { x: [], y: [1, 2], z: [0] }, this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize); let i = n != null, p = a != null; @@ -28450,14 +28453,14 @@ var Yg = class { } getUserCode() { return ` - ${fr(this.activation, this.hasPreluActivationWeights)} - ${hm(this.addBias, this.activation, this.transposeA, this.transposeB)} + ${dr(this.activation, this.hasPreluActivationWeights)} + ${dm(this.addBias, this.activation, this.transposeA, this.transposeB)} ${zie(this.workgroupSize[0])} `; } }; -function Vie(r) { - let t8 = r[1], e = r[0], o = t8 > e ? t8 : e; +function Vie(r15) { + let t8 = r15[1], e = r15[0], o = t8 > e ? t8 : e; return ` var mm_Asub : array, ${t8}>; var mm_Bsub : array, ${o}>; @@ -28512,7 +28515,7 @@ function Vie(r) { } `; } -var Qg = class { +var Yg = class { constructor(t8, e, o, n = false, s = false, a = null, i = null, p = null) { this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [16, 8, 1], this.outputShape = o, this.dispatchLayout = { x: [2], y: [1], z: [0] }, this.dispatch = [Math.ceil(o[2] / this.workgroupSize[0]), Math.ceil(o[1] / this.workgroupSize[1]), o[0]]; let u = a != null; @@ -28522,13 +28525,13 @@ var Qg = class { } getUserCode() { return ` - ${fr(this.activation, this.hasPreluActivationWeights)} - ${hm(this.addBias, this.activation, this.transposeA, this.transposeB)} + ${dr(this.activation, this.hasPreluActivationWeights)} + ${dm(this.addBias, this.activation, this.transposeA, this.transposeB)} ${Vie(this.workgroupSize)} `; } }; -var Zg = class { +var Qg = class { constructor(t8, e, o = false, n = false) { this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [8, 8, 1], this.atomic = true, this.splitedDimInner = 128, y.assert(t8[0] === 1, () => "MatMulSplitKProgram only supports batch = 1."), this.outputShape = t8, this.dispatchLayout = { x: [2], y: [1], z: [0, 3] }; let s = (o && this.outputShape[1] % 4 === 0 || !o && e % 4 === 0) && this.outputShape[2] % 4 === 0; @@ -28537,7 +28540,7 @@ var Zg = class { getUserCode() { let t8 = this.outputComponent; return ` - ${Qv(false, this.transposeB, false, false, false, t8)} + ${Zv(false, this.transposeB, false, false, false, t8)} fn mm_write(batch: i32, row : i32, col : i32, value : ${Ae(t8)}) { if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { let coords = vec3(batch, row, col); @@ -28545,33 +28548,33 @@ var Zg = class { // The problem is that we should initialize output to zero before using. // Otherwise, the original value will be added to the result. for (var i = 0; i < ${t8}; i = i + 1) { - ${Qr("&result[flatIndex + i]", `${t8 > 1 ? "value[i]" : "value"}`, "float32")} + ${Yr("&result[flatIndex + i]", `${t8 > 1 ? "value[i]" : "value"}`, "float32")} } } } - ${t8 === 4 ? Fp(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner) : Pp(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner)} + ${t8 === 4 ? Np(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner) : Tp(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner)} `; } }; -var Jg = class { +var Zg = class { constructor(t8, e = null, o = null, n = null) { this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = e != null, this.hasPreluActivationWeights = n != null, this.activation = o, this.addBias && this.variableNames.push("bias"), this.hasPreluActivationWeights && this.variableNames.push("preluActivationWeights"), this.shaderKey = `biasActivation_${o}`; } getUserCode() { return ` - ${fr(this.activation, this.hasPreluActivationWeights)} + ${dr(this.activation, this.hasPreluActivationWeights)} ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); - ${Zr(this.addBias, this.activation)} + ${Qr(this.addBias, this.activation)} setOutputAtIndex(index, value); } } `; } }; -var ex = class { +var Jg = class { constructor(t8) { this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "fill"; } @@ -28585,38 +28588,38 @@ var ex = class { `; } }; -function kt(r) { - let { backend: t8, attrs: e } = r, { shape: o, value: n } = e, { dtype: s } = e; +function vt(r15) { + let { backend: t8, attrs: e } = r15, { shape: o, value: n } = e, { dtype: s } = e; if (s = s || y.inferDtype(n), s === "string") { let a = y.getArrayFromDType(s, y.sizeFromShape(o)); return a.fill(n), t8.makeTensorInfo(o, s, a); } else { - let a = new ex(o), i = [{ type: "float32", data: [n] }]; + let a = new Jg(o), i = [{ type: "float32", data: [n] }]; return t8.runWebGPUProgram(a, [], s, i); } } -var ez = { kernelName: sa, backendName: "webgpu", kernelFunc: kt }; -function pe(r) { - let { inputs: t8, attrs: e } = r, { x: o } = t8, { shape: n } = e, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s), i = y.sizeFromShape(a); - return y.assert(s === i, () => `The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`), r.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype }; +var tz = { kernelName: sa, backendName: "webgpu", kernelFunc: vt }; +function pe(r15) { + let { inputs: t8, attrs: e } = r15, { x: o } = t8, { shape: n } = e, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s), i = y.sizeFromShape(a); + return y.assert(s === i, () => `The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`), r15.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype }; } -var tz = { kernelName: da, backendName: "webgpu", kernelFunc: pe }; -function Op({ a: r, b: t8, transposeA: e, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) { - let u = r.shape.length, c = t8.shape.length, l = e ? r.shape[u - 2] : r.shape[u - 1], m = o ? t8.shape[c - 1] : t8.shape[c - 2], d = e ? r.shape[u - 1] : r.shape[u - 2], f = o ? t8.shape[c - 2] : t8.shape[c - 1], h = r.shape.slice(0, -2), g = t8.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), S = Ir.assertAndGetBroadcastShape(r.shape.slice(0, -2), t8.shape.slice(0, -2)).concat([d, f]); - y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${t8.shape} and transposeA=${e} and transposeB=${o} must match.`); - let k = e ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], E = pe({ inputs: { x: r }, backend: n, attrs: { shape: k } }), R = pe({ inputs: { x: t8 }, backend: n, attrs: { shape: _ } }), D = [E, R], P = Math.max(x, b), O = [E, R], M = [{ type: "int32", data: [d] }, { type: "int32", data: [f] }, { type: "int32", data: [l] }], L, B, z = [P, d, f], U = A().get("WEBGPU_MATMUL_PROGRAM_TYPE"); +var rz = { kernelName: da, backendName: "webgpu", kernelFunc: pe }; +function _p({ a: r15, b: t8, transposeA: e, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) { + let u = r15.shape.length, c = t8.shape.length, l = e ? r15.shape[u - 2] : r15.shape[u - 1], m = o ? t8.shape[c - 1] : t8.shape[c - 2], d = e ? r15.shape[u - 1] : r15.shape[u - 2], f = o ? t8.shape[c - 2] : t8.shape[c - 1], h = r15.shape.slice(0, -2), g = t8.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), S = Sr.assertAndGetBroadcastShape(r15.shape.slice(0, -2), t8.shape.slice(0, -2)).concat([d, f]); + y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r15.shape} and ${t8.shape} and transposeA=${e} and transposeB=${o} must match.`); + let k = e ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], E = pe({ inputs: { x: r15 }, backend: n, attrs: { shape: k } }), R = pe({ inputs: { x: t8 }, backend: n, attrs: { shape: _ } }), D = [E, R], P = Math.max(x, b), O = [E, R], M = [{ type: "int32", data: [d] }, { type: "int32", data: [f] }, { type: "int32", data: [l] }], L, B, z = [P, d, f], U = A().get("WEBGPU_MATMUL_PROGRAM_TYPE"); if (U < 0) { let q = A().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), Y = q > 0 ? q : n.thresholdToIncreaseWorkgroups, J = P * Math.ceil(d / 32) * Math.ceil(f / 32); - J <= Y || d <= 8 && J <= Y * 2 ? P * d * f <= 128 ? U = Lo.MatMulReduceProgram : P === 1 && m >= 2e3 ? U = Lo.MatMulSplitKProgram : U = Lo.MatMulSmallOutputSizeProgram : U = Lo.MatMulPackedProgram; + J <= Y || d <= 8 && J <= Y * 2 ? P * d * f <= 128 ? U = Mo.MatMulReduceProgram : P === 1 && m >= 2e3 ? U = Mo.MatMulSplitKProgram : U = Mo.MatMulSmallOutputSizeProgram : U = Mo.MatMulPackedProgram; } switch (U) { - case Lo.MatMulReduceProgram: - L = new Yg(z, e, o, s, p, a); + case Mo.MatMulReduceProgram: + L = new Xg(z, e, o, s, p, a); break; - case Lo.MatMulSplitKProgram: { - if (B = kt({ backend: n, attrs: { shape: z, value: 0, dtype: r.dtype } }), L = new Zg(z, m, e, o), s || p) { - B = n.runWebGPUProgram(L, O, r.dtype, M, B); - let Y = new Jg(B.shape, s, p, a), J = null, re = [B]; + case Mo.MatMulSplitKProgram: { + if (B = vt({ backend: n, attrs: { shape: z, value: 0, dtype: r15.dtype } }), L = new Qg(z, m, e, o), s || p) { + B = n.runWebGPUProgram(L, O, r15.dtype, M, B); + let Y = new Zg(B.shape, s, p, a), J = null, re = [B]; s && re.push(s), a && re.push(a), p === "leakyrelu" && (J = [{ type: "float32", data: [i] }], Y.uniforms += " alpha : f32,"); let ne = n.runWebGPUProgram(Y, re, B.dtype, J); D.push(B); @@ -28628,29 +28631,29 @@ function Op({ a: r, b: t8, transposeA: e, transposeB: o, backend: n, bias: s = n } break; } - case Lo.MatMulSmallOutputSizeProgram: - L = new Qg(k, _, z, e, o, s, p, a); + case Mo.MatMulSmallOutputSizeProgram: + L = new Yg(k, _, z, e, o, s, p, a); break; - case Lo.MatMulPackedProgram: + case Mo.MatMulPackedProgram: let q = n.adapterInfo.isIntel(); - L = new Xg(k, z, e, o, s, p, a, q); + L = new jg(k, z, e, o, s, p, a, q); break; default: throw new Error(`Unsupported MatMulProgramType ${U}.`); } - s && O.push(s), a && O.push(a), p === "leakyrelu" && (M.push({ type: "float32", data: [i] }), L.uniforms += " alpha : f32,"), B = n.runWebGPUProgram(L, O, r.dtype, M, B); + s && O.push(s), a && O.push(a), p === "leakyrelu" && (M.push({ type: "float32", data: [i] }), L.uniforms += " alpha : f32,"), B = n.runWebGPUProgram(L, O, r15.dtype, M, B); let j = pe({ inputs: { x: B }, backend: n, attrs: { shape: S } }); D.push(B); for (let q of D) n.disposeData(q.dataId); return j; } -function Wie(r) { - let { inputs: t8, backend: e, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = t8, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o; - return Op({ a: n, b: s, transposeA: p, transposeB: u, backend: e, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c }); +function Wie(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { a: n, b: s, bias: a, preluActivationWeights: i } = t8, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o; + return _p({ a: n, b: s, transposeA: p, transposeB: u, backend: e, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c }); } -var rz = { kernelName: Io, backendName: "webgpu", kernelFunc: Wie }; -var gm = class { +var oz = { kernelName: So, backendName: "webgpu", kernelFunc: Wie }; +var fm = class { constructor(t8, e, o) { this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workgroupSize = [128, 1, 1], this.size = true, this.outputShape = w.assertAndGetBroadcastShape(e, o), this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `binaryOpComplex_${t8}`, this.op = t8; } @@ -28658,7 +28661,7 @@ var gm = class { return ` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { - ${Qc(this.op, false)} + ${qc(this.op, false)} } ${G("index")} { @@ -28673,7 +28676,7 @@ var gm = class { `; } }; -var ki = class { +var Si = class { constructor(t8, e, o) { if (this.size = true, this.variableNames = ["A", "B"], this.outputShape = w.assertAndGetBroadcastShape(e, o), this.dispatchLayout = X(this.outputShape), this.op = t8, this.useSharedMemoryWithA = e.length <= 1 && o.length > 1 && e[0] < 128, this.useSharedMemoryWithB = o.length <= 1 && e.length > 1 && o[0] < 128, this.useSharedMemoryWithA || this.useSharedMemoryWithB) this.outputComponent = 1, this.variableComponents = [1, 1], this.lastDimensionSize = this.useSharedMemoryWithB ? o[0] : e[0], this.shaderKey = `binary_${t8}_${this.lastDimensionSize}`, this.type = "shared", this.workgroupSize = [256, 1, 1]; @@ -28686,7 +28689,7 @@ var ki = class { getUserCode() { let t8, e = this.outputComponent === 4 ? "vec4" : "f32", o = ` fn binaryOperation(a : ${e}, b : ${e}) -> ${e} { - ${Qc(this.op, this.outputComponent === 4)} + ${qc(this.op, this.outputComponent === 4)} }; `; if (this.type === "shared") { @@ -28726,17 +28729,17 @@ var ki = class { return t8; } }; -function Ft(r) { - let { inputs: t8 } = r, { x: e } = t8; - return r.backend.incRef(e.dataId), { dataId: e.dataId, shape: e.shape, dtype: e.dtype }; +function At(r15) { + let { inputs: t8 } = r15, { x: e } = t8; + return r15.backend.incRef(e.dataId), { dataId: e.dataId, shape: e.shape, dtype: e.dtype }; } -var oz = { kernelName: wo, backendName: "webgpu", kernelFunc: Ft }; -function yo(r) { - let { inputs: t8, backend: e } = r, { real: o, imag: n } = t8, s = e.makeTensorInfo(o.shape, "complex64"), a = e.tensorMap.get(s.dataId), i = Ft({ inputs: { x: o }, backend: e }), p = Ft({ inputs: { x: n }, backend: e }); +var nz = { kernelName: Co, backendName: "webgpu", kernelFunc: At }; +function xo(r15) { + let { inputs: t8, backend: e } = r15, { real: o, imag: n } = t8, s = e.makeTensorInfo(o.shape, "complex64"), a = e.tensorMap.get(s.dataId), i = At({ inputs: { x: o }, backend: e }), p = At({ inputs: { x: n }, backend: e }); return a.complexTensorInfos = { real: i, imag: p }, s; } -var nz = { kernelName: Fi, backendName: "webgpu", kernelFunc: yo }; -var Jr = class { +var sz = { kernelName: Ri, backendName: "webgpu", kernelFunc: xo }; +var Zr = class { constructor(t8, e, o = "") { this.variableNames = ["A"], this.size = true; let n = 128; @@ -28745,7 +28748,7 @@ var Jr = class { getUserCode() { return ` fn unaryOperation(a : f32) -> f32 { - ${vi(this.op, false)} + ${wi(this.op, false)} } ${G("index")} { if (index < uniforms.size) { @@ -28756,32 +28759,32 @@ var Jr = class { `; } }; -function ye({ opType: r, cpuKernelImpl: t8, dtype: e }) { +function ye({ opType: r15, cpuKernelImpl: t8, dtype: e }) { return ({ inputs: o, backend: n }) => { let { x: s } = o, a = n, i = e || s.dtype; if (a.shouldExecuteOnCPU([s]) && t8 != null) { let u = a.tensorMap.get(s.dataId), c = t8(u.values, i); return a.makeTensorInfo(s.shape, i, c); } - let p = new Jr(s.shape, r); + let p = new Zr(s.shape, r15); return a.runWebGPUProgram(p, [s], i); }; } -function et({ opType: r, cpuKernelImpl: t8, supportsComplex: e = false, dtype: o }) { +function et({ opType: r15, cpuKernelImpl: t8, supportsComplex: e = false, dtype: o }) { return ({ inputs: n, backend: s }) => { let { a, b: i } = n, p = s; if (e && a.dtype === "complex64") { let l = p.tensorMap.get(a.dataId), m = p.tensorMap.get(i.dataId), d, f; - if (r !== fe.MUL) + if (r15 !== fe.MUL) [d, f] = [[l.complexTensorInfos.real, m.complexTensorInfos.real], [l.complexTensorInfos.imag, m.complexTensorInfos.imag]].map((g) => { - let [x, b] = g, C = { dataId: x.dataId, dtype: x.dtype, shape: a.shape }, S = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, k = new ki(r, a.shape, i.shape); + let [x, b] = g, C = { dataId: x.dataId, dtype: x.dtype, shape: a.shape }, S = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, k = new Si(r15, a.shape, i.shape); return p.runWebGPUProgram(k, [C, S], dt(x.dtype, b.dtype)); }); else { - let g = new gm(fe.COMPLEX_MULTIPLY_REAL, a.shape, i.shape), x = new gm(fe.COMPLEX_MULTIPLY_IMAG, a.shape, i.shape), b = [{ dataId: l.complexTensorInfos.real.dataId, dtype: l.complexTensorInfos.real.dtype, shape: a.shape }, { dataId: l.complexTensorInfos.imag.dataId, dtype: l.complexTensorInfos.imag.dtype, shape: a.shape }, { dataId: m.complexTensorInfos.real.dataId, dtype: m.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: m.complexTensorInfos.imag.dataId, dtype: m.complexTensorInfos.imag.dtype, shape: i.shape }]; + let g = new fm(fe.COMPLEX_MULTIPLY_REAL, a.shape, i.shape), x = new fm(fe.COMPLEX_MULTIPLY_IMAG, a.shape, i.shape), b = [{ dataId: l.complexTensorInfos.real.dataId, dtype: l.complexTensorInfos.real.dtype, shape: a.shape }, { dataId: l.complexTensorInfos.imag.dataId, dtype: l.complexTensorInfos.imag.dtype, shape: a.shape }, { dataId: m.complexTensorInfos.real.dataId, dtype: m.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: m.complexTensorInfos.imag.dataId, dtype: m.complexTensorInfos.imag.dtype, shape: i.shape }]; d = p.runWebGPUProgram(g, b, "float32"), f = p.runWebGPUProgram(x, b, "float32"); } - let h = yo({ inputs: { real: d, imag: f }, backend: p }); + let h = xo({ inputs: { real: d, imag: f }, backend: p }); return p.disposeData(d.dataId), p.disposeData(f.dataId), h; } let u = o || dt(a.dtype, i.dtype); @@ -28789,20 +28792,20 @@ function et({ opType: r, cpuKernelImpl: t8, supportsComplex: e = false, dtype: o let l = p.tensorMap.get(a.dataId).values, m = p.tensorMap.get(i.dataId).values, d = a.dtype === "string" ? w.fromUint8ToStringArray(l) : l, f = a.dtype === "string" ? w.fromUint8ToStringArray(m) : m, [h, g] = t8(a.shape, i.shape, d, f, u); return p.makeTensorInfo(g, u, h); } - let c = new ki(r, a.shape, i.shape); + let c = new Si(r15, a.shape, i.shape); return p.runWebGPUProgram(c, [a, i], u); }; } -var { addImpl: sz, castImpl: az, ceilImpl: iz, concatImpl: uz, equalImpl: pz, expImpl: cz, expm1Impl: lz, floorImpl: mz, floorDivImpl: dz, gatherNdImpl: fz, gatherV2Impl: hz, greaterEqualImpl: gz, greaterImpl: xz, lessEqualImpl: yz, lessImpl: bz, logImpl: Cz, maxImpl: wz, maximumImpl: Sz, minimumImpl: Iz, multiplyImpl: vz, negImpl: kz, notEqualImpl: Nz, prodImpl: Tz, rangeImpl: _z, rsqrtImpl: $z, scatterImpl: Ez, simpleAbsImpl: Rz, sliceImpl: Dz, stridedSliceImpl: Az, stringNGramsImpl: Fz, subImpl: Pz, tileImpl: Oz, topKImpl: Mz, transposeImpl: Lz, uniqueImpl: HPt } = Tc; -var Uie = ye({ opType: Z.ABS, cpuKernelImpl: Rz }); -var Bz = { kernelName: Xs, backendName: "webgpu", kernelFunc: Uie }; +var { addImpl: az, castImpl: iz, ceilImpl: uz, concatImpl: pz, equalImpl: cz, expImpl: lz, expm1Impl: mz, floorImpl: dz, floorDivImpl: fz, gatherNdImpl: hz, gatherV2Impl: gz, greaterEqualImpl: xz, greaterImpl: yz, lessEqualImpl: bz, lessImpl: Cz, logImpl: wz, maxImpl: Sz, maximumImpl: Iz, minimumImpl: vz, multiplyImpl: kz, negImpl: Nz, notEqualImpl: Tz, prodImpl: _z, rangeImpl: $z, rsqrtImpl: Ez, scatterImpl: Rz, simpleAbsImpl: Dz, sliceImpl: Az, stridedSliceImpl: Fz, stringNGramsImpl: Pz, subImpl: Oz, tileImpl: Mz, topKImpl: Lz, transposeImpl: Bz, uniqueImpl: KPt } = wc; +var Uie = ye({ opType: Z.ABS, cpuKernelImpl: Dz }); +var zz = { kernelName: Xs, backendName: "webgpu", kernelFunc: Uie }; var Gie = ye({ opType: Z.ACOS }); -var zz = { kernelName: Wo, backendName: "webgpu", kernelFunc: Gie }; +var Vz = { kernelName: Vo, backendName: "webgpu", kernelFunc: Gie }; var Hie = ye({ opType: Z.ACOSH }); -var Vz = { kernelName: Uo, backendName: "webgpu", kernelFunc: Hie }; -var Kie = et({ opType: fe.ADD, cpuKernelImpl: sz, supportsComplex: true }); -var Wz = { kernelName: uo, backendName: "webgpu", kernelFunc: Kie }; -var tx = class { +var Wz = { kernelName: Wo, backendName: "webgpu", kernelFunc: Hie }; +var Kie = et({ opType: fe.ADD, cpuKernelImpl: az, supportsComplex: true }); +var Uz = { kernelName: io, backendName: "webgpu", kernelFunc: Kie }; +var ex = class { constructor(t8) { this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8[0], this.variableNames = t8.map((e, o) => `T${o}`), this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "addN"; } @@ -28827,15 +28830,15 @@ var tx = class { `; } }; -function qie(r) { - let { inputs: t8, backend: e } = r, o = t8; +function qie(r15) { + let { inputs: t8, backend: e } = r15, o = t8; if (o.length === 1) - return Ft({ inputs: { x: o[0] }, backend: e }); - let n = o.map((i) => i.dtype).reduce((i, p) => dt(i, p)), s = o.map((i) => i.shape), a = new tx(s); + return At({ inputs: { x: o[0] }, backend: e }); + let n = o.map((i) => i.dtype).reduce((i, p) => dt(i, p)), s = o.map((i) => i.shape), a = new ex(s); return e.runWebGPUProgram(a, o, n); } -var Uz = { kernelName: Go, backendName: "webgpu", kernelFunc: qie }; -var rx = class { +var Gz = { kernelName: Uo, backendName: "webgpu", kernelFunc: qie }; +var tx = class { constructor(t8, e) { this.variableNames = ["A"], this.workgroupSize = [16, 16, 1]; let o = new Array(t8.length); @@ -28868,7 +28871,7 @@ var rx = class { `; } }; -var ox = class { +var rx = class { constructor(t8, e) { this.variableNames = ["A"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true; let o = new Array(t8.length); @@ -28877,7 +28880,7 @@ var ox = class { this.outputShape = o, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.newDim = e, this.shaderKey = `transpose_${e}`; } getUserCode() { - let t8 = ht(this.outputShape.length), e = Zv(this.newDim); + let t8 = ft(this.outputShape.length), e = Jv(this.newDim); return ` ${G("index")} { for(var i = 0; i < ${this.workPerThread}; i = i + 1) { @@ -28892,32 +28895,32 @@ var ox = class { `; } }; -function Zv(r) { - let t8 = r.length; +function Jv(r15) { + let t8 = r15.length; if (t8 > 6) throw Error(`Transpose for rank ${t8} is not yet supported`); let e = new Array(t8); - for (let o = 0; o < r.length; o++) - e[r[o]] = `coords.${Mo(o)}`; + for (let o = 0; o < r15.length; o++) + e[r15[o]] = `coords.${Oo(o)}`; return e.join(); } -function yr(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { perm: s } = o, a = e, i = n.shape.length, p = new Array(i); +function xr(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { perm: s } = o, a = e, i = n.shape.length, p = new Array(i); for (let c = 0; c < p.length; c++) p[c] = n.shape[s[c]]; if (e.shouldExecuteOnCPU([n])) { - let l = a.tensorMap.get(n.dataId).values, m = Lz(l, n.shape, n.dtype, s, p); + let l = a.tensorMap.get(n.dataId).values, m = Bz(l, n.shape, n.dtype, s, p); return e.makeTensorInfo(p, n.dtype, m); } if (n.shape.length === 2 && y.arraysEqual(s, [1, 0])) { - let c = new rx(n.shape, s); + let c = new tx(n.shape, s); return a.runWebGPUProgram(c, [n], n.dtype); } - let u = new ox(n.shape, s); + let u = new rx(n.shape, s); return a.runWebGPUProgram(u, [n], n.dtype); } -var Gz = { kernelName: co, backendName: "webgpu", kernelFunc: yr }; -var nx = class { +var Hz = { kernelName: po, backendName: "webgpu", kernelFunc: xr }; +var ox = class { constructor(t8, e, o) { this.variableNames = ["x"], this.uniforms = "reduceSize : i32,", this.size = true, this.inputShape = [t8.batchSize, t8.inSize]; let [n] = w.computeOutAndReduceShapes(this.inputShape, [1]); @@ -28979,9 +28982,9 @@ var nx = class { } }; var jie = { mean: "float32", all: "bool", any: "bool" }; -function eo(r, t8, e, o, n) { - let s = r.shape.length, a = [], i = y.parseAxisParam(t8, r.shape), p = i, u = w.getAxesPermutation(p, s), c = r; - u != null && (c = yr({ inputs: { x: r }, attrs: { perm: u }, backend: n }), p = w.getInnerMostAxes(p.length, s), a.push(c)), w.assertAxesAreInnerMostDims(o, p, s); +function Jr(r15, t8, e, o, n) { + let s = r15.shape.length, a = [], i = y.parseAxisParam(t8, r15.shape), p = i, u = w.getAxesPermutation(p, s), c = r15; + u != null && (c = xr({ inputs: { x: r15 }, attrs: { perm: u }, backend: n }), p = w.getInnerMostAxes(p.length, s), a.push(c)), w.assertAxesAreInnerMostDims(o, p, s); let [l, m] = w.computeOutAndReduceShapes(c.shape, p), d = l; e && (d = w.expandShapeToKeepDim(l, i)); let f; @@ -28989,33 +28992,33 @@ function eo(r, t8, e, o, n) { let h = n.tensorMap.get(c.dataId).values; switch (o) { case "max": - let g = wz(h, y.sizeFromShape(m), d, r.dtype); - f = n.makeTensorInfo(d, r.dtype, g); + let g = Sz(h, y.sizeFromShape(m), d, r15.dtype); + f = n.makeTensorInfo(d, r15.dtype, g); break; case "prod": - let { outVals: x, outShape: b, outDtype: C } = Tz(c.shape, c.dtype, h, p); + let { outVals: x, outShape: b, outDtype: C } = _z(c.shape, c.dtype, h, p); f = n.makeTensorInfo(b, C, x); break; default: throw new Error(`${o} CPU implementation is not yet supported.`); } } else { - let h = y.sizeFromShape(m), x = y.sizeFromShape(c.shape) / h, b = { windowSize: h, inSize: h, batchSize: x, outSize: 1 }, C = jie[o] || ni(r.dtype), S = [{ type: "int32", data: [h] }], k = new nx(b, o, n.device.limits.maxComputeWorkgroupSizeX), _ = n.runWebGPUProgram(k, [c], C, S); + let h = y.sizeFromShape(m), x = y.sizeFromShape(c.shape) / h, b = { windowSize: h, inSize: h, batchSize: x, outSize: 1 }, C = jie[o] || oi(r15.dtype), S = [{ type: "int32", data: [h] }], k = new ox(b, o, n.device.limits.maxComputeWorkgroupSizeX), _ = n.runWebGPUProgram(k, [c], C, S); a.push(_), f = pe({ inputs: { x: _ }, attrs: { shape: d }, backend: n }); } return a.forEach((h) => n.disposeData(h.dataId)), f; } -function Xie(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { keepDims: s, axis: a } = o; - return eo(n, a, s, "all", e); +function Xie(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { keepDims: s, axis: a } = o; + return Jr(n, a, s, "all", e); } -var Hz = { kernelName: Ho, backendName: "webgpu", kernelFunc: Xie }; -function Yie(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { keepDims: s, axis: a } = o; - return eo(n, a, s, "any", e); +var Kz = { kernelName: Go, backendName: "webgpu", kernelFunc: Xie }; +function Yie(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { keepDims: s, axis: a } = o; + return Jr(n, a, s, "any", e); } -var Kz = { kernelName: Ko, backendName: "webgpu", kernelFunc: Yie }; -var Zc = class { +var qz = { kernelName: Ho, backendName: "webgpu", kernelFunc: Yie }; +var jc = class { constructor(t8, e, o) { this.workgroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "infinityValue : f32,", this.size = true; let n = [e]; @@ -29024,13 +29027,13 @@ var Zc = class { this.outputShape = s.length === 0 ? [1] : s, this.dispatchLayout = X(this.outputShape), y.sizeFromShape(a) < 32 ? (this.type = "plain", this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize)) : (this.type = "shared", this.dispatch = H(this.dispatchLayout, this.outputShape, [1, 1, 1])), this.inputShape = t8, this.shaderKey = `argMinMax_${this.op}_${this.type}`; } getUserCode() { - let t8 = this.workgroupSize[0], e = () => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape.${Mo(this.inputShape.length - 1)}`, o = () => { + let t8 = this.workgroupSize[0], e = () => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape.${Oo(this.inputShape.length - 1)}`, o = () => { let n = ""; if (this.outputShape.length === 1) this.inputShape.length !== 1 && (n += "outputCoords,"); else for (let s = 0; s < this.outputShape.length; s++) - n += `outputCoords.${Mo(s)},`; + n += `outputCoords.${Oo(s)},`; return n; }; return this.type === "shared" ? ` @@ -29102,31 +29105,31 @@ var Zc = class { `; } }; -function Qie(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = yr({ inputs: { x: n }, backend: e, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length); - let c = new Zc(p.shape, a[0], "max"), l = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }], m = e.runWebGPUProgram(c, [p], "int32", l); +function Qie(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; + i != null && (p = xr({ inputs: { x: n }, backend: e, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length); + let c = new jc(p.shape, a[0], "max"), l = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }], m = e.runWebGPUProgram(c, [p], "int32", l); return u.forEach((d) => e.disposeData(d.dataId)), m; } -var qz = { kernelName: Ys, backendName: "webgpu", kernelFunc: Qie }; -function Zie(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; - i != null && (p = yr({ inputs: { x: n }, backend: e, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length); - let c = new Zc(p.shape, a[0], "min"), l = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }], m = e.runWebGPUProgram(c, [p], "int32", l); +var jz = { kernelName: Ys, backendName: "webgpu", kernelFunc: Qie }; +function Zie(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = w.getAxesPermutation(a, n.shape.length), p = n, u = []; + i != null && (p = xr({ inputs: { x: n }, backend: e, attrs: { perm: i } }), u.push(p), a = w.getInnerMostAxes(a.length, p.shape.length)), w.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length); + let c = new jc(p.shape, a[0], "min"), l = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }], m = e.runWebGPUProgram(c, [p], "int32", l); return u.forEach((d) => e.disposeData(d.dataId)), m; } -var jz = { kernelName: Qs, backendName: "webgpu", kernelFunc: Zie }; +var Xz = { kernelName: Qs, backendName: "webgpu", kernelFunc: Zie }; var Jie = ye({ opType: Z.ASIN }); -var Xz = { kernelName: qo, backendName: "webgpu", kernelFunc: Jie }; +var Yz = { kernelName: Ko, backendName: "webgpu", kernelFunc: Jie }; var eue = ye({ opType: Z.ASINH }); -var Yz = { kernelName: jo, backendName: "webgpu", kernelFunc: eue }; +var Qz = { kernelName: qo, backendName: "webgpu", kernelFunc: eue }; var tue = ye({ opType: Z.ATAN }); -var Qz = { kernelName: Xo, backendName: "webgpu", kernelFunc: tue }; +var Zz = { kernelName: jo, backendName: "webgpu", kernelFunc: tue }; var rue = et({ opType: fe.ATAN2 }); -var Zz = { kernelName: Qo, backendName: "webgpu", kernelFunc: rue }; +var Jz = { kernelName: Yo, backendName: "webgpu", kernelFunc: rue }; var oue = ye({ opType: Z.ATANH }); -var Jz = { kernelName: Yo, backendName: "webgpu", kernelFunc: oue }; -var sx = class { +var eV = { kernelName: Xo, backendName: "webgpu", kernelFunc: oue }; +var nx = class { constructor(t8) { this.variableNames = ["x"], this.uniforms = "strides : vec2,", this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = t8.outShape, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "poolWithFilterSizeEqualsOne"; } @@ -29149,7 +29152,7 @@ var sx = class { `; } }; -var za = class { +var Ba = class { constructor(t8, e, o = false, n = false, s = false) { if (this.variableNames = ["x"], this.uniforms = "strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,", this.workgroupSize = [128, 1, 1], this.size = true, e === "avg" && o) throw new Error("Cannot compute positions for average pool."); @@ -29203,7 +29206,7 @@ var za = class { `; } }; -var _u = class { +var wu = class { constructor(t8, e, o = false, n = false, s = false) { if (this.variableNames = ["x"], this.uniforms = "strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,", this.workgroupSize = [128, 1, 1], this.size = true, e === "avg" && o) throw new Error("Cannot compute positions for average pool."); @@ -29265,39 +29268,39 @@ var _u = class { `; } }; -function Jv(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { reductionIndices: s, keepDims: a } = o; - return eo(n, s, a, "max", e); +function e0(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { reductionIndices: s, keepDims: a } = o; + return Jr(n, s, a, "max", e); } -var eV = { kernelName: Vn, backendName: "webgpu", kernelFunc: Jv }; -function e0(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { keepDims: s, axis: a } = o; - return eo(n, a, s, "mean", e); +var tV = { kernelName: zn, backendName: "webgpu", kernelFunc: e0 }; +function t0(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { keepDims: s, axis: a } = o; + return Jr(n, a, s, "mean", e); } -var tV = { kernelName: Gn, backendName: "webgpu", kernelFunc: e0 }; -function ax(r, t8, e, o) { +var rV = { kernelName: Un, backendName: "webgpu", kernelFunc: t0 }; +function sx(r15, t8, e, o) { if (t8.filterWidth === 1 && t8.filterHeight === 1 && y.arraysEqual(t8.inShape, t8.outShape)) - return Ft({ inputs: { x: r }, backend: o }); + return At({ inputs: { x: r15 }, backend: o }); if (t8.filterWidth === t8.inWidth && t8.filterHeight === t8.inHeight && t8.batchSize === 1 && t8.padInfo.type === "VALID") { - let a = r.shape.length, i = pe({ inputs: { x: r }, backend: o, attrs: { shape: [r.shape[a - 3] * r.shape[a - 2], r.shape[a - 1]] } }), p; - e === "avg" ? p = e0({ inputs: { x: i }, backend: o, attrs: { axis: 0, keepDims: false } }) : (y.assert(e === "max", () => `Invalid pool type ${e}`), p = Jv({ inputs: { x: i }, backend: o, attrs: { reductionIndices: 0, keepDims: false } })); + let a = r15.shape.length, i = pe({ inputs: { x: r15 }, backend: o, attrs: { shape: [r15.shape[a - 3] * r15.shape[a - 2], r15.shape[a - 1]] } }), p; + e === "avg" ? p = t0({ inputs: { x: i }, backend: o, attrs: { axis: 0, keepDims: false } }) : (y.assert(e === "max", () => `Invalid pool type ${e}`), p = e0({ inputs: { x: i }, backend: o, attrs: { reductionIndices: 0, keepDims: false } })); let u = pe({ inputs: { x: p }, backend: o, attrs: { shape: t8.outShape } }); return o.disposeData(i.dataId), o.disposeData(p.dataId), u; } let n, s = [{ type: "int32", data: [t8.strideHeight, t8.strideWidth] }]; - return t8.filterHeight === 1 && t8.filterWidth === 1 ? n = new sx(t8) : (e === "avg" ? n = new za(t8, "avg") : (y.assert(e === "max", () => `Invalid pool type ${e}`), n = new za(t8, "max")), s.push({ type: "int32", data: [t8.padInfo.top, t8.padInfo.left] }, { type: "int32", data: [t8.dilationHeight, t8.dilationWidth] }, { type: "int32", data: [t8.inHeight, t8.inWidth] }, { type: "int32", data: [t8.effectiveFilterHeight, t8.effectiveFilterWidth] })), o.runWebGPUProgram(n, [r], r.dtype, s); + return t8.filterHeight === 1 && t8.filterWidth === 1 ? n = new nx(t8) : (e === "avg" ? n = new Ba(t8, "avg") : (y.assert(e === "max", () => `Invalid pool type ${e}`), n = new Ba(t8, "max")), s.push({ type: "int32", data: [t8.padInfo.top, t8.padInfo.left] }, { type: "int32", data: [t8.dilationHeight, t8.dilationWidth] }, { type: "int32", data: [t8.inHeight, t8.inWidth] }, { type: "int32", data: [t8.effectiveFilterHeight, t8.effectiveFilterWidth] })), o.runWebGPUProgram(n, [r15], r15.dtype, s); } -function nue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = w.computePool2DInfo(n.shape, s, a, u, i, p); - return ax(n, c, "avg", e); +function nue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = w.computePool2DInfo(n.shape, s, a, u, i, p); + return sx(n, c, "avg", e); } -var rV = { kernelName: Zo, backendName: "webgpu", kernelFunc: nue }; -function sue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new _u(l, "avg"), d = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.front, l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.inDepth, l.inHeight, l.inWidth] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }]; +var oV = { kernelName: Qo, backendName: "webgpu", kernelFunc: nue }; +function sue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new wu(l, "avg"), d = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.front, l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.inDepth, l.inHeight, l.inWidth] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }]; return e.runWebGPUProgram(m, [n], n.dtype, d); } -var oV = { kernelName: Zs, backendName: "webgpu", kernelFunc: sue }; -var ix = class { +var nV = { kernelName: Zs, backendName: "webgpu", kernelFunc: sue }; +var ax = class { constructor(t8) { this.variableNames = ["dy"], this.uniforms = `strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, avgMultiplier : f32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.inShape, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "avgPool2DBackprop"; @@ -29344,7 +29347,7 @@ var ix = class { `; } }; -var ux = class { +var ix = class { constructor(t8) { this.variableNames = ["dy"], this.uniforms = `strides : vec3, pads : vec3, filterDims : vec3, outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.inShape, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "avgPool3DBackprop"; @@ -29401,30 +29404,30 @@ var ux = class { `; } }; -function aue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s } = t8, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = w.computePool3DInfo(a.shape, i, p, 1, u, c), m = new ux(l), d = 1 / (l.filterDepth * l.filterHeight * l.filterWidth), f = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.effectiveFilterDepth - 1 - l.padInfo.front, l.effectiveFilterHeight - 1 - l.padInfo.top, l.effectiveFilterWidth - 1 - l.padInfo.left] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }, { type: "int32", data: [l.outDepth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "float32", data: [d] }]; +function aue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s } = t8, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = w.computePool3DInfo(a.shape, i, p, 1, u, c), m = new ix(l), d = 1 / (l.filterDepth * l.filterHeight * l.filterWidth), f = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.effectiveFilterDepth - 1 - l.padInfo.front, l.effectiveFilterHeight - 1 - l.padInfo.top, l.effectiveFilterWidth - 1 - l.padInfo.left] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }, { type: "int32", data: [l.outDepth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "float32", data: [d] }]; return e.runWebGPUProgram(m, [n], a.dtype, f); } -var nV = { kernelName: Ai, backendName: "webgpu", kernelFunc: aue }; -function iue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s } = t8, a = s; - fm([n, s], "avgPoolGrad"); - let { filterSize: i, strides: p, pad: u } = o, c = w.computePool2DInfo(a.shape, i, p, 1, u), l = new ix(c), m = 1 / (c.filterHeight * c.filterWidth), d = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.effectiveFilterHeight - 1 - c.padInfo.top, c.effectiveFilterWidth - 1 - c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] }, { type: "int32", data: [c.outHeight] }, { type: "int32", data: [c.outWidth] }, { type: "float32", data: [m] }]; +var sV = { kernelName: Ei, backendName: "webgpu", kernelFunc: aue }; +function iue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s } = t8, a = s; + mm([n, s], "avgPoolGrad"); + let { filterSize: i, strides: p, pad: u } = o, c = w.computePool2DInfo(a.shape, i, p, 1, u), l = new ax(c), m = 1 / (c.filterHeight * c.filterWidth), d = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.effectiveFilterHeight - 1 - c.padInfo.top, c.effectiveFilterWidth - 1 - c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] }, { type: "int32", data: [c.outHeight] }, { type: "int32", data: [c.outWidth] }, { type: "float32", data: [m] }]; return e.runWebGPUProgram(l, [n], a.dtype, d); } -var sV = { kernelName: Di, backendName: "webgpu", kernelFunc: iue }; -function uue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { a: n, b: s } = t8, { transposeA: a, transposeB: i } = o; - return Op({ a: n, b: s, transposeA: a, transposeB: i, backend: e }); +var aV = { kernelName: $i, backendName: "webgpu", kernelFunc: iue }; +function uue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { a: n, b: s } = t8, { transposeA: a, transposeB: i } = o; + return _p({ a: n, b: s, transposeA: a, transposeB: i, backend: e }); } -var aV = { kernelName: Jo, backendName: "webgpu", kernelFunc: uue }; -var px = class { +var iV = { kernelName: Zo, backendName: "webgpu", kernelFunc: uue }; +var ux = class { constructor(t8, e) { - this.variableNames = ["source"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.rank = e.length, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.start = t8, this.uniforms = `start : ${ht(t8.length)}, `, this.shaderKey = "slice"; + this.variableNames = ["source"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.rank = e.length, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.start = t8, this.uniforms = `start : ${ft(t8.length)}, `, this.shaderKey = "slice"; } getUserCode() { - let t8 = ht(this.rank), e = pue(this.rank), o; - return this.start.length === 1 ? o = this.outputShape.map((s, a) => "sourceLoc = uniforms.start + coords;") : o = this.outputShape.map((s, a) => `sourceLoc.${t0[a]} = uniforms.start.${Mo(a)} + coords.${t0[a]};`), ` + let t8 = ft(this.rank), e = pue(this.rank), o; + return this.start.length === 1 ? o = this.outputShape.map((s, a) => "sourceLoc = uniforms.start + coords;") : o = this.outputShape.map((s, a) => `sourceLoc.${r0[a]} = uniforms.start.${Oo(a)} + coords.${r0[a]};`), ` ${G("index")} { if (index < uniforms.size) { var sourceLoc : ${t8}; @@ -29437,36 +29440,36 @@ var px = class { `; } }; -var t0 = ["x", "y", "z", "w", "u", "v"]; -function pue(r) { - if (r === 1) +var r0 = ["x", "y", "z", "w", "u", "v"]; +function pue(r15) { + if (r15 === 1) return "sourceLoc"; - if (r <= 6) - return t0.slice(0, r).map((t8) => `sourceLoc.${t8}`).join(","); - throw Error(`Slicing for rank ${r} is not yet supported`); + if (r15 <= 6) + return r0.slice(0, r15).map((t8) => `sourceLoc.${t8}`).join(","); + throw Error(`Slicing for rank ${r15} is not yet supported`); } -function Hs(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { begin: s, size: a } = o, [i, p] = ct.parseSliceParams(n, s, a); - if (ct.assertParamsValid(n, i, p), e.shouldExecuteOnCPU([n]) || n.dtype === "string") { - let l = e.tensorMap.get(n.dataId), m = Dz(l.values, i, p, n.shape, n.dtype); +function Hs(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { begin: s, size: a } = o, [i, p] = pt.parseSliceParams(n, s, a); + if (pt.assertParamsValid(n, i, p), e.shouldExecuteOnCPU([n]) || n.dtype === "string") { + let l = e.tensorMap.get(n.dataId), m = Az(l.values, i, p, n.shape, n.dtype); return e.makeTensorInfo(p, n.dtype, m); } if (y.sizeFromShape(p) === 0) return e.makeTensorInfo(p, n.dtype, []); - let u = new px(i, p), c = [{ type: "int32", data: i }]; + let u = new ux(i, p), c = [{ type: "int32", data: i }]; return e.runWebGPUProgram(u, [n], n.dtype, c); } -var iV = { kernelName: ha, backendName: "webgpu", kernelFunc: Hs }; -var cue = (r) => { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { blockShape: s, crops: a } = o; +var uV = { kernelName: ha, backendName: "webgpu", kernelFunc: Hs }; +var cue = (r15) => { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { blockShape: s, crops: a } = o; y.assert(n.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet"); - let i = s.reduce((b, C) => b * C), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = [], f = pe({ inputs: { x: n }, backend: e, attrs: { shape: p } }), h = yr({ inputs: { x: f }, backend: e, attrs: { perm: u } }), g = pe({ inputs: { x: h }, backend: e, attrs: { shape: c } }), x = Hs({ inputs: { x: g }, backend: e, attrs: { begin: l, size: m } }); + let i = s.reduce((b, C) => b * C), p = w.getReshaped(n.shape, s, i), u = w.getPermuted(p.length, s.length), c = w.getReshapedPermuted(n.shape, s, i), l = w.getSliceBeginCoords(a, s.length), m = w.getSliceSize(c, a, s.length), d = [], f = pe({ inputs: { x: n }, backend: e, attrs: { shape: p } }), h = xr({ inputs: { x: f }, backend: e, attrs: { perm: u } }), g = pe({ inputs: { x: h }, backend: e, attrs: { shape: c } }), x = Hs({ inputs: { x: g }, backend: e, attrs: { begin: l, size: m } }); return d.push(f), d.push(h), d.push(g), d.forEach((b) => e.disposeData(b.dataId)), x; }; -var uV = { kernelName: Js, backendName: "webgpu", kernelFunc: cue }; +var pV = { kernelName: Js, backendName: "webgpu", kernelFunc: cue }; var lue = ` fn bincount_write(index: i32, value: f32) { - ${Qr("&result[index]", "value", "float32")} + ${Yr("&result[index]", "value", "float32")} } `; var mue = ` @@ -29474,7 +29477,7 @@ var mue = ` atomicStore(&result[index], bitcast(value)); } `; -var Jc = class { +var Xc = class { constructor(t8, e, o = false) { this.outputShape = [], this.variableNames = ["x"], this.uniforms = "binCountSize : i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.hasWeights = true, this.binaryOutput = false, this.outputShape = t8, this.rank = t8.length, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.binaryOutput = o, o && (this.atomic = false), this.hasWeights = e, this.hasWeights && this.variableNames.push("w"), this.shaderKey = `bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`; } @@ -29500,12 +29503,12 @@ var Jc = class { `; } }; -function due(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, weights: s } = t8, { size: a } = o, i = y.sizeFromShape(n.shape), u = y.sizeFromShape(s.shape) > 0, c = [a], l = s.dtype, m = kt({ backend: e, attrs: { shape: c, value: 0, dtype: l } }), d = new Jc([i], u), f = [{ type: "int32", data: [a] }], h = u ? [n, s] : [n]; +function due(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, weights: s } = t8, { size: a } = o, i = y.sizeFromShape(n.shape), u = y.sizeFromShape(s.shape) > 0, c = [a], l = s.dtype, m = vt({ backend: e, attrs: { shape: c, value: 0, dtype: l } }), d = new Xc([i], u), f = [{ type: "int32", data: [a] }], h = u ? [n, s] : [n]; return e.runWebGPUProgram(d, h, l, f, m); } -var pV = { kernelName: en, backendName: "webgpu", kernelFunc: due }; -var cx = class { +var cV = { kernelName: Jo, backendName: "webgpu", kernelFunc: due }; +var px = class { constructor(t8) { this.outputShape = [], this.variableNames = ["s0", "s1"], this.uniforms = "s0Size : i32, s1Size : i32, ", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [t8], this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "broadcastArgs"; } @@ -29538,59 +29541,59 @@ var cx = class { `; } }; -function fue(r) { - let { inputs: t8, backend: e } = r, { s0: o, s1: n } = t8; +function fue(r15) { + let { inputs: t8, backend: e } = r15, { s0: o, s1: n } = t8; if (e.shouldExecuteOnCPU([o, n])) { let c = e.tensorMap.get(o.dataId), l = e.tensorMap.get(n.dataId), m = c.values, d = l.values, f = w.assertAndGetBroadcastShape(Array.from(m), Array.from(d)); return e.makeTensorInfo([f.length], "int32", Int32Array.from(f)); } - let s = y.sizeFromShape(o.shape), a = y.sizeFromShape(n.shape), i = Math.max(s, a), p = new cx(i), u = [{ type: "int32", data: [s] }, { type: "int32", data: [a] }]; + let s = y.sizeFromShape(o.shape), a = y.sizeFromShape(n.shape), i = Math.max(s, a), p = new px(i), u = [{ type: "int32", data: [s] }, { type: "int32", data: [a] }]; return e.runWebGPUProgram(p, [o, n], "int32", u); } -var cV = { kernelName: ea, backendName: "webgpu", kernelFunc: fue }; -var r0 = et({ opType: fe.NOT_EQUAL, dtype: "bool", cpuKernelImpl: Nz }); -var lV = { kernelName: Qn, backendName: "webgpu", kernelFunc: r0 }; -function Ni(r) { - let { inputs: t8, backend: e } = r, { input: o } = t8, n = e.tensorMap.get(o.dataId); - return Ft({ inputs: { x: n.complexTensorInfos.real }, backend: e }); +var lV = { kernelName: ea, backendName: "webgpu", kernelFunc: fue }; +var o0 = et({ opType: fe.NOT_EQUAL, dtype: "bool", cpuKernelImpl: Tz }); +var mV = { kernelName: Yn, backendName: "webgpu", kernelFunc: o0 }; +function Ii(r15) { + let { inputs: t8, backend: e } = r15, { input: o } = t8, n = e.tensorMap.get(o.dataId); + return At({ inputs: { x: n.complexTensorInfos.real }, backend: e }); } -var mV = { kernelName: qi, backendName: "webgpu", kernelFunc: Ni }; -function dV(r, t8) { - let e = new Jr(r.shape, Z.TO_INT), o = t8.runWebGPUProgram(e, [r], "int32"); +var dV = { kernelName: Gi, backendName: "webgpu", kernelFunc: Ii }; +function fV(r15, t8) { + let e = new Zr(r15.shape, Z.TO_INT), o = t8.runWebGPUProgram(e, [r15], "int32"); return { dataId: o.dataId, shape: o.shape, dtype: o.dtype }; } -function o0(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { dtype: s } = o; +function n0(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { dtype: s } = o; if (s === "complex64") { if (n.dtype === "complex64") - return Ft({ inputs: { x: n }, backend: e }); - let a = Gr(n.shape), i = o0({ inputs: { x: n }, backend: e, attrs: { dtype: "float32" } }), p = yo({ inputs: { real: i, imag: a }, backend: e }); + return At({ inputs: { x: n }, backend: e }); + let a = Ur(n.shape), i = n0({ inputs: { x: n }, backend: e, attrs: { dtype: "float32" } }), p = xo({ inputs: { real: i, imag: a }, backend: e }); return a.dispose(), e.disposeData(i.dataId), p; } if (n.dtype === "complex64") { - let a = Ni({ inputs: { input: n }, backend: e }), i = o0({ inputs: { x: a }, backend: e, attrs: { dtype: s } }); + let a = Ii({ inputs: { input: n }, backend: e }), i = n0({ inputs: { x: a }, backend: e, attrs: { dtype: s } }); return e.disposeData(a.dataId), i; } if (!y.hasEncodingLoss(n.dtype, s)) { - let a = Ft({ inputs: { x: n }, backend: e }); + let a = At({ inputs: { x: n }, backend: e }); return { dataId: a.dataId, shape: a.shape, dtype: s }; } if (e.shouldExecuteOnCPU([n])) { - let a = e.tensorMap.get(n.dataId).values, [i, p, u] = az(a, n.shape, n.dtype, s); + let a = e.tensorMap.get(n.dataId).values, [i, p, u] = iz(a, n.shape, n.dtype, s); return e.makeTensorInfo(i, p, u); } if (s === "int32") - return dV(n, e); + return fV(n, e); if (s === "bool") { - let a = e.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = r0({ inputs: { a: n, b: a }, backend: e }); + let a = e.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = o0({ inputs: { a: n, b: a }, backend: e }); return e.disposeData(a.dataId), p; } throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`); } -var fV = { kernelName: bo, backendName: "webgpu", kernelFunc: o0 }; -var hue = ye({ opType: Z.CEIL, cpuKernelImpl: iz }); -var hV = { kernelName: tn, backendName: "webgpu", kernelFunc: hue }; -var lx = class { +var hV = { kernelName: yo, backendName: "webgpu", kernelFunc: n0 }; +var hue = ye({ opType: Z.CEIL, cpuKernelImpl: uz }); +var gV = { kernelName: en, backendName: "webgpu", kernelFunc: hue }; +var cx = class { constructor(t8) { this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workPerThread = 4, this.workgroupSize = [64, 1, 1], this.outputComponent = 4, this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "clipVec4"; } @@ -29608,7 +29611,7 @@ var lx = class { `; } }; -var mx = class { +var lx = class { constructor(t8) { this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "clip"; } @@ -29627,12 +29630,12 @@ var mx = class { `; } }; -function gue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { clipValueMin: s, clipValueMax: a } = o, i, p = [{ type: "float32", data: [s] }, { type: "float32", data: [a] }]; - return y.sizeFromShape(n.shape) % 4 === 0 ? i = new lx(n.shape) : i = new mx(n.shape), e.runWebGPUProgram(i, [n], n.dtype, p); +function gue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { clipValueMin: s, clipValueMax: a } = o, i, p = [{ type: "float32", data: [s] }, { type: "float32", data: [a] }]; + return y.sizeFromShape(n.shape) % 4 === 0 ? i = new cx(n.shape) : i = new lx(n.shape), e.runWebGPUProgram(i, [n], n.dtype, p); } -var gV = { kernelName: Co, backendName: "webgpu", kernelFunc: gue }; -var dx = class { +var xV = { kernelName: bo, backendName: "webgpu", kernelFunc: gue }; +var mx = class { constructor(t8) { this.outputShape = [], this.variableNames = ["real", "imag"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "complexAbs"; } @@ -29652,15 +29655,15 @@ var dx = class { `; } }; -function xV(r, t8) { - return { dataId: t8.dataId, dtype: t8.dtype, shape: r.shape }; +function yV(r15, t8) { + return { dataId: t8.dataId, dtype: t8.dtype, shape: r15.shape }; } -function xue(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8, n = e.tensorMap.get(o.dataId), s = new dx(o.shape), a = [xV(o, n.complexTensorInfos.real), xV(o, n.complexTensorInfos.imag)]; +function xue(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8, n = e.tensorMap.get(o.dataId), s = new mx(o.shape), a = [yV(o, n.complexTensorInfos.real), yV(o, n.complexTensorInfos.imag)]; return e.runWebGPUProgram(s, a, a[0].dtype); } -var yV = { kernelName: Pi, backendName: "webgpu", kernelFunc: xue }; -var fx = class { +var bV = { kernelName: Di, backendName: "webgpu", kernelFunc: xue }; +var dx = class { constructor(t8) { this.uniforms = "", this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = w.computeOutShape(t8, 1), this.variableNames = t8.map((e, o) => `T${o}`), this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.offsetLength = t8.length - 1; for (let e = 0; e < this.offsetLength; e++) @@ -29694,38 +29697,38 @@ var fx = class { `; } }; -function Mp(r) { - let { inputs: t8, backend: e } = r, { input: o } = t8, n = e.tensorMap.get(o.dataId); - return Ft({ inputs: { x: n.complexTensorInfos.imag }, backend: e }); +function $p(r15) { + let { inputs: t8, backend: e } = r15, { input: o } = t8, n = e.tensorMap.get(o.dataId); + return At({ inputs: { x: n.complexTensorInfos.imag }, backend: e }); } -var bV = { kernelName: Gi, backendName: "webgpu", kernelFunc: Mp }; -function el(r, t8, e) { - let o = r[0].dtype; +var CV = { kernelName: Vi, backendName: "webgpu", kernelFunc: $p }; +function Yc(r15, t8, e) { + let o = r15[0].dtype; if (o === "complex64") { - let f = r.map((C) => Ni({ inputs: { input: C }, backend: e })), h = r.map((C) => Mp({ inputs: { input: C }, backend: e })), g = el(f, t8, e), x = el(h, t8, e), b = yo({ inputs: { real: g, imag: x }, backend: e }); + let f = r15.map((C) => Ii({ inputs: { input: C }, backend: e })), h = r15.map((C) => $p({ inputs: { input: C }, backend: e })), g = Yc(f, t8, e), x = Yc(h, t8, e), b = xo({ inputs: { real: g, imag: x }, backend: e }); return f.forEach((C) => e.disposeData(C.dataId)), h.forEach((C) => e.disposeData(C.dataId)), e.disposeData(g.dataId), e.disposeData(x.dataId), b; } - let n = e.shouldExecuteOnCPU(r); + let n = e.shouldExecuteOnCPU(r15); if (o === "string" && (n = true), n) { - let f = r.map((k) => { + let f = r15.map((k) => { let E = [-1, y.sizeFromShape(k.shape.slice(t8))]; return pe({ inputs: { x: k }, backend: e, attrs: { shape: E } }); - }), h = f.map((k) => ({ vals: e.readSync(k.dataId), shape: k.shape })), g = w.computeOutShape(f.map((k) => k.shape), 1), x = f[0].shape[0] === 1, b = uz(h, g, o, x), C = w.computeOutShape(r.map((k) => k.shape), t8), S = e.makeTensorInfo(C, o, b); + }), h = f.map((k) => ({ vals: e.readSync(k.dataId), shape: k.shape })), g = w.computeOutShape(f.map((k) => k.shape), 1), x = f[0].shape[0] === 1, b = pz(h, g, o, x), C = w.computeOutShape(r15.map((k) => k.shape), t8), S = e.makeTensorInfo(C, o, b); return f.forEach((k) => e.disposeData(k.dataId)), S; } let s = e.device.limits.maxStorageBuffersPerShaderStage - 1; - if (r.length > s) { + if (r15.length > s) { let f = []; - for (let g = 0; g < r.length; g += s) { - let x = r.slice(g, g + s); - f.push(el(x, t8, e)); + for (let g = 0; g < r15.length; g += s) { + let x = r15.slice(g, g + s); + f.push(Yc(x, t8, e)); } - let h = el(f, t8, e); + let h = Yc(f, t8, e); for (let g of f) e.disposeData(g.dataId); return h; } - let { tensors2D: a, outShape: i } = yue(r, t8, e), p = a.map((f) => f.shape), u = new fx(p), c = [], l = new Array(p.length - 1); + let { tensors2D: a, outShape: i } = yue(r15, t8, e), p = a.map((f) => f.shape), u = new dx(p), c = [], l = new Array(p.length - 1); if (l.length > 0) { l[0] = p[0][1], c.push({ type: "int32", data: [l[0]] }); for (let f = 1; f < l.length; f++) @@ -29736,21 +29739,21 @@ function el(r, t8, e) { let d = pe({ inputs: { x: m }, backend: e, attrs: { shape: i } }); return e.disposeData(m.dataId), d; } -function yue(r, t8, e) { - let o = w.computeOutShape(r.map((s) => s.shape), t8); - return { tensors2D: r.map((s) => pe({ inputs: { x: s }, backend: e, attrs: { shape: [y.sizeFromShape(s.shape.slice(0, t8)), y.sizeFromShape(s.shape.slice(t8))] } })), outShape: o }; +function yue(r15, t8, e) { + let o = w.computeOutShape(r15.map((s) => s.shape), t8); + return { tensors2D: r15.map((s) => pe({ inputs: { x: s }, backend: e, attrs: { shape: [y.sizeFromShape(s.shape.slice(0, t8)), y.sizeFromShape(s.shape.slice(t8))] } })), outShape: o }; } -function n0(r) { - let { inputs: t8, backend: e, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, t8[0].shape)[0], a = t8.map((u) => u.shape); +function s0(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { axis: n } = o, s = y.parseAxisParam(n, t8[0].shape)[0], a = t8.map((u) => u.shape); w.assertParamsConsistent(a, s); let i = w.computeOutShape(t8.map((u) => u.shape), s); if (y.sizeFromShape(i) === 0) return e.makeTensorInfo(i, t8[0].dtype, []); let p = t8.filter((u) => y.sizeFromShape(u.shape) > 0); - return p.length === 1 ? Ft({ inputs: { x: p[0] }, backend: e }) : el(p, s, e); + return p.length === 1 ? At({ inputs: { x: p[0] }, backend: e }) : Yc(p, s, e); } -var CV = { kernelName: ta, backendName: "webgpu", kernelFunc: n0 }; -function bue(r, t8, e, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) { +var wV = { kernelName: ta, backendName: "webgpu", kernelFunc: s0 }; +function bue(r15, t8, e, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) { let c = (D) => { switch (D) { case 1: @@ -29771,11 +29774,11 @@ function bue(r, t8, e, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) { default: throw new Error(`innerElementSize ${D} is not supported.`); } - }, m = r ? ` + }, m = r15 ? ` let coord = vec4(batch, xRow, xCol, xCh); ` : ` let coord = vec4(batch, xCh, xRow, xCol); - `, d = r ? ` + `, d = r15 ? ` let coords = vec4( batch, row / outWidth, @@ -29787,9 +29790,9 @@ function bue(r, t8, e, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) { row, col / outWidth, col % outWidth); - `, f = r ? "uniforms.xShape[1]" : "uniforms.xShape[2]", h = r ? "uniforms.xShape[2]" : "uniforms.xShape[3]", g = r ? "row" : "col", x = r ? "col" : "row", b = ` + `, f = r15 ? "uniforms.xShape[1]" : "uniforms.xShape[2]", h = r15 ? "uniforms.xShape[2]" : "uniforms.xShape[3]", g = r15 ? "row" : "col", x = r15 ? "col" : "row", b = ` let inChannels = uniforms.wShape[2]; - let outWidth = ${r ? "uniforms.outShape[2]" : "uniforms.outShape[3]"}; + let outWidth = ${r15 ? "uniforms.outShape[2]" : "uniforms.outShape[3]"}; let outRow = ${g} / outWidth; let outCol = ${g} % outWidth; @@ -29806,7 +29809,7 @@ function bue(r, t8, e, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) { let xIndex = getIndexFromCoords4D(coord, uniforms.xShape); ${c(i)} } - return resData;`, C = r ? t8 && o ? ` + return resData;`, C = r15 ? t8 && o ? ` ${b}` : ` if (row < uniforms.dimAOuter && col < uniforms.dimInner) { ${b} @@ -29816,47 +29819,47 @@ function bue(r, t8, e, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) { if (row < uniforms.dimInner && col < uniforms.dimBOuter) { ${b} } - return ${Ae(i)}(0.0);`, S = `${l(p)}`, k = Ae(u), _ = r ? Ae(i) : Ae(p), E = r ? Ae(p) : Ae(i); + return ${Ae(i)}(0.0);`, S = `${l(p)}`, k = Ae(u), _ = r15 ? Ae(i) : Ae(p), E = r15 ? Ae(p) : Ae(i); return ` - ${fr(s, a, u === 4, 4)} + ${dr(s, a, u === 4, 4)} fn mm_readA(batch: i32, row : i32, col : i32) -> ${_} { - ${r ? C : S} + ${r15 ? C : S} } fn mm_readB(batch: i32, row : i32, col : i32) -> ${E} { - ${r ? S : C} + ${r15 ? S : C} } fn mm_write(batch: i32, row : i32, col : i32, valueIn : ${k}) { if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { var value = valueIn; - let outWidth = ${r ? "uniforms.outShape[2]" : "uniforms.outShape[3]"}; + let outWidth = ${r15 ? "uniforms.outShape[2]" : "uniforms.outShape[3]"}; ${d} - ${Zr(n, s)} + ${Qr(n, s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } }`; } -var hx = class { +var fx = class { constructor(t8, e, o, n, s = false, a = null, i = false, p = false) { - this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = t8.outShape, this.isChannelsLast = t8.dataFormat === "channelsLast", this.isVec4 = ((t8.inChannels % 4 === 0 || t8.inChannels % 3 === 0) && this.isChannelsLast || t8.outWidth % 4 === 0 && !this.isChannelsLast) && t8.outChannels % 4 === 0, this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [2, 3], y: [1], z: [0] }, this.workgroupSize = lm(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = mm(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 ? (this.outputComponent = 4, this.isChannelsLast && t8.inChannels % 4 !== 0 ? (this.innerElementSize = 3, this.variableComponents = [1, 4]) : (this.innerElementSize = 4, this.variableComponents = [4, 4]), s && (this.variableNames.push("bias"), this.variableComponents.push(4)), i && (this.variableNames.push("preluActivationWeights"), this.variableComponents.push(4))) : (this.innerElementSize = this.elementsPerThread[0], s && this.variableNames.push("bias"), i && this.variableNames.push("preluActivationWeights")), this.sequentialAccessByThreads = p, this.addBias = s, this.activation = a, this.hasPreluActivationWeights = i, this.tileAOuter = this.workgroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workgroupSize[0] * this.elementsPerThread[0], this.tileInner = Math.max(this.workgroupSize[0] * this.innerElementSize, this.workgroupSize[1]), this.fitAOuter = e % this.tileAOuter === 0, this.fitBOuter = o % this.tileBOuter === 0, this.fitInner = n % this.tileInner === 0, this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`; + this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = t8.outShape, this.isChannelsLast = t8.dataFormat === "channelsLast", this.isVec4 = ((t8.inChannels % 4 === 0 || t8.inChannels % 3 === 0) && this.isChannelsLast || t8.outWidth % 4 === 0 && !this.isChannelsLast) && t8.outChannels % 4 === 0, this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [2, 3], y: [1], z: [0] }, this.workgroupSize = pm(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = cm(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 ? (this.outputComponent = 4, this.isChannelsLast && t8.inChannels % 4 !== 0 ? (this.innerElementSize = 3, this.variableComponents = [1, 4]) : (this.innerElementSize = 4, this.variableComponents = [4, 4]), s && (this.variableNames.push("bias"), this.variableComponents.push(4)), i && (this.variableNames.push("preluActivationWeights"), this.variableComponents.push(4))) : (this.innerElementSize = this.elementsPerThread[0], s && this.variableNames.push("bias"), i && this.variableNames.push("preluActivationWeights")), this.sequentialAccessByThreads = p, this.addBias = s, this.activation = a, this.hasPreluActivationWeights = i, this.tileAOuter = this.workgroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workgroupSize[0] * this.elementsPerThread[0], this.tileInner = Math.max(this.workgroupSize[0] * this.innerElementSize, this.workgroupSize[1]), this.fitAOuter = e % this.tileAOuter === 0, this.fitBOuter = o % this.tileBOuter === 0, this.fitInner = n % this.tileInner === 0, this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`; } getUserCode() { - let t8 = this.isVec4 ? Fp(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner) : Pp(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner, false, null, this.sequentialAccessByThreads), e = this.isVec4 ? [this.innerElementSize, 4, 4] : [1, 1, 1]; + let t8 = this.isVec4 ? Np(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner) : Tp(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner, false, null, this.sequentialAccessByThreads), e = this.isVec4 ? [this.innerElementSize, 4, 4] : [1, 1, 1]; return ` ${bue(this.isChannelsLast, this.fitAOuter, this.fitBOuter, this.fitInner, this.addBias, this.activation, this.hasPreluActivationWeights, e[0], e[1], e[2])} ${t8} `; } }; -var gx = class { +var hx = class { constructor(t8, e = false, o = null, n = false) { this.variableNames = ["x", "W"], this.uniforms = "filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,", this.workgroupSize = [4, 4, 8], this.outputShape = t8.outShape, this.isChannelsLast = t8.dataFormat === "channelsLast", this.dispatchLayout = this.isChannelsLast ? { x: [2], y: [1], z: [0, 3] } : { x: [3], y: [2], z: [0, 1] }, this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = e, this.activation = o, this.hasPreluActivationWeights = n, e && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.shaderKey = `conv2dnaive_${this.activation}_${this.isChannelsLast}`; } getUserCode() { return ` - ${fr(this.activation, this.hasPreluActivationWeights, false, 4)} + ${dr(this.activation, this.hasPreluActivationWeights, false, 4)} fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{ let coords = vec4(batch, row, col, chan); if (coordsInBounds4D(coords, uniforms.xShape)) { @@ -29877,7 +29880,7 @@ var gx = class { let coords = ${this.isChannelsLast ? "vec4(batch, row, col, chan);" : "vec4(batch, chan, row, col);"} if (coordsInBounds4D(coords, uniforms.outShape)) { var value = valueIn; - ${Zr(this.addBias, this.activation)} + ${Qr(this.addBias, this.activation)} setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value); } } @@ -29904,7 +29907,7 @@ var gx = class { `; } }; -var xx = class { +var gx = class { constructor(t8, e) { this.variableNames = ["x"], this.uniforms = `pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32, inChannels : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e, this.shaderKey = `im2col_${this.isChannelsLast}`; @@ -29937,78 +29940,78 @@ var xx = class { `; } }; -function yx(r, t8) { - let e = r.length; - return e >= 3 ? t8 ? [...r.slice(0, -3), r[e - 3] * r[e - 2], r[e - 1]] : [...r.slice(0, -3), r[e - 3], r[e - 2] * r[e - 1]] : !t8 && e === 1 && r[0] > 1 ? [r[0], 1] : null; +function xx(r15, t8) { + let e = r15.length; + return e >= 3 ? t8 ? [...r15.slice(0, -3), r15[e - 3] * r15[e - 2], r15[e - 1]] : [...r15.slice(0, -3), r15[e - 3], r15[e - 2] * r15[e - 1]] : !t8 && e === 1 && r15[0] > 1 ? [r15[0], 1] : null; } -function Cue({ x: r, filter: t8, convInfo: e, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { +function Cue({ x: r15, filter: t8, convInfo: e, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { let p = e.dataFormat === "channelsLast", u = !p, c = false, l = p && e.filterHeight === e.inHeight && e.filterWidth === e.inWidth && e.padInfo.type === "VALID", m = [], d, f; if (l) { let x = e.inHeight * e.inWidth * e.inChannels; - d = pe({ inputs: { x: r }, backend: o, attrs: { shape: [1, e.batchSize, x] } }), f = pe({ inputs: { x: t8 }, backend: o, attrs: { shape: [1, x, e.outChannels] } }); + d = pe({ inputs: { x: r15 }, backend: o, attrs: { shape: [1, e.batchSize, x] } }), f = pe({ inputs: { x: t8 }, backend: o, attrs: { shape: [1, x, e.outChannels] } }); } else - d = pe({ inputs: { x: r }, backend: o, attrs: { shape: p ? [e.batchSize, e.inHeight * e.inWidth, e.inChannels] : [e.batchSize, e.inChannels, e.inHeight * e.inWidth] } }), f = pe({ inputs: { x: t8 }, backend: o, attrs: { shape: [1, e.inChannels, e.outChannels] } }); + d = pe({ inputs: { x: r15 }, backend: o, attrs: { shape: p ? [e.batchSize, e.inHeight * e.inWidth, e.inChannels] : [e.batchSize, e.inChannels, e.inHeight * e.inWidth] } }), f = pe({ inputs: { x: t8 }, backend: o, attrs: { shape: [1, e.inChannels, e.outChannels] } }); if (m.push(d), m.push(f), s != null) { - let x = yx(s.shape, p); + let x = xx(s.shape, p); x != null && (s = pe({ inputs: { x: s }, backend: o, attrs: { shape: x } }), m.push(s)); } if (n != null) { - let x = yx(n.shape, p); + let x = xx(n.shape, p); x != null && (n = pe({ inputs: { x: n }, backend: o, attrs: { shape: x } }), m.push(n)); } - let h = Op({ a: p ? d : f, b: p ? f : d, transposeA: u, transposeB: c, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), g = pe({ inputs: { x: h }, backend: o, attrs: { shape: e.outShape } }); + let h = _p({ a: p ? d : f, b: p ? f : d, transposeA: u, transposeB: c, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), g = pe({ inputs: { x: h }, backend: o, attrs: { shape: e.outShape } }); m.push(h); for (let x of m) o.disposeData(x.dataId); return g; } -function wue({ x: r, filter: t8, convInfo: e, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { - let { filterWidth: p, filterHeight: u, inChannels: c, strideWidth: l, strideHeight: m, padInfo: d, outWidth: f, outHeight: h, dilationWidth: g, dilationHeight: x, dataFormat: b } = e, C = b === "channelsLast", S = p * u * c, k = h * f, _ = C ? [e.batchSize, k, S] : [e.batchSize, S, k], E = new xx(_, C), R = [{ type: "int32", data: [d.top, d.left] }, { type: "int32", data: [m, l] }, { type: "int32", data: [x, g] }, { type: "int32", data: [f] }, { type: "int32", data: [c * p] }, { type: "int32", data: [c] }], D = o.runWebGPUProgram(E, [r], r.dtype, R), P = []; +function wue({ x: r15, filter: t8, convInfo: e, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { + let { filterWidth: p, filterHeight: u, inChannels: c, strideWidth: l, strideHeight: m, padInfo: d, outWidth: f, outHeight: h, dilationWidth: g, dilationHeight: x, dataFormat: b } = e, C = b === "channelsLast", S = p * u * c, k = h * f, _ = C ? [e.batchSize, k, S] : [e.batchSize, S, k], E = new gx(_, C), R = [{ type: "int32", data: [d.top, d.left] }, { type: "int32", data: [m, l] }, { type: "int32", data: [x, g] }, { type: "int32", data: [f] }, { type: "int32", data: [c * p] }, { type: "int32", data: [c] }], D = o.runWebGPUProgram(E, [r15], r15.dtype, R), P = []; P.push(D); let O = pe({ inputs: { x: t8 }, backend: o, attrs: { shape: [1, S, -1] } }); if (P.push(O), s != null) { - let U = yx(s.shape, C); + let U = xx(s.shape, C); U != null && (s = pe({ inputs: { x: s }, backend: o, attrs: { shape: U } }), P.push(s)); } if (n != null) { - let U = yx(n.shape, C); + let U = xx(n.shape, C); U != null && (n = pe({ inputs: { x: n }, backend: o, attrs: { shape: U } }), P.push(n)); } - let B = Op({ a: C ? D : O, b: C ? O : D, transposeA: !C, transposeB: false, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), z = pe({ inputs: { x: B }, backend: o, attrs: { shape: e.outShape } }); + let B = _p({ a: C ? D : O, b: C ? O : D, transposeA: !C, transposeB: false, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), z = pe({ inputs: { x: B }, backend: o, attrs: { shape: e.outShape } }); P.push(B); for (let U of P) o.disposeData(U.dataId); return z; } -function bx({ x: r, filter: t8, convInfo: e, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { +function yx({ x: r15, filter: t8, convInfo: e, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) { let p = n != null, u = s != null, c = e.dataFormat === "channelsLast", l = c && e.filterHeight === e.inHeight && e.filterWidth === e.inWidth && e.padInfo.type === "VALID", m = A().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG"); if (!m && (l || e.filterHeight === 1 && e.filterWidth === 1 && e.dilationHeight === 1 && e.dilationWidth === 1 && e.strideHeight === 1 && e.strideWidth === 1 && (e.padInfo.type === "SAME" || e.padInfo.type === "VALID"))) - return Cue({ x: r, filter: t8, convInfo: e, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }); + return Cue({ x: r15, filter: t8, convInfo: e, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }); let d = A().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), f = d > -1 ? d : o.thresholdToIncreaseWorkgroups, h = e.batchSize * Math.ceil(e.outHeight * e.outWidth / 32) * Math.ceil(e.outChannels / 32); if (A().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER") || h <= f) - return wue({ x: r, filter: t8, convInfo: e, backend: o, bias: n, preluActivationWeights: s, leakyreluAlpha: a, activation: i }); + return wue({ x: r15, filter: t8, convInfo: e, backend: o, bias: n, preluActivationWeights: s, leakyreluAlpha: a, activation: i }); let g, x = [e.padInfo.top, e.padInfo.left], b = [{ type: "int32", data: [e.filterHeight, e.filterWidth] }, { type: "int32", data: [...x] }, { type: "int32", data: [e.strideHeight, e.strideWidth] }, { type: "int32", data: [e.dilationHeight, e.dilationWidth] }]; if (m) - g = new gx(e, p, i, u); + g = new hx(e, p, i, u); else { let _ = c ? e.outHeight * e.outWidth : e.outChannels, E = c ? e.outChannels : e.outHeight * e.outWidth, R = e.filterHeight * e.filterWidth * e.inChannels; b.push({ type: "int32", data: [_] }, { type: "int32", data: [E] }, { type: "int32", data: [R] }); let D = o.adapterInfo.isIntel(); - g = new hx(e, _, E, R, p, i, u, D); + g = new fx(e, _, E, R, p, i, u, D); } - let C = [], S = [r, t8]; + let C = [], S = [r15, t8]; p && (!c && n.shape.length === 1 && (n = pe({ inputs: { x: n }, backend: o, attrs: { shape: [n.shape[0], 1, 1] } }), C.push(n)), S.push(n)), u && (!c && s.shape.length === 1 && (s = pe({ inputs: { x: s }, backend: o, attrs: { shape: [s.shape[0], 1, 1] } }), C.push(s)), S.push(s)), i === "leakyrelu" && (b.push({ type: "float32", data: [a] }), g.uniforms += " alpha : f32,"); - let k = o.runWebGPUProgram(g, S, r.dtype, b); + let k = o.runWebGPUProgram(g, S, r15.dtype, b); for (let _ of C) o.disposeData(_.dataId); return k; } -function Sue(r) { - let { inputs: t8, attrs: e, backend: o } = r, { x: n, filter: s } = t8, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = e, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l); - return bx({ x: n, filter: s, convInfo: m, backend: o }); +function Sue(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, { x: n, filter: s } = t8, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = e, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l); + return yx({ x: n, filter: s, convInfo: m, backend: o }); } -var wV = { kernelName: rn, backendName: "webgpu", kernelFunc: Sue }; -var Cx = class { +var SV = { kernelName: tn, backendName: "webgpu", kernelFunc: Sue }; +var bx = class { constructor(t8) { this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,", this.workgroupSize = [64, 1, 1], this.size = false, this.isVec4 = false, this.workPerThread = 1, this.outputShape = t8.inShape, this.isChannelsLast = t8.dataFormat === "channelsLast", this.isVec4 = this.isChannelsLast && t8.outChannels % 4 === 0 && t8.inChannels % 4 === 0, this.isVec4 ? (this.workPerThread = 2, this.outputComponent = 4, this.workgroupSize = [4, 4, 4], this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, [4, this.workPerThread, 1])) : (this.size = true, this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize)), this.shaderKey = `conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`; } @@ -30160,7 +30163,7 @@ var Cx = class { `; } }; -var wx = class { +var Cx = class { constructor(t8) { this.variableNames = ["x", "dy"], this.uniforms = "pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.filterShape, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = t8.dataFormat === "channelsLast", this.shaderKey = `conv2DDerFilter_${this.isChannelsLast}`; } @@ -30209,7 +30212,7 @@ var wx = class { `; } }; -var Sx = class { +var wx = class { constructor(t8) { this.variableNames = ["x", "dy"], this.uniforms = `pads : vec3, strides : vec3, batchSize : i32, outDepth : i32, outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.filterShape, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3DDerFilter"; @@ -30258,7 +30261,7 @@ var Sx = class { `; } }; -var Ix = class { +var Sx = class { constructor(t8) { this.variableNames = ["dy", "W"], this.uniforms = `filterDims : vec3, pads : vec3, strides : vec3, outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.inShape, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3DDerInput"; @@ -30320,12 +30323,12 @@ var Ix = class { `; } }; -function Iue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, dy: s } = t8, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new wx(m), f = [{ type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize] }, { type: "int32", data: [m.outHeight] }, { type: "int32", data: [m.outWidth] }, { type: "int32", data: [m.inHeight] }, { type: "int32", data: [m.inWidth] }]; +function Iue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, dy: s } = t8, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = w.convertConv2DDataFormat(p), m = w.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new Cx(m), f = [{ type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize] }, { type: "int32", data: [m.outHeight] }, { type: "int32", data: [m.outWidth] }, { type: "int32", data: [m.inHeight] }, { type: "int32", data: [m.inWidth] }]; return e.runWebGPUProgram(d, [n, s], n.dtype, f); } -var SV = { kernelName: Oi, backendName: "webgpu", kernelFunc: Iue }; -function vue(r = 4) { +var IV = { kernelName: Ai, backendName: "webgpu", kernelFunc: Iue }; +function vue(r15 = 4) { let t8 = (s) => { switch (s) { case 1: @@ -30354,25 +30357,25 @@ function vue(r = 4) { let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.strides[0]); let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.strides[1]); if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) { - return ${Ae(r)}(0.0); + return ${Ae(r15)}(0.0); } if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) { - return ${Ae(r)}(0.0); + return ${Ae(r15)}(0.0); } let coord = vec4( batch, i32(xR), i32(xC), col % uniforms.outBackprop[3]); - return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`} + return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r15}];`} } - return ${Ae(r)}(0.0);`; + return ${Ae(r15)}(0.0);`; return ` - fn mm_readA(batch: i32, row : i32, col : i32) -> ${Ae(r)} { + fn mm_readA(batch: i32, row : i32, col : i32) -> ${Ae(r15)} { ${o} } - fn mm_readB(batch: i32, row : i32, col : i32) -> ${Ae(r)} { + fn mm_readB(batch: i32, row : i32, col : i32) -> ${Ae(r15)} { let coordX = uniforms.filterDims.x - 1 - row / (uniforms.filterDims[1] * uniforms.outBackprop[3]); let coordY = uniforms.filterDims.y - 1 - @@ -30381,12 +30384,12 @@ function vue(r = 4) { coordX >= 0 && coordY >= 0) { let rowInner = row % uniforms.outBackprop[3]; let coord = vec4(coordX, coordY, col, rowInner); - ${t8(r)} + ${t8(r15)} } - return ${Ae(r)}(0.0); + return ${Ae(r15)}(0.0); } - fn mm_write(batch: i32, row : i32, col : i32, valueInput : ${Ae(r)}) { + fn mm_write(batch: i32, row : i32, col : i32, valueInput : ${Ae(r15)}) { if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { var value = valueInput; let outCoord = vec4( @@ -30394,35 +30397,35 @@ function vue(r = 4) { row / uniforms.outShape[2], row % uniforms.outShape[2], col); - result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value; + result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r15}] = value; } }`; } -var vx = class { +var Ix = class { constructor(t8) { - this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = t8.inShape, y.assert(t8.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.isVec4 = t8.inChannels % 4 === 0 && t8.outChannels % 4 === 0, this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workgroupSize = lm(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = mm(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 && (this.outputComponent = 4, this.variableComponents = [4, 1]), this.shaderKey = `conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`; + this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = t8.inShape, y.assert(t8.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.isVec4 = t8.inChannels % 4 === 0 && t8.outChannels % 4 === 0, this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workgroupSize = pm(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = cm(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 && (this.outputComponent = 4, this.variableComponents = [4, 1]), this.shaderKey = `conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`; } getUserCode() { - let t8 = this.isVec4 ? Fp(this.elementsPerThread, this.workgroupSize) : Pp(this.elementsPerThread, this.workgroupSize); + let t8 = this.isVec4 ? Np(this.elementsPerThread, this.workgroupSize) : Tp(this.elementsPerThread, this.workgroupSize); return ` ${vue(this.isVec4 ? 4 : 1)} ${t8} `; } }; -function kue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, filter: s } = t8, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(u), m = w.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l), d = [{ type: "int32", data: [m.filterHeight, m.filterWidth] }, { type: "int32", data: [m.filterHeight - 1 - m.padInfo.top, m.filterWidth - 1 - m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize, m.outHeight, m.outWidth, m.outChannels] }], f; +function kue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, filter: s } = t8, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(u), m = w.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l), d = [{ type: "int32", data: [m.filterHeight, m.filterWidth] }, { type: "int32", data: [m.filterHeight - 1 - m.padInfo.top, m.filterWidth - 1 - m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize, m.outHeight, m.outWidth, m.outChannels] }], f; if (A().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE") || m.dataFormat !== "channelsLast") - f = new Cx(m); + f = new bx(m); else { - f = new vx(m); + f = new Ix(m); let h = m.inHeight * m.inWidth, g = m.inChannels, x = m.filterHeight * m.filterWidth * m.outChannels; d.push({ type: "uint32", data: [h] }, { type: "uint32", data: [g] }, { type: "uint32", data: [x] }); } return e.runWebGPUProgram(f, [n, s], "float32", d); } -var IV = { kernelName: on, backendName: "webgpu", kernelFunc: kue }; -var kx = class { +var vV = { kernelName: rn, backendName: "webgpu", kernelFunc: kue }; +var vx = class { constructor(t8) { this.variableNames = ["x", "W"], this.uniforms = "filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.outShape, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3dnaive"; } @@ -30512,26 +30515,26 @@ var kx = class { }`; } }; -function Nue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p } = o, u = w.computeConv3DInfo(n.shape, s.shape, a, p, i), c = [u.padInfo.front, u.padInfo.top, u.padInfo.left], l = [{ type: "int32", data: [u.filterDepth, u.filterHeight, u.filterWidth] }, { type: "int32", data: [...c] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.dilationDepth, u.dilationHeight, u.dilationWidth] }], m = new kx(u), d = dt(n.dtype, s.dtype); +function Nue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p } = o, u = w.computeConv3DInfo(n.shape, s.shape, a, p, i), c = [u.padInfo.front, u.padInfo.top, u.padInfo.left], l = [{ type: "int32", data: [u.filterDepth, u.filterHeight, u.filterWidth] }, { type: "int32", data: [...c] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.dilationDepth, u.dilationHeight, u.dilationWidth] }], m = new vx(u), d = dt(n.dtype, s.dtype); return e.runWebGPUProgram(m, [n, s], d, l); } -var vV = { kernelName: nn, backendName: "webgpu", kernelFunc: Nue }; -function Tue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, dy: s } = t8, { strides: a, pad: i, filterShape: p } = o, u = w.computeConv3DInfo(n.shape, p, a, 1, i), c = new Sx(u), l = [{ type: "int32", data: [u.padInfo.front, u.padInfo.top, u.padInfo.left] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.batchSize] }, { type: "int32", data: [u.outDepth] }, { type: "int32", data: [u.outHeight] }, { type: "int32", data: [u.outWidth] }, { type: "int32", data: [u.inDepth] }, { type: "int32", data: [u.inHeight] }, { type: "int32", data: [u.inWidth] }]; +var kV = { kernelName: on, backendName: "webgpu", kernelFunc: Nue }; +function Tue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, dy: s } = t8, { strides: a, pad: i, filterShape: p } = o, u = w.computeConv3DInfo(n.shape, p, a, 1, i), c = new wx(u), l = [{ type: "int32", data: [u.padInfo.front, u.padInfo.top, u.padInfo.left] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.batchSize] }, { type: "int32", data: [u.outDepth] }, { type: "int32", data: [u.outHeight] }, { type: "int32", data: [u.outWidth] }, { type: "int32", data: [u.inDepth] }, { type: "int32", data: [u.inHeight] }, { type: "int32", data: [u.inWidth] }]; return e.runWebGPUProgram(c, [n, s], s.dtype, l); } -var kV = { kernelName: Xa, backendName: "webgpu", kernelFunc: Tue }; -function _ue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, filter: s } = t8, { strides: a, pad: i, inputShape: p } = o, u = w.computeConv3DInfo(p, s.shape, a, 1, i), c = new Ix(u), l = [{ type: "int32", data: [u.filterDepth, u.filterHeight, u.filterWidth] }, { type: "int32", data: [u.filterDepth - 1 - u.padInfo.front, u.filterHeight - 1 - u.padInfo.top, u.filterWidth - 1 - u.padInfo.left] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.outDepth] }, { type: "int32", data: [u.outHeight] }, { type: "int32", data: [u.outWidth] }, { type: "int32", data: [u.outChannels] }]; +var NV = { kernelName: ja, backendName: "webgpu", kernelFunc: Tue }; +function _ue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, filter: s } = t8, { strides: a, pad: i, inputShape: p } = o, u = w.computeConv3DInfo(p, s.shape, a, 1, i), c = new Sx(u), l = [{ type: "int32", data: [u.filterDepth, u.filterHeight, u.filterWidth] }, { type: "int32", data: [u.filterDepth - 1 - u.padInfo.front, u.filterHeight - 1 - u.padInfo.top, u.filterWidth - 1 - u.padInfo.left] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.outDepth] }, { type: "int32", data: [u.outHeight] }, { type: "int32", data: [u.outWidth] }, { type: "int32", data: [u.outChannels] }]; return e.runWebGPUProgram(c, [n, s], n.dtype, l); } -var NV = { kernelName: sn, backendName: "webgpu", kernelFunc: _ue }; +var TV = { kernelName: nn, backendName: "webgpu", kernelFunc: _ue }; var $ue = ye({ opType: Z.COS }); -var TV = { kernelName: an, backendName: "webgpu", kernelFunc: $ue }; +var _V = { kernelName: sn, backendName: "webgpu", kernelFunc: $ue }; var Eue = ye({ opType: Z.COSH }); -var _V = { kernelName: un, backendName: "webgpu", kernelFunc: Eue }; -var Nx = class { +var $V = { kernelName: an, backendName: "webgpu", kernelFunc: Eue }; +var kx = class { constructor(t8, e, o, n) { this.variableNames = ["Image", "Boxes", "BoxInd"], this.uniforms = "extrapolationValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true; let [s] = e; @@ -30598,33 +30601,33 @@ var Nx = class { `; } }; -var Rue = (r) => { - let { inputs: t8, backend: e, attrs: o } = r, { image: n, boxes: s, boxInd: a } = t8, { cropSize: i, method: p, extrapolationValue: u } = o, c = new Nx(n.shape[3], s.shape, i, p), l = [{ type: "float32", data: [u] }]; +var Rue = (r15) => { + let { inputs: t8, backend: e, attrs: o } = r15, { image: n, boxes: s, boxInd: a } = t8, { cropSize: i, method: p, extrapolationValue: u } = o, c = new kx(n.shape[3], s.shape, i, p), l = [{ type: "float32", data: [u] }]; return e.runWebGPUProgram(c, [n, s, a], "float32", l); }; -var $V = { kernelName: ln, backendName: "webgpu", kernelFunc: Rue }; -var Lp; -(function(r) { - r.Prod = "*", r.Sum = "+"; -})(Lp || (Lp = {})); -var xm = class { +var EV = { kernelName: cn, backendName: "webgpu", kernelFunc: Rue }; +var Ep; +(function(r15) { + r15.Prod = "*", r15.Sum = "+"; +})(Ep || (Ep = {})); +var hm = class { constructor(t8, e, o, n) { this.variableNames = ["x"], this.uniforms = "index : f32,", this.size = true, this.workgroupSize = [128, 1, 1], this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.exclusive = o, this.reverse = n, this.op = t8, this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`; } getUserCode() { - let t8 = this.outputShape.length, e = this.op === Lp.Prod ? "1.0" : "0.0", o = this.exclusive ? e : `getX(${EV(t8, "coords", this.op)})`, n = this.outputShape[this.outputShape.length - 1], s = "", a = ""; + let t8 = this.outputShape.length, e = this.op === Ep.Prod ? "1.0" : "0.0", o = this.exclusive ? e : `getX(${RV(t8, "coords", this.op)})`, n = this.outputShape[this.outputShape.length - 1], s = "", a = ""; return this.exclusive ? (s = this.reverse ? `end != ${n - 1}` : "end != 0", a = this.reverse ? "end + 1" : "end - 1") : (s = this.reverse ? `end + pow2 < ${n}` : "end >= pow2", a = this.reverse ? "end + pow2" : "end - pow2"), ` ${G("index")} { if (index < uniforms.size) { var coords = getCoordsFromIndex(index); - let end = ${RV(t8, "coords", this.op)}; + let end = ${DV(t8, "coords", this.op)}; var val = ${o}; let pow2 = i32(pow(2.0, uniforms.index)); if (${s}) { let idx = ${a}; - ${RV(t8, "coords", this.op)} = idx; - val ${this.op}= getX(${EV(t8, "coords", this.op)}); + ${DV(t8, "coords", this.op)} = idx; + val ${this.op}= getX(${RV(t8, "coords", this.op)}); } setOutputAtIndex(index, val); } @@ -30632,65 +30635,65 @@ var xm = class { `; } }; -function EV(r, t8, e) { - if (r === 1) +function RV(r15, t8, e) { + if (r15 === 1) return `${t8}`; - if (r === 2) + if (r15 === 2) return `${t8}.x, ${t8}.y`; - if (r === 3) + if (r15 === 3) return `${t8}.x, ${t8}.y, ${t8}.z`; - if (r === 4) + if (r15 === 4) return `${t8}.x, ${t8}.y, ${t8}.z, ${t8}.w`; - throw Error(`Cumulative ${e} for rank ${r} is not yet supported`); + throw Error(`Cumulative ${e} for rank ${r15} is not yet supported`); } -function RV(r, t8, e) { - if (r === 1) +function DV(r15, t8, e) { + if (r15 === 1) return `${t8}`; - if (r === 2) + if (r15 === 2) return `${t8}.y`; - if (r === 3) + if (r15 === 3) return `${t8}.z`; - if (r === 4) + if (r15 === 4) return `${t8}.w`; - throw Error(`Cumulative ${e} for rank ${r} is not yet supported`); + throw Error(`Cumulative ${e} for rank ${r15} is not yet supported`); } -function Tx(r, t8, e, o, n, s) { +function Nx(r15, t8, e, o, n, s) { let a = t8.shape.length, i = w.getAxesPermutation([o], a), p = t8; - i != null && (p = yr({ inputs: { x: t8 }, backend: e, attrs: { perm: i } })); + i != null && (p = xr({ inputs: { x: t8 }, backend: e, attrs: { perm: i } })); let u = w.getInnerMostAxes(1, a)[0]; if (u !== a - 1) throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t8.shape.length - 1} but got axis=${o}`); - let c = p.shape[u], l = Ft({ inputs: { x: p }, backend: e }); + let c = p.shape[u], l = At({ inputs: { x: p }, backend: e }); for (let m = 0; m <= Math.ceil(Math.log2(c)) - 1; m++) { - let d = new xm(r, p.shape, false, s), f = l, h = [{ type: "float32", data: [m] }]; + let d = new hm(r15, p.shape, false, s), f = l, h = [{ type: "float32", data: [m] }]; l = e.runWebGPUProgram(d, [l], l.dtype, h), e.disposeData(f.dataId); } if (n) { - let m = new xm(r, p.shape, n, s), d = l, f = [{ type: "float32", data: [0] }]; + let m = new hm(r15, p.shape, n, s), d = l, f = [{ type: "float32", data: [0] }]; l = e.runWebGPUProgram(m, [l], l.dtype, f), e.disposeData(d.dataId); } if (i != null) { - let m = w.getUndoAxesPermutation(i), d = yr({ inputs: { x: l }, backend: e, attrs: { perm: m } }); + let m = w.getUndoAxesPermutation(i), d = xr({ inputs: { x: l }, backend: e, attrs: { perm: m } }); return e.disposeData(l.dataId), e.disposeData(p.dataId), d; } return l; } -function Due(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o; - return Tx(Lp.Prod, n, e, s, a, i); +function Due(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o; + return Nx(Ep.Prod, n, e, s, a, i); } -var DV = { kernelName: pn, backendName: "webgpu", kernelFunc: Due }; -function Aue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o; - return Tx(Lp.Sum, n, e, s, a, i); +var AV = { kernelName: un, backendName: "webgpu", kernelFunc: Due }; +function Aue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, exclusive: a, reverse: i } = o; + return Nx(Ep.Sum, n, e, s, a, i); } -var AV = { kernelName: cn, backendName: "webgpu", kernelFunc: Aue }; -function Fue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, weights: s } = t8, { size: a, binaryOutput: i } = o, p = n.shape.length === 1, c = y.sizeFromShape(s.shape) > 0, l = s.dtype, m = p ? [n.shape[0]] : [n.shape[0], n.shape[1]], d = p ? [a] : [n.shape[0], a], f = kt({ backend: e, attrs: { shape: d, value: 0, dtype: l } }), h = new Jc(m, c, i), g = [{ type: "int32", data: [a] }], x = c ? [n, s] : [n]; +var FV = { kernelName: pn, backendName: "webgpu", kernelFunc: Aue }; +function Fue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, weights: s } = t8, { size: a, binaryOutput: i } = o, p = n.shape.length === 1, c = y.sizeFromShape(s.shape) > 0, l = s.dtype, m = p ? [n.shape[0]] : [n.shape[0], n.shape[1]], d = p ? [a] : [n.shape[0], a], f = vt({ backend: e, attrs: { shape: d, value: 0, dtype: l } }), h = new Xc(m, c, i), g = [{ type: "int32", data: [a] }], x = c ? [n, s] : [n]; return e.runWebGPUProgram(h, x, l, g, f); } -var FV = { kernelName: ra, backendName: "webgpu", kernelFunc: Fue }; -var _x = class { +var PV = { kernelName: ra, backendName: "webgpu", kernelFunc: Fue }; +var Tx = class { constructor(t8, e) { this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `depthToSpace_${e}`, this.dataFormat = e; } @@ -30733,19 +30736,19 @@ var _x = class { return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)"; } }; -function Pue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = [{ type: "int32", data: [s] }], g = new _x(f, a); +function Pue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = [{ type: "int32", data: [s] }], g = new Tx(f, a); return e.runWebGPUProgram(g, [n], n.dtype, h); } -var PV = { kernelName: mn, backendName: "webgpu", kernelFunc: Pue }; -var $x = class { +var OV = { kernelName: ln, backendName: "webgpu", kernelFunc: Pue }; +var _x = class { constructor(t8, e, o, n = false, s = null, a = false) { this.variableNames = ["x", "W"], this.uniforms = "pads : vec2, inDims : vec2,", this.workgroupSize = [16, 16, 1], this.outputShape = t8, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), n && this.variableNames.push("bias"), a && this.variableNames.push("preluActivationWeights"), this.addBias = n, this.activation = s, this.hasPreluActivation = a, this.filterHeight = e, this.filterWidth = o, this.shaderKey = `depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`; } getUserCode() { let t8 = this.filterWidth * this.filterHeight, e = this.workgroupSize[0] * this.workgroupSize[1] * this.workgroupSize[2], o = this.workgroupSize[1] + this.filterHeight - 1, n = this.workgroupSize[0] + this.filterWidth - 1; return ` - ${fr(this.activation, this.hasPreluActivation, false, 4)} + ${dr(this.activation, this.hasPreluActivation, false, 4)} var mm_Asub : array, ${o}>; var mm_Bsub : array, ${this.filterHeight}>; @@ -30801,7 +30804,7 @@ var $x = class { value = fma(xVal, wVal, value); } } - ${Zr(this.addBias, this.activation)} + ${Qr(this.addBias, this.activation)} if (coordsInBounds4D(coords, uniforms.outShape)) { setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } @@ -30809,7 +30812,7 @@ var $x = class { `; } }; -var tl = class { +var Qc = class { constructor(t8, e = false, o = null, n = false) { this.variableNames = ["x", "W"], this.uniforms = "pads : vec2, inDims : vec2, virtualWidth : i32,", this.workgroupSize = [64, 1, 1], this.workPerThread = 4, this.outputComponent = 4, this.outputShape = t8.outShape, this.virtualWidth = Math.ceil(this.outputShape[2] / this.workPerThread) * this.workPerThread; let s = [this.outputShape[0], this.outputShape[1], this.virtualWidth, this.outputShape[3]]; @@ -30818,7 +30821,7 @@ var tl = class { getUserCode() { let t8 = (this.workPerThread - 1) * this.convInfo.strideWidth + this.convInfo.filterWidth, e = this.convInfo.strideHeight, o = this.convInfo.strideWidth; return ` - ${fr(this.activation, this.hasPreluActivation, true, 4)} + ${dr(this.activation, this.hasPreluActivation, true, 4)} fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 { var value = vec4(0.0); if (col >=0 && col < uniforms.inDims[1]) { @@ -30867,7 +30870,7 @@ var tl = class { let coords = vec4(batch, r, c + i, d1); if (coordsInBounds4D(coords, uniforms.outShape)) { var value = dotProd[i]; - ${Zr(this.addBias, this.activation)} + ${Qr(this.addBias, this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } @@ -30875,7 +30878,7 @@ var tl = class { `; } }; -var rl = class { +var Zc = class { constructor(t8, e = false, o = null, n = false) { this.variableNames = ["x", "W"], this.uniforms = `pads : vec2, inDims : vec2, filterHeight : i32, filterWidth : i32, strides : vec2, dilations : vec2,`, this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = t8.outShape, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = t8.dataFormat === "channelsLast", e && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = t8, this.addBias = e, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwise_${this.activation}_${this.isChannelsLast}`; @@ -30883,7 +30886,7 @@ var rl = class { getUserCode() { let t8 = this.isChannelsLast ? "getX(batch, xR, xC, d1);" : "getX(batch, d1, xR, xC);"; return ` - ${fr(this.activation, this.hasPreluActivation, false, 4)} + ${dr(this.activation, this.hasPreluActivation, false, 4)} ${G("index")} { if (index < uniforms.size) { @@ -30944,21 +30947,21 @@ var rl = class { } } } - ${Zr(this.addBias, this.activation)} + ${Qr(this.addBias, this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } `; } }; -function Oue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s } = t8, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(p), m = u; +function Oue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s } = t8, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = w.convertConv2DDataFormat(p), m = u; m == null && (m = [1, 1]); let d = w.computeConv2DInfo(n.shape, s.shape, a, m, i, c, true, l), f = [{ type: "int32", data: [d.padInfo.top, d.padInfo.left] }, { type: "int32", data: [d.inHeight, d.inWidth] }], h = d.dataFormat === "channelsLast", g; - return !h && d.inHeight > 16 && d.inWidth > 16 && d.strideHeight === 1 && d.strideWidth === 1 && d.dilationWidth === 1 && d.dilationHeight === 1 && d.inChannels === d.outChannels ? g = new $x(d.outShape, d.filterHeight, d.filterWidth) : h && d.outHeight > 4 && d.outWidth > 4 && d.strideWidth <= 2 && d.inChannels === d.outChannels && d.dilationHeight === 1 && d.dilationWidth === 1 && d.inChannels % 4 === 0 ? (g = new tl(d), f.push({ type: "int32", data: [g.virtualWidth] })) : (g = new rl(d), f.push({ type: "int32", data: [d.filterHeight] }, { type: "int32", data: [d.filterWidth] }, { type: "int32", data: [d.strideHeight, d.strideWidth] }, { type: "int32", data: [d.dilationHeight, d.dilationWidth] })), e.runWebGPUProgram(g, [n, s], n.dtype, f); + return !h && d.inHeight > 16 && d.inWidth > 16 && d.strideHeight === 1 && d.strideWidth === 1 && d.dilationWidth === 1 && d.dilationHeight === 1 && d.inChannels === d.outChannels ? g = new _x(d.outShape, d.filterHeight, d.filterWidth) : h && d.outHeight > 4 && d.outWidth > 4 && d.strideWidth <= 2 && d.inChannels === d.outChannels && d.dilationHeight === 1 && d.dilationWidth === 1 && d.inChannels % 4 === 0 ? (g = new Qc(d), f.push({ type: "int32", data: [g.virtualWidth] })) : (g = new Zc(d), f.push({ type: "int32", data: [d.filterHeight] }, { type: "int32", data: [d.filterWidth] }, { type: "int32", data: [d.strideHeight, d.strideWidth] }, { type: "int32", data: [d.dilationHeight, d.dilationWidth] })), e.runWebGPUProgram(g, [n, s], n.dtype, f); } -var OV = { kernelName: dn, backendName: "webgpu", kernelFunc: Oue }; -var Ex = class { +var MV = { kernelName: mn, backendName: "webgpu", kernelFunc: Oue }; +var $x = class { constructor(t8) { this.variableNames = ["x", "dy"], this.uniforms = `strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.filterShape, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "depthwise_conv2d_backprop_filter"; @@ -31002,7 +31005,7 @@ var Ex = class { `; } }; -var Rx = class { +var Ex = class { constructor(t8) { this.variableNames = ["dy", "W"], this.uniforms = `strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, channelMul : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.inShape, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "depthwise_conv2d_backprop_input"; @@ -31053,17 +31056,17 @@ var Rx = class { `; } }; -function Mue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, dy: s } = t8, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = w.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new Ex(l), d = [{ type: "int32", data: [l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.filterHeight, l.filterWidth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "int32", data: [l.inHeight] }, { type: "int32", data: [l.inWidth] }, { type: "int32", data: [l.batchSize] }, { type: "int32", data: [l.outChannels / l.inChannels] }]; +function Mue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, dy: s } = t8, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = w.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new $x(l), d = [{ type: "int32", data: [l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.filterHeight, l.filterWidth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "int32", data: [l.inHeight] }, { type: "int32", data: [l.inWidth] }, { type: "int32", data: [l.batchSize] }, { type: "int32", data: [l.outChannels / l.inChannels] }]; return e.runWebGPUProgram(m, [n, s], "float32", d); } -var MV = { kernelName: Mi, backendName: "webgpu", kernelFunc: Mue }; -function Lue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, filter: s } = t8, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = w.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new Rx(l), d = [{ type: "int32", data: [l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.filterHeight - 1 - l.padInfo.top, l.filterWidth - 1 - l.padInfo.left] }, { type: "int32", data: [l.filterHeight, l.filterWidth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "int32", data: [l.outChannels / l.inChannels] }]; +var LV = { kernelName: Fi, backendName: "webgpu", kernelFunc: Mue }; +function Lue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, filter: s } = t8, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = w.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new Ex(l), d = [{ type: "int32", data: [l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.filterHeight - 1 - l.padInfo.top, l.filterWidth - 1 - l.padInfo.left] }, { type: "int32", data: [l.filterHeight, l.filterWidth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "int32", data: [l.outChannels / l.inChannels] }]; return e.runWebGPUProgram(m, [n, s], n.dtype, d); } -var LV = { kernelName: Li, backendName: "webgpu", kernelFunc: Lue }; -var Dx = class { +var BV = { kernelName: Pi, backendName: "webgpu", kernelFunc: Lue }; +var Rx = class { constructor(t8) { this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [t8, t8], this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "diag"; } @@ -31079,12 +31082,12 @@ var Dx = class { `; } }; -function Bue(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = pe({ inputs: { x: o }, backend: e, attrs: { shape: [s] } }), i = new Dx(s), p = e.runWebGPUProgram(i, [a], a.dtype), u = pe({ inputs: { x: p }, backend: e, attrs: { shape: n } }); +function Bue(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = pe({ inputs: { x: o }, backend: e, attrs: { shape: [s] } }), i = new Rx(s), p = e.runWebGPUProgram(i, [a], a.dtype), u = pe({ inputs: { x: p }, backend: e, attrs: { shape: n } }); return e.disposeData(a.dataId), e.disposeData(p.dataId), u; } -var BV = { kernelName: oa, backendName: "webgpu", kernelFunc: Bue }; -var Ax = class { +var zV = { kernelName: oa, backendName: "webgpu", kernelFunc: Bue }; +var Dx = class { constructor(t8) { this.variableNames = ["x", "w"], this.uniforms = "filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.outShape, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "dilation2d"; } @@ -31124,12 +31127,12 @@ var Ax = class { `; } }; -function zue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p } = o, u = w.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c = [u.padInfo.top, u.padInfo.left], l = [{ type: "int32", data: [u.filterHeight, u.filterWidth] }, { type: "int32", data: [...c] }, { type: "int32", data: [u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.dilationHeight, u.dilationWidth] }], m = new Ax(u); +function zue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s } = t8, { strides: a, pad: i, dilations: p } = o, u = w.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c = [u.padInfo.top, u.padInfo.left], l = [{ type: "int32", data: [u.filterHeight, u.filterWidth] }, { type: "int32", data: [...c] }, { type: "int32", data: [u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.dilationHeight, u.dilationWidth] }], m = new Dx(u); return e.runWebGPUProgram(m, [n, s], n.dtype, l); } -var zV = { kernelName: fn, backendName: "webgpu", kernelFunc: zue }; -var Fx = class { +var VV = { kernelName: dn, backendName: "webgpu", kernelFunc: zue }; +var Ax = class { constructor(t8, e) { if (this.variableNames = ["x", "w", "dy"], this.uniforms = "filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = t8.inShape, this.dispatchLayout = X(t8.outShape), this.dispatch = H(this.dispatchLayout, t8.outShape, this.workgroupSize), e !== "float32" && e !== "int32") throw new Error(`Dilation2DBackpropInput only supports float32 and int32 @@ -31177,13 +31180,13 @@ var Fx = class { let flatIndexIn = d + uniforms.xShape[3] * (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b)); let value = getDy(b, r, c, d); - ${Qr("&result[flatIndexIn]", "value", this.type)} + ${Yr("&result[flatIndexIn]", "value", this.type)} } } `; } }; -var Px = class { +var Fx = class { constructor(t8, e, o) { if (this.variableNames = ["x", "w", "dy"], this.uniforms = "filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = t8.filterShape, this.dispatchLayout = X(t8.outShape), this.dispatch = H(this.dispatchLayout, t8.outShape, this.workgroupSize), o !== "float32" && o !== "int32") throw new Error(`Dilation2DBackpropFilter only supports float32 and int32 @@ -31230,25 +31233,25 @@ var Px = class { let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]); let value = getDy(b, r, c, d); - ${Qr("&result[flatIndexIn]", "value", this.type)} + ${Yr("&result[flatIndexIn]", "value", this.type)} } } `; } }; -function Vue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s, dy: a } = t8, { strides: i, pad: p, dilations: u } = o, c = w.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = s.dtype, m = new Px(c, s.shape, l), d = [{ type: "int32", data: [c.filterHeight, c.filterWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [y.sizeFromShape(c.outShape)] }], f = kt({ backend: e, attrs: { shape: s.shape, value: 0, dtype: l } }); +function Vue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s, dy: a } = t8, { strides: i, pad: p, dilations: u } = o, c = w.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = s.dtype, m = new Fx(c, s.shape, l), d = [{ type: "int32", data: [c.filterHeight, c.filterWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [y.sizeFromShape(c.outShape)] }], f = vt({ backend: e, attrs: { shape: s.shape, value: 0, dtype: l } }); return e.runWebGPUProgram(m, [n, s, a], l, d, f); } -var VV = { kernelName: zi, backendName: "webgpu", kernelFunc: Vue }; -function Wue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s, dy: a } = t8, { strides: i, pad: p, dilations: u } = o, c = w.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = n.dtype, m = new Fx(c, l), d = [{ type: "int32", data: [c.filterHeight, c.filterWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [y.sizeFromShape(c.outShape)] }], f = kt({ backend: e, attrs: { shape: c.inShape, value: 0, dtype: l } }); +var WV = { kernelName: Mi, backendName: "webgpu", kernelFunc: Vue }; +function Wue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s, dy: a } = t8, { strides: i, pad: p, dilations: u } = o, c = w.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = n.dtype, m = new Ax(c, l), d = [{ type: "int32", data: [c.filterHeight, c.filterWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [y.sizeFromShape(c.outShape)] }], f = vt({ backend: e, attrs: { shape: c.inShape, value: 0, dtype: l } }); return e.runWebGPUProgram(m, [n, s, a], l, d, f); } -var WV = { kernelName: Bi, backendName: "webgpu", kernelFunc: Wue }; -var Ox = class { +var UV = { kernelName: Oi, backendName: "webgpu", kernelFunc: Wue }; +var Px = class { constructor(t8, e, o) { - this.variableNames = ["Image"], this.uniforms = "alpha: f32,", this.workgroupSize = [64, 1, 1], this.pixelsOpType = Ii.DRAW, this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.type = e, this.textureFormat = o, this.shaderKey = `draw_${e}_${o}`; + this.variableNames = ["Image"], this.uniforms = "alpha: f32,", this.workgroupSize = [64, 1, 1], this.pixelsOpType = Ci.DRAW, this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.type = e, this.textureFormat = o, this.shaderKey = `draw_${e}_${o}`; } getUserCode() { let t8, e = this.type === "float32" ? "value" : "value / 255.0"; @@ -31278,8 +31281,8 @@ var Ox = class { `; } }; -function Uue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { image: n } = t8, { canvas: s, options: a } = o, [i, p] = n.shape.slice(0, 2), { imageOptions: u } = a || {}, c = (u == null ? void 0 : u.alpha) || 1, l = e.device.features.has("bgra8unorm-storage") ? "bgra8unorm" : "rgba8unorm", m = [i, p], d = new Ox(m, n.dtype, l); +function Uue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { image: n } = t8, { canvas: s, options: a } = o, [i, p] = n.shape.slice(0, 2), { imageOptions: u } = a || {}, c = (u == null ? void 0 : u.alpha) || 1, l = e.device.features.has("bgra8unorm-storage") ? "bgra8unorm" : "rgba8unorm", m = [i, p], d = new Px(m, n.dtype, l); s.width = p, s.height = i; let f = "webgpu", h = s.getContext(f), g; h || (g = new OffscreenCanvas(p, i), h = g.getContext(f)); @@ -31296,55 +31299,55 @@ function Uue(r) { } return e.disposeData(C.dataId), n; } -var UV = { kernelName: Pu, backendName: "webgpu", kernelFunc: Uue }; -var s0 = et({ opType: fe.MUL, cpuKernelImpl: vz, supportsComplex: true }); -var GV = { kernelName: Yn, backendName: "webgpu", kernelFunc: s0 }; -function a0(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, keepDims: a } = o; - return eo(n, s, a, "sum", e); +var GV = { kernelName: _u, backendName: "webgpu", kernelFunc: Uue }; +var a0 = et({ opType: fe.MUL, cpuKernelImpl: kz, supportsComplex: true }); +var HV = { kernelName: Xn, backendName: "webgpu", kernelFunc: a0 }; +function i0(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, keepDims: a } = o; + return Jr(n, s, a, "sum", e); } -var HV = { kernelName: Is, backendName: "webgpu", kernelFunc: a0 }; -function Gue(r) { - let { inputs: t8, backend: e, attrs: o } = r, { equation: n } = o, s = t8, { allDims: a, summedDims: i, idDims: p } = w.decodeEinsumEquation(n, s.length); +var KV = { kernelName: Ss, backendName: "webgpu", kernelFunc: i0 }; +function Gue(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { equation: n } = o, s = t8, { allDims: a, summedDims: i, idDims: p } = w.decodeEinsumEquation(n, s.length); w.checkEinsumDimSizes(a.length, p, s); let { path: u, steps: c } = w.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = []; for (let h = 0; h < l; ++h) { for (let g of c[h]) { let { permutationIndices: x, expandDims: b } = w.getEinsumPermutation(d, p[g]), C; - w.isIdentityPermutation(x) ? C = s[g] : (C = yr({ inputs: { x: s[g] }, backend: e, attrs: { perm: x } }), f.push(C)); + w.isIdentityPermutation(x) ? C = s[g] : (C = xr({ inputs: { x: s[g] }, backend: e, attrs: { perm: x } }), f.push(C)); let S = C.shape.slice(); for (let k = 0; k < b.length; ++k) S.splice(b[k], 0, 1); - y.arraysEqual(C.shape, S) || (C = pe({ inputs: { x: C }, backend: e, attrs: { shape: S } }), f.push(C)), m === null ? m = C : (m = s0({ inputs: { a: C, b: m }, backend: e }), f.push(m)); + y.arraysEqual(C.shape, S) || (C = pe({ inputs: { x: C }, backend: e, attrs: { shape: S } }), f.push(C)), m === null ? m = C : (m = a0({ inputs: { a: C, b: m }, backend: e }), f.push(m)); } - h < l - 1 && (u[h] >= 0 && (m = a0({ inputs: { x: m }, backend: e, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); + h < l - 1 && (u[h] >= 0 && (m = i0({ inputs: { x: m }, backend: e, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--); } for (let h of f) h !== m && e.disposeData(h.dataId); return m; } -var KV = { kernelName: Vi, backendName: "webgpu", kernelFunc: Gue }; +var qV = { kernelName: Li, backendName: "webgpu", kernelFunc: Gue }; var Hue = ye({ opType: Z.ELU }); -var qV = { kernelName: gn, backendName: "webgpu", kernelFunc: Hue }; -var Kue = (r) => { - let { inputs: t8, backend: e } = r, { dy: o, y: n } = t8, s = new ki(fe.ELU_DER, o.shape, n.shape); +var jV = { kernelName: hn, backendName: "webgpu", kernelFunc: Hue }; +var Kue = (r15) => { + let { inputs: t8, backend: e } = r15, { dy: o, y: n } = t8, s = new Si(fe.ELU_DER, o.shape, n.shape); return e.runWebGPUProgram(s, [o, n], o.dtype); }; -var jV = { kernelName: Ya, backendName: "webgpu", kernelFunc: Kue }; -var que = et({ opType: fe.EQUAL, dtype: "bool", cpuKernelImpl: pz }); -var XV = { kernelName: yn, backendName: "webgpu", kernelFunc: que }; +var XV = { kernelName: Xa, backendName: "webgpu", kernelFunc: Kue }; +var que = et({ opType: fe.EQUAL, dtype: "bool", cpuKernelImpl: cz }); +var YV = { kernelName: xn, backendName: "webgpu", kernelFunc: que }; var jue = ye({ opType: Z.ERF }); -var YV = { kernelName: xn, backendName: "webgpu", kernelFunc: jue }; -var Xue = ye({ opType: Z.EXP, cpuKernelImpl: cz, dtype: "float32" }); -var QV = { kernelName: bn, backendName: "webgpu", kernelFunc: Xue }; -function Mx(r) { - let { inputs: t8, attrs: e, backend: o } = r, { dim: n } = e, { input: s } = t8, a = s.shape.length, i = s.shape.slice(), p = n; +var QV = { kernelName: gn, backendName: "webgpu", kernelFunc: jue }; +var Xue = ye({ opType: Z.EXP, cpuKernelImpl: lz, dtype: "float32" }); +var ZV = { kernelName: yn, backendName: "webgpu", kernelFunc: Xue }; +function Ox(r15) { + let { inputs: t8, attrs: e, backend: o } = r15, { dim: n } = e, { input: s } = t8, a = s.shape.length, i = s.shape.slice(), p = n; return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), pe({ inputs: { x: s }, backend: o, attrs: { shape: i } }); } -var ZV = { kernelName: na, backendName: "webgpu", kernelFunc: Mx }; -var Yue = ye({ opType: Z.EXPM1, cpuKernelImpl: lz }); -var JV = { kernelName: Cn, backendName: "webgpu", kernelFunc: Yue }; -var ym = class { +var JV = { kernelName: na, backendName: "webgpu", kernelFunc: Ox }; +var Yue = ye({ opType: Z.EXPM1, cpuKernelImpl: mz }); +var eW = { kernelName: bn, backendName: "webgpu", kernelFunc: Yue }; +var gm = class { constructor(t8, e) { this.variableNames = ["real", "imag"], this.outputShape = [], this.uniforms = "exponentMultiplier : f32, denominator: f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.component = t8, this.shaderKey = `fft_${t8}`; } @@ -31385,24 +31388,24 @@ var ym = class { `; } }; -function Lx(r, t8, e) { - let o = e.tensorMap.get(r.dataId), n = y.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = [], p = pe({ inputs: { x: r }, backend: e, attrs: { shape: [a, s] } }); +function Mx(r15, t8, e) { + let o = e.tensorMap.get(r15.dataId), n = y.sizeFromShape(r15.shape), s = r15.shape[r15.shape.length - 1], a = n / s, i = [], p = pe({ inputs: { x: r15 }, backend: e, attrs: { shape: [a, s] } }); i.push(p); - let u = p.shape, c = new ym("real", u), l = new ym("imag", u), m = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: u }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: u }], d = t8 ? 2 * Math.PI : -2 * Math.PI, f = t8 ? u[1] : 1, h = [{ type: "float32", data: [d] }, { type: "float32", data: [f] }], g = e.runWebGPUProgram(c, m, "float32", h); + let u = p.shape, c = new gm("real", u), l = new gm("imag", u), m = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: u }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: u }], d = t8 ? 2 * Math.PI : -2 * Math.PI, f = t8 ? u[1] : 1, h = [{ type: "float32", data: [d] }, { type: "float32", data: [f] }], g = e.runWebGPUProgram(c, m, "float32", h); i.push(g); let x = e.runWebGPUProgram(l, m, "float32", h); i.push(x); - let b = yo({ inputs: { real: g, imag: x }, backend: e }); + let b = xo({ inputs: { real: g, imag: x }, backend: e }); i.push(b); - let C = pe({ inputs: { x: b }, backend: e, attrs: { shape: r.shape } }); + let C = pe({ inputs: { x: b }, backend: e, attrs: { shape: r15.shape } }); return i.forEach((S) => e.disposeData(S.dataId)), C; } -function Que(r) { - let { inputs: t8, backend: e } = r, { input: o } = t8; - return Lx(o, false, e); +function Que(r15) { + let { inputs: t8, backend: e } = r15, { input: o } = t8; + return Mx(o, false, e); } -var eW = { kernelName: Wi, backendName: "webgpu", kernelFunc: Que }; -var Bx = class { +var tW = { kernelName: Bi, backendName: "webgpu", kernelFunc: Que }; +var Lx = class { constructor(t8) { this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "flipLeftRight"; } @@ -31419,17 +31422,17 @@ var Bx = class { `; } }; -var tW = { kernelName: wn, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: t8 }) => { - let { image: e } = r, o = t8, n = new Bx(e.shape); +var rW = { kernelName: Cn, backendName: "webgpu", kernelFunc: ({ inputs: r15, backend: t8 }) => { + let { image: e } = r15, o = t8, n = new Lx(e.shape); return o.runWebGPUProgram(n, [e], e.dtype); } }; -var Zue = ye({ opType: Z.FLOOR, cpuKernelImpl: mz }); -var rW = { kernelName: Sn, backendName: "webgpu", kernelFunc: Zue }; -var Jue = et({ opType: fe.FLOOR_DIV, cpuKernelImpl: dz, dtype: "int32" }); -var oW = { kernelName: In, backendName: "webgpu", kernelFunc: Jue }; -var zx = class { +var Zue = ye({ opType: Z.FLOOR, cpuKernelImpl: dz }); +var oW = { kernelName: wn, backendName: "webgpu", kernelFunc: Zue }; +var Jue = et({ opType: fe.FLOOR_DIV, cpuKernelImpl: fz, dtype: "int32" }); +var nW = { kernelName: Sn, backendName: "webgpu", kernelFunc: Jue }; +var Bx = class { constructor(t8, e, o = false) { - this.pixelsOpType = Ii.FROM_PIXELS, this.outputShape = [0], this.variableNames = [], this.workgroupSize = [256, 1, 1], this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, [e, 1, 1]), this.importVideo = o, this.shaderKey = `fromPixels_${this.importVideo}`; + this.pixelsOpType = Ci.FROM_PIXELS, this.outputShape = [0], this.variableNames = [], this.workgroupSize = [256, 1, 1], this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, [e, 1, 1]), this.importVideo = o, this.shaderKey = `fromPixels_${this.importVideo}`; } getUserCode() { let t8 = this.importVideo ? "textureLoad(src, vec2(coords.yx));" : "textureLoad(src, vec2(coords.yx), 0)"; @@ -31448,14 +31451,14 @@ var zx = class { `; } }; -var nW = { kernelName: Mu, backendName: "webgpu", kernelFunc: epe }; -var ol; -var i0 = A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); -function epe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { pixels: n } = t8, { numChannels: s } = o; +var sW = { kernelName: Eu, backendName: "webgpu", kernelFunc: epe }; +var Jc; +var u0 = A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); +function epe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { pixels: n } = t8, { numChannels: s } = o; if (n == null) throw new Error("pixels passed to tf.browser.fromPixels() can not be null"); - let a = typeof HTMLVideoElement != "undefined" && n instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && n instanceof HTMLImageElement, p = typeof HTMLCanvasElement != "undefined" && n instanceof HTMLCanvasElement || typeof OffscreenCanvas != "undefined" && n instanceof OffscreenCanvas, u = typeof ImageBitmap != "undefined" && n instanceof ImageBitmap, [c, l] = a ? [n.videoWidth, n.videoHeight] : [n.width, n.height], m = [l, c, s], d = false, f = a || i; + let a = typeof HTMLVideoElement != "undefined" && n instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && n instanceof HTMLImageElement, p = typeof HTMLCanvasElement != "undefined" && n instanceof HTMLCanvasElement || typeof OffscreenCanvas != "undefined" && n instanceof OffscreenCanvas, u = typeof ImageBitmap != "undefined" && n instanceof ImageBitmap, [c, l] = a ? [n.videoWidth, n.videoHeight] : [n.width, n.height], m = [l, c, s], d = A().getBool("WEBGPU_IMPORT_EXTERNAL_TEXTURE") && a, f = a || i; if (u || p || f) { let b; if (d) @@ -31463,12 +31466,12 @@ function epe(r) { else { if (f) { let L = A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"); - (ol == null || L !== i0) && (i0 = L, ol = document.createElement("canvas").getContext("2d", { willReadFrequently: i0 })), ol.canvas.width = c, ol.canvas.height = l, ol.drawImage(n, 0, 0, c, l), n = ol.canvas; + (Jc == null || L !== u0) && (u0 = L, Jc = document.createElement("canvas").getContext("2d", { willReadFrequently: u0 })), Jc.canvas.width = c, Jc.canvas.height = l, Jc.drawImage(n, 0, 0, c, l), n = Jc.canvas; } let P = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING, O = "rgba8unorm", M = e.textureManager.acquireTexture(m[1], m[0], O, P); e.queue.copyExternalImageToTexture({ source: n }, { texture: M }, [m[1], m[0]]), b = M; } - let C = y.sizeFromShape(m), S = y.computeStrides(m), k = new zx(m, s, d), _ = [{ type: "uint32", data: [C] }, { type: "uint32", data: [s] }, { type: "uint32", data: [...S] }], E = e.makeTensorInfo([l, c], "int32"), R = e.tensorMap.get(E.dataId); + let C = y.sizeFromShape(m), S = y.computeStrides(m), k = new Bx(m, s, d), _ = [{ type: "uint32", data: [C] }, { type: "uint32", data: [s] }, { type: "uint32", data: [...S] }], E = e.makeTensorInfo([l, c], "int32"), R = e.tensorMap.get(E.dataId); R.resource = b; let D = e.runWebGPUProgram(k, [E], "int32", _); return e.disposeData(E.dataId), D; @@ -31483,7 +31486,7 @@ function epe(r) { let x = e.makeTensorInfo(m, "int32", new Int32Array(g)); return e.uploadToGPU(x.dataId), x; } -var Vx = class { +var zx = class { constructor(t8, e, o, n, s) { this.uniforms = "varianceEpsilon : f32,", this.workgroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], w.assertAndGetBroadcastShape(t8, e), w.assertAndGetBroadcastShape(t8, o), this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), n != null && (w.assertAndGetBroadcastShape(t8, n), this.variableNames.push("offset")), s != null && (w.assertAndGetBroadcastShape(t8, s), this.variableNames.push("scale")), this.offsetShape = n, this.scaleShape = s, this.shaderKey = "batchNorm"; } @@ -31507,31 +31510,31 @@ var Vx = class { `; } }; -var sW = { kernelName: vn, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: t8, backend: e }) => { - let { x: o, scale: n, offset: s, mean: a, variance: i } = r, { varianceEpsilon: p } = t8, u = e, c = [o, a, i], l = null; +var aW = { kernelName: In, backendName: "webgpu", kernelFunc: ({ inputs: r15, attrs: t8, backend: e }) => { + let { x: o, scale: n, offset: s, mean: a, variance: i } = r15, { varianceEpsilon: p } = t8, u = e, c = [o, a, i], l = null; s != null && (l = s.shape, c.push(s)); let m = null; n != null && (m = n.shape, c.push(n)); - let d = new Vx(o.shape, a.shape, i.shape, l, m), f = [{ type: "float32", data: [p] }]; + let d = new zx(o.shape, a.shape, i.shape, l, m), f = [{ type: "float32", data: [p] }]; return u.runWebGPUProgram(d, c, o.dtype, f); } }; -function tpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = w.convertConv2DDataFormat(c), g = w.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h); - return bx({ x: n, filter: s, convInfo: g, backend: e, bias: a, preluActivationWeights: i, leakyreluAlpha: f, activation: d }); +function tpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = w.convertConv2DDataFormat(c), g = w.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h); + return yx({ x: n, filter: s, convInfo: g, backend: e, bias: a, preluActivationWeights: i, leakyreluAlpha: f, activation: d }); } -var aW = { kernelName: vo, backendName: "webgpu", kernelFunc: tpe }; -function rpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = c; +var iW = { kernelName: Io, backendName: "webgpu", kernelFunc: tpe }; +function rpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, filter: s, bias: a, preluActivationWeights: i } = t8, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = c; f == null && (f = [1, 1]), y.assert(w.eitherStridesOrDilationsAreOne(p, f), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`); let h = w.computeConv2DInfo(n.shape, s.shape, p, f, u, l, true), g = [n, s], x = a != null, b = i != null; x && g.push(a), b && g.push(i); let C = [{ type: "int32", data: [h.padInfo.top, h.padInfo.left] }, { type: "int32", data: [h.inHeight, h.inWidth] }], S; - return h.outHeight > 4 && h.outWidth > 4 && h.strideWidth <= 2 && h.inChannels === h.outChannels && h.dilationHeight === 1 && h.dilationWidth === 1 && h.inChannels % 4 === 0 ? (S = new tl(h, x, m, b), C.push({ type: "int32", data: [S.virtualWidth] })) : (S = new rl(h, x, m, b), C.push({ type: "int32", data: [h.filterHeight] }, { type: "int32", data: [h.filterWidth] }, { type: "int32", data: [h.strideHeight, h.strideWidth] }, { type: "int32", data: [h.dilationHeight, h.dilationWidth] })), m === "leakyrelu" && (C.push({ type: "float32", data: [d] }), S.uniforms += " alpha : f32,"), e.runWebGPUProgram(S, g, "float32", C); + return h.outHeight > 4 && h.outWidth > 4 && h.strideWidth <= 2 && h.inChannels === h.outChannels && h.dilationHeight === 1 && h.dilationWidth === 1 && h.inChannels % 4 === 0 ? (S = new Qc(h, x, m, b), C.push({ type: "int32", data: [S.virtualWidth] })) : (S = new Zc(h, x, m, b), C.push({ type: "int32", data: [h.filterHeight] }, { type: "int32", data: [h.filterWidth] }, { type: "int32", data: [h.strideHeight, h.strideWidth] }, { type: "int32", data: [h.dilationHeight, h.dilationWidth] })), m === "leakyrelu" && (C.push({ type: "float32", data: [d] }), S.uniforms += " alpha : f32,"), e.runWebGPUProgram(S, g, "float32", C); } -var iW = { kernelName: ko, backendName: "webgpu", kernelFunc: rpe }; -var Wx = class { +var uW = { kernelName: vo, backendName: "webgpu", kernelFunc: rpe }; +var Vx = class { constructor(t8, e) { - this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `gathernd_${t8}`, this.sliceDim = t8, this.uniforms = `sliceDim : i32, strides : ${ht(t8)},`; + this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `gathernd_${t8}`, this.sliceDim = t8, this.uniforms = `sliceDim : i32, strides : ${ft(t8)},`; } getUserCode() { let t8; @@ -31552,17 +31555,17 @@ var Wx = class { `; } }; -function ope(r) { - let { inputs: t8, backend: e } = r, { params: o, indices: n } = t8, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = w.prepareAndValidate(o, n), m = pe({ inputs: { x: n }, backend: e, attrs: { shape: [u, a] } }), d = pe({ inputs: { x: o }, backend: e, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } }); +function ope(r15) { + let { inputs: t8, backend: e } = r15, { params: o, indices: n } = t8, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = w.prepareAndValidate(o, n), m = pe({ inputs: { x: n }, backend: e, attrs: { shape: [u, a] } }), d = pe({ inputs: { x: o }, backend: e, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } }); if (e.shouldExecuteOnCPU([o, n]) || o.dtype === "string") { - let b = e.readSync(n.dataId), C = e.bufferSync(o), S = fz(b, C, o.dtype, u, a, c, l, o.shape, i); + let b = e.readSync(n.dataId), C = e.bufferSync(o), S = hz(b, C, o.dtype, u, a, c, l, o.shape, i); return e.makeTensorInfo(p, o.dtype, S.values); } - let f = new Wx(a, [u, c]), h = [{ type: "int32", data: [a] }, { type: "int32", data: l }], g = e.runWebGPUProgram(f, [d, m], d.dtype, h), x = pe({ inputs: { x: g }, backend: e, attrs: { shape: p } }); + let f = new Vx(a, [u, c]), h = [{ type: "int32", data: [a] }, { type: "int32", data: l }], g = e.runWebGPUProgram(f, [d, m], d.dtype, h), x = pe({ inputs: { x: g }, backend: e, attrs: { shape: p } }); return e.disposeData(m.dataId), e.disposeData(d.dataId), e.disposeData(g.dataId), x; } -var uW = { kernelName: kn, backendName: "webgpu", kernelFunc: ope }; -var Ux = class { +var pW = { kernelName: vn, backendName: "webgpu", kernelFunc: ope }; +var Wx = class { constructor(t8, e) { this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.slice(), this.aShape = t8, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "gather"; } @@ -31580,51 +31583,51 @@ var Ux = class { `; } }; -function npe(r) { +function npe(r15) { let t8 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], e = []; - for (let o = 0; o < r.length; o++) + for (let o = 0; o < r15.length; o++) o === 2 ? e.push("indexZ") : e.push(`${t8[o]}`); return e.join(); } -function u0(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, indices: s } = t8, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = w.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = pe({ inputs: { x: n }, backend: e, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = pe({ inputs: { x: s }, backend: e, attrs: { shape: [u.batchSize, c / u.batchSize] } }); +function p0(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, indices: s } = t8, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = w.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = pe({ inputs: { x: n }, backend: e, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = pe({ inputs: { x: s }, backend: e, attrs: { shape: [u.batchSize, c / u.batchSize] } }); l.push(m), l.push(d); let f = [u.batchSize, u.outerSize, c / u.batchSize, u.sliceSize]; if (e.shouldExecuteOnCPU([n, s])) { - let C = e.tensorMap.get(d.dataId).values, S = me(d.shape, d.dtype, C), _ = e.tensorMap.get(m.dataId).values, E = me(m.shape, m.dtype, _), R = hz(E, S, f); + let C = e.tensorMap.get(d.dataId).values, S = me(d.shape, d.dtype, C), _ = e.tensorMap.get(m.dataId).values, E = me(m.shape, m.dtype, _), R = gz(E, S, f); return l.forEach((D) => e.disposeData(D.dataId)), e.makeTensorInfo(u.outputShape, R.dtype, R.values); } - let h = new Ux(m.shape, f), g = e.runWebGPUProgram(h, [m, d], m.dtype); + let h = new Wx(m.shape, f), g = e.runWebGPUProgram(h, [m, d], m.dtype); l.push(g); let x = pe({ inputs: { x: g }, backend: e, attrs: { shape: u.outputShape } }); return l.forEach((b) => e.disposeData(b.dataId)), x; } -var pW = { kernelName: aa, backendName: "webgpu", kernelFunc: u0 }; -var spe = et({ opType: fe.GREATER, cpuKernelImpl: xz, dtype: "bool" }); -var cW = { kernelName: Nn, backendName: "webgpu", kernelFunc: spe }; -var ape = et({ opType: fe.GREATER_EQUAL, dtype: "bool", cpuKernelImpl: gz }); -var lW = { kernelName: Tn, backendName: "webgpu", kernelFunc: ape }; -function ipe(r) { - let { inputs: t8, backend: e } = r, { input: o } = t8; - return Lx(o, true, e); +var cW = { kernelName: aa, backendName: "webgpu", kernelFunc: p0 }; +var spe = et({ opType: fe.GREATER, cpuKernelImpl: yz, dtype: "bool" }); +var lW = { kernelName: kn, backendName: "webgpu", kernelFunc: spe }; +var ape = et({ opType: fe.GREATER_EQUAL, dtype: "bool", cpuKernelImpl: xz }); +var mW = { kernelName: Nn, backendName: "webgpu", kernelFunc: ape }; +function ipe(r15) { + let { inputs: t8, backend: e } = r15, { input: o } = t8; + return Mx(o, true, e); } -var mW = { kernelName: Ui, backendName: "webgpu", kernelFunc: ipe }; +var dW = { kernelName: zi, backendName: "webgpu", kernelFunc: ipe }; var upe = ye({ opType: Z.IS_FINITE, dtype: "bool" }); -var dW = { kernelName: _n, backendName: "webgpu", kernelFunc: upe }; +var fW = { kernelName: Tn, backendName: "webgpu", kernelFunc: upe }; var ppe = ye({ opType: Z.IS_INF, dtype: "bool" }); -var fW = { kernelName: $n, backendName: "webgpu", kernelFunc: ppe }; +var hW = { kernelName: _n, backendName: "webgpu", kernelFunc: ppe }; var cpe = ye({ opType: Z.IS_NAN, dtype: "bool" }); -var hW = { kernelName: En, backendName: "webgpu", kernelFunc: cpe }; -function lpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { alpha: s } = o, a = [{ type: "float32", data: [s] }], i = new Jr(n.shape, Z.LEAKYRELU, "alpha : f32,"); +var gW = { kernelName: $n, backendName: "webgpu", kernelFunc: cpe }; +function lpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { alpha: s } = o, a = [{ type: "float32", data: [s] }], i = new Zr(n.shape, Z.LEAKYRELU, "alpha : f32,"); return e.runWebGPUProgram(i, [n], "float32", a); } -var gW = { kernelName: Rn, backendName: "webgpu", kernelFunc: lpe }; -var mpe = et({ opType: fe.LESS, dtype: "bool", cpuKernelImpl: bz }); -var xW = { kernelName: Dn, backendName: "webgpu", kernelFunc: mpe }; -var dpe = et({ opType: fe.LESS_EQUAL, dtype: "bool", cpuKernelImpl: yz }); -var yW = { kernelName: An, backendName: "webgpu", kernelFunc: dpe }; -var Gx = class { +var xW = { kernelName: En, backendName: "webgpu", kernelFunc: lpe }; +var mpe = et({ opType: fe.LESS, dtype: "bool", cpuKernelImpl: Cz }); +var yW = { kernelName: Rn, backendName: "webgpu", kernelFunc: mpe }; +var dpe = et({ opType: fe.LESS_EQUAL, dtype: "bool", cpuKernelImpl: bz }); +var bW = { kernelName: Dn, backendName: "webgpu", kernelFunc: dpe }; +var Ux = class { constructor(t8) { this.variableNames = [], this.outputShape = [], this.uniforms = "start : f32, step : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [t8], this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "linSpace"; } @@ -31638,22 +31641,22 @@ var Gx = class { `; } }; -function fpe(r) { - let { backend: t8, attrs: e } = r, { start: o, stop: n, num: s } = e, a = (n - o) / (s - 1), i = new Gx(s), p = [{ type: "float32", data: [o] }, { type: "float32", data: [a] }]; +function fpe(r15) { + let { backend: t8, attrs: e } = r15, { start: o, stop: n, num: s } = e, a = (n - o) / (s - 1), i = new Ux(s), p = [{ type: "float32", data: [o] }, { type: "float32", data: [a] }]; return t8.runWebGPUProgram(i, [], "float32", p); } -var bW = { kernelName: Fn, backendName: "webgpu", kernelFunc: fpe }; -var hpe = ye({ opType: Z.LOG, cpuKernelImpl: Cz }); -var CW = { kernelName: Pn, backendName: "webgpu", kernelFunc: hpe }; +var CW = { kernelName: An, backendName: "webgpu", kernelFunc: fpe }; +var hpe = ye({ opType: Z.LOG, cpuKernelImpl: wz }); +var wW = { kernelName: Fn, backendName: "webgpu", kernelFunc: hpe }; var gpe = ye({ opType: Z.LOG1P }); -var wW = { kernelName: On, backendName: "webgpu", kernelFunc: gpe }; +var SW = { kernelName: Pn, backendName: "webgpu", kernelFunc: gpe }; var xpe = et({ opType: fe.LOGICAL_AND, dtype: "bool" }); -var SW = { kernelName: Mn, backendName: "webgpu", kernelFunc: xpe }; +var IW = { kernelName: On, backendName: "webgpu", kernelFunc: xpe }; var ype = ye({ opType: Z.LOGICAL_NOT }); -var IW = { kernelName: Ln, backendName: "webgpu", kernelFunc: ype }; +var vW = { kernelName: Mn, backendName: "webgpu", kernelFunc: ype }; var bpe = et({ opType: fe.LOGICAL_OR }); -var vW = { kernelName: Bn, backendName: "webgpu", kernelFunc: bpe }; -var kW = ` +var kW = { kernelName: Ln, backendName: "webgpu", kernelFunc: bpe }; +var NW = ` var powValue = 0.0; let basis = uniforms.bias + uniforms.alpha * sum; if (uniforms.beta == 0.5) { @@ -31664,7 +31667,7 @@ var kW = ` powValue = exp(log(basis) * (-uniforms.beta)); } `; -var Hx = class { +var Gx = class { constructor(t8) { this.outputShape = [], this.variableNames = ["x"], this.uniforms = "radius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "lrn"; } @@ -31687,7 +31690,7 @@ var Hx = class { sum = sum + z * z; } } - ${kW} + ${NW} setOutputAtIndex(index, x * powValue); } @@ -31695,7 +31698,7 @@ var Hx = class { `; } }; -var Kx = class { +var Hx = class { constructor(t8, e) { this.outputShape = [], this.variableNames = ["x"], this.uniforms = "radius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [256, 1, 1], this.maxAllowRadius = 16, y.assert(e <= this.maxAllowRadius, () => `Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${e}`), this.outputShape = t8, this.elementsPerWorkgroup = this.workgroupSize[0] - 2 * this.maxAllowRadius, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = H(this.dispatchLayout, this.outputShape, [this.elementsPerWorkgroup, this.workgroupSize[1], this.workgroupSize[2]]), this.shaderKey = "lrn_shared"; } @@ -31728,21 +31731,21 @@ var Kx = class { let z = lrnSub[index + i]; sum = sum + z * z; } - ${kW} + ${NW} setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue); } } `; } }; -function Cpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u; - s > 16 ? u = new Hx(n.shape) : u = new Kx(n.shape, s); +function Cpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u; + s > 16 ? u = new Gx(n.shape) : u = new Hx(n.shape, s); let c = [{ type: "int32", data: [s] }, { type: "float32", data: [a] }, { type: "float32", data: [i] }, { type: "float32", data: [p] }]; return e.runWebGPUProgram(u, [n], n.dtype, c); } -var NW = { kernelName: zn, backendName: "webgpu", kernelFunc: Cpe }; -var qx = class { +var TW = { kernelName: Bn, backendName: "webgpu", kernelFunc: Cpe }; +var Kx = class { constructor(t8) { this.outputShape = [], this.variableNames = ["inputImage", "outputImage", "dy"], this.uniforms = "depthRadius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "lrn_grad"; } @@ -31800,24 +31803,24 @@ var qx = class { `; } }; -function wpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, y: s, dy: a } = t8, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new qx(n.shape), m = [{ type: "int32", data: [i] }, { type: "float32", data: [p] }, { type: "float32", data: [u] }, { type: "float32", data: [c] }]; +function wpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, y: s, dy: a } = t8, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new Kx(n.shape), m = [{ type: "int32", data: [i] }, { type: "float32", data: [p] }, { type: "float32", data: [u] }, { type: "float32", data: [c] }]; return e.runWebGPUProgram(l, [n, s, a], n.dtype, m); } -var TW = { kernelName: Qa, backendName: "webgpu", kernelFunc: wpe }; -var Spe = et({ opType: fe.MAX, cpuKernelImpl: Sz }); -var _W = { kernelName: Wn, backendName: "webgpu", kernelFunc: Spe }; -function Ipe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = w.computePool2DInfo(n.shape, s, a, u, i, p); - return ax(n, c, "max", e); +var _W = { kernelName: Ya, backendName: "webgpu", kernelFunc: wpe }; +var Spe = et({ opType: fe.MAX, cpuKernelImpl: Iz }); +var $W = { kernelName: Vn, backendName: "webgpu", kernelFunc: Spe }; +function Ipe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = w.computePool2DInfo(n.shape, s, a, u, i, p); + return sx(n, c, "max", e); } -var $W = { kernelName: Un, backendName: "webgpu", kernelFunc: Ipe }; -function vpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new _u(l, "max"), d = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.front, l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.inDepth, l.inHeight, l.inWidth] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }]; +var EW = { kernelName: Wn, backendName: "webgpu", kernelFunc: Ipe }; +function vpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = w.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new wu(l, "max"), d = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.front, l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.inDepth, l.inHeight, l.inWidth] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }]; return e.runWebGPUProgram(m, [n], n.dtype, d); } -var EW = { kernelName: ia, backendName: "webgpu", kernelFunc: vpe }; -var jx = class { +var RW = { kernelName: ia, backendName: "webgpu", kernelFunc: vpe }; +var qx = class { constructor(t8) { this.variableNames = ["dy", "maxPos"], this.uniforms = `strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, outHeight : i32, outWidth : i32`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.inShape, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "maxPool2DBackprop"; @@ -31870,7 +31873,7 @@ var jx = class { `; } }; -var Xx = class { +var jx = class { constructor(t8) { this.variableNames = ["dy", "maxPos"], this.uniforms = `strides : vec3, pads : vec3, filterDims : vec3, outDepth : i32, outHeight : i32, outWidth : i32`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8.inShape, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "maxPool3DBackprop"; @@ -31936,48 +31939,48 @@ var Xx = class { `; } }; -function kpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s } = t8, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = w.computePool3DInfo(a.shape, i, p, l, u, c), d = new _u(m, "max", true), f = [{ type: "int32", data: [m.strideDepth, m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.padInfo.front, m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.inDepth, m.inHeight, m.inWidth] }, { type: "int32", data: [m.effectiveFilterDepth, m.effectiveFilterHeight, m.effectiveFilterWidth] }], h = e.runWebGPUProgram(d, [a], "int32", f), g = new Xx(m); +function kpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s } = t8, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = w.computePool3DInfo(a.shape, i, p, l, u, c), d = new wu(m, "max", true), f = [{ type: "int32", data: [m.strideDepth, m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.padInfo.front, m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.inDepth, m.inHeight, m.inWidth] }, { type: "int32", data: [m.effectiveFilterDepth, m.effectiveFilterHeight, m.effectiveFilterWidth] }], h = e.runWebGPUProgram(d, [a], "int32", f), g = new jx(m); f = [{ type: "int32", data: [m.strideDepth, m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.effectiveFilterDepth - 1 - m.padInfo.front, m.effectiveFilterHeight - 1 - m.padInfo.top, m.effectiveFilterWidth - 1 - m.padInfo.left] }, { type: "int32", data: [m.effectiveFilterDepth, m.effectiveFilterHeight, m.effectiveFilterWidth] }, { type: "int32", data: [m.outDepth] }, { type: "int32", data: [m.outHeight] }, { type: "int32", data: [m.outWidth] }]; let x = e.runWebGPUProgram(g, [n, h], a.dtype, f); return e.disposeData(h.dataId), x; } -var RW = { kernelName: Ki, backendName: "webgpu", kernelFunc: kpe }; -function Npe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { dy: n, input: s, output: a } = t8, i = s; - fm([s, a], "maxPoolGrad"); - let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = w.computePool2DInfo(i.shape, p, u, 1, c, l), d = new za(m, "max", true), f = [{ type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.dilationHeight, m.dilationWidth] }, { type: "int32", data: [m.inHeight, m.inWidth] }, { type: "int32", data: [m.effectiveFilterHeight, m.effectiveFilterWidth] }], h = e.runWebGPUProgram(d, [i], "int32", f), g = new jx(m); +var DW = { kernelName: Ui, backendName: "webgpu", kernelFunc: kpe }; +function Npe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { dy: n, input: s, output: a } = t8, i = s; + mm([s, a], "maxPoolGrad"); + let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = w.computePool2DInfo(i.shape, p, u, 1, c, l), d = new Ba(m, "max", true), f = [{ type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.dilationHeight, m.dilationWidth] }, { type: "int32", data: [m.inHeight, m.inWidth] }, { type: "int32", data: [m.effectiveFilterHeight, m.effectiveFilterWidth] }], h = e.runWebGPUProgram(d, [i], "int32", f), g = new qx(m); f = [{ type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.effectiveFilterHeight - 1 - m.padInfo.top, m.effectiveFilterWidth - 1 - m.padInfo.left] }, { type: "int32", data: [m.dilationHeight, m.dilationWidth] }, { type: "int32", data: [m.effectiveFilterHeight, m.effectiveFilterWidth] }, { type: "int32", data: [m.outHeight] }, { type: "int32", data: [m.outWidth] }]; let x = e.runWebGPUProgram(g, [n, h], i.dtype, f); return e.disposeData(h.dataId), x; } -var DW = { kernelName: Hi, backendName: "webgpu", kernelFunc: Npe }; -function Tpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = o, { x: p } = t8; +var AW = { kernelName: Wi, backendName: "webgpu", kernelFunc: Npe }; +function Tpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = o, { x: p } = t8; y.assert(p.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`); let u = [1, 1]; y.assert(w.eitherStridesOrDilationsAreOne(s, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`); - let c = w.computePool2DInfo(p.shape, n, s, u, a), l = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] }], m = new za(c, "max", false), d = e.runWebGPUProgram(m, [p], p.dtype, l); - m = new za(c, "max", true, true, i); + let c = w.computePool2DInfo(p.shape, n, s, u, a), l = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] }], m = new Ba(c, "max", false), d = e.runWebGPUProgram(m, [p], p.dtype, l); + m = new Ba(c, "max", true, true, i); let f = e.runWebGPUProgram(m, [p], "int32", l); return [d, f]; } -var AW = { kernelName: ua, backendName: "webgpu", kernelFunc: Tpe }; -function _pe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, keepDims: a } = o; - return eo(n, s, a, "min", e); +var FW = { kernelName: ua, backendName: "webgpu", kernelFunc: Tpe }; +function _pe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, keepDims: a } = o; + return Jr(n, s, a, "min", e); } -var FW = { kernelName: Hn, backendName: "webgpu", kernelFunc: _pe }; -var $pe = et({ opType: fe.MIN, cpuKernelImpl: Iz }); -var PW = { kernelName: Kn, backendName: "webgpu", kernelFunc: $pe }; -var Yx = class { +var PW = { kernelName: Gn, backendName: "webgpu", kernelFunc: _pe }; +var $pe = et({ opType: fe.MIN, cpuKernelImpl: vz }); +var OW = { kernelName: Hn, backendName: "webgpu", kernelFunc: $pe }; +var Xx = class { constructor(t8, e, o) { this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.map((n, s) => n[0] + t8[s] + n[1]), this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.xShape = t8, e.map((n, s) => { this.uniforms += ` pad${s} : vec2,`; }), this.offset = o === "reflect" ? 0 : 1, this.shaderKey = `mirrorPad_${o}`; } getUserCode() { - let t8 = this.xShape.length, e = this.xShape.map((u, c) => `uniforms.pad${c}[0]`).join(","), o = this.xShape.map((u, c) => `uniforms.pad${c}[0] + uniforms.xShape${t8 > 1 ? `[${c}]` : ""}`).join(","), n = t8 === 1 ? "start" : "start[i]", s = t8 === 1 ? "end" : "end[i]", a = t8 === 1 ? "outC" : "outC[i]", i = ht(t8), p = t8 > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, t8) : "coords"; + let t8 = this.xShape.length, e = this.xShape.map((u, c) => `uniforms.pad${c}[0]`).join(","), o = this.xShape.map((u, c) => `uniforms.pad${c}[0] + uniforms.xShape${t8 > 1 ? `[${c}]` : ""}`).join(","), n = t8 === 1 ? "start" : "start[i]", s = t8 === 1 ? "end" : "end[i]", a = t8 === 1 ? "outC" : "outC[i]", i = ft(t8), p = t8 > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, t8) : "coords"; return ` ${G("index")} { if (index < uniforms.size) { @@ -31998,13 +32001,13 @@ var Yx = class { `; } }; -var OW = { kernelName: qn, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: t8, backend: e }) => { - let { x: o } = r, { paddings: n, mode: s } = t8, a = e, i = n.map((c) => ({ type: "int32", data: [c[0], c[1]] })), p = new Yx(o.shape, n, s); +var MW = { kernelName: Kn, backendName: "webgpu", kernelFunc: ({ inputs: r15, attrs: t8, backend: e }) => { + let { x: o } = r15, { paddings: n, mode: s } = t8, a = e, i = n.map((c) => ({ type: "int32", data: [c[0], c[1]] })), p = new Xx(o.shape, n, s); return a.runWebGPUProgram(p, [o], o.dtype, i); } }; var Epe = et({ opType: fe.MOD }); -var MW = { kernelName: jn, backendName: "webgpu", kernelFunc: Epe }; -var Qx = class { +var LW = { kernelName: qn, backendName: "webgpu", kernelFunc: Epe }; +var Yx = class { constructor(t8, e) { this.variableNames = ["probs"], this.outputShape = [], this.uniforms = "seed : f32, numOutcomes: i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [t8, e], this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "multinomial"; } @@ -32045,7 +32048,7 @@ var Qx = class { `; } }; -var Zx = class { +var Qx = class { constructor(t8) { this.variableNames = ["logits"], this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = [this.outputShape[0], 1, 1], this.outputShape[1] >= 4096 ? this.workgroupSize = [256, 1, 1] : this.workgroupSize = [64, 1, 1], this.shaderKey = "softmax"; } @@ -32112,39 +32115,39 @@ var Zx = class { `; } }; -function p0(r) { - let { inputs: t8, backend: e, attrs: o } = r, { logits: n } = t8, { dim: s } = o, a = pe({ inputs: { x: n }, backend: e, attrs: { shape: [y.sizeFromShape(n.shape) / n.shape[s], n.shape[s]] } }), i = new Zx(a.shape), p = e.runWebGPUProgram(i, [a], n.dtype), u = pe({ inputs: { x: p }, backend: e, attrs: { shape: n.shape } }); +function c0(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { logits: n } = t8, { dim: s } = o, a = pe({ inputs: { x: n }, backend: e, attrs: { shape: [y.sizeFromShape(n.shape) / n.shape[s], n.shape[s]] } }), i = new Qx(a.shape), p = e.runWebGPUProgram(i, [a], n.dtype), u = pe({ inputs: { x: p }, backend: e, attrs: { shape: n.shape } }); return e.disposeData(a.dataId), e.disposeData(p.dataId), u; } -var LW = { kernelName: vs, backendName: "webgpu", kernelFunc: p0 }; -function Rpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { logits: n } = t8, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : p0({ inputs: { logits: n }, backend: e, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new Qx(u, s), m = [{ type: "float32", data: [a] }, { type: "int32", data: [c] }], d = e.runWebGPUProgram(l, [p], "int32", m); +var BW = { kernelName: Is, backendName: "webgpu", kernelFunc: c0 }; +function Rpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { logits: n } = t8, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : c0({ inputs: { logits: n }, backend: e, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new Yx(u, s), m = [{ type: "float32", data: [a] }, { type: "int32", data: [c] }], d = e.runWebGPUProgram(l, [p], "int32", m); return i || e.disposeData(p.dataId), d; } -var BW = { kernelName: Xn, backendName: "webgpu", kernelFunc: Rpe }; -function Dpe(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8; +var zW = { kernelName: jn, backendName: "webgpu", kernelFunc: Rpe }; +function Dpe(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8; if (e.shouldExecuteOnCPU([o])) { - let s = e.tensorMap.get(o.dataId), [a, i] = kz(s.values, o.shape, o.dtype); + let s = e.tensorMap.get(o.dataId), [a, i] = Nz(s.values, o.shape, o.dtype); return e.makeTensorInfo(i, o.dtype, a); } - let n = new Jr(o.shape, Z.NEG); + let n = new Zr(o.shape, Z.NEG); return e.runWebGPUProgram(n, [o], o.dtype); } -var zW = { kernelName: pa, backendName: "webgpu", kernelFunc: Dpe }; -function Ape(r) { +var VW = { kernelName: pa, backendName: "webgpu", kernelFunc: Dpe }; +function Ape(r15) { console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: t8, backend: e, attrs: o } = r, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = e.readSync(n.dataId), c = e.readSync(s.dataId), { selectedIndices: l } = Wt.nonMaxSuppressionV3Impl(u, c, a, i, p); + let { inputs: t8, backend: e, attrs: o } = r15, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = e.readSync(n.dataId), c = e.readSync(s.dataId), { selectedIndices: l } = Vt.nonMaxSuppressionV3Impl(u, c, a, i, p); return e.makeTensorInfo([l.length], "int32", new Int32Array(l)); } -var VW = { kernelName: Zn, backendName: "webgpu", kernelFunc: Ape }; -function Fpe(r) { +var WW = { kernelName: Qn, backendName: "webgpu", kernelFunc: Ape }; +function Fpe(r15) { console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); - let { inputs: t8, backend: e, attrs: o } = r, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = e.readSync(n.dataId), l = e.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = Wt.nonMaxSuppressionV5Impl(c, l, m, d, f, h); + let { inputs: t8, backend: e, attrs: o } = r15, { boxes: n, scores: s } = t8, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = e.readSync(n.dataId), l = e.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = Vt.nonMaxSuppressionV5Impl(c, l, m, d, f, h); return [e.makeTensorInfo([g.length], "int32", new Int32Array(g)), e.makeTensorInfo([x.length], "float32", new Float32Array(x))]; } -var WW = { kernelName: Jn, backendName: "webgpu", kernelFunc: Fpe }; -var Jx = class { +var UW = { kernelName: Zn, backendName: "webgpu", kernelFunc: Fpe }; +var Zx = class { constructor(t8, e) { this.variableNames = ["x"], this.uniforms = "onValue : f32, offValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [t8, e], this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "onehot"; } @@ -32160,50 +32163,50 @@ var Jx = class { `; } }; -function Ppe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { indices: n } = t8, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new Jx(u, a), l = pe({ inputs: { x: n }, backend: e, attrs: { shape: [u] } }), m = [{ type: "float32", data: [i] }, { type: "float32", data: [p] }], d = e.runWebGPUProgram(c, [l], s, m); +function Ppe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { indices: n } = t8, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new Zx(u, a), l = pe({ inputs: { x: n }, backend: e, attrs: { shape: [u] } }), m = [{ type: "float32", data: [i] }, { type: "float32", data: [p] }], d = e.runWebGPUProgram(c, [l], s, m); e.disposeData(l.dataId); let f = [...n.shape, a], h = pe({ inputs: { x: d }, backend: e, attrs: { shape: f } }); return e.disposeData(d.dataId), h; } -var UW = { kernelName: es, backendName: "webgpu", kernelFunc: Ppe }; -function bm(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8; +var GW = { kernelName: Jn, backendName: "webgpu", kernelFunc: Ppe }; +function xm(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8; if (o.dtype === "complex64") { - let n = Ni({ inputs: { input: o }, backend: e }), s = bm({ inputs: { x: n }, backend: e }), a = Mp({ inputs: { input: o }, backend: e }), i = bm({ inputs: { x: a }, backend: e }), p = yo({ inputs: { real: s, imag: i }, backend: e }); + let n = Ii({ inputs: { input: o }, backend: e }), s = xm({ inputs: { x: n }, backend: e }), a = $p({ inputs: { input: o }, backend: e }), i = xm({ inputs: { x: a }, backend: e }), p = xo({ inputs: { real: s, imag: i }, backend: e }); return e.disposeData(n.dataId), e.disposeData(s.dataId), e.disposeData(a.dataId), e.disposeData(i.dataId), p; } else - return kt({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: e }); + return vt({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: e }); } -var GW = { kernelName: Sa, backendName: "webgpu", kernelFunc: bm }; -function HW(r) { - let { inputs: t8, backend: e } = r, { x: o } = t8; +var HW = { kernelName: Sa, backendName: "webgpu", kernelFunc: xm }; +function KW(r15) { + let { inputs: t8, backend: e } = r15, { x: o } = t8; if (o.dtype === "string") throw new Error("onesLike is not supported under string dtype"); if (o.dtype === "complex64") { - let n = Ni({ inputs: { input: o }, backend: e }), s = HW({ inputs: { x: n }, backend: e }), a = Mp({ inputs: { input: o }, backend: e }), i = bm({ inputs: { x: a }, backend: e }), p = yo({ inputs: { real: s, imag: i }, backend: e }); + let n = Ii({ inputs: { input: o }, backend: e }), s = KW({ inputs: { x: n }, backend: e }), a = $p({ inputs: { input: o }, backend: e }), i = xm({ inputs: { x: a }, backend: e }), p = xo({ inputs: { real: s, imag: i }, backend: e }); return e.disposeData(n.dataId), e.disposeData(s.dataId), e.disposeData(a.dataId), e.disposeData(i.dataId), p; } else - return kt({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: e }); + return vt({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: e }); } -var KW = { kernelName: ca, backendName: "webgpu", kernelFunc: HW }; -function Ope(r) { - let { inputs: t8, backend: e, attrs: o } = r, { axis: n } = o; +var qW = { kernelName: ca, backendName: "webgpu", kernelFunc: KW }; +function Ope(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { axis: n } = o; if (t8.length === 1) - return Mx({ inputs: { input: t8[0] }, backend: e, attrs: { dim: n } }); + return Ox({ inputs: { input: t8[0] }, backend: e, attrs: { dim: n } }); let s = t8[0].shape, a = t8[0].dtype; t8.forEach((c) => { y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes"); }); let i = [], p = t8.map((c) => { - let l = Mx({ inputs: { input: c }, backend: e, attrs: { dim: n } }); + let l = Ox({ inputs: { input: c }, backend: e, attrs: { dim: n } }); return i.push(l), l; - }), u = n0({ inputs: p, backend: e, attrs: { axis: n } }); + }), u = s0({ inputs: p, backend: e, attrs: { axis: n } }); return i.forEach((c) => e.disposeData(c.dataId)), u; } -var qW = { kernelName: la, backendName: "webgpu", kernelFunc: Ope }; -function c0(r, t8 = false) { - let e = r.length, o = ht(e), n = r.map((l, m) => `uniforms.pad${m}[0]`).join(","), s = r.map((l, m) => `uniforms.pad${m}[0] + uniforms.xShape${e > 1 ? `[${m}]` : ""}`).join(","), a = e > 1 ? `${o}(${n})` : `${n}`, i = e > 1 ? `${o}(${s})` : `${s}`, p = e > 1 ? "any(paddedCoords < start)" : "paddedCoords < start", u = e > 1 ? "any(paddedCoords >= end)" : "paddedCoords >= end", c = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords"; +var jW = { kernelName: la, backendName: "webgpu", kernelFunc: Ope }; +function l0(r15, t8 = false) { + let e = r15.length, o = ft(e), n = r15.map((l, m) => `uniforms.pad${m}[0]`).join(","), s = r15.map((l, m) => `uniforms.pad${m}[0] + uniforms.xShape${e > 1 ? `[${m}]` : ""}`).join(","), a = e > 1 ? `${o}(${n})` : `${n}`, i = e > 1 ? `${o}(${s})` : `${s}`, p = e > 1 ? "any(paddedCoords < start)" : "paddedCoords < start", u = e > 1 ? "any(paddedCoords >= end)" : "paddedCoords >= end", c = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords"; return ` let start = ${a}; let end = ${i}; @@ -32215,7 +32218,7 @@ function c0(r, t8 = false) { } `; } -var ey = class { +var Jx = class { constructor(t8, e) { this.variableNames = ["x"], this.uniforms = "constantValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.map((o, n) => o[0] + t8[n] + o[1]), this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), e.map((o, n) => { this.uniforms += ` pad${n} : vec2,`; @@ -32226,52 +32229,52 @@ var ey = class { ${G("index")} { if (index < uniforms.size) { let paddedCoords = getCoordsFromIndex(index); - ${c0(this.xShape)} + ${l0(this.xShape)} } } `; } }; -var Mpe = (r) => { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { paddings: s, constantValue: a } = o; +var Mpe = (r15) => { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { paddings: s, constantValue: a } = o; if (s.every((u) => y.arraysEqual(u, [0, 0]))) - return Ft({ inputs: { x: n }, backend: e }); + return At({ inputs: { x: n }, backend: e }); if (y.sizeFromShape(n.shape) === 0) { let u = s.map((c, l) => c[0] + n.shape[l] + c[1]); - return kt({ backend: e, attrs: { shape: u, value: a, dtype: n.dtype } }); + return vt({ backend: e, attrs: { shape: u, value: a, dtype: n.dtype } }); } let i = [{ type: "float32", data: [a] }]; s.map((u) => i.push({ type: "int32", data: [u[0], u[1]] })); - let p = new ey(n.shape, s); + let p = new Jx(n.shape, s); return e.runWebGPUProgram(p, [n], n.dtype, i); }; -var jW = { kernelName: ts, backendName: "webgpu", kernelFunc: Mpe }; +var XW = { kernelName: es, backendName: "webgpu", kernelFunc: Mpe }; var Lpe = et({ opType: fe.POW }); -var XW = { kernelName: rs, backendName: "webgpu", kernelFunc: Lpe }; -function Bpe(r) { - let { inputs: t8, backend: e } = r, { x: o, alpha: n } = t8, s = new ki(fe.PRELU, o.shape, n.shape); +var YW = { kernelName: ts, backendName: "webgpu", kernelFunc: Lpe }; +function Bpe(r15) { + let { inputs: t8, backend: e } = r15, { x: o, alpha: n } = t8, s = new Si(fe.PRELU, o.shape, n.shape); return e.runWebGPUProgram(s, [o, n], "float32"); } -var YW = { kernelName: os, backendName: "webgpu", kernelFunc: Bpe }; -function zpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { axis: s, keepDims: a } = o; - return eo(n, s, a, "prod", e); +var QW = { kernelName: rs, backendName: "webgpu", kernelFunc: Bpe }; +function zpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { axis: s, keepDims: a } = o; + return Jr(n, s, a, "prod", e); } -var QW = { kernelName: ns, backendName: "webgpu", kernelFunc: zpe }; -var Vpe = (r) => { - let { backend: t8, attrs: e } = r, { start: o, stop: n, step: s, dtype: a } = e, i = _z(o, n, s, a); +var ZW = { kernelName: os, backendName: "webgpu", kernelFunc: zpe }; +var Vpe = (r15) => { + let { backend: t8, attrs: e } = r15, { start: o, stop: n, step: s, dtype: a } = e, i = $z(o, n, s, a); return t8.makeTensorInfo([i.length], a, i); }; -var ZW = { kernelName: ma, backendName: "webgpu", kernelFunc: Vpe }; +var JW = { kernelName: ma, backendName: "webgpu", kernelFunc: Vpe }; var Wpe = et({ opType: fe.DIV }); -var JW = { kernelName: hn, backendName: "webgpu", kernelFunc: Wpe }; +var eU = { kernelName: fn, backendName: "webgpu", kernelFunc: Wpe }; var Upe = ye({ opType: Z.RECIPROCAL }); -var eU = { kernelName: ss, backendName: "webgpu", kernelFunc: Upe }; +var tU = { kernelName: ns, backendName: "webgpu", kernelFunc: Upe }; var Gpe = ye({ opType: Z.RELU }); -var tU = { kernelName: as, backendName: "webgpu", kernelFunc: Gpe }; +var rU = { kernelName: ss, backendName: "webgpu", kernelFunc: Gpe }; var Hpe = ye({ opType: Z.RELU6 }); -var rU = { kernelName: ps, backendName: "webgpu", kernelFunc: Hpe }; -var ty = class { +var oU = { kernelName: us, backendName: "webgpu", kernelFunc: Hpe }; +var ey = class { constructor(t8, e, o) { this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2, halfPixelCenters : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [t8[0], e, o, t8[3]], this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "resizeBilinear"; } @@ -32322,12 +32325,12 @@ var ty = class { `; } }; -function Kpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { images: n } = t8, { alignCorners: s, size: a, halfPixelCenters: i } = o, [p, u] = a, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [i ? 0.5 : 0] }], f = new ty(n.shape, p, u); +function Kpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { images: n } = t8, { alignCorners: s, size: a, halfPixelCenters: i } = o, [p, u] = a, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [i ? 0.5 : 0] }], f = new ey(n.shape, p, u); return e.runWebGPUProgram(f, [n], "float32", d); } -var oU = { kernelName: us, backendName: "webgpu", kernelFunc: Kpe }; -var ry = class { +var nU = { kernelName: is, backendName: "webgpu", kernelFunc: Kpe }; +var ty = class { constructor(t8, e) { this.variableNames = ["dy"], this.uniforms = `effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32, invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.alignCorners = e, this.shaderKey = `resizeBilinearBackprop_${e}`; @@ -32410,12 +32413,12 @@ var ry = class { `; } }; -function qpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { images: n, dy: s } = t8, { alignCorners: a } = o, [, i, p] = n.shape, [, u, c] = s.shape, l = [a && u > 1 ? i - 1 : i, a && c > 1 ? p - 1 : p], m = [a && u > 1 ? u - 1 : u, a && c > 1 ? c - 1 : c], d = l[0] / m[0], f = l[1] / m[1], h = 1 / d, g = 1 / f, x = Math.ceil(h) * 2 + 2, b = Math.ceil(g) * 2 + 2, C = new ry(n.shape, a), S = [{ type: "int32", data: l }, { type: "int32", data: m }, { type: "float32", data: [d] }, { type: "float32", data: [f] }, { type: "float32", data: [h] }, { type: "float32", data: [g] }, { type: "int32", data: [x] }, { type: "int32", data: [b] }]; +function qpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { images: n, dy: s } = t8, { alignCorners: a } = o, [, i, p] = n.shape, [, u, c] = s.shape, l = [a && u > 1 ? i - 1 : i, a && c > 1 ? p - 1 : p], m = [a && u > 1 ? u - 1 : u, a && c > 1 ? c - 1 : c], d = l[0] / m[0], f = l[1] / m[1], h = 1 / d, g = 1 / f, x = Math.ceil(h) * 2 + 2, b = Math.ceil(g) * 2 + 2, C = new ty(n.shape, a), S = [{ type: "int32", data: l }, { type: "int32", data: m }, { type: "float32", data: [d] }, { type: "float32", data: [f] }, { type: "float32", data: [h] }, { type: "float32", data: [g] }, { type: "int32", data: [x] }, { type: "int32", data: [b] }]; return e.runWebGPUProgram(C, [s], s.dtype, S); } -var nU = { kernelName: ei, backendName: "webgpu", kernelFunc: qpe }; -var oy = class { +var sU = { kernelName: Ja, backendName: "webgpu", kernelFunc: qpe }; +var ry = class { constructor(t8, e, o, n) { this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2, roundBase : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [t8[0], e, o, t8[3]], this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.halfPixelCenters = n, this.shaderKey = `resizeNearest_${n}`; } @@ -32455,12 +32458,12 @@ var oy = class { `; } }; -function jpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { images: n } = t8, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [s ? 0.5 : 0] }], f = new oy(n.shape, p, u, a); +function jpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { images: n } = t8, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [s ? 0.5 : 0] }], f = new ry(n.shape, p, u, a); return e.runWebGPUProgram(f, [n], n.dtype, d); } -var sU = { kernelName: is, backendName: "webgpu", kernelFunc: jpe }; -var ny = class { +var aU = { kernelName: as, backendName: "webgpu", kernelFunc: jpe }; +var oy = class { constructor(t8, e) { this.variableNames = ["dy"], this.uniforms = `effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.alignCorners = e, this.shaderKey = `resizeNearestNeigborBackprop_${e}`; @@ -32528,12 +32531,12 @@ var ny = class { `; } }; -function Xpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { images: n, dy: s } = t8, { alignCorners: a } = o, [, i, p] = n.shape, [, u, c] = s.shape, l = [a && u > 1 ? i - 1 : i, a && c > 1 ? p - 1 : p], m = [a && u > 1 ? u - 1 : u, a && c > 1 ? c - 1 : c], d = l[0] / m[0], f = l[1] / m[1], h = 1 / d, g = 1 / f, x = Math.ceil(h) * 2 + 2, b = Math.ceil(g) * 2 + 2, C = new ny(n.shape, a), S = [{ type: "int32", data: l }, { type: "int32", data: m }, { type: "float32", data: [h] }, { type: "float32", data: [g] }, { type: "int32", data: [x] }, { type: "int32", data: [b] }]; +function Xpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { images: n, dy: s } = t8, { alignCorners: a } = o, [, i, p] = n.shape, [, u, c] = s.shape, l = [a && u > 1 ? i - 1 : i, a && c > 1 ? p - 1 : p], m = [a && u > 1 ? u - 1 : u, a && c > 1 ? c - 1 : c], d = l[0] / m[0], f = l[1] / m[1], h = 1 / d, g = 1 / f, x = Math.ceil(h) * 2 + 2, b = Math.ceil(g) * 2 + 2, C = new oy(n.shape, a), S = [{ type: "int32", data: l }, { type: "int32", data: m }, { type: "float32", data: [h] }, { type: "float32", data: [g] }, { type: "int32", data: [x] }, { type: "int32", data: [b] }]; return e.runWebGPUProgram(C, [s], s.dtype, S); } -var aU = { kernelName: Ja, backendName: "webgpu", kernelFunc: Xpe }; -var sy = class { +var iU = { kernelName: Za, backendName: "webgpu", kernelFunc: Xpe }; +var ny = class { constructor(t8) { this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = " axis : vec4,", this.shaderKey = "reverse"; } @@ -32571,10 +32574,10 @@ var sy = class { `; } }; -function Ype(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { dims: s } = o, a = n.shape.length; +function Ype(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { dims: s } = o, a = n.shape.length; if (a === 0) - return Ft({ inputs: { x: n }, backend: e }); + return At({ inputs: { x: n }, backend: e }); let i = n.shape, p = [1, 1, 1, 1]; i.forEach((g, x) => { let b = x + 4 - a; @@ -32585,13 +32588,13 @@ function Ype(r) { let x = g + 4 - a; c[x] = 1; }); - let l = [{ type: "int32", data: c }], m = pe({ inputs: { x: n }, backend: e, attrs: { shape: p } }), d = new sy(p), f = e.runWebGPUProgram(d, [m], m.dtype, l); + let l = [{ type: "int32", data: c }], m = pe({ inputs: { x: n }, backend: e, attrs: { shape: p } }), d = new ny(p), f = e.runWebGPUProgram(d, [m], m.dtype, l); e.disposeData(m.dataId); let h = pe({ inputs: { x: f }, backend: e, attrs: { shape: i } }); return e.disposeData(f.dataId), h; } -var iU = { kernelName: cs, backendName: "webgpu", kernelFunc: Ype }; -var ay = class { +var uU = { kernelName: ps, backendName: "webgpu", kernelFunc: Ype }; +var sy = class { constructor(t8, e) { this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32, cosRadians : f32,`, this.shaderKey = "rotate", this.outputShape = t8, typeof e == "number" ? (this.uniforms += " fillValue : f32,", this.fillSnippet = "var outputValue = uniforms.fillValue;", this.shaderKey += "_float") : (this.uniforms += " fillValue : vec3,", this.fillSnippet = "var outputValue = uniforms.fillValue[coords[3]];", this.shaderKey += "_vec3"); @@ -32620,18 +32623,18 @@ var ay = class { `; } }; -var uU = { kernelName: As, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: t8, backend: e }) => { - let { image: o } = r, { radians: n, fillValue: s, center: a } = t8, i = e, p = new ay(o.shape, s), [u, c] = w.getImageCenter(a, o.shape[1], o.shape[2]), l = [{ type: "float32", data: [u] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(n)] }, { type: "float32", data: [Math.cos(n)] }]; +var pU = { kernelName: Ds, backendName: "webgpu", kernelFunc: ({ inputs: r15, attrs: t8, backend: e }) => { + let { image: o } = r15, { radians: n, fillValue: s, center: a } = t8, i = e, p = new sy(o.shape, s), [u, c] = w.getImageCenter(a, o.shape[1], o.shape[2]), l = [{ type: "float32", data: [u] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(n)] }, { type: "float32", data: [Math.cos(n)] }]; return typeof s == "number" ? l.push({ type: "float32", data: [Number.parseFloat(s.toFixed(2))] }) : l.push({ type: "float32", data: s }), i.runWebGPUProgram(p, [o], o.dtype, l); } }; var Qpe = ye({ opType: Z.ROUND }); -var pU = { kernelName: ls, backendName: "webgpu", kernelFunc: Qpe }; -var Zpe = ye({ opType: Z.RSQRT, cpuKernelImpl: $z }); -var cU = { kernelName: ms, backendName: "webgpu", kernelFunc: Zpe }; -var Va = class { +var cU = { kernelName: cs, backendName: "webgpu", kernelFunc: Qpe }; +var Zpe = ye({ opType: Z.RSQRT, cpuKernelImpl: Ez }); +var lU = { kernelName: ls, backendName: "webgpu", kernelFunc: Zpe }; +var za = class { constructor(t8, e, o, n, s, a, i, p = true) { - this.variableNames = ["updates", "indices"], this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.sumDupeIndices = p, this.dispatchLayout = X(t8), this.dispatch = H(this.dispatchLayout, t8, this.workgroupSize), this.sliceDimGreaterThanOne = e > 1, this.shaderKey = `scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`; - let u = ht(s.length); + this.variableNames = ["updates", "indices"], this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.sumDupeIndices = p, this.dispatchLayout = X(t8), this.dispatch = H(this.dispatchLayout, t8, this.workgroupSize), this.sliceDimGreaterThanOne = e > 1, this.shaderKey = `scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}_${s.length}`; + let u = ft(s.length); this.uniforms = `sliceDim : i32, strides: ${u}, updatesSize: i32,`, this.updatesRank = n, this.indicesRank = o; } getUserCode() { @@ -32666,23 +32669,23 @@ var Va = class { flattenedIndex = flattenedIndex + indexInside * ${o}; } let updateValue = - ${Nu(this.type)}(${i}); + ${Cu(this.type)}(${i}); let flatIndex = getOutputIndexFromCoords(${n}); - ${this.sumDupeIndices ? Qr("&result[flatIndex]", "updateValue", this.type) : "atomicStore(&result[flatIndex], bitcast(updateValue));"} + ${this.sumDupeIndices ? Yr("&result[flatIndex]", "updateValue", this.type) : "atomicStore(&result[flatIndex], bitcast(updateValue));"} } }`; } }; -function Jpe(r) { - let { inputs: t8, backend: e, attrs: o } = r, { indices: n, updates: s } = t8, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(s, n, a), m = [l / u, u]; +function Jpe(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { indices: n, updates: s } = t8, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(s, n, a), m = [l / u, u]; if (l === 0) return e.makeTensorInfo(a, n.dtype); - let d = pe({ inputs: { x: n }, backend: e, attrs: { shape: [p, i] } }), f = pe({ inputs: { x: s }, backend: e, attrs: { shape: [p, u] } }), h = f.dtype, g = kt({ backend: e, attrs: { shape: m, value: 0, dtype: h } }), x = y.sizeFromShape(f.shape), b = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [x] }], C = new Va(f.shape, i, d.shape.length, f.shape.length, c, m, h), S = e.runWebGPUProgram(C, [f, d], h, b, g), k = pe({ inputs: { x: S }, backend: e, attrs: { shape: a } }); + let d = pe({ inputs: { x: n }, backend: e, attrs: { shape: [p, i] } }), f = pe({ inputs: { x: s }, backend: e, attrs: { shape: [p, u] } }), h = f.dtype, g = vt({ backend: e, attrs: { shape: m, value: 0, dtype: h } }), x = y.sizeFromShape(f.shape), b = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [x] }], C = new za(f.shape, i, d.shape.length, f.shape.length, c, m, h), S = e.runWebGPUProgram(C, [f, d], h, b, g), k = pe({ inputs: { x: S }, backend: e, attrs: { shape: a } }); return e.disposeData(d.dataId), e.disposeData(f.dataId), e.disposeData(S.dataId), k; } -var lU = { kernelName: ds, backendName: "webgpu", kernelFunc: Jpe }; -var iy = class { +var mU = { kernelName: ms, backendName: "webgpu", kernelFunc: Jpe }; +var ay = class { constructor(t8, e) { this.outputShape = [], this.variableNames = ["sortedSequence", "values"], this.uniforms = "numInputs : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.side = e, this.shaderKey = `search_sorted_${e}`; } @@ -32712,12 +32715,12 @@ var iy = class { `; } }; -function ece(r) { - let { inputs: t8, backend: e, attrs: o } = r, { sortedSequence: n, values: s } = t8, { side: a } = o, i = new iy([s.shape[0], s.shape[1]], a), p = [{ type: "int32", data: [n.shape[1]] }]; +function ece(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { sortedSequence: n, values: s } = t8, { side: a } = o, i = new ay([s.shape[0], s.shape[1]], a), p = [{ type: "int32", data: [n.shape[1]] }]; return e.runWebGPUProgram(i, [n, s], "int32", p); } -var mU = { kernelName: hs, backendName: "webgpu", kernelFunc: ece }; -var uy = class { +var dU = { kernelName: fs, backendName: "webgpu", kernelFunc: ece }; +var iy = class { constructor(t8, e, o) { this.variableNames = ["c", "a", "b"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.cRank = t8, this.rank = o, this.shaderKey = "select"; } @@ -32748,62 +32751,62 @@ var uy = class { `; } }; -function tce(r) { - let { inputs: t8, backend: e } = r, { condition: o, t: n, e: s } = t8, a = new uy(o.shape.length, n.shape, n.shape.length); +function tce(r15) { + let { inputs: t8, backend: e } = r15, { condition: o, t: n, e: s } = t8, a = new iy(o.shape.length, n.shape, n.shape.length); return e.runWebGPUProgram(a, [o, n, s], dt(n.dtype, s.dtype)); } -var dU = { kernelName: fa, backendName: "webgpu", kernelFunc: tce }; +var fU = { kernelName: fa, backendName: "webgpu", kernelFunc: tce }; var rce = ye({ opType: Z.SELU }); -var fU = { kernelName: gs, backendName: "webgpu", kernelFunc: rce }; +var hU = { kernelName: hs, backendName: "webgpu", kernelFunc: rce }; var oce = ye({ opType: Z.SIGMOID }); -var hU = { kernelName: Cs, backendName: "webgpu", kernelFunc: oce }; +var gU = { kernelName: bs, backendName: "webgpu", kernelFunc: oce }; var nce = ye({ opType: Z.SIGN }); -var gU = { kernelName: bs, backendName: "webgpu", kernelFunc: nce }; +var xU = { kernelName: ys, backendName: "webgpu", kernelFunc: nce }; var sce = ye({ opType: Z.SIN }); -var xU = { kernelName: xs, backendName: "webgpu", kernelFunc: sce }; +var yU = { kernelName: gs, backendName: "webgpu", kernelFunc: sce }; var ace = ye({ opType: Z.SINH }); -var yU = { kernelName: ys, backendName: "webgpu", kernelFunc: ace }; +var bU = { kernelName: xs, backendName: "webgpu", kernelFunc: ace }; var ice = ye({ opType: Z.SOFTPLUS }); -var bU = { kernelName: ws, backendName: "webgpu", kernelFunc: ice }; -var py = class { +var CU = { kernelName: Cs, backendName: "webgpu", kernelFunc: ice }; +var uy = class { constructor(t8, e, o, n, s, a) { this.variableNames = ["x"], this.outputShape = [], this.uniforms = "", this.workgroupSize = [64, 1, 1], this.size = true; let i = new Array(n.length); for (let p = 0; p < i.length; p++) i[p] = n[s[p]]; - this.outputShape = i, this.newDim = s, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.xShape = t8, this.paddedXShape = e, this.uniforms += `reshapedPaddedXShape : ${ht(n.length)}, paddedXShapeStrides : ${ht(a)}, `, o.map((p, u) => { + this.outputShape = i, this.newDim = s, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.xShape = t8, this.paddedXShape = e, this.uniforms += `reshapedPaddedXShape : ${ft(n.length)}, paddedXShapeStrides : ${ft(a)}, `, o.map((p, u) => { this.uniforms += ` pad${u} : vec2,`; }), this.shaderKey = `spaceToBatchND_${s}`; } getUserCode() { - let t8 = ht(this.outputShape.length), e = Zv(this.newDim); + let t8 = ft(this.outputShape.length), e = Jv(this.newDim); return ` - ${cm(this.paddedXShape, "PaddedX")} + ${um(this.paddedXShape, "PaddedX")} ${G("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${t8}(${e}), uniforms.reshapedPaddedXShape); let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex); - ${c0(this.xShape, true)} + ${l0(this.xShape, true)} } } `; } }; -var uce = (r) => { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { blockShape: s, paddings: a } = o; +var uce = (r15) => { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { blockShape: s, paddings: a } = o; y.assert(n.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet"); let i = s.reduce((b, C) => b * C), p = [[0, 0]]; p.push(...a); for (let b = 1 + s.length; b < n.shape.length; ++b) p.push([0, 0]); - let u = p.map((b, C) => b[0] + n.shape[C] + b[1]), c = w.getReshaped(u, s, i, false), l = w.getPermuted(c.length, s.length, false), m = w.getReshapedPermuted(u, s, i, false), d = y.computeStrides(u), f = new py(n.shape, u, p, c, l, d.length), h = [{ type: "int32", data: c }, { type: "int32", data: d }]; + let u = p.map((b, C) => b[0] + n.shape[C] + b[1]), c = w.getReshaped(u, s, i, false), l = w.getPermuted(c.length, s.length, false), m = w.getReshapedPermuted(u, s, i, false), d = y.computeStrides(u), f = new uy(n.shape, u, p, c, l, d.length), h = [{ type: "int32", data: c }, { type: "int32", data: d }]; p.map((b) => h.push({ type: "int32", data: [b[0], b[1]] })); let g = e.runWebGPUProgram(f, [n], n.dtype, h), x = pe({ inputs: { x: g }, backend: e, attrs: { shape: m } }); return e.disposeData(g.dataId), x; }; -var CU = { kernelName: ga, backendName: "webgpu", kernelFunc: uce }; -var cy = class { +var wU = { kernelName: ga, backendName: "webgpu", kernelFunc: uce }; +var py = class { constructor(t8, e, o) { this.variableNames = ["input", "indices", "segmentIds"], this.outputShape = [], this.uniforms = "segmentSize : i32, sparseSize : i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = t8, this.type = o, this.dispatchLayout = X([e]), this.dispatch = H(this.dispatchLayout, [e], this.workgroupSize), this.shaderKey = "sparseSegmentSum"; } @@ -32818,13 +32821,13 @@ var cy = class { let value = input[indexInInput * uniforms.segmentSize + indexInSegment]; let outIndex = segmentId * uniforms.segmentSize + indexInSegment; - ${Qr("&result[outIndex]", "value", this.type)} + ${Yr("&result[outIndex]", "value", this.type)} } } `; } }; -var ly = class { +var cy = class { constructor(t8, e) { this.variableNames = ["segmentIds"], this.outputShape = [], this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = [t8], this.dispatchLayout = X(e), this.dispatch = H(this.dispatchLayout, e, this.workgroupSize), this.shaderKey = "sparseSegmentIdCountProgram"; } @@ -32833,13 +32836,13 @@ var ly = class { ${G("index")} { if (index < uniforms.segmentIdsShape) { let segmentId = segmentIds[index]; - ${Qr("&result[segmentId]", "1", "int32")} + ${Yr("&result[segmentId]", "1", "int32")} } } `; } }; -var my = class { +var ly = class { constructor(t8, e) { this.variableNames = ["segmentSum", "sameSegmentIdCount"], this.outputShape = [], this.uniforms = "segmentSize : i32", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.type = e, this.dispatchLayout = X(t8), this.dispatch = H(this.dispatchLayout, t8, this.workgroupSize), this.shaderKey = "sparseSegmentMean"; } @@ -32857,32 +32860,32 @@ var my = class { `; } }; -function dy(r, t8, e, o = false, n) { - let a = y.sizeFromShape(r.shape) / r.shape[0], i = r.dtype, p = y.sizeFromShape(t8.shape), u = n.readSync(e.dataId), l = p > 0 ? u[p - 1] + 1 : 0, m, d = r.shape.slice(); +function my(r15, t8, e, o = false, n) { + let a = y.sizeFromShape(r15.shape) / r15.shape[0], i = r15.dtype, p = y.sizeFromShape(t8.shape), u = n.readSync(e.dataId), l = p > 0 ? u[p - 1] + 1 : 0, m, d = r15.shape.slice(); d[0] = l; - let f = p * a, h = kt({ backend: n, attrs: { shape: d, value: 0, dtype: i } }); - m = new cy(d, f, i); - let g = [{ type: "int32", data: [a] }, { type: "int32", data: [f] }], x = n.runWebGPUProgram(m, [r, t8, e], i, g, h); + let f = p * a, h = vt({ backend: n, attrs: { shape: d, value: 0, dtype: i } }); + m = new py(d, f, i); + let g = [{ type: "int32", data: [a] }, { type: "int32", data: [f] }], x = n.runWebGPUProgram(m, [r15, t8, e], i, g, h); if (o) return x; - let b = kt({ backend: n, attrs: { shape: [l], value: 0, dtype: "int32" } }); - m = new ly(l, e.shape); - let C = n.runWebGPUProgram(m, [e], "int32", null, b), S = kt({ backend: n, attrs: { shape: d, value: 0, dtype: i } }); - m = new my(d, i), g = [{ type: "int32", data: [a] }]; + let b = vt({ backend: n, attrs: { shape: [l], value: 0, dtype: "int32" } }); + m = new cy(l, e.shape); + let C = n.runWebGPUProgram(m, [e], "int32", null, b), S = vt({ backend: n, attrs: { shape: d, value: 0, dtype: i } }); + m = new ly(d, i), g = [{ type: "int32", data: [a] }]; let k = n.runWebGPUProgram(m, [x, C], i, g, S); return n.disposeData(x.dataId), n.disposeData(C.dataId), k; } -function pce(r) { - let { inputs: t8, backend: e } = r, { data: o, indices: n, segmentIds: s } = t8; - return dy(o, n, s, false, e); +function pce(r15) { + let { inputs: t8, backend: e } = r15, { data: o, indices: n, segmentIds: s } = t8; + return my(o, n, s, false, e); } -var wU = { kernelName: ya, backendName: "webgpu", kernelFunc: pce }; -function cce(r) { - let { inputs: t8, backend: e } = r, { data: o, indices: n, segmentIds: s } = t8; - return dy(o, n, s, true, e); +var SU = { kernelName: ya, backendName: "webgpu", kernelFunc: pce }; +function cce(r15) { + let { inputs: t8, backend: e } = r15, { data: o, indices: n, segmentIds: s } = t8; + return my(o, n, s, true, e); } -var SU = { kernelName: ba, backendName: "webgpu", kernelFunc: cce }; -var fy = class { +var IU = { kernelName: ba, backendName: "webgpu", kernelFunc: cce }; +var dy = class { constructor(t8, e) { this.variableNames = ["A"], this.workgroupSize = [64, 1, 1], this.size = true; let o = new Array(t8.length); @@ -32902,58 +32905,58 @@ var fy = class { `; } }; -function lce(r, t8 = "") { - if (r >= 5) - throw Error(`Tile for rank ${r} is not yet supported`); - if (r === 1) +function lce(r15, t8 = "") { + if (r15 >= 5) + throw Error(`Tile for rank ${r15} is not yet supported`); + if (r15 === 1) return `(resRC % ${t8}aShape)`; let e = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = []; - for (let n = 0; n < r; n++) + for (let n = 0; n < r15; n++) o.push(`(${e[n]} % ${t8}aShape[${n}])`); return o.join(); } -function Cm(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { reps: s } = o; +function ym(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { reps: s } = o; if (e.shouldExecuteOnCPU([n]) || n.dtype === "string" || n.shape.length >= 5) { - let p = e.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = me(n.shape, n.dtype, u), l = Oz(c, s); + let p = e.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = me(n.shape, n.dtype, u), l = Mz(c, s); return e.makeTensorInfo(l.shape, l.dtype, l.values); } - let a = new fy(n.shape, s); + let a = new dy(n.shape, s); return e.runWebGPUProgram(a, [n], n.dtype); } -var IU = { kernelName: po, backendName: "webgpu", kernelFunc: Cm }; -function mce(r) { - let { inputs: t8, backend: e, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = t8, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = w.calculateShapes(s, n, i), d = false; +var vU = { kernelName: uo, backendName: "webgpu", kernelFunc: ym }; +function mce(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { sparseIndices: n, sparseValues: s, defaultValue: a } = t8, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = w.calculateShapes(s, n, i), d = false; if (s.dtype === "string") { - let R = e.bufferSync(n), D = e.bufferSync(s), P = y.decodeString(e.readSync(a.dataId)[0]), O = Ez(R, D, i, m, c, u, p, l, P, d); + let R = e.bufferSync(n), D = e.bufferSync(s), P = y.decodeString(e.readSync(a.dataId)[0]), O = Rz(R, D, i, m, c, u, p, l, P, d); return e.makeTensorInfo(i, O.dtype, O.values); } - let f = [m / c, c], h = pe({ inputs: { x: n }, backend: e, attrs: { shape: [u, p] } }), g = s.shape.length ? pe({ inputs: { x: s }, backend: e, attrs: { shape: [u, c] } }) : Ft({ inputs: { x: s }, backend: e }), x = g.dtype, b = e.makeTensorInfo([], x, y.makeZerosTypedArray(1, x)), C = pe({ inputs: { x: a }, backend: e, attrs: { shape: Array(f.length).fill(1) } }), S = Cm({ inputs: { x: C }, backend: e, attrs: { reps: f } }), k = y.sizeFromShape([u, c]), _ = [{ type: "int32", data: [p] }, { type: "int32", data: l }, { type: "int32", data: [k] }]; + let f = [m / c, c], h = pe({ inputs: { x: n }, backend: e, attrs: { shape: [u, p] } }), g = s.shape.length ? pe({ inputs: { x: s }, backend: e, attrs: { shape: [u, c] } }) : At({ inputs: { x: s }, backend: e }), x = g.dtype, b = e.makeTensorInfo([], x, y.makeZerosTypedArray(1, x)), C = pe({ inputs: { x: a }, backend: e, attrs: { shape: Array(f.length).fill(1) } }), S = ym({ inputs: { x: C }, backend: e, attrs: { reps: f } }), k = y.sizeFromShape([u, c]), _ = [{ type: "int32", data: [p] }, { type: "int32", data: l }, { type: "int32", data: [k] }]; switch (u) { case 0: break; case 1: { - let R = new Va([u, c], p, h.shape.length, g.shape.length, l, f, x, d); + let R = new za([u, c], p, h.shape.length, g.shape.length, l, f, x, d); e.runWebGPUProgram(R, [g, h], x, _, S); } break; default: { - let R = new Va([u, c], p, h.shape.length, b.shape.length, l, f, x, d); + let R = new za([u, c], p, h.shape.length, b.shape.length, l, f, x, d); e.runWebGPUProgram(R, [b, h], x, _, S); } { - let R = new Va([u, c], p, h.shape.length, g.shape.length, l, f, x); + let R = new za([u, c], p, h.shape.length, g.shape.length, l, f, x); e.runWebGPUProgram(R, [g, h], x, _, S); } } let E = pe({ inputs: { x: S }, backend: e, attrs: { shape: i } }); return e.disposeData(h.dataId), e.disposeData(g.dataId), e.disposeData(C.dataId), e.disposeData(b.dataId), e.disposeData(S.dataId), E; } -var vU = { kernelName: ks, backendName: "webgpu", kernelFunc: mce }; -function dce(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = w.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice(); +var kU = { kernelName: vs, backendName: "webgpu", kernelFunc: mce }; +function dce(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = w.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice(); return p.map((m) => { let d = [...l]; d[i] = m; @@ -32961,24 +32964,24 @@ function dce(r) { return c[i] += m, f; }); } -var kU = { kernelName: xa, backendName: "webgpu", kernelFunc: dce }; +var NU = { kernelName: xa, backendName: "webgpu", kernelFunc: dce }; var fce = ye({ opType: Z.SQRT }); -var NU = { kernelName: Ss, backendName: "webgpu", kernelFunc: fce }; -var TU = { kernelName: Xi, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: t8 }) => { - let { x: e } = r, o = t8, n = new Jr(e.shape, Z.SQUARE); +var TU = { kernelName: ws, backendName: "webgpu", kernelFunc: fce }; +var _U = { kernelName: Ki, backendName: "webgpu", kernelFunc: ({ inputs: r15, backend: t8 }) => { + let { x: e } = r15, o = t8, n = new Zr(e.shape, Z.SQUARE); return o.runWebGPUProgram(n, [e], e.dtype); } }; var hce = et({ opType: fe.SQUARED_DIFFERENCE }); -var _U = { kernelName: Ns, backendName: "webgpu", kernelFunc: hce }; -function gce({ inputs: r, attrs: t8, backend: e }) { - let { x: o } = r, n = new Jr(o.shape, Z.STEP, "stepAlpha : f32,"), s = [{ type: "float32", data: [t8.alpha] }]; +var $U = { kernelName: ks, backendName: "webgpu", kernelFunc: hce }; +function gce({ inputs: r15, attrs: t8, backend: e }) { + let { x: o } = r15, n = new Zr(o.shape, Z.STEP, "stepAlpha : f32,"), s = [{ type: "float32", data: [t8.alpha] }]; return e.runWebGPUProgram(n, [o], o.dtype, s); } -var $U = { kernelName: So, backendName: "webgpu", kernelFunc: gce }; -var hy = class { +var EU = { kernelName: wo, backendName: "webgpu", kernelFunc: gce }; +var fy = class { constructor(t8) { this.variableNames = ["x"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]); - let e = ht(this.outputShape.length); + let e = ft(this.outputShape.length); this.uniforms = `begin : ${e}, strides : ${e}, `, this.shaderKey = "stridedSlice"; } getUserCode() { @@ -32999,37 +33002,37 @@ var hy = class { `; } }; -function xce(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; +function xce(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: S } = pt.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k; if (h) k = pe({ inputs: { x: n }, backend: e, attrs: { shape: f } }); else if (g || x) { y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`); - let _ = ct.computeOutShape(b, C, S), E = Hs({ inputs: { x: n }, backend: e, attrs: { begin: b, size: _ } }); + let _ = pt.computeOutShape(b, C, S), E = Hs({ inputs: { x: n }, backend: e, attrs: { begin: b, size: _ } }); k = pe({ inputs: { x: E }, backend: e, attrs: { shape: f } }), e.disposeData(E.dataId); } else if (e.shouldExecuteOnCPU([n])) { - let E = e.readSync(n.dataId), R = me(n.shape, n.dtype, E), D = Az(d, R, S, b); + let E = e.readSync(n.dataId), R = me(n.shape, n.dtype, E), D = Fz(d, R, S, b); k = e.makeTensorInfo(f, n.dtype, D.values); } else { - let E = new hy(d), R = [{ type: "int32", data: b }, { type: "int32", data: S }], D = e.runWebGPUProgram(E, [n], n.dtype, R); + let E = new fy(d), R = [{ type: "int32", data: b }, { type: "int32", data: S }], D = e.runWebGPUProgram(E, [n], n.dtype, R); k = pe({ inputs: { x: D }, backend: e, attrs: { shape: f } }), e.disposeData(D.dataId); } return k; } -var EU = { kernelName: Ts, backendName: "webgpu", kernelFunc: xce }; -function yce(r) { - let { inputs: t8, backend: e, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = t8, m = e.readSync(c.dataId), d = e.readSync(l.dataId), [f, h] = Fz(m, d, n, s, a, i, p, u); +var RU = { kernelName: Ns, backendName: "webgpu", kernelFunc: xce }; +function yce(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = t8, m = e.readSync(c.dataId), d = e.readSync(l.dataId), [f, h] = Pz(m, d, n, s, a, i, p, u); return [e.makeTensorInfo([f.length], "string", f), e.makeTensorInfo(l.shape, "int32", h)]; } -var RU = { kernelName: Ca, backendName: "webgpu", kernelFunc: yce }; -var bce = et({ opType: fe.SUB, cpuKernelImpl: Pz, supportsComplex: true }); -var DU = { kernelName: _s, backendName: "webgpu", kernelFunc: bce }; +var DU = { kernelName: Ca, backendName: "webgpu", kernelFunc: yce }; +var bce = et({ opType: fe.SUB, cpuKernelImpl: Oz, supportsComplex: true }); +var AU = { kernelName: Ts, backendName: "webgpu", kernelFunc: bce }; var Cce = ye({ opType: Z.TAN }); -var AU = { kernelName: $s, backendName: "webgpu", kernelFunc: Cce }; +var FU = { kernelName: _s, backendName: "webgpu", kernelFunc: Cce }; var wce = ye({ opType: Z.TANH }); -var FU = { kernelName: Es, backendName: "webgpu", kernelFunc: wce }; -function Sce(r) { - let { inputs: t8, backend: e, attrs: o } = r, { tensor: n, indices: s, updates: a } = t8, {} = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(a, s, n.shape), m = [l / u, u]; +var PU = { kernelName: $s, backendName: "webgpu", kernelFunc: wce }; +function Sce(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { tensor: n, indices: s, updates: a } = t8, {} = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = w.calculateShapes(a, s, n.shape), m = [l / u, u]; if (l === 0) return e.makeTensorInfo(n.shape, s.dtype); let d = [], f = pe({ inputs: { x: s }, backend: e, attrs: { shape: [p, i] } }); @@ -33038,13 +33041,13 @@ function Sce(r) { d.push(h); let g = pe({ inputs: { x: n }, backend: e, attrs: { shape: m } }); d.push(g); - let x = Cm({ inputs: { x: g }, backend: e, attrs: { reps: Array(m.length).fill(1) } }), b = new Va([p, u], i, f.shape.length, h.shape.length, c, m, n.dtype, false), C = y.sizeFromShape([p, u]), S = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [C] }], k = e.runWebGPUProgram(b, [h, f], g.dtype, S, x); + let x = ym({ inputs: { x: g }, backend: e, attrs: { reps: Array(m.length).fill(1) } }), b = new za([p, u], i, f.shape.length, h.shape.length, c, m, n.dtype, false), C = y.sizeFromShape([p, u]), S = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [C] }], k = e.runWebGPUProgram(b, [h, f], g.dtype, S, x); d.push(k); let _ = pe({ inputs: { x: k }, backend: e, attrs: { shape: n.shape } }); return d.forEach((E) => e.disposeData(E.dataId)), _; } -var PU = { kernelName: fs, backendName: "webgpu", kernelFunc: Sce }; -var gy = class { +var OU = { kernelName: ds, backendName: "webgpu", kernelFunc: Sce }; +var hy = class { constructor(t8) { this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32, dir : i32, inc : i32,`, this.shaderKey = "swap"; @@ -33120,7 +33123,7 @@ var gy = class { `; } }; -var xy = class { +var gy = class { constructor(t8) { this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = "inputSize : i32, firstPass : i32, k : i32,", this.shaderKey = "merge"; } @@ -33187,28 +33190,28 @@ var xy = class { `; } }; -function nl(r, t8) { - t8 !== null && r.disposeData(t8.dataId); +function el(r15, t8) { + t8 !== null && r15.disposeData(t8.dataId); } -function OU(r) { +function MU(r15) { let t8 = 1; - for (; t8 < r; ) + for (; t8 < r15; ) t8 *= 2; return t8; } -function Ice(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n } = t8, { k: s, sorted: a } = o, i = n.shape, p = i[i.length - 1]; +function Ice(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n } = t8, { k: s, sorted: a } = o, i = n.shape, p = i[i.length - 1]; if (e.shouldExecuteOnCPU([n])) { - let k = e.readSync(n.dataId), [_, E] = Mz(k, i, n.dtype, s, a); + let k = e.readSync(n.dataId), [_, E] = Lz(k, i, n.dtype, s, a); return [e.makeTensorInfo(_.shape, _.dtype, _.values), e.makeTensorInfo(E.shape, E.dtype, E.values)]; } if (s === 0) return i[i.length - 1] = 0, [e.makeTensorInfo(i, n.dtype, []), e.makeTensorInfo(i, "int32", [])]; if (p === 1) - return [n, kt({ attrs: { shape: i, dtype: "int32", value: 0 }, backend: e })]; - let c = y.sizeFromShape(i) / p, l = pe({ inputs: { x: n }, attrs: { shape: [c, p] }, backend: e }), m = OU(s), d = OU(p), f = null, h = () => f === null ? [l, l] : [l, f], g = (k, _, E) => { - let R = h(), D = new gy(E), O = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [_] }], M = f; - f = e.runWebGPUProgram(D, R, "int32", O), nl(e, M); + return [n, vt({ attrs: { shape: i, dtype: "int32", value: 0 }, backend: e })]; + let c = y.sizeFromShape(i) / p, l = pe({ inputs: { x: n }, attrs: { shape: [c, p] }, backend: e }), m = MU(s), d = MU(p), f = null, h = () => f === null ? [l, l] : [l, f], g = (k, _, E) => { + let R = h(), D = new hy(E), O = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [_] }], M = f; + f = e.runWebGPUProgram(D, R, "int32", O), el(e, M); }; for (let k = 1; k < m; k *= 2) { let _ = k * 2; @@ -33216,23 +33219,23 @@ function Ice(r) { g(_, E, [c, d]); } for (let k = d; k > m; k /= 2) { - let _ = h(), E = new xy([c, k / 2]), D = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [m] }], P = f; - f = e.runWebGPUProgram(E, _, "int32", D), nl(e, P); + let _ = h(), E = new gy([c, k / 2]), D = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [m] }], P = f; + f = e.runWebGPUProgram(E, _, "int32", D), el(e, P); let O = m / 2, M = O * 2; for (let L = O; L >= 1; L /= 2) g(M, L, f.shape); } let x = f; - f = Hs({ inputs: { x: f }, backend: e, attrs: { begin: 0, size: [c, s] } }), nl(e, x); - let b = u0({ inputs: { x: l, indices: f }, backend: e, attrs: { axis: 1, batchDims: 1 } }); - nl(e, l); + f = Hs({ inputs: { x: f }, backend: e, attrs: { begin: 0, size: [c, s] } }), el(e, x); + let b = p0({ inputs: { x: l, indices: f }, backend: e, attrs: { axis: 1, batchDims: 1 } }); + el(e, l); let C = i.slice(0, -1); - C.push(s), x = f, f = pe({ inputs: { x: f }, attrs: { shape: C }, backend: e }), nl(e, x); + C.push(s), x = f, f = pe({ inputs: { x: f }, attrs: { shape: C }, backend: e }), el(e, x); let S = b; - return b = pe({ inputs: { x: b }, attrs: { shape: C }, backend: e }), nl(e, S), [b, f]; + return b = pe({ inputs: { x: b }, attrs: { shape: C }, backend: e }), el(e, S), [b, f]; } -var MU = { kernelName: Rs, backendName: "webgpu", kernelFunc: Ice }; -var yy = class { +var LU = { kernelName: Es, backendName: "webgpu", kernelFunc: Ice }; +var xy = class { constructor(t8) { this.variableNames = ["Image", "Transforms"], this.uniforms = "interpolationModeId : i32, fillModeId : i32, fillValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t8, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "transform"; } @@ -33356,8 +33359,8 @@ var yy = class { `; } }; -function vce(r) { - let { inputs: t8, backend: e, attrs: o } = r, { image: n, transforms: s } = t8, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new yy(g), b = a === "nearest" ? 1 : 2, C; +function vce(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { image: n, transforms: s } = t8, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new xy(g), b = a === "nearest" ? 1 : 2, C; switch (i) { case "constant": C = 1; @@ -33378,9 +33381,9 @@ function vce(r) { let S = [{ type: "int32", data: [b] }, { type: "int32", data: [C] }, { type: "float32", data: [p] }]; return e.runWebGPUProgram(x, [n, s], "float32", S); } -var LU = { kernelName: Ds, backendName: "webgpu", kernelFunc: vce }; -function kce(r) { - let { inputs: t8, backend: e, attrs: o } = r, { value: n } = t8, { axis: s } = o; +var BU = { kernelName: Rs, backendName: "webgpu", kernelFunc: vce }; +function kce(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { value: n } = t8, { axis: s } = o; s < 0 && (s += n.shape.length); let a = n, i = a.shape.length, p = n.shape[s], u = new Array(i - 1), c = 0; for (let h = 0; h < i; h++) @@ -33395,8 +33398,8 @@ function kce(r) { } return l.forEach((h) => e.disposeData(h.dataId)), f; } -var BU = { kernelName: wa, backendName: "webgpu", kernelFunc: kce }; -var by = class { +var zU = { kernelName: wa, backendName: "webgpu", kernelFunc: kce }; +var yy = class { constructor(t8, e, o) { if (this.outputShape = [], this.variableNames = ["x", "segmentIds"], this.uniforms = "numSegments : i32, xSize: i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = e, this.dispatchLayout = X(t8), this.dispatch = H(this.dispatchLayout, t8, this.workgroupSize), o !== "float32" && o !== "int32") throw new Error(`UnsortedSegmentSum only supports float32 and int32 @@ -33416,39 +33419,40 @@ var by = class { let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments; let value = getX(b, inCol); - ${Qr("&result[flatIndex]", "value", this.type)} + ${Yr("&result[flatIndex]", "value", this.type)} } } } `; } }; -function Nce(r) { - let { inputs: t8, backend: e, attrs: o } = r, { x: n, segmentIds: s } = t8, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = w.getAxesPermutation([u], i), l = n; - c != null && (l = yr({ inputs: { x: n }, backend: e, attrs: { perm: c } }), p.push(l), u = w.getInnerMostAxes(1, i)[0]); +function Nce(r15) { + let { inputs: t8, backend: e, attrs: o } = r15, { x: n, segmentIds: s } = t8, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = w.getAxesPermutation([u], i), l = n; + c != null && (l = xr({ inputs: { x: n }, backend: e, attrs: { perm: c } }), p.push(l), u = w.getInnerMostAxes(1, i)[0]); let m = w.segment_util.computeOutShape(l.shape, u, a), d = y.sizeFromShape([l.shape[u]]), f = pe({ inputs: { x: l }, backend: e, attrs: { shape: [-1, d] } }); p.push(f); - let h = n.dtype, g = [f.shape[0], a], x = kt({ backend: e, attrs: { shape: g, value: 0, dtype: h } }), b = new by(f.shape, g, h), C = [{ type: "int32", data: [a] }, { type: "int32", data: [y.sizeFromShape(f.shape)] }], S = e.runWebGPUProgram(b, [f, s], h, C, x), k = pe({ inputs: { x: S }, backend: e, attrs: { shape: m } }); + let h = n.dtype, g = [f.shape[0], a], x = vt({ backend: e, attrs: { shape: g, value: 0, dtype: h } }), b = new yy(f.shape, g, h), C = [{ type: "int32", data: [a] }, { type: "int32", data: [y.sizeFromShape(f.shape)] }], S = e.runWebGPUProgram(b, [f, s], h, C, x), k = pe({ inputs: { x: S }, backend: e, attrs: { shape: m } }); p.push(S); let _ = k; if (c != null) { p.push(k); let E = w.getUndoAxesPermutation(c); - _ = yr({ inputs: { x: _ }, backend: e, attrs: { perm: E } }); + _ = xr({ inputs: { x: _ }, backend: e, attrs: { perm: E } }); } return p.forEach((E) => e.disposeData(E.dataId)), _; } -var zU = { kernelName: Ji, backendName: "webgpu", kernelFunc: Nce }; -var Tce = [rz, Bz, zz, Vz, Wz, Uz, Hz, Kz, qz, jz, Xz, Yz, Qz, Zz, Jz, rV, oV, nV, sV, aV, uV, pV, cV, fV, hV, gV, nz, yV, CV, wV, SV, IV, vV, kV, NV, TV, _V, $V, DV, AV, FV, PV, MV, LV, OV, BV, zV, VV, WV, UV, KV, qV, jV, XV, YV, QV, ZV, JV, eW, ez, tW, nW, rW, oW, sW, aW, iW, uW, pW, cW, lW, oz, mW, bV, dW, fW, hW, gW, xW, yW, bW, wW, CW, SW, IW, vW, NW, TW, eV, _W, $W, DW, EW, RW, AW, tV, FW, PW, OW, MW, BW, GV, zW, VW, WW, lV, UW, KW, qW, jW, XW, YW, QW, ZW, mV, JW, eU, tU, rU, tz, oU, nU, sU, aU, iU, uU, pU, cU, lU, mU, dU, fU, hU, gU, xU, yU, iV, $U, EU, RU, LW, bU, CU, wU, SU, vU, kU, NU, TU, _U, DU, HV, AU, FU, PU, IU, MU, LU, Gz, BU, zU, GW]; -for (let r of Tce) - ri(r); -var VU = "4.11.0"; -var _ce = "4.11.0"; -var $ce = "4.11.0"; -var Ece = "4.11.0"; -var Rce = "4.11.0"; -var Dce = "4.11.0"; -var Ace = { tfjs: VU, "tfjs-core": VU, "tfjs-converter": _ce, "tfjs-backend-cpu": $ce, "tfjs-backend-webgl": Ece, "tfjs-backend-wasm": Rce, "tfjs-backend-webgpu": Dce }; +var VU = { kernelName: Yi, backendName: "webgpu", kernelFunc: Nce }; +var Tce = [oz, zz, Vz, Wz, Uz, Gz, Kz, qz, jz, Xz, Yz, Qz, Zz, Jz, eV, oV, nV, sV, aV, iV, pV, cV, lV, hV, gV, xV, sz, bV, wV, SV, IV, vV, kV, NV, TV, _V, $V, EV, AV, FV, PV, OV, LV, BV, MV, zV, VV, WV, UV, GV, qV, jV, XV, YV, QV, ZV, JV, eW, tW, tz, rW, sW, oW, nW, aW, iW, uW, pW, cW, lW, mW, nz, dW, CV, fW, hW, gW, xW, yW, bW, CW, SW, wW, IW, vW, kW, TW, _W, tV, $W, EW, AW, RW, DW, FW, rV, PW, OW, MW, LW, zW, HV, VW, WW, UW, mV, GW, qW, jW, XW, YW, QW, ZW, JW, dV, eU, tU, rU, oU, rz, nU, sU, aU, iU, uU, pU, cU, lU, mU, dU, fU, hU, gU, xU, yU, bU, uV, EU, RU, DU, BW, CU, wU, SU, IU, kU, NU, TU, _U, $U, AU, KV, FU, PU, OU, vU, LU, BU, Hz, zU, VU, HW]; +for (let r15 of Tce) + ti(r15); +var WU = "4.14.0"; +var _ce = "4.14.0"; +var $ce = "4.14.0"; +var Ece = "4.14.0"; +var Rce = "4.14.0"; +var Dce = "4.14.0"; +var Ace = { tfjs: WU, "tfjs-core": WU, "tfjs-converter": _ce, "tfjs-backend-cpu": $ce, "tfjs-backend-webgl": Ece, "tfjs-backend-wasm": Rce, "tfjs-backend-webgpu": Dce }; +var bQt = void 0; // src/util/util.ts function log(...msg) { @@ -33742,8 +33746,8 @@ var convolution = ` // src/image/imagefx.ts var collect = (source, prefix, collection) => { - const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); - source.replace(r, (match2, name) => { + const r15 = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); + source.replace(r15, (match2, name) => { collection[name] = 0; return match2; }); @@ -34347,9 +34351,9 @@ function GLImageFilter() { // src/image/enhance.ts async function histogramEqualization(inputImage) { - const squeeze = inputImage.shape.length === 4 ? gc(inputImage) : inputImage; - const rgb3 = li(squeeze, 3, 2); - const min = [Tl(rgb3[0]), Tl(rgb3[1]), Tl(rgb3[2])]; + const squeeze = inputImage.shape.length === 4 ? uc(inputImage) : inputImage; + const rgb3 = ci(squeeze, 3, 2); + const min = [vl(rgb3[0]), vl(rgb3[1]), vl(rgb3[2])]; const max = [Ra(rgb3[0]), Ra(rgb3[1]), Ra(rgb3[2])]; const absMax = await Promise.all(max.map((channel) => channel.data())); const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); @@ -34360,13 +34364,13 @@ async function histogramEqualization(inputImage) { const sub = [Te(rgb3[0], min[0]), Te(rgb3[1], min[1]), Te(rgb3[2], min[2])]; const range = [Te(max[0], min[0]), Te(max[1], min[1]), Te(max[2], min[2])]; const enh = [se(sub[0], factor), se(sub[1], factor), se(sub[2], factor)]; - const stack = kr([enh[0], enh[1], enh[2]], 2); + const stack = vr([enh[0], enh[1], enh[2]], 2); final = W(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]); - Mt([...sub, ...range, ...enh, stack]); + Ot([...sub, ...range, ...enh, stack]); } else { final = Ms(squeeze, 0); } - Mt([...rgb3, ...min, ...max, rgb3, squeeze, inputImage]); + Ot([...rgb3, ...min, ...max, rgb3, squeeze, inputImage]); return final; } @@ -34432,10 +34436,10 @@ async function process2(input, config3, getTensor = true) { log("input error: input is missing"); return { tensor: null, canvas: null }; } - if (!(input instanceof ut) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { + if (!(input instanceof mt) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { throw new Error("input error: type not recognized"); } - if (input instanceof ut) { + if (input instanceof mt) { let tensor2 = null; if (input["isDisposedInternal"]) throw new Error("input error: attempted to use tensor but it is disposed"); @@ -34445,22 +34449,22 @@ async function process2(input, config3, getTensor = true) { if (input.shape[2] === 3) { tensor2 = Ms(input, 0); } else if (input.shape[2] === 4) { - const rgb3 = A1(input, [0, 0, 0], [-1, -1, 3]); + const rgb3 = F1(input, [0, 0, 0], [-1, -1, 3]); tensor2 = Ms(rgb3, 0); - Mt(rgb3); + Ot(rgb3); } } else if (input.shape.length === 4) { if (input.shape[3] === 3) { - tensor2 = Ur(input); + tensor2 = Wr(input); } else if (input.shape[3] === 4) { - tensor2 = F1(input, [0, 0, 0, 0], [-1, -1, -1, 3]); + tensor2 = P1(input, [0, 0, 0, 0], [-1, -1, -1, 3]); } } if (tensor2 == null || tensor2.shape.length !== 4 || tensor2.shape[0] !== 1 || tensor2.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); if (tensor2.dtype === "int32") { const cast = We(tensor2, "float32"); - Mt(tensor2); + Ot(tensor2); tensor2 = cast; } return { tensor: tensor2, canvas: config3.filter.return ? outCanvas : null }; @@ -34572,22 +34576,22 @@ async function process2(input, config3, getTensor = true) { let pixels; let depth = 3; if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { - if (env.browser && oT) { - pixels = oT ? oT.fromPixels(input) : null; + if (env.browser && nT) { + pixels = nT ? nT.fromPixels(input) : null; } else { depth = input.data.length / input.height / input.width; const arr = new Uint8Array(input.data.buffer); - pixels = ur(arr, [input.height, input.width, depth], "int32"); + pixels = ar(arr, [input.height, input.width, depth], "int32"); } } else { if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) tmpCanvas = canvas(outCanvas.width, outCanvas.height); - if (oT && env.browser) { + if (nT && env.browser) { if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { - pixels = oT.fromPixels(outCanvas); + pixels = nT.fromPixels(outCanvas); } else { tmpCanvas = copy(outCanvas); - pixels = oT.fromPixels(tmpCanvas); + pixels = nT.fromPixels(tmpCanvas); } } else { const tempCanvas = copy(outCanvas); @@ -34595,24 +34599,24 @@ async function process2(input, config3, getTensor = true) { const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight); depth = tempData.data.length / targetWidth / targetHeight; const arr = new Uint8Array(tempData.data.buffer); - pixels = ur(arr, [targetWidth, targetHeight, depth]); + pixels = ar(arr, [targetWidth, targetHeight, depth]); } } if (depth === 4) { - const rgb3 = A1(pixels, [0, 0, 0], [-1, -1, 3]); - Mt(pixels); + const rgb3 = F1(pixels, [0, 0, 0], [-1, -1, 3]); + Ot(pixels); pixels = rgb3; } if (!pixels) throw new Error("input error: cannot create tensor"); const casted = We(pixels, "float32"); const tensor = config3.filter.equalization ? await histogramEqualization(casted) : Ms(casted, 0); - Mt([pixels, casted]); + Ot([pixels, casted]); if (config3.filter.autoBrightness) { const max = Ra(tensor); const maxVal = await max.data(); config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; - Mt(max); + Ot(max); } return { tensor, canvas: config3.filter.return ? outCanvas : null }; } @@ -34621,10 +34625,10 @@ async function skip(config3, input) { if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; if (!last.inputTensor) { - last.inputTensor = Ur(input); + last.inputTensor = Wr(input); } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { - Mt(last.inputTensor); - last.inputTensor = Ur(input); + Ot(last.inputTensor); + last.inputTensor = Wr(input); } else { const t8 = {}; t8.diff = Te(input, last.inputTensor); @@ -34632,8 +34636,8 @@ async function skip(config3, input) { t8.sum = ot(t8.squared); const diffSum = await t8.sum.data(); const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; - Mt([last.inputTensor, t8.diff, t8.squared, t8.sum]); - last.inputTensor = Ur(input); + Ot([last.inputTensor, t8.diff, t8.squared, t8.sum]); + last.inputTensor = Wr(input); skipFrame = diffRelative <= (config3.cacheSensitivity || 0); } return skipFrame; @@ -34650,14 +34654,14 @@ async function compare(config3, input1, input2) { log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); return 0; } - t8.input1 = Ur(input1); - t8.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? Kj.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : Ur(input2); + t8.input1 = Wr(input1); + t8.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? Kj.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : Wr(input2); t8.diff = Te(t8.input1, t8.input2); t8.squared = se(t8.diff, t8.diff); t8.sum = ot(t8.squared); const diffSum = await t8.sum.data(); const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; - Mt([t8.input1, t8.input2, t8.diff, t8.squared, t8.sum]); + Ot([t8.input1, t8.input2, t8.diff, t8.squared, t8.sum]); return diffRelative; } @@ -34725,7 +34729,7 @@ var Env = class { __privateAdd(this, _canvas, void 0); __privateAdd(this, _image, void 0); __privateAdd(this, _imageData, void 0); - this.browser = typeof navigator !== "undefined"; + this.browser = typeof navigator !== "undefined" && typeof navigator.appVersion !== "undefined"; this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; this.tfjs = { version: Ace["tfjs-core"] }; this.offscreen = typeof OffscreenCanvas !== "undefined"; @@ -34772,7 +34776,7 @@ var Env = class { } /** update backend information */ async updateBackend() { - this.backends = Object.keys(pr().registryFactory); + this.backends = Object.keys(ur().registryFactory); try { this.tensorflow = { version: vde()["binding"] ? vde()["binding"].TF_Version : void 0, @@ -34807,7 +34811,7 @@ var Env = class { this.webgpu.supported = false; } try { - this.kernels = Ym(Cde()).map((kernel) => kernel.kernelName.toLowerCase()); + this.kernels = jm(Cde()).map((kernel) => kernel.kernelName.toLowerCase()); } catch (e) { } } @@ -35246,14 +35250,14 @@ async function loadModel(modelPath) { options.cacheSupported = typeof indexedDB !== "undefined"; let cachedModels = {}; try { - cachedModels = options.cacheSupported && options.cacheModels ? await fi.listModels() : {}; + cachedModels = options.cacheSupported && options.cacheModels ? await mi.listModels() : {}; } catch (e) { options.cacheSupported = false; } modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl; const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) }; - let model23 = new Bl(modelStats[shortModelName].url, tfLoadOptions); + let model23 = new Ml(modelStats[shortModelName].url, tfLoadOptions); let loaded = false; try { model23.findIOHandler(); @@ -35289,7 +35293,7 @@ async function loadModel(modelPath) { } // package.json -var version = "3.1.2"; +var version = "3.2.0"; // src/tfjs/humangl.ts var config2 = { @@ -35322,7 +35326,7 @@ function register(instance) { var _a; if (instance.config.backend !== "humangl") return; - if (config2.name in pr().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) { + if (config2.name in ur().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) { log("humangl error: backend invalid context"); instance.models.reset(); } @@ -35364,23 +35368,23 @@ function register(instance) { return; } try { - vI(2, config2.gl); + kI(2, config2.gl); } catch (err) { log("humangl error: cannot set webgl context:", err); return; } try { - const ctx = new kp(config2.gl); - su(config2.name, () => new wu(ctx), config2.priority); + const ctx = new xp(config2.gl); + tu(config2.name, () => new Oc(ctx), config2.priority); } catch (err) { log("humangl error: cannot register webgl backend:", err); return; } try { - const kernels = Ym("webgl"); + const kernels = jm("webgl"); kernels.forEach((kernelConfig) => { const newKernelConfig = { ...kernelConfig, backendName: config2.name }; - ri(newKernelConfig); + ti(newKernelConfig); }); } catch (err) { log("humangl error: cannot update webgl backend registration:", err); @@ -35420,7 +35424,7 @@ function init() { constants.tf2 = ke(2, "float32"); constants.tf05 = ke(0.5, "float32"); constants.tf127 = ke(127.5, "float32"); - constants.rgb = tr([0.2989, 0.587, 0.114], "float32"); + constants.rgb = Jt([0.2989, 0.587, 0.114], "float32"); } // src/tfjs/backend.ts @@ -35445,7 +35449,7 @@ function registerCustomOps(config3) { backendName: Cde(), kernelFunc: (op2) => De(() => Te(op2.inputs.a, se(je(op2.inputs.a, op2.inputs.b), op2.inputs.b))) }; - ri(kernelMod); + ti(kernelMod); env.kernels.push("mod"); newKernels.push("mod"); } @@ -35453,9 +35457,9 @@ function registerCustomOps(config3) { const kernelFloorMod = { kernelName: "FloorMod", backendName: Cde(), - kernelFunc: (op2) => De(() => Ce(se(dd(op2.inputs.a, op2.inputs.b), op2.inputs.b), F2(op2.inputs.a, op2.inputs.b))) + kernelFunc: (op2) => De(() => Ce(se(ld(op2.inputs.a, op2.inputs.b), op2.inputs.b), P2(op2.inputs.a, op2.inputs.b))) }; - ri(kernelFloorMod); + ti(kernelFloorMod); env.kernels.push("floormod"); newKernels.push("floormod"); } @@ -35471,7 +35475,7 @@ function registerCustomOps(config3) { return t8; }) }; - ri(kernelRotateWithOffset); + ti(kernelRotateWithOffset); env.kernels.push("rotatewithoffset"); newKernels.push("rotatewithoffset"); } @@ -35495,10 +35499,10 @@ async function check(instance, force = false) { if (instance.config.debug) log("running inside electron"); } - let available = Object.keys(pr().registryFactory); + let available = Object.keys(ur().registryFactory); if (instance.config.backend === "humangl" && !available.includes("humangl")) { register(instance); - available = Object.keys(pr().registryFactory); + available = Object.keys(ur().registryFactory); } if (instance.config.debug) log("available backends:", available); @@ -35546,13 +35550,13 @@ async function check(instance, force = false) { Yse(instance.config.wasmPath, instance.config.wasmPlatformFetch); else throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); - let mt = false; + let mt2 = false; let simd = false; try { - mt = await A().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + mt2 = await A().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); simd = await A().getAsync("WASM_HAS_SIMD_SUPPORT"); if (instance.config.debug) - log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); + log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt2 ? "multithreaded" : "singlethreaded"}`); if (instance.config.debug && !simd) log("warning: wasm simd support is not enabled"); } catch (e) { @@ -35622,9 +35626,9 @@ function fakeOps(kernelNames, config3) { // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); }, // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); }, }; - ri(kernelConfig); + ti(kernelConfig); } - env.kernels = Ym(Cde()).map((kernel) => kernel.kernelName.toLowerCase()); + env.kernels = jm(Cde()).map((kernel) => kernel.kernelName.toLowerCase()); } // src/draw/draw.ts @@ -39579,7 +39583,7 @@ var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [ // src/draw/face.ts var localOptions; function drawLabels(f, ctx) { - var _a, _b, _c2, _d2, _e, _f2, _g2, _h2, _i; + var _a, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2; if (!localOptions.drawLabels || ((_a = localOptions.faceLabels) == null ? void 0 : _a.length) === 0) return; let l = localOptions.faceLabels.slice(); @@ -39610,7 +39614,7 @@ function drawLabels(f, ctx) { l = replace(l, "[yaw]", rad2deg(f.rotation.angle.yaw)); if ((_g2 = (_f2 = f.rotation) == null ? void 0 : _f2.angle) == null ? void 0 : _g2.pitch) l = replace(l, "[pitch]", rad2deg(f.rotation.angle.pitch)); - if ((_i = (_h2 = f.rotation) == null ? void 0 : _h2.gaze) == null ? void 0 : _i.bearing) + if ((_i2 = (_h2 = f.rotation) == null ? void 0 : _h2.gaze) == null ? void 0 : _i2.bearing) l = replace(l, "[gaze]", rad2deg(f.rotation.gaze.bearing)); labels(ctx, l, f.box[0], f.box[1], localOptions); } @@ -40143,7 +40147,7 @@ function createAnchors() { } layerId = lastSameStrideLayer; } - anchorTensor = { x: tr(anchors3.map((a) => a.x)), y: tr(anchors3.map((a) => a.y)) }; + anchorTensor = { x: Jt(anchors3.map((a) => a.x)), y: Jt(anchors3.map((a) => a.y)) }; } async function loadDetector(config3) { if (env.initial) @@ -40160,11 +40164,11 @@ async function loadDetector(config3) { var cropFactor = [5, 5]; function decodeBoxes(boxesTensor, anchor) { return De(() => { - const split = li(boxesTensor, 12, 1); - let xCenter = gc(split[0]); - let yCenter = gc(split[1]); - let width = gc(split[2]); - let height = gc(split[3]); + const split = ci(boxesTensor, 12, 1); + let xCenter = uc(split[0]); + let yCenter = uc(split[1]); + let width = uc(split[2]); + let height = uc(split[3]); xCenter = Ce(je(xCenter, inputSize), anchor.x); yCenter = Ce(je(yCenter, inputSize), anchor.y); width = se(je(width, inputSize), cropFactor[0]); @@ -40173,7 +40177,7 @@ function decodeBoxes(boxesTensor, anchor) { const yMin = Te(yCenter, je(height, 2)); const xMax = Ce(xMin, width); const yMax = Ce(yMin, height); - const boxes = kr([xMin, yMin, xMax, yMax], 1); + const boxes = vr([xMin, yMin, xMax, yMax], 1); return boxes; }); } @@ -40194,7 +40198,7 @@ async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { const detectedBox = { score, boxRaw, box }; detectedBoxes.push(detectedBox); } - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return detectedBoxes; } async function detectBoxes(input, config3, outputSize2) { @@ -40202,10 +40206,10 @@ async function detectBoxes(input, config3, outputSize2) { t8.res = model == null ? void 0 : model.execute(input, ["Identity"]); t8.logitsRaw = Xe(t8.res, [0, 0, 0], [1, -1, 1]); t8.boxesRaw = Xe(t8.res, [0, 0, 1], [1, -1, -1]); - t8.logits = gc(t8.logitsRaw); - t8.boxes = gc(t8.boxesRaw); + t8.logits = uc(t8.logitsRaw); + t8.boxes = uc(t8.boxesRaw); const boxes = await decodeResults(t8.boxes, t8.logits, config3, outputSize2); - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return boxes; } @@ -40300,7 +40304,7 @@ function prepareImage(input, size2, cropBox) { } else { final = je(t8.cropped || input, constants.tf255); } - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return final; } function rescaleKeypoints(keypoints, outputSize2, cropBox) { @@ -40357,7 +40361,7 @@ async function detectLandmarks(input, config3, outputSize2) { const poseScore = (await t8.poseflag.data())[0]; const points = await t8.ld.data(); const distances = await t8.world.data(); - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); const keypointsRelative = []; const depth = 5; for (let i = 0; i < points.length / depth; i++) { @@ -40401,7 +40405,7 @@ async function predict(input, config3) { if ((_b = (_a = config3.body) == null ? void 0 : _a["detector"]) == null ? void 0 : _b["enabled"]) { const preparedImage = prepareImage(input, 224); boxes = await detectBoxes(preparedImage, config3, outputSize2); - Mt(preparedImage); + Ot(preparedImage); } else { boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }]; } @@ -40409,7 +40413,7 @@ async function predict(input, config3) { const preparedBox = prepareImage(input, 256, (_c2 = boxes[i]) == null ? void 0 : _c2.boxRaw); cache.length = 0; const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2); - Mt(preparedBox); + Ot(preparedBox); if (!bodyResult) continue; bodyResult.id = i; @@ -40528,13 +40532,13 @@ async function process3(res, outputShape, config3) { const t8 = {}; const results = []; const detections = await res.array(); - t8.squeeze = gc(res); - const arr = li(t8.squeeze, 6, 1); - t8.stack = kr([arr[1], arr[0], arr[3], arr[2]], 1); - t8.boxes = gc(t8.stack); - t8.scores = gc(arr[4]); - t8.classes = gc(arr[5]); - Mt([res, ...arr]); + t8.squeeze = uc(res); + const arr = ci(t8.squeeze, 6, 1); + t8.stack = vr([arr[1], arr[0], arr[3], arr[2]], 1); + t8.boxes = uc(t8.stack); + t8.scores = uc(arr[4]); + t8.classes = uc(arr[5]); + Ot([res, ...arr]); t8.nms = await Kj.nonMaxSuppressionAsync(t8.boxes, t8.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); const nms = await t8.nms.data(); let i = 0; @@ -40562,7 +40566,7 @@ async function process3(res, outputShape, config3) { ]; results.push({ id: i++, score, class: classVal, label, box, boxRaw }); } - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return results; } async function predict2(input, config3) { @@ -40580,7 +40584,7 @@ async function predict2(input, config3) { const resize = Kj.resizeBilinear(input, [inputSize3, inputSize3]); const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; lastTime2 = now(); - Mt(resize); + Ot(resize); const obj = await process3(objectT, outputSize2, config3); last2 = obj; resolve(obj); @@ -40640,15 +40644,15 @@ async function max2d(inputs, minScore) { const max = Ra(reshaped, 0); const newScore = (await max.data())[0]; if (newScore > minScore) { - const coordinates = Ek(reshaped, 0); - const mod = F2(coordinates, width); + const coordinates = Rk(reshaped, 0); + const mod = P2(coordinates, width); const x = (await mod.data())[0]; const div = je(coordinates, width); const y8 = (await div.data())[0]; - Mt([reshaped, max, coordinates, mod, div]); + Ot([reshaped, max, coordinates, mod, div]); return [x, y8, newScore]; } - Mt([reshaped, max]); + Ot([reshaped, max]); return [0, 0, newScore]; } async function predict3(image, config3) { @@ -40673,13 +40677,13 @@ async function predict3(image, config3) { if (config3.body.enabled) resT = model4 == null ? void 0 : model4.execute(tensor); lastTime3 = now(); - Mt(tensor); + Ot(tensor); if (resT) { cache2.keypoints.length = 0; - const squeeze = gc(resT); - Mt(resT); - const stack = fo(squeeze, 2); - Mt(squeeze); + const squeeze = uc(resT); + Ot(resT); + const stack = mo(squeeze, 2); + Ot(squeeze); for (let id2 = 0; id2 < stack.length; id2++) { const [x8, y10, partScore] = await max2d(stack[id2], config3.body.minConfidence); if (partScore > (config3.body.minConfidence || 0)) { @@ -40701,7 +40705,7 @@ async function predict3(image, config3) { }); } } - stack.forEach((s) => Mt(s)); + stack.forEach((s) => Ot(s)); } cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); const x = cache2.keypoints.map((a) => a.position[0]); @@ -40761,7 +40765,7 @@ var cutAndResize = (box, image, cropSize) => { const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w10, box.endPoint[1] / h, box.endPoint[0] / w10]; const crop = Kj.cropAndResize(image, [cutBox], [0], cropSize); const norm = je(crop, constants.tf255); - Mt(crop); + Ot(crop); return norm; }; var enlargeBox = (box, factor) => { @@ -40892,7 +40896,7 @@ function correctFaceRotation(rotate, box, input, inputSize10) { const rotated = Kj.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); rotationMatrix = buildRotationMatrix(-angle, center); face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); - Mt(rotated); + Ot(rotated); } else { face4 = cutAndResize(box, input, [inputSize10, inputSize10]); } @@ -40934,12 +40938,12 @@ async function load3(config3) { log("cached model:", model5["modelUrl"]); inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; inputSizeT = ke(inputSize4, "int32"); - anchors = fu(generateAnchors(inputSize4)); + anchors = cu(generateAnchors(inputSize4)); return model5; } function decodeBoxes2(boxOutputs) { if (!anchors || !inputSizeT) - return Gr([0, 0]); + return Ur([0, 0]); const t8 = {}; t8.boxStarts = Xe(boxOutputs, [0, 1], [-1, 2]); t8.centers = Ce(t8.boxStarts, anchors); @@ -40951,8 +40955,8 @@ function decodeBoxes2(boxOutputs) { t8.ends = Ce(t8.centersNormalized, t8.halfBoxSize); t8.startNormalized = se(t8.starts, inputSizeT); t8.endNormalized = se(t8.ends, inputSizeT); - const boxes = Yk([t8.startNormalized, t8.endNormalized], 1); - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + const boxes = Qk([t8.startNormalized, t8.endNormalized], 1); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return boxes; } async function getBoxes(inputImage, config3) { @@ -40966,20 +40970,20 @@ async function getBoxes(inputImage, config3) { const res = model5 == null ? void 0 : model5.execute(t8.normalized); if (Array.isArray(res) && res.length > 2) { const sorted = res.sort((a, b) => a.size - b.size); - t8.concat384 = bt([sorted[0], sorted[2]], 2); - t8.concat512 = bt([sorted[1], sorted[3]], 2); - t8.concat = bt([t8.concat512, t8.concat384], 1); - t8.batch = gc(t8.concat, [0]); + t8.concat384 = yt([sorted[0], sorted[2]], 2); + t8.concat512 = yt([sorted[1], sorted[3]], 2); + t8.concat = yt([t8.concat512, t8.concat384], 1); + t8.batch = uc(t8.concat, [0]); } else if (Array.isArray(res)) { - t8.batch = gc(res[0]); + t8.batch = uc(res[0]); } else { - t8.batch = gc(res); + t8.batch = uc(res); } - Mt(res); + Ot(res); t8.boxes = decodeBoxes2(t8.batch); t8.logits = Xe(t8.batch, [0, 0], [-1, 1]); t8.sigmoid = $a(t8.logits); - t8.scores = gc(t8.sigmoid); + t8.scores = uc(t8.sigmoid); t8.nms = await Kj.nonMaxSuppressionAsync(t8.boxes, t8.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c2 = config3.face.detector) == null ? void 0 : _c2.minConfidence) || 0); const nms = await t8.nms.array(); const boxes = []; @@ -40990,7 +40994,7 @@ async function getBoxes(inputImage, config3) { const b = {}; b.bbox = Xe(t8.boxes, [nms[i], 0], [1, -1]); b.slice = Xe(t8.batch, [nms[i], keypointsCount - 1], [1, -1]); - b.squeeze = gc(b.slice); + b.squeeze = uc(b.slice); b.landmarks = W(b.squeeze, [keypointsCount, -1]); const points = await b.bbox.data(); const rawBox = { @@ -41006,10 +41010,10 @@ async function getBoxes(inputImage, config3) { const squaredBox = squarifyBox(enlargedBox); if (squaredBox.size[0] > (((_e = config3.face.detector) == null ? void 0 : _e["minSize"]) || 0) && squaredBox.size[1] > (((_f2 = config3.face.detector) == null ? void 0 : _f2["minSize"]) || 0)) boxes.push(squaredBox); - Object.keys(b).forEach((tensor) => Mt(b[tensor])); + Object.keys(b).forEach((tensor) => Ot(b[tensor])); } } - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return boxes; } @@ -41074,7 +41078,7 @@ var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, mes ]], [0], [inputSize5, inputSize5]); if (flip && env.kernels.includes("flipleftright")) { const flipped = Kj.flipLeftRight(crop); - Mt(crop); + Ot(crop); crop = flipped; } return { box, boxSize, crop }; @@ -41112,13 +41116,13 @@ async function augmentIris(rawCoords, face4, meshSize) { return rawCoords; const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true); const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true); - const combined = bt([leftEyeCrop, rightEyeCrop]); - Mt(leftEyeCrop); - Mt(rightEyeCrop); + const combined = yt([leftEyeCrop, rightEyeCrop]); + Ot(leftEyeCrop); + Ot(rightEyeCrop); const eyePredictions = model6.execute(combined); - Mt(combined); + Ot(combined); const eyePredictionsData = await eyePredictions.data(); - Mt(eyePredictions); + Ot(eyePredictions); const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); @@ -41140,20 +41144,20 @@ async function augmentIris(rawCoords, face4, meshSize) { // src/face/attention.ts async function augment(rawCoords, results) { - var _a, _b, _c2, _d2, _e, _f2, _g2, _h2, _i, _j2; + var _a, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2; const t8 = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords // mesh: results[0], // already have it in rawCoords // output_mesh_identity // flag: results[1], // already processed in parent // conv_faceflag - lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()), + lips: await ((_b = (_a = results.filter((r15) => r15.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()), // 80 x 2d = 160 // output_lips - irisL: await ((_d2 = (_c2 = results.filter((r) => r.size === 10)) == null ? void 0 : _c2[0]) == null ? void 0 : _d2.data()), + irisL: await ((_d2 = (_c2 = results.filter((r15) => r15.size === 10)) == null ? void 0 : _c2[0]) == null ? void 0 : _d2.data()), // 5 x 2d = 10 // output_right_iris - eyeL: await ((_f2 = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f2.data()), + eyeL: await ((_f2 = (_e = results.filter((r15) => r15.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f2.data()), // 71 x 2d = 142 // output_right_eye - irisR: await ((_h2 = (_g2 = results.filter((r) => r.size === 10)) == null ? void 0 : _g2[1]) == null ? void 0 : _h2.data()), + irisR: await ((_h2 = (_g2 = results.filter((r15) => r15.size === 10)) == null ? void 0 : _g2[1]) == null ? void 0 : _h2.data()), // 5 x 2d = 10 // output_left_iris - eyeR: await ((_j2 = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j2.data()) + eyeR: await ((_j2 = (_i2 = results.filter((r15) => r15.size === 142)) == null ? void 0 : _i2[1]) == null ? void 0 : _j2.data()) // 71 x 2d = 142// output_left_eye }; for (const val of Object.values(t8)) { @@ -41184,7 +41188,7 @@ var cache3 = { var model7 = null; var inputSize6 = 0; async function predict4(input, config3) { - var _a, _b, _c2, _d2, _e, _f2, _g2, _h2, _i, _j2; + var _a, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2; const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp; const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0); if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) { @@ -41220,7 +41224,7 @@ async function predict4(input, config3) { [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c2 = config3.face.detector) == null ? void 0 : _c2.rotation, box, input, ((_d2 = config3.face.mesh) == null ? void 0 : _d2.enabled) ? inputSize6 : size()); if (config3.filter.equalization) { const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0; - Mt(face4.tensor); + Ot(face4.tensor); if (equilized) face4.tensor = equilized; } @@ -41240,7 +41244,7 @@ async function predict4(input, config3) { } else { if (((_f2 = config3.face.attention) == null ? void 0 : _f2.enabled) && !env.kernels.includes("atan2")) { config3.face.attention.enabled = false; - Mt(face4.tensor); + Ot(face4.tensor); return faces; } const results = model7.execute(face4.tensor); @@ -41264,10 +41268,10 @@ async function predict4(input, config3) { const meshT = results.find((t8) => t8.shape[t8.shape.length - 1] === 1404); const coordsReshaped = W(meshT, [-1, 3]); let rawCoords = await coordsReshaped.array(); - Mt(coordsReshaped); + Ot(coordsReshaped); if ((_h2 = config3.face.attention) == null ? void 0 : _h2.enabled) { rawCoords = await augment(rawCoords, results); - } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) { + } else if ((_i2 = config3.face.iris) == null ? void 0 : _i2.enabled) { rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6); } face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6); @@ -41286,12 +41290,12 @@ async function predict4(input, config3) { face4.size = calculatedBox.size; newCache.push(calculatedBox); } - Mt(results); + Ot(results); } if (face4.score > (((_j2 = config3.face.detector) == null ? void 0 : _j2.minConfidence) || 1)) faces.push(face4); else - Mt(face4.tensor); + Ot(face4.tensor); } cache3.boxes = newCache; return faces; @@ -41384,7 +41388,7 @@ async function predict5(image, config3, idx, count2) { obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); } obj.sort((a, b) => b.score - a.score); - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); } last3[idx] = obj; lastCount = count2; @@ -41422,7 +41426,7 @@ function enhance(input, config3) { crop = Kj.resizeBilinear(tensor, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); } const norm = se(crop, constants.tf255); - Mt(crop); + Ot(crop); return norm; } async function predict6(image, config3, idx, count2) { @@ -41448,7 +41452,7 @@ async function predict6(image, config3, idx, count2) { const enhanced = enhance(image, config3); const resT = model9 == null ? void 0 : model9.execute(enhanced); lastTime5 = now(); - Mt(enhanced); + Ot(enhanced); const genderT = resT.find((t8) => t8.shape[1] === 1); const gender2 = await genderT.data(); const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; @@ -41456,9 +41460,9 @@ async function predict6(image, config3, idx, count2) { obj.gender = gender2[0] <= 0.5 ? "female" : "male"; obj.genderScore = Math.min(0.99, confidence); } - const argmax = Ek(resT.find((t8) => t8.shape[1] === 100), 1); + const argmax = Rk(resT.find((t8) => t8.shape[1] === 100), 1); const ageIdx = (await argmax.data())[0]; - Mt(argmax); + Ot(argmax); const ageT = resT.find((t8) => t8.shape[1] === 100); const all2 = await ageT.data(); obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; @@ -41467,7 +41471,7 @@ async function predict6(image, config3, idx, count2) { const desc = resT.find((t8) => t8.shape[1] === 1024); const descriptor = desc ? await desc.data() : []; obj.descriptor = Array.from(descriptor); - resT.forEach((t8) => Mt(t8)); + resT.forEach((t8) => Ot(t8)); } last4[idx] = obj; lastCount2 = count2; @@ -41548,7 +41552,7 @@ async function predict7(image, config3, idx, count2) { cached[idx] = Math.round(100 * num) / 100; lastCount3 = count2; lastTime6 = now(); - Mt([resize, res]); + Ot([resize, res]); resolve(cached[idx]); }); } @@ -41587,7 +41591,7 @@ async function predict8(image, config3, idx, count2) { cached2[idx] = Math.round(100 * num) / 100; lastCount4 = count2; lastTime7 = now(); - Mt([resize, res]); + Ot([resize, res]); resolve(cached2[idx]); }); } @@ -41650,7 +41654,7 @@ async function predict9(image, config3, idx, count2) { for (let i = 1; i < ageSorted.length; i++) age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); obj.age = Math.round(10 * age2) / 10; - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); last5[idx] = obj; lastCount5 = count2; lastTime8 = now(); @@ -41704,7 +41708,7 @@ async function predict10(image, config3, idx, count2) { const data = await t8.age.data(); obj.age = Math.trunc(10 * data[0]) / 10; } - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); last6[idx] = obj; lastCount6 = count2; lastTime9 = now(); @@ -41756,11 +41760,11 @@ async function predict11(image, config3, idx, count2) { var _a3, _b3; let normalize2; if (((_b3 = (_a3 = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a3[0].shape) == null ? void 0 : _b3[3]) === 1) { - const [red, green, blue] = li(t8.resize, 3, 3); + const [red, green, blue] = ci(t8.resize, 3, 3); const redNorm = se(red, rgb2[0]); const greenNorm = se(green, rgb2[1]); const blueNorm = se(blue, rgb2[2]); - const grayscale = Tk([redNorm, greenNorm, blueNorm]); + const grayscale = _k([redNorm, greenNorm, blueNorm]); normalize2 = se(Te(grayscale, constants.tf05), 2); } else { normalize2 = se(Te(t8.resize, constants.tf05), 2); @@ -41773,7 +41777,7 @@ async function predict11(image, config3, idx, count2) { const data = await t8.gender.data(); obj.gender = data[0] > data[1] ? "female" : "male"; obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); last7[idx] = obj; lastCount7 = count2; lastTime10 = now(); @@ -41816,7 +41820,7 @@ async function predict12(input, config3, idx, count2) { t8.data = model15.execute(t8.crop); const output = await t8.data.data(); data = Array.from(output); - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); } last8[idx] = data; lastCount8 = count2; @@ -41859,7 +41863,7 @@ async function predict13(input, config3, idx, count2) { t8.data = model16.execute(t8.crop); const output = await t8.data.data(); data = Array.from(output); - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); } last9[idx] = data; lastCount9 = count2; @@ -41909,24 +41913,24 @@ var calculateFaceAngle = (face4, imageSize) => { const z = a[0] * b[1] - a[1] * b[0]; return [x, y8, z]; }; - const rotationMatrixToEulerAngle = (r) => { - const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; + const rotationMatrixToEulerAngle = (r15) => { + const [r00, _r01, _r02, r102, r112, r122, r20, r21, r222] = r15; let thetaX; let thetaY; let thetaZ; - if (r10 < 1) { - if (r10 > -1) { - thetaZ = Math.asin(r10); + if (r102 < 1) { + if (r102 > -1) { + thetaZ = Math.asin(r102); thetaY = Math.atan2(-r20, r00); - thetaX = Math.atan2(-r12, r11); + thetaX = Math.atan2(-r122, r112); } else { thetaZ = -Math.PI / 2; - thetaY = -Math.atan2(r21, r22); + thetaY = -Math.atan2(r21, r222); thetaX = 0; } } else { thetaZ = Math.PI / 2; - thetaY = Math.atan2(r21, r22); + thetaY = Math.atan2(r21, r222); thetaX = 0; } if (Number.isNaN(thetaX)) @@ -41974,7 +41978,7 @@ function calculateCameraDistance(face4, width) { // src/face/face.ts var detectFace = async (instance, input) => { - var _a, _b, _c2, _d2, _e, _f2, _g2, _h2, _i, _j2, _k2, _l2, _m, _n2, _o, _p2, _q, _r2, _s2, _t, _u2, _v2, _w2; + var _a, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q, _r2, _s2, _t, _u2, _v2, _w2; let timeStamp = now(); let ageRes; let gearRes; @@ -42001,90 +42005,90 @@ var detectFace = async (instance, input) => { } if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) { const masked = await mask(faces[i]); - Mt(faces[i].tensor); + Ot(faces[i].tensor); if (masked) faces[i].tensor = masked; } const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null; instance.analyze("Start Emotion:"); if (instance.config.async) { - emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || ur([]), instance.config, i, faces.length) : []; + emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || ar([]), instance.config, i, faces.length) : []; } else { instance.state = "run:emotion"; timeStamp = now(); - emotionRes = ((_c2 = instance.config.face.emotion) == null ? void 0 : _c2.enabled) ? await predict5(faces[i].tensor || ur([]), instance.config, i, faces.length) : []; + emotionRes = ((_c2 = instance.config.face.emotion) == null ? void 0 : _c2.enabled) ? await predict5(faces[i].tensor || ar([]), instance.config, i, faces.length) : []; instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); } instance.analyze("End Emotion:"); instance.analyze("Start AntiSpoof:"); if (instance.config.async) { - antispoofRes = ((_d2 = instance.config.face.antispoof) == null ? void 0 : _d2.enabled) ? predict7(faces[i].tensor || ur([]), instance.config, i, faces.length) : 0; + antispoofRes = ((_d2 = instance.config.face.antispoof) == null ? void 0 : _d2.enabled) ? predict7(faces[i].tensor || ar([]), instance.config, i, faces.length) : 0; } else { instance.state = "run:antispoof"; timeStamp = now(); - antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || ur([]), instance.config, i, faces.length) : 0; + antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || ar([]), instance.config, i, faces.length) : 0; instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); } instance.analyze("End AntiSpoof:"); instance.analyze("Start Liveness:"); if (instance.config.async) { - livenessRes = ((_f2 = instance.config.face.liveness) == null ? void 0 : _f2.enabled) ? predict8(faces[i].tensor || ur([]), instance.config, i, faces.length) : 0; + livenessRes = ((_f2 = instance.config.face.liveness) == null ? void 0 : _f2.enabled) ? predict8(faces[i].tensor || ar([]), instance.config, i, faces.length) : 0; } else { instance.state = "run:liveness"; timeStamp = now(); - livenessRes = ((_g2 = instance.config.face.liveness) == null ? void 0 : _g2.enabled) ? await predict8(faces[i].tensor || ur([]), instance.config, i, faces.length) : 0; + livenessRes = ((_g2 = instance.config.face.liveness) == null ? void 0 : _g2.enabled) ? await predict8(faces[i].tensor || ar([]), instance.config, i, faces.length) : 0; instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); } instance.analyze("End Liveness:"); instance.analyze("Start GEAR:"); if (instance.config.async) { - gearRes = ((_h2 = instance.config.face.gear) == null ? void 0 : _h2.enabled) ? predict9(faces[i].tensor || ur([]), instance.config, i, faces.length) : null; + gearRes = ((_h2 = instance.config.face.gear) == null ? void 0 : _h2.enabled) ? predict9(faces[i].tensor || ar([]), instance.config, i, faces.length) : null; } else { instance.state = "run:gear"; timeStamp = now(); - gearRes = ((_i = instance.config.face.gear) == null ? void 0 : _i.enabled) ? await predict9(faces[i].tensor || ur([]), instance.config, i, faces.length) : null; + gearRes = ((_i2 = instance.config.face.gear) == null ? void 0 : _i2.enabled) ? await predict9(faces[i].tensor || ar([]), instance.config, i, faces.length) : null; instance.performance.gear = Math.trunc(now() - timeStamp); } instance.analyze("End GEAR:"); instance.analyze("Start SSRNet:"); if (instance.config.async) { - ageRes = ((_j2 = instance.config.face["ssrnet"]) == null ? void 0 : _j2.enabled) ? predict10(faces[i].tensor || ur([]), instance.config, i, faces.length) : null; - genderRes = ((_k2 = instance.config.face["ssrnet"]) == null ? void 0 : _k2.enabled) ? predict11(faces[i].tensor || ur([]), instance.config, i, faces.length) : null; + ageRes = ((_j2 = instance.config.face["ssrnet"]) == null ? void 0 : _j2.enabled) ? predict10(faces[i].tensor || ar([]), instance.config, i, faces.length) : null; + genderRes = ((_k2 = instance.config.face["ssrnet"]) == null ? void 0 : _k2.enabled) ? predict11(faces[i].tensor || ar([]), instance.config, i, faces.length) : null; } else { instance.state = "run:ssrnet"; timeStamp = now(); - ageRes = ((_l2 = instance.config.face["ssrnet"]) == null ? void 0 : _l2.enabled) ? await predict10(faces[i].tensor || ur([]), instance.config, i, faces.length) : null; - genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || ur([]), instance.config, i, faces.length) : null; + ageRes = ((_l2 = instance.config.face["ssrnet"]) == null ? void 0 : _l2.enabled) ? await predict10(faces[i].tensor || ar([]), instance.config, i, faces.length) : null; + genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || ar([]), instance.config, i, faces.length) : null; instance.performance.ssrnet = Math.trunc(now() - timeStamp); } instance.analyze("End SSRNet:"); instance.analyze("Start MobileFaceNet:"); if (instance.config.async) { - mobilefacenetRes = ((_n2 = instance.config.face["mobilefacenet"]) == null ? void 0 : _n2.enabled) ? predict12(faces[i].tensor || ur([]), instance.config, i, faces.length) : null; + mobilefacenetRes = ((_n2 = instance.config.face["mobilefacenet"]) == null ? void 0 : _n2.enabled) ? predict12(faces[i].tensor || ar([]), instance.config, i, faces.length) : null; } else { instance.state = "run:mobilefacenet"; timeStamp = now(); - mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict12(faces[i].tensor || ur([]), instance.config, i, faces.length) : null; + mobilefacenetRes = ((_o2 = instance.config.face["mobilefacenet"]) == null ? void 0 : _o2.enabled) ? await predict12(faces[i].tensor || ar([]), instance.config, i, faces.length) : null; instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); } instance.analyze("End MobileFaceNet:"); instance.analyze("Start InsightFace:"); if (instance.config.async) { - insightfaceRes = ((_p2 = instance.config.face["insightface"]) == null ? void 0 : _p2.enabled) ? predict13(faces[i].tensor || ur([]), instance.config, i, faces.length) : null; + insightfaceRes = ((_p2 = instance.config.face["insightface"]) == null ? void 0 : _p2.enabled) ? predict13(faces[i].tensor || ar([]), instance.config, i, faces.length) : null; } else { instance.state = "run:mobilefacenet"; timeStamp = now(); - insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || ur([]), instance.config, i, faces.length) : null; + insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || ar([]), instance.config, i, faces.length) : null; instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); } instance.analyze("End InsightFace:"); instance.analyze("Start Description:"); if (instance.config.async) { - descRes = predict6(faces[i].tensor || ur([]), instance.config, i, faces.length); + descRes = predict6(faces[i].tensor || ar([]), instance.config, i, faces.length); } else { instance.state = "run:description"; timeStamp = now(); - descRes = await predict6(faces[i].tensor || ur([]), instance.config, i, faces.length); + descRes = await predict6(faces[i].tensor || ar([]), instance.config, i, faces.length); instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); } instance.analyze("End Description:"); @@ -42116,8 +42120,8 @@ var detectFace = async (instance, input) => { descRes.descriptor = insightfaceRes; } const irisSize = ((_v2 = instance.config.face.iris) == null ? void 0 : _v2.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; - const tensor = ((_w2 = instance.config.face.detector) == null ? void 0 : _w2.return) ? gc(faces[i].tensor) : null; - Mt(faces[i].tensor); + const tensor = ((_w2 = instance.config.face.detector) == null ? void 0 : _w2.return) ? uc(faces[i].tensor) : null; + Ot(faces[i].tensor); if (faces[i].tensor) delete faces[i].tensor; const res = { @@ -45770,10 +45774,10 @@ var HandDetector = class { var _a, _b, _c2, _d2; this.model = model23; this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); - this.anchorsTensor = fu(this.anchors); + this.anchorsTensor = cu(this.anchors); this.inputSize = ((_d2 = (_c2 = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c2.shape) == null ? void 0 : _d2[2]) || 0; - this.inputSizeTensor = tr([this.inputSize, this.inputSize]); - this.doubleInputSizeTensor = tr([this.inputSize * 2, this.inputSize * 2]); + this.inputSizeTensor = Jt([this.inputSize, this.inputSize]); + this.doubleInputSizeTensor = Jt([this.inputSize * 2, this.inputSize * 2]); } normalizeBoxes(boxes) { const t8 = {}; @@ -45786,8 +45790,8 @@ var HandDetector = class { t8.startPoints = se(t8.sub, this.inputSizeTensor); t8.add = Ce(t8.boxCenterPoints, t8.halfBoxSizes); t8.endPoints = se(t8.add, this.inputSizeTensor); - const res = Yk([t8.startPoints, t8.endPoints], 1); - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + const res = Qk([t8.startPoints, t8.endPoints], 1); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return res; } normalizeLandmarks(rawPalmLandmarks, index2) { @@ -45796,7 +45800,7 @@ var HandDetector = class { t8.div = je(t8.reshape, this.inputSizeTensor); t8.landmarks = Ce(t8.div, this.anchors[index2] ? this.anchors[index2] : 0); const res = se(t8.landmarks, this.inputSizeTensor); - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return res; } async predict(input, config3) { @@ -45806,10 +45810,10 @@ var HandDetector = class { t8.div = je(t8.resize, constants.tf127); t8.image = Te(t8.div, constants.tf1); t8.batched = this.model.execute(t8.image); - t8.predictions = gc(t8.batched); + t8.predictions = uc(t8.batched); t8.slice = Xe(t8.predictions, [0, 0], [-1, 1]); t8.sigmoid = $a(t8.slice); - t8.scores = gc(t8.sigmoid); + t8.scores = uc(t8.sigmoid); const scores = await t8.scores.data(); t8.boxes = Xe(t8.predictions, [0, 1], [-1, 4]); t8.norm = this.normalizeBoxes(t8.boxes); @@ -45829,9 +45833,9 @@ var HandDetector = class { const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]); hands.push(scaled); - Object.keys(p).forEach((tensor) => Mt(p[tensor])); + Object.keys(p).forEach((tensor) => Ot(p[tensor])); } - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return hands; } }; @@ -45936,18 +45940,18 @@ var HandPipeline = class { const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); const handImage = je(croppedInput, constants.tf255); - Mt(croppedInput); - Mt(rotatedImage); + Ot(croppedInput); + Ot(rotatedImage); const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); lastTime13 = now(); - Mt(handImage); + Ot(handImage); const confidence = (await confidenceT.data())[0]; - Mt(confidenceT); + Ot(confidenceT); if (confidence >= config3.hand.minConfidence / 4) { const keypointsReshaped = W(keypoints, [-1, 3]); const rawCoords = await keypointsReshaped.array(); - Mt(keypoints); - Mt(keypointsReshaped); + Ot(keypoints); + Ot(keypointsReshaped); const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); const nextBoundingBox = this.getBoxForHandLandmarks(coords); this.storedBoxes[i] = { ...nextBoundingBox, confidence }; @@ -45962,7 +45966,7 @@ var HandPipeline = class { } else { this.storedBoxes[i] = null; } - Mt(keypoints); + Ot(keypoints); } else { const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); const result = { @@ -46153,15 +46157,15 @@ async function detectHands(input, config3) { t8.resize = Kj.resizeBilinear(input, [height, width]); t8.cast = We(t8.resize, "int32"); [t8.rawScores, t8.rawBoxes] = await models2[0].executeAsync(t8.cast, modelOutputNodes); - t8.boxes = gc(t8.rawBoxes, [0, 2]); - t8.scores = gc(t8.rawScores, [0]); - const classScores = fo(t8.scores, 1); - Mt(classScores[faceIndex]); + t8.boxes = uc(t8.rawBoxes, [0, 2]); + t8.scores = uc(t8.rawScores, [0]); + const classScores = mo(t8.scores, 1); + Ot(classScores[faceIndex]); classScores.splice(faceIndex, 1); - t8.filtered = kr(classScores, 1); - Mt(classScores); + t8.filtered = vr(classScores, 1); + Ot(classScores); t8.max = Ra(t8.filtered, 1); - t8.argmax = Ek(t8.filtered, 1); + t8.argmax = Rk(t8.filtered, 1); let id2 = 0; t8.nms = await Kj.nonMaxSuppressionAsync(t8.boxes, t8.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); const nms = await t8.nms.data(); @@ -46170,7 +46174,7 @@ async function detectHands(input, config3) { for (const nmsIndex of Array.from(nms)) { const boxSlice = Xe(t8.boxes, nmsIndex, 1); const boxYX = await boxSlice.data(); - Mt(boxSlice); + Ot(boxSlice); const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; const boxRaw = scale(boxData, detectorExpandFact); const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; @@ -46179,7 +46183,7 @@ async function detectHands(input, config3) { const hand3 = { id: id2++, score, box: boxFull, boxRaw, label }; hands.push(hand3); } - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); hands.sort((a, b) => b.score - a.score); if (hands.length > (config3.hand.maxDetected || 1)) hands.length = config3.hand.maxDetected || 1; @@ -46219,7 +46223,7 @@ async function detectFingers(input, h, config3) { hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); } } - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); } return hand3; } @@ -46337,7 +46341,7 @@ var connected3 = { var bufferedResult = empty(); var interpolateTime = 0; function calc2(newResult, config3) { - var _a, _b, _c2, _d2, _e, _f2, _g2, _h2, _i, _j2, _k2, _l2, _m, _n2, _o, _p2, _q, _r2, _s2, _t, _u2, _v2, _w2, _x2, _y, _z2; + var _a, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q, _r2, _s2, _t, _u2, _v2, _w2, _x2, _y, _z2; const t02 = now(); if (!newResult) return empty(); @@ -46354,7 +46358,7 @@ function calc2(newResult, config3) { const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { - var _a2, _b2, _c3, _d3, _e2, _f3, _g3, _h3, _i2; + var _a2, _b2, _c3, _d3, _e2, _f3, _g3, _h3, _i3; return { score: newKpt.score, part: newKpt.part, @@ -46371,7 +46375,7 @@ function calc2(newResult, config3) { distance: [ bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c3 = newKpt.distance) == null ? void 0 : _c3[0], bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d3[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f3 = newKpt.distance) == null ? void 0 : _f3[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g3[2]) || 0) + (((_h3 = newKpt.distance) == null ? void 0 : _h3[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2] + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g3[2]) || 0) + (((_h3 = newKpt.distance) == null ? void 0 : _h3[2]) || 0)) / bufferedFactor : (_i3 = newKpt.distance) == null ? void 0 : _i3[2] ] }; }); @@ -46429,7 +46433,7 @@ function calc2(newResult, config3) { annotations2 = bufferedResult.face[i].annotations; } else if (newResult.face[i].annotations) { for (const key of Object.keys(newResult.face[i].annotations)) { - annotations2[key] = ((_i = (_h2 = (_g2 = newResult.face[i]) == null ? void 0 : _g2.annotations) == null ? void 0 : _h2[key]) == null ? void 0 : _i[0]) ? newResult.face[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; + annotations2[key] = ((_i2 = (_h2 = (_g2 = newResult.face[i]) == null ? void 0 : _g2.annotations) == null ? void 0 : _h2[key]) == null ? void 0 : _i2[0]) ? newResult.face[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; } } if (newResult.face[i].rotation) { @@ -46437,7 +46441,7 @@ function calc2(newResult, config3) { rotation.matrix = (_j2 = newResult.face[i].rotation) == null ? void 0 : _j2.matrix; rotation.angle = { roll: ((bufferedFactor - 1) * (((_l2 = (_k2 = bufferedResult.face[i].rotation) == null ? void 0 : _k2.angle) == null ? void 0 : _l2.roll) || 0) + (((_n2 = (_m = newResult.face[i].rotation) == null ? void 0 : _m.angle) == null ? void 0 : _n2.roll) || 0)) / bufferedFactor, - yaw: ((bufferedFactor - 1) * (((_p2 = (_o = bufferedResult.face[i].rotation) == null ? void 0 : _o.angle) == null ? void 0 : _p2.yaw) || 0) + (((_r2 = (_q = newResult.face[i].rotation) == null ? void 0 : _q.angle) == null ? void 0 : _r2.yaw) || 0)) / bufferedFactor, + yaw: ((bufferedFactor - 1) * (((_p2 = (_o2 = bufferedResult.face[i].rotation) == null ? void 0 : _o2.angle) == null ? void 0 : _p2.yaw) || 0) + (((_r2 = (_q = newResult.face[i].rotation) == null ? void 0 : _q.angle) == null ? void 0 : _r2.yaw) || 0)) / bufferedFactor, pitch: ((bufferedFactor - 1) * (((_t = (_s2 = bufferedResult.face[i].rotation) == null ? void 0 : _s2.angle) == null ? void 0 : _t.pitch) || 0) + (((_v2 = (_u2 = newResult.face[i].rotation) == null ? void 0 : _u2.angle) == null ? void 0 : _v2.pitch) || 0)) / bufferedFactor }; rotation.gaze = { @@ -46474,8 +46478,8 @@ function calc2(newResult, config3) { bufferedResult.gesture = newResult.gesture; bufferedResult.width = newResult.width; bufferedResult.height = newResult.height; - const t12 = now(); - interpolateTime = env.perfadd ? interpolateTime + Math.round(t12 - t02) : Math.round(t12 - t02); + const t1 = now(); + interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t02) : Math.round(t1 - t02); if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; return bufferedResult; @@ -46500,26 +46504,26 @@ async function predict16(input, config3) { t8.resize = Kj.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); t8.norm = je(t8.resize, constants.tf255); t8.res = model17.execute(t8.norm); - t8.squeeze = gc(t8.res, [0]); - [t8.bgRaw, t8.fgRaw] = fo(t8.squeeze, 2); - t8.fg = P1(t8.fgRaw); + t8.squeeze = uc(t8.res, [0]); + [t8.bgRaw, t8.fgRaw] = mo(t8.squeeze, 2); + t8.fg = O1(t8.fgRaw); t8.mul = se(t8.fg, constants.tf255); t8.expand = Ms(t8.mul, 2); t8.output = Kj.resizeBilinear(t8.expand, [input.shape[1] || 0, input.shape[2] || 0]); let rgba; switch (config3.segmentation.mode || "default") { case "default": - t8.input = gc(input); - t8.concat = bt([t8.input, t8.output], -1); + t8.input = uc(input); + t8.concat = yt([t8.input, t8.output], -1); rgba = We(t8.concat, "int32"); break; case "alpha": rgba = We(t8.output, "int32"); break; default: - rgba = ur(0); + rgba = ar(0); } - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return rgba; } @@ -46660,7 +46664,7 @@ function padInput(input, inputSize10) { t8.pad = Aa(input, cache5.padding); t8.resize = Kj.resizeBilinear(t8.pad, [inputSize10, inputSize10]); const final = We(t8.resize, "int32"); - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return final; } function rescaleBody(body4, outputSize2) { @@ -46811,7 +46815,7 @@ async function predict17(input, config3) { rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); jitter(body4.keypoints); } - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); resolve(cache6.bodies); }); } @@ -46839,11 +46843,11 @@ async function process4(res, outputShape, config3) { const size2 = inputSize9; for (const strideSize of [1, 2, 4]) { const baseSize = strideSize * 13; - const scoresT = gc(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); + const scoresT = uc(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); const scores = await scoresT.array(); - const featuresT = gc(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); + const featuresT = uc(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); const boxesMaxT = W(featuresT, [-1, 4, (((_a = featuresT.shape) == null ? void 0 : _a[1]) || 0) / 4]); - const boxIdxT = Ek(boxesMaxT, 2); + const boxIdxT = Rk(boxesMaxT, 2); const boxIdx = await boxIdxT.array(); for (let i = 0; i < scoresT.shape[0]; i++) { for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) { @@ -46884,7 +46888,7 @@ async function process4(res, outputShape, config3) { } } } - Mt([scoresT, featuresT, boxesMaxT, boxIdxT]); + Ot([scoresT, featuresT, boxesMaxT, boxIdxT]); } const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); const nmsScores = results.map((a) => a.score); @@ -46892,7 +46896,7 @@ async function process4(res, outputShape, config3) { if (nmsBoxes && nmsBoxes.length > 0) { const nms = await Kj.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); nmsIdx = Array.from(await nms.data()); - Mt(nms); + Ot(nms); } results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); return results; @@ -46913,14 +46917,14 @@ async function predict18(image, config3) { const outputSize2 = [image.shape[2] || 0, image.shape[1] || 0]; const resizeT = Kj.resizeBilinear(image, [inputSize9, inputSize9], false); const normT = je(resizeT, constants.tf255); - const transposeT = yc(normT, [0, 3, 1, 2]); + const transposeT = cc(normT, [0, 3, 1, 2]); let objectT; if (config3.object.enabled) objectT = model19.execute(transposeT); lastTime15 = now(); const obj = await process4(objectT, outputSize2, config3); last10 = obj; - Mt([resizeT, normT, transposeT, ...objectT]); + Ot([resizeT, normT, transposeT, ...objectT]); resolve(obj); }); } @@ -47254,13 +47258,13 @@ async function predict19(input, config3) { const resized = Kj.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); const normalized = Te(je(We(resized, "float32"), 127.5), 1); const results = model20.execute(normalized, poseNetOutputs); - const results3d = results.map((y8) => gc(y8, [0])); + const results3d = results.map((y8) => uc(y8, [0])); results3d[1] = $a(results3d[1]); return results3d; }); const buffers = await Promise.all(res.map((tensor) => tensor.buffer())); for (const t8 of res) - Mt(t8); + Ot(t8); const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); if (!model20.inputs[0].shape) return []; @@ -47281,13 +47285,13 @@ var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"]; var t = {}; var ratio = 0; function init3(config3) { - Mt([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); - t.r1i = ur(0); - t.r2i = ur(0); - t.r3i = ur(0); - t.r4i = ur(0); + Ot([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); + t.r1i = ar(0); + t.r2i = ar(0); + t.r3i = ar(0); + t.r4i = ar(0); ratio = config3.segmentation.ratio || 0.5; - t.downsample_ratio = ur(ratio); + t.downsample_ratio = ar(ratio); } async function load19(config3) { if (!model21 || env.initial) @@ -47297,8 +47301,8 @@ async function load19(config3) { init3(config3); return model21; } -var normalize = (r) => De(() => { - const squeeze = gc(r, [0]); +var normalize = (r15) => De(() => { + const squeeze = uc(r15, [0]); const mul = se(squeeze, constants.tf255); const cast = We(mul, "int32"); return cast; @@ -47306,25 +47310,25 @@ var normalize = (r) => De(() => { function getRGBA(fgr, pha) { const rgb3 = fgr ? normalize(fgr) : Ea([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); const a = pha ? normalize(pha) : Ea([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); - const rgba = bt([rgb3, a], -1); - Mt([rgb3, a]); + const rgba = yt([rgb3, a], -1); + Ot([rgb3, a]); return rgba; } function getState(state) { return De(() => { - const r = {}; - r.unstack = fo(state, -1); - r.concat = bt(r.unstack, 1); - r.split = li(r.concat, 4, 1); - r.stack = bt(r.split, 2); - r.squeeze = gc(r.stack, [0]); - r.expand = Ms(r.squeeze, -1); - r.add = Ce(r.expand, 1); - r.mul = se(r.add, 127.5); - r.cast = We(r.mul, "int32"); - r.tile = cu(r.cast, [1, 1, 3]); - r.alpha = Ea([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); - return bt([r.tile, r.alpha], -1); + const r15 = {}; + r15.unstack = mo(state, -1); + r15.concat = yt(r15.unstack, 1); + r15.split = ci(r15.concat, 4, 1); + r15.stack = yt(r15.split, 2); + r15.squeeze = uc(r15.stack, [0]); + r15.expand = Ms(r15.squeeze, -1); + r15.add = Ce(r15.expand, 1); + r15.mul = se(r15.add, 127.5); + r15.cast = We(r15.mul, "int32"); + r15.tile = au(r15.cast, [1, 1, 3]); + r15.alpha = Ea([r15.tile.shape[0] || 0, r15.tile.shape[1] || 0, 1], 255, "int32"); + return yt([r15.tile, r15.alpha], -1); }); } async function predict20(input, config3) { @@ -47351,9 +47355,9 @@ async function predict20(input, config3) { rgba = getState(r1o); break; default: - rgba = ur(0); + rgba = ar(0); } - Mt([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); + Ot([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; return rgba; } @@ -47377,23 +47381,23 @@ async function predict21(input, config3) { t8.resize = Kj.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); t8.norm = je(t8.resize, constants.tf255); t8.res = model22.execute(t8.norm); - t8.squeeze = gc(t8.res, [0]); + t8.squeeze = uc(t8.res, [0]); t8.alpha = Kj.resizeBilinear(t8.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); t8.mul = se(t8.alpha, constants.tf255); let rgba; switch (config3.segmentation.mode || "default") { case "default": - t8.input = gc(input); - t8.concat = bt([t8.input, t8.mul], -1); + t8.input = uc(input); + t8.concat = yt([t8.input, t8.mul], -1); rgba = We(t8.concat, "int32"); break; case "alpha": rgba = We(t8.mul, "int32"); break; default: - rgba = ur(0); + rgba = ar(0); } - Object.keys(t8).forEach((tensor) => Mt(t8[tensor])); + Object.keys(t8).forEach((tensor) => Ot(t8[tensor])); return rgba; } @@ -47462,7 +47466,7 @@ var Models = class { this.models[model23] = null; } async load(instance) { - var _a, _b, _c2, _d2, _e, _f2, _g2, _h2, _i, _j2, _k2, _l2, _m, _n2, _o, _p2, _q, _r2, _s2, _t, _u2, _v2, _w2, _x2, _y, _z2, _A2; + var _a, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q, _r2, _s2, _t, _u2, _v2, _w2, _x2, _y, _z2, _A2; if (env.initial) this.reset(); if (instance) @@ -47476,14 +47480,14 @@ var Models = class { m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f2 = this.instance.config.face.attention) == null ? void 0 : _f2.enabled) && !this.models.iris ? load4(this.instance.config) : null; m.facemesh = this.instance.config.face.enabled && ((_g2 = this.instance.config.face.mesh) == null ? void 0 : _g2.enabled) && !this.models.facemesh ? load5(this.instance.config) : null; m.gear = this.instance.config.face.enabled && ((_h2 = this.instance.config.face["gear"]) == null ? void 0 : _h2.enabled) && !this.models.gear ? load10(this.instance.config) : null; - m.ssrnetage = this.instance.config.face.enabled && ((_i = this.instance.config.face["ssrnet"]) == null ? void 0 : _i.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; + m.ssrnetage = this.instance.config.face.enabled && ((_i2 = this.instance.config.face["ssrnet"]) == null ? void 0 : _i2.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; m.ssrnetgender = this.instance.config.face.enabled && ((_j2 = this.instance.config.face["ssrnet"]) == null ? void 0 : _j2.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null; m.mobilefacenet = this.instance.config.face.enabled && ((_k2 = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k2.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; m.insightface = this.instance.config.face.enabled && ((_l2 = this.instance.config.face["insightface"]) == null ? void 0 : _l2.enabled) && !this.models.insightface ? load14(this.instance.config) : null; m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null; m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n2 = this.instance.config.body.modelPath) == null ? void 0 : _n2.includes("efficientpose")) ? load2(this.instance.config) : null; - m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o = this.instance.config.body.modelPath) == null ? void 0 : _o.includes("movenet")) ? load16(this.instance.config) : null; + m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o2 = this.instance.config.body.modelPath) == null ? void 0 : _o2.includes("movenet")) ? load16(this.instance.config) : null; m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p2 = this.instance.config.body.modelPath) == null ? void 0 : _p2.includes("posenet")) ? load18(this.instance.config) : null; m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r2 = (_q = this.instance.config.hand.detector) == null ? void 0 : _q.modelPath) == null ? void 0 : _r2.includes("handtrack")) ? loadDetect3(this.instance.config) : null; m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s2 = this.instance.config.hand.detector) == null ? void 0 : _s2.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton2(this.instance.config) : null; @@ -48391,7 +48395,7 @@ async function warmupNode(instance) { img = atob2(body3); let res; if ("node" in tfjs_esm_exports && Cde() === "tensorflow") { - const data = (void 0).decodeJpeg(img); + const data = bQt.decodeJpeg(img); const expanded = Ms(data, 0); instance.tf.dispose(data); res = await instance.detect(expanded, instance.config); @@ -48422,7 +48426,7 @@ async function runCompile(instance) { return; } A().set("ENGINE_COMPILE_ONLY", true); - const numTensorsStart = pr().state.numTensors; + const numTensorsStart = ur().state.numTensors; const compiledModels = []; for (const [modelName, model23] of Object.entries(instance.models.models)) { if (!model23) @@ -48433,26 +48437,26 @@ async function runCompile(instance) { if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; } - const tensor = Gr(shape, dtype); + const tensor = Ur(shape, dtype); try { const res = model23.execute(tensor); compiledModels.push(modelName); if (Array.isArray(res)) - res.forEach((t8) => Mt(t8)); + res.forEach((t8) => Ot(t8)); else - Mt(res); + Ot(res); } catch (e) { if (instance.config.debug) log("compile fail model:", modelName); } - Mt(tensor); + Ot(tensor); } const kernels = await webGLBackend["checkCompileCompletionAsync"](); webGLBackend["getUniformLocations"](); if (instance.config.debug) log("compile pass:", { models: compiledModels, kernels: kernels.length }); A().set("ENGINE_COMPILE_ONLY", false); - const numTensorsEnd = pr().state.numTensors; + const numTensorsEnd = ur().state.numTensors; if (numTensorsEnd - numTensorsStart > 0) log("tensor leak:", numTensorsEnd - numTensorsStart); } @@ -48469,9 +48473,9 @@ async function warmup(instance, userConfig) { await instance.models.load(); await runCompile(instance); const res = await runInference(instance); - const t12 = now(); + const t1 = now(); if (instance.config.debug) - log("warmup", instance.config.warmup, Math.round(t12 - t02), "ms"); + log("warmup", instance.config.warmup, Math.round(t1 - t02), "ms"); instance.emit("warmup"); resolve(res); }); @@ -48564,7 +48568,7 @@ var Human = class { return null; if (!input) return "input is not defined"; - if (this.env.node && !(input instanceof ut)) + if (this.env.node && !(input instanceof mt)) return "input must be a tensor"; try { this.tf.getBackend(); @@ -48672,7 +48676,7 @@ var Human = class { tensor = await predict16(processed.tensor, this.config); if ((_c2 = this.config.segmentation.modelPath) == null ? void 0 : _c2.includes("selfie")) tensor = await predict21(processed.tensor, this.config); - Mt(processed.tensor); + Ot(processed.tensor); return tensor; } /** Compare two input tensors for pixel similarity @@ -48748,8 +48752,8 @@ var Human = class { async warmup(userConfig) { const t02 = now(); const res = await warmup(this, userConfig); - const t12 = now(); - this.performance.warmup = Math.trunc(t12 - t02); + const t1 = now(); + this.performance.warmup = Math.trunc(t1 - t02); return res; } /** Run detect with tensorflow profiling @@ -48791,7 +48795,7 @@ var Human = class { async detect(input, userConfig) { this.state = "detect"; return new Promise(async (resolve) => { - var _a, _b, _c2, _d2, _e, _f2, _g2, _h2, _i, _j2, _k2, _l2, _m, _n2, _o, _p2, _q, _r2, _s2, _t, _u2; + var _a, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q, _r2, _s2, _t, _u2; this.state = "config"; let timeStamp; this.config = mergeDeep(this.config, userConfig); @@ -48877,7 +48881,7 @@ var Human = class { this.state = "detect:hand"; const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config; if (this.config.async) { - if ((_j2 = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j2.includes("handdetect")) + if ((_j2 = (_i2 = this.config.hand.detector) == null ? void 0 : _i2.modelPath) == null ? void 0 : _j2.includes("handdetect")) handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : []; else if ((_l2 = (_k2 = this.config.hand.detector) == null ? void 0 : _k2.modelPath) == null ? void 0 : _l2.includes("handtrack")) handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : []; @@ -48887,7 +48891,7 @@ var Human = class { timeStamp = now(); if ((_n2 = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n2.includes("handdetect")) handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : []; - else if ((_p2 = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p2.includes("handtrack")) + else if ((_p2 = (_o2 = this.config.hand.detector) == null ? void 0 : _o2.modelPath) == null ? void 0 : _p2.includes("handtrack")) handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : []; this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); } @@ -48941,7 +48945,7 @@ var Human = class { return join2(faceRes, bodyRes, handRes, gestureRes, shape); } }; - Mt(img.tensor); + Ot(img.tensor); this.emit("detect"); this.state = "idle"; resolve(this.result); diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map index ca48ffc4..8f824470 100644 --- a/dist/human.esm.js.map +++ b/dist/human.esm.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["tfjs.esm.js", "../src/util/util.ts", "../src/config.ts", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/util/webcam.ts", "../models/models.json", "../src/tfjs/load.ts", "../package.json", "../src/tfjs/humangl.ts", "../src/tfjs/constants.ts", "../src/tfjs/backend.ts", "../src/draw/draw.ts", "../src/draw/primitives.ts", "../src/draw/options.ts", "../src/face/facemeshcoords.ts", "../src/face/constants.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/draw/labels.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/face/iris.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/gear/emotion.ts", "../src/face/faceres.ts", "../src/face/mask.ts", "../src/face/antispoof.ts", "../src/face/liveness.ts", "../src/gear/gear.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/mobilefacenet.ts", "../src/face/insightface.ts", "../src/face/angles.ts", "../src/face/anthropometry.ts", "../src/face/face.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/gesture/gesture.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/result.ts", "../src/body/movenetcoords.ts", "../src/util/interpolate.ts", "../src/segmentation/meet.ts", "../src/face/match.ts", "../src/models.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/rvm.ts", "../src/segmentation/selfie.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"], - "sourcesContent": ["/*\n Human\n homepage: \n author: '\n*/\n\nvar wG=Object.create;var QC=Object.defineProperty;var SG=Object.getOwnPropertyDescriptor;var IG=Object.getOwnPropertyNames;var vG=Object.getPrototypeOf,kG=Object.prototype.hasOwnProperty;var qt=(r,t)=>()=>(t||r((t={exports:{}}).exports,t),t.exports),qe=(r,t)=>{for(var e 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xw;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(xw||(xw={}));var yw;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(yw||(yw={}));var bw;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(bw||(bw={}));var u4={float32:yw,int32:gw,bool:xw,complex64:bw};function dt(r,t){if(r===\"string\"||t===\"string\"){if(r===\"string\"&&t===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${t}`)}return u4[r][t]}function ni(r){return dt(r,\"int32\")}function rd(r){return r!=null&&typeof r==\"object\"&&\"texture\"in r&&r.texture instanceof WebGLTexture}function od(r){return typeof GPUBuffer!=\"undefined\"&&r!=null&&typeof r==\"object\"&&\"buffer\"in r&&r.buffer instanceof GPUBuffer}function Oe(r,t){if(r.dtype===t.dtype)return[r,t];let e=dt(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function Cw(r,t){$(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${t.dtype}) input must match`)}function p4(r,t){return t.some(e=>e.id===r.id)}function Sl(r){let t=[];return J0(r,t,new Set),t}function J0(r,t,e){if(r==null)return;if(r instanceof ut){t.push(r);return}if(!c4(r))return;let o=r;for(let n in o){let s=o[n];e.has(s)||(e.add(s),J0(s,t,e))}}function c4(r){return Array.isArray(r)||typeof r==\"object\"}function ww(r){return r.kernelName!=null}var nd=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(t=>t.name)))}}}dispose(){for(let t in this.registeredVariables)this.registeredVariables[t].dispose()}},ru=class{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new nd}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let e=0;e{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){Ym(t).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let o=e.factory();if(o&&!(o instanceof ao)&&typeof o.then==\"function\"){let n=++this.pendingBackendInitId,s=o.then(a=>n(nthis.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;ethis.startScope(o),()=>this.endScope(n),()=>(n=e(),n instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),n))}scopedRun(t,e,o){t();try{let n=o();return e(),n}catch(n){throw e(),n}}nextTensorId(){return ru.nextTensorId++}nextVariableId(){return ru.nextVariableId++}clone(t){let e=T.runKernel(wo,{x:t}),o={x:t},n=a=>({x:()=>{let i=\"float32\",p={x:a},u={dtype:i};return T.runKernel(bo,p,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[e],n,s,{}),e}runKernel(t,e,o){if(this.backendName==null&&this.backend,!(tc(t,this.backendName)!=null))throw new Error(`Kernel '${t}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:t,inputs:e,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(t,e,o){let n=this.backend.numDataIds(),s=0;o.forEach(p=>{s+=p.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-e-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${t}'`)}runKernelFunc(t){let e,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=ww(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(ww(t)){let{kernelName:f,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=tc(f,this.backendName);$(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let S=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,S);o=this.saveTensorsForBackwardMode(k)}return S}}else{let{forwardFunc:f}=t,h=g=>{n&&(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=t,m=ww(t)?null:t.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?e=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),e=d.outputs)}),n&&this.addTapeNode(u,c,e,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:e.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(t,e,o){let n=iw(t);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?($(Array.isArray(e),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(e).map(u=>e[u])):i=s.map(u=>e[u]);let p=o.filter((u,c)=>a[c]);return i.concat(p)}return[]}makeTensor(t,e,o,n){if(t==null)throw new Error(\"Values passed to engine.makeTensor() are null\");o=o||\"float32\",n=n||this.backend;let s=t;o===\"string\"&&Vo(t[0])&&(s=t.map(p=>tu(p)));let a=n.write(s,e,o),i=new ut(e,o,a,this.nextTensorId());if(this.trackTensor(i,n),o===\"string\"){let p=this.state.tensorInfo.get(a),u=ow(s);this.state.numBytes+=u-p.bytes,p.bytes=u}return i}makeTensorFromDataId(t,e,o,n){o=o||\"float32\";let s={dataId:t,shape:e,dtype:o};return this.makeTensorFromTensorInfo(s,n)}makeTensorFromTensorInfo(t,e){let{dataId:o,shape:n,dtype:s}=t,a=new ut(n,s,o,this.nextTensorId());return this.trackTensor(a,e),a}makeVariable(t,e=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==t.dtype&&(t=t.cast(n));let s=new oi(t,e,o,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(t,e){this.state.numTensors++,t.dtype===\"string\"&&this.state.numStringTensors++;let o=0;t.dtype!==\"complex64\"&&t.dtype!==\"string\"&&(o=t.size*jp(t.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:o})),t instanceof oi||this.track(t)}incRef(t,e){this.trackTensor(t,e),this.backend.incRef(t.dataId)}removeDataId(t,e){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===e&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;let e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),t.dtype!==\"complex64\"&&t.dtype!==\"string\"){let o=t.size*jp(t.dtype);this.state.numBytes-=o}e.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,e.backend)}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:o,saved:s},p=iw(t);p!=null&&(n=p.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,l)=>{if(c==null){let m=o[l],d=Yp(m.size,m.dtype);return this.makeTensor(d,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,a))),this.state.activeTape.push(i)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=Sl(t),o=new Set(e.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(t,e,o,n=!1){if($(e.length>0,()=>\"gradients() received an empty list of xs.\"),o!=null&&o.dtype!==\"float32\")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",t));$(s instanceof ut,()=>\"The result y returned by f() must be a tensor.\");let a=H0(this.state.activeTape,e,s);if(!n&&a.length===0&&e.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");return this.tidy(\"backward\",()=>{let i={};i[s.id]=o==null?l4(s.shape):o,K0(i,a,u=>this.tidy(u),m4);let p=e.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:p}})}customGrad(t){return $(qs(t),()=>\"The f passed in customGrad(f) must be a function.\"),(...e)=>{$(e.every(i=>i instanceof ut),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let o,n={};e.forEach((i,p)=>{n[p]=i});let s=(i,p)=>(o=t(...e,p),$(o.value instanceof ut,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),$(qs(o.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),o.value),a=(i,p)=>{let u=o.gradFunc(i,p),c=Array.isArray(u)?u:[u];$(c.length===e.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),$(c.every(m=>m instanceof ut),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");let l={};return c.forEach((m,d)=>{l[d]=()=>m}),l};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:n})}}readSync(t){return this.state.tensorInfo.get(t).backend.readSync(t)}read(t){return this.state.tensorInfo.get(t).backend.read(t)}readToGPU(t,e){return this.state.tensorInfo.get(t).backend.readToGPU(t,e)}async time(t){let e=Wu(),o=await this.backend.time(t);return o.wallMs=Wu()-e,o}track(t){return this.state.activeScope!=null&&(t.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(t)),t}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new nd;for(let 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Rt;(function(r){r[r.NONE=0]=\"NONE\",r[r.MEAN=1]=\"MEAN\",r[r.SUM=2]=\"SUM\",r[r.SUM_BY_NONZERO_WEIGHTS=3]=\"SUM_BY_NONZERO_WEIGHTS\"})(Rt||(Rt={}));function kj(r,t,e=Rt.SUM_BY_NONZERO_WEIGHTS){let o=v(r,\"losses\",\"computeWeightedLoss\"),n=null;t!=null&&(n=v(t,\"weights\",\"computeWeightedLoss\"));let s=n==null?o:se(o,n);if(e===Rt.NONE)return s;if(e===Rt.SUM)return ot(s);if(e===Rt.MEAN){if(n==null)return Xu(s);{let a=o.size/n.size,i=je(ot(s),ot(n));return a>1?je(i,ke(a)):i}}if(e===Rt.SUM_BY_NONZERO_WEIGHTS){if(n==null)return je(ot(s),ke(o.size));{let a=se(n,Da(o.shape)),i=We(ot(Pd(a,ke(0))),\"float32\");return je(ot(s),i)}}throw Error(`Unknown reduction: ${e}`)}var lr=N({computeWeightedLoss_:kj});function Nj(r,t,e,o=Rt.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"absoluteDifference\"),s=v(t,\"predictions\",\"absoluteDifference\"),a=null;e!=null&&(a=v(e,\"weights\",\"absoluteDifference\")),yt(n.shape,s.shape,\"Error in absoluteDifference: \");let i=Jt(Te(n,s));return lr(i,a,o)}var 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a={inputIndices:o,inputShape:n,newShape:s},i=T.runKernel(ti,a);return{outputIndices:i[0],outputShape:i[1]}}var PN=N({sparseReshape_:Mj});function Lj(r,t,e){let o=v(r,\"data\",\"sparseSegmentMean\"),n=v(t,\"indices\",\"sparseSegmentMean\",\"int32\"),s=v(e,\"segmentIds\",\"sparseSegmentMean\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(ya,a)}var ON=N({sparseSegmentMean_:Lj});function Bj(r,t,e){let o=v(r,\"data\",\"sparseSegmentSum\"),n=v(t,\"indices\",\"sparseSegmentSum\",\"int32\"),s=v(e,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but 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${o.shape}`);if(n.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);let s={skipEmpty:e},a={input:o,delimiter:n},i=T.runKernel(Yi,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var BN=N({stringSplit_:Vj});function Wj(r,t){let e=v(r,\"input\",\"stringToHashBucketFast\",\"string\"),o={numBuckets:t};if(t<=0)throw new Error(\"Number of buckets must be at least 1\");let n={input:e};return T.runKernel(Qi,n,o)}var zN=N({stringToHashBucketFast_:Wj});function Uj(r,t,e,o=!0){let n=v(r,\"input\",\"staticRegexReplace\",\"string\"),s={pattern:t,rewrite:e,replaceGlobal:o};return T.runKernel(Ou,{x:n},s)}var VN=N({staticRegexReplace_:Uj});var Gj={fft:fc,ifft:Ju,rfft:hc,irfft:Kd},Hj={hammingWindow:oN,hannWindow:Zd,frame:Jd,stft:nN},Kj={flipLeftRight:aN,grayscaleToRGB:iN,resizeNearestNeighbor:bN,resizeBilinear:yN,rgbToGrayscale:uN,rotateWithOffset:pN,cropAndResize:sN,nonMaxSuppression:cN,nonMaxSuppressionAsync:dN,nonMaxSuppressionWithScore:fN,nonMaxSuppressionWithScoreAsync:hN,nonMaxSuppressionPadded:gN,nonMaxSuppressionPaddedAsync:xN,threshold:CN,transform:wN},qj={bandPart:SN,gramSchmidt:IN,qr:kN},jj={absoluteDifference:NN,computeWeightedLoss:lr,cosineDistance:TN,hingeLoss:_N,huberLoss:$N,logLoss:EN,meanSquaredError:RN,sigmoidCrossEntropy:DN,softmaxCrossEntropy:AN},Xj={sparseFillEmptyRows:FN,sparseReshape:PN,sparseSegmentMean:ON,sparseSegmentSum:MN},Yj={stringNGrams:LN,stringSplit:BN,stringToHashBucketFast:zN,staticRegexReplace:VN};var WN={};qe(WN,{Serializable:()=>Dl,SerializationMap:()=>Fa,getRegisteredName:()=>Zj,registerClass:()=>tS});var Qj=new Map,eS=new Map,Dl=class{getClassName(){return this.constructor.className}static fromConfig(t,e){return new t(e)}},Fa=class{constructor(){this.classNameMap={}}static getMap(){return Fa.instance==null&&(Fa.instance=new Fa),Fa.instance}static register(t){Fa.getMap().classNameMap[t.className]=[t,t.fromConfig]}};function tS(r,t,e){$(r.className!=null,()=>\"Class being registered does not have the static className property defined.\"),$(typeof r.className==\"string\",()=>\"className is required to be a string, but got type \"+typeof r.className),$(r.className.length>0,()=>\"Class being registered has an empty-string as its className, which is disallowed.\"),typeof t==\"undefined\"&&(t=\"Custom\"),typeof e==\"undefined\"&&(e=r.className);let o=e,n=t+\">\"+o;return Fa.register(r),Qj.set(n,r),eS.set(r,n),r}function Zj(r){return eS.has(r)?eS.get(r):r.className}var Nr=class extends Dl{minimize(t,e=!1,o){let{value:n,grads:s}=this.computeGradients(t,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Mt(s),e?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(t,e){return zw(t,e)}dispose(){this.iterations_!=null&&Mt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:\"iter\",tensor:ke(this.iterations_,\"int32\")}}async getWeights(){throw new Error(\"getWeights() is not implemented for this optimizer yet.\")}async setWeights(t){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(t){return this.iterations_=(await t[0].tensor.data())[0],t.slice(1)}};Object.defineProperty(Nr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var np=class extends Nr{static get className(){return\"Adadelta\"}constructor(t,e,o=null){super(),this.learningRate=t,this.rho=e,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:De(()=>Ht(s).variable(a))});let i=Array.isArray(t)?t[n].tensor:t[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;De(()=>{let c=Ce(se(p,this.rho),se(er(i),1-this.rho)),l=se(je(Dr(Ce(u,this.epsilon)),Dr(Ce(p,this.epsilon))),i),m=Ce(se(u,this.rho),se(er(l),1-this.rho));p.assign(c),u.assign(m);let d=Ce(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Mt(this.accumulatedGrads.map(t=>t.variable)),Mt(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,o=!1;this.accumulatedGrads=t.slice(0,e).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=t.slice(e,e*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var sp=class extends Nr{static get className(){return\"Adagrad\"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>Ea(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(t)?t[n].tensor:t[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=Ce(i,er(a));i.assign(p);let u=Ce(se(je(a,Dr(Ce(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Mt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(o=>({originalName:o.name,variable:o.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};var ap=class extends Nr{static get className(){return\"Adam\"}constructor(t,e,o,n=null){super(),this.learningRate=t,this.beta1=e,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(e).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(o=>o.name):Object.keys(t);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);e.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Ht(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Ht(i).variable(p))});let u=Array.isArray(t)?t[a].tensor:t[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=Ce(se(l,this.beta2),se(er(u),1-this.beta2)),f=je(m,o),h=je(d,n);c.assign(m),l.assign(d);let g=Ce(se(je(f,Ce(Dr(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(se(this.accBeta1,this.beta1)),this.accBeta2.assign(se(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Mt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Mt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),De(()=>{this.accBeta1.assign(ui(this.beta1,this.iterations_+1)),this.accBeta2.assign(ui(this.beta2,this.iterations_+1))});let e=t.length/2,o=!1;this.accumulatedFirstMoment=t.slice(0,e).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}};var ip=class extends Nr{static get className(){return\"Adamax\"}constructor(t,e,o,n=null,s=0){super(),this.learningRate=t,this.beta1=e,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(e).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(o=>o.name):Object.keys(t);De(()=>{let o=Te(1,this.accBeta1),n=je(-this.learningRate,Ce(se(this.iteration,this.decay),1));e.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ht(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ht(i).variable(p)});let u=Array.isArray(t)?t[a].tensor:t[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=se(l,this.beta2),f=Jt(u),h=Fd(d,f);c.assign(m),l.assign(h);let g=Ce(se(je(n,o),je(m,Ce(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Ce(this.iteration,1)),this.accBeta1.assign(se(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Mt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Mt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(t){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};var mi=class extends Nr{static get className(){return\"SGD\"}constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=Array.isArray(t)?t[n].tensor:t[o];if(s==null)return;let a=T.registeredVariables[o];De(()=>{let i=Ce(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=Rr(ke(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(t,e){return new t(e.learningRate)}};var up=class extends mi{static get className(){return\"Momentum\"}constructor(t,e,o=!1){super(t),this.learningRate=t,this.momentum=e,this.useNesterov=o,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:De(()=>Ht(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(t)?t[n].tensor:t[o];i!=null&&De(()=>{let p,u=Ce(se(this.m,a),i);this.useNesterov?p=Ce(se(this.c,Ce(i,se(u,this.m))),s):p=Ce(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Mt(this.accumulations.map(t=>t.variable))}setMomentum(t){this.momentum=t}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(o=>({originalName:o.name,variable:o.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var pp=class extends Nr{static get className(){return\"RMSProp\"}constructor(t,e=.9,o=0,n=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Ht(s).variable(a))});let i=Array.isArray(t)?t[n].tensor:t[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;De(()=>{let c=Ce(se(p,this.decay),se(er(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=Ce(se(l,this.decay),se(i,1-this.decay)),d=je(se(i,this.learningRate),Dr(Te(c,Ce(er(m),this.epsilon)))),f=Ce(se(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Te(s,f);s.assign(h)}else{let l=Ce(se(p,this.decay),se(er(i),1-this.decay)),m=Ce(se(u,this.momentum),je(se(i,this.learningRate),Dr(Ce(l,this.epsilon))));p.assign(l),u.assign(m);let d=Te(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Mt(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Mt(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Mt(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=this.centered?t.length/3:t.length/2,o=!1;this.accumulatedMeanSquares=t.slice(0,e).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=t.slice(e,e*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}};var Jj=[np,sp,ap,ip,up,pp,mi];function UN(){for(let r of Jj)tS(r)}var fi={};qe(fi,{CompositeArrayBuffer:()=>jt,browserFiles:()=>HN,browserHTTPRequest:()=>jN,concatenateArrayBuffers:()=>ik,copyModel:()=>wk,decodeWeights:()=>ad,encodeWeights:()=>nk,fromMemory:()=>XN,fromMemorySync:()=>iS,getLoadHandlers:()=>lk,getModelArtifactsForJSON:()=>ic,getModelArtifactsForJSONSync:()=>_w,getModelArtifactsInfoForJSON:()=>va,getSaveHandlers:()=>ck,getWeightSpecs:()=>ud,http:()=>nf,isHTTPScheme:()=>of,listModels:()=>bk,loadWeights:()=>KN,moveModel:()=>Sk,registerLoadRouter:()=>pk,registerSaveRouter:()=>uk,removeModel:()=>Ck,weightsLoaderFactory:()=>sS,withSaveHandler:()=>YN,withSaveHandlerSync:()=>QN});var eX=\"model\",tX=\".json\",rX=\".weights.bin\";function GN(r){return new Promise(t=>setTimeout(t)).then(r)}var di=class{constructor(t){if(!A().getBool(\"IS_BROWSER\"))throw new Error(\"browserDownloads() cannot proceed because the current environment is not a 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TypeError(`Node type ${r.op} is not implemented`)}};function Hr(r,t,e=\"\"){if(!(typeof r==\"number\"||typeof t==\"number\")){y.assert(r.length===t.length,()=>e+` Shapes ${r} and ${t} must match`);for(let o=0;oe+` Shapes ${r} and ${t} must match`)}}}function ST(r){return!(typeof r==\"number\"||r.some(t=>t<0))}function bc(r,t,e){let o=Sf(r,e),n=!ST(o);if(n&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${o}`);if(n&&t.forEach(s=>{o=Sf(s.shape,o)}),!ST(o))throw new Error(`Non-fully-defined elementShape: ${o}`);return o}function Sf(r,t){if(typeof r==\"number\")return t;if(typeof t==\"number\")return r;if(r.length!==t.length)throw new Error(`Incompatible ranks during merge: ${r} vs. ${t}`);let e=[];for(let o=0;o=0&&s>=0&&n!==s)throw new Error(`Incompatible shape during merge: ${r} vs. ${t}`);e[o]=n>=0?n:s}return e}var If=class{constructor(t,e,o,n,s,a,i){this.name=t,this.dtype=e,this.maxSize=o,this.elementShape=n,this.identicalElementShapes=s,this.dynamicSize=a,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=ke(0),Rr(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(t){this.tensors.forEach(e=>{(t==null||!t.has(e.tensor.id))&&e.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(t<0||t>=this.size())throw new Error(`Tried to read from index ${t}, but array size is: ${this.size()}`);let e=this.tensors[t];if(e.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${t} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(e.cleared=!0),e.read=!0,e.tensor}readMany(t){return 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o=I(\"tensorListId\",r,t,e),n=e.getTensorList(o.id);return[ke(n.size(),\"int32\")]}case\"TensorListResize\":{let o=I(\"tensorListId\",r,t,e),n=I(\"size\",r,t,e),a=e.getTensorList(o.id).resize(n);return e.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function _T(r,t,e){let[o,n]=I(\"fusedOps\",r,t,e),s=o===\"biasadd\",a=!s,i=n===\"prelu\",p=o===\"fusedbatchnorm\",u=I(\"numArgs\",r,t,e);if(s){if(i&&u!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&s&&u!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(p)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,t,e),l=Ol(r,t,e),m=I(\"dataFormat\",r,t,e).toUpperCase(),d=I(\"dilations\",r,t,e),[f,h]=I(\"args\",r,t,e);a&&(h=f,f=void 0);let g=I(\"leakyreluAlpha\",r,t,e);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var $T=(r,t,e,o=Je)=>{switch(r.op){case\"Conv1D\":{let n=I(\"stride\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"dataFormat\",r,t,e).toUpperCase(),i=I(\"dilation\",r,t,e);return[o.conv1d(I(\"x\",r,t,e),I(\"filter\",r,t,e),n,s,a,i)]}case\"Conv2D\":{let n=I(\"strides\",r,t,e),s=Ol(r,t,e),a=I(\"dataFormat\",r,t,e).toUpperCase(),i=I(\"dilations\",r,t,e);return[o.conv2d(I(\"x\",r,t,e),I(\"filter\",r,t,e),[n[1],n[2]],s,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=_T(r,t,e);return[o.fused.conv2d({x:I(\"x\",r,t,e),filter:I(\"filter\",r,t,e),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"FusedDepthwiseConv2dNative\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=_T(r,t,e);return[o.fused.depthwiseConv2d({x:I(\"x\",r,t,e),filter:I(\"filter\",r,t,e),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let n=I(\"outputShape\",r,t,e),s=I(\"strides\",r,t,e),a=Ol(r,t,e);return[o.conv2dTranspose(I(\"x\",r,t,e),I(\"filter\",r,t,e),n,[s[1],s[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let n=I(\"strides\",r,t,e),s=Ol(r,t,e),a=I(\"dilations\",r,t,e),i=I(\"dataFormat\",r,t,e).toUpperCase();return[o.depthwiseConv2d(I(\"input\",r,t,e),I(\"filter\",r,t,e),[n[1],n[2]],s,i,[a[1],a[2]])]}case\"Conv3D\":{let n=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"dataFormat\",r,t,e).toUpperCase(),i=I(\"dilations\",r,t,e);return[o.conv3d(I(\"x\",r,t,e),I(\"filter\",r,t,e),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let n=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"kernelSize\",r,t,e);return[o.avgPool(I(\"x\",r,t,e),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPool\":{let n=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"kernelSize\",r,t,e);return[o.maxPool(I(\"x\",r,t,e),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPoolWithArgmax\":{let n=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"kernelSize\",r,t,e),i=I(\"includeBatchInIndex\",r,t,e),{result:p,indexes:u}=o.maxPoolWithArgmax(I(\"x\",r,t,e),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case\"AvgPool3D\":{let n=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"kernelSize\",r,t,e);return[o.avgPool3d(I(\"x\",r,t,e),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"MaxPool3D\":{let n=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"kernelSize\",r,t,e);return[o.maxPool3d(I(\"x\",r,t,e),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"Dilation2D\":{let n=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"dilations\",r,t,e),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I(\"x\",r,t,e),I(\"filter\",r,t,e),[i,p],s,[u,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ET=(r,t,e,o=Je)=>{switch(r.op){case\"Fill\":{let n=I(\"shape\",r,t,e),s=I(\"dtype\",r,t,e),a=I(\"value\",r,t,e);return[o.fill(n,a,s)]}case\"LinSpace\":{let n=I(\"start\",r,t,e),s=I(\"stop\",r,t,e),a=I(\"num\",r,t,e);return[o.linspace(n,s,a)]}case\"Multinomial\":{let n=I(\"logits\",r,t,e),s=I(\"numSamples\",r,t,e),a=I(\"seed\",r,t,e);return[o.multinomial(n,s,a)]}case\"OneHot\":{let n=I(\"indices\",r,t,e),s=I(\"depth\",r,t,e),a=I(\"onValue\",r,t,e),i=I(\"offValue\",r,t,e),p=I(\"dtype\",r,t,e);return[o.oneHot(n,s,a,i,p)]}case\"Ones\":return[o.ones(I(\"shape\",r,t,e),I(\"dtype\",r,t,e))];case\"OnesLike\":return[o.onesLike(I(\"x\",r,t,e))];case\"RandomStandardNormal\":return[o.randomStandardNormal(I(\"shape\",r,t,e),I(\"dtype\",r,t,e),I(\"seed\",r,t,e))];case\"RandomUniform\":return[o.randomUniform(I(\"shape\",r,t,e),I(\"minval\",r,t,e),I(\"maxval\",r,t,e),I(\"dtype\",r,t,e))];case\"RandomUniformInt\":return[o.randomUniformInt(I(\"shape\",r,t,e),I(\"minval\",r,t,e),I(\"maxval\",r,t,e),I(\"seed\",r,t,e))];case\"Range\":{let n=I(\"start\",r,t,e),s=I(\"stop\",r,t,e),a=I(\"step\",r,t,e);return[o.range(n,s,a,I(\"dtype\",r,t,e))]}case\"TruncatedNormal\":{let n=I(\"shape\",r,t,e),s=I(\"mean\",r,t,e),a=I(\"stdDev\",r,t,e),i=I(\"seed\",r,t,e);return[o.truncatedNormal(n,s,a,I(\"dtype\",r,t,e),i)]}case\"Zeros\":return[o.zeros(I(\"shape\",r,t,e),I(\"dtype\",r,t,e))];case\"ZerosLike\":return[o.zerosLike(I(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function PS(r,t,e){let o=I(\"boxes\",r,t,e),n=I(\"scores\",r,t,e),s=I(\"maxOutputSize\",r,t,e),a=I(\"iouThreshold\",r,t,e),i=I(\"scoreThreshold\",r,t,e),p=I(\"softNmsSigma\",r,t,e);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var RT=async(r,t,e,o,n=Je)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=PS(r,t,e),l=await n.image.nonMaxSuppressionWithScoreAsync(s,a,i,p,u,c);return[l.selectedIndices,l.selectedScores]}case\"NonMaxSuppressionV4\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=PS(r,t,e),c=I(\"padToMaxOutputSize\",r,t,e),l=await n.image.nonMaxSuppressionPaddedAsync(s,a,i,p,u,c);return[l.selectedIndices,l.validOutputs]}case\"NonMaxSuppressionV3\":case\"NonMaxSuppressionV2\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=PS(r,t,e);return[await n.image.nonMaxSuppressionAsync(s,a,i,p,u)]}case\"Where\":{let s=n.cast(I(\"condition\",r,t,e),\"bool\"),a=[await n.whereAsync(s)];return s.dispose(),a}case\"ListDiff\":return n.setdiff1dAsync(I(\"x\",r,t,e),I(\"y\",r,t,e));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var DT=(r,t,e,o=Je)=>{switch(r.op){case\"LowerBound\":{let n=I(\"sortedSequence\",r,t,e),s=I(\"values\",r,t,e);return[o.lowerBound(n,s)]}case\"TopKV2\":{let n=I(\"x\",r,t,e),s=I(\"k\",r,t,e),a=I(\"sorted\",r,t,e),i=o.topk(n,s,a);return[i.values,i.indices]}case\"UpperBound\":{let n=I(\"sortedSequence\",r,t,e),s=I(\"values\",r,t,e);return[o.upperBound(n,s)]}case\"Unique\":{let n=I(\"x\",r,t,e),s=o.unique(n);return[s.values,s.indices]}case\"UniqueV2\":{let n=I(\"x\",r,t,e),s=I(\"axis\",r,t,e),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var AT=(r,t,e,o=Je)=>{switch(r.op){case\"Const\":return t[r.name];case\"PlaceholderWithDefault\":let n=I(\"default\",r,t,e);return[zt(r.name,t,e)||n];case\"Placeholder\":return[zt(r.name,t,e)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,t,e);return[Bs(c)]}case\"IdentityN\":return I(\"x\",r,t,e).map(c=>Bs(c));case\"Snapshot\":let s=I(\"x\",r,t,e);return[Bs(s)];case\"Shape\":return[o.tensor1d(I(\"x\",r,t,e).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,t,e).map(c=>o.tensor1d(c.shape));case\"Size\":return[o.scalar(I(\"x\",r,t,e).size,\"int32\")];case\"Rank\":return[o.scalar(I(\"x\",r,t,e).rank,\"int32\")];case\"NoOp\":return[o.scalar(1)];case\"Print\":let a=I(\"x\",r,t,e),i=I(\"data\",r,t,e),p=I(\"message\",r,t,e),u=I(\"summarize\",r,t,e);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(p);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ke(this.size(),\"int32\")}async import(t,e){this.checkKeyAndValueTensor(t,e);let o=await t.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),De(()=>{let n=fo(e),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I(\"keyDType\",r,t,e),a=I(\"valueDType\",r,t,e),i=new vf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let n=I(\"tableHandle\",r,t,e,o),s=I(\"keys\",r,t,e),a=I(\"values\",r,t,e);return[await o.getHashTableById(n.id).import(s,a)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let n=I(\"tableHandle\",r,t,e,o),s=I(\"keys\",r,t,e),a=I(\"defaultValue\",r,t,e);return[await o.getHashTableById(n.id).find(s,a)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let n=I(\"tableHandle\",r,t,e,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var PT=(r,t,e,o=Je)=>{switch(r.op){case\"ResizeBilinear\":{let n=I(\"images\",r,t,e),s=I(\"size\",r,t,e),a=I(\"alignCorners\",r,t,e),i=I(\"halfPixelCenters\",r,t,e);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case\"ResizeNearestNeighbor\":{let n=I(\"images\",r,t,e),s=I(\"size\",r,t,e),a=I(\"alignCorners\",r,t,e),i=I(\"halfPixelCenters\",r,t,e);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case\"CropAndResize\":{let n=I(\"image\",r,t,e),s=I(\"boxes\",r,t,e),a=I(\"boxInd\",r,t,e),i=I(\"cropSize\",r,t,e),p=I(\"method\",r,t,e),u=I(\"extrapolationValue\",r,t,e);return[o.image.cropAndResize(n,s,a,i,p,u)]}case\"ImageProjectiveTransformV3\":{let n=I(\"images\",r,t,e),s=I(\"transforms\",r,t,e),a=I(\"outputShape\",r,t,e),i=I(\"fillValue\",r,t,e),p=I(\"interpolation\",r,t,e),u=I(\"fillMode\",r,t,e);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var OT=(r,t,e,o=Je)=>{switch(r.op){case\"Equal\":return[o.equal(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"NotEqual\":return[o.notEqual(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"Greater\":return[o.greater(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"GreaterEqual\":return[o.greaterEqual(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"Less\":return[o.less(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"LessEqual\":return[o.lessEqual(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"LogicalAnd\":return[o.logicalAnd(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"LogicalNot\":return[o.logicalNot(I(\"a\",r,t,e))];case\"LogicalOr\":return[o.logicalOr(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"Select\":case\"SelectV2\":return[o.where(I(\"condition\",r,t,e),I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"BitwiseAnd\":return[o.bitwiseAnd(I(\"a\",r,t,e),I(\"b\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MT=(r,t,e,o=Je)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[o.matMul(I(\"a\",r,t,e),I(\"b\",r,t,e),I(\"transposeA\",r,t,e),I(\"transposeB\",r,t,e))];case\"Einsum\":return[o.einsum(I(\"equation\",r,t,e),...I(\"tensors\",r,t,e))];case\"Transpose\":return[o.transpose(I(\"x\",r,t,e),I(\"perm\",r,t,e))];case\"_FusedMatMul\":let[n,s]=I(\"fusedOps\",r,t,e),a=n===\"biasadd\",i=s===\"prelu\",p=I(\"numArgs\",r,t,e),u=I(\"leakyreluAlpha\",r,t,e);if(a){if(i&&p!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&p!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,l]=I(\"args\",r,t,e);return[o.fused.matMul({a:I(\"a\",r,t,e),b:I(\"b\",r,t,e),transposeA:I(\"transposeA\",r,t,e),transposeB:I(\"transposeB\",r,t,e),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];case\"MatrixBandPart\":return[o.linalg.bandPart(I(\"a\",r,t,e),I(\"numLower\",r,t,e),I(\"numUpper\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LT=(r,t,e,o=Je)=>{switch(r.op){case\"EuclideanNorm\":return[o.euclideanNorm(I(\"x\",r,t,e),I(\"axis\",r,t,e),I(\"keepDims\",r,t,e))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[o.batchNorm(I(\"x\",r,t,e),I(\"mean\",r,t,e),I(\"variance\",r,t,e),I(\"offset\",r,t,e),I(\"scale\",r,t,e),I(\"epsilon\",r,t,e))];case\"FusedBatchNormV3\":return[o.batchNorm(I(\"x\",r,t,e),I(\"mean\",r,t,e),I(\"variance\",r,t,e),I(\"offset\",r,t,e),I(\"scale\",r,t,e),I(\"epsilon\",r,t,e))];case\"LRN\":return[o.localResponseNormalization(I(\"x\",r,t,e),I(\"radius\",r,t,e),I(\"bias\",r,t,e),I(\"alpha\",r,t,e),I(\"beta\",r,t,e))];case\"Softmax\":return[o.softmax(I(\"x\",r,t,e))];case\"LogSoftmax\":return[o.logSoftmax(I(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var BT=(r,t,e,o=Je)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I(\"paramsNestedSplits\",r,t,e),I(\"paramsDenseValues\",r,t,e),I(\"indices\",r,t,e),I(\"outputRaggedRank\",r,t,e));return n.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I(\"starts\",r,t,e),I(\"limits\",r,t,e),I(\"splits\",r,t,e));return[n,s]}case\"RaggedTensorToTensor\":return[o.raggedTensorToTensor(I(\"shape\",r,t,e),I(\"values\",r,t,e),I(\"defaultValue\",r,t,e),I(\"rowPartitionTensors\",r,t,e),I(\"rowPartitionTypes\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var zT=(r,t,e,o=Je)=>{switch(r.op){case\"Max\":{let i=I(\"axis\",r,t,e),p=I(\"keepDims\",r,t,e);return[o.max(I(\"x\",r,t,e),i,p)]}case\"Mean\":{let i=I(\"axis\",r,t,e),p=I(\"keepDims\",r,t,e);return[o.mean(I(\"x\",r,t,e),i,p)]}case\"Min\":{let i=I(\"axis\",r,t,e),p=I(\"keepDims\",r,t,e);return[o.min(I(\"x\",r,t,e),i,p)]}case\"Sum\":{let i=I(\"axis\",r,t,e),p=I(\"keepDims\",r,t,e);return[o.sum(I(\"x\",r,t,e),i,p)]}case\"All\":{let i=I(\"axis\",r,t,e),p=I(\"keepDims\",r,t,e);return[o.all(I(\"x\",r,t,e),i,p)]}case\"Any\":{let i=I(\"axis\",r,t,e),p=I(\"keepDims\",r,t,e);return[o.any(I(\"x\",r,t,e),i,p)]}case\"ArgMax\":{let i=I(\"axis\",r,t,e);return[o.argMax(I(\"x\",r,t,e),i)]}case\"ArgMin\":{let i=I(\"axis\",r,t,e);return[o.argMin(I(\"x\",r,t,e),i)]}case\"Prod\":{let i=I(\"axis\",r,t,e),p=I(\"keepDims\",r,t,e);return[o.prod(I(\"x\",r,t,e),i,p)]}case\"Cumprod\":{let i=I(\"axis\",r,t,e),p=I(\"exclusive\",r,t,e),u=I(\"reverse\",r,t,e);return[o.cumprod(I(\"x\",r,t,e),i,p,u)]}case\"Cumsum\":{let i=I(\"axis\",r,t,e),p=I(\"exclusive\",r,t,e),u=I(\"reverse\",r,t,e);return[o.cumsum(I(\"x\",r,t,e),i,p,u)]}case\"Bincount\":let n=I(\"x\",r,t,e),s=I(\"weights\",r,t,e),a=I(\"size\",r,t,e);return[o.bincount(n,s,a)];case\"DenseBincount\":{let i=I(\"x\",r,t,e),p=I(\"weights\",r,t,e),u=I(\"size\",r,t,e),c=I(\"binaryOutput\",r,t,e);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var VT=(r,t,e,o=Je)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let n=I(\"n\",r,t,e),s=I(\"axis\",r,t,e),a=I(\"tensors\",r,t,e);return a=a.slice(0,n),[o.concat(a,s)]}case\"Gather\":{let n=I(\"x\",r,t,e),s=I(\"indices\",r,t,e);return[o.gather(n,o.cast(s,\"int32\"),0)]}case\"GatherV2\":{let n=I(\"axis\",r,t,e),s=I(\"batchDims\",r,t,e),a=I(\"x\",r,t,e),i=I(\"indices\",r,t,e);return[o.gather(a,o.cast(i,\"int32\"),n,s)]}case\"Reverse\":{let n=I(\"dims\",r,t,e),s=[];for(let i=0;i{let n=I(\"axis\",r,t,e),s=I(\"tensors\",r,t,e),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error(\"the input tensors shape does not match\");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case\"Unpack\":{let n=I(\"axis\",r,t,e),s=I(\"tensor\",r,t,e);return o.unstack(s,n)}case\"Tile\":{let n=I(\"reps\",r,t,e);return[o.tile(I(\"x\",r,t,e),n)]}case\"Split\":case\"SplitV\":{let n=I(\"axis\",r,t,e),s=I(\"numOrSizeSplits\",r,t,e),a=I(\"x\",r,t,e);return o.split(a,s,n)}case\"ScatterNd\":{let n=I(\"indices\",r,t,e),s=I(\"values\",r,t,e),a=I(\"shape\",r,t,e);return[o.scatterND(n,s,a)]}case\"GatherNd\":{let n=I(\"x\",r,t,e),s=I(\"indices\",r,t,e);return[o.gatherND(n,s)]}case\"SparseToDense\":{let n=I(\"sparseIndices\",r,t,e),s=I(\"outputShape\",r,t,e),a=I(\"sparseValues\",r,t,e),i=I(\"defaultValue\",r,t,e);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}case\"TensorScatterUpdate\":{let n=I(\"indices\",r,t,e),s=I(\"values\",r,t,e),a=I(\"tensor\",r,t,e);return[o.tensorScatterUpdate(a,n,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var WT=(r,t,e,o=Je)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I(\"indices\",r,t,e),I(\"values\",r,t,e),I(\"denseShape\",r,t,e),I(\"defaultValue\",r,t,e));return[n,s,a,i]}case\"SparseReshape\":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I(\"inputIndices\",r,t,e),I(\"inputShape\",r,t,e),I(\"newShape\",r,t,e));return[n,s]}case\"SparseSegmentMean\":return[o.sparse.sparseSegmentMean(I(\"data\",r,t,e),I(\"indices\",r,t,e),I(\"segmentIds\",r,t,e))];case\"SparseSegmentSum\":return[o.sparse.sparseSegmentSum(I(\"data\",r,t,e),I(\"indices\",r,t,e),I(\"segmentIds\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UT=(r,t,e,o=Je)=>{switch(r.op){case\"FFT\":return[o.fft(I(\"x\",r,t,e))];case\"IFFT\":return[o.ifft(I(\"x\",r,t,e))];case\"RFFT\":return[o.rfft(I(\"x\",r,t,e))];case\"IRFFT\":return[o.irfft(I(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GT=(r,t,e,o=Je)=>{switch(r.op){case\"StaticRegexReplace\":return[o.string.staticRegexReplace(I(\"input\",r,t,e),I(\"pattern\",r,t,e),I(\"rewrite\",r,t,e),I(\"replaceGlobal\",r,t,e))];case\"StringNGrams\":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I(\"data\",r,t,e),I(\"dataSplits\",r,t,e),I(\"separator\",r,t,e),I(\"nGramWidths\",r,t,e),I(\"leftPad\",r,t,e),I(\"rightPad\",r,t,e),I(\"padWidth\",r,t,e),I(\"preserveShortSequences\",r,t,e));return[n,s]}case\"StringSplit\":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I(\"input\",r,t,e),I(\"delimiter\",r,t,e),I(\"skipEmpty\",r,t,e));return[n,s,a]}case\"StringToHashBucketFast\":return[o.string.stringToHashBucketFast(I(\"input\",r,t,e),I(\"numBuckets\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HT=(r,t,e,o=Je)=>{switch(r.op){case\"Cast\":return[o.cast(I(\"x\",r,t,e),I(\"dtype\",r,t,e))];case\"ExpandDims\":{let n=I(\"axis\",r,t,e);return[o.expandDims(I(\"x\",r,t,e),n)]}case\"Squeeze\":{let n=I(\"axis\",r,t,e);return[o.squeeze(I(\"x\",r,t,e),n)]}case\"Reshape\":return[o.reshape(I(\"x\",r,t,e),I(\"shape\",r,t,e))];case\"EnsureShape\":return[o.ensureShape(I(\"x\",r,t,e),I(\"shape\",r,t,e))];case\"MirrorPad\":return[o.mirrorPad(I(\"x\",r,t,e),I(\"padding\",r,t,e),I(\"mode\",r,t,e))];case\"PadV2\":case\"Pad\":return[o.pad(I(\"x\",r,t,e),I(\"padding\",r,t,e),I(\"constantValue\",r,t,e))];case\"SpaceToBatchND\":{let n=I(\"blockShape\",r,t,e),s=I(\"paddings\",r,t,e);return[o.spaceToBatchND(I(\"x\",r,t,e),n,s)]}case\"BatchToSpaceND\":{let n=I(\"blockShape\",r,t,e),s=I(\"crops\",r,t,e);return[o.batchToSpaceND(I(\"x\",r,t,e),n,s)]}case\"DepthToSpace\":{let n=I(\"blockSize\",r,t,e),s=I(\"dataFormat\",r,t,e).toUpperCase();return[o.depthToSpace(I(\"x\",r,t,e),n,s)]}case\"BroadcastTo\":return[o.broadcastTo(I(\"x\",r,t,e),I(\"shape\",r,t,e))];case\"BroadcastArgs\":return[o.broadcastArgs(I(\"s0\",r,t,e),I(\"s1\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function OS(r,t,e,o,n=De){let s=((a,i,p)=>{switch(a.category){case\"arithmetic\":return n(()=>CT(a,i,p));case\"basic_math\":return n(()=>wT(a,i,p));case\"control\":return TT(a,i,p);case\"convolution\":return n(()=>$T(a,i,p));case\"creation\":return n(()=>ET(a,i,p));case\"dynamic\":return RT(a,i,p);case\"evaluation\":return n(()=>DT(a,i,p));case\"image\":return n(()=>PT(a,i,p));case\"graph\":return n(()=>AT(a,i,p));case\"logical\":return n(()=>OT(a,i,p));case\"matrices\":return n(()=>MT(a,i,p));case\"normalization\":return n(()=>LT(a,i,p));case\"ragged\":return n(()=>BT(a,i,p));case\"reduction\":return n(()=>zT(a,i,p));case\"slice_join\":return n(()=>VT(a,i,p));case\"sparse\":return n(()=>WT(a,i,p));case\"spectral\":return n(()=>UT(a,i,p));case\"string\":return n(()=>GT(a,i,p));case\"transformation\":return n(()=>HT(a,i,p));case\"hash_table\":return FT(a,i,p,o);case\"custom\":let u=pf(a.op);if(u&&u.customExecutor)return u.customExecutor(new wf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Ll=class{constructor(t={},e={},o={},n={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=o,this.functionMap=n,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?\"\":`${e.frameName}-${e.iterationId}`).join(\"/\"):\"\"}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function MS(r,t,e,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=new Set(Object.keys(r).map(m=>Tr(m)[0]));o=o||[];let c=new Set(o.map(m=>Tr(m.name)[0])),l=[...t];for(;l.length>0;){let m=l.pop();if((gu(m)||k8(m)||N8(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),e[m.name]==null&&!u.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:t,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function KT(r,t){let{usedNodes:e,inputs:o}=t,n=Object.keys(o).map(g=>Tr(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>e.has(typeof g==\"string\"?g:g.name);function i(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let p=i([...n,...r.weights,...s]).filter(a),u=i([...p,...Object.values(r.nodes)]).filter(a),c=new Map(u.map(g=>[g.name,g])),l={};for(let g of u){l[g.name]=l[g.name]||0;for(let x of g.children)a(x)||(l[x.name]=Number.POSITIVE_INFINITY),l[x.name]=(l[x.name]||0)+1}let m=Object.entries(l).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(a))--l[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>c.get(g)),h=C8(f,p);return w8(h,p),h}function C8(r,t){let e=new Map(r.map(a=>[a.name,a])),o=t.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=e.get(a);for(let p of i.children)!e.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var Cc=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function w8(r,t){let e=new Map(r.map((i,p)=>[i.name,p])),o=new Set(t.map(i=>i.name)),n=i=>o.has(typeof i==\"string\"?i:i.name),s=new Set(r.map(i=>i.name)),a=i=>s.has(typeof i==\"string\"?i:i.name);for(let i of r){for(let p of i.children.filter(a)){if(!e.has(p.name))throw new Cc(`Child ${p.name} of node ${i.name} is unreachable.`);if(e.get(i.name)>e.get(p.name))throw new Cc(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!e.has(p.name))throw new Cc(`Input ${p.name} of node ${i.name} is unreachable.`);if(e.get(p.name)>e.get(i.name))throw new Cc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function qT(r){let t=new Map(r.map((i,p)=>[i.name,p])),e=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>gu(i)?e:p),n=i=>{let p=o[t.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;it[o].map(n=>n.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(o=>{this._functionExecutorMap[o]=new lp(t.functions[o],this)})}getCompilationKey(t,e){let o=t.map(s=>s.name).sort(),n=e.map(s=>s.name).sort();return o.join(this.SEPARATOR)+\"--\"+n.join(this.SEPARATOR)}compile(t,e){let o=MS(t,e,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let u=e.map(l=>l.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`)}let i=KT(this.graph,o),p=qT(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return Rr(e),e}cloneTensorList(t){return t?t.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,o])=>[e,this.cloneTensorList(o)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let o=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let n=o.map(m=>this.graph.nodes[Tr(m)[0]]),s=e.map(m=>Tr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(t,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=A().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Ll(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,C]=Tr(x,m),S=[];S[C]=t[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=OS(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);d[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,d,m,f,a,g.get(x.name))}return this.parent==null&&m.dispose(f),e.map(x=>zt(x,d,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(o=>t[o]).map(o=>o.map(n=>n.id)));return new Set(e)}checkTensorForDisposal(t,e,o,n,s,a,i){if(!(gu(e)||a.has(t))){for(let p of o[t])p!=null&&(i[p.id]=(i[p.id]||0)+e.children.length);for(let p of e.inputs){if(gu(p))continue;let u=fS(p.name,o,n);if(u!=null)for(let c of u){if(!c||c.kept||s.has(c.id))continue;let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,o,n,s,a){function i(p){return gu(p)||s.has(p.name)}if(!(gu(t)||a==null))for(let p of a){if(i(p))continue;let u=fS(p.name,e,o);for(let c of u)!c||c.kept||n.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=A().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Ll(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(t,a,e,o),p=e.map(m=>zt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(t,e,o){let n=t.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,e,o)}async executeWithControlFlow(t,e,o,n){let s=Object.keys(t),a=s.map(S=>this.graph.nodes[Tr(S)[0]]),i=o.map(S=>Tr(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:c,missingInputs:l,dynamicNode:m,syncInputs:d}=MS(t,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(S=>{let[k,_]=Tr(S),E=[];E[_]=t[S],h[k]=E});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,e,h,b,x,p,g,c);await Promise.all(S)}m==null&&!n&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let C=u.filter(S=>!gu(S)&&!zt(S.name,h,e)).map(S=>S.name);if(C.length>0){let S=\"\";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`)}return h}processStack(t,e,o,n,s,a,i,p,u){let c=[];for(;e.length>0;){let l=e.pop();o.currentContext=l.contexts;let m=\"\";if(l.node.op===\"Enter\"&&I(\"isConstant\",l.node,n,o)&&([m]=Ls(l.node.name,o)),n[l.node.name]==null){let d=OS(l.node,n,o,this._resourceManager);m||([m]=Ls(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,e,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,e,o,n,s,u))}else this.processChildNodes(l.node,e,o,n,s,u)}return c}processChildNodes(t,e,o,n,s,a){t.children.forEach(i=>{let[p]=Ls(i.name,o);s[p]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!zt(u,n,o))&&(s[p]=!0,e.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!zt(u,n,o))&&(s[p]=!0,e.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let o=t[e],[n]=Tr(e),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(t){var e,o;let n={};for(let s in t){let a=(o=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=t[s]:n[s]=t[s]}return n}checkInputs(t){let e=Object.keys(t).filter(o=>{let[n]=Tr(o);return this.graph.nodes[n]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[o]=Tr(e);if(!this.graph.nodes[o])throw new Error(`The output '${e}' is not found in the graph`)})}};var kf=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var T8=\"?tfjs-format=file\",_8=\"model.json\",Bl=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},o=fi){this.modelUrl=t,this.loadOptions=e,this.version=\"n/a\",this.io=o,e==null&&(this.loadOptions={}),this.resourceManager=new kf}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let t=this.handler.load();return y.isPromise(t)?t.then(e=>this.loadSync(e)):this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new lp(Ml.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Ml.Instance.transformGraph(t.modelInitializer);this.initializer=new lp(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t==\"string\"){let o=this.io.getSaveHandlers(t);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${t}'`);t=o[0]}if(t.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof ut?[t]:t,o={};return e.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return t}predict(t,e){let o=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(t,e){let o=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(t){var e;if(!(t instanceof ut)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(t[a]=this.resourceIdToCapturedInput[i.resourceId])}return t}t=Array.isArray(t)?t:[t];let o=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+o!==this.inputNodes.length)throw new Error(`Input tensor count 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Tc={};qe(Tc,{addImpl:()=>zS,bincountImpl:()=>Ic,bincountReduceImpl:()=>Nf,bitwiseAndImpl:()=>VS,castImpl:()=>BS,ceilImpl:()=>WS,concatImpl:()=>mp,equalImpl:()=>US,expImpl:()=>HS,expm1Impl:()=>qS,floorDivImpl:()=>XS,floorImpl:()=>jS,gatherNdImpl:()=>Tf,gatherV2Impl:()=>_f,greaterEqualImpl:()=>QS,greaterImpl:()=>YS,lessEqualImpl:()=>JS,lessImpl:()=>ZS,linSpaceImpl:()=>$f,logImpl:()=>eI,maxImpl:()=>Ef,maximumImpl:()=>tI,minimumImpl:()=>rI,multiplyImpl:()=>zl,negImpl:()=>oI,notEqualImpl:()=>nI,prodImpl:()=>sI,raggedGatherImpl:()=>Rf,raggedRangeImpl:()=>Df,raggedTensorToTensorImpl:()=>Af,rangeImpl:()=>fp,rsqrtImpl:()=>aI,scatterImpl:()=>zs,sigmoidImpl:()=>v_,simpleAbsImpl:()=>LS,sliceImpl:()=>hp,sparseFillEmptyRowsImpl:()=>Ff,sparseReshapeImpl:()=>Pf,sparseSegmentReductionImpl:()=>Nc,sqrtImpl:()=>T_,squaredDifferenceImpl:()=>uI,staticRegexReplaceImpl:()=>pI,stridedSliceImpl:()=>Of,stringNGramsImpl:()=>gp,stringSplitImpl:()=>xp,stringToHashBucketFastImpl:()=>yp,subImpl:()=>lI,tileImpl:()=>Mf,topKImpl:()=>Lf,transposeImpl:()=>vc,uniqueImpl:()=>bp});function 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WY=Ie(un,r=>Math.cosh(r)),b$={kernelName:un,backendName:\"cpu\",kernelFunc:WY};function UY(r){let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],\"float32\"),b=e.data.get(s.dataId).values,C=e.data.get(a.dataId).values,S=e.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let E=0;E=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-P)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let q=0;q1?P*(m-1)+re*z:.5*(P+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?P*(m-1)+q*z:.5*(P+M)*(m-1);if(Y<0||Y>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. 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Got strides ${a} and dilations '${m}'`);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,C=b.left,S=b.top,k=d.outChannels/d.inChannels,_=new tt(d.outShape,n.dtype),E=e.data.get(n.dataId).values,R=e.data.get(s.dataId).values,D=_.values;for(let P=0;P=d.inHeight)continue;let q=U*l[0],Y=O+j*c[1];for(let J=0;J=d.inWidth)continue;let ie=q+ee*l[1],le=Y+oe*d.inChannels,be=re,_e=ie;for(let ve=0;ve{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=e,p=t,u=p.data.get(o.dataId).values,c=o.shape.length,l=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:C,strideHeight:S,strideWidth:k,filterHeight:_,filterWidth:E,dilationHeight:R,dilationWidth:D,outShape:P}=w.computeDilation2DInfo(o.shape,n.shape,s,a,\"NHWC\",i),O=y.sizeFromShape(P),M=P.length,L=y.getArrayFromDType(o.dtype,O);for(let z=0;z=0&&oe=0&&lere&&(re=ve)}}}let ne=y.locToIndex([z,U,q,J],M,y.computeStrides(P));L[ne]=re}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),P,o.dtype),shape:P,dtype:o.dtype}}};var E$={kernelName:zi,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=e,u=t,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=w.computeDilation2DInfo(o.shape,n.shape,a,i,\"NHWC\",p);y.assert(s.rank===D.length,()=>`Error in ${zi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ne,re=oe)}}}O[J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var R$={kernelName:Bi,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=e,u=t,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=w.computeDilation2DInfo(o.shape,n.shape,a,i,\"NHWC\",p);y.assert(s.rank===D.length,()=>`Error in ${Bi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ee,re=ie)}}}O[L][J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function QY(r){let{inputs:t,backend:e,attrs:o}=r,{image:n}=t,{canvas:s,options:a}=o,{contextOptions:i,imageOptions:p}=a||{},u=(p==null?void 0:p.alpha)||1,c=(i==null?void 0:i.contextType)||\"2d\";if(c!==\"2d\")throw new Error(`Context type ${i.contextType} is not supported by the CPU backend.`);let l=s.getContext(c,(i==null?void 0:i.contextAttributes)||{});if(l==null)throw new Error(`Could not get the context with ${c} type.`);let[m,d]=n.shape.slice(0,2),f=n.shape.length===2?1:n.shape[2],h=e.data.get(n.dataId).values,g=n.dtype===\"float32\"?255:1,x=new Uint8ClampedArray(d*m*4);for(let C=0;C1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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k=y.computeStrides(c);f=`${k[0]===p[1]}_${k[k.length-1]===p[1]}`}let h=a.shape.length,g=c.length===2&&y.arraysEqual(a.shape,p),x=y.sizeFromShape(a.shape)===1,b=w.getBroadcastDims(a.shape,e.shape),C=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(p,e.texData.texShape),S=r.packedInputs||c.length>2?\"\":`${p[0]>1}_${p[1]>1}`;o+=`${h}_${C}_${u?l:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${S}_${i}`}else{let p=a.isUniform?\"uniform\":a.texData.texShape;o+=`${a.shape}_${p}_${i}`}});let n=r.userCode,s=r.constructor.name;return s+=\"_\"+o+\"_\"+n+`${A().getNumber(\"WEBGL_VERSION\")}`,s}function pt(r){return A().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Jf=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=bu.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let e=vt();this.outputShape=t,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n 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o=\"WEBGL_color_buffer_float\",n=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),A().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=Ec(this.gl,s),qr(this.gl,a))this.textureHalfFloatExtension=Ec(this.gl,a);else if(A().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(o),qr(this.gl,n))this.colorBufferHalfFloatExtension=Ec(this.gl,n);else if(A().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(o=\"EXT_color_buffer_float\",qr(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else 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e=this.gl;ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),rv(e,t,this.vertexBuffer)}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ce(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&Yl(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,o=!0){return this.throwIfDisposed(),o?MI(this.gl,t,e):LI(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,o){this.throwIfDisposed(),this.throwIfNoProgram(),BI(this.gl,t,e,o)}setOutputMatrixTexture(t,e,o){this.setOutputMatrixTextureDriver(t,o,e)}setOutputPackedMatrixTexture(t,e,o){this.throwIfDisposed();let[n,s]=La(e,o);this.setOutputMatrixTextureDriver(t,n,s)}setOutputMatrixWriteRegion(t,e,o,n){this.setOutputMatrixWriteRegionDriver(o,t,n,e)}setOutputPackedMatrixWriteRegion(t,e,o,n){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&Yl(this.gl,this.program),Rc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,\"VAO changed between setProgram and 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this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(t,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=HZ(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:o}=this.itemsToPoll[e];o()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let o;\"setTimeoutCustom\"in 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currently set.\")}};function HZ(r){let t=0;for(;t`${r}.${e}`)}function Dt(r,t){return t===1?[r]:lv(r,t)}function SD(r,t){if(r===1)return\"rc\";let e=\"\";for(let o=0;o ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let e=\"\";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o};\n bool rEdge = rp1 >= ${n};\n `}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),\n cEdge ? 0. : getA(${e[1]}),\n rEdge ? 0. : getA(${e[2]}),\n rEdge || cEdge ? 0. : getA(${e[3]})`}};var Vc=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=t,this.enableShapeUniforms=pt(this.outputShape.length);let o=\"\";for(let n=0;n<4;n++){let s=\"thisRC = rc;\";n%2===1&&(s+=\"thisRC.z += 1;\"),n>1&&(s+=\"thisRC.y += 1;\"),o+=`\n ${s}\n ${n>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${n}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${n>0?\"}\":\"\"}\n `}this.userCode=`\n ${KZ(e,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?Pc():Fc(t)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":t[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":t[2]};\n\n ${o}\n\n setOutput(result);\n }\n `}};function KZ(r,t){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${t?SR([\"r\",\"c\",\"d\"],\"inputShape\"):Ws([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var dh=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,o){let n=vD(e,o),s=kD(t,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=ID(t,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].pop();return this.usedTextures[s].push(p),p}let i;return n===rr.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):n===rr.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):n===rr.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):n===rr.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):n===rr.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(t,e,o,n){if(this.freeTextures==null)return;let s=vD(o,n),a=kD(e,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=ID(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=A().getNumber(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(t),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(t);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function qZ(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function ID(r,t,e,o,n){let s=jZ(t,o),a;if(n){let[p,u]=La(r[0],r[1]);a=p*u}else{let[p,u]=Sp(r[0],r[1]);a=p*u}let i=qZ(e,s);return a*i}function jZ(r,t){switch(r){case rr.PACKED_2X2_FLOAT32:return ih(t);case rr.PACKED_2X2_FLOAT16:return uh(t);case rr.UNPACKED_FLOAT32:return nh(t);case rr.UNPACKED_FLOAT16:return sh(t);case rr.PACKED_4X1_UNSIGNED_BYTE:return ah(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function XZ(r){return A().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?rr.PACKED_2X2_FLOAT32:rr.UNPACKED_FLOAT32:r?rr.PACKED_2X2_FLOAT16:rr.UNPACKED_FLOAT16}function vD(r,t){if(r===dr.UPLOAD)return rr.PACKED_2X2_FLOAT32;if(r===dr.RENDER||r==null)return XZ(t);if(r===dr.DOWNLOAD||r===dr.PIXELS)return rr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function kD(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var or=class{constructor(t,e){this.variableNames=[\"A\"],this.outputShape=t,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${e}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},Ut=\"if (isnan(x)) return x;\",ND=\"return x;\",mv=\"return abs(x);\";var TD=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",_D=Ut+`\n return (x < 0.0) ? 0.0 : x;\n`,$D=Ut+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Ba=\"return x;\",ED=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var DD=\"return x;\",AD=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,FD=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,PD=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,OD=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Fr=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${e}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var fh=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=pt(this.outputShape.length);let e=t.length,o=Dt(\"rc\",e),n=Re(e),s=SD(e,o),a=o.slice(-2),i=e<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n void main() {\n ${n} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${i}));\n }\n `}};var QZ=Wt.whereImpl,ZZ=1e-7,JZ=1e-4,hh={};function e9(r){return r in hh||(hh[r]={}),hh[r]}var t9=A().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),r9=600;function o9(){return A().global.screen==null?1024:A().global.screen.height*A().global.screen.width*window.devicePixelRatio*r9/1024/1024}var wu=class extends ao{nextDataId(){return wu.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!A().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let e;if(t!=null){if(t instanceof kp)e=t;else{let o=Kr(A().getNumber(\"WEBGL_VERSION\"),t);e=new kp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Kr(A().getNumber(\"WEBGL_VERSION\"));e=new kp(o),this.binaryCache=e9(A().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new dh(this.gpgpu),this.numMBBeforeWarning=o9(),this.texData=new zo(this,pr())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,o,n,s,a){let i=this.makeTensorInfo(e,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:t,texShape:[n,s]},p.texShape=[n,s];let u=Dc(e),c=new Zl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=e,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(t,e,o){if((A().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||A().getBool(\"DEBUG\"))&&this.checkNumericalProblems(t),o===\"complex64\"&&t!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.texData.set(n,{shape:e,dtype:o,values:t,usage:dr.UPLOAD,refCount:1}),n}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,o,n,s){if(A().getBool(\"DEBUG\")&&this.checkNumericalProblems(e),n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(t,{shape:o,dtype:n,values:e,usage:dr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=e;if(a!=null){let m;p?m=new Fr(i,Ba):m=new or(i,Ba);let d=this.runWebGLProgram(m,[{dataId:t,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(t);if(n===\"string\")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n===\"complex64\"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=w.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,l)}async read(t){if(this.pendingRead.has(t)){let f=this.pendingRead.get(t);return new Promise(h=>f.push(h))}let e=this.texData.get(t),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=e;if(s!=null){let f;p?f=new Fr(n,Ba):f=new or(n,Ba);let h=this.runWebGLProgram(f,[{dataId:t,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(t);if(A().getBool(\"DEBUG\")&&!A().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&A().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,c;if(a!==\"complex64\"&&A().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(t);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...jl(n))}this.pendingRead.set(t,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let l;if(a===\"complex64\"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=w.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(t);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(t,l),d=this.pendingRead.get(t);return this.pendingRead.delete(t),d.forEach(f=>f(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&pr().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let o=this.texData.get(t),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;p?d=new Fr(s,Ba):d=new or(s,Ba);let f=this.runWebGLProgram(d,[{dataId:t,shape:s,dtype:i}],i),h=this.readToGPU(f,e);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(t,e.customTexShape),l=pr().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype===\"string\")try{let o=e.map(n=>y.decodeString(n));return me(t.shape,t.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,t();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=e,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:o}=this.texData.get(t);return o!=null&&(this.disposeData(o.real.dataId,e),this.disposeData(o.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(t),p=i&&i.origDataId||t,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),e!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(e,n,s,a)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=t9){return A().getBool(\"WEBGL_CPU_FORWARD\")&&t.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,t,e)}else n=this.write(o,t,e);return this.texData.get(n).usage=null,{dataId:n,shape:t,dtype:e}}makeOutput(t,e,o){return pr().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,o),this)}unpackTensor(t){let e=new fh(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new mh(t.shape),o=!0;return this.runWebGLProgram(e,[t],t.dtype,null,o)}packedReshape(t,e){let o=[yi(t.shape),...bi(t.shape)],n={dtype:t.dtype,shape:o,dataId:t.dataId},s=[yi(e),...bi(e)],a=new Vc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],t.dtype,p,i);return{dataId:u.dataId,shape:e,dtype:u.dtype}}decode(t,e){let o=this.texData.get(t),{isPacked:n,shape:s,dtype:a}=o;if(e!=null){let m=y.sizeFromShape(s),d=e[0]*e[1]*4;y.assert(m<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=Dc(s),p;n?p=new eh(i):p=new Jf(i);let u=!0,c=[e!=null?e:jl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:t}],a,c,u,e);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(t,e,o,n,s=!1,a){let i=this.makeTensorInfo(t.outputShape,o),p=this.texData.get(i.dataId);if(t.packedOutput&&(p.isPacked=!0),t.outPackingScheme===bu.DENSE){let x=a!=null?a:jl(t.outputShape);p.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(p.usage=t.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=e.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=A().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Cu(b.shape,x.shape)){let C=x,S=x.shape;x.shape=b.shape,x=this.packedReshape(x,S),u.push(x),b=this.texData.get(x.dataId),C.shape=S}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=ER(t,c,l),d=this.getAndSaveBinary(m,()=>_R(this.gpgpu,t,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),A().get(\"ENGINE_COMPILE_ONLY\")||$R(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=A().getNumber(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!A().getBool(\"WEBGL_LAZILY_UNPACK\")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(t,e,o,n,s=!1){return o=o||e[0].dtype,this.runWebGLProgram(t,e,o,n,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(A().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!A().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let t=A().getBool(\"DEBUG\");A().set(\"DEBUG\",!1);let e=this.abs(ke(1e-8)).dataSync()[0];if(A().set(\"DEBUG\",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?ZZ:JZ}uploadToGPU(t){let e=this.texData.get(t),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=e;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=e.texShape;if(l==null&&(l=zI(o,p),e.texShape=l),s!=null){let m=Dc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=La(l[0],l[1])),p?d=new oh(m,g):d=new Zl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=dr.PIXELS:C.usage=dr.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let S=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,S,k),E=this.texData.get(_.dataId);e.texShape=E.texShape,e.isPacked=E.isPacked,e.usage=E.usage,A().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(e.texture=E.texture,e.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);e.texture=m}}convertAndCacheOnCPU(t,e){let o=this.texData.get(t),{dtype:n}=o;return e!=null&&(o.values=n9(e,n)),o.values}acquireTexture(t,e,o,n){if(this.numBytesInGPU+=this.computeBytes(t,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,n)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(e),n(!0)}catch(s){throw s}});t.push(o)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await pS(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(qf(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:o,infLoc:n,nanLoc:s,outShapeLocation:a,outShapeStridesLocation:i,outTexShapeLocation:p}=qI(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=o,t.infLoc=n,t.nanLoc=s,t.outShapeLocation=a,t.outShapeStridesLocation=i,t.outTexShapeLocation=p}}createTensorFromGPUData(t,e,o){t.channels=t.channels||\"RGBA\";let{texture:n,height:s,width:a,channels:i}=t,p=pr().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let u=p.writeTexture(n,e,o,s,a,i);return pr().makeTensorFromDataId(u,e,o,p)}};wu.nextDataId=0;function n9(r,t){if(t===\"float32\"||t===\"complex64\")return r;if(t===\"int32\"||t===\"bool\"){let e=t===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew wu,2);var Cat={forceHalfFloat:MD};var Wc=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var Pr=class{constructor(t,e,o){this.variableNames=[\"A\",\"B\"],this.outputShape=w.assertAndGetBroadcastShape(e,o),this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${t}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Xr=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var jr=class{constructor(t,e,o,n=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=w.assertAndGetBroadcastShape(e,o);let s=this.outputShape.length;this.enableShapeUniforms=pt(s);let a=\"\";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(a=`\n ${Re(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?a+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:a+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let p=Dt(\"coords\",s);this.enableShapeUniforms?a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${t}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${a}\n\n setOutput(result);\n }\n `}};function At(r){let{inputs:t,backend:e}=r,{x:o}=t;return e.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var LD={kernelName:wo,backendName:\"webgl\",kernelFunc:At};function Or(r){let{inputs:t,backend:e}=r,{real:o,imag:n}=t,s=e.makeTensorInfo(o.shape,\"complex64\"),a=e.texData.get(s.dataId),i=At({inputs:{x:o},backend:e}),p=At({inputs:{x:n},backend:e});return a.complexTensorInfos={real:i,imag:p},s}var BD={kernelName:Fi,backendName:\"webgl\",kernelFunc:Or};var dv=\"return (a < 0.) ? b * a : a;\",fv=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function a9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{alpha:s}=o,a=e.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),i=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(fv,n.shape,a.shape):new Pr(dv,n.shape,a.shape),p=e.runWebGLProgram(i,[n,a],\"float32\");return e.disposeIntermediateTensorInfo(a),p}var zD={kernelName:Rn,backendName:\"webgl\",kernelFunc:a9};var hv=\"return (a < 0.) ? b * a : a;\",gv=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function i9(r){let{inputs:t,backend:e}=r,{x:o,alpha:n}=t,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(gv,o.shape,n.shape):new Pr(hv,o.shape,n.shape);return e.runWebGLProgram(s,[o,n],\"float32\")}var VD={kernelName:os,backendName:\"webgl\",kernelFunc:i9};var Po=\"if (isnan(x)) return x;\";function xe({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&e!=null){let l=i.texData.get(a.dataId),m=e(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=A().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return u?c=new Fr(a.shape,t):c=new or(a.shape,r),i.runWebGLProgram(c,[a],p)}}function nt({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype===\"complex64\"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[S,k]=C,_={dataId:S.dataId,dtype:S.dtype,shape:p.shape},E={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new Pr(r,p.shape,u.shape);return c.runWebGLProgram(R,[_,E],dt(S.dtype,k.dtype))}),b=Or({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype===\"string\"||u.dtype===\"string\"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype===\"string\"?w.fromUint8ToStringArray(f):f,x=p.dtype===\"string\"?w.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),S=c.makeTensorInfo(C,l),k=c.texData.get(S.dataId);return k.values=b,S}let m=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,d;return m?d=new jr(t,p.shape,u.shape,e):d=new Pr(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function Ci(r,t=!1){if(r===\"linear\")return t?DD:ND;if(r===\"relu\")return t?FD:_D;if(r===\"elu\")return t?AD:TD;if(r===\"relu6\")return t?PD:$D;if(r===\"prelu\")return t?gv:hv;if(r===\"leakyrelu\")return t?fv:dv;if(r===\"sigmoid\")return t?OD:ED;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Uc=class{constructor(t,e,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=pt(this.outputShape.length);let c=n?t[1]:t[2],l=Math.ceil(c/2),m=n?\"i * 2, rc.y\":\"rc.y, i * 2\",d=s?\"rc.z, i * 2\":\"i * 2, rc.z\",f=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";i&&(p?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${i}\n }`:u?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${i}\n }`:g=`vec4 activation(vec4 x) {\n ${i}\n }`,x=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let C=\"rc.x\",S=\"rc.x\";t[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!Cu(n.shape,p)&&!(c.texture!==null&&Cu(c.shape,p))?GD(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var HD={kernelName:da,backendName:\"webgl\",kernelFunc:te};var rm=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=t;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u=\"sumValue += dot(values, ones);\";if(e!=null){let l=1/e;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c=\"\";s%o>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${u}\n }\n\n int inIdx = inOffset + ${i};\n if (${p===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${u}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${u}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${u}\n }\n setOutput(sumValue);\n }\n `}};var gh=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=t;this.outputShape=[n,a];let i=\"0.0\",p=\"\";e===\"prod\"?i=\"1.0\":e===\"min\"?(i=\"1.0 / 1e-20\",p=\"min\"):e===\"max\"&&(i=\"-1.0 / 1e-20\",p=\"max\");let u=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"sum\"?u=\"sumValue\":e===\"prod\"?u=\"prodValue\":e===\"all\"?u=\"allValue\":e===\"any\"&&(u=\"anyValue\");let c=Math.floor(o/4)*4,l=o%4,m=`\n if (${e===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${e===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${p}(values, minMaxValue);\n if (${e===\"min\"} || ${e===\"max\"}) {\n minMaxValue = ${p}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,d=\"vec4\";e===\"all\"?(i=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,d=\"bvec4\"):e===\"any\"&&(i=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `,d=\"bvec4\");let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${i};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n vec4 minMaxValue = vec4(${i});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===2}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===3}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function p9(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],o=w.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:o,outSize:Math.ceil(e/o)})}return t}function Yr(r,t,e,o){let n=p9(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],o=new Array(t);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Re(this.rank),s=lv(\"rc\",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes 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ne=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));re.push(ne),D.push(ne)}j=n.runWebGLProgram(J,re,Y)}let q=te({inputs:{x:j},backend:n,attrs:{shape:S}});D.push(j);for(let Y of D)n.disposeIntermediateTensorInfo(Y);return q}function l9(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return _p({a:n,b:s,transposeA:p,transposeB:u,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var XD={kernelName:Io,backendName:\"webgl\",kernelFunc:l9};var YD=\"return abs(x);\";function m9(r){let{inputs:t,backend:e}=r,{x:o}=t;if(e.shouldExecuteOnCPU([o])&&o.dtype!==\"complex64\"){let s=e.texData.get(o.dataId),a=ch(s.values);return e.makeTensorInfo(o.shape,o.dtype,a)}let n;return A().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?n=new Fr(o.shape,YD):n=new or(o.shape,YD),e.runWebGLProgram(n,[o],o.dtype)}var QD={kernelName:Xs,backendName:\"webgl\",kernelFunc:m9};var d9=Ut+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,f9=xe({opSnippet:d9}),ZD={kernelName:Wo,backendName:\"webgl\",kernelFunc:f9};var h9=Ut+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,g9=xe({opSnippet:h9}),JD={kernelName:Uo,backendName:\"webgl\",kernelFunc:g9};var eA=\"return a + b;\",x9=nt({opSnippet:eA,packedOpSnippet:eA,supportsComplex:!0,cpuKernelImpl:RR}),tA={kernelName:uo,backendName:\"webgl\",kernelFunc:x9};var bh=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n float result = ${n};\n setOutput(result);\n }\n `}};var Ch=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n vec4 result = ${n};\n setOutput(result);\n }\n `}};function wh(r){let{inputs:t,backend:e}=r,o=t;if(o.length===1)return At({inputs:{x:o[0]},backend:e});if(o.length>A().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let p=Math.floor(o.length/2),u=wh({inputs:o.slice(0,p),backend:e}),c=wh({inputs:o.slice(p),backend:e});return wh({inputs:[u,c],backend:e})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=A().getBool(\"WEBGL_PACK\")?new Ch(o[0].shape,s):new bh(o[0].shape,s);return e.runWebGLProgram(i,o,n)}var rA={kernelName:Go,backendName:\"webgl\",kernelFunc:wh};function y9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=Ct({inputs:{x:n},backend:e,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims(\"all\",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:e,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,\"all\",e),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(l),x}var oA={kernelName:Ho,backendName:\"webgl\",kernelFunc:y9};function 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i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${n}; i++) {\n int inIdx = ${p};\n float candidate = getA(batch, inIdx);\n if (candidate ${i} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var Ih=class{constructor(t,e,o,n){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],a=Math.ceil(s/e);this.outputShape=t.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,p=i.length,u=Re(p),c=Dt(\"coords\",p),l,m;if(a===1){m=p+1;let R=Re(m);l=`\n ${R} sourceLocR = ${R}(${c.join()}, 0);\n ++${c[p-1]};\n ${R} sourceLocG = ${R}(${c.join()}, 0);\n ++${c[p-2]};\n ${R} sourceLocA = ${R}(${c.join()}, 0);\n --${c[p-1]};\n ${R} sourceLocB = ${R}(${c.join()}, 0);\n --${c[p-2]};`}else m=p,l=`\n ${u} sourceLocR = coords;\n ++${c[p-1]};\n ${u} sourceLocG = coords;\n ++${c[p-2]};\n ${u} sourceLocA = coords;\n --${c[p-1]};\n ${u} sourceLocB = coords;\n --${c[p-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),f=\".\"+d[m-1],h=d.map(R=>\"int \"+R),g=Dt(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=Dt(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=Dt(\"sourceLocB\",m-1).concat(\"inIdx.b\"),C=Dt(\"sourceLocA\",m-1).concat(\"inIdx.a\"),S=o===\"max\"?\"greaterThan\":\"lessThan\",k=n?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${C.join()})));`,_=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? 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n=[e];if(w.assertAxesAreInnerMostDims(\"arg\"+o.charAt(0).toUpperCase()+o.slice(1),n,t.shape.length),!A().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let s=[],a=r.texData.get(t.dataId),i=a!==null&&a.isPacked,p=t;i&&(p=r.unpackTensor(t),s.push(p));let[u,c]=w.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=sA(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return aA(r,t,o)}function C9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Ct({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=vh(e,p,a[0],\"max\");return u.forEach(l=>e.disposeIntermediateTensorInfo(l)),c}var iA={kernelName:Ys,backendName:\"webgl\",kernelFunc:C9};function w9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Ct({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=vh(e,p,a[0],\"min\");return u.forEach(l=>e.disposeIntermediateTensorInfo(l)),c}var uA={kernelName:Qs,backendName:\"webgl\",kernelFunc:w9};var S9=Ut+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,I9=xe({opSnippet:S9}),pA={kernelName:qo,backendName:\"webgl\",kernelFunc:I9};var v9=Ut+\"return log(x + sqrt(x * x + 1.0));\",k9=xe({opSnippet:v9}),cA={kernelName:jo,backendName:\"webgl\",kernelFunc:k9};var N9=Ut+`\n return atan(x);\n`,T9=xe({opSnippet:N9}),lA={kernelName:Xo,backendName:\"webgl\",kernelFunc:T9};var _9=Wc+`\n return atan(a, b);\n`,$9=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Xr+`\n return result;\n`,E9=nt({opSnippet:_9,packedOpSnippet:$9}),mA={kernelName:Qo,backendName:\"webgl\",kernelFunc:E9};var R9=Ut+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelName:Yo,backendName:\"webgl\",kernelFunc:D9};var Us=class{constructor(t,e,o,n=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=t.filterWidth,i=t.strideHeight,p=t.strideWidth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterHeight,m=t.effectiveFilterWidth,d=t.padInfo.top,f=t.padInfo.left;this.outputShape=t.outShape;let h=e===\"avg\",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),o){let R=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${R} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let C=\"max\",S=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(S=\"avgValue / max(count, 1.0)\");let k=Math.floor(a/4)*4,_=a%4,E=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${C}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${k}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${E}\n }\n\n int xC = xCCorner + ${k};\n if (${_===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${E}\n } else if (${_===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${E}\n } else if (${_===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${E}\n }\n }\n setOutput(${S});\n }\n `}},Iu=class{constructor(t,e,o,n=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=t.filterWidth,i=t.strideDepth,p=t.strideHeight,u=t.strideWidth,c=t.dilationDepth,l=t.dilationHeight,m=t.dilationWidth,d=t.effectiveFilterDepth,f=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let C=e===\"avg\",S=\"0.0\";if(C||(S=\"-1.0 / 1e-20\"),o){let P=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${P} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${f} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let k=\"max\",_=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(_=\"avgValue / max(count, 1.0)\");let E=Math.floor(a/4)*4,R=a%4,D=`\n if (${C}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${k}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${S};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${S});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${E}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${D}\n }\n\n int xC = xCCorner + ${E};\n if (${R===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${D}\n }\n }\n }\n setOutput(${_});\n }\n `}};function A9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t;Vs(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:e});let l=new Us(c,\"avg\",!1);return e.runWebGLProgram(l,[n],\"float32\")}var fA={kernelName:Zo,backendName:\"webgl\",kernelFunc:A9};function F9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new Iu(l,\"avg\",!1);return e.runWebGLProgram(m,[n],\"float32\")}var hA={kernelName:Zs,backendName:\"webgl\",kernelFunc:F9};var kh=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,p=t.effectiveFilterHeight,u=t.effectiveFilterWidth,c=p-1-t.padInfo.top,l=u-1-t.padInfo.left,m=1/(e*o);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${l});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${i}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},Nh=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterDepth,o=t.filterHeight,n=t.filterWidth,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterDepth,m=t.effectiveFilterHeight,d=t.effectiveFilterWidth,f=l-1-t.padInfo.front,h=m-1-t.padInfo.top,g=d-1-t.padInfo.left,x=1/(e*o*n);this.userCode=`\n const ivec3 pads = ivec3(${f}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${l};\n wD += ${p}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${d};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function P9(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Nh(m);return e.runWebGLProgram(d,[n],a.dtype)}var gA={kernelName:Ai,backendName:\"webgl\",kernelFunc:P9};function O9(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;Vs([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new kh(c);return e.runWebGLProgram(l,[n],a.dtype)}var xA={kernelName:Di,backendName:\"webgl\",kernelFunc:O9};function M9(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s}=t,{transposeA:a,transposeB:i}=o;return _p({a:n,b:s,transposeA:a,transposeB:i,backend:e})}var yA={kernelName:Jo,backendName:\"webgl\",kernelFunc:M9};var Th=class{constructor(t,e,o,n,s,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],w.assertAndGetBroadcastShape(t,e),w.assertAndGetBroadcastShape(t,o);let i=\"0.0\";n!=null&&(w.assertAndGetBroadcastShape(t,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"1.0\";s!=null&&(w.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${i};\n float scale = ${p};\n float inv = scale * inversesqrt(variance + float(${a}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var _h=class{constructor(t,e,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],w.assertAndGetBroadcastShape(t,e),w.assertAndGetBroadcastShape(t,o);let i=\"vec4(0.0)\";n!=null&&(w.assertAndGetBroadcastShape(t,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"vec4(1.0)\";s!=null&&(w.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n vec4 offset = ${i};\n vec4 scale = ${p};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var L9=({inputs:r,backend:t,attrs:e})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||n.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(i==null||n.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:p}=e;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=A().getBool(\"WEBGL_PACK_NORMALIZATION\")?new _h(o.shape,n.shape,s.shape,c,l,p):new Th(o.shape,n.shape,s.shape,c,l,p);return t.runWebGLProgram(m,u,u[0].dtype)},bA={kernelName:vn,backendName:\"webgl\",kernelFunc:L9};var $h=class{constructor(t){this.variableNames=[\"source\"],this.outputShape=t,this.rank=t.length;let e=Re(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let o=B9(this.rank),n,s=t.map((a,i)=>`sourceLoc.${bv[i]} = start[${i}] + coords.${bv[i]};`);n=`\n ${e} sourceLoc;\n ${e} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${n}\n setOutput(getSource(${o}));\n }\n `}},bv=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function B9(r){if(r===1)return\"sourceLoc\";if(r<=6)return bv.slice(0,r).map(t=>\"sourceLoc.\"+t).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var Eh=class{constructor(t){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let e=Re(this.rank),o=Dt(\"coords\",this.rank),n=Dt(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`\n result.x = ${a};\n if (++${o[this.rank-1]} < ${t[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.y = ${a};\n --${n[this.rank-1]};\n }\n `,p=this.rank===1?\"\":`\n --${o[this.rank-1]};\n if (++${o[this.rank-2]} < ${t[this.rank-2]}) {\n ++${n[this.rank-2]};\n result.z = ${a};\n if (++${o[this.rank-1]} < ${t[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.w = ${a};\n }\n }\n `,u=this.rank<=4?`sourceLoc = coords +\n ${e}(${t.map((c,l)=>`start[${l}]`).join()});`:t.map((c,l)=>`${n[l]} = ${o[l]} + start[${l}];`).join(`\n`);this.userCode=`\n void main() {\n ${e} coords = getOutputCoords();\n ${e} sourceLoc;\n ${u}\n vec4 result = vec4(0.);\n ${i}\n ${p}\n setOutput(result);\n }\n `}};function z9(r,t,e,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(e,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=e,a.dtype=r.dtype;let i=ct.computeFlatOffset(t,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function Gs(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,size:a}=o,[i,p]=ct.parseSliceParams(n,s,a);if(ct.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return e.makeTensorInfo(p,n.dtype,[]);if(e.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=e.texData.get(n.dataId),m=pD(l.values,i,p,n.shape,n.dtype);return e.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=e.texData.get(n.dataId),c=ct.isSliceContinous(n.shape,i,p);if(u||!c){let l=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new Eh(p):new $h(p),m=[i];return e.runWebGLProgram(l,[n],n.dtype,m)}return e.uploadToGPU(n.dataId),z9(n,i,p,e)}var CA={kernelName:ha,backendName:\"webgl\",kernelFunc:Gs};var V9=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:e,attrs:{shape:p}}),h=Ct({inputs:{x:f},backend:e,attrs:{perm:u}}),g=te({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Gs({inputs:{x:g},backend:e,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},wA={kernelName:Js,backendName:\"webgl\",kernelFunc:V9};function W9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=e.readSync(n.dataId),p=e.readSync(s.dataId),u=ph(i,p,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,u)}var SA={kernelName:en,backendName:\"webgl\",kernelFunc:W9};var U9=`\n int r = int(a.r) & int(b.r);\n int g = int(a.g) & int(b.g);\n int rb = int(a.b) & int(b.b);\n int ra = int(a.a) & int(b.a);\n return vec4(r, g, rb, ra);\n`,G9=`\n return float(int(a.r) & int(b.r));\n`;function H9(r){let{inputs:t,backend:e}=r,{a:o,b:n}=t,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"),a=A().getNumber(\"WEBGL_VERSION\");if(e.shouldExecuteOnCPU([o,n])||a===1){let p=e.texData.get(o.dataId).values,u=e.texData.get(n.dataId).values,[c,l]=AR(o.shape,n.shape,p,u,o.dtype),m=e.makeTensorInfo(l,o.dtype),d=e.texData.get(m.dataId);return d.values=c,m}let i;return s?i=new jr(U9,o.shape,n.shape,!1):i=new Pr(G9,o.shape,n.shape),e.runWebGLProgram(i,[o,n],o.dtype)}var IA={kernelName:ja,backendName:\"webgl\",kernelFunc:H9};function K9(r){let{inputs:t,backend:e}=r,{s0:o,s1:n}=t,s=e.readSync(o.dataId),a=e.readSync(n.dataId),i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return e.makeTensorInfo([i.length],\"int32\",Int32Array.from(i))}var vA={kernelName:ea,backendName:\"webgl\",kernelFunc:K9};var q9=\"return float(a != b);\",Cv=nt({opSnippet:q9,cpuKernelImpl:eD,dtype:\"bool\"}),kA={kernelName:Qn,backendName:\"webgl\",kernelFunc:Cv};function wi(r){let{inputs:t,backend:e}=r,{input:o}=t,n=e.texData.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.real},backend:e})}var NA={kernelName:qi,backendName:\"webgl\",kernelFunc:wi};var j9=\"return float(int(x));\";function TA(r,t){let e=new or(r.shape,j9),o=t.runWebGLProgram(e,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function wv(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return At({inputs:{x:n},backend:e});let a=Gr(n.shape),i=wv({inputs:{x:n},backend:e,attrs:{dtype:\"float32\"}}),p=Or({inputs:{real:i,imag:a},backend:e});return a.dispose(),e.disposeIntermediateTensorInfo(i),p}if(n.dtype===\"complex64\"){let a=wi({inputs:{input:n},backend:e}),i=wv({inputs:{x:a},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:e});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(e.shouldExecuteOnCPU([n])){let a=e.texData.get(n.dataId).values,[i,p,u]=FR(a,n.shape,n.dtype,s);return e.makeTensorInfo(i,p,u)}if(s===\"int32\")return TA(n,e);if(s===\"bool\"){let a=e.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=Cv({inputs:{a:n,b:a},backend:e});return e.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var _A={kernelName:bo,backendName:\"webgl\",kernelFunc:wv};var $A=\"return ceil(x);\",X9=xe({opSnippet:$A,packedOpSnippet:$A,cpuKernelImpl:PR}),EA={kernelName:tn,backendName:\"webgl\",kernelFunc:X9};var Rh=class{constructor(t){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var Dh=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function Y9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{clipValueMin:s,clipValueMax:a}=o,i;A().getBool(\"WEBGL_PACK_CLIP\")?i=new Dh(n.shape):i=new Rh(n.shape);let p=[[s],[a]];return e.runWebGLProgram(i,[n],n.dtype,p)}var RA={kernelName:Co,backendName:\"webgl\",kernelFunc:Y9};var Ah=class{constructor(t){this.variableNames=[\"real\",\"imag\"],this.outputShape=t,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). 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pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${f}) *\n getW(wR, wC, ${f}, d2);\n } else {\n dotProd +=\n getX(batch, ${f}, xR, xC) *\n getW(wR, wC, ${f}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2),\n getW(wR, wC, ${f} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1),\n getX(batch, xR, xC, ${f} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC),\n getX(batch, ${f} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${k}\n setOutput(result);\n }\n `}},Mh=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let e=t.padInfo.front,o=t.padInfo.top,n=t.padInfo.left,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.filterDepth,m=t.filterHeight,d=t.filterWidth,f=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${a}, ${i});\n const ivec3 pads = ivec3(${e}, ${o}, ${n});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${l}; wF++) {\n int xF = xFCorner + wF * ${p};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${f}) *\n getW(wF, wR, wC, ${f}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1),\n getX(batch, xF, xR, xC, ${f} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2),\n getW(wF, wR, wC, ${f} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var Kc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=t.padInfo.left,i=t.strideWidth,p=t.dilationWidth,u=t.filterHeight,c=t.filterWidth,l=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*p};\n `,i===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,p===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,p>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${i}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+l}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+l}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${n.output} = result;\n }\n `}};function Bh(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function zh({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=e.inChannels,l=p[0]*p[1]*p[2],m=e.outChannels,d=e.dataFormat===\"channelsLast\",f=!1,h=!1,g,x=[];if(s!=null){let S=Bh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Bh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((l===1||m===1)&&c>yv)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let S=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,S,e.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(Cu(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let E=te({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(E);let R=_p({a:k,b:E,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),D=o.texData.get(R.dataId);y.assert(D.isPacked,()=>\"batchMatMul result is expected to be packed\"),u.shape=_,D.shape=e.outShape,g=At({inputs:{x:R},backend:o}),g.shape=e.outShape,x.push(R)}else{let S=e.outHeight*e.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[e.batchSize,S,e.inChannels]:[e.batchSize,e.inChannels,S]}}),_=te({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}}),E=_p({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:E},backend:o,attrs:{shape:e.outShape}}),x.push(k),x.push(_),x.push(E)}for(let S of x)o.disposeIntermediateTensorInfo(S);return g}function Vh({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=e,f=d===\"channelsLast\",h=p*u*c,g=m*l,x=[e.batchSize,h,g],b=!0,C=!1,S=[];if(s!=null){let q=Bh(s.shape,f);q!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:q}}),S.push(s))}if(n!=null){let q=Bh(n.shape,f);q!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:q}}),S.push(n))}let k=te({inputs:{x:t},backend:o,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});S.push(k);let _=new Lh(x,e),E=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],R=o.runWebGLProgram(_,[r],\"float32\",E),D=te({inputs:{x:R},backend:o,attrs:{shape:x}});S.push(R),S.push(D);let P=n!=null,O=s!=null,M=i===\"leakyrelu\",L=i?Ci(i,!0):null,B=new Uc(f?D.shape:k.shape,f?k.shape:D.shape,f?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,C,P,L,O,M),z=f?[D,k]:[k,D];if(n&&z.push(n),O&&z.push(s),M){let q=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));z.push(q),S.push(q)}let U=o.runWebGLProgram(B,z,\"float32\"),j=te({inputs:{x:U},backend:o,attrs:{shape:e.outShape}});S.push(U);for(let q of S)o.disposeIntermediateTensorInfo(q);return j}function J9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))d=zh({x:n,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&l===\"channelsLast\"&&A().getBool(\"WEBGL_EXP_CONV\")){let h=new Kc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=e.runWebGLProgram(h,[n,s],\"float32\",g)}else if(A().getBool(\"WEBGL_CONV_IM2COL\"))d=Vh({x:n,filter:s,convInfo:m,backend:e});else{let h=new Hc(m);d=e.runWebGLProgram(h,[n,s],\"float32\")}let f=te({inputs:{x:d},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(d),f}var OA={kernelName:rn,backendName:\"webgl\",kernelFunc:J9};var Wh=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,o=t.strideWidth,n=t.padInfo.top,s=t.padInfo.left,a=t.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${n};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n ${a?`float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Uh=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=t.dataFormat===\"channelsLast\",i=e-1-t.padInfo.top,p=o-1-t.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${l}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n\n if (${a}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Gh=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideDepth,o=t.strideHeight,n=t.strideWidth,s=t.padInfo.front,a=t.padInfo.top,i=t.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yF = 0; yF < ${t.outDepth}; yF++) {\n int xF = wF + yF * ${e} - ${s};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${o} - ${a};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${i};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Hh=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterDepth,o=t.filterHeight,n=t.filterWidth,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=e-1-t.padInfo.front,u=o-1-t.padInfo.top,c=n-1-t.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${u}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${e}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${e} - 1 - wF;\n\n for (int wR = 0; wR < ${o}; wR++) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${o} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function eJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Wh(m);return e.runWebGLProgram(d,[n,s],\"float32\")}var MA={kernelName:Oi,backendName:\"webgl\",kernelFunc:eJ};var Kh=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=t.inShape,this.enableShapeUniforms=pt(this.outputShape.length);let e=t.filterHeight,o=t.filterWidth,n=e-1-t.padInfo.top,s=o-1-t.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${n}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n vec4 result = vec4(0.);\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n int wCPerm = ${o} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${t.outWidth}.0)\n && (fract(dyC) == 0.0);\n int idyC = int(dyC);\n\n float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${t.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n dySample : getDy(batch, idyR, idyC2, d2);\n\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n\n dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample2.xy : dySample2.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC2, d2);\n vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n }\n }\n }\n setOutput(result);\n }\n `}};function tJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l);if(A().getBool(\"WEBGL_PACK_CONV2DTRANSPOSE\")&&l===\"channelsLast\"){let d=[[m.strideHeight,m.strideWidth]],f=new Kh(m);return e.runWebGLProgram(f,[n,s],\"float32\",d)}else{let d=new Uh(m);return e.runWebGLProgram(d,[n,s],\"float32\")}}var LA={kernelName:on,backendName:\"webgl\",kernelFunc:tJ};function rJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Mh(u);return e.runWebGLProgram(c,[n,s],\"float32\")}var BA={kernelName:nn,backendName:\"webgl\",kernelFunc:rJ};function oJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Gh(u);return e.runWebGLProgram(c,[n,s],\"float32\")}var zA={kernelName:Xa,backendName:\"webgl\",kernelFunc:oJ};function nJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{pad:a,strides:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,i,1,a),c=new Hh(u);return e.runWebGLProgram(c,[n,s],\"float32\")}var VA={kernelName:sn,backendName:\"webgl\",kernelFunc:nJ};var sJ=Po+`\n return cos(x);\n`,aJ=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${Xr}\n return result;\n`,iJ=xe({opSnippet:sJ,packedOpSnippet:aJ}),WA={kernelName:an,backendName:\"webgl\",kernelFunc:iJ};var uJ=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,pJ=xe({opSnippet:uJ}),UA={kernelName:un,backendName:\"webgl\",kernelFunc:pJ};var qh=class{constructor(t,e,o,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,p,u]=t,[c]=e,[l,m]=o;this.outputShape=[c,l,m,u];let d=n===\"bilinear\"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,\"(y2-y1) * height_ratio\",`y1*${f} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${f}`],[C,S,k]=m>1?[`${(p-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${C});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${a}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${S};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${f} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${k};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${d} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var cJ=r=>{let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,c=new qh(n.shape,s.shape,i,p,u);return e.runWebGLProgram(c,[n,s,a],\"float32\")},GA={kernelName:ln,backendName:\"webgl\",kernelFunc:cJ};var Ep;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Ep||(Ep={}));var om=class{constructor(t,e,o,n){this.op=t,this.outputShape=e,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,a=this.op===Ep.Prod?\"1.0\":\"0.0\",i=o?a:`getX(${HA(s,\"coords\",this.op)})`,p=this.outputShape[this.outputShape.length-1],u=\"\",c=\"\";o?(u=n?`end != ${p-1}`:\"end != 0\",c=n?\"end + 1\":\"end - 1\"):(u=n?`end + pow2 < ${p}`:\"end >= pow2\",c=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${Re(s)} coords = getOutputCoords();\n int end = ${KA(s,\"coords\",this.op)};\n float val = ${i};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${c};\n ${KA(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${HA(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function HA(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function KA(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function jh(r,t,e,o,n,s){let a=t.shape.length,i=w.getAxesPermutation([o],a),p=t;i!=null&&(p=Ct({inputs:{x:t},backend:e,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new om(r,p.shape,!1,s),f=[[m]],h=l;l=e.runWebGLProgram(d,[l],l.dtype,f),e.disposeIntermediateTensorInfo(h)}if(n){let m=new om(r,p.shape,n,s),d=l;l=e.runWebGLProgram(m,[l],l.dtype),e.disposeIntermediateTensorInfo(d)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=Ct({inputs:{x:l},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(p),d}return l}function lJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return jh(Ep.Prod,n,e,s,a,i)}var qA={kernelName:pn,backendName:\"webgl\",kernelFunc:lJ};function mJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return jh(Ep.Sum,n,e,s,a,i)}var jA={kernelName:cn,backendName:\"webgl\",kernelFunc:mJ};function dJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=e.readSync(n.dataId),u=e.readSync(s.dataId),c=ph(p,u,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=e.bufferSync(n),u=e.bufferSync(s),c=DR(p,u,a,i);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var XA={kernelName:ra,backendName:\"webgl\",kernelFunc:dJ};var Xh=class{constructor(t,e,o){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=o,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${e};\n int offset_h = imod(h, ${e});\n int in_w = w / ${e};\n int offset_w = imod(w, ${e});\n int offset_d = (offset_h * ${e} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function fJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=new Xh(f,s,a);return e.runWebGLProgram(h,[n],n.dtype)}var YA={kernelName:mn,backendName:\"webgl\",kernelFunc:fJ};var qc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=t.filterHeight,i=t.filterWidth,p=t.outChannels/t.inChannels,u=\"\",c=\"\";o&&(n?u=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?u=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:u=`\n float activation(float x) {\n ${o}\n }\n `,c=\"result = activation(result);\");let l=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${p};\n int q = d2 - d1 * ${p};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${a}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${i}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${l}\n ${c}\n setOutput(result);\n }\n `}};var jc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=t.outChannels/t.inChannels,i=t.padInfo.left,p=t.strideWidth,u=t.dilationWidth,c=t.filterHeight,l=t.filterWidth,m=l,d=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=`\n xC = xCCorner + ${b*u};\n `,p===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,u===1&&b>0?d+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:d+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,u>1?d+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:d+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):C===1?d+=`\n xC${b+1} = xTexelC${b};\n `:d+=`\n xCOffset = xC + ${C};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=w.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;A().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new jc(l):m=new qc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return e.runWebGLProgram(m,[n,s],\"float32\",d)}var QA={kernelName:dn,backendName:\"webgl\",kernelFunc:hJ};var Yh=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,o=t.strideWidth,n=t.padInfo.top,s=t.padInfo.left,a=t.outChannels/t.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${a} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${n};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Qh=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=e-1-t.padInfo.top,i=o-1-t.padInfo.left,p=t.outChannels/t.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${p}; dm++) {\n int d2 = d1 * ${p} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function gJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Yh(l);return e.runWebGLProgram(m,[n,s],\"float32\")}var ZA={kernelName:Mi,backendName:\"webgl\",kernelFunc:gJ};function xJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Qh(l);return e.runWebGLProgram(m,[n,s],\"float32\")}var JA={kernelName:Li,backendName:\"webgl\",kernelFunc:xJ};var Zh=class{constructor(t){this.variableNames=[\"X\"],this.outputShape=[t,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function yJ(r){let{inputs:t,backend:e}=r,{x:o}=t,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:e,attrs:{shape:[s]}}),i=new Zh(s),p=e.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:e,attrs:{shape:n}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(p),u}var eF={kernelName:oa,backendName:\"webgl\",kernelFunc:yJ};var Jh=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let{inHeight:e,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=t,{top:l,left:m}=n;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${a});\n const ivec2 pads = ivec2(${l}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${i}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${e}) {\n for (int w = 0; w < ${p}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${o}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function bJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c,l=new Jh(u);c=e.runWebGLProgram(l,[n,s],\"float32\");let m=te({inputs:{x:c},backend:e,attrs:{shape:u.outShape}});return e.disposeIntermediateTensorInfo(c),m}var tF={kernelName:fn,backendName:\"webgl\",kernelFunc:bJ};function CJ(r){let{inputs:t,backend:e,attrs:o}=r,{equation:n}=o,s=t,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Tp({inputs:{x:m},backend:e,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var rF={kernelName:Vi,backendName:\"webgl\",kernelFunc:CJ};var wJ=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",SJ=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,IJ=xe({opSnippet:wJ,packedOpSnippet:SJ}),oF={kernelName:gn,backendName:\"webgl\",kernelFunc:IJ};var vJ=\"return (b >= 0.0) ? a : a * (b + 1.0);\",kJ=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,NJ=r=>{let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(kJ,o.shape,n.shape):new Pr(vJ,o.shape,n.shape);return e.runWebGLProgram(s,[o,n],o.dtype)},nF={kernelName:Ya,backendName:\"webgl\",kernelFunc:NJ};var TJ=`\n return vec4(equal(a, b));\n`,_J=\"return float(a == b);\",$J=nt({opSnippet:_J,packedOpSnippet:TJ,dtype:\"bool\",cpuKernelImpl:MR}),sF={kernelName:yn,backendName:\"webgl\",kernelFunc:$J};var EJ=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${w.ERF_P};\n float a1 = ${w.ERF_A1};\n float a2 = ${w.ERF_A2};\n float a3 = ${w.ERF_A3};\n float a4 = ${w.ERF_A4};\n float a5 = ${w.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,RJ=xe({opSnippet:EJ}),aF={kernelName:xn,backendName:\"webgl\",kernelFunc:RJ};var DJ=Po+`\n return exp(x);\n`,AJ=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Iv=xe({opSnippet:DJ,packedOpSnippet:AJ,cpuKernelImpl:LR,dtype:\"float32\"}),iF={kernelName:bn,backendName:\"webgl\",kernelFunc:Iv};function eg(r){let{inputs:t,attrs:e,backend:o}=r,{dim:n}=e,{input:s}=t,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var uF={kernelName:na,backendName:\"webgl\",kernelFunc:eg};var pF=\"return exp(x) - 1.0;\",FJ=xe({opSnippet:pF,packedOpSnippet:pF,cpuKernelImpl:BR}),cF={kernelName:Cn,backendName:\"webgl\",kernelFunc:FJ};var nm=class{constructor(t,e,o){this.variableNames=[\"real\",\"imag\"];let n=e[1];this.outputShape=e;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:\"1.0\",i;if(t===\"real\")i=\"return real * expR - imag * expI;\";else if(t===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${t}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${i}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${n});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${n}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${a};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function tg(r,t,e){let o=e.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:e,attrs:{shape:[a,s]}}),p=i.shape,u=new nm(\"real\",p,t),c=new nm(\"imag\",p,t),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=e.runWebGLProgram(u,l,\"float32\"),d=e.runWebGLProgram(c,l,\"float32\"),f=Or({inputs:{real:m,imag:d},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(f),h}function PJ(r){let{inputs:t,backend:e}=r,{input:o}=t;return tg(o,!1,e)}var lF={kernelName:Wi,backendName:\"webgl\",kernelFunc:PJ};var rg=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=t,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Si(r){let{backend:t,attrs:e}=r,{shape:o,value:n}=e,{dtype:s}=e;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),t.makeTensorInfo(o,s,a)}else{let a=new rg(o,n),i=[[n]];return t.runWebGLProgram(a,[],s,i)}}var mF={kernelName:sa,backendName:\"webgl\",kernelFunc:Si};var og=class{constructor(t){this.variableNames=[\"Image\"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${e} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${e}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var dF={kernelName:wn,backendName:\"webgl\",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,o=t,n=new og(e.shape);return o.runWebGLProgram(n,[e],e.dtype)}};var fF=\"return floor(x);\",OJ=xe({opSnippet:fF,packedOpSnippet:fF,cpuKernelImpl:zR}),hF={kernelName:Sn,backendName:\"webgl\",kernelFunc:OJ};var MJ=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,LJ=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,BJ=nt({opSnippet:MJ,packedOpSnippet:LJ,dtype:\"int32\"}),gF={kernelName:In,backendName:\"webgl\",kernelFunc:BJ};var ng=class{constructor(t){this.variableNames=[\"A\"];let e=vt(),[o,n]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);\n\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var sg=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let e=vt(),[o,n]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${n}.0, ${o}.0);\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${e.output} = result;\n }\n `}};var xF={kernelName:Mu,backendName:\"webgl\",kernelFunc:zJ},Xc,vv=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function zJ(r){let{inputs:t,backend:e,attrs:o}=r,{pixels:n}=t,{numChannels:s}=o,a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Xc==null||h!==vv)&&(vv=h,Xc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:vv})),Xc.canvas.width=p,Xc.canvas.height=u,Xc.drawImage(n,0,0,p,u),n=Xc.canvas}let m=e.makeTensorInfo(c,\"int32\");e.texData.get(m.dataId).usage=dr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),n);let d=A().getBool(\"WEBGL_PACK\")?new sg(l):new ng(l),f=e.runWebGLProgram(d,[m],\"int32\");return e.disposeData(m.dataId),f}function VJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,S=i!=null,k=d===\"leakyrelu\",_=()=>{let R=[n,s],D=(P,O)=>{if(O===\"NCHW\"&&P.shape.length===1&&P.shape[0]!==1){let M=te({inputs:{x:P},backend:e,attrs:{shape:[P.shape[0],1,1]}});return b.push(M),M}return P};if(C&&R.push(D(a,c)),S&&R.push(D(i,c)),k){let P=e.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));R.push(P),b.push(P)}return R};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=zh({x:n,filter:s,convInfo:g,backend:e,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h===\"channelsLast\"&&A().getBool(\"WEBGL_EXP_CONV\")){let R=d?Ci(d,!0):null,D=new Kc(g,C,R,S,k),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],O=_();x=e.runWebGLProgram(D,O,\"float32\",P)}else if(A().getBool(\"WEBGL_CONV_IM2COL\"))x=Vh({x:n,filter:s,convInfo:g,backend:e,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let R=d?Ci(d,!1):null,D=new Hc(g,C,R,S,k),P=_();x=e.runWebGLProgram(D,P,\"float32\")}let E=te({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach(R=>e.disposeIntermediateTensorInfo(R)),E}var yF={kernelName:vo,backendName:\"webgl\",kernelFunc:VJ};function WJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${a}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function UJ(r){let{inputs:t,backend:e}=r,{params:o,indices:n}=t,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=te({inputs:{x:n},backend:e,attrs:{shape:[u,a]}}),d=te({inputs:{x:o},backend:e,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(e.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let x=e.readSync(n.dataId),b=e.bufferSync(o),C=VR(x,b,o.dtype,u,a,c,l,o.shape,i);return e.makeTensorInfo(p,o.dtype,C.values)}let f=new ag(a,l,[u,c],o.shape),h=e.runWebGLProgram(f,[d,m],d.dtype),g=te({inputs:{x:h},backend:e,attrs:{shape:p}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var CF={kernelName:kn,backendName:\"webgl\",kernelFunc:UJ};var ig=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.outputShape=e,this.rank=e.length;let o=Re(this.rank),n=GJ(t,2);this.userCode=`\n void main() {\n ${o} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${n}));\n }\n `}};function GJ(r,t){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=0,()=>`GatherV2: the index value ${k} is not in [0, ${C-1}]`)}}let u=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=te({inputs:{x:n},backend:e,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=te({inputs:{x:s},backend:e,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(e.shouldExecuteOnCPU([n,s])||n.dtype===\"string\"){let b=e.bufferSync(d),C=e.bufferSync(m),S=WR(C,b,f);return l.forEach(k=>e.disposeIntermediateTensorInfo(k)),e.makeTensorInfo(u.outputShape,S.dtype,S.values)}let h=new ig(m.shape,f),g=e.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=te({inputs:{x:g},backend:e,attrs:{shape:u.outputShape}});return l.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var wF={kernelName:aa,backendName:\"webgl\",kernelFunc:kv};var HJ=\"return float(a > b);\",KJ=`\n return vec4(greaterThan(a, b));\n`,qJ=nt({opSnippet:HJ,packedOpSnippet:KJ,cpuKernelImpl:UR,dtype:\"bool\"}),SF={kernelName:Nn,backendName:\"webgl\",kernelFunc:qJ};var jJ=\"return float(a >= b);\",XJ=`\n return vec4(greaterThanEqual(a, b));\n`,YJ=nt({opSnippet:jJ,packedOpSnippet:XJ,dtype:\"bool\",cpuKernelImpl:GR}),IF={kernelName:Tn,backendName:\"webgl\",kernelFunc:YJ};function QJ(r){let{inputs:t,backend:e}=r,{input:o}=t;return tg(o,!0,e)}var vF={kernelName:Ui,backendName:\"webgl\",kernelFunc:QJ};var ZJ=\"return float(!isnan(x) && !isinf(x));\",JJ=xe({opSnippet:ZJ,dtype:\"bool\"}),kF={kernelName:_n,backendName:\"webgl\",kernelFunc:JJ};var eee=\"return float(isinf(x));\",tee=xe({opSnippet:eee,dtype:\"bool\"}),NF={kernelName:$n,backendName:\"webgl\",kernelFunc:tee};var ree=\"return float(isnan(x));\",oee=xe({opSnippet:ree,dtype:\"bool\"}),TF={kernelName:En,backendName:\"webgl\",kernelFunc:oee};var nee=\"return float(a < b);\",see=`\n return vec4(lessThan(a, b));\n`,aee=nt({opSnippet:nee,packedOpSnippet:see,cpuKernelImpl:HR,dtype:\"bool\"}),_F={kernelName:Dn,backendName:\"webgl\",kernelFunc:aee};var iee=\"return float(a <= b);\",uee=`\n return vec4(lessThanEqual(a, b));\n`,pee=nt({opSnippet:iee,packedOpSnippet:uee,cpuKernelImpl:KR,dtype:\"bool\"}),$F={kernelName:An,backendName:\"webgl\",kernelFunc:pee};function cee(r){let{backend:t,attrs:e}=r,{start:o,stop:n,num:s}=e,a=qR(o,n,s);return t.makeTensorInfo([a.length],\"float32\",a)}var EF={kernelName:Fn,backendName:\"webgl\",kernelFunc:cee};var lee=Po+`\n return x < 0.0 ? 0./0. : log(x);\n`,mee=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,dee=xe({opSnippet:lee,packedOpSnippet:mee,cpuKernelImpl:jR}),RF={kernelName:Pn,backendName:\"webgl\",kernelFunc:dee};var fee=Po+`\n return log(1.0 + x);\n`,hee=xe({opSnippet:fee}),DF={kernelName:On,backendName:\"webgl\",kernelFunc:hee};var gee=\"return float(a >= 1.0 && b >= 1.0);\",xee=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,yee=nt({opSnippet:gee,packedOpSnippet:xee,dtype:\"bool\"}),AF={kernelName:Mn,backendName:\"webgl\",kernelFunc:yee};var bee=\"return float(!(x >= 1.0));\",Cee=xe({opSnippet:bee}),FF={kernelName:Ln,backendName:\"webgl\",kernelFunc:Cee};var wee=\"return float(a >= 1.0 || b >= 1.0);\",See=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,Iee=nt({opSnippet:wee,packedOpSnippet:See,dtype:\"bool\"}),PF={kernelName:Bn,backendName:\"webgl\",kernelFunc:Iee};var ug=class{constructor(t,e,o,n,s){this.variableNames=[\"x\"],this.outputShape=[];let a=e,i=t[3]-1;this.outputShape=t;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${a}; j <= ${a}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${i}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${p};\n setOutput(val);\n }\n `}};var pg=class{constructor(t,e,o,n,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=e,i=t[3]-1;this.outputShape=t;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${a};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${a}; j <= ${a}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${p};\n setOutput(result);\n }\n `}};var vee=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=A().getBool(\"WEBGL_PACK_NORMALIZATION\")?new pg(n.shape,s,a,i,p):new ug(n.shape,s,a,i,p);return e.runWebGLProgram(u,[n],n.dtype)},OF={kernelName:zn,backendName:\"webgl\",kernelFunc:vee};var cg=class{constructor(t,e,o,n,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${e})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${e} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${n}) * norm + float(${o});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${n})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var kee=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new cg(n.shape,i,p,u,c);return e.runWebGLProgram(l,[n,s,a],n.dtype)},MF={kernelName:Qa,backendName:\"webgl\",kernelFunc:kee};function LF(r,t,e,o){let n=y.sizeFromShape(t),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Yr(i,r.dtype,\"max\",o),u=te({inputs:{x:p},attrs:{shape:e},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function Nv(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=e.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=e.texData.get(d.dataId).values,S=new Array(i);for(let E=0;E`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:e});let l=new Us(c,\"max\",!1);return e.runWebGLProgram(l,[n],n.dtype)}var VF={kernelName:Un,backendName:\"webgl\",kernelFunc:$ee};function Eee(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Iu(l,\"max\",!1);return e.runWebGLProgram(m,[n],n.dtype)}var WF={kernelName:ia,backendName:\"webgl\",kernelFunc:Eee};var lg=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideHeight,o=t.strideWidth,n=t.dilationHeight,s=t.effectiveFilterHeight,a=t.effectiveFilterWidth,i=s-1-t.padInfo.top,p=a-1-t.padInfo.left,u=s*a-1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${n}) {\n float dyR = float(dyRCorner + wR) / ${e}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${a}; wC++) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${a} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},mg=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideDepth,o=t.strideHeight,n=t.strideWidth,s=t.dilationDepth,a=t.dilationHeight,i=t.dilationWidth,p=t.effectiveFilterDepth,u=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=p-1-t.padInfo.front,m=u-1-t.padInfo.top,d=c-1-t.padInfo.left,f=p*u*c-1;this.userCode=`\n const ivec3 pads = ivec3(${l}, ${m}, ${d});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${e}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${i}) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${f} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Ree(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Iu(m,\"max\",!0),f=e.runWebGLProgram(d,[a],a.dtype),h=new mg(m),g=e.runWebGLProgram(h,[n,f],a.dtype);return e.disposeIntermediateTensorInfo(f),g}var UF={kernelName:Ki,backendName:\"webgl\",kernelFunc:Ree};function Dee(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s,output:a}=t,i=s;Vs([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new Us(m,\"max\",d),h=e.runWebGLProgram(f,[i],i.dtype),g=new lg(m),x=e.runWebGLProgram(g,[n,h],i.dtype);return e.disposeIntermediateTensorInfo(h),x}var GF={kernelName:Hi,backendName:\"webgl\",kernelFunc:Dee};function HF(r,t,e,o){let n=new Us(e,\"max\",!1),s=o.runWebGLProgram(n,[r],\"float32\");n=new Us(e,\"max\",!0,!0,t);let a=o.runWebGLProgram(n,[r],\"float32\");return[s,a]}var KF={kernelName:ua,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=t,p=e;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(o.shape,n,s,u,a),[l,m]=HF(o,i,c,p);return[l,m]}};function qF(r,t,e,o){let n=y.sizeFromShape(t),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Yr(i,\"float32\",\"mean\",o),u=te({inputs:{x:p},attrs:{shape:e},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}var jF={kernelName:Gn,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o}=r,{keepDims:n,axis:s}=t,a=e,i=o.shape.length,p=y.parseAxisParam(s,o.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=a.shouldExecuteOnCPU([o]),d=[],f=o;if(l){if(m){let S=a.texData.get(f.dataId).values,k=new Array(i);for(let R=0;Rc[0]+t[l]+c[1]);let n=t.length,s=Re(n),a=e.map(c=>c[0]).join(\",\"),i=e.map((c,l)=>c[0]+t[l]).join(\",\"),p=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,n),u=o===\"reflect\"?0:1;if(n===1){this.userCode=`\n int start = ${a};\n int end = ${i};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start) {\n outC = start * 2 - outC - ${u};\n } else if(outC >= end) {\n outC = (end - 1) * 2 - outC + ${u};\n }\n setOutput(getX(outC - start));\n }\n `;return}this.userCode=`\n ${s} start = ${s}(${a});\n ${s} end = ${s}(${i});\n\n void main() {\n ${s} outC = getOutputCoords();\n for (int i = 0; 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yg=class{constructor(t,e,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let n=t.length,s=Re(n),a=e.map(h=>h[0]).join(\",\"),i=e.map((h,g)=>h[0]+t[g]).join(\",\"),p=Dt(\"rc\",n),u=Dt(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1;\n if(${c}) {\n `,n===1?\"\":`}\n rc = outputLoc;\n ${p[n-2]} += 1;\n if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?\"\":` ${p[n-1]} += 1;\n if(${c}) {`],d=n===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",f=\"\";for(let h=0,g=n===1?2:4;h{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Si({backend:e,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new yg(n.shape,s,a):new xg(n.shape,s,a),p=[[a]];return e.runWebGLProgram(i,[n],n.dtype,p)},d3={kernelName:ts,backendName:\"webgl\",kernelFunc:Ev};var tte=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,rte=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = lessThan(floor(b), b);\n bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);\n `+Xr+`\n return result;\n`,ote=nt({opSnippet:tte,packedOpSnippet:rte}),f3={kernelName:rs,backendName:\"webgl\",kernelFunc:ote};function nte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o,i=n.shape.length,p=[],u=y.parseAxisParam(s,n.shape),c=u,l=w.getAxesPermutation(c,i),m=n;l!=null&&(m=Ct({inputs:{x:n},backend:e,attrs:{perm:l}}),c=w.getInnerMostAxes(c.length,i),p.push(m)),w.assertAxesAreInnerMostDims(\"prod\",c,i);let d;if(e.shouldExecuteOnCPU([m])){let f=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=tD(m.shape,m.dtype,f,c);d=e.makeTensorInfo(g,x,h)}else{let[f,h]=w.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=te({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=ni(n.dtype),C=Yr(x,b,\"prod\",e);d=te({inputs:{x:C},backend:e,attrs:{shape:f}}),p.push(x),p.push(C)}if(a){p.push(d);let f=w.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:e,attrs:{shape:f}})}return p.forEach(f=>e.disposeIntermediateTensorInfo(f)),d}var h3={kernelName:ns,backendName:\"webgl\",kernelFunc:nte};function ste(r){let{inputs:t,backend:e,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=t,{outputRaggedRank:i}=o,p=n.map(x=>e.readSync(x.dataId)),u=n.map(x=>x.shape),c=e.readSync(s.dataId),l=e.readSync(a.dataId),[m,d,f]=rD(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>e.makeTensorInfo([x.length],\"int32\",x)),g=e.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var g3={kernelName:Qp,backendName:\"webgl\",kernelFunc:ste};function ate(r){let{inputs:t,backend:e}=r,{starts:o,limits:n,deltas:s}=t,a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=oD(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=e.makeTensorInfo([u.length],\"int32\",u),m=e.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var x3={kernelName:Zp,backendName:\"webgl\",kernelFunc:ate};function ite(r){let{inputs:t,backend:e,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=t,{rowPartitionTypes:p}=o,u=e.readSync(n.dataId),c=e.readSync(s.dataId),l=e.readSync(a.dataId),m=i.map(g=>e.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=nD(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return e.makeTensorInfo(f,s.dtype,h)}var y3={kernelName:Jp,backendName:\"webgl\",kernelFunc:ite};var Rv=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=sD(o,n,s,a);return t.makeTensorInfo([i.length],a,i)},b3={kernelName:ma,backendName:\"webgl\",kernelFunc:Rv};var ute=\"return 1.0 / x;\",pte=xe({opSnippet:ute}),C3={kernelName:ss,backendName:\"webgl\",kernelFunc:pte};var cte=Ut+`\n return (x < 0.0) ? 0.0 : x;\n`,lte=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,mte=xe({opSnippet:cte,packedOpSnippet:lte}),w3={kernelName:as,backendName:\"webgl\",kernelFunc:mte};var dte=Ut+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,fte=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,hte=xe({opSnippet:dte,packedOpSnippet:fte}),S3={kernelName:ps,backendName:\"webgl\",kernelFunc:hte};var bg=class{constructor(t,e,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var Cg=class{constructor(t,e,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function gte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new Cg(n.shape,p,u,s,a):new bg(n.shape,p,u,s,a);return e.runWebGLProgram(c,[n],\"float32\")}var I3={kernelName:us,backendName:\"webgl\",kernelFunc:gte};var wg=class{constructor(t,e,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,n,s]=e,[,a,i]=t,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function xte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=new wg(s.shape,n.shape,a);return e.runWebGLProgram(i,[s],s.dtype)}var v3={kernelName:ei,backendName:\"webgl\",kernelFunc:xte};var Sg=class{constructor(t,e,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var Ig=class{constructor(t,e,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function yte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new Ig(n.shape,p,u,s,a):new Sg(n.shape,p,u,s,a);return e.runWebGLProgram(c,[n],n.dtype)}var k3={kernelName:is,backendName:\"webgl\",kernelFunc:yte};var vg=class{constructor(t,e,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,n,s]=e,[,a,i]=t,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float sourceFracRow =\n float(${p[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${p[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${n}) - 1),\n ${o} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${o} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function bte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=new vg(s.shape,n.shape,a);return e.runWebGLProgram(i,[s],s.dtype)}var N3={kernelName:Ja,backendName:\"webgl\",kernelFunc:bte};var kg=class{constructor(t,e){this.variableNames=[\"x\"];let o=t.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=t,o===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${t[0]} - coord - 1));\n }\n `;return}let n=i=>e.indexOf(i)!==-1&&t[i]!==1?`${t[i]} - coords[${i}] - 1`:`coords[${i}]`,s=t.map((i,p)=>n(p)).join(\",\"),a=Re(o);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var Ng=class{constructor(t,e){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let o=t.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=t;let n=Dt(\"rc\",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Re(o);o===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${t[0]} - rc - 1),\n ${t[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${t[0]} - (rc + 1) - 1),\n ${t[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${i} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${p(n.slice())};\n if(${s}){\n result.g = ${u(n.slice())};\n }\n if(${a}) {\n result.b = ${c(n.slice())};\n if(${s}) {\n result.a = ${l(n.slice())};\n }\n }\n setOutput(result);\n }\n `;function p(f){return m(f)}function u(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",m(f)}function c(f){return f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function l(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function m(f){let h=t.map((b,C)=>d(C,f)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return e.indexOf(f)!==-1&&t[f]!==1?`${t[f]} - ${h[f]} - 1`:`${h[f]}`}}};function Cte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return At({inputs:{x:n},backend:e});let p=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new Ng(n.shape,i):new kg(n.shape,i);return e.runWebGLProgram(p,[n],n.dtype)}var T3={kernelName:cs,backendName:\"webgl\",kernelFunc:Cte};var Tg=class{constructor(t,e){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let o=t[1],n=t[2];this.outputShape=t;let s=\"\";typeof e==\"number\"?s=`float outputValue = ${e.toFixed(2)};`:s=`\n vec3 fill = vec3(${e.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var _3={kernelName:As,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=t,i=e,p=new Tg(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var wte=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`,Ste=xe({opSnippet:wte}),$3={kernelName:ls,backendName:\"webgl\",kernelFunc:Ste};var Ite=\"return inversesqrt(x);\",vte=xe({opSnippet:Ite,cpuKernelImpl:aD}),E3={kernelName:ms,backendName:\"webgl\",kernelFunc:vte};var vu=class{constructor(t,e,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${t}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${e}; j++) {\n int index = round(${m});\n flattenedIndex += index * ${x};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${f};\n found = true;\n }\n }\n setOutput(mix(${g}, sum, float(found)));\n }\n `}};var _g=class{constructor(t,e,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\",b=e>1?\"strides[j + 1]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${t}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${e}; j+=2) {\n ivec4 index = round(${m});\n flattenedIndex += index.xz * ${x};\n if (j + 1 < ${e}) {\n flattenedIndex += index.yw * ${b};\n }\n }\n if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n vec4 updVals = ${f};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${g}, sum, found));\n }\n `}};function kte(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n,updates:s}=t,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return e.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:e,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:e,attrs:{shape:[p,u]}}),h=e.makeTensorInfo([],\"float32\",new Float32Array([0])),g;A().getBool(\"WEBGL_PACK\")?g=new _g(p,i,d.shape.length,f.shape.length,c,m):g=new vu(p,i,d.shape.length,f.shape.length,c,m);let x=e.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var R3={kernelName:ds,backendName:\"webgl\",kernelFunc:kte};var $g=class{constructor(t,e,o,n){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[t,o];let s=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,i=A().getNumber(\"WEBGL_VERSION\")===2?s:a,p=n===\"left\"?\"<\":\"<=\";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${i}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${p} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}};function Nte(r){let{inputs:t,backend:e,attrs:o}=r,{sortedSequence:n,values:s}=t,{side:a}=o,i=new $g(n.shape[0],n.shape[1],s.shape[1],a),p=[[n.shape[1]]];return e.runWebGLProgram(i,[n,s],\"int32\",p)}var D3={kernelName:hs,backendName:\"webgl\",kernelFunc:Nte};var Eg=class{constructor(t,e,o){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=e;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s=\"resRC\",n=\"resRC\";else{let i=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],p=[],u=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function Tte(r){let{inputs:t,backend:e}=r,{condition:o,t:n,e:s}=t,a=new Eg(o.shape.length,n.shape,n.shape.length);return e.runWebGLProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var A3={kernelName:fa,backendName:\"webgl\",kernelFunc:Tte};var _te=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${w.SELU_SCALEALPHA};\n float scale = ${w.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,$te=xe({opSnippet:_te}),F3={kernelName:gs,backendName:\"webgl\",kernelFunc:$te};var Ete=Po+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,Rte=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Dte=xe({opSnippet:Ete,packedOpSnippet:Rte,cpuKernelImpl:uD}),P3={kernelName:Cs,backendName:\"webgl\",kernelFunc:Dte};var Ate=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,Fte=xe({opSnippet:Ate}),O3={kernelName:bs,backendName:\"webgl\",kernelFunc:Fte};var Pte=Po+`\n return sin(x);\n`,Ote=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${Xr}\n return result;\n`,Mte=xe({opSnippet:Pte,packedOpSnippet:Ote}),M3={kernelName:xs,backendName:\"webgl\",kernelFunc:Mte};var Lte=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Bte=xe({opSnippet:Lte}),L3={kernelName:ys,backendName:\"webgl\",kernelFunc:Bte};var zte=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,Vte=xe({opSnippet:zte}),B3={kernelName:ws,backendName:\"webgl\",kernelFunc:Vte};var Wte=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xe.disposeIntermediateTensorInfo(x)),g},z3={kernelName:ga,backendName:\"webgl\",kernelFunc:Wte};function Ute(r){let{inputs:t,backend:e}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(o.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${o.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${n.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${a.shape}`);let i=e.readSync(o.dataId),p=e.readSync(n.dataId),u=e.readSync(s.dataId),c=e.readSync(a.dataId)[0],[l,m,d,f,h]=cD(i,o.shape,o.dtype,p,n.dtype,u,c);return[e.makeTensorInfo(m,o.dtype,l),e.makeTensorInfo([m[0]],n.dtype,d),e.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),e.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var V3={kernelName:ji,backendName:\"webgl\",kernelFunc:Ute};function Gte(r){let{inputs:t,backend:e}=r,{inputIndices:o,inputShape:n,newShape:s}=t;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(e.readSync(n.dataId)),i=e.readSync(o.dataId),p=Array.from(e.readSync(s.dataId)),[u,c,l]=lD(i,o.shape,o.dtype,a,p);return[e.makeTensorInfo(c,o.dtype,u),e.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var W3={kernelName:ti,backendName:\"webgl\",kernelFunc:Gte};function Hte(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=lh(a,o.shape,o.dtype,i,p,!0);return e.makeTensorInfo(c,o.dtype,u)}var U3={kernelName:ya,backendName:\"webgl\",kernelFunc:Hte};function Kte(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=lh(a,o.shape,o.dtype,i,p);return e.makeTensorInfo(c,o.dtype,u)}var G3={kernelName:ba,backendName:\"webgl\",kernelFunc:Kte};function qte(r){let{inputs:t,backend:e,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=t,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let x=e.bufferSync(n),b=e.bufferSync(s),C=y.decodeString(e.readSync(a.dataId)[0]),S=iD(x,b,i,m,c,u,p,l,C,d);return e.makeTensorInfo(i,S.dtype,S.values)}let f=new vu(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=e.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(h),g}var H3={kernelName:ks,backendName:\"webgl\",kernelFunc:qte};function jte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Gs({inputs:{x:n},backend:e,attrs:{begin:c,size:d}});return c[i]+=m,f})}var K3={kernelName:xa,backendName:\"webgl\",kernelFunc:jte};var q3=\"return sqrt(x);\",Xte=xe({opSnippet:q3,packedOpSnippet:q3,cpuKernelImpl:mD}),j3={kernelName:Ss,backendName:\"webgl\",kernelFunc:Xte};var Yte=\"return x * x;\",Qte=xe({opSnippet:Yte}),X3={kernelName:Xi,backendName:\"webgl\",kernelFunc:Qte};var Y3=\"return (a - b) * (a - b);\",Zte=nt({opSnippet:Y3,packedOpSnippet:Y3}),Q3={kernelName:Ns,backendName:\"webgl\",kernelFunc:Zte};function Jte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t;if(n.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");let s=e.readSync(n.dataId),a=w.fromUint8ToStringArray(s),i=dD(a,\"string\",o);return e.makeTensorInfo(n.shape,\"string\",i)}var Z3={kernelName:Ou,backendName:\"webgl\",kernelFunc:Jte};function ere({inputs:r,attrs:t,backend:e}){let{x:o}=r,n=Ut+`\n return x > 0.0 ? 1.0 : float(${t.alpha});\n `,s=new or(o.shape,n);return e.runWebGLProgram(s,[o],o.dtype)}var J3={kernelName:So,backendName:\"webgl\",kernelFunc:ere};var Rg=class{constructor(t,e,o){this.variableNames=[\"x\"],this.outputShape=o;let n=o.length,s=Re(o.length),a=Re(o.length),i=\"\";if(n===1)i=\"coords * strides + begin\";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${t});\n ${s} strides = ${s}(${e});\n\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${i}));\n }\n `}};function tre(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=ct.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=te({inputs:{x:n},backend:e,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let E=ct.computeOutShape(b,C,S),R=Gs({inputs:{x:n},backend:e,attrs:{begin:b,size:E}});k=te({inputs:{x:R},backend:e,attrs:{shape:f}}),e.disposeIntermediateTensorInfo(R)}else if(e.shouldExecuteOnCPU([n])){let R=e.readSync(n.dataId),D=me(n.shape,n.dtype,R),P=fD(d,D,S,b);k=e.makeTensorInfo(f,n.dtype,P.values)}else{let R=new Rg(b,S,d);k=e.runWebGLProgram(R,[n],n.dtype)}let _=te({inputs:{x:k},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(k),_}var eP={kernelName:Ts,backendName:\"webgl\",kernelFunc:tre};function rre(r){let{inputs:t,backend:e,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=t,m=e.readSync(c.dataId),d=e.readSync(l.dataId),[f,h]=hD(m,d,n,s,a,i,p,u);return[e.makeTensorInfo([f.length],\"string\",f),e.makeTensorInfo(l.shape,\"int32\",h)]}var tP={kernelName:Ca,backendName:\"webgl\",kernelFunc:rre};function ore(r){let{inputs:t,backend:e,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=t;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=e.readSync(s.dataId),p=e.readSync(a.dataId)[0],[u,c,l]=gD(i,p,n),m=c.length;return[e.makeTensorInfo([m,2],\"int32\",u),e.makeTensorInfo([m],\"string\",c),e.makeTensorInfo([2],\"int32\",new Int32Array(l))]}var rP={kernelName:Yi,backendName:\"webgl\",kernelFunc:ore};function nre(r){let{inputs:t,backend:e,attrs:o}=r,{numBuckets:n}=o,{input:s}=t;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(n<=0)throw new Error(\"Number of buckets must be at least 1\");let a=e.readSync(s.dataId),i=xD(a,n);return e.makeTensorInfo(s.shape,\"int32\",i)}var oP={kernelName:Qi,backendName:\"webgl\",kernelFunc:nre};var sre=\"return tan(x);\",are=xe({opSnippet:sre}),nP={kernelName:$s,backendName:\"webgl\",kernelFunc:are};var ire=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,ure=xe({opSnippet:ire}),sP={kernelName:Es,backendName:\"webgl\",kernelFunc:ure};function pre(r){let{inputs:t,backend:e,attrs:o}=r,{tensor:n,indices:s,updates:a}=t,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return e.makeTensorInfo(n.shape,s.dtype);let d=te({inputs:{x:s},backend:e,attrs:{shape:[p,i]}}),f=te({inputs:{x:a},backend:e,attrs:{shape:[p,u]}}),h=te({inputs:{x:n},backend:e,attrs:{shape:m}}),g=new vu(p,i,d.shape.length,f.shape.length,c,m,!1,!0),x=e.runWebGLProgram(g,[f,d,h],h.dtype),b=te({inputs:{x},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(x),b}var aP={kernelName:fs,backendName:\"webgl\",kernelFunc:pre};var Dg=class{constructor(t,e){this.variableNames=[\"A\"];let o=new Array(t.length);for(let a=0;a5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],o=[];for(let n=0;n5){let p=e.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=bD(c,s);return e.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new Dg(n.shape,s);return e.runWebGLProgram(a,[n],n.dtype)}var iP={kernelName:po,backendName:\"webgl\",kernelFunc:Dv};var Ag=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n `}},Fg=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function Rp(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function uP(r){let t=1;for(;tp){let P=e.readSync(n.dataId),[O,M]=CD(P,u,n.dtype,s,a);return[e.makeTensorInfo(O.shape,O.dtype,O.values),e.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[e.makeTensorInfo(u,n.dtype,[]),e.makeTensorInfo(u,\"int32\",[])];if(c===1)return[n,Si({attrs:{shape:u,dtype:\"int32\",value:0},backend:e})];let l=e.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?e.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=te({inputs:{x:d},attrs:{shape:[h,c]},backend:e});m&&Rp(e,d);let x=uP(s),b=uP(c),C=null,S=()=>C===null?[g,g]:[g,C],k=(P,O,M)=>{let L=S(),B=new Ag(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[P],[O]],j=C;C=e.runWebGLProgram(B,L,\"int32\",U),Rp(e,j)};for(let P=1;P=1;M/=2)k(O,M,[h,b])}for(let P=b;P>x;P/=2){let O=S(),M=new Fg([h,P/2]),B=[[c],[C===null?1:0],[x]],z=C;C=e.runWebGLProgram(M,O,\"int32\",B),Rp(e,z);let U=x/2,j=U*2;for(let q=U;q>=1;q/=2)k(j,q,C.shape)}let _=C;C=Gs({inputs:{x:C},backend:e,attrs:{begin:0,size:[h,s]}}),Rp(e,_);let E=kv({inputs:{x:g,indices:C},backend:e,attrs:{axis:1,batchDims:1}});Rp(e,g);let R=u.slice(0,-1);R.push(s),_=C,C=te({inputs:{x:C},attrs:{shape:R},backend:e}),Rp(e,_);let D=E;return E=te({inputs:{x:E},attrs:{shape:R},backend:e}),Rp(e,D),[E,C]}var pP={kernelName:Rs,backendName:\"webgl\",kernelFunc:lre};var Pg=class{constructor(t,e,o,n,s,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=o===\"nearest\"?1:2,p;switch(n){case\"constant\":p=1;break;case\"reflect\":p=2;break;case\"wrap\":p=3;break;case\"nearest\":p=4;break;default:p=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${p} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = getTransforms(batch, 2);\n float b1 = getTransforms(batch, 3);\n float b2 = getTransforms(batch, 4);\n float b3 = getTransforms(batch, 5);\n float c1 = getTransforms(batch, 6);\n float c2 = getTransforms(batch, 7);\n float projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = float(${s});\n } else {\n float inX = (a1 * xf + a2 * yf + a3) / projection;\n float inY = (b1 * xf + b2 * yf + b3) / projection;\n float mapX = mapCoord(inX, float(${e}));\n float mapY = mapCoord(inY, float(${t}));\n\n if (${i} == 1) {\n int coordY = int(round(mapY));\n int coordX = int(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n float yFloor = floor(mapY);\n float xFloor = floor(mapX);\n float yCeil = yFloor + 1.0;\n float xCeil = xFloor + 1.0;\n float valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n float valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function mre(r){let{inputs:t,backend:e,attrs:o}=r,{image:n,transforms:s}=t,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Pg(l,m,a,i,p,g);return e.runWebGLProgram(x,[n,s],\"float32\")}var cP={kernelName:Ds,backendName:\"webgl\",kernelFunc:mre};function dre(r){let{inputs:t,attrs:e,backend:o}=r,{axis:n}=e,{x:s}=t;Vs(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let a=o.readSync(s.dataId),{outputValues:i,outputShape:p,indices:u}=wD(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],\"int32\",u)]}var lP={kernelName:Zi,backendName:\"webgl\",kernelFunc:dre};function fre(r){let{inputs:t,backend:e,attrs:o}=r,{value:n}=t,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;he.disposeIntermediateTensorInfo(h)),f}var mP={kernelName:wa,backendName:\"webgl\",kernelFunc:fre};var Og=class{constructor(t,e){this.variableNames=[\"x\",\"segmentIds\"];let o=t.windowSize,n=t.batchSize,s=t.inSize,a=t.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p=\"0.0\",u=\"sumValue\",c=Math.floor(o/4)*4,l=o%4,m=`\n sumValue += dot(values, segFilter);\n `,d=\"\";s%o>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${p};\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${f}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${a})) * float(${o}));\n int currentSeg = int(mod(float(outIdx), float(${a})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function hre(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,segmentIds:s}=t,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=Ct({inputs:{x:n},backend:e,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=te({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});p.push(f);let h=ni(n.dtype),g=(S,k,_,E,R)=>{let D=S.shape[0],P=S.shape[1],O=w.segment_util.segOpComputeOptimalWindowSize(P,R),M={windowSize:O,inSize:P,batchSize:D,numSegments:R},L=new Og(M,k),B=e.compileAndRun(L,[S,_],E);if(p.push(B),B.shape[1]===R)return B;let z=Rv({backend:e,attrs:{start:0,stop:R,step:1,dtype:\"float32\"}}),U=Dv({inputs:{x:z},backend:e,attrs:{reps:[P/O]}});return p.push(z),p.push(U),g(B,k,U,E,R)},x=g(f,\"unsortedSegmentSum\",s,h,a),b=te({inputs:{x},backend:e,attrs:{shape:m}}),C=b;if(c!=null){p.push(b);let S=w.getUndoAxesPermutation(c);C=Ct({inputs:{x:C},backend:e,attrs:{perm:S}})}return p.forEach(S=>e.disposeIntermediateTensorInfo(S)),C}var dP={kernelName:Ji,backendName:\"webgl\",kernelFunc:hre};var gre=[XD,QD,ZD,JD,tA,rA,oA,nA,iA,uA,pA,cA,lA,mA,dA,fA,hA,gA,xA,yA,bA,wA,SA,IA,vA,_A,EA,RA,BD,AA,PA,OA,MA,LA,BA,zA,VA,WA,UA,GA,qA,jA,XA,YA,QA,ZA,JA,eF,tF,rF,oF,nF,sF,aF,iF,uF,cF,lF,mF,dF,hF,gF,xF,yF,bF,CF,wF,SF,IF,LD,vF,FA,kF,NF,TF,zD,_F,$F,EF,RF,DF,AF,FF,PF,OF,MF,BF,zF,VF,WF,UF,GF,KF,jF,XF,YF,QF,ZF,o3,UD,n3,s3,a3,i3,kA,u3,l3,m3,d3,f3,VD,h3,g3,x3,y3,b3,NA,JF,C3,w3,S3,HD,I3,v3,k3,N3,T3,_3,$3,E3,R3,D3,A3,F3,P3,O3,M3,L3,CA,r3,B3,z3,V3,W3,U3,G3,H3,K3,j3,X3,Q3,Z3,J3,eP,tP,rP,oP,t3,qD,nP,sP,aP,iP,pP,cP,jD,lP,mP,dP,p3];for(let r of gre)ri(r);var we;(function(r){r[r.float32=0]=\"float32\",r[r.int32=1]=\"int32\",r[r.bool=2]=\"bool\",r[r.string=3]=\"string\",r[r.complex64=4]=\"complex64\"})(we||(we={}));var ku;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(ku||(ku={}));var fP;function xre(r){fP=r.wasm.cwrap(Io,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function yre(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t;if(n.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o,m=e.dataIdMap.get(n.dataId).id,d=e.dataIdMap.get(s.dataId).id,f=0;if(a!=null){let R=e.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let h=i==null?0:e.dataIdMap.get(i.dataId).id,g=ku[c];if(g==null)throw new Error(`${c} 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n(a){o=a.wasm.cwrap(r,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function s(a){let{backend:i,inputs:p}=a,{a:u,b:c}=p,l=i.dataIdMap.get(u.dataId).id,m=i.dataIdMap.get(c.dataId).id,d=e!=null?e:u.dtype,f=w.assertAndGetBroadcastShape(u.shape,c.shape),h=i.makeOutput(f,d);if(y.sizeFromShape(f)===0)return h;let g=new Uint8Array(new Int32Array(u.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=i.dataIdMap.get(h.dataId).id;return(()=>o(l,g,u.shape.length,m,x,c.shape.length,we[u.dtype],b))(),h}return{kernelName:r,backendName:\"wasm\",setupFunc:n,kernelFunc:s}}var bre=!0,bP=Ue(uo,bre);var CP;function Cre(r){CP=r.wasm.cwrap(Go,null,[\"array\",\"number\",\"number\",\"number\"])}function wre(r){let{inputs:t,backend:e}=r,o=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(o.shape)===0)return o;let n=t.map(i=>e.dataIdMap.get(i.dataId).id),s=new Uint8Array(new Int32Array(n).buffer),a=e.dataIdMap.get(o.dataId).id;return CP(s,n.length,we[o.dtype],a),o}var wP={kernelName:Go,backendName:\"wasm\",setupFunc:Cre,kernelFunc:wre};function Dp(r){let{inputs:{x:t},backend:e}=r;if(t.dtype===\"string\")return ur(e.readSync(t.dataId),t.shape,t.dtype);let o=e.makeOutput(t.shape,t.dtype),n=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(o).set(n),o}var SP={kernelName:wo,backendName:\"wasm\",kernelFunc:Dp};var IP;function Sre(r){IP=r.wasm.cwrap(co,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function go(r){let{inputs:t,backend:e,attrs:o}=r,[n,s]=vre(t.x.shape,o.perm),a=!0;for(let f=0;f=n&&(s===-1||o[s]>o[a])&&(s=a);o[s]=n}return[e,o]}var vP={kernelName:co,backendName:\"wasm\",kernelFunc:go,setupFunc:Sre};function _r(r,t,e){let o=r.shape,n=r.shape.length,s=y.parseAxisParam(t,o),a=s,i=w.getAxesPermutation(a,n),p=null,u=!1;if(i!=null){let c=new Array(n);for(let d=0;d`new shape: ${a}, old shape: ${o.shape}. 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JP={kernelName:bo,backendName:\"wasm\",kernelFunc:Mr};var eO=he(tn);var tO;function qre(r){tO=r.wasm.cwrap(Co,null,[\"number\",\"number\",\"number\",\"number\"])}function jre(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{clipValueMin:s,clipValueMax:a}=o,i=e.dataIdMap.get(n.dataId).id,p=e.makeOutput(n.shape,n.dtype),u=e.dataIdMap.get(p.dataId).id;return tO(i,s,a,u),p}var rO={kernelName:Co,backendName:\"wasm\",setupFunc:qre,kernelFunc:jre};function Av(r){let{inputs:t,backend:e}=r,o=y.parseAxisParam(r.attrs.axis,t[0].shape)[0],n=t.map(d=>d.shape);w.assertParamsConsistent(n,o);let s=w.computeOutShape(t.map(d=>d.shape),o),a=t.filter(d=>y.sizeFromShape(d.shape)>0);if(a.length===1)return Dp({inputs:{x:a[0]},backend:e});let i=e.makeOutput(s,t[0].dtype);if(y.sizeFromShape(s)===0)return i;if(a[0].dtype===\"string\"){let d=a.map(C=>{let k=[-1,y.sizeFromShape(C.shape.slice(o))];return 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goe(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s}=t,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l}=e,m=u==null?[1,1]:u,d=w.computeConv2DInfo(n.shape,s.shape,p,m,c,l,!0),f=d.filterHeight,h=d.filterWidth,g=d.padInfo.top,x=d.padInfo.right,b=d.padInfo.bottom,C=d.padInfo.left,S=d.dilationHeight,k=d.dilationWidth,_=d.strideHeight,E=d.strideWidth,R=d.inChannels,D=d.outChannels,P=d.padInfo.type===\"SAME\"?1:0;if(d.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. 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Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=e.makeOutput(s.shape,s.dtype);return DO(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var AO={kernelName:zi,backendName:\"wasm\",setupFunc:woe,kernelFunc:Soe};var FO;function Ioe(r){FO=r.wasm.cwrap(Bi,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function voe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=e.makeOutput(n.shape,n.dtype);return FO(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var PO={kernelName:Bi,backendName:\"wasm\",setupFunc:Ioe,kernelFunc:voe};var OO=he(gn);var MO;function koe(r){MO=r.wasm.cwrap(Ya,null,[\"number\",\"number\",\"number\"])}function Noe(r){let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=e.makeOutput(n.shape,\"float32\"),a=i=>e.dataIdMap.get(i.dataId).id;return MO(a(n),a(o),a(s)),s}var LO={kernelName:Ya,backendName:\"wasm\",setupFunc:koe,kernelFunc:Noe};var Toe=!1,BO=Ue(yn,Toe,\"bool\");var zO=he(xn);var VO=he(bn,\"float32\");function Lg(r){let{inputs:t,attrs:e,backend:o}=r,{input:n}=t,{dim:s}=e,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),Vt({inputs:{x:n},backend:o,attrs:{shape:i}})}var WO={kernelName:na,backendName:\"wasm\",kernelFunc:Lg};var UO=he(Cn,\"float32\");function Pv(r){let{attrs:{shape:t,value:e,dtype:o},backend:n}=r,s=n.makeOutput(t,o);return n.typedArrayFromHeap(s).fill(e),s}var GO={kernelName:sa,backendName:\"wasm\",kernelFunc:Pv};var HO;function _oe(r){HO=r.wasm.cwrap(wn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function $oe(r){let{inputs:t,backend:e}=r,{image:o}=t,n=e.makeOutput(o.shape,o.dtype),s=e.dataIdMap.get(o.dataId).id,a=e.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return HO(s,i,p,u,c,a),n}var KO={kernelName:wn,backendName:\"wasm\",kernelFunc:$oe,setupFunc:_oe};var qO=he(Sn);var Eoe=!1,jO=Ue(In,Eoe);var XO;function Roe(r){XO=r.wasm.cwrap(vn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Doe(r){let{backend:t,inputs:e,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=e,c=t.dataIdMap.get(s.dataId).id,l=t.dataIdMap.get(a.dataId).id,m=t.dataIdMap.get(i.dataId).id,d=p!=null?t.dataIdMap.get(p.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return XO(c,l,m,d,f,n,g),h}var YO={kernelName:vn,backendName:\"wasm\",setupFunc:Roe,kernelFunc:Doe};var QO;function Aoe(r){QO=r.wasm.cwrap(vo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Foe(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=e,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=ku[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return QO(x,j,q,Y,b,k,_,S,E,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var ZO={kernelName:vo,backendName:\"wasm\",setupFunc:Aoe,kernelFunc:Foe};var JO;function Poe(r){JO=r.wasm.cwrap(ko,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ooe(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=e,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m,!0),g=ku[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return JO(x,j,q,Y,b,k,_,S,E,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var eM={kernelName:ko,backendName:\"wasm\",setupFunc:Poe,kernelFunc:Ooe};var tM;function Moe(r){tM=r.wasm.cwrap(kn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Loe(r){let{backend:t,inputs:e}=r,{params:o,indices:n}=e,[s,a,i,p]=af.prepareAndValidate(o,n),u=t.makeOutput(s,o.dtype);if(a===0)return u;let c=n.shape,l=c[c.length-1],d=t.dataIdMap.get(o.dataId).id,h=t.dataIdMap.get(n.dataId).id,g=new Uint8Array(new Int32Array(p).buffer),x=t.dataIdMap.get(u.dataId).id;return tM(d,we[o.dtype],h,a,l,i,g,x),u}var rM={kernelName:kn,backendName:\"wasm\",setupFunc:Moe,kernelFunc:Loe};var oM;function Boe(r){oM=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function zoe(r){let{backend:t,inputs:e,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=t.readSync(s.dataId),c=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let l=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=Vt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:t}),d=y.sizeFromShape(s.shape),f=Vt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:t}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=t.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,C=t.dataIdMap.get(m.dataId).id,k=t.dataIdMap.get(f.dataId).id,_=t.dataIdMap.get(g.dataId).id,E=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return oM(C,we[n.dtype],E,x,k,l.batchSize,R,_),t.disposeData(m.dataId),t.disposeData(f.dataId),g.shape=l.outputShape,g}var nM={kernelName:aa,backendName:\"wasm\",setupFunc:Boe,kernelFunc:zoe};var Voe=!1,sM=Ue(Nn,Voe,\"bool\");var Woe=!1,aM=Ue(Tn,Woe,\"bool\");var iM=he(_n,\"bool\");var uM=he($n,\"bool\");var pM=he(En,\"bool\");var cM;function Uoe(r){cM=r.wasm.cwrap(Rn,null,[\"number\",\"number\",\"number\",\"number\"])}function Goe(r){let{inputs:{x:t},attrs:{alpha:e},backend:o}=r,n=o.dataIdMap.get(t.dataId).id,s=o.makeOutput(t.shape,\"float32\");if(y.sizeFromShape(t.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;cM(n,we[t.dtype],e,a)}return s}var lM={kernelName:Rn,backendName:\"wasm\",setupFunc:Uoe,kernelFunc:Goe};var Hoe=!1,mM=Ue(Dn,Hoe,\"bool\");var Koe=!1,dM=Ue(An,Koe,\"bool\");var fM;function qoe(r){fM=r.wasm.cwrap(Fn,null,[\"number\",\"number\",\"number\",\"number\"])}function joe(r){let{attrs:t,backend:e}=r,{start:o,stop:n,num:s}=t,a=Math.floor(s),i=e.makeOutput([a],\"float32\");return fM(e.dataIdMap.get(i.dataId).id,o,n,a),i}var hM={kernelName:Fn,backendName:\"wasm\",setupFunc:qoe,kernelFunc:joe};var gM=he(Pn);var xM=he(On);var Xoe=!1,yM=Ue(Mn,Xoe,\"bool\");var bM=he(Ln);var Yoe=!1,CM=Ue(Bn,Yoe,\"bool\");var Qoe=!1,wM=Ue($0,Qoe,\"bool\");var SM;function Zoe(r){SM=r.wasm.cwrap(zn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Joe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o;if(n.dtype!==\"float32\")throw new Error(\"LRN error: x must have dtype float32\");let u=e.makeOutput(n.shape,n.dtype);return SM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var IM={kernelName:zn,backendName:\"wasm\",setupFunc:Zoe,kernelFunc:Joe};var vM;function ene(r){vM=r.wasm.cwrap(Qa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function tne(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o;if(n.dtype!==\"float32\"||s.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");let l=e.makeOutput(n.shape,n.dtype);return vM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,a.shape[3],i,p,u,c),l}var kM={kernelName:Qa,backendName:\"wasm\",setupFunc:ene,kernelFunc:tne};var NM;function rne(r){NM=r.wasm.cwrap(Vn,null,[\"number\",\"number\",\"number\",\"number\"])}function one(r){let{backend:t,inputs:e,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=e,p=t.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,t);if(d){let C=t.dataIdMap.get(c.dataId).id;u=c,p=C}let f=u.shape.length;w.assertAxesAreInnerMostDims(\"max\",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=t.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let C=t.dataIdMap.get(b.dataId).id;NM(p,we[a.dtype],x,C)}if(d&&t.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var TM={kernelName:Vn,backendName:\"wasm\",setupFunc:rne,kernelFunc:one};var nne=!1,_M=Ue(Wn,nne);var $M;function sne(r){$M=r.wasm.cwrap(Un,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ane(r){let{inputs:t,attrs:e,backend:o}=r,n=t.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=e,c=w.computePool2DInfo(n.shape,a,i,1,p,u),l=c.filterHeight,m=c.filterWidth,d=c.padInfo.top,f=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,C=c.strideHeight,S=c.strideWidth,k=c.inChannels,_=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let E=o.makeOutput(c.outShape,\"float32\"),R=o.dataIdMap.get(E.dataId).id;return $M(s,n.shape[0],n.shape[1],n.shape[2],l,m,d,f,h,g,x,b,C,S,k,_,R),E}var EM={kernelName:Un,backendName:\"wasm\",setupFunc:sne,kernelFunc:ane};var RM;function ine(r){RM=r.wasm.cwrap(\"MaxPool3D\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function une(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=e.makeOutput(c.outShape,n.dtype);return RM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var DM={kernelName:ia,backendName:\"wasm\",setupFunc:ine,kernelFunc:une};var AM;function pne(r){AM=r.wasm.cwrap(\"MaxPool3DGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function cne(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=e.makeOutput(s.shape,s.dtype);return AM(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var FM={kernelName:Ki,backendName:\"wasm\",setupFunc:pne,kernelFunc:cne};var PM;function lne(r){PM=r.wasm.cwrap(\"MaxPoolGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function mne(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool2DInfo(s.shape,a,i,1,p,u),l=e.makeOutput(s.shape,s.dtype);return PM(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),l}var OM={kernelName:Hi,backendName:\"wasm\",setupFunc:lne,kernelFunc:mne};var MM;function dne(r){MM=r.wasm.cwrap(\"MaxPoolWithArgmax\",null,[\"number\",\"number\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function fne(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,includeBatchInIndex:p}=o;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,[1,1],i),l=e.makeOutput(c.outShape,n.dtype),m=e.makeOutput(c.outShape,\"int32\");return MM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,e.dataIdMap.get(m.dataId).id,we[n.dtype],p,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[l,m]}var LM={kernelName:ua,backendName:\"wasm\",setupFunc:dne,kernelFunc:fne};var BM;function hne(r){BM=r.wasm.cwrap(Gn,null,[\"number, number, number\"])}function gne(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,t),f=l;if(d){let S=t.dataIdMap.get(c.dataId).id;S!==i&&(u=c,p=S,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"mean\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!==\"float32\"&&(b=Mr({backend:t,inputs:{x:u},attrs:{dtype:\"float32\"}}),p=t.dataIdMap.get(b.dataId).id);let C=t.makeOutput(h,\"float32\");if(y.sizeFromShape(u.shape)!==0){let S=t.dataIdMap.get(C.dataId).id;BM(p,x,S)}if(d&&t.disposeData(c.dataId),s){let S=w.expandShapeToKeepDim(C.shape,m);C.shape=S}return u.dtype!==\"float32\"&&t.disposeData(b.dataId),C}var zM={kernelName:Gn,backendName:\"wasm\",setupFunc:hne,kernelFunc:gne};var VM;function xne(r){VM=r.wasm.cwrap(Hn,null,[\"number\",\"number\",\"number\",\"number\"])}function yne(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,t);if(d){let 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dL={kernelName:os,backendName:\"wasm\",setupFunc:zne,kernelFunc:Vne};var fL;function Wne(r){fL=r.wasm.cwrap(ns,null,[\"number\",\"number\",\"number\",\"number\"])}function Une(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,t),f=l;if(d){let C=t.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"prod\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=t.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=t.dataIdMap.get(b.dataId).id;fL(p,x,we[b.dtype],C)}if(d&&t.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var hL={kernelName:ns,backendName:\"wasm\",setupFunc:Wne,kernelFunc:Une};var Gne=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=fp(o,n,s,a),p=t.makeOutput([i.length],a);return 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_L={kernelName:Ja,backendName:\"wasm\",setupFunc:Zne,kernelFunc:Jne};var $L;function ese(r){$L=r.wasm.cwrap(cs,null,[\"number\",\"array\",\"number\",\"array\",\"number\",\"number\"])}function tse(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return Dp({inputs:{x:n},backend:e});let i=e.makeOutput(n.shape,n.dtype),p=e.dataIdMap.get(n.dataId).id,u=e.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),l=new Uint8Array(new Int32Array(n.shape).buffer);$L(p,c,a.length,l,n.shape.length,u);let m=Vt({inputs:{x:i},attrs:{shape:n.shape},backend:e});return e.disposeData(i.dataId),m}var EL={kernelName:cs,backendName:\"wasm\",kernelFunc:tse,setupFunc:ese};var RL;function rse(r){RL=r.wasm.cwrap(As,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\"])}function ose(r){let{inputs:t,backend:e,attrs:o}=r,{image:n}=t,{radians:s,fillValue:a,center:i}=o,p=e.makeOutput(n.shape,n.dtype),u=e.dataIdMap.get(n.dataId).id,c=e.dataIdMap.get(p.dataId).id,[l,m,d,f]=n.shape,[h,g]=w.getImageCenter(i,m,d),x=a===0,b=255,C=typeof a==\"number\"?[a,a,a,x?0:b]:[...a,b],S=new Uint8Array(new Int32Array(C).buffer);return RL(u,l,m,d,f,s,h,g,S,C.length,c),p}var DL={kernelName:As,backendName:\"wasm\",kernelFunc:ose,setupFunc:rse};var AL=he(ls);var FL=he(ms);var PL;function nse(r){PL=r.wasm.cwrap(ds,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\"])}function sse(r){let{backend:t,inputs:e,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,i=t.makeOutput(a,s.dtype);if(y.sizeFromShape(a)===0)return i;let{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=hu.calculateShapes(s,n,a),f=t.dataIdMap.get(n.dataId).id,g=t.dataIdMap.get(s.dataId).id,x=new Uint8Array(new 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lse(r){let{backend:t,inputs:{x:e}}=r,o=t.dataIdMap.get(e.dataId).id,n=t.makeOutput(e.shape,e.dtype),s=t.dataIdMap.get(n.dataId).id;return y.sizeFromShape(n.shape)===0||WL(o,s),n}var UL={kernelName:\"Sigmoid\",backendName:\"wasm\",setupFunc:cse,kernelFunc:lse};var GL=he(bs);var HL=he(xs);var KL=he(ys);var qL=he(ws);function mse(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,paddings:a}=o,i=y.sizeFromShape(s),p=[[0,0]];p.push(...a);for(let _=1+s.length;_0?p+1:0;if(c<0)throw new Error(w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let l=n.shape.slice();l[0]=c;let m=e.dataIdMap.get(n.dataId).id,d=e.dataIdMap.get(s.dataId).id,f=e.dataIdMap.get(a.dataId).id,h=e.makeOutput(l,n.dtype),g=e.dataIdMap.get(h.dataId).id,x=e.makeOutput([4],\"int32\"),b=e.dataIdMap.get(x.dataId).id;JL(m,we[n.dtype],n.shape[0],d,f,g,b,t,0);let C=e.readSync(x.dataId),S;switch(C[0]){case 0:{S=w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 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xB={kernelName:Is,backendName:\"wasm\",setupFunc:Rse,kernelFunc:Dse};var yB=he($s);var bB=he(Es);var CB;function Ase(r){CB=r.wasm.cwrap(fs,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function Fse(r){let{backend:t,inputs:e,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,i=t.makeOutput(n.shape,n.dtype);if(y.sizeFromShape(n.shape)===0)return i;let{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=hu.calculateShapes(a,s,n.shape),f=t.dataIdMap.get(s.dataId).id,g=t.dataIdMap.get(a.dataId).id,b=t.dataIdMap.get(n.dataId).id,C=new Uint8Array(new Int32Array(l).buffer),S=t.dataIdMap.get(i.dataId).id;return CB(f,g,we[a.dtype],p,u,c,C,m,S,b),i}var wB={kernelName:fs,backendName:\"wasm\",setupFunc:Ase,kernelFunc:Fse};var SB;function Pse(r){SB=r.wasm.cwrap(po,null,[\"number\",\"array\",\"number\",\"array\",\"number\",\"number\"])}function 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e=this.dataIdMap.get(t);e!=null&&e.refCount++}floatPrecision(){return 32}getMemoryOffset(t){return this.dataIdMap.get(t).memoryOffset}dispose(){this.wasm.tfjs.dispose(),\"PThread\"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(t,e,o,n){let s;if(o==null)s=this.write(n!=null?n:null,t,e);else{let a=this.dataIdNextNumber++;s={id:a},this.dataIdMap.set(s,{id:a,memoryOffset:o,shape:t,dtype:e,refCount:1});let i=y.sizeFromShape(t);this.wasm.tfjs.registerTensor(a,i,o)}return{dataId:s,shape:t,dtype:e}}typedArrayFromHeap({shape:t,dtype:e,dataId:o}){let n=this.wasm.HEAPU8.buffer,{memoryOffset:s}=this.dataIdMap.get(o),a=y.sizeFromShape(t);switch(e){case\"float32\":return new Float32Array(n,s,a);case\"int32\":return new Int32Array(n,s,a);case\"bool\":return new Uint8Array(n,s,a);default:throw new Error(`Unknown dtype ${e}`)}}};function Kse(r){return(t,e)=>(y.fetch(r,{credentials:\"same-origin\"}).then(o=>{o.ok||t.env.a(`failed to load wasm binary file at '${r}'`),o.arrayBuffer().then(n=>{WebAssembly.instantiate(n,t).then(s=>{e(s.instance,s.module)})})}),{})}function BB(r,t,e){if(Gg!=null)return Gg;let o=\"tfjs-backend-wasm.wasm\";return r&&t?o=\"tfjs-backend-wasm-threaded-simd.wasm\":r&&(o=\"tfjs-backend-wasm-simd.wasm\"),im!=null&&im[o]!=null?im[o]:e+o}async function VB(){let[r,t]=await Promise.all([A().getAsync(\"WASM_HAS_SIMD_SUPPORT\"),A().getAsync(\"WASM_HAS_MULTITHREAD_SUPPORT\")]);return new Promise((e,o)=>{let n={};n.locateFile=(i,p)=>{if(i.endsWith(\".worker.js\")){let u=zB.wasmWorkerContents.replace(/\\n/g,\"\\\\n\"),c=new Blob([u],{type:\"application/javascript\"});return URL.createObjectURL(c)}return i.endsWith(\".wasm\")?BB(r,t,am!=null?am:p):p+i},jv&&(n.instantiateWasm=Kse(BB(r,t,am!=null?am:\"\")));let s=!1;n.onAbort=()=>{if(s||um)return;um=!0,o({message:\"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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xo=A();xo.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\",()=>15);xo.registerFlag(\"WEBGPU_CPU_FORWARD\",()=>!0);xo.registerFlag(\"WEBGPU_MATMUL_PROGRAM_TYPE\",()=>-1);xo.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\",()=>!0);xo.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\",()=>!1);xo.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e3);xo.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\",()=>!1);xo.registerFlag(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\",()=>!0);xo.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\",()=>!1);xo.registerFlag(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\",()=>-1);xo.registerFlag(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\",()=>!1);xo.registerFlag(\"WEBGPU_PRINT_SHADER\",()=>\"\");xo.registerFlag(\"WEBGPU_ENGINE_COMPILE_ONLY\",()=>!1);var Hg=class{constructor(t){t&&(this.vendor=t.vendor,this.architecture=t.architecture,this.intelGPUGeneration=this.getIntelGPUGeneration())}getIntelGPUGeneration(){if(this.isIntel()){if(this.architecture.startsWith(\"gen\"))return Number(this.architecture.match(/\\d+/));if(this.architecture.startsWith(\"xe\"))return 12}return 0}isIntel(){return this.vendor===\"intel\"}};var Kg=class{constructor(t){this.device=t,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(t,e,o=!1,n=!0){let s,a=UB(t,e);return n?(this.freeBuffers.has(a)||this.freeBuffers.set(a,[]),this.freeBuffers.get(a).length>0?(s=this.freeBuffers.get(a).pop(),this.numFreeBuffers--):(s=this.device.createBuffer({size:t,usage:e,mappedAtCreation:o}),this.numBytesAllocated+=t)):(s=this.device.createBuffer({size:t,usage:e,mappedAtCreation:o}),this.numBytesAllocated+=t),this.usedBuffers.has(a)||this.usedBuffers.set(a,[]),this.usedBuffers.get(a).push(s),this.numUsedBuffers++,this.numBytesUsed+=t,s}releaseBuffer(t,e=!0){if(this.freeBuffers.size===0)return;let o=t.size,n=t.usage,s=UB(o,n),a=this.usedBuffers.get(s),i=a.indexOf(t);if(i<0)throw new Error(\"Cannot find the buffer in buffer manager\");a[i]=a[a.length-1],a.pop(),this.numUsedBuffers--,this.numBytesUsed-=o,e?(this.freeBuffers.get(s).push(t),this.numFreeBuffers++):(t.destroy(),this.numBytesAllocated-=o)}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((t,e)=>{t.forEach(o=>{o.destroy()})}),this.usedBuffers.forEach((t,e)=>{t.forEach(o=>{o.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function UB(r,t){return`${r}_${t}`}var qg=class{constructor(t){this.device=t,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(t,e,o,n){let s=HB(o),a=t*e*s,i=GB(t,e,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[t,e],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(t){if(this.freeTextures.size===0)return;let e=t.width,o=t.height,n=t.format,s=t.usage,a=GB(e,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(t),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(t);if(p<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");i.splice(p,1);let u=HB(n),c=e*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((t,e)=>{t.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((t,e)=>{t.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function GB(r,t,e,o){return`${r}_${t}_${e}_${o}`}function HB(r){if(r===\"rgba8unorm\")return 16;throw new Error(`${r} is not supported!`)}function KB(r,t){if(Math.max(...r)>5)throw new Error(\"Cannot symbolically compute strides for rank > 6 tensor.\");let e=r.length,o=\"xyzwuv\",n=r.map(a=>`${t}.${o[a]}`),s=new Array(e-1);s[e-2]=n[e-1];for(let a=e-3;a>=0;--a)s[a]=`(${s[a+1]} * ${n[a+1]})`;return s}var Qr=(r,t,e)=>e===\"int32\"?`atomicAdd(${r}, bitcast(${t}));`:`\n {\n var oldValue = 0;\n loop {\n let newValueF32 = bitcast(oldValue) + (${t});\n let newValue = bitcast(newValueF32);\n let res = atomicCompareExchangeWeak(${r}, oldValue, newValue);\n if res.exchanged {\n break;\n }\n oldValue = res.old_value;\n }\n }`;var Ii;(function(r){r[r.FROM_PIXELS=0]=\"FROM_PIXELS\",r[r.DRAW=1]=\"DRAW\"})(Ii||(Ii={}));var YB=(r,t,e,o,n)=>{let s={dtype:o.dtype,shape:o.shape},a=rae(e,s,t),i=r.createShaderModule({code:a,label:t.constructor.name}),p=A().get(\"WEBGPU_PRINT_SHADER\");if(p!==\"\"){p=p.toLowerCase();let u=p.split(\",\");(p===\"all\"||u.some(c=>t.shaderKey.toLowerCase().includes(c)))&&(console.group(t.shaderKey),console.debug(a),console.groupEnd())}return n?r.createComputePipelineAsync({compute:{module:i,entryPoint:\"_start\"},label:t.constructor.name,layout:\"auto\"}):r.createComputePipeline({compute:{module:i,entryPoint:\"_start\"},label:t.constructor.name,layout:\"auto\"})},Ae=(r,t=\"f32\")=>{switch(r){case 1:return`${t}`;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${r}-component ${t} is not supported.`)}};function ht(r){if(r<=1)return\"i32\";if(r===2)return\"vec2\";if(r===3)return\"vec3\";if(r===4)return\"vec4\";if(r===5)return\"vec5\";if(r===6)return\"vec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Mo(r){if(r===0)return\"x\";if(r===1)return\"y\";if(r===2)return\"z\";if(r===3)return\"w\";if(r===4)return\"u\";if(r===5)return\"v\";throw Error(`Index ${r} is not yet supported`)}function G(...r){let t;switch(r.length){case 0:t=`\n fn main()\n `;break;case 1:t=`\n fn main(${r[0]} : i32)\n `;break;default:throw Error(\"Unreachable\")}return t}function qB(r,t){let e;return e=`\n ${tae(t)}\n fn _start(@builtin(local_invocation_id) LocalId : vec3,\n @builtin(global_invocation_id) GlobalId : vec3,\n @builtin(local_invocation_index) LocalIndex: u32,\n @builtin(workgroup_id) WorkgroupId : vec3,\n @builtin(num_workgroups) NumWorkgroups : vec3) {\n localId = LocalId;\n localIndex = LocalIndex;\n globalId = GlobalId;\n numWorkgroups = NumWorkgroups;\n workgroupId = WorkgroupId;\n ${r?\"main(getGlobalIndex());\":\"main();\"};\n }\n `,e}function tae(r){return`\n @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]})\n`}function rae(r,t,e){let o=[],n=e.workgroupSize[0]*e.workgroupSize[1]*e.workgroupSize[2];if(e.outputComponent=e.outputComponent?e.outputComponent:1,o.push(`\n\n var localId: vec3;\n var localIndex: u32;\n var globalId: vec3;\n var numWorkgroups: vec3;\n var workgroupId: vec3;\n\n // Only used when the y/z dimension of workgroup size is 1.\n fn getGlobalIndex() -> i32 {\n ${ZB(e)?\" return i32(globalId.x);\":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +\n workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u +\n localIndex);\n `}\n }\n `),e.pixelsOpType!=null){let f=e.pixelsOpType===Ii.FROM_PIXELS?`@group(0) @binding(0) var result: array<${Nu(t.dtype,e.outputComponent)}>;`:`@group(0) @binding(1) var inBuf : array<${Nu(r[0].dtype,e.outputComponent)}>;`,h=t.shape.length===3?\"vec2\":\"i32\";o.push(`\n struct Uniform {\n outShapeStrides : ${h},\n size : i32,\n numChannels : i32,\n alpha : f32,\n };\n\n ${f}\n @group(0) @binding(2) var uniforms: Uniform;\n `);let g=XB(e);return[jB,o.join(`\n`),cm(t.shape),e.getUserCode(),qB(g,e)].join(`\n`)}let s,a,i=\"struct Uniforms { NAN : f32, INFINITY : f32, \";e.variableNames.forEach((f,h)=>{let g=ht(r[h].shape.length);i+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `,s=r[h].shape.length-1,a=ht(s),i+=`${f.charAt(0).toLowerCase()+f.slice(1)}ShapeStrides: ${a}, `});let p=ht(t.shape.length);i+=`outShape : ${p}, `,s=t.shape.length-1,a=ht(s),i+=`\n outShapeStrides: ${a}, `,e.size&&(i+=\"size : i32, \"),e.uniforms&&(i+=e.uniforms),i+=\"};\",i=cae(i),o.push(i),e.atomic?o.push(`\n @group(0) @binding(0) var result: array>;\n `):o.push(`\n @group(0) @binding(0) var result: array<${Nu(t.dtype,e.outputComponent)}>;\n `),e.variableNames.forEach((f,h)=>{o.push(`\n @group(0) @binding(${1+h}) var ${f}: array<${e.variableComponents?Nu(r[h].dtype,e.variableComponents[h]):Nu(r[h].dtype,e.outputComponent)}>;\n `)}),i!==\"\"&&o.push(`\n @group(0) @binding(${1+e.variableNames.length}) var uniforms: Uniforms;\n `);let u=iae(t.shape,e.dispatchLayout),c=[jB,o.join(`\n`)+oae,cm(t.shape),u,uae(t.shape.length)];e.atomic||c.push(pae(t.shape,t.dtype,e.outputComponent)),e.variableNames.forEach((f,h)=>{c.push(`${cm(r[h].shape,f)}`)});let l=r.map((f,h)=>aae(f,t.shape,e.variableComponents?e.variableComponents[h]:e.outputComponent,e.dispatchLayout.x.length===t.shape.length)).join(`\n`);c.push(l),c.push(e.getUserCode());let m=XB(e);return c.push(qB(m,e)),c.join(`\n`)}function QB(r,t,e){let o=r.shaderKey;if(r.pixelsOpType!=null)return o;let n=[],s=[];t.forEach(c=>{n.push(c.shape),s.push(c.dtype)}),n.push(e.shape),s.push(e.dtype);let a=t.map(c=>w.getBroadcastDims(c.shape,e.shape)),i=t.map(c=>y.arraysEqual(c.shape,e.shape)).join(\"_\"),p=a.map(c=>c.join(\"_\")).join(\";\"),u=ZB(r)?\"flatDispatch\":\"\";return o+=\"_\"+(r.workgroupSize?r.workgroupSize.join(\",\"):\"\")+n.map(c=>c.length).join(\",\")+s.join(\",\")+r.variableNames.join(\",\")+p+i+u,o}var jB=`\n struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n // Checks whether coordinates lie within the bounds of the shape.\n fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool {\n return all(coord >= vec2(0)) && all(coord < shape);\n }\n fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool {\n return all(coord >= vec3(0)) && all(coord < shape);\n }\n fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool {\n return all(coord >= vec4(0)) && all(coord < shape);\n }\n\n fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n return coord;\n }\n fn getIndexFromCoords2D(coords : vec2, shape : vec2) -> i32 {\n return dot(coords, vec2(shape.y, 1));\n }\n fn getIndexFromCoords3D(coords : vec3, shape : vec3) -> i32 {\n return dot(coords, vec3(shape.y * shape.z, shape.z, 1));\n }\n fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n }\n fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n }\n fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n }\n\n // NaN defination in IEEE 754-1985 is :\n // - sign = either 0 or 1.\n // - biased exponent = all 1 bits.\n // - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n fn isnan(val: f32) -> bool {\n let floatToUint: u32 = bitcast(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n fn isnanVec4(val : vec4) -> vec4 {\n let floatToUint: vec4 = bitcast>(val);\n return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u);\n }\n`,oae=`\n fn isinf(val: f32) -> bool {\n return abs(val) == uniforms.INFINITY;\n }\n`;function cm(r,t=\"\"){let e=r.length,o=t!==\"\"?`get${t.charAt(0).toUpperCase()+t.slice(1)}CoordsFromIndex`:\"getCoordsFromIndex\",n=t!==\"\"?`${t.charAt(0).toLowerCase()+t.slice(1)}ShapeStrides`:\"outShapeStrides\";if(e<=1)return`fn ${o}(index : i32) -> i32 { return index; }`;let s=y.computeStrides(r),a=ht(e),i=[];for(let u=0;u vec2 {\n let d0 = index / uniforms.${n}; let d1 = index - d0 * uniforms.${n};\n return vec2(d0, d1);\n }`;let p;return p=\"var index2 = index;\"+s.map((u,c)=>{let l=`let ${i[c]} = index2 / uniforms.${n}.${Mo(c)}`,m=c===s.length-1?`let ${i[c+1]} = index2 - ${i[c]} * uniforms.${n}.${Mo(c)}`:`index2 = index2 - ${i[c]} * uniforms.${n}.${Mo(c)}`;return`${l}; ${m};`}).join(\"\"),`\n fn ${o}(index : i32) -> ${a} {\n ${p}\n return ${a}(${i.join(\",\")});\n }\n `}function nae(r,t){let e=r.name,o=r.shape.length,n=ht(o),s=\"get\"+e.charAt(0).toUpperCase()+e.slice(1),a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=a.map(c=>`${c} : i32`).join(\", \");if(o<1)return`\n fn ${s}() -> ${Ae(t)} {\n return ${Ae(t)}(${e}[0]);\n }\n `;let p=`uniforms.${e.charAt(0).toLowerCase()+e.slice(1)}Shape`,u=`${o}D`;return o===0&&(u=\"1D\"),`\n fn ${s}(${i}) -> ${Ae(t)} {\n return ${Ae(t)}(${e}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p})${t===1?\"\":` / ${t}`}]);\n }\n `}function sae(r,t,e,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+s+\"ByOutput\",i=r.shape.length,p=t.length,u=ht(p);if(y.arraysEqual(r.shape,t)&&o)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(e)} {\n return ${Ae(e)}(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(e)} {\n return ${Ae(e)}(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}${e===1?\"\":` / ${e}`}]);\n }\n `;let c=w.getBroadcastDims(r.shape,t),l=p-i,m=\"\";if(i===0)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(e)}{\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(e)}{\n return get${s}();\n }\n `;p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(g=>`coords.${Mo(g+l)} = 0;`).join(`\n`);let d=\"\";if(p<2&&i>0)d=\"coords\";else if(p>1){let g=ht(i),x=r.shape.map((b,C)=>`coords.${Mo(C+l)}`).join(\", \");d=`${g}(${x})`}else d=\"coords\";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(e)} {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return ${Ae(e)}(${n}[getIndexFromCoords${h}(${d}, ${f})${e===1?\"\":` / ${e}`}]);\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> ${Ae(e)} {\n var coords = coordsIn;\n ${m}\n return ${Ae(e)}(${n}[getIndexFromCoords${h}(${d}, ${f})${e===1?\"\":` / ${e}`}]);\n }\n`}function aae(r,t,e,o){let n=nae(r,e);return r.shape.length<=t.length&&(n+=sae(r,t,e,o)),n}function iae(r,t){let{x:e,y:o=[],z:n=[]}=t,s=r.length,a=e.length+o.length+n.length;if(a!==s)return\"\";if(e.length===s)return`fn getOutputCoords() -> ${ht(s)}{\n let globalIndex = getGlobalIndex();\n return getCoordsFromIndex(globalIndex);\n }\n `;let i=\"\",p=[e,o,n];for(let m=0;m ${c} {\n ${i}\n`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(\",\")}); }`,l}function uae(r){let t=\"\";switch(r){case 0:case 1:t+=`\n fn getOutputIndexFromCoords(coords : i32) -> i32 {\n return coords;\n }\n `;break;case 2:t+=`\n fn getOutputIndexFromCoords(coords : vec2) -> i32 {\n return dot(coords, vec2(uniforms.outShapeStrides, 1));\n }\n `;break;case 3:t+=`\n fn getOutputIndexFromCoords(coords : vec3) -> i32 {\n return dot(coords, vec3(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n }\n `;break;case 4:t+=`\n fn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n }\n `;break;case 5:t+=`\n fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u;\n }\n `;break;case 6:t+=`\n fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u * uniforms.outShapeStrides.u +\n coords.v;\n }\n `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return t}function ZB(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function Nu(r,t=1){if(r===\"float32\")return Ae(t,\"f32\");if(r===\"int32\"||r===\"bool\")return Ae(t,\"i32\");throw new Error(`type ${r} is not supported.`)}function pae(r,t,e){let o=r.length,n=Nu(t,e),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Ae(e)}) {\n result[flatIndex] = ${n}(value);\n }\n\n fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(e,\"i32\")}) {\n result[flatIndex] = ${n}(value);\n }\n `;if(o>=2){let a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=ht(o);s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(e)}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex${e===1?\"\":` / ${e}`}, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(e,\"i32\")}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex${e===1?\"\":` / ${e}`}, value);\n }\n `}return s}function cae(r){let t=/(\\w+)\\s*:\\s*vec(5|6)/g;r=r.replace(t,o=>\"@align(16) \"+o);let e=/vec(5|6)\\s*,\\s*(\\w+)/g;return r=r.replace(e,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function XB(r){return!(r.dispatchLayout.hasOwnProperty(\"y\")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty(\"z\")&&r.dispatchLayout.z.length!==0)}var Yv={};qe(Yv,{GPUBytesPerElement:()=>jg,MatMulProgramType:()=>Lo,assertNotComplex:()=>fm,computeDispatch:()=>H,computeWorkPerThreadForConv2d:()=>mm,computeWorkgroupInfoForMatMul:()=>Xv,computeWorkgroupSizeForConv2d:()=>lm,flatDispatchLayout:()=>X,isWebGPUSupported:()=>dm,tilesFitEvenlyIntoShape:()=>mae});var Ap=r=>{let t=1;for(let e=0;ee%r[o]===0)}function H(r,t,e=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Ap(r.x.map(i=>t[i]))/(e[0]*o[0])),r.y?Math.ceil(Ap(r.y.map(i=>t[i]))/(e[1]*o[1])):1,r.z?Math.ceil(Ap(r.z.map(i=>t[i]))/(e[2]*o[2])):1];return[n,s,a]}function Xv(r,t,e,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),t<=16&&e<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function lm(r,t,e=!1){if(e)return[8,8,1];let o=Ap(r.x.map(s=>t[s])),n=Ap(r.y.map(s=>t[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function mm(r,t,e=!1){if(e)return[4,4,1];let o=Ap(r.x.map(s=>t[s])),n=Ap(r.y.map(s=>t[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function X(r){return{x:r.map((t,e)=>e)}}function jg(r){if(r===\"float32\"||r===\"int32\"||r===\"bool\"||r===\"string\")return 4;if(r===\"complex64\")return 8;throw new Error(`Unknown dtype ${r}`)}function dm(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}function fm(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!==\"complex64\",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var Lo;(function(r){r[r.MatMulReduceProgram=0]=\"MatMulReduceProgram\",r[r.MatMulSplitKProgram=1]=\"MatMulSplitKProgram\",r[r.MatMulSmallOutputSizeProgram=2]=\"MatMulSmallOutputSizeProgram\",r[r.MatMulPackedProgram=3]=\"MatMulPackedProgram\",r[r.MatMulMax=4]=\"MatMulMax\"})(Lo||(Lo={}));var dae=A().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),fae=(r,t)=>{let e=r.limits.maxComputeWorkgroupsPerDimension,o=t.dispatchLayout,n=t.dispatch;if(n.every(a=>a<=e))return n;y.assert(n[0]>e&&o.y===void 0&&o.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let s=Math.ceil(Math.sqrt(n[0]));return s>e?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=e,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[s,s,s]):[s,s,1]},Tu=class extends ao{nextDataId(){return Tu.nextDataId++}constructor(t,e){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchCountInPass=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.queryResolveBuffer=null,this.querySet=null,this.querySetCount=2,this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,this.hasReadSyncWarned=!1,this.hasTimestampQueryWarned=!1,!dm())throw new Error(\"WebGPU is not supported on this device\");this.pipelineCache={},this.device=t,this.queue=t.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new Hg(e),this.supportTimestampQuery=this.device.features.has(\"timestamp-query\"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Kg(this.device),this.textureManager=new qg(this.device),this.tensorMap=new zo(this,pr()),A().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:t,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(t,e=!1){if(!this.tensorMap.has(t))return!0;let o=this.tensorMap.get(t);return e?o.refCount=0:o.refCount--,o.refCount>0?!1:(o.complexTensorInfos!=null&&(this.disposeData(o.complexTensorInfos.real.dataId),this.disposeData(o.complexTensorInfos.imag.dataId)),this.commandQueueOwnedIds.has(t)?(this.tensorDataPendingDisposal.push(t),!0):(this.releaseResource(t),this.tensorMap.delete(t),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(t){let e=this.tensorMap.get(t);if(!(!e||!e.resource)){if(e.external){e.resource=null;return}e.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(e.resource):e.resource instanceof GPUTexture&&this.textureManager.releaseTexture(e.resource),e.resource=null}}refCount(t){return this.tensorMap.has(t)?this.tensorMap.get(t).refCount:0}incRef(t){let e=this.tensorMap.get(t);e.refCount++}decRef(t){if(this.tensorMap.has(t)){let e=this.tensorMap.get(t);e.refCount--}}write(t,e,o){if(o===\"complex64\"&&t!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:e,values:t,refCount:1}),n}move(t,e,o,n,s){if(n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.tensorMap.set(t,{dtype:n,shape:o,values:e,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(t=>{this.releaseResource(t),this.tensorMap.delete(t)}),this.uniformPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t)),this.stagingPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let t;try{t=await Promise.all(Object.values(this.pipelineCache))}catch(e){throw new Error(e.message)}Object.keys(this.pipelineCache).map((e,o)=>{this.pipelineCache[e]=t[o]})}async getBufferData(t){if(A().getBool(\"WEBGPU_ENGINE_COMPILE_ONLY\"))return console.warn(\"The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false\"),null;let e=t.size,o=this.bufferManager.acquireBuffer(e,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,o,0,e),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o),A().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(y.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(t,e){let o=this.tensorMap.get(t);return o.values=e,o.values}readSync(t){let e=this.tensorMap.get(t),{values:o,complexTensorInfos:n}=e;if(o!=null||e.dtype===\"string\")return o;if(e.dtype===\"complex64\"){let h=this.readSync(n.real.dataId),g=this.readSync(n.imag.dataId),x=y.convertBackendValuesAndArrayBuffer(w.mergeRealAndImagArrays(h,g).buffer,\"float32\");return this.convertAndCacheOnCPU(t,x),x}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn(\"The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead.\"));let s=[\"opaque\",\"premultiplied\"],a=e.resource,i=a.size;y.assert(i%4===0,()=>\"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.\");let p=i/4,u=new ArrayBuffer(i),c=256,l=256,m=s.map(h=>new OffscreenCanvas(c,l)),d=new OffscreenCanvas(c,l);this.endComputePassEncoder(),m.map((h,g)=>{let x=h.getContext(\"webgpu\");return x.configure({device:this.device,format:\"bgra8unorm\",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),x.getCurrentTexture()}).map((h,g)=>{let x=c*4,b=(R,D,P)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:a,bytesPerRow:x,offset:P},{texture:h},{width:R,height:D}),this.submitQueue();let O=d.getContext(\"2d\",{willReadFrequently:!0});O.clearRect(0,0,R,D),O.drawImage(m[g],0,0);let M=O.getImageData(0,0,R,D).data,L=s[g],B=new Uint8ClampedArray(u,P,R*D*4);for(let z=0;z0&&(b(S,k,_),_+=k*(c*4)),S=E%c,S>0&&b(S,1,_)});let f=y.convertBackendValuesAndArrayBuffer(u,e.dtype);return this.convertAndCacheOnCPU(t,f),f}async read(t){if(!this.tensorMap.has(t))throw new Error(`Tensor ${t} was not registered!`);let e=this.tensorMap.get(t),{values:o}=e;if(o!=null)return o;let n;if(e.dtype===\"complex64\"){let s=await Promise.all([this.read(e.complexTensorInfos.real.dataId),this.read(e.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=w.mergeRealAndImagArrays(a,i)}else{let s=await this.getBufferData(e.resource);n=y.convertBackendValuesAndArrayBuffer(s,e.dtype)}return this.convertAndCacheOnCPU(t,n),n}copyBuffer(t){let e=t.size,o=t.usage,n=this.bufferManager.acquireBuffer(e,o);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,n,0,e),this.submitQueue(),n}createTensorFromGPUData(t,e,o){let n=t.buffer;if(o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. \");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:e,values:null,refCount:1,external:t.zeroCopy});let a=this.tensorMap.get(s),i=jg(a.dtype)*y.sizeFromShape(a.shape);if(t.buffer.sizey.decodeString(n));return me(t.shape,t.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(t.shape,t.dtype,e)}async time(t){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled.\"),this.hasTimestampQueryWarned=!0);let e=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(t,e,o){return e===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,t,e),shape:t,dtype:e}}tensorToBinding(t){if(!t)return null;let o=this.tensorMap.get(t.dataId).resource;return o instanceof GPUBuffer?{buffer:o}:o instanceof GPUTexture?o.createView():o}uploadToGPU(t){let e=this.tensorMap.get(t);if(e.resource!=null)return;let o=jg(e.dtype)*y.sizeFromShape(e.shape),n,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(e.values){if(n=this.bufferManager.acquireBuffer(o,s,!0),n.mapState===\"unmapped\"){let a=this.bufferManager.acquireBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),i=a.getMappedRange();e.dtype===\"int32\"||e.dtype===\"bool\"?new Int32Array(i).set(e.values):new Float32Array(i).set(e.values),a.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(a,0,n,0,o),this.stagingPendingDisposal.push(a)}else{let a=n.getMappedRange();e.dtype===\"int32\"||e.dtype===\"bool\"?new Int32Array(a).set(e.values):new Float32Array(a).set(e.values),n.unmap()}e.values=null}else n=this.bufferManager.acquireBuffer(o,s);e.resource=n}makeUniforms(t){let e=0,o=0,n=[],s=1;t.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),u>s&&(s=u),e=Math.ceil(e/u)*u,o=p.data.length,n.push(e),e+=p.data.length*4}),e=Math.ceil(e/s)*s;let a=new ArrayBuffer(e);t.forEach((p,u)=>{let c=n[u];p.type===\"int32\"?new Int32Array(a,c,p.data.length).set(p.data):p.type===\"uint32\"?new Uint32Array(a,c,p.data.length).set(p.data):new Float32Array(a,c,p.data.length).set(p.data)});let i=this.bufferManager.acquireBuffer(e,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(i,0,a,0,e),this.uniformPendingDisposal.push(i),{offset:0,size:e,buffer:i}}runWebGPUProgram(t,e,o,n,s){if(s||(s=this.makeTensorInfo(t.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),t.dispatch=fae(this.device,t);let a=e.map((p,u)=>{if(p.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(p.dataId),{dtype:this.tensorMap.get(p.dataId).dtype,shape:p.shape,name:t.variableNames[u]}});t.shaderKey=QB(t,a,s);let i=A().getBool(\"WEBGPU_ENGINE_COMPILE_ONLY\");return t.shaderKey in this.pipelineCache||(this.pipelineCache[t.shaderKey]=YB(this.device,t,a,s,i)),t.pipeline=this.pipelineCache[t.shaderKey],i||this.recordAndSubmit(t,s,e,n),s}recordAndSubmit(t,e,o,n){if(t.pipeline instanceof Promise)throw new Error(\"Please call checkCompileCompletionAsync to ensure parallel compilation is done!\");let s=[],a=[],i=\"int32\";if(t.pixelsOpType==null){s.push({type:\"float32\",data:[NaN]},{type:\"float32\",data:[1/0]}),a=o.concat(e).map(d=>d.shape);let m=\"int32\";a.map(d=>{s.push({type:m,data:d});let f=y.computeStrides(d);s.push({type:m,data:f})})}else{let m=y.computeStrides(e.shape);s.push({type:i,data:m})}if(t.size){let m=y.sizeFromShape(t.outputShape);s.push({type:i,data:[t.outputComponent?m/t.outputComponent:m]})}n&&(s=[...s,...n]);let p=[this.tensorToBinding(e),...o.map(m=>this.tensorToBinding(m)),this.makeUniforms(s)];o.forEach(m=>{this.commandQueueOwnedIds.add(m.dataId)}),this.commandQueueOwnedIds.add(e.dataId);let u=this.device.createBindGroup({layout:t.pipeline.getBindGroupLayout(0),entries:p.map((m,d)=>({binding:d,resource:m}))}),c=this.activeTimers!=null;this.ensureCommandEncoderReady();let l={};c&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:this.querySetCount})),l.timestampWrites=[{querySet:this.querySet,queryIndex:0,location:\"beginning\"},{querySet:this.querySet,queryIndex:1,location:\"end\"}],this.computePassEncoder=this.commandEncoder.beginComputePass(l)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(l)),this.computePassEncoder.setPipeline(t.pipeline),this.computePassEncoder.setBindGroup(0,u),this.computePassEncoder.dispatchWorkgroups(t.dispatch[0],t.dispatch[1],t.dispatch[2]),this.dispatchCountInPass++,(c||A().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchCountInPass||t.pixelsOpType===Ii.DRAW)&&(this.endComputePassEncoder(),c?this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let t=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.querySetCount*8),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let e=new BigUint64Array(t.getMappedRange()),o=Number(e[1]-e[0])/1e6;return t.unmap(),this.bufferManager.releaseBuffer(t),o}shouldExecuteOnCPU(t,e=dae){return A().getBool(\"WEBGPU_CPU_FORWARD\")&&t.every(o=>this.tensorMap.get(o.dataId).resource==null&&y.sizeFromShape(o.shape){let r={powerPreference:A().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},t=await navigator.gpu.requestAdapter(r),e={},o=[];t.features.has(\"timestamp-query\")&&o.push(\"timestamp-query\"),t.features.has(\"bgra8unorm-storage\")&&o.push([\"bgra8unorm-storage\"]),e.requiredFeatures=o;let n=t.limits;e.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize,maxBufferSize:n.maxBufferSize,maxComputeWorkgroupSizeX:n.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:n.maxComputeInvocationsPerWorkgroup};let s=await t.requestDevice(e),a=await t.requestAdapterInfo();return new Tu(s,a)},3);var fe;(function(r){r[r.ADD=0]=\"ADD\",r[r.ATAN2=1]=\"ATAN2\",r[r.COMPLEX_MULTIPLY_IMAG=2]=\"COMPLEX_MULTIPLY_IMAG\",r[r.COMPLEX_MULTIPLY_REAL=3]=\"COMPLEX_MULTIPLY_REAL\",r[r.DIV=4]=\"DIV\",r[r.ELU_DER=5]=\"ELU_DER\",r[r.EQUAL=6]=\"EQUAL\",r[r.FLOOR_DIV=7]=\"FLOOR_DIV\",r[r.GREATER=8]=\"GREATER\",r[r.GREATER_EQUAL=9]=\"GREATER_EQUAL\",r[r.LESS=10]=\"LESS\",r[r.LESS_EQUAL=11]=\"LESS_EQUAL\",r[r.LOGICAL_AND=12]=\"LOGICAL_AND\",r[r.LOGICAL_OR=13]=\"LOGICAL_OR\",r[r.MAX=14]=\"MAX\",r[r.MIN=15]=\"MIN\",r[r.MOD=16]=\"MOD\",r[r.MUL=17]=\"MUL\",r[r.NOT_EQUAL=18]=\"NOT_EQUAL\",r[r.POW=19]=\"POW\",r[r.PRELU=20]=\"PRELU\",r[r.SQUARED_DIFFERENCE=21]=\"SQUARED_DIFFERENCE\",r[r.SUB=22]=\"SUB\"})(fe||(fe={}));var hae=\"let resultTemp = a + b;\",gae=\"let resultTemp = atan2(a, b);\",xae=\"let resultTemp = areal * breal - aimag * bimag;\",yae=\"let resultTemp = areal * bimag + aimag * breal;\",bae=\"let resultTemp = a / b;\",Cae=\"let resultTemp = select(a * (b + 1.0), a, b >= b - b);\",wae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a == b);\n`,Sae=`\n let remainder =\n select(a % b, round(a % b), (round(a) == a) & (round(b) == b));\n let quotient = (a - remainder) / b;\n let resultTemp =\n round(select(quotient, quotient - 1, sign(remainder) == -sign(b)));\n`,Iae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a > b);\n`,vae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a >= b);\n`,kae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a < b);\n`,Nae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a <= b);\n`,Tae=\"return f32(a >= 1.0 && b >= 1.0);\",_ae=`return (vec4(a >= vec4(1.0)) *\n vec4(b >= vec4(1.0)));`,$ae=\"return f32(a >= 1.0 || b >= 1.0);\",Eae=`return min(vec4(a >= vec4(1.0)) +\n vec4(b >= vec4(1.0)), vec4(1.0));`,Rae=\"let resultTemp = max(a, b);\",Dae=\"let resultTemp = min(a, b);\",Aae=`\n let isNaN = b == 0.;\n var resultTemp = a % b;\n resultTemp = select((resultTemp + b) % b, resultTemp,\n (a < 0. && b < 0.) || (a >= 0. && b > 0.));\n`,Fae=`\n let isNaN = !vec4(b);\n var resultTemp = vec4(a % b);\n if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {\n resultTemp[0] = (resultTemp[0] + b[0]) % b[0];\n }\n if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {\n resultTemp[1] = (resultTemp[1] + b[1]) % b[1];\n }\n if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {\n resultTemp[2] = (resultTemp[2] + b[2]) % b[2];\n }\n if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {\n resultTemp[3] = (resultTemp[3] + b[3]) % b[3];\n }\n`,Pae=\"let resultTemp = a * b;\",Oae=`\n var resultTemp = f32(a != b);\n let valueForNaN = 1.0;\n`,Mae=`\n var resultTemp = vec4(a != b);\n let valueForNaN = 1.0;\n`,Lae=`\n let isNaN = a < 0.0 && floor(b) < b;\n if (b == 0.0) {\n return 1.0;\n }\n var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b),\n round(abs(b) % 2.0) != 1.0);\n`,Bae=`\n let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1);\n let isModRound1 = vec4(isModRound1Bool);\n let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n var resultTemp = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n let isExpZero = b == vec4(0.0);\n if (isExpZero.r) {\n resultTemp.r = 1.0;\n }\n if (isExpZero.g) {\n resultTemp.g = 1.0;\n }\n if (isExpZero.b) {\n resultTemp.b = 1.0;\n }\n if (isExpZero.a) {\n resultTemp.a = 1.0;\n }\n let isNaN = (a < vec4(0.0)) & (floor(b) < b);\n`,zae=\"if (a < 0.0) { return b * a; } return a;\",Vae=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,Wae=\"let resultTemp = (a - b) * (a - b);\",Uae=\"let resultTemp = a - b;\";function Qc(r,t){let e;do{switch(r){case fe.ATAN2:e=gae;break;case fe.MAX:e=Rae;break;case fe.MIN:e=Dae;break;case fe.MOD:e=t?Fae:Aae;break;case fe.NOT_EQUAL:e=t?Mae:Oae;break;case fe.POW:e=t?Bae:Lae;break;default:continue}let o,n,s;return t?(o=\"isnanVec4\",n=\"vec4\",s=\"vec4\"):(o=\"isnan\",n=\"f32\",s=\"bool\"),`\n let aIsNaN = ${o}(a);\n let aPostLegalization = select(a, ${n}(42), aIsNaN);\n let bIsNaN = ${o}(b);\n let bPostLegalization = select(b, ${n}(42), bIsNaN);\n let isNaN = false;\n let valueForNaN = uniforms.NAN;\n {\n let a = aPostLegalization;\n let b = bPostLegalization;\n ${e}\n return select(\n resultTemp, ${n}(valueForNaN),\n ${s}(isNaN) | aIsNaN | bIsNaN);\n }\n `}while(!1);switch(r){case fe.ADD:e=hae;break;case fe.COMPLEX_MULTIPLY_IMAG:e=yae;break;case fe.COMPLEX_MULTIPLY_REAL:e=xae;break;case fe.DIV:e=bae;break;case fe.ELU_DER:e=Cae;break;case fe.EQUAL:e=wae;break;case fe.FLOOR_DIV:e=Sae;break;case fe.GREATER:e=Iae;break;case fe.GREATER_EQUAL:e=vae;break;case fe.LESS:e=kae;break;case fe.LESS_EQUAL:e=Nae;break;case fe.LOGICAL_AND:return t?_ae:Tae;case fe.LOGICAL_OR:return t?Eae:$ae;case fe.MUL:e=Pae;break;case fe.PRELU:return t?Vae:zae;case fe.SQUARED_DIFFERENCE:e=Wae;break;case fe.SUB:e=Uae;break;default:}return`\n ${e}\n return resultTemp;\n `}var Z;(function(r){r[r.ABS=0]=\"ABS\",r[r.ACOS=1]=\"ACOS\",r[r.ACOSH=2]=\"ACOSH\",r[r.ASIN=3]=\"ASIN\",r[r.ASINH=4]=\"ASINH\",r[r.ATAN=5]=\"ATAN\",r[r.ATANH=6]=\"ATANH\",r[r.CEIL=7]=\"CEIL\",r[r.COS=8]=\"COS\",r[r.COSH=9]=\"COSH\",r[r.ELU=10]=\"ELU\",r[r.ERF=11]=\"ERF\",r[r.EXP=12]=\"EXP\",r[r.EXPM1=13]=\"EXPM1\",r[r.FLOOR=14]=\"FLOOR\",r[r.IS_FINITE=15]=\"IS_FINITE\",r[r.IS_INF=16]=\"IS_INF\",r[r.IS_NAN=17]=\"IS_NAN\",r[r.LINEAR=18]=\"LINEAR\",r[r.LOG=19]=\"LOG\",r[r.LOG1P=20]=\"LOG1P\",r[r.LOGICAL_NOT=21]=\"LOGICAL_NOT\",r[r.NEG=22]=\"NEG\",r[r.RELU=23]=\"RELU\",r[r.RELU6=24]=\"RELU6\",r[r.LEAKYRELU=25]=\"LEAKYRELU\",r[r.RECIPROCAL=26]=\"RECIPROCAL\",r[r.ROUND=27]=\"ROUND\",r[r.RSQRT=28]=\"RSQRT\",r[r.SELU=29]=\"SELU\",r[r.SIGMOID=30]=\"SIGMOID\",r[r.SIGN=31]=\"SIGN\",r[r.SIN=32]=\"SIN\",r[r.SINH=33]=\"SINH\",r[r.SOFTPLUS=34]=\"SOFTPLUS\",r[r.SQRT=35]=\"SQRT\",r[r.SQUARE=36]=\"SQUARE\",r[r.STEP=37]=\"STEP\",r[r.TAN=38]=\"TAN\",r[r.TANH=39]=\"TANH\",r[r.TO_INT=40]=\"TO_INT\"})(Z||(Z={}));var Gae=\"return abs(a);\",Hae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return acos(a);\n`,Kae=`\n if (a < 1.) {\n return uniforms.NAN;\n }\n return acosh(a);\n`,qae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return asin(a);\n`,jae=\"return asinh(a);\",Xae=`\n if (isnan(a)) {\n return uniforms.NAN;\n }\n return atan(a);\n`,Yae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n if (a == 1.) {\n return uniforms.INFINITY;\n }\n if (a == -1.) {\n return -uniforms.INFINITY;\n }\n return atanh(a);\n`,Qae=\"return ceil(a);\",Zae=\"return cos(a);\",Jae=`\n let e2x = exp(-a);\n return (e2x + 1.0 / e2x) / 2.0;\n`,eie=\"return exp(a) - 1.0;\",tie=\"if (a >= 0.0) { return a; } return (exp(a) - 1.0);\",rie=`\n var resFloat = exp(a) - vec4(1.0);\n if (a.r >= 0.0) {\n resFloat.r = a.r;\n }\n if (a.g >= 0.0) {\n resFloat.g = a.g;\n }\n if (a.b >= 0.0) {\n resFloat.b = a.b;\n }\n if (a.a >= 0.0) {\n resFloat.a = a.a;\n }\n return resFloat;\n`,oie=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n let p = ${w.ERF_P};\n let a1 = ${w.ERF_A1};\n let a2 = ${w.ERF_A2};\n let a3 = ${w.ERF_A3};\n let a4 = ${w.ERF_A4};\n let a5 = ${w.ERF_A5};\n\n let sign = sign(a);\n let absA = abs(a);\n let t = 1.0 / (1.0 + p * absA);\n return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));\n`,nie=\"return exp(a);\",sie=\"return floor(a);\",aie=\"return f32(!isnan(a) && !isinf(a));\",iie=\"return f32(isinf(a));\",uie=\"return f32(isnan(a));\",pie=\"return a;\",cie=`if (a < 0.0) { return uniforms.NAN; }\n return log(a);`,lie=`\n if (isnan(a)) { return a; }\n return log(1.0 + a);\n`,mie=\"return f32(!(a >= 1.0));\",die=\"return -a;\",fie=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",hie=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,gie=\"return 1.0 / a;\",xie=\"return select(a, 0.0, a < 0.0);\",yie=\"return clamp(a, 0.0, 6.0);\",bie=\"return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));\",Cie=`\n return select(a, vec4(0.0), a < vec4(0.0));\n`,wie=\"return round(a);\",Sie=\"return inverseSqrt(a);\",Iie=`\n if (a >= 0.0) {\n return ${w.SELU_SCALE} * a;\n } else {\n return ${w.SELU_SCALEALPHA} * (exp(a) - 1.0);\n }\n`,vie=\"return 1.0 / (1.0 + exp(-1.0 * a));\",kie=\"return sign(a);\",Nie=\"return sin(a);\",Tie=`\n let e2x = exp(a);\n return (e2x - 1.0 / e2x) / 2.0;\n`,_ie=`\n let epsilon = 1.1920928955078125e-7;\n let threshold = log(epsilon) + 2.0;\n\n let too_large = a > -threshold;\n let too_small = a < threshold;\n let exp_a = exp(a);\n\n if (too_large) {\n return a;\n } else if (too_small) {\n return exp_a;\n } else {\n return log(exp_a + 1.0);\n }\n`,$ie=\"return sqrt(a);\",Eie=\"return a * a;\",Rie=`\n if (isnan(a)) {\n return a;\n }\n\n return select(uniforms.stepAlpha, 1.0, a > 0.0);\n`,Die=\"return tan(a);\",Aie=`\n let e2x = exp(-2.0 * abs(a));\n return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,Fie=\"return f32(i32((a)));\";function vi(r,t){switch(r){case Z.ABS:return Gae;case Z.ACOS:return Hae;case Z.ACOSH:return Kae;case Z.ASIN:return qae;case Z.ASINH:return jae;case Z.ATAN:return Xae;case Z.ATANH:return Yae;case Z.COS:return Zae;case Z.COSH:return Jae;case Z.CEIL:return Qae;case Z.ELU:return t?rie:tie;case Z.ERF:return oie;case Z.EXP:return nie;case Z.EXPM1:return eie;case Z.FLOOR:return sie;case Z.IS_FINITE:return aie;case Z.IS_INF:return iie;case Z.IS_NAN:return uie;case Z.LINEAR:return pie;case Z.LOG:return cie;case Z.LOG1P:return lie;case Z.LOGICAL_NOT:return mie;case Z.NEG:return die;case Z.LEAKYRELU:return t?hie:fie;case Z.RECIPROCAL:return gie;case Z.RELU:return t?Cie:xie;case Z.RELU6:return t?bie:yie;case Z.ROUND:return wie;case Z.RSQRT:return Sie;case Z.SELU:return Iie;case Z.SIGMOID:return vie;case Z.SIGN:return kie;case Z.SIN:return Nie;case Z.SINH:return Tie;case Z.SOFTPLUS:return _ie;case Z.SQRT:return $ie;case Z.SQUARE:return Eie;case Z.STEP:return Rie;case Z.TAN:return Die;case Z.TANH:return Aie;case Z.TO_INT:return Fie;default:throw new Error(`BinaryType ${r} is not implemented!`)}}function fr(r,t=!1,e=!1,o=3){if(r===null)return\"\";let n=\"\";if(r===\"linear\")n=vi(Z.LINEAR);else if(r===\"relu\")n=vi(Z.RELU,e);else if(r===\"elu\")n=vi(Z.ELU,e);else if(r===\"relu6\")n=vi(Z.RELU6,e);else if(r===\"prelu\")n=Qc(fe.PRELU,e);else if(r===\"sigmoid\")n=vi(Z.SIGMOID,e);else if(r===\"leakyrelu\")n=vi(Z.LEAKYRELU,e);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=Ae(e?4:1),i=\"\";return t?i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n let b = getPreluActivationWeightsByOutputCoords(coords);\n ${n}\n }`:i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n ${n}\n }`,i}function Zr(r,t){return`\n ${r?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n ${t?\"value = activation(value, coords);\":\"\"}\n `}function Qv(r,t,e=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=`\n ${r?\"value = getA(batch, col, row);\":\"value = getA(batch, row, col);\"}\n\n `,i=t?\"value = getB(batch, col, row);\":\"value = getB(batch, row, col);\";return`\n fn mm_readA(batch: i32, row: i32, col: i32) -> ${Ae(s)} {\n var value = ${Ae(s)}(0.0);\n ${e&&n?a:`\n ${r?\"if(row < uniforms.dimAOuter && col < uniforms.dimInner)\":\"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])\"}\n {\n ${a}\n }\n `}\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, col: i32) -> ${Ae(s)} {\n var value = ${Ae(s)}(0.0);\n ${i}\n return value;\n }\n `}function hm(r,t,e,o,n=!1,s=!1,a=!1,i=1){return`\n ${Qv(e,o,n,s,a,i)}\n fn mm_write(batch: i32, row: i32, col: i32, valueIn: ${Ae(i)}) {\n ${n&&s?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n {\n var value = valueIn;\n let coords = vec3(batch, row, col);\n ${Zr(r,t)}\n setOutputAtCoords(coords[0], coords[1], coords[2], value);\n }\n }\n `}var Pie=(r,t)=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol * ${t});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRow + innerRow,\n kStart + inputCol * ${t});\n `,Oie=(r,t,e,o)=>{if(r)return`\n for (var k = 0; k < ${o}; k++) {\n let BCached0 = mm_Bsub[k][tileCol];\n let ACached0 = mm_Asub[k][localRow];\n for (var i = 0; i < ${e}; i++) {\n acc[i] = fma(BCached0, vec4(ACached0[i]), acc[i]);\n }\n }`;{let n=\"\",s=\"\";for(let a=0;a(ACached[${a}]), acc[i]);`;return`\n for (var k = 0; k < ${o/t}; k++) {\n ${n}\n for (var i = 0; i < ${e}; i++) {\n let ACached = mm_Asub[tileRow + i][k];\n ${s}\n }\n }`}};function Fp(r,t,e=!1,o=32,n=!1,s=32,a=!1){let i=t[1]*r[1],p=t[0]*r[0],u=e?i:o,c=e?o:i,l=u/t[0],m=o/t[1],d=r[1],f=r[0];return y.assert((e&&l===4&&r[1]===4||!e&&(l===3||l===4))&&u%t[0]===0&&o%t[1]===0&&r[0]===4,()=>`If transposeA ${e} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4.\n Otherwise, innerElementSize ${l} must be 3 or 4.\n tileAWidth ${u} must be divisible by workgroupSize[0]${t[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${r[0]} must be 4.`),`\n var mm_Asub : array, ${u/l}>, ${c}>;\n var mm_Bsub : array, ${p/r[0]}>, ${o}>;\n\n ${G()} {\n let localRow = i32(localId.y);\n let tileRow = localRow * ${d};\n let tileCol = i32(localId.x);\n\n let globalRow = i32(globalId.y) * ${d};\n let globalCol = i32(globalId.x) * ${f};\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!a?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!a?\"batch\":\"batch % uniforms.bShape[0]\"};\n let globalRowStart = i32(workgroupId.y) * ${i};\n\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc: array, ${d}>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${m};\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${Pie(e,l)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n ${Oie(e,l,d,o)}\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n }`}var JB=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRowStart + inputRow,\n kStart + inputCol);\n `,Mie=r=>r?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function Pp(r,t,e=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*t[1],u=r[0]*t[0],c=e?p:o,l=e?o:p;y.assert(l%t[1]===0&&c%t[0]===0&&o%t[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${t[0]}, tileInner ${o} must be divisible by workgroupSize[1]${t[1]}`);let m=l/t[1],d=c/t[0],f=o/t[1],h=r[1],g=r[0],x=a?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${p};\n let globalColStart = i32(workgroupId.x) * ${u};\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${t[1]}) {\n for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${t[0]}) {\n ${JB(e)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${t[1]}) {\n for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${t[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalColStart + inputCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let ACached = ${e?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let gRow = globalRowStart + localRow + innerRow * ${t[1]};\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let gCol = globalColStart + localCol + innerCol * ${t[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\n let tileRow = i32(localId.y) * ${h};\n let tileCol = i32(localId.x) * ${g};\n\n let globalRow = i32(globalId.y) * ${h};\n let globalCol = i32(globalId.x) * ${g};\n let globalRowStart = i32(workgroupId.y) * ${p};\n\n let tileRowA = i32(localId.y) * ${m};\n let tileColA = i32(localId.x) * ${d};\n let tileRowB = i32(localId.y) * ${f};\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n for (var innerCol = 0; innerCol < ${d}; innerCol++) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${JB(e)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalCol + innerCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n ${Mie(e)}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n }\n `;return`\n var mm_Asub : array, ${l}>;\n var mm_Bsub : array, ${o}>;\n\n ${G()} {\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!i?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!i?\"batch\":\"batch % uniforms.bShape[0]\"};\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc : array, ${h}>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${x}\n }\n `}var Lie=r=>r?`\n mm_readA(batchA, colA, globalRow),\n mm_readA(batchA, colA + 1, globalRow),\n mm_readA(batchA, colA + 2, globalRow),\n mm_readA(batchA, colA + 3, globalRow)\n `:`\n mm_readA(batchA, globalRow, colA),\n mm_readA(batchA, globalRow, colA + 1),\n mm_readA(batchA, globalRow, colA + 2),\n mm_readA(batchA, globalRow, colA + 3)\n `;function Bie(r,t=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let e=r[0]*4;return`\n var mm_Asub : array, ${r[0]}>;\n\n ${G()} {\n let tileCol = i32(localId.x);\n let globalCol = i32(globalId.x);\n let globalRow = i32(globalId.y);\n\n let numTiles = (uniforms.dimInner - 1) / ${e} + 1;\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n // Without this initialization strange values show up in acc.\n var acc = 0.0;\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n let colA = t * ${e} + tileCol * 4;\n mm_Asub[tileCol] = vec4(${Lie(t)});\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < ${e/4}; k++) {\n let rowB = t * ${e} + k * 4;\n let BCached = vec4(mm_readB(batchB, rowB, globalCol),\n mm_readB(batchB, rowB + 1, globalCol),\n mm_readB(batchB, rowB + 2, globalCol),\n mm_readB(batchB, rowB + 3, globalCol));\n\n let ACached = mm_Asub[k];\n acc = acc + dot(ACached, BCached);\n }\n\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Xg=class{constructor(t,e,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?t[1]:t[2];if(this.isVec4=(u%4===0&&!o||e[1]%4===0&&o)&&e[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=e[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=Xv(e[1],u,e[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(e[1],e[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(t,e,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=t%n===0,i=e%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return`\n ${fr(this.activation,this.hasPreluActivationWeights,this.isVec4)}\n ${hm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}\n ${this.isVec4?Fp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?Bie(this.workgroupSize,this.transposeA):Pp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)}\n `}};function zie(r){return`\n var sumValues : array;\n ${G()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n let row = coords[1];\n let col = coords[2];\n var sum = 0.0;\n let Length = uniforms.dimInner;\n for (var k = i32(localId.x); k < Length; k = k + ${r}) {\n let dataA = mm_readA(batchA, row, k);\n let dataB = mm_readB(batchB, k, col);\n sum = sum + dataA * dataB;\n }\n sumValues[localId.x] = sum;\n workgroupBarrier();\n\n for(var currentSize = ${r/2}u; currentSize > 1u;\n currentSize = currentSize / 2u) {\n if (localId.x < currentSize)\n {\n sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n }\n workgroupBarrier();\n }\n\n if (localId.x == 0u) {\n sum = sumValues[0] + sumValues[1];\n mm_write(batch, row, col, sum);\n }\n }\n `}var Yg=class{constructor(t,e=!1,o=!1,n=null,s=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[256,1,1],this.outputShape=t,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=e,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${e}_${o}`}getUserCode(){return`\n ${fr(this.activation,this.hasPreluActivationWeights)}\n ${hm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${zie(this.workgroupSize[0])}\n `}};function Vie(r){let t=r[1],e=r[0],o=t>e?t:e;return`\n var mm_Asub : array, ${t}>;\n var mm_Bsub : array, ${o}>;\n\n // If the output size is small for matrix multiplication, avoid to use vec4\n // and handle some elements per thread to optimally utilize the ALU.\n // Read data from global memory to registers firstly, then store them into\n // shared memory, so it is instruction-Level parallelism for arithmetic\n // operations and others handle IO operations between barrier api, makes ALU\n // and load/store units work simultaneously, could improves the performance.\n ${G()} {\n let tileRow = i32(localId.y);\n let tileCol = i32(localId.x);\n let globalRow = i32(globalId.y);\n let globalCol = i32(globalId.x);\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n\n // uniforms.dimInner should be greater than 0.\n let numTiles = (uniforms.dimInner - 1) / ${o} + 1;\n var acc = 0.0;\n\n var globalColA = tileCol;\n var globalRowB = 0;\n var regA = mm_readA(batchA, globalRow, globalColA);\n var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var t = 0; t < numTiles; t = t + 1) {\n mm_Asub[tileRow][tileCol] = regA;\n mm_Bsub[2 * tileRow][tileCol] = regB0;\n mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n workgroupBarrier();\n\n regA = mm_readA(batchA, globalRow, globalColA);\n regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var k = 0; k < ${o}; k = k + 1) {\n acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n }\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Qg=class{constructor(t,e,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push(\"bias\");let c=p!=null;c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return`\n ${fr(this.activation,this.hasPreluActivationWeights)}\n ${hm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${Vie(this.workgroupSize)}\n `}};var Zg=class{constructor(t,e,o=!1,n=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(t[0]===1,()=>\"MatMulSplitKProgram only supports batch = 1.\"),this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&e%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=H(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],e],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let t=this.outputComponent;return`\n ${Qv(!1,this.transposeB,!1,!1,!1,t)}\n fn mm_write(batch: i32, row : i32, col : i32, value : ${Ae(t)}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n let coords = vec3(batch, row, col);\n let flatIndex = getOutputIndexFromCoords(coords);\n // The problem is that we should initialize output to zero before using.\n // Otherwise, the original value will be added to the result.\n for (var i = 0; i < ${t}; i = i + 1) {\n ${Qr(\"&result[flatIndex + i]\",`${t>1?\"value[i]\":\"value\"}`,\"float32\")}\n }\n }\n }\n ${t===4?Fp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Pp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}\n `}},Jg=class{constructor(t,e=null,o=null,n=null){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=e!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return`\n ${fr(this.activation,this.hasPreluActivationWeights)}\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var value = getXByOutputIndex(index);\n ${Zr(this.addBias,this.activation)}\n setOutputAtIndex(index, value);\n }\n }\n `}};var ex=class{constructor(t){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.value);\n }\n }\n `}};function kt(r){let{backend:t,attrs:e}=r,{shape:o,value:n}=e,{dtype:s}=e;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),t.makeTensorInfo(o,s,a)}else{let a=new ex(o),i=[{type:\"float32\",data:[n]}];return t.runWebGPUProgram(a,[],s,i)}}var ez={kernelName:sa,backendName:\"webgpu\",kernelFunc:kt};function pe(r){let{inputs:t,attrs:e}=r,{x:o}=t,{shape:n}=e,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var tz={kernelName:da,backendName:\"webgpu\",kernelFunc:pe};function Op({a:r,b:t,transposeA:e,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=t.shape.length,l=e?r.shape[u-2]:r.shape[u-1],m=o?t.shape[c-1]:t.shape[c-2],d=e?r.shape[u-1]:r.shape[u-2],f=o?t.shape[c-2]:t.shape[c-1],h=r.shape.slice(0,-2),g=t.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=Ir.assertAndGetBroadcastShape(r.shape.slice(0,-2),t.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${o} must match.`);let k=e?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=pe({inputs:{x:r},backend:n,attrs:{shape:k}}),R=pe({inputs:{x:t},backend:n,attrs:{shape:_}}),D=[E,R],P=Math.max(x,b),O=[E,R],M=[{type:\"int32\",data:[d]},{type:\"int32\",data:[f]},{type:\"int32\",data:[l]}],L,B,z=[P,d,f],U=A().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");if(U<0){let q=A().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),Y=q>0?q:n.thresholdToIncreaseWorkgroups,J=P*Math.ceil(d/32)*Math.ceil(f/32);J<=Y||d<=8&&J<=Y*2?P*d*f<=128?U=Lo.MatMulReduceProgram:P===1&&m>=2e3?U=Lo.MatMulSplitKProgram:U=Lo.MatMulSmallOutputSizeProgram:U=Lo.MatMulPackedProgram}switch(U){case Lo.MatMulReduceProgram:L=new Yg(z,e,o,s,p,a);break;case Lo.MatMulSplitKProgram:{if(B=kt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Zg(z,m,e,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let Y=new Jg(B.shape,s,p,a),J=null,re=[B];s&&re.push(s),a&&re.push(a),p===\"leakyrelu\"&&(J=[{type:\"float32\",data:[i]}],Y.uniforms+=\" alpha : f32,\");let ne=n.runWebGPUProgram(Y,re,B.dtype,J);D.push(B);let ee=pe({inputs:{x:ne},backend:n,attrs:{shape:S}});D.push(ne);for(let oe of D)n.disposeData(oe.dataId);return ee}break}case Lo.MatMulSmallOutputSizeProgram:L=new Qg(k,_,z,e,o,s,p,a);break;case Lo.MatMulPackedProgram:let q=n.adapterInfo.isIntel();L=new Xg(k,z,e,o,s,p,a,q);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p===\"leakyrelu\"&&(M.push({type:\"float32\",data:[i]}),L.uniforms+=\" alpha : f32,\"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=pe({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let q of D)n.disposeData(q.dataId);return j}function Wie(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Op({a:n,b:s,transposeA:p,transposeB:u,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var rz={kernelName:Io,backendName:\"webgpu\",kernelFunc:Wie};var gm=class{constructor(t,e,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=w.assertAndGetBroadcastShape(e,o),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${t}`,this.op=t}getUserCode(){return`\n fn binaryOpComplex(\n areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n ${Qc(this.op,!1)}\n }\n\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let areal = getARealByOutputIndex(index);\n let aimag = getAImagByOutputIndex(index);\n let breal = getBRealByOutputIndex(index);\n let bimag = getBImagByOutputIndex(index);\n setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n }\n }\n `}};var ki=class{constructor(t,e,o){if(this.size=!0,this.variableNames=[\"A\",\"B\"],this.outputShape=w.assertAndGetBroadcastShape(e,o),this.dispatchLayout=X(this.outputShape),this.op=t,this.useSharedMemoryWithA=e.length<=1&&o.length>1&&e[0]<128,this.useSharedMemoryWithB=o.length<=1&&e.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:e[0],this.shaderKey=`binary_${t}_${this.lastDimensionSize}`,this.type=\"shared\",this.workgroupSize=[256,1,1];else{let n=e.length>0&&e[e.length-1]%4===0,s=o.length>0&&o[o.length-1]%4===0;n&&s?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(y.isScalarShape(o)||o[o.length-1]===1)||s&&(y.isScalarShape(e)||e[e.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type=\"nonshared\",this.shaderKey=`binary_${t}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let t,e=this.outputComponent===4?\"vec4\":\"f32\",o=`\n fn binaryOperation(a : ${e}, b : ${e}) -> ${e} {\n ${Qc(this.op,this.outputComponent===4)}\n };\n `;if(this.type===\"shared\"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);\n let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];\n let b = getBByOutputIndex(index);`;t=`\n ${o}\n var sharedBuf : array;\n ${G(\"index\")} {\n // Fill in the shared memory buffer.\n let localIndex = i32(localId.x);\n if(localIndex < ${this.lastDimensionSize}) {\n sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n }\n workgroupBarrier();\n\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n ${s}\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `}else t=`\n ${o}\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index * ${this.outputComponent});\n let a = ${e}(getAByOutputCoords(coords));\n let b = ${e}(getBByOutputCoords(coords));\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `;return t}};function Ft(r){let{inputs:t}=r,{x:e}=t;return r.backend.incRef(e.dataId),{dataId:e.dataId,shape:e.shape,dtype:e.dtype}}var oz={kernelName:wo,backendName:\"webgpu\",kernelFunc:Ft};function yo(r){let{inputs:t,backend:e}=r,{real:o,imag:n}=t,s=e.makeTensorInfo(o.shape,\"complex64\"),a=e.tensorMap.get(s.dataId),i=Ft({inputs:{x:o},backend:e}),p=Ft({inputs:{x:n},backend:e});return a.complexTensorInfos={real:i,imag:p},s}var nz={kernelName:Fi,backendName:\"webgpu\",kernelFunc:yo};var Jr=class{constructor(t,e,o=\"\"){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=e,o!==\"\"&&(this.uniforms=o),this.shaderKey=`unary_${e}`}getUserCode(){return`\n fn unaryOperation(a : f32) -> f32 {\n ${vi(this.op,!1)}\n }\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n setOutputAtIndex(index, unaryOperation(a));\n }\n }\n `}};function ye({opType:r,cpuKernelImpl:t,dtype:e}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=e||s.dtype;if(a.shouldExecuteOnCPU([s])&&t!=null){let u=a.tensorMap.get(s.dataId),c=t(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Jr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function et({opType:r,cpuKernelImpl:t,supportsComplex:e=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(e&&a.dtype===\"complex64\"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new ki(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,S],dt(x.dtype,b.dtype))});else{let g=new gm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new gm(fe.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,\"float32\"),f=p.runWebGPUProgram(x,b,\"float32\")}let h=yo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype===\"string\"||i.dtype===\"string\"||p.shouldExecuteOnCPU([a,i]))&&t!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype===\"string\"?w.fromUint8ToStringArray(l):l,f=a.dtype===\"string\"?w.fromUint8ToStringArray(m):m,[h,g]=t(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new ki(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:sz,castImpl:az,ceilImpl:iz,concatImpl:uz,equalImpl:pz,expImpl:cz,expm1Impl:lz,floorImpl:mz,floorDivImpl:dz,gatherNdImpl:fz,gatherV2Impl:hz,greaterEqualImpl:gz,greaterImpl:xz,lessEqualImpl:yz,lessImpl:bz,logImpl:Cz,maxImpl:wz,maximumImpl:Sz,minimumImpl:Iz,multiplyImpl:vz,negImpl:kz,notEqualImpl:Nz,prodImpl:Tz,rangeImpl:_z,rsqrtImpl:$z,scatterImpl:Ez,simpleAbsImpl:Rz,sliceImpl:Dz,stridedSliceImpl:Az,stringNGramsImpl:Fz,subImpl:Pz,tileImpl:Oz,topKImpl:Mz,transposeImpl:Lz,uniqueImpl:HPt}=Tc;var Uie=ye({opType:Z.ABS,cpuKernelImpl:Rz}),Bz={kernelName:Xs,backendName:\"webgpu\",kernelFunc:Uie};var Gie=ye({opType:Z.ACOS}),zz={kernelName:Wo,backendName:\"webgpu\",kernelFunc:Gie};var Hie=ye({opType:Z.ACOSH}),Vz={kernelName:Uo,backendName:\"webgpu\",kernelFunc:Hie};var Kie=et({opType:fe.ADD,cpuKernelImpl:sz,supportsComplex:!0}),Wz={kernelName:uo,backendName:\"webgpu\",kernelFunc:Kie};var tx=class{constructor(t){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t[0],this.variableNames=t.map((e,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let t=[];this.variableNames.forEach(n=>{t.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let e=this.variableNames.map(n=>`v${n}`).join(\" + \");return`\n ${G(\"index\")} {\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let flatIndex = index * ${this.workPerThread} + i;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n ${t.join(`\n `)}\n setOutputAtIndex(flatIndex, ${e});\n }\n }\n }\n `}};function qie(r){let{inputs:t,backend:e}=r,o=t;if(o.length===1)return Ft({inputs:{x:o[0]},backend:e});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new tx(s);return e.runWebGPUProgram(a,o,n)}var Uz={kernelName:Go,backendName:\"webgpu\",kernelFunc:qie};var rx=class{constructor(t,e){this.variableNames=[\"A\"],this.workgroupSize=[16,16,1];let o=new Array(t.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let t=this.workgroupSize[0];return`\n var tile : array, ${this.workgroupSize[0]}>;\n ${G()} {\n var x = i32(workgroupId.x) * ${t} + i32(localId.x);\n var y = i32(workgroupId.y) * ${t} + i32(localId.y);\n let width = uniforms.outShape[0];\n let height = uniforms.outShape[1];\n if (x < width && y < height) {\n tile[localId.y][localId.x] = f32(A[y * width + x]);\n }\n workgroupBarrier();\n\n x = i32(workgroupId.y) * ${t} + i32(localId.x);\n y = i32(workgroupId.x) * ${t} + i32(localId.y);\n if (x < height && y < width) {\n setOutputAtIndex((y * height + x), tile[localId.x]\n [localId.y]);\n }\n }\n `}};var ox=class{constructor(t,e){this.variableNames=[\"A\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(t.length);for(let n=0;n6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=new Array(t);for(let o=0;o=32768&&o>=512?this.workgroupSize=[512,1,1]:t.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=e,this.shaderKey=`reduce_${e}`}getUserCode(){let t=\"\",e=\"0.0\",o=this.workgroupSize[0];this.reduceType===\"min\"||this.reduceType===\"max\"?(t=`\n if (isnan(candidate)) {\n bestValue = uniforms.NAN;\n } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n { bestValue = candidate; }`,e=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?t=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"?(t=\" bestValue = bestValue * candidate; \",e=\"1.0\"):this.reduceType===\"all\"?(t=\" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); \",e=\"1.0\"):this.reduceType===\"any\"&&(t=\" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); \",e=\"0.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n fn DIV_CEIL(a : u32, b : u32) -> u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestValues : array;\n `}\n fn getOffset(outputIndex : i32) -> i32 {\n let outputCoords = getCoordsFromIndex(outputIndex);\n let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n return offset;\n }\n ${G(\"index\")} {\n let outputIndex = index / ${o};\n let offset = getOffset(outputIndex);\n var bestValue = ${e};\n let Length = uniforms.reduceSize;\n let WorkPerThread = DIV_CEIL(u32(Length), ${o}u);\n for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n k = k + ${o}) {\n let candidate = f32(x[offset + k]);\n ${t}\n }\n xBestValues[localId.x] = bestValue;\n workgroupBarrier();\n\n var reduceSize = min(u32(Length), ${o}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n ${t}\n xBestValues[localId.x] = bestValue;\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n ${n}\n }\n }\n `}};var jie={mean:\"float32\",all:\"bool\",any:\"bool\"};function eo(r,t,e,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(t,r.shape),p=i,u=w.getAxesPermutation(p,s),c=r;u!=null&&(c=yr({inputs:{x:r},attrs:{perm:u},backend:n}),p=w.getInnerMostAxes(p.length,s),a.push(c)),w.assertAxesAreInnerMostDims(o,p,s);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=l;e&&(d=w.expandShapeToKeepDim(l,i));let f;if((o===\"max\"||o===\"prod\")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case\"max\":let g=wz(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case\"prod\":let{outVals:x,outShape:b,outDtype:C}=Tz(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=jie[o]||ni(r.dtype),S=[{type:\"int32\",data:[h]}],k=new nx(b,o,n.device.limits.maxComputeWorkgroupSizeX),_=n.runWebGPUProgram(k,[c],C,S);a.push(_),f=pe({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function Xie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return eo(n,a,s,\"all\",e)}var Hz={kernelName:Ho,backendName:\"webgpu\",kernelFunc:Xie};function Yie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return eo(n,a,s,\"any\",e)}var Kz={kernelName:Ko,backendName:\"webgpu\",kernelFunc:Yie};var Zc=class{constructor(t,e,o){this.workgroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let n=[e];this.op=o===\"min\"?\"<\":\">\";let[s,a]=w.computeOutAndReduceShapes(t,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=X(this.outputShape),y.sizeFromShape(a)<32?(this.type=\"plain\",this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type=\"shared\",this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=t,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let t=this.workgroupSize[0],e=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${Mo(this.inputShape.length-1)}`,o=()=>{let n=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+=\"outputCoords,\");else for(let s=0;s u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestIndices : array;\n var xBestValues : array;\n `}\n\n ${G(\"index\")} {\n let outputIndex = index / ${t};\n let reduceLength = ${e()};\n\n var bestIndex = i32(localId.x);\n var bestValue = uniforms.infinityValue;\n let outputCoords = getCoordsFromIndex(outputIndex);\n for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n k = k + ${t}) {\n let candidate = getX(${o()} k);\n if (!isnan(candidate) && candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = k;\n }\n }\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = bestIndex;\n workgroupBarrier();\n\n var reduceSize = min(u32(reduceLength), ${t}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n }\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n }\n }\n `:`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outputCoords = getCoordsFromIndex(index);\n var bestIndex = 0;\n var bestValue = getX(${o()} 0);\n let reduceLength = ${e()};\n for (var i = 1; i < reduceLength; i++) {\n let candidate = getX(${o()} i);\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = i;\n }\n }\n setOutputAtIndexI32(index, bestIndex);\n }\n }\n `}};function Qie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yr({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=new Zc(p.shape,a[0],\"max\"),l=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],m=e.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>e.disposeData(d.dataId)),m}var qz={kernelName:Ys,backendName:\"webgpu\",kernelFunc:Qie};function Zie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yr({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=new Zc(p.shape,a[0],\"min\"),l=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],m=e.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>e.disposeData(d.dataId)),m}var jz={kernelName:Qs,backendName:\"webgpu\",kernelFunc:Zie};var Jie=ye({opType:Z.ASIN}),Xz={kernelName:qo,backendName:\"webgpu\",kernelFunc:Jie};var eue=ye({opType:Z.ASINH}),Yz={kernelName:jo,backendName:\"webgpu\",kernelFunc:eue};var tue=ye({opType:Z.ATAN}),Qz={kernelName:Xo,backendName:\"webgpu\",kernelFunc:tue};var rue=et({opType:fe.ATAN2}),Zz={kernelName:Qo,backendName:\"webgpu\",kernelFunc:rue};var oue=ye({opType:Z.ATANH}),Jz={kernelName:Yo,backendName:\"webgpu\",kernelFunc:oue};var sx=class{constructor(t){this.variableNames=[\"x\"],this.uniforms=\"strides : vec2,\",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let xRCCorner = coords.yz * uniforms.strides;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n let value = getX(batch, xRCorner, xCCorner, d);\n setOutputAtIndex(index, value);\n }\n }\n `}};var za=class{constructor(t,e,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,\",this.workgroupSize=[128,1,1],this.size=!0,e===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=e,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${e}_${o}_${n}_${s}`}getUserCode(){let t;this.poolType===\"avg\"?t=\"resultValue = resultValue + value; count = count + 1.0;\":this.computePositions?t=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"((batch * uniforms.xShape[1] + xR) * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"(xR * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"wR * uniforms.filterDims.y + wC\"};\n }`:t=\"resultValue = max(value, resultValue);\";let e=\"resultValue\";return this.poolType===\"avg\"&&(e=\"resultValue / max(count, 1.0)\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n let xRCCorner = vec2(coords.yz) * uniforms.strides - uniforms.pads;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilations.x) {\n let xR = xRCorner + wR;\n\n if (xR < 0 || xR >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilations.y) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.y) {\n continue;\n }\n\n let value = getX(batch, xR, xC, d);\n ${t}\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${e});`}\n }\n }\n `}},_u=class{constructor(t,e,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,\",this.workgroupSize=[128,1,1],this.size=!0,e===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=e,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${e}_${o}_${n}_${s}`}getUserCode(){let t;this.poolType===\"avg\"?t=\"resultValue += value; count += 1.0;\":this.computePositions?t=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"(((batch * uniforms.xShape.y + xD) * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"((xD * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"wD * uniforms.filterDims.y * uniforms.filterDims.y + wR * uniforms.filterDims.z + wC\"};\n }`:t=\"resultValue = max(value, resultValue);\";let e=\"resultValue\";return this.poolType===\"avg\"&&(e=\"resultValue / max(count, 1.0)\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let xCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xDCorner = xCorner.x;\n let xRCorner = xCorner.y;\n let xCCorner = xCorner.z;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wD = 0; wD < uniforms.filterDims.x; wD++) {\n let xD = xDCorner + wD;\n if (xD < 0 || xD >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims.y; wR++) {\n let xR = xRCorner + wR;\n if (xR < 0 || xR >= uniforms.convDims.y) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.z; wC++) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.z) {\n continue;\n }\n\n let value = getX(batch, xD, xR, xC, ch);\n ${t}\n }\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${e});`}\n }\n }\n `}};function Jv(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{reductionIndices:s,keepDims:a}=o;return eo(n,s,a,\"max\",e)}var eV={kernelName:Vn,backendName:\"webgpu\",kernelFunc:Jv};function e0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return eo(n,a,s,\"mean\",e)}var tV={kernelName:Gn,backendName:\"webgpu\",kernelFunc:e0};function ax(r,t,e,o){if(t.filterWidth===1&&t.filterHeight===1&&y.arraysEqual(t.inShape,t.outShape))return Ft({inputs:{x:r},backend:o});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type===\"VALID\"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;e===\"avg\"?p=e0({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(e===\"max\",()=>`Invalid pool type ${e}`),p=Jv({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:t.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:\"int32\",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?n=new sx(t):(e===\"avg\"?n=new za(t,\"avg\"):(y.assert(e===\"max\",()=>`Invalid pool type ${e}`),n=new za(t,\"max\")),s.push({type:\"int32\",data:[t.padInfo.top,t.padInfo.left]},{type:\"int32\",data:[t.dilationHeight,t.dilationWidth]},{type:\"int32\",data:[t.inHeight,t.inWidth]},{type:\"int32\",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function nue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=w.computePool2DInfo(n.shape,s,a,u,i,p);return ax(n,c,\"avg\",e)}var rV={kernelName:Zo,backendName:\"webgpu\",kernelFunc:nue};function sue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new _u(l,\"avg\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return e.runWebGPUProgram(m,[n],n.dtype,d)}var oV={kernelName:Zs,backendName:\"webgpu\",kernelFunc:sue};var ix=class{constructor(t){this.variableNames=[\"dy\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool2DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n\n dotProd = dotProd + dyValue * uniforms.avgMultiplier;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},ux=class{constructor(t){this.variableNames=[\"dy\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool3DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n dotProd += dyValue * uniforms.avgMultiplier;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function aue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=w.computePool3DInfo(a.shape,i,p,1,u,c),m=new ux(l),d=1/(l.filterDepth*l.filterHeight*l.filterWidth),f=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.effectiveFilterDepth-1-l.padInfo.front,l.effectiveFilterHeight-1-l.padInfo.top,l.effectiveFilterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]},{type:\"int32\",data:[l.outDepth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"float32\",data:[d]}];return e.runWebGPUProgram(m,[n],a.dtype,f)}var nV={kernelName:Ai,backendName:\"webgpu\",kernelFunc:aue};function iue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;fm([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new ix(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:\"int32\",data:[c.outHeight]},{type:\"int32\",data:[c.outWidth]},{type:\"float32\",data:[m]}];return e.runWebGPUProgram(l,[n],a.dtype,d)}var sV={kernelName:Di,backendName:\"webgpu\",kernelFunc:iue};function uue(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s}=t,{transposeA:a,transposeB:i}=o;return Op({a:n,b:s,transposeA:a,transposeB:i,backend:e})}var aV={kernelName:Jo,backendName:\"webgpu\",kernelFunc:uue};var px=class{constructor(t,e){this.variableNames=[\"source\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.rank=e.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=t,this.uniforms=`start : ${ht(t.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let t=ht(this.rank),e=pue(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>\"sourceLoc = uniforms.start + coords;\"):o=this.outputShape.map((s,a)=>`sourceLoc.${t0[a]} = uniforms.start.${Mo(a)} + coords.${t0[a]};`),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var sourceLoc : ${t};\n let coords = getCoordsFromIndex(index);\n ${o.join(`\n`)}\n setOutputAtIndex(index, getSource(${e}));\n }\n }\n `}},t0=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function pue(r){if(r===1)return\"sourceLoc\";if(r<=6)return t0.slice(0,r).map(t=>`sourceLoc.${t}`).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}function Hs(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,size:a}=o,[i,p]=ct.parseSliceParams(n,s,a);if(ct.assertParamsValid(n,i,p),e.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=e.tensorMap.get(n.dataId),m=Dz(l.values,i,p,n.shape,n.dtype);return e.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return e.makeTensorInfo(p,n.dtype,[]);let u=new px(i,p),c=[{type:\"int32\",data:i}];return e.runWebGPUProgram(u,[n],n.dtype,c)}var iV={kernelName:ha,backendName:\"webgpu\",kernelFunc:Hs};var cue=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=pe({inputs:{x:n},backend:e,attrs:{shape:p}}),h=yr({inputs:{x:f},backend:e,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Hs({inputs:{x:g},backend:e,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>e.disposeData(b.dataId)),x},uV={kernelName:Js,backendName:\"webgpu\",kernelFunc:cue};var lue=`\n fn bincount_write(index: i32, value: f32) {\n ${Qr(\"&result[index]\",\"value\",\"float32\")}\n }\n`,mue=`\n fn bincount_write(index: i32, value: f32) {\n atomicStore(&result[index], bitcast(value));\n }\n`,Jc=class{constructor(t,e,o=!1){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"binCountSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=t,this.rank=t.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=e,this.hasWeights&&this.variableNames.push(\"w\"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`\n ${this.binaryOutput?mue:lue}\n ${G(\"index\")} {\n ${this.rank===1?`if (index < uniforms.xShape) {\n let indexVal = i32(getX(index));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(index)\":\"1.\"};\n bincount_write(indexVal, value);\n }\n }`:`let coord = getCoordsFromIndex(index);\n if (coordsInBounds2D(coord, uniforms.xShape)) {\n let indexVal = i32(getX(coord[0], coord[1]));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(coord[0], coord[1])\":\"1.\"};\n bincount_write(coord.x * uniforms.binCountSize + indexVal, value);\n }\n }`}\n }\n `}};function due(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=kt({backend:e,attrs:{shape:c,value:0,dtype:l}}),d=new Jc([i],u),f=[{type:\"int32\",data:[a]}],h=u?[n,s]:[n];return e.runWebGPUProgram(d,h,l,f,m)}var pV={kernelName:en,backendName:\"webgpu\",kernelFunc:due};var cx=class{constructor(t){this.outputShape=[],this.variableNames=[\"s0\",\"s1\"],this.uniforms=\"s0Size : i32, s1Size : i32, \",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"broadcastArgs\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var s0 = 1.0;\n var s1 = 1.0;\n let indexS0 = index - uniforms.size + uniforms.s0Size;\n let indexS1 = index - uniforms.size + uniforms.s1Size;\n if (indexS0 >= 0) {\n s0 = getS0(indexS0);\n }\n if (indexS1 >= 0) {\n s1 = getS1(indexS1);\n }\n\n if (s0 == 1.0) {\n setOutputAtIndex(index, s1);\n } else if (s1 == 1.0) {\n setOutputAtIndex(index, s0);\n } else if (s0 != s1) {\n setOutputAtIndex(index, uniforms.NAN);\n } else {\n setOutputAtIndex(index, s0);\n }\n }\n }\n `}};function fue(r){let{inputs:t,backend:e}=r,{s0:o,s1:n}=t;if(e.shouldExecuteOnCPU([o,n])){let c=e.tensorMap.get(o.dataId),l=e.tensorMap.get(n.dataId),m=c.values,d=l.values,f=w.assertAndGetBroadcastShape(Array.from(m),Array.from(d));return e.makeTensorInfo([f.length],\"int32\",Int32Array.from(f))}let s=y.sizeFromShape(o.shape),a=y.sizeFromShape(n.shape),i=Math.max(s,a),p=new cx(i),u=[{type:\"int32\",data:[s]},{type:\"int32\",data:[a]}];return e.runWebGPUProgram(p,[o,n],\"int32\",u)}var cV={kernelName:ea,backendName:\"webgpu\",kernelFunc:fue};var r0=et({opType:fe.NOT_EQUAL,dtype:\"bool\",cpuKernelImpl:Nz}),lV={kernelName:Qn,backendName:\"webgpu\",kernelFunc:r0};function Ni(r){let{inputs:t,backend:e}=r,{input:o}=t,n=e.tensorMap.get(o.dataId);return Ft({inputs:{x:n.complexTensorInfos.real},backend:e})}var mV={kernelName:qi,backendName:\"webgpu\",kernelFunc:Ni};function dV(r,t){let e=new Jr(r.shape,Z.TO_INT),o=t.runWebGPUProgram(e,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function o0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return Ft({inputs:{x:n},backend:e});let a=Gr(n.shape),i=o0({inputs:{x:n},backend:e,attrs:{dtype:\"float32\"}}),p=yo({inputs:{real:i,imag:a},backend:e});return a.dispose(),e.disposeData(i.dataId),p}if(n.dtype===\"complex64\"){let a=Ni({inputs:{input:n},backend:e}),i=o0({inputs:{x:a},backend:e,attrs:{dtype:s}});return e.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Ft({inputs:{x:n},backend:e});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(e.shouldExecuteOnCPU([n])){let a=e.tensorMap.get(n.dataId).values,[i,p,u]=az(a,n.shape,n.dtype,s);return e.makeTensorInfo(i,p,u)}if(s===\"int32\")return dV(n,e);if(s===\"bool\"){let a=e.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=r0({inputs:{a:n,b:a},backend:e});return e.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var fV={kernelName:bo,backendName:\"webgpu\",kernelFunc:o0};var hue=ye({opType:Z.CEIL,cpuKernelImpl:iz}),hV={kernelName:tn,backendName:\"webgpu\",kernelFunc:hue};var lx=class{constructor(t){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n var clampedValue = clamp(\n value, vec4(uniforms.minVal), vec4(uniforms.maxVal));\n clampedValue = select(clampedValue, value, isnanVec4(value));\n setOutputAtIndex(index, clampedValue);\n }\n }\n `}};var mx=class{constructor(t){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n if (isnan(value)) {\n setOutputAtIndex(index, value);\n return;\n }\n setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n }\n }\n `}};function gue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:\"float32\",data:[s]},{type:\"float32\",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new lx(n.shape):i=new mx(n.shape),e.runWebGPUProgram(i,[n],n.dtype,p)}var gV={kernelName:Co,backendName:\"webgpu\",kernelFunc:gue};var dx=class{constructor(t){this.outputShape=[],this.variableNames=[\"real\",\"imag\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"complexAbs\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let re = abs(getRealByOutputIndex(index));\n let im = abs(getImagByOutputIndex(index));\n let mx = max(re, im);\n\n // The length function in wgsl may be not underflow-safe on some GPUs.\n // So the safe solution is to ensure underflow-safety in all cases.\n setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0));\n }\n }\n `}};function xV(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function xue(r){let{inputs:t,backend:e}=r,{x:o}=t,n=e.tensorMap.get(o.dataId),s=new dx(o.shape),a=[xV(o,n.complexTensorInfos.real),xV(o,n.complexTensorInfos.imag)];return e.runWebGPUProgram(s,a,a[0].dtype)}var yV={kernelName:Pi,backendName:\"webgpu\",kernelFunc:xue};var fx=class{constructor(t){this.uniforms=\"\",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=w.computeOutShape(t,1),this.variableNames=t.map((e,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=t.length-1;for(let e=0;e0){t.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let s=1;sNi({inputs:{input:C},backend:e})),h=r.map(C=>Mp({inputs:{input:C},backend:e})),g=el(f,t,e),x=el(h,t,e),b=yo({inputs:{real:g,imag:x},backend:e});return f.forEach(C=>e.disposeData(C.dataId)),h.forEach(C=>e.disposeData(C.dataId)),e.disposeData(g.dataId),e.disposeData(x.dataId),b}let n=e.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let f=r.map(k=>{let E=[-1,y.sizeFromShape(k.shape.slice(t))];return pe({inputs:{x:k},backend:e,attrs:{shape:E}})}),h=f.map(k=>({vals:e.readSync(k.dataId),shape:k.shape})),g=w.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=uz(h,g,o,x),C=w.computeOutShape(r.map(k=>k.shape),t),S=e.makeTensorInfo(C,o,b);return f.forEach(k=>e.disposeData(k.dataId)),S}let s=e.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new fx(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:\"int32\",data:[l[0]]});for(let f=1;fe.disposeData(f.dataId));let d=pe({inputs:{x:m},backend:e,attrs:{shape:i}});return e.disposeData(m.dataId),d}function yue(r,t,e){let o=w.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>pe({inputs:{x:s},backend:e,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,t)),y.sizeFromShape(s.shape.slice(t))]}})),outShape:o}}function n0(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,t[0].shape)[0],a=t.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(t.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let p=t.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Ft({inputs:{x:p[0]},backend:e}):el(p,s,e)}var CV={kernelName:ta,backendName:\"webgpu\",kernelFunc:n0};function bue(r,t,e,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=D=>{switch(D){case 1:return\"resData = f32(x[xIndex]);\";case 3:return\"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";case 4:return\"resData = vec4(x[xIndex / 4]);\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},l=D=>{switch(D){case 1:return\"return f32(W[row * uniforms.wShape[3] + col]);\";case 4:return\"return vec4(W[(row * uniforms.wShape[3] + col) / 4]);\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=r?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,d=r?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,f=r?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",h=r?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=r?\"row\":\"col\",x=r?\"col\":\"row\",b=`\n let inChannels = uniforms.wShape[2];\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n let outRow = ${g} / outWidth;\n let outCol = ${g} % outWidth;\n\n let WRow = ${x} / (uniforms.filterDims[1] * inChannels);\n let WCol = ${x} / inChannels % uniforms.filterDims[1];\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * WRow - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * WCol - uniforms.pads[1];\n let xCh = ${x} % inChannels;\n var resData = ${Ae(i)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the 'same' padding type.\n if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n ${m}\n let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n ${c(i)}\n }\n return resData;`,C=r?t&&o?`\n ${b}`:`\n if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${b}\n }\n return ${Ae(i)}(0.0);`:o&&e?`\n ${b}`:`\n if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n ${b}\n }\n return ${Ae(i)}(0.0);`,S=`${l(p)}`,k=Ae(u),_=r?Ae(i):Ae(p),E=r?Ae(p):Ae(i);return`\n ${fr(s,a,u===4,4)}\n fn mm_readA(batch: i32, row : i32, col : i32) -> ${_} {\n ${r?C:S}\n }\n\n fn mm_readB(batch: i32, row : i32, col : i32) -> ${E} {\n ${r?S:C}\n }\n\n fn mm_write(batch: i32, row : i32, col : i32, valueIn : ${k}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n {\n var value = valueIn;\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n ${d}\n ${Zr(n,s)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`}var hx=class{constructor(t,e,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t.outShape,this.isChannelsLast=t.dataFormat===\"channelsLast\",this.isVec4=((t.inChannels%4===0||t.inChannels%3===0)&&this.isChannelsLast||t.outWidth%4===0&&!this.isChannelsLast)&&t.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=lm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=mm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&t.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push(\"bias\"),this.variableComponents.push(4)),i&&(this.variableNames.push(\"preluActivationWeights\"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=e%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let t=this.isVec4?Fp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Pp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),e=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`\n ${bue(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,e[0],e[1],e[2])}\n ${t}\n `}};var gx=class{constructor(t,e=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,\",this.workgroupSize=[4,4,8],this.outputShape=t.outShape,this.isChannelsLast=t.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=e,this.activation=o,this.hasPreluActivationWeights=n,e&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`\n ${fr(this.activation,this.hasPreluActivationWeights,!1,4)}\n fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{\n let coords = vec4(batch, row, col, chan);\n if (coordsInBounds4D(coords, uniforms.xShape)) {\n return getX(batch, row, col, chan);\n } else {\n return 0.0;\n }\n }\n fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n let coords = vec4(row, col, xChannel, outChannel);\n if(coordsInBounds4D(coords, uniforms.wShape)) {\n return getW(row, col, xChannel, outChannel);\n } else {\n return 0.0;\n }\n }\n fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {\n let coords = ${this.isChannelsLast?\"vec4(batch, row, col, chan);\":\"vec4(batch, chan, row, col);\"}\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = valueIn;\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);\n }\n }\n ${G(\"index\")} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let outChannel = ${this.isChannelsLast?\"coords[3];\":\"coords[1];\"}\n let outRow = ${this.isChannelsLast?\"coords[1];\":\"coords[2];\"}\n let outCol = ${this.isChannelsLast?\"coords[2];\":\"coords[3];\"}\n var acc : f32 = 0.0;\n for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * row - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * col - uniforms.pads[1];\n for (var xChannel = 0; xChannel < ${this.isChannelsLast?\"uniforms.xShape[3];\":\"uniforms.xShape[1];\"} xChannel = xChannel + 1) {\n ${this.isChannelsLast?\"let v = readInp(batch, xRow, xCol, xChannel);\":\"let v = readInp(batch, xChannel, xRow, xCol);\"}\n let f = readFilt(row, col, xChannel, outChannel);\n acc = acc + v * f;\n }\n }\n }\n writeResult(batch, outRow, outCol, outChannel, acc);\n }\n `}};var xx=class{constructor(t,e){this.variableNames=[\"x\"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32,\n inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let t=this.isChannelsLast?1:2,e=this.isChannelsLast?2:3,o=this.isChannelsLast?\"coords[1]\":\"coords[2]\",n=this.isChannelsLast?\"coords[2]\":\"coords[1]\",s=this.isChannelsLast?\"getX(batch, xRow, xCol, ch)\":\"getX(batch, ch, xRow, xCol)\";return`\n ${G(\"index\")} {\n let coords = getCoordsFromIndex(index);\n if(index < uniforms.size) {\n let batch = coords[0];\n let row = ${o};\n let col = ${n};\n let offsetY = (row / uniforms.outWidth) * uniforms.strides[0] - uniforms.pads[0];\n let xRow = offsetY + uniforms.dilations[0] * (col / uniforms.itemsPerBlockRow);\n var value = 0.0;\n if(xRow < uniforms.xShape[${t}] && xRow >= 0) {\n let offsetX = (row % uniforms.outWidth) * uniforms.strides[1] -\n uniforms.pads[1];\n let xCol = offsetX + uniforms.dilations[1] * ((col %\n uniforms.itemsPerBlockRow) / uniforms.inChannels);\n let ch = col % uniforms.inChannels;\n if(xCol < uniforms.xShape[${e}] && xCol >= 0) {\n value = ${s};\n }\n }\n setOutputAtIndex(index, value);\n }\n }\n `}};function yx(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function Cue({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=e.dataFormat===\"channelsLast\",u=!p,c=!1,l=p&&e.filterHeight===e.inHeight&&e.filterWidth===e.inWidth&&e.padInfo.type===\"VALID\",m=[],d,f;if(l){let x=e.inHeight*e.inWidth*e.inChannels;d=pe({inputs:{x:r},backend:o,attrs:{shape:[1,e.batchSize,x]}}),f=pe({inputs:{x:t},backend:o,attrs:{shape:[1,x,e.outChannels]}})}else d=pe({inputs:{x:r},backend:o,attrs:{shape:p?[e.batchSize,e.inHeight*e.inWidth,e.inChannels]:[e.batchSize,e.inChannels,e.inHeight*e.inWidth]}}),f=pe({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=yx(s.shape,p);x!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=yx(n.shape,p);x!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Op({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=pe({inputs:{x:h},backend:o,attrs:{shape:e.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function wue({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=e,C=b===\"channelsLast\",S=p*u*c,k=h*f,_=C?[e.batchSize,k,S]:[e.batchSize,S,k],E=new xx(_,C),R=[{type:\"int32\",data:[d.top,d.left]},{type:\"int32\",data:[m,l]},{type:\"int32\",data:[x,g]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*p]},{type:\"int32\",data:[c]}],D=o.runWebGPUProgram(E,[r],r.dtype,R),P=[];P.push(D);let O=pe({inputs:{x:t},backend:o,attrs:{shape:[1,S,-1]}});if(P.push(O),s!=null){let U=yx(s.shape,C);U!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:U}}),P.push(s))}if(n!=null){let U=yx(n.shape,C);U!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:U}}),P.push(n))}let B=Op({a:C?D:O,b:C?O:D,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=pe({inputs:{x:B},backend:o,attrs:{shape:e.outShape}});P.push(B);for(let U of P)o.disposeData(U.dataId);return z}function bx({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=e.dataFormat===\"channelsLast\",l=c&&e.filterHeight===e.inHeight&&e.filterWidth===e.inWidth&&e.padInfo.type===\"VALID\",m=A().getBool(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\");if(!m&&(l||e.filterHeight===1&&e.filterWidth===1&&e.dilationHeight===1&&e.dilationWidth===1&&e.strideHeight===1&&e.strideWidth===1&&(e.padInfo.type===\"SAME\"||e.padInfo.type===\"VALID\")))return Cue({x:r,filter:t,convInfo:e,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=A().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),f=d>-1?d:o.thresholdToIncreaseWorkgroups,h=e.batchSize*Math.ceil(e.outHeight*e.outWidth/32)*Math.ceil(e.outChannels/32);if(A().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")||h<=f)return wue({x:r,filter:t,convInfo:e,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[e.padInfo.top,e.padInfo.left],b=[{type:\"int32\",data:[e.filterHeight,e.filterWidth]},{type:\"int32\",data:[...x]},{type:\"int32\",data:[e.strideHeight,e.strideWidth]},{type:\"int32\",data:[e.dilationHeight,e.dilationWidth]}];if(m)g=new gx(e,p,i,u);else{let _=c?e.outHeight*e.outWidth:e.outChannels,E=c?e.outChannels:e.outHeight*e.outWidth,R=e.filterHeight*e.filterWidth*e.inChannels;b.push({type:\"int32\",data:[_]},{type:\"int32\",data:[E]},{type:\"int32\",data:[R]});let D=o.adapterInfo.isIntel();g=new hx(e,_,E,R,p,i,u,D)}let C=[],S=[r,t];p&&(!c&&n.shape.length===1&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),S.push(n)),u&&(!c&&s.shape.length===1&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),S.push(s)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[a]}),g.uniforms+=\" alpha : f32,\");let k=o.runWebGPUProgram(g,S,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function Sue(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=e,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return bx({x:n,filter:s,convInfo:m,backend:o})}var wV={kernelName:rn,backendName:\"webgpu\",kernelFunc:Sue};var Cx=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,\",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=t.inShape,this.isChannelsLast=t.dataFormat===\"channelsLast\",this.isVec4=this.isChannelsLast&&t.outChannels%4===0&&t.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let t=this.isChannelsLast?1:2,e=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1,n=`\n ${G()} {\n let batch = i32(globalId.z) / uniforms.outShape[1];\n let r = i32(globalId.z) % uniforms.outShape[1];\n let c = i32(globalId.y) * ${this.workPerThread};\n let d1 = i32(globalId.x) * 4;\n\n let dyCorner = vec2(r, c) - uniforms.pads;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd: array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = f32(dyCorner.x + wR) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) ||\n fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = f32(dyCorner.y + wC) / f32(uniforms.strides.y);\n let dyC2 = f32(dyCorner.y + 1 + wC) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n var bDyCVal = true;\n var bDyCVal2 = true;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0) {\n bDyCVal = false;\n }\n if (dyC2 < 0.0 || dyC2 >= f32(uniforms.outBackprop[2]) ||\n fract(dyC2) > 0.0) {\n bDyCVal2 = false;\n }\n\n let idyC = i32(dyC);\n let idyC2 = i32(dyC2);\n if (bDyCVal && bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n xValue = getDy(batch, idyR, idyC2, d2);\n dotProd[1] = dotProd[1] + vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n }\n } else if (bDyCVal) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n }\n } else if (bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC2, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[1] = dotProd[1] + tmpval;\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n }\n }\n }\n `;return this.isVec4?`\n ${n}\n `:`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[${o}];\n\n let dyCorner = vec2(coords[${t}], coords[${e}]) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC = i32(dyC);\n\n for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n let xValue = ${this.isChannelsLast?\"getDy(batch, idyR, idyC, d2)\":\"getDy(batch, d2, idyR, idyC)\"};\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},wx=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.uniforms=\"pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let d2 = coords[3];\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b = b + 1) {\n for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n if (${this.isChannelsLast}) {\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd = dotProd + xValue * dyValue;\n } else {\n let dyValue = getDy(b, d2, yR, yC);\n let xValue = getX(b, d1, xR, xC);\n dotProd = dotProd + xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Sx=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32,\n outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerFilter\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wF = coords.x;\n let wR = coords.y;\n let wC = coords.z;\n let d1 = coords.w;\n let d2 = coords.u;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yF = 0; yF < uniforms.outDepth; yF++) {\n let xF = wF + yF * uniforms.strides[0] - uniforms.pads[0];\n if (xF < 0 || xF >= uniforms.inDepth) {\n continue;\n }\n\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[1] - uniforms.pads[1];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[2] - uniforms.pads[2];\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yF, yR, yC, d2);\n let xValue = getX(b, xF, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Ix=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerInput\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let d1 = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyFCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let dyF = f32(dyFCorner + wF) / f32(uniforms.strides[0]);\n if (dyF < 0.0 || dyF >= f32(uniforms.outDepth) || fract(dyF) > 0.0) {\n continue;\n }\n let idyF = i32(dyF);\n\n let wFPerm = uniforms.filterDims[0] - 1 - wF;\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n let wRPerm = uniforms.filterDims[1] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let wCPerm = uniforms.filterDims[2] - 1 - wC;\n\n for (var d2 = 0; d2 < uniforms.outChannels; d2++) {\n let xValue = getDy(batch, idyF, idyR, idyC, d2);\n let wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Iue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new wx(m),f=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]},{type:\"int32\",data:[m.inHeight]},{type:\"int32\",data:[m.inWidth]}];return e.runWebGPUProgram(d,[n,s],n.dtype,f)}var SV={kernelName:Oi,backendName:\"webgpu\",kernelFunc:Iue};function vue(r=4){let t=s=>{switch(s){case 1:return\"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n return vec4(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${`\n let outRow = row / uniforms.outShape[2];\n let outCol = row % uniforms.outShape[2];\n\n let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.strides[0]);\n let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.strides[1]);\n if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n let coord = vec4(\n batch,\n i32(xR),\n i32(xC),\n col % uniforms.outBackprop[3]);\n return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}\n }\n return ${Ae(r)}(0.0);`;return`\n fn mm_readA(batch: i32, row : i32, col : i32) -> ${Ae(r)} {\n ${o}\n }\n\n fn mm_readB(batch: i32, row : i32, col : i32) -> ${Ae(r)} {\n let coordX = uniforms.filterDims.x - 1 -\n row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let coordY = uniforms.filterDims.y - 1 -\n (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n coordX >= 0 && coordY >= 0) {\n let rowInner = row % uniforms.outBackprop[3];\n let coord = vec4(coordX, coordY, col, rowInner);\n ${t(r)}\n }\n return ${Ae(r)}(0.0);\n }\n\n fn mm_write(batch: i32, row : i32, col : i32, valueInput : ${Ae(r)}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n var value = valueInput;\n let outCoord = vec4(\n batch,\n row / uniforms.outShape[2],\n row % uniforms.outShape[2],\n col);\n result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;\n }\n }`}var vx=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t.inShape,y.assert(t.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.isVec4=t.inChannels%4===0&&t.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=lm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=mm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let t=this.isVec4?Fp(this.elementsPerThread,this.workgroupSize):Pp(this.elementsPerThread,this.workgroupSize);return`\n ${vue(this.isVec4?4:1)}\n ${t}\n `}};function kue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:\"int32\",data:[m.filterHeight,m.filterWidth]},{type:\"int32\",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(A().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")||m.dataFormat!==\"channelsLast\")f=new Cx(m);else{f=new vx(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:\"uint32\",data:[h]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[x]})}return e.runWebGPUProgram(f,[n,s],\"float32\",d)}var IV={kernelName:on,backendName:\"webgpu\",kernelFunc:kue};var kx=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3dnaive\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords.x;\n let d2 = coords.u;\n\n let xFRCCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xFCorner = xFRCCorner.x;\n let xRCorner = xFRCCorner.y;\n let xCCorner = xFRCCorner.z;\n\n let inputDepthNearestVec4 = (uniforms.xShape.u / 4) * 4;\n let inputDepthVec4Remainder = uniforms.xShape.u % 4;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let xF = xFCorner + wF * uniforms.dilations[0];\n if (xF < 0 || xF >= uniforms.xShape.y) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let xR = xRCorner + wR * uniforms.dilations[1];\n if (xR < 0 || xR >= uniforms.xShape.z) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let xC = xCCorner + wC * uniforms.dilations[2];\n if (xC < 0 || xC >= uniforms.xShape.w) {\n continue;\n }\n\n for (var d1 = 0; d1 < inputDepthNearestVec4; d1 += 4) {\n let xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n let wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (inputDepthVec4Remainder == 1) {\n dotProd += getX(batch, xF, xR, xC, inputDepthNearestVec4) *\n getW(wF, wR, wC, inputDepthNearestVec4, d2);\n } else if (inputDepthVec4Remainder == 2) {\n let xValues = vec2(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)\n );\n let wValues = vec2(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (inputDepthVec4Remainder == 3) {\n let xValues = vec3(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)\n );\n let wValues = vec3(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }`}};function Nue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new kx(u),d=dt(n.dtype,s.dtype);return e.runWebGPUProgram(m,[n,s],d,l)}var vV={kernelName:nn,backendName:\"webgpu\",kernelFunc:Nue};function Tue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Sx(u),l=[{type:\"int32\",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.batchSize]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.inDepth]},{type:\"int32\",data:[u.inHeight]},{type:\"int32\",data:[u.inWidth]}];return e.runWebGPUProgram(c,[n,s],s.dtype,l)}var kV={kernelName:Xa,backendName:\"webgpu\",kernelFunc:Tue};function _ue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,pad:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,a,1,i),c=new Ix(u),l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.outChannels]}];return e.runWebGPUProgram(c,[n,s],n.dtype,l)}var NV={kernelName:sn,backendName:\"webgpu\",kernelFunc:_ue};var $ue=ye({opType:Z.COS}),TV={kernelName:an,backendName:\"webgpu\",kernelFunc:$ue};var Eue=ye({opType:Z.COSH}),_V={kernelName:un,backendName:\"webgpu\",kernelFunc:Eue};var Nx=class{constructor(t,e,o,n){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0;let[s]=e;this.outputShape=[s,o[0],o[1],t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[t,e]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[o,n,s]=this.cropHeightBiggerThan1?[`(${t} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${t} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${t}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${e} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${e} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${e}`];return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let height_ratio = f32(${o});\n let width_ratio = f32(${a});\n let b = coords[0];\n let y = coords[1];\n let x = coords[2];\n let d = coords[3];\n // get box vals\n let y1 = getBoxes(b, 0);\n let x1 = getBoxes(b, 1);\n let y2 = getBoxes(b, 2);\n let x2 = getBoxes(b, 3);\n // get image in batch index\n let bInd = i32(round(getBoxInd(b)));\n if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n return;\n }\n let height_scale = ${n};\n let width_scale = ${i};\n let in_y = ${s};\n if( in_y < 0.0 || in_y > ${t} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let in_x = ${p};\n if( in_x < 0.0 || in_x > ${e} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let sourceFracIndexCR = vec2(in_x,in_y);\n if(${this.methodId} == 1) {\n // Compute the four integer indices.\n let sourceFloorCR = vec2(sourceFracIndexCR);\n let sourceCeilCR = vec2(ceil(sourceFracIndexCR));\n let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n let fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n let top = topLeft + (topRight - topLeft) * fracCR.x;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n let newValue = top + (bottom - top) * fracCR.y;\n setOutputAtIndex(index, newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n let sourceNearestCR = vec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n let newValue = getImage(\n bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutputAtIndex(index, newValue);\n }\n }\n }\n `}};var Rue=r=>{let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Nx(n.shape[3],s.shape,i,p),l=[{type:\"float32\",data:[u]}];return e.runWebGPUProgram(c,[n,s,a],\"float32\",l)},$V={kernelName:ln,backendName:\"webgpu\",kernelFunc:Rue};var Lp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Lp||(Lp={}));var xm=class{constructor(t,e,o,n){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=t,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let t=this.outputShape.length,e=this.op===Lp.Prod?\"1.0\":\"0.0\",o=this.exclusive?e:`getX(${EV(t,\"coords\",this.op)})`,n=this.outputShape[this.outputShape.length-1],s=\"\",a=\"\";return this.exclusive?(s=this.reverse?`end != ${n-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(s=this.reverse?`end + pow2 < ${n}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var coords = getCoordsFromIndex(index);\n\n let end = ${RV(t,\"coords\",this.op)};\n var val = ${o};\n let pow2 = i32(pow(2.0, uniforms.index));\n if (${s}) {\n let idx = ${a};\n ${RV(t,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${EV(t,\"coords\",this.op)});\n }\n setOutputAtIndex(index, val);\n }\n }\n `}};function EV(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function RV(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function Tx(r,t,e,o,n,s){let a=t.shape.length,i=w.getAxesPermutation([o],a),p=t;i!=null&&(p=yr({inputs:{x:t},backend:e,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Ft({inputs:{x:p},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new xm(r,p.shape,!1,s),f=l,h=[{type:\"float32\",data:[m]}];l=e.runWebGPUProgram(d,[l],l.dtype,h),e.disposeData(f.dataId)}if(n){let m=new xm(r,p.shape,n,s),d=l,f=[{type:\"float32\",data:[0]}];l=e.runWebGPUProgram(m,[l],l.dtype,f),e.disposeData(d.dataId)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=yr({inputs:{x:l},backend:e,attrs:{perm:m}});return e.disposeData(l.dataId),e.disposeData(p.dataId),d}return l}function Due(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return Tx(Lp.Prod,n,e,s,a,i)}var DV={kernelName:pn,backendName:\"webgpu\",kernelFunc:Due};function Aue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return Tx(Lp.Sum,n,e,s,a,i)}var AV={kernelName:cn,backendName:\"webgpu\",kernelFunc:Aue};function Fue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=kt({backend:e,attrs:{shape:d,value:0,dtype:l}}),h=new Jc(m,c,i),g=[{type:\"int32\",data:[a]}],x=c?[n,s]:[n];return e.runWebGPUProgram(h,x,l,g,f)}var FV={kernelName:ra,backendName:\"webgpu\",kernelFunc:Fue};var _x=class{constructor(t,e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${e}`,this.dataFormat=e}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let h = ${this.getHeightCoordString()};\n let w = ${this.getWidthCoordString()};\n let d = ${this.getDepthCoordString()};\n\n let in_h = h / uniforms.blockSize;\n let offset_h = h % uniforms.blockSize;\n let in_w = w / uniforms.blockSize;\n let offset_w = w % uniforms.blockSize;\n let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n ${this.getOutputDepthSize()};\n let in_d = d + offset_d;\n\n let rlt = ${this.getInputSamplingString()};\n setOutputAtIndex(index, rlt);\n }\n }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Pue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=[{type:\"int32\",data:[s]}],g=new _x(f,a);return e.runWebGPUProgram(g,[n],n.dtype,h)}var PV={kernelName:mn,backendName:\"webgpu\",kernelFunc:Pue};var $x=class{constructor(t,e,o,n=!1,s=null,a=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pads : vec2, inDims : vec2,\",this.workgroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push(\"bias\"),a&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=e,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let t=this.filterWidth*this.filterHeight,e=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`\n ${fr(this.activation,this.hasPreluActivation,!1,4)}\n\n var mm_Asub : array, ${o}>;\n var mm_Bsub : array, ${this.filterHeight}>;\n fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n var value = 0.0;\n if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n {\n value = getX(batch, channel, row, col);\n }\n return value;\n }\n\n ${G()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.zw) - uniforms.pads;\n let channelMul = uniforms.wShape[3];\n let d1 = coords[1] / channelMul;\n let q = coords[1] % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n\n // Load one tile of X into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {\n for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {\n let rowOffset = inputRow - localRow;\n let colOffset = inputCol - localCol;\n mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n }\n }\n\n // Load one tile of W into local memory.\n var wIndex = i32(localIndex);\n ${t, inDims : vec2, virtualWidth : i32,\",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=t.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let s=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=X(s),this.dispatch=H(this.dispatchLayout,s,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),y.assert(t.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),e&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=t,this.addBias=e,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let t=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,e=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return`\n ${fr(this.activation,this.hasPreluActivation,!0,4)}\n fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 {\n var value = vec4(0.0);\n if (col >=0 && col < uniforms.inDims[1]) {\n value = getX(batch, row, col, channel);\n }\n return value;\n }\n\n ${G(\"index\")} {\n let width0 = uniforms.outShape[3] / ${this.outputComponent};\n let d1 = (index % width0) * ${this.outputComponent};\n var index1 = index / width0;\n let width1 = uniforms.virtualWidth / ${this.workPerThread};\n let c = (index1 % width1) * ${this.workPerThread};\n index1 = index1 / width1;\n let r = index1 % uniforms.outShape[1];\n let batch = index1 / uniforms.outShape[1];\n\n let xRCCorner = vec2(r, c) * vec2(${e}, ${o}) - uniforms.pads;\n\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n var xVals : array, ${t}>;\n var dotProd : array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n\n // Use constant instead of uniform can give better performance.\n for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n let xR = xRCorner + wR;\n if (xR >=0 && xR < uniforms.inDims[0]) {\n for (var i = 0; i < ${t}; i++) {\n xVals[i] = readX(batch, xR, xCCorner + i, d1);\n }\n for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n let wValue = getW(wR, wC, d1, 0);\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]);\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = dotProd[i];\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n }\n `}};var rl=class{constructor(t,e=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32,\n filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t.dataFormat===\"channelsLast\",e&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=t,this.addBias=e,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let t=this.isChannelsLast?\"getX(batch, xR, xC, d1);\":\"getX(batch, d1, xR, xC);\";return`\n ${fr(this.activation,this.hasPreluActivation,!1,4)}\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.${this.isChannelsLast?\"yz\":\"zw\"}) * uniforms.strides - uniforms.pads;\n let d2 = coords[${this.isChannelsLast?3:1}];\n let channelMul = uniforms.wShape[3];\n let d1 = d2 / channelMul;\n let q = d2 % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n let inputRowEnd = inputRowStart + uniforms.filterHeight *\n uniforms.dilations[0];\n let inputColEnd = inputColStart + uniforms.filterWidth *\n uniforms.dilations[1];\n\n // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get\n // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n var value = 0.0;\n\n // Extract if checking out of for loop for performance.\n if (inputRowStart >= 0 && inputColStart >= 0 &&\n inputRowEnd < uniforms.inDims[0] &&\n inputColEnd < uniforms.inDims[1]) {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n let xVal = ${t};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n } else {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n if (xR < 0 || xR >= uniforms.inDims[0]) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n if (xC < 0 || xC >= uniforms.inDims[1]) {\n continue;\n }\n\n let xVal = ${t};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n }\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n `}};function Oue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:\"int32\",data:[d.padInfo.top,d.padInfo.left]},{type:\"int32\",data:[d.inHeight,d.inWidth]}],h=d.dataFormat===\"channelsLast\",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new $x(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?(g=new tl(d),f.push({type:\"int32\",data:[g.virtualWidth]})):(g=new rl(d),f.push({type:\"int32\",data:[d.filterHeight]},{type:\"int32\",data:[d.filterWidth]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.dilationHeight,d.dilationWidth]})),e.runWebGPUProgram(g,[n,s],n.dtype,f)}var OV={kernelName:dn,backendName:\"webgpu\",kernelFunc:Oue};var Ex=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32,\n outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_filter\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let dm = coords[3];\n let d2 = d1 * uniforms.channelMul + dm;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Rx=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_input\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[3];\n let dyCorner = coords.yz - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n\n let idyR = i32(dyR);\n let wRPerm = uniforms.filterDims[0] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n\n let idyC = i32(dyC);\n let wCPerm = uniforms.filterDims[1] - 1 - wC;\n\n for (var dm = 0; dm < uniforms.channelMul; dm++) {\n let d2 = d1 * uniforms.channelMul + dm;\n let xValue = getDy(batch, idyR, idyC, d2);\n let wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Mue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Ex(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.inHeight]},{type:\"int32\",data:[l.inWidth]},{type:\"int32\",data:[l.batchSize]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return e.runWebGPUProgram(m,[n,s],\"float32\",d)}var MV={kernelName:Mi,backendName:\"webgpu\",kernelFunc:Mue};function Lue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Rx(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.filterHeight-1-l.padInfo.top,l.filterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return e.runWebGPUProgram(m,[n,s],n.dtype,d)}var LV={kernelName:Li,backendName:\"webgpu\",kernelFunc:Lue};var Dx=class{constructor(t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"diag\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);\n setOutputAtIndex(index, value);\n }\n }\n `}};function Bue(r){let{inputs:t,backend:e}=r,{x:o}=t,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:e,attrs:{shape:[s]}}),i=new Dx(s),p=e.runWebGPUProgram(i,[a],a.dtype),u=pe({inputs:{x:p},backend:e,attrs:{shape:n}});return e.disposeData(a.dataId),e.disposeData(p.dataId),u}var BV={kernelName:oa,backendName:\"webgpu\",kernelFunc:Bue};var Ax=class{constructor(t){this.variableNames=[\"x\",\"w\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"dilation2d\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let neg_infinity = -3.4e38;\n let coords = getOutputCoords();\n let batch = coords.x;\n let d1 = coords.w;\n let outTopLeftCorner = coords.yz * uniforms.strides - uniforms.pads;\n let hBeg = outTopLeftCorner.x;\n let wBeg = outTopLeftCorner.y;\n\n var curVal = neg_infinity;\n for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) {\n let hIn = hBeg + h * uniforms.dilations[0];\n\n if (hIn >= 0 && hIn < uniforms.xShape[1]) {\n for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {\n let wIn = wBeg + w * uniforms.dilations[1];\n\n if (wIn >= 0 && wIn < uniforms.xShape[2]) {\n let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1);\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n setOutputAtIndex(index, curVal);\n }\n }\n `}};function zue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationHeight,u.dilationWidth]}],m=new Ax(u);return e.runWebGPUProgram(m,[n,s],n.dtype,l)}var zV={kernelName:fn,backendName:\"webgpu\",kernelFunc:zue};var Fx=class{constructor(t,e){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t.inShape,this.dispatchLayout=X(t.outShape),this.dispatch=H(this.dispatchLayout,t.outShape,this.workgroupSize),e!==\"float32\"&&e!==\"int32\")throw new Error(`Dilation2DBackpropInput only supports float32 and int32\n types, does not support ${e} type.`);this.type=e,this.shaderKey=\"dilation2DBackpropInput\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var xRMax = 0;\n var xCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n xRMax = xR;\n xCMax = xC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.xShape[3] *\n (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b));\n let value = getDy(b, r, c, d);\n ${Qr(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}},Px=class{constructor(t,e,o){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(t.outShape),this.dispatch=H(this.dispatchLayout,t.outShape,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"dilation2DBackpropFilter\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var wRMax = 0;\n var wCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n wRMax = wR;\n wCMax = wC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]);\n let value = getDy(b, r, c, d);\n ${Qr(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}};function Vue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=s.dtype,m=new Px(c,s.shape,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=kt({backend:e,attrs:{shape:s.shape,value:0,dtype:l}});return e.runWebGPUProgram(m,[n,s,a],l,d,f)}var VV={kernelName:zi,backendName:\"webgpu\",kernelFunc:Vue};function Wue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=n.dtype,m=new Fx(c,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=kt({backend:e,attrs:{shape:c.inShape,value:0,dtype:l}});return e.runWebGPUProgram(m,[n,s,a],l,d,f)}var WV={kernelName:Bi,backendName:\"webgpu\",kernelFunc:Wue};var Ox=class{constructor(t,e,o){this.variableNames=[\"Image\"],this.uniforms=\"alpha: f32,\",this.workgroupSize=[64,1,1],this.pixelsOpType=Ii.DRAW,this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=e,this.textureFormat=o,this.shaderKey=`draw_${e}_${o}`}getUserCode(){let t,e=this.type===\"float32\"?\"value\":\"value / 255.0\";return t=`\n if (uniforms.numChannels == 1) {\n rgba[0] = ${e};\n rgba[1] = ${e};\n rgba[2] = ${e};\n } else {\n rgba[d] = ${e};\n }`,`\n @group(0) @binding(0) var outImage : texture_storage_2d<${this.textureFormat}, write>;\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var rgba = vec4(0.0, 0.0, 0.0, uniforms.alpha);\n for (var d = 0; d < uniforms.numChannels; d = d + 1) {\n let value = f32(inBuf[index * uniforms.numChannels + d]);\n ${t}\n }\n rgba.x = rgba.x * rgba.w;\n rgba.y = rgba.y * rgba.w;\n rgba.z = rgba.z * rgba.w;\n let coords = getCoordsFromIndex(index);\n textureStore(outImage, vec2(coords.yx), rgba);\n }\n }\n `}};function Uue(r){let{inputs:t,backend:e,attrs:o}=r,{image:n}=t,{canvas:s,options:a}=o,[i,p]=n.shape.slice(0,2),{imageOptions:u}=a||{},c=(u==null?void 0:u.alpha)||1,l=e.device.features.has(\"bgra8unorm-storage\")?\"bgra8unorm\":\"rgba8unorm\",m=[i,p],d=new Ox(m,n.dtype,l);s.width=p,s.height=i;let f=\"webgpu\",h=s.getContext(f),g;h||(g=new OffscreenCanvas(p,i),h=g.getContext(f));let x=n.shape.length===3?n.shape[2]:1;h.configure({device:e.device,format:l,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:\"premultiplied\"});let b=\"int32\",C=e.makeTensorInfo(m,b),S=e.tensorMap.get(C.dataId);S.resource=h.getCurrentTexture(),S.external=!0;let k=[{type:\"uint32\",data:[x]},{type:\"float32\",data:[c]}];if(e.runWebGPUProgram(d,[n],b,k,C),g){let _=s.getContext(\"2d\");if(!_)throw new Error(\"Please make sure this canvas has only been used for 2d or webgpu context!\");_.drawImage(g,0,0)}return e.disposeData(C.dataId),n}var UV={kernelName:Pu,backendName:\"webgpu\",kernelFunc:Uue};var s0=et({opType:fe.MUL,cpuKernelImpl:vz,supportsComplex:!0}),GV={kernelName:Yn,backendName:\"webgpu\",kernelFunc:s0};function a0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return eo(n,s,a,\"sum\",e)}var HV={kernelName:Is,backendName:\"webgpu\",kernelFunc:a0};function Gue(r){let{inputs:t,backend:e,attrs:o}=r,{equation:n}=o,s=t,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=a0({inputs:{x:m},backend:e,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&e.disposeData(h.dataId);return m}var KV={kernelName:Vi,backendName:\"webgpu\",kernelFunc:Gue};var Hue=ye({opType:Z.ELU}),qV={kernelName:gn,backendName:\"webgpu\",kernelFunc:Hue};var Kue=r=>{let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=new ki(fe.ELU_DER,o.shape,n.shape);return e.runWebGPUProgram(s,[o,n],o.dtype)},jV={kernelName:Ya,backendName:\"webgpu\",kernelFunc:Kue};var que=et({opType:fe.EQUAL,dtype:\"bool\",cpuKernelImpl:pz}),XV={kernelName:yn,backendName:\"webgpu\",kernelFunc:que};var jue=ye({opType:Z.ERF}),YV={kernelName:xn,backendName:\"webgpu\",kernelFunc:jue};var Xue=ye({opType:Z.EXP,cpuKernelImpl:cz,dtype:\"float32\"}),QV={kernelName:bn,backendName:\"webgpu\",kernelFunc:Xue};function Mx(r){let{inputs:t,attrs:e,backend:o}=r,{dim:n}=e,{input:s}=t,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var ZV={kernelName:na,backendName:\"webgpu\",kernelFunc:Mx};var Yue=ye({opType:Z.EXPM1,cpuKernelImpl:lz}),JV={kernelName:Cn,backendName:\"webgpu\",kernelFunc:Yue};var ym=class{constructor(t,e){this.variableNames=[\"real\",\"imag\"],this.outputShape=[],this.uniforms=\"exponentMultiplier : f32, denominator: f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=t,this.shaderKey=`fft_${t}`}getUserCode(){return`\n fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {\n ${this.component===\"real\"?\"return real * expR - imag * expI;\":\"return real * expI + imag * expR;\"}\n }\n\n fn mulMatDFT(batch: i32, index: i32) -> f32 {\n let indexRatio = f32(index) / f32(uniforms.realShape[1]);\n let exponentMultiplierTimesIndexRatio =\n uniforms.exponentMultiplier * indexRatio;\n\n var result = 0.0;\n\n for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {\n // x = (-2|2 * PI / N) * index * i;\n let x = exponentMultiplierTimesIndexRatio * f32(i);\n let expR = cos(x);\n let expI = sin(x);\n let real = getReal(batch, i);\n let imag = getImag(batch, i);\n\n result = result +\n unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;\n }\n\n return result;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));\n }\n }\n `}};function Lx(r,t,e){let o=e.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:e,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new ym(\"real\",u),l=new ym(\"imag\",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=t?2*Math.PI:-2*Math.PI,f=t?u[1]:1,h=[{type:\"float32\",data:[d]},{type:\"float32\",data:[f]}],g=e.runWebGPUProgram(c,m,\"float32\",h);i.push(g);let x=e.runWebGPUProgram(l,m,\"float32\",h);i.push(x);let b=yo({inputs:{real:g,imag:x},backend:e});i.push(b);let C=pe({inputs:{x:b},backend:e,attrs:{shape:r.shape}});return i.forEach(S=>e.disposeData(S.dataId)),C}function Que(r){let{inputs:t,backend:e}=r,{input:o}=t;return Lx(o,!1,e)}var eW={kernelName:Wi,backendName:\"webgpu\",kernelFunc:Que};var Bx=class{constructor(t){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordX = uniforms.xShape[2] - coords[2] - 1;\n let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var tW={kernelName:wn,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,o=t,n=new Bx(e.shape);return o.runWebGPUProgram(n,[e],e.dtype)}};var Zue=ye({opType:Z.FLOOR,cpuKernelImpl:mz}),rW={kernelName:Sn,backendName:\"webgpu\",kernelFunc:Zue};var Jue=et({opType:fe.FLOOR_DIV,cpuKernelImpl:dz,dtype:\"int32\"}),oW={kernelName:In,backendName:\"webgpu\",kernelFunc:Jue};var zx=class{constructor(t,e,o=!1){this.pixelsOpType=Ii.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[e,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let t=this.importVideo?\"textureLoad(src, vec2(coords.yx));\":\"textureLoad(src, vec2(coords.yx), 0)\";return`\n @binding(1) @group(0) var src: ${this.importVideo?\"texture_external\":\"texture_2d\"};\n ${G(\"index\")} {\n let flatIndex = index * uniforms.numChannels;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n let values = ${t};\n for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n result[flatIndex + i] = i32(floor(255.0 * values[i]));\n }\n }\n }\n `}};var nW={kernelName:Mu,backendName:\"webgpu\",kernelFunc:epe},ol,i0=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function epe(r){let{inputs:t,backend:e,attrs:o}=r,{pixels:n}=t,{numChannels:s}=o;if(n==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!=\"undefined\"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!=\"undefined\"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=!1,f=a||i;if(u||p||f){let b;if(d)b=e.device.importExternalTexture({source:n});else{if(f){let L=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(ol==null||L!==i0)&&(i0=L,ol=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:i0})),ol.canvas.width=c,ol.canvas.height=l,ol.drawImage(n,0,0,c,l),n=ol.canvas}let P=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,O=\"rgba8unorm\",M=e.textureManager.acquireTexture(m[1],m[0],O,P);e.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b=M}let C=y.sizeFromShape(m),S=y.computeStrides(m),k=new zx(m,s,d),_=[{type:\"uint32\",data:[C]},{type:\"uint32\",data:[s]},{type:\"uint32\",data:[...S]}],E=e.makeTensorInfo([l,c],\"int32\"),R=e.tensorMap.get(E.dataId);R.resource=b;let D=e.runWebGPUProgram(k,[E],\"int32\",_);return e.disposeData(E.dataId),D}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,C=0;for(let S=0;S(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0)));\n }\n }\n `}};var sW={kernelName:vn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=t,u=e,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new Vx(o.shape,a.shape,i.shape,l,m),f=[{type:\"float32\",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function tpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return bx({x:n,filter:s,convInfo:g,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var aW={kernelName:vo,backendName:\"webgpu\",kernelFunc:tpe};function rpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=w.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:\"int32\",data:[h.padInfo.top,h.padInfo.left]},{type:\"int32\",data:[h.inHeight,h.inWidth]}],S;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(S=new tl(h,x,m,b),C.push({type:\"int32\",data:[S.virtualWidth]})):(S=new rl(h,x,m,b),C.push({type:\"int32\",data:[h.filterHeight]},{type:\"int32\",data:[h.filterWidth]},{type:\"int32\",data:[h.strideHeight,h.strideWidth]},{type:\"int32\",data:[h.dilationHeight,h.dilationWidth]})),m===\"leakyrelu\"&&(C.push({type:\"float32\",data:[d]}),S.uniforms+=\" alpha : f32,\"),e.runWebGPUProgram(S,g,\"float32\",C)}var iW={kernelName:ko,backendName:\"webgpu\",kernelFunc:rpe};var Wx=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${t}`,this.sliceDim=t,this.uniforms=`sliceDim : i32, strides : ${ht(t)},`}getUserCode(){let t;return this.sliceDim>1?t=\"uniforms.strides[j]\":t=\"uniforms.strides\",`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var flattenIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexTemp = i32(round(getIndices(coords[0], j)));\n let strideNum = ${t};\n flattenIndex = flattenIndex + indexTemp * strideNum;\n }\n\n setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n }\n }\n `}};function ope(r){let{inputs:t,backend:e}=r,{params:o,indices:n}=t,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=pe({inputs:{x:n},backend:e,attrs:{shape:[u,a]}}),d=pe({inputs:{x:o},backend:e,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(e.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let b=e.readSync(n.dataId),C=e.bufferSync(o),S=fz(b,C,o.dtype,u,a,c,l,o.shape,i);return e.makeTensorInfo(p,o.dtype,S.values)}let f=new Wx(a,[u,c]),h=[{type:\"int32\",data:[a]},{type:\"int32\",data:l}],g=e.runWebGPUProgram(f,[d,m],d.dtype,h),x=pe({inputs:{x:g},backend:e,attrs:{shape:p}});return e.disposeData(m.dataId),e.disposeData(d.dataId),e.disposeData(g.dataId),x}var uW={kernelName:kn,backendName:\"webgpu\",kernelFunc:ope};var Ux=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.slice(),this.aShape=t,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"gather\"}getUserCode(){let t=npe(this.aShape);return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let resRC = getCoordsFromIndex(index);\n let indexZ = i32(getIndices(resRC.x, resRC.z));\n let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n setOutputAtIndex(index, inBounds * getA(${t}));\n }\n }\n `}};function npe(r){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],e=[];for(let o=0;oe.disposeData(D.dataId)),e.makeTensorInfo(u.outputShape,R.dtype,R.values)}let h=new Ux(m.shape,f),g=e.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=pe({inputs:{x:g},backend:e,attrs:{shape:u.outputShape}});return l.forEach(b=>e.disposeData(b.dataId)),x}var pW={kernelName:aa,backendName:\"webgpu\",kernelFunc:u0};var spe=et({opType:fe.GREATER,cpuKernelImpl:xz,dtype:\"bool\"}),cW={kernelName:Nn,backendName:\"webgpu\",kernelFunc:spe};var ape=et({opType:fe.GREATER_EQUAL,dtype:\"bool\",cpuKernelImpl:gz}),lW={kernelName:Tn,backendName:\"webgpu\",kernelFunc:ape};function ipe(r){let{inputs:t,backend:e}=r,{input:o}=t;return Lx(o,!0,e)}var mW={kernelName:Ui,backendName:\"webgpu\",kernelFunc:ipe};var upe=ye({opType:Z.IS_FINITE,dtype:\"bool\"}),dW={kernelName:_n,backendName:\"webgpu\",kernelFunc:upe};var ppe=ye({opType:Z.IS_INF,dtype:\"bool\"}),fW={kernelName:$n,backendName:\"webgpu\",kernelFunc:ppe};var cpe=ye({opType:Z.IS_NAN,dtype:\"bool\"}),hW={kernelName:En,backendName:\"webgpu\",kernelFunc:cpe};function lpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{alpha:s}=o,a=[{type:\"float32\",data:[s]}],i=new Jr(n.shape,Z.LEAKYRELU,\"alpha : f32,\");return e.runWebGPUProgram(i,[n],\"float32\",a)}var gW={kernelName:Rn,backendName:\"webgpu\",kernelFunc:lpe};var mpe=et({opType:fe.LESS,dtype:\"bool\",cpuKernelImpl:bz}),xW={kernelName:Dn,backendName:\"webgpu\",kernelFunc:mpe};var dpe=et({opType:fe.LESS_EQUAL,dtype:\"bool\",cpuKernelImpl:yz}),yW={kernelName:An,backendName:\"webgpu\",kernelFunc:dpe};var Gx=class{constructor(t){this.variableNames=[],this.outputShape=[],this.uniforms=\"start : f32, step : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"linSpace\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);\n }\n }\n `}};function fpe(r){let{backend:t,attrs:e}=r,{start:o,stop:n,num:s}=e,a=(n-o)/(s-1),i=new Gx(s),p=[{type:\"float32\",data:[o]},{type:\"float32\",data:[a]}];return t.runWebGPUProgram(i,[],\"float32\",p)}var bW={kernelName:Fn,backendName:\"webgpu\",kernelFunc:fpe};var hpe=ye({opType:Z.LOG,cpuKernelImpl:Cz}),CW={kernelName:Pn,backendName:\"webgpu\",kernelFunc:hpe};var gpe=ye({opType:Z.LOG1P}),wW={kernelName:On,backendName:\"webgpu\",kernelFunc:gpe};var xpe=et({opType:fe.LOGICAL_AND,dtype:\"bool\"}),SW={kernelName:Mn,backendName:\"webgpu\",kernelFunc:xpe};var ype=ye({opType:Z.LOGICAL_NOT}),IW={kernelName:Ln,backendName:\"webgpu\",kernelFunc:ype};var bpe=et({opType:fe.LOGICAL_OR}),vW={kernelName:Bn,backendName:\"webgpu\",kernelFunc:bpe};var kW=`\n var powValue = 0.0;\n let basis = uniforms.bias + uniforms.alpha * sum;\n if (uniforms.beta == 0.5) {\n powValue = inverseSqrt(basis);\n } else if (uniforms.beta == 1.0) {\n powValue = 1.0 / basis;\n } else {\n powValue = exp(log(basis) * (-uniforms.beta));\n }\n`,Hx=class{constructor(t){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let x = getX(b, r, c, d);\n var sum = 0.0;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let idx = d + i;\n if (idx >= 0 && idx < uniforms.xShape[3]) {\n let z = getX(b, r, c, idx);\n sum = sum + z * z;\n }\n }\n ${kW}\n\n setOutputAtIndex(index, x * powValue);\n }\n }\n `}},Kx=class{constructor(t,e){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(e<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${e}`),this.outputShape=t,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey=\"lrn_shared\"}getUserCode(){return`\n var lrnSub: array;\n const elementsPerWorkgroup = ${this.elementsPerWorkgroup};\n const maxAllowRadius = ${this.maxAllowRadius};\n\n ${G()} {\n let localDepth = i32(localId.x);\n let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup;\n let xDepth = workgroupDepth + localDepth - maxAllowRadius;\n let b = i32(globalId.z) / uniforms.xShape[1];\n let r = i32(globalId.z) - b * uniforms.xShape[1];\n let c = i32(globalId.y);\n let d = workgroupDepth + localDepth;\n\n var x = 0.0;\n if (xDepth >= 0 && xDepth < uniforms.xShape[3]) {\n x = getX(b, r, c, xDepth);\n }\n lrnSub[localDepth] = x;\n workgroupBarrier();\n\n if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) {\n var sum = 0.0;\n let index = localDepth + maxAllowRadius;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let z = lrnSub[index + i];\n sum = sum + z * z;\n }\n ${kW}\n\n setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);\n }\n } `}};function Cpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Hx(n.shape):u=new Kx(n.shape,s);let c=[{type:\"int32\",data:[s]},{type:\"float32\",data:[a]},{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}];return e.runWebGPUProgram(u,[n],n.dtype,c)}var NW={kernelName:zn,backendName:\"webgpu\",kernelFunc:Cpe};var qx=class{constructor(t){this.outputShape=[],this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.uniforms=\"depthRadius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn_grad\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n\n let MIN_DEPTH_BEGIN = 0;\n let MAX_DEPTH_END = uniforms.outShape[3];\n var result = 0.0;\n for (var d = MIN_DEPTH_BEGIN; d < MAX_DEPTH_END; d++) {\n let depthBegin = max(MIN_DEPTH_BEGIN, d - uniforms.depthRadius);\n let depthEnd = min(MAX_DEPTH_END, d + uniforms.depthRadius + 1);\n\n var norm = 0.0;\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n } else {\n break;\n }\n }\n\n norm = uniforms.alpha * norm + uniforms.bias;\n\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n var dyi = -2.0 * uniforms.alpha * uniforms.beta\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * uniforms.beta);\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n } else {\n break;\n }\n }\n }\n\n setOutputAtIndex(index, result);\n }\n }\n `}};function wpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new qx(n.shape),m=[{type:\"int32\",data:[i]},{type:\"float32\",data:[p]},{type:\"float32\",data:[u]},{type:\"float32\",data:[c]}];return e.runWebGPUProgram(l,[n,s,a],n.dtype,m)}var TW={kernelName:Qa,backendName:\"webgpu\",kernelFunc:wpe};var Spe=et({opType:fe.MAX,cpuKernelImpl:Sz}),_W={kernelName:Wn,backendName:\"webgpu\",kernelFunc:Spe};function Ipe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=w.computePool2DInfo(n.shape,s,a,u,i,p);return ax(n,c,\"max\",e)}var $W={kernelName:Un,backendName:\"webgpu\",kernelFunc:Ipe};function vpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new _u(l,\"max\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return e.runWebGPUProgram(m,[n],n.dtype,d)}var EW={kernelName:ia,backendName:\"webgpu\",kernelFunc:vpe};var jx=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool2DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] - 1;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR += uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC += uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wR * uniforms.filterDims[1] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Xx=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool3DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] * uniforms.filterDims[2] - 1;\n\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wD * uniforms.filterDims[1] * uniforms.filterDims[2] + wR * uniforms.filterDims[2] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n }\n\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function kpe(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new _u(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.inDepth,m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=e.runWebGPUProgram(d,[a],\"int32\",f),g=new Xx(m);f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outDepth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=e.runWebGPUProgram(g,[n,h],a.dtype,f);return e.disposeData(h.dataId),x}var RW={kernelName:Ki,backendName:\"webgpu\",kernelFunc:kpe};function Npe(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s,output:a}=t,i=s;fm([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=new za(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=e.runWebGPUProgram(d,[i],\"int32\",f),g=new jx(m);f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=e.runWebGPUProgram(g,[n,h],i.dtype,f);return e.disposeData(h.dataId),x}var DW={kernelName:Hi,backendName:\"webgpu\",kernelFunc:Npe};function Tpe(r){let{inputs:t,backend:e,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=t;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(p.shape,n,s,u,a),l=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new za(c,\"max\",!1),d=e.runWebGPUProgram(m,[p],p.dtype,l);m=new za(c,\"max\",!0,!0,i);let f=e.runWebGPUProgram(m,[p],\"int32\",l);return[d,f]}var AW={kernelName:ua,backendName:\"webgpu\",kernelFunc:Tpe};function _pe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return eo(n,s,a,\"min\",e)}var FW={kernelName:Hn,backendName:\"webgpu\",kernelFunc:_pe};var $pe=et({opType:fe.MIN,cpuKernelImpl:Iz}),PW={kernelName:Kn,backendName:\"webgpu\",kernelFunc:$pe};var Yx=class{constructor(t,e,o){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.map((n,s)=>n[0]+t[s]+n[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=t,e.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let t=this.xShape.length,e=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(\",\"),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${t>1?`[${c}]`:\"\"}`).join(\",\"),n=t===1?\"start\":\"start[i]\",s=t===1?\"end\":\"end[i]\",a=t===1?\"outC\":\"outC[i]\",i=ht(t),p=t>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,t):\"coords\";return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let start = ${i}(${e});\n let end = ${i}(${o});\n var outC = getCoordsFromIndex(index);\n for (var i = 0; i < ${t}; i = i + 1) {\n if (${a} < ${n}) {\n ${a} = ${n} * 2 - ${a} - ${this.offset};\n } else if(${a} >= ${s}) {\n ${a} = (${s} - 1) * 2 - ${a} + ${this.offset};\n }\n }\n let coords = outC - start;\n setOutputAtIndex(index, getX(${p}));\n }\n }\n `}};var OW={kernelName:qn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=e,i=n.map(c=>({type:\"int32\",data:[c[0],c[1]]})),p=new Yx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var Epe=et({opType:fe.MOD}),MW={kernelName:jn,backendName:\"webgpu\",kernelFunc:Epe};var Qx=class{constructor(t,e){this.variableNames=[\"probs\"],this.outputShape=[],this.uniforms=\"seed : f32, numOutcomes: i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"multinomial\"}getUserCode(){return`\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n fn random (seed : f32, resultUV : vec2) -> f32 {\n let HASHSCALE1 = 443.8975;\n let p = resultUV * seed;\n var p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 = p3 + dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n\n let resUV = vec2(f32(coords[1]) / f32(uniforms.outShape[1]),\n f32(coords[0]) / f32(uniforms.outShape[0]));\n let r = random(uniforms.seed, resUV);\n var cdf = 0.0;\n for (var i = 0; i < uniforms.numOutcomes - 1; i = i + 1) {\n cdf = cdf + getProbs(batch, i);\n\n if (r < cdf) {\n setOutputAtIndexI32(index, i);\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutputAtIndexI32(index, uniforms.numOutcomes - 1);\n }\n }\n `}};var Zx=class{constructor(t){this.variableNames=[\"logits\"],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey=\"softmax\"}getUserCode(){return`\n var buf : array;\n var rowMaxShared : f32;\n var rowSumShared : f32;\n const blockSize = ${this.workgroupSize[0]};\n ${G(\"index\")} {\n let row = index / blockSize;\n let tid = i32(localId.x);\n let cols = uniforms.outShape[1];\n\n var threadMax = -3.402823e+38f;\n for (var col = tid; col < cols; col += blockSize) {\n let value = getLogits(row, col);\n threadMax = max(threadMax, value);\n }\n if (tid < cols) {\n buf[tid] = threadMax;\n }\n workgroupBarrier();\n\n var reduceSize = min(cols, blockSize);\n for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) {\n reduceSize = currSize + (reduceSize & 1);\n if (tid < currSize) {\n buf[tid] = max(buf[tid], buf[tid + reduceSize]);\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowMaxShared = buf[0];\n }\n workgroupBarrier();\n\n var threadSum = 0.0;\n for (var col = tid; col < cols; col += blockSize) {\n let subExp = exp(getLogits(row, col) - rowMaxShared);\n threadSum += subExp;\n }\n buf[tid] = threadSum;\n workgroupBarrier();\n\n for (var currSize = blockSize >> 1; currSize > 0; currSize = currSize >> 1) {\n if (tid < currSize) {\n buf[tid] = buf[tid] + buf[tid + currSize];\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowSumShared = buf[0];\n }\n workgroupBarrier();\n\n for (var col = tid; col < cols; col += blockSize) {\n let value = exp(getLogits(row, col) - rowMaxShared) / rowSumShared;\n setOutputAtCoords(row, col, value);\n }\n }\n `}};function p0(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{dim:s}=o,a=pe({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new Zx(a.shape),p=e.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:e,attrs:{shape:n.shape}});return e.disposeData(a.dataId),e.disposeData(p.dataId),u}var LW={kernelName:vs,backendName:\"webgpu\",kernelFunc:p0};function Rpe(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{numSamples:s,seed:a,normalized:i}=o,p=i?n:p0({inputs:{logits:n},backend:e,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Qx(u,s),m=[{type:\"float32\",data:[a]},{type:\"int32\",data:[c]}],d=e.runWebGPUProgram(l,[p],\"int32\",m);return i||e.disposeData(p.dataId),d}var BW={kernelName:Xn,backendName:\"webgpu\",kernelFunc:Rpe};function Dpe(r){let{inputs:t,backend:e}=r,{x:o}=t;if(e.shouldExecuteOnCPU([o])){let s=e.tensorMap.get(o.dataId),[a,i]=kz(s.values,o.shape,o.dtype);return e.makeTensorInfo(i,o.dtype,a)}let n=new Jr(o.shape,Z.NEG);return e.runWebGPUProgram(n,[o],o.dtype)}var zW={kernelName:pa,backendName:\"webgpu\",kernelFunc:Dpe};function Ape(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=e.readSync(n.dataId),c=e.readSync(s.dataId),{selectedIndices:l}=Wt.nonMaxSuppressionV3Impl(u,c,a,i,p);return e.makeTensorInfo([l.length],\"int32\",new Int32Array(l))}var VW={kernelName:Zn,backendName:\"webgpu\",kernelFunc:Ape};function Fpe(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=e.readSync(n.dataId),l=e.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Wt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[e.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),e.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var WW={kernelName:Jn,backendName:\"webgpu\",kernelFunc:Fpe};var Jx=class{constructor(t,e){this.variableNames=[\"x\"],this.uniforms=\"onValue : f32, offValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"onehot\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,\n f32(i32(round(getX(coords.x))) == coords.y)));\n }\n }\n `}};function Ppe(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n}=t,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Jx(u,a),l=pe({inputs:{x:n},backend:e,attrs:{shape:[u]}}),m=[{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}],d=e.runWebGPUProgram(c,[l],s,m);e.disposeData(l.dataId);let f=[...n.shape,a],h=pe({inputs:{x:d},backend:e,attrs:{shape:f}});return e.disposeData(d.dataId),h}var UW={kernelName:es,backendName:\"webgpu\",kernelFunc:Ppe};function bm(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype===\"complex64\"){let n=Ni({inputs:{input:o},backend:e}),s=bm({inputs:{x:n},backend:e}),a=Mp({inputs:{input:o},backend:e}),i=bm({inputs:{x:a},backend:e}),p=yo({inputs:{real:s,imag:i},backend:e});return e.disposeData(n.dataId),e.disposeData(s.dataId),e.disposeData(a.dataId),e.disposeData(i.dataId),p}else return kt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:e})}var GW={kernelName:Sa,backendName:\"webgpu\",kernelFunc:bm};function HW(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(o.dtype===\"complex64\"){let n=Ni({inputs:{input:o},backend:e}),s=HW({inputs:{x:n},backend:e}),a=Mp({inputs:{input:o},backend:e}),i=bm({inputs:{x:a},backend:e}),p=yo({inputs:{real:s,imag:i},backend:e});return e.disposeData(n.dataId),e.disposeData(s.dataId),e.disposeData(a.dataId),e.disposeData(i.dataId),p}else return kt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:e})}var KW={kernelName:ca,backendName:\"webgpu\",kernelFunc:HW};function Ope(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o;if(t.length===1)return Mx({inputs:{input:t[0]},backend:e,attrs:{dim:n}});let s=t[0].shape,a=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=t.map(c=>{let l=Mx({inputs:{input:c},backend:e,attrs:{dim:n}});return i.push(l),l}),u=n0({inputs:p,backend:e,attrs:{axis:n}});return i.forEach(c=>e.disposeData(c.dataId)),u}var qW={kernelName:la,backendName:\"webgpu\",kernelFunc:Ope};function c0(r,t=!1){let e=r.length,o=ht(e),n=r.map((l,m)=>`uniforms.pad${m}[0]`).join(\",\"),s=r.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:\"\"}`).join(\",\"),a=e>1?`${o}(${n})`:`${n}`,i=e>1?`${o}(${s})`:`${s}`,p=e>1?\"any(paddedCoords < start)\":\"paddedCoords < start\",u=e>1?\"any(paddedCoords >= end)\":\"paddedCoords >= end\",c=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n let start = ${a};\n let end = ${i};\n if (${p} || ${u}) {\n setOutputAtIndex(index, ${t?0:\"uniforms.constantValue\"});\n } else {\n let coords = paddedCoords - start;\n setOutputAtIndex(index, getX(${c}));\n }\n `}var ey=class{constructor(t,e){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.map((o,n)=>o[0]+t[n]+o[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),e.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=t,this.shaderKey=\"pad\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let paddedCoords = getCoordsFromIndex(index);\n ${c0(this.xShape)}\n }\n }\n `}};var Mpe=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return Ft({inputs:{x:n},backend:e});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return kt({backend:e,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:\"float32\",data:[a]}];s.map(u=>i.push({type:\"int32\",data:[u[0],u[1]]}));let p=new ey(n.shape,s);return e.runWebGPUProgram(p,[n],n.dtype,i)},jW={kernelName:ts,backendName:\"webgpu\",kernelFunc:Mpe};var Lpe=et({opType:fe.POW}),XW={kernelName:rs,backendName:\"webgpu\",kernelFunc:Lpe};function Bpe(r){let{inputs:t,backend:e}=r,{x:o,alpha:n}=t,s=new ki(fe.PRELU,o.shape,n.shape);return e.runWebGPUProgram(s,[o,n],\"float32\")}var YW={kernelName:os,backendName:\"webgpu\",kernelFunc:Bpe};function zpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return eo(n,s,a,\"prod\",e)}var QW={kernelName:ns,backendName:\"webgpu\",kernelFunc:zpe};var Vpe=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=_z(o,n,s,a);return t.makeTensorInfo([i.length],a,i)},ZW={kernelName:ma,backendName:\"webgpu\",kernelFunc:Vpe};var Wpe=et({opType:fe.DIV}),JW={kernelName:hn,backendName:\"webgpu\",kernelFunc:Wpe};var Upe=ye({opType:Z.RECIPROCAL}),eU={kernelName:ss,backendName:\"webgpu\",kernelFunc:Upe};var Gpe=ye({opType:Z.RELU}),tU={kernelName:as,backendName:\"webgpu\",kernelFunc:Gpe};var Hpe=ye({opType:Z.RELU6}),rU={kernelName:ps,backendName:\"webgpu\",kernelFunc:Hpe};var ty=class{constructor(t,e,o){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, halfPixelCenters : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t[0],e,o,t[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC =\n (vec2(rc) + vec2(uniforms.halfPixelCenters)) *\n effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters);\n\n // Compute the four integer indices.\n let sourceFloorRC = vec2(sourceFracIndexRC);\n let sourceCeilRC = vec2(\n min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC)));\n\n let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n let fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n let top = topLeft + (topRight - topLeft) * fracRC.y;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n let newValue = top + (bottom - top) * fracRC.x;\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function Kpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[i?.5:0]}],f=new ty(n.shape,p,u);return e.runWebGPUProgram(f,[n],\"float32\",d)}var oU={kernelName:us,backendName:\"webgpu\",kernelFunc:Kpe};var ry=class{constructor(t,e){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32,\n invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=e,this.shaderKey=`resizeBilinearBackprop_${e}`}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(startRLerp - f32(uniforms.winHeight / 2));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(startCLerp - f32(uniforms.winWidth / 2));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let dxR = f32(dyR) * uniforms.heightScale;\n let topDxRIndex = i32(floor(dxR));\n let bottomDxRIndex = i32(min(ceil(dxR), f32(uniforms.outShape[1] - 1)));\n let dxRLerp = dxR - f32(topDxRIndex);\n let inverseDxRLerp = 1.0 - dxRLerp;\n\n let dxC = f32(dyC) * uniforms.widthScale;\n let leftDxCIndex = i32(floor(dxC));\n let rightDxCIndex = i32(min(ceil(dxC), f32(uniforms.outShape[2] - 1)));\n let dxCLerp = dxC - f32(leftDxCIndex);\n let inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function qpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ry(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[d]},{type:\"float32\",data:[f]},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return e.runWebGPUProgram(C,[s],s.dtype,S)}var nU={kernelName:ei,backendName:\"webgpu\",kernelFunc:qpe};var oy=class{constructor(t,e,o,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, roundBase : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t[0],e,o,t[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let t;return this.halfPixelCenters?t=\"max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":t=\"vec2(rc) * effectiveInputOverOutputRatioRC\",`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC = ${t};\n\n // Compute the coordinators of nearest neighbor point.\n let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n let sourceNearestRC = vec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function jpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[s?.5:0]}],f=new oy(n.shape,p,u,a);return e.runWebGPUProgram(f,[n],n.dtype,d)}var sU={kernelName:is,backendName:\"webgpu\",kernelFunc:jpe};var ny=class{constructor(t,e){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32,\n winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=e,this.shaderKey=`resizeNearestNeigborBackprop_${e}`}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(floor(startRLerp - f32(uniforms.winHeight / 2)));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(floor(startCLerp - f32(uniforms.winWidth / 2)));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let sourceFracRow = f32(uniforms.effectiveXSize[0]) *\n (f32(dyR) / f32(uniforms.effectiveYSize[0]));\n\n let sourceFracCol = f32(uniforms.effectiveXSize[1]) *\n (f32(dyC) / f32(uniforms.effectiveYSize[1]));\n\n let sourceNearestRow =\n i32(min(f32(uniforms.outShape[1] - 1),\n ${this.alignCorners?\"floor(sourceFracRow + 0.5)\":\"floor(sourceFracRow)\"}));\n\n let sourceNearestCol =\n i32(min(f32(uniforms.outShape[2] - 1),\n ${this.alignCorners?\"floor(sourceFracCol + 0.5)\":\"floor(sourceFracCol)\"}));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function Xpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ny(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return e.runWebGPUProgram(C,[s],s.dtype,S)}var aU={kernelName:Ja,backendName:\"webgpu\",kernelFunc:Xpe};var sy=class{constructor(t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\" axis : vec4,\",this.shaderKey=\"reverse\"}getUserCode(){return`\n \n // Using uniform variables as judging conditions, so the function has\n // coherent execution within all threads.\n fn getReverseCoords(coords : vec4) -> vec4 {\n var reverseCoords = coords;\n if (uniforms.axis[0] == 1) {\n reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;\n }\n if (uniforms.axis[1] == 1) {\n reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;\n }\n if (uniforms.axis[2] == 1) {\n reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;\n }\n if (uniforms.axis[3] == 1) {\n reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;\n }\n\n return reverseCoords;\n }\n \n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let reverseCoords = getReverseCoords(coords);\n setOutputAtIndex(index, getX(reverseCoords[0],\n reverseCoords[1], reverseCoords[2], reverseCoords[3]));\n }\n }\n `}};function Ype(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=n.shape.length;if(a===0)return Ft({inputs:{x:n},backend:e});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:\"int32\",data:c}],m=pe({inputs:{x:n},backend:e,attrs:{shape:p}}),d=new sy(p),f=e.runWebGPUProgram(d,[m],m.dtype,l);e.disposeData(m.dataId);let h=pe({inputs:{x:f},backend:e,attrs:{shape:i}});return e.disposeData(f.dataId),h}var iU={kernelName:cs,backendName:\"webgpu\",kernelFunc:Ype};var ay=class{constructor(t,e){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=t,typeof e==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n uniforms.sinRadians;\n let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n uniforms.cosRadians;\n let coordX = i32(round(coordXFloat + uniforms.centerX));\n let coordY = i32(round(coordYFloat + uniforms.centerY));\n ${this.fillSnippet}\n if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n coordY < uniforms.xShape[1]) {\n outputValue = getX(coords[0], coordY, coordX, coords[3]);\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var uU={kernelName:As,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=t,i=e,p=new ay(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:\"float32\",data:[u]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(n)]},{type:\"float32\",data:[Math.cos(n)]}];return typeof s==\"number\"?l.push({type:\"float32\",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:\"float32\",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Qpe=ye({opType:Z.ROUND}),pU={kernelName:ls,backendName:\"webgpu\",kernelFunc:Qpe};var Zpe=ye({opType:Z.RSQRT,cpuKernelImpl:$z}),cU={kernelName:ms,backendName:\"webgpu\",kernelFunc:Zpe};var Va=class{constructor(t,e,o,n,s,a,i,p=!0){this.variableNames=[\"updates\",\"indices\"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.sliceDimGreaterThanOne=e>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=ht(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let t=\"\";this.indicesRank===1?t=\"coords[0]\":this.indicesRank===2&&(t=\"coords[0], j\");let e=`getIndices(${t})`,o=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",n=\"\",s=\"\";this.dispatchLayout.x.length===1?(n=\"flattenedIndex\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n return index;\n }\n `):this.dispatchLayout.x.length===2&&(n=\"vec2(flattenedIndex, coords[1])\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 {\n // N.B. |updates| could be a scalar tensor, conceptually representing a\n // 2D tensor with all values equal to that. By design, its size must be\n // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n // gives the other.\n let sliceSize = uniforms.outShape[1];\n let d0 = index / sliceSize;\n let d1 = index - d0 * sliceSize;\n return vec2(d0, d1);\n }\n `);let i=`getUpdates(${Array.from({length:this.updatesRank},(u,c)=>`coords[${c}]`).join(\", \")})`;return`\n ${s}\n ${G(\"index\")} {\n if (index < uniforms.updatesSize) {\n let coords = getUpdatesCoordsFromFlatIndex(index);\n var flattenedIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexInside = i32(round(${e}));\n flattenedIndex = flattenedIndex + indexInside * ${o};\n }\n let updateValue =\n ${Nu(this.type)}(${i});\n let flatIndex = getOutputIndexFromCoords(${n});\n\n ${this.sumDupeIndices?Qr(\"&result[flatIndex]\",\"updateValue\",this.type):\"atomicStore(&result[flatIndex], bitcast(updateValue));\"}\n }\n }`}};function Jpe(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n,updates:s}=t,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return e.makeTensorInfo(a,n.dtype);let d=pe({inputs:{x:n},backend:e,attrs:{shape:[p,i]}}),f=pe({inputs:{x:s},backend:e,attrs:{shape:[p,u]}}),h=f.dtype,g=kt({backend:e,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[x]}],C=new Va(f.shape,i,d.shape.length,f.shape.length,c,m,h),S=e.runWebGPUProgram(C,[f,d],h,b,g),k=pe({inputs:{x:S},backend:e,attrs:{shape:a}});return e.disposeData(d.dataId),e.disposeData(f.dataId),e.disposeData(S.dataId),k}var lU={kernelName:ds,backendName:\"webgpu\",kernelFunc:Jpe};var iy=class{constructor(t,e){this.outputShape=[],this.variableNames=[\"sortedSequence\",\"values\"],this.uniforms=\"numInputs : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=e,this.shaderKey=`search_sorted_${e}`}getUserCode(){return`\n fn findBound(batch: i32, value: f32) -> i32 {\n var left = i32(0);\n var right = uniforms.numInputs;\n while (left < right) {\n var mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${this.side===\"left\"?\"<\":\"<=\"} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let value = getValuesByOutputIndex(index);\n setOutputAtIndexI32(index, findBound(coords[0], value));\n }\n }\n `}};function ece(r){let{inputs:t,backend:e,attrs:o}=r,{sortedSequence:n,values:s}=t,{side:a}=o,i=new iy([s.shape[0],s.shape[1]],a),p=[{type:\"int32\",data:[n.shape[1]]}];return e.runWebGPUProgram(i,[n,s],\"int32\",p)}var mU={kernelName:hs,backendName:\"webgpu\",kernelFunc:ece};var uy=class{constructor(t,e,o){this.variableNames=[\"c\",\"a\",\"b\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=t,this.rank=o,this.shaderKey=\"select\"}getUserCode(){let t,e;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)e=\"resRC\",t=\"resRC\";else{let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[],a=[];for(let i=0;i= 1.0) {\n setOutputAtIndex(index, getA(${e}));\n } else {\n setOutputAtIndex(index, getB(${e}));\n }\n }\n }\n `}};function tce(r){let{inputs:t,backend:e}=r,{condition:o,t:n,e:s}=t,a=new uy(o.shape.length,n.shape,n.shape.length);return e.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var dU={kernelName:fa,backendName:\"webgpu\",kernelFunc:tce};var rce=ye({opType:Z.SELU}),fU={kernelName:gs,backendName:\"webgpu\",kernelFunc:rce};var oce=ye({opType:Z.SIGMOID}),hU={kernelName:Cs,backendName:\"webgpu\",kernelFunc:oce};var nce=ye({opType:Z.SIGN}),gU={kernelName:bs,backendName:\"webgpu\",kernelFunc:nce};var sce=ye({opType:Z.SIN}),xU={kernelName:xs,backendName:\"webgpu\",kernelFunc:sce};var ace=ye({opType:Z.SINH}),yU={kernelName:ys,backendName:\"webgpu\",kernelFunc:ace};var ice=ye({opType:Z.SOFTPLUS}),bU={kernelName:ws,backendName:\"webgpu\",kernelFunc:ice};var py=class{constructor(t,e,o,n,s,a){this.variableNames=[\"x\"],this.outputShape=[],this.uniforms=\"\",this.workgroupSize=[64,1,1],this.size=!0;let i=new Array(n.length);for(let p=0;p{this.uniforms+=` pad${u} : vec2,`}),this.shaderKey=`spaceToBatchND_${s}`}getUserCode(){let t=ht(this.outputShape.length),e=Zv(this.newDim);return`\n ${cm(this.paddedXShape,\"PaddedX\")}\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${t}(${e}), uniforms.reshapedPaddedXShape);\n let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex);\n ${c0(this.xShape,!0)}\n }\n }\n `}};var uce=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=[[0,0]];p.push(...a);for(let b=1+s.length;bb[0]+n.shape[C]+b[1]),c=w.getReshaped(u,s,i,!1),l=w.getPermuted(c.length,s.length,!1),m=w.getReshapedPermuted(u,s,i,!1),d=y.computeStrides(u),f=new py(n.shape,u,p,c,l,d.length),h=[{type:\"int32\",data:c},{type:\"int32\",data:d}];p.map(b=>h.push({type:\"int32\",data:[b[0],b[1]]}));let g=e.runWebGPUProgram(f,[n],n.dtype,h),x=pe({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeData(g.dataId),x},CU={kernelName:ga,backendName:\"webgpu\",kernelFunc:uce};var cy=class{constructor(t,e,o){this.variableNames=[\"input\",\"indices\",\"segmentIds\"],this.outputShape=[],this.uniforms=\"segmentSize : i32, sparseSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.type=o,this.dispatchLayout=X([e]),this.dispatch=H(this.dispatchLayout,[e],this.workgroupSize),this.shaderKey=\"sparseSegmentSum\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.sparseSize) {\n let indexInSegmentIds = index / uniforms.segmentSize;\n let indexInSegment = index % uniforms.segmentSize;\n let indexInInput = indices[indexInSegmentIds];\n let segmentId = segmentIds[indexInSegmentIds];\n\n let value = input[indexInInput * uniforms.segmentSize + indexInSegment];\n let outIndex = segmentId * uniforms.segmentSize + indexInSegment;\n ${Qr(\"&result[outIndex]\",\"value\",this.type)}\n }\n }\n `}},ly=class{constructor(t,e){this.variableNames=[\"segmentIds\"],this.outputShape=[],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=[t],this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),this.shaderKey=\"sparseSegmentIdCountProgram\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.segmentIdsShape) {\n let segmentId = segmentIds[index];\n ${Qr(\"&result[segmentId]\",\"1\",\"int32\")}\n }\n }\n `}},my=class{constructor(t,e){this.variableNames=[\"segmentSum\",\"sameSegmentIdCount\"],this.outputShape=[],this.uniforms=\"segmentSize : i32\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.type=e,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.shaderKey=\"sparseSegmentMean\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let segmentId = index / uniforms.segmentSize;\n let count = sameSegmentIdCount[segmentId];\n if (count != 0) {\n ${this.type===\"float32\"?\"setOutputAtIndex(index, segmentSum[index] / f32(count));\":\"setOutputAtIndexI32(index, segmentSum[index] / count);\"}\n }\n }\n }\n `}};function dy(r,t,e,o=!1,n){let a=y.sizeFromShape(r.shape)/r.shape[0],i=r.dtype,p=y.sizeFromShape(t.shape),u=n.readSync(e.dataId),l=p>0?u[p-1]+1:0,m,d=r.shape.slice();d[0]=l;let f=p*a,h=kt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new cy(d,f,i);let g=[{type:\"int32\",data:[a]},{type:\"int32\",data:[f]}],x=n.runWebGPUProgram(m,[r,t,e],i,g,h);if(o)return x;let b=kt({backend:n,attrs:{shape:[l],value:0,dtype:\"int32\"}});m=new ly(l,e.shape);let C=n.runWebGPUProgram(m,[e],\"int32\",null,b),S=kt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new my(d,i),g=[{type:\"int32\",data:[a]}];let k=n.runWebGPUProgram(m,[x,C],i,g,S);return n.disposeData(x.dataId),n.disposeData(C.dataId),k}function pce(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;return dy(o,n,s,!1,e)}var wU={kernelName:ya,backendName:\"webgpu\",kernelFunc:pce};function cce(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;return dy(o,n,s,!0,e)}var SU={kernelName:ba,backendName:\"webgpu\",kernelFunc:cce};var fy=class{constructor(t,e){this.variableNames=[\"A\"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(t.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${t}aShape)`;let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=5){let p=e.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=Oz(c,s);return e.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new fy(n.shape,s);return e.runWebGPUProgram(a,[n],n.dtype)}var IU={kernelName:po,backendName:\"webgpu\",kernelFunc:Cm};function mce(r){let{inputs:t,backend:e,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=t,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let R=e.bufferSync(n),D=e.bufferSync(s),P=y.decodeString(e.readSync(a.dataId)[0]),O=Ez(R,D,i,m,c,u,p,l,P,d);return e.makeTensorInfo(i,O.dtype,O.values)}let f=[m/c,c],h=pe({inputs:{x:n},backend:e,attrs:{shape:[u,p]}}),g=s.shape.length?pe({inputs:{x:s},backend:e,attrs:{shape:[u,c]}}):Ft({inputs:{x:s},backend:e}),x=g.dtype,b=e.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=pe({inputs:{x:a},backend:e,attrs:{shape:Array(f.length).fill(1)}}),S=Cm({inputs:{x:C},backend:e,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:\"int32\",data:[p]},{type:\"int32\",data:l},{type:\"int32\",data:[k]}];switch(u){case 0:break;case 1:{let R=new Va([u,c],p,h.shape.length,g.shape.length,l,f,x,d);e.runWebGPUProgram(R,[g,h],x,_,S)}break;default:{let R=new Va([u,c],p,h.shape.length,b.shape.length,l,f,x,d);e.runWebGPUProgram(R,[b,h],x,_,S)}{let R=new Va([u,c],p,h.shape.length,g.shape.length,l,f,x);e.runWebGPUProgram(R,[g,h],x,_,S)}}let E=pe({inputs:{x:S},backend:e,attrs:{shape:i}});return e.disposeData(h.dataId),e.disposeData(g.dataId),e.disposeData(C.dataId),e.disposeData(b.dataId),e.disposeData(S.dataId),E}var vU={kernelName:ks,backendName:\"webgpu\",kernelFunc:mce};function dce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Hs({inputs:{x:n},backend:e,attrs:{begin:c,size:d}});return c[i]+=m,f})}var kU={kernelName:xa,backendName:\"webgpu\",kernelFunc:dce};var fce=ye({opType:Z.SQRT}),NU={kernelName:Ss,backendName:\"webgpu\",kernelFunc:fce};var TU={kernelName:Xi,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:t})=>{let{x:e}=r,o=t,n=new Jr(e.shape,Z.SQUARE);return o.runWebGPUProgram(n,[e],e.dtype)}};var hce=et({opType:fe.SQUARED_DIFFERENCE}),_U={kernelName:Ns,backendName:\"webgpu\",kernelFunc:hce};function gce({inputs:r,attrs:t,backend:e}){let{x:o}=r,n=new Jr(o.shape,Z.STEP,\"stepAlpha : f32,\"),s=[{type:\"float32\",data:[t.alpha]}];return e.runWebGPUProgram(n,[o],o.dtype,s)}var $U={kernelName:So,backendName:\"webgpu\",kernelFunc:gce};var hy=class{constructor(t){this.variableNames=[\"x\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let e=ht(this.outputShape.length);this.uniforms=`begin : ${e}, strides : ${e}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let t=this.outputShape.length,e=\"\";if(t===1)e=\"coords * uniforms.strides + uniforms.begin\";else{let n=0;e=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, getX(${e}));\n }\n }\n `}};function xce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=ct.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=pe({inputs:{x:n},backend:e,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ct.computeOutShape(b,C,S),E=Hs({inputs:{x:n},backend:e,attrs:{begin:b,size:_}});k=pe({inputs:{x:E},backend:e,attrs:{shape:f}}),e.disposeData(E.dataId)}else if(e.shouldExecuteOnCPU([n])){let E=e.readSync(n.dataId),R=me(n.shape,n.dtype,E),D=Az(d,R,S,b);k=e.makeTensorInfo(f,n.dtype,D.values)}else{let E=new hy(d),R=[{type:\"int32\",data:b},{type:\"int32\",data:S}],D=e.runWebGPUProgram(E,[n],n.dtype,R);k=pe({inputs:{x:D},backend:e,attrs:{shape:f}}),e.disposeData(D.dataId)}return k}var EU={kernelName:Ts,backendName:\"webgpu\",kernelFunc:xce};function yce(r){let{inputs:t,backend:e,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=t,m=e.readSync(c.dataId),d=e.readSync(l.dataId),[f,h]=Fz(m,d,n,s,a,i,p,u);return[e.makeTensorInfo([f.length],\"string\",f),e.makeTensorInfo(l.shape,\"int32\",h)]}var RU={kernelName:Ca,backendName:\"webgpu\",kernelFunc:yce};var bce=et({opType:fe.SUB,cpuKernelImpl:Pz,supportsComplex:!0}),DU={kernelName:_s,backendName:\"webgpu\",kernelFunc:bce};var Cce=ye({opType:Z.TAN}),AU={kernelName:$s,backendName:\"webgpu\",kernelFunc:Cce};var wce=ye({opType:Z.TANH}),FU={kernelName:Es,backendName:\"webgpu\",kernelFunc:wce};function Sce(r){let{inputs:t,backend:e,attrs:o}=r,{tensor:n,indices:s,updates:a}=t,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return e.makeTensorInfo(n.shape,s.dtype);let d=[],f=pe({inputs:{x:s},backend:e,attrs:{shape:[p,i]}});d.push(f);let h=pe({inputs:{x:a},backend:e,attrs:{shape:[p,u]}});d.push(h);let g=pe({inputs:{x:n},backend:e,attrs:{shape:m}});d.push(g);let x=Cm({inputs:{x:g},backend:e,attrs:{reps:Array(m.length).fill(1)}}),b=new Va([p,u],i,f.shape.length,h.shape.length,c,m,n.dtype,!1),C=y.sizeFromShape([p,u]),S=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[C]}],k=e.runWebGPUProgram(b,[h,f],g.dtype,S,x);d.push(k);let _=pe({inputs:{x:k},backend:e,attrs:{shape:n.shape}});return d.forEach(E=>e.disposeData(E.dataId)),_}var PU={kernelName:fs,backendName:\"webgpu\",kernelFunc:Sce};var gy=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced\n // above, Figure5(a) shows that element[1] is in the second half of\n // the group when group size is 2, but it is in the first half of\n // the group when group size is 4.\n let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n var i = 0;\n if (isFirstInPair) {\n i = elemIdx;\n } else {\n i = elemIdx - uniforms.inc;\n }\n\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.inc;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.inc));\n }\n\n var x0 = f32(0.0);\n var x1 = f32(0.0);\n if (i0 < uniforms.inputSize) {\n x0 = getX(batch, i0);\n } else {\n x0 = uniforms.negativeInf;\n }\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = uniforms.negativeInf;\n }\n\n let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) {\n // Elements in opposite order of direction\n let iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}},xy=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _\n // (k=4), we only need to output the indices at positions |, the\n // indices at positions _ can be thrown away, see Figure5(b) After\n // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n // above.\n // For example, the paper shows we only need to output the orange\n // bars. The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back to\n // the previous sequence to find the corresponding value, we need\n // to double the index. When we double the index, we basically\n // interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n // position of each 2k positions by - elemIdx % k. E.g. for output\n // at index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n var i = 0;\n if (elemIdx < uniforms.k) {\n i = elemIdx;\n } else {\n i = elemIdx * 2 - elemIdx % uniforms.k;\n }\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.k;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.k));\n }\n\n let x0 = getX(batch, i0);\n var x1 = f32(0.0);\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = x0;\n }\n\n if (x0 >= x1) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}};function nl(r,t){t!==null&&r.disposeData(t.dataId)}function OU(r){let t=1;for(;tf===null?[l,l]:[l,f],g=(k,_,E)=>{let R=h(),D=new gy(E),O=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[_]}],M=f;f=e.runWebGPUProgram(D,R,\"int32\",O),nl(e,M)};for(let k=1;k=1;E/=2)g(_,E,[c,d])}for(let k=d;k>m;k/=2){let _=h(),E=new xy([c,k/2]),D=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[m]}],P=f;f=e.runWebGPUProgram(E,_,\"int32\",D),nl(e,P);let O=m/2,M=O*2;for(let L=O;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=Hs({inputs:{x:f},backend:e,attrs:{begin:0,size:[c,s]}}),nl(e,x);let b=u0({inputs:{x:l,indices:f},backend:e,attrs:{axis:1,batchDims:1}});nl(e,l);let C=i.slice(0,-1);C.push(s),x=f,f=pe({inputs:{x:f},attrs:{shape:C},backend:e}),nl(e,x);let S=b;return b=pe({inputs:{x:b},attrs:{shape:C},backend:e}),nl(e,S),[b,f]}var MU={kernelName:Rs,backendName:\"webgpu\",kernelFunc:Ice};var yy=class{constructor(t){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n fn mapCoord(outCoord : f32, len : f32) -> f32{\n var inCoord = outCoord;\n if(uniforms.fillModeId == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n inCoord;\n }\n if (inCoord < -len) {\n inCoord = inCoord + sz2;\n } else {\n inCoord = -inCoord - 1.0;\n }\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n }\n return outCoord;\n }\n fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n channel : i32) -> f32 {\n var outputValue : f32;\n if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = uniforms.fillValue;\n }\n return outputValue;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var outputValue : f32;\n let batch = coords[0];\n let x = coords[2];\n let y = coords[1];\n let channel = coords[3];\n let xf = f32(x);\n let yf = f32(y);\n let a1 = getTransforms(batch, 0);\n let a2 = getTransforms(batch, 1);\n let a3 = getTransforms(batch, 2);\n let b1 = getTransforms(batch, 3);\n let b2 = getTransforms(batch, 4);\n let b3 = getTransforms(batch, 5);\n let c1 = getTransforms(batch, 6);\n let c2 = getTransforms(batch, 7);\n let projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = uniforms.fillValue;\n } else {\n let inX = (a1 * xf + a2 * yf + a3) / projection;\n let inY = (b1 * xf + b2 * yf + b3) / projection;\n let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n if (uniforms.interpolationModeId == 1) {\n let coordY = i32(round(mapY));\n let coordX = i32(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n let yFloor = floor(mapY);\n let xFloor = floor(mapX);\n let yCeil = yFloor + 1.0;\n let xCeil = xFloor + 1.0;\n let valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n let valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};function vce(r){let{inputs:t,backend:e,attrs:o}=r,{image:n,transforms:s}=t,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new yy(g),b=a===\"nearest\"?1:2,C;switch(i){case\"constant\":C=1;break;case\"reflect\":C=2;break;case\"wrap\":C=3;break;case\"nearest\":C=4;break;default:C=1;break}let S=[{type:\"int32\",data:[b]},{type:\"int32\",data:[C]},{type:\"float32\",data:[p]}];return e.runWebGPUProgram(x,[n,s],\"float32\",S)}var LU={kernelName:Ds,backendName:\"webgpu\",kernelFunc:vce};function kce(r){let{inputs:t,backend:e,attrs:o}=r,{value:n}=t,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;he.disposeData(h.dataId)),f}var BU={kernelName:wa,backendName:\"webgpu\",kernelFunc:kce};var by=class{constructor(t,e,o){if(this.outputShape=[],this.variableNames=[\"x\",\"segmentIds\"],this.uniforms=\"numSegments : i32, xSize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`UnsortedSegmentSum only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"unsortedSegmentSum\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.xSize) {\n let coords = getXCoordsFromIndex(index);\n let b = coords[0];\n let inCol = coords[1];\n\n let segmentId = i32(getSegmentIds(inCol));\n if (segmentId >= 0) {\n let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments;\n let value = getX(b, inCol);\n\n ${Qr(\"&result[flatIndex]\",\"value\",this.type)}\n }\n }\n }\n `}};function Nce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,segmentIds:s}=t,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=yr({inputs:{x:n},backend:e,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=pe({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=kt({backend:e,attrs:{shape:g,value:0,dtype:h}}),b=new by(f.shape,g,h),C=[{type:\"int32\",data:[a]},{type:\"int32\",data:[y.sizeFromShape(f.shape)]}],S=e.runWebGPUProgram(b,[f,s],h,C,x),k=pe({inputs:{x:S},backend:e,attrs:{shape:m}});p.push(S);let _=k;if(c!=null){p.push(k);let E=w.getUndoAxesPermutation(c);_=yr({inputs:{x:_},backend:e,attrs:{perm:E}})}return p.forEach(E=>e.disposeData(E.dataId)),_}var zU={kernelName:Ji,backendName:\"webgpu\",kernelFunc:Nce};var Tce=[rz,Bz,zz,Vz,Wz,Uz,Hz,Kz,qz,jz,Xz,Yz,Qz,Zz,Jz,rV,oV,nV,sV,aV,uV,pV,cV,fV,hV,gV,nz,yV,CV,wV,SV,IV,vV,kV,NV,TV,_V,$V,DV,AV,FV,PV,MV,LV,OV,BV,zV,VV,WV,UV,KV,qV,jV,XV,YV,QV,ZV,JV,eW,ez,tW,nW,rW,oW,sW,aW,iW,uW,pW,cW,lW,oz,mW,bV,dW,fW,hW,gW,xW,yW,bW,wW,CW,SW,IW,vW,NW,TW,eV,_W,$W,DW,EW,RW,AW,tV,FW,PW,OW,MW,BW,GV,zW,VW,WW,lV,UW,KW,qW,jW,XW,YW,QW,ZW,mV,JW,eU,tU,rU,tz,oU,nU,sU,aU,iU,uU,pU,cU,lU,mU,dU,fU,hU,gU,xU,yU,iV,$U,EU,RU,LW,bU,CU,wU,SU,vU,kU,NU,TU,_U,DU,HV,AU,FU,PU,IU,MU,LU,Gz,BU,zU,GW];for(let r of Tce)ri(r);var VU=\"4.11.0\",_ce=\"4.11.0\",$ce=\"4.11.0\",Ece=\"4.11.0\",Rce=\"4.11.0\",Dce=\"4.11.0\",Ace={tfjs:VU,\"tfjs-core\":VU,\"tfjs-converter\":_ce,\"tfjs-backend-cpu\":$ce,\"tfjs-backend-webgl\":Ece,\"tfjs-backend-wasm\":Rce,\"tfjs-backend-webgpu\":Dce};export{Xs as Abs,Wo as Acos,Uo as Acosh,np as AdadeltaOptimizer,sp as AdagradOptimizer,ap as AdamOptimizer,ip as AdamaxOptimizer,uo as Add,Go as AddN,Ho as All,Ko as Any,Ys as ArgMax,Qs as ArgMin,qo as Asin,jo as Asinh,Xo as Atan,Qo as Atan2,Yo as Atanh,Zo as AvgPool,Zs as AvgPool3D,Ai as AvgPool3DGrad,Di as AvgPoolGrad,pm as BackendWasm,Jo as BatchMatMul,Js as BatchToSpaceND,en as Bincount,ja as BitwiseAnd,ea as BroadcastArgs,Bce as BroadcastTo,bo as Cast,tn as Ceil,Co as ClipByValue,Fi as Complex,Pi as ComplexAbs,ta as Concat,rn as Conv2D,Oi as Conv2DBackpropFilter,on as Conv2DBackpropInput,nn as Conv3D,Xa as Conv3DBackpropFilterV2,sn as Conv3DBackpropInputV2,an as Cos,un as Cosh,ln as CropAndResize,pn as Cumprod,cn as Cumsum,zo as DataStorage,ra as DenseBincount,mn as DepthToSpace,dn as DepthwiseConv2dNative,Mi as DepthwiseConv2dNativeBackpropFilter,Li as DepthwiseConv2dNativeBackpropInput,oa as Diag,fn as Dilation2D,zi as Dilation2DBackpropFilter,Bi as Dilation2DBackpropInput,Pu as Draw,nw as ENV,Vi as Einsum,gn as Elu,Ya as EluGrad,hl as Environment,yn as Equal,xn as Erf,bn as Exp,na as ExpandDims,Cn as Expm1,Wi as FFT,sa as Fill,wn as FlipLeftRight,Sn as Floor,In as FloorDiv,Mu as FromPixels,vn as FusedBatchNorm,vo as FusedConv2D,ko as FusedDepthwiseConv2D,kp as GPGPUContext,kn as GatherNd,aa as GatherV2,Bl as GraphModel,Nn as Greater,Tn as GreaterEqual,Ui as IFFT,wo as Identity,Gi as Imag,_n as IsFinite,$n as IsInf,En as IsNan,ao as KernelBackend,zn as LRN,Qa as LRNGrad,Rn as LeakyRelu,Dn as Less,An as LessEqual,Fn as LinSpace,Pn as Log,On as Log1p,zce as LogSoftmax,Mn as LogicalAnd,Ln as LogicalNot,Bn as LogicalOr,$0 as LogicalXor,Vce as LowerBound,xu as MathBackendCPU,wu as MathBackendWebGL,Wce as MatrixBandPart,Vn as Max,Un as MaxPool,ia as MaxPool3D,Ki as MaxPool3DGrad,Hi as MaxPoolGrad,ua as MaxPoolWithArgmax,Wn as Maximum,Gn as Mean,Hn as Min,Kn as Minimum,qn as MirrorPad,jn as Mod,up as MomentumOptimizer,Xn as Multinomial,Yn as Multiply,pa as Neg,Zn as NonMaxSuppressionV3,Za as NonMaxSuppressionV4,Jn as NonMaxSuppressionV5,Qn as NotEqual,kw as OP_SCOPE_SUFFIX,es as OneHot,ca as OnesLike,Nr as Optimizer,Pl as OptimizerConstructors,la as Pack,ts as PadV2,Uce as Pool,rs as Pow,os as Prelu,ns as Prod,pp as RMSPropOptimizer,Qp as RaggedGather,Zp as RaggedRange,Jp as RaggedTensorToTensor,ma as Range,hw as Rank,qi as Real,hn as RealDiv,ss as Reciprocal,Rt as Reduction,as as Relu,ps as Relu6,da as Reshape,us as ResizeBilinear,ei as ResizeBilinearGrad,is as ResizeNearestNeighbor,Ja as ResizeNearestNeighborGrad,cs as Reverse,As as RotateWithOffset,ls as Round,ms as Rsqrt,mi as SGDOptimizer,ds as ScatterNd,hs as SearchSorted,fa as Select,gs as Selu,Cs as Sigmoid,bs as Sign,xs as Sin,ys as Sinh,ha as Slice,vs as Softmax,ws as Softplus,ga as SpaceToBatchND,ji as SparseFillEmptyRows,ti as SparseReshape,ya as SparseSegmentMean,ba as SparseSegmentSum,ks as SparseToDense,xa as SplitV,Ss as Sqrt,Xi as Square,Ns as SquaredDifference,Ou as StaticRegexReplace,So as Step,Ts as StridedSlice,Ca as StringNGrams,Yi as StringSplit,Qi as StringToHashBucketFast,_s as Sub,Is as Sum,$s as Tan,Es as Tanh,ut as Tensor,tt as TensorBuffer,fs as TensorScatterUpdate,po as Tile,Rs as TopK,Ds as Transform,co as Transpose,Zi as Unique,wa as Unpack,Ji as UnsortedSegmentSum,Gce as UpperBound,oi as Variable,Tu as WebGPUBackend,Sa as ZerosLike,Io as _FusedMatMul,Jt as abs,kk as acos,Nk as acosh,Ce as add,Tk as addN,_k as all,$k as any,Ek as argMax,Rk as argMin,Dk as asin,Ak as asinh,Fk as atan,Pk as atan2,Ok as atanh,fd as avgPool,Bk as avgPool3d,vde as backend,w as backend_util,zk as basicLSTMCell,au as batchNorm,Wk as batchNorm2d,Uk as batchNorm3d,Gk as batchNorm4d,hd as batchToSpaceND,gd as bincount,Hk as bitwiseAnd,E6 as booleanMaskAsync,Kk as broadcastArgs,iu as broadcastTo,Ir as broadcast_util,oT as browser,me as buffer,We as cast,qk as ceil,jk as clipByValue,Ur as clone,Er as complex,bt as concat,Xk as concat1d,Yk as concat2d,Qk as concat3d,Zk as concat4d,Jk as conv1d,uu as conv2d,e2 as conv2dTranspose,t2 as conv3d,o2 as conv3dTranspose,Zce as copyRegisteredKernels,n2 as cos,s2 as cosh,Rl as cosineWindow,a2 as cumprod,i2 as cumsum,vr as customGrad,u2 as denseBincount,Pw as deprecationWarn,p2 as depthToSpace,lc as depthwiseConv2d,A5 as deregisterOp,ou as device_util,c2 as diag,l2 as dilation2d,dde as disableDeprecationWarnings,Mt as dispose,fde as disposeVariables,je as div,d2 as divNoNan,f2 as dot,W6 as dropout,pu as einsum,Cd as elu,mde as enableDebugMode,lde as enableProdMode,Qw as enclosingPowerOfTwo,pr as engine,h2 as ensureShape,A as env,bd as equal,g2 as erf,b2 as euclideanNorm,$o as exp,Ms as expandDims,C2 as expm1,wd as eye,fc as fft,Ea as fill,Sde as findBackend,Ide as findBackendFactory,Sd as floor,dd as floorDiv,MD as forceHalfFloat,Zw as fused,Id as gather,z6 as gatherND,af as gather_util,Cde as getBackend,iw as getGradient,tc as getKernel,Ym as getKernelsForBackend,Zse as getThreadsCount,cv as gpgpu_util,AK as grad,FK as grads,qu as greater,vd as greaterEqual,Ju as ifft,lu as imag,Kj as image,G6 as inTopKAsync,fi as io,Kd as irfft,w2 as isFinite,S2 as isInf,I2 as isNaN,Rr as keep,Wt as kernel_impls,kd as leakyRelu,_l as less,mc as lessEqual,qj as linalg,v2 as linspace,$8 as loadGraphModel,E8 as loadGraphModelSync,k2 as localResponseNormalization,pi as log,Nd as log1p,N2 as logSigmoid,T2 as logSoftmax,$d as logSumExp,ju as logicalAnd,Ed as logicalNot,Rd as logicalOr,_2 as logicalXor,jj as losses,$2 as lowerBound,Ze as matMul,JN as math,Ra as max,Ad as maxPool,E2 as maxPool3d,R2 as maxPoolWithArgmax,Fd as maximum,Xu as mean,hde as memory,D2 as meshgrid,Tl as min,Yu as minimum,A2 as mirrorPad,F2 as mod,P2 as moments,A6 as movingAverage,se as mul,O2 as multiRNNCell,M2 as multinomial,cr as neg,pS as nextFrame,Ku as norm,Pd as notEqual,El as oneHot,Da as ones,L2 as onesLike,N as op,B2 as outerProduct,Aa as pad,z2 as pad1d,V2 as pad2d,W2 as pad3d,U2 as pad4d,G2 as pool,ui as pow,Md as prelu,md as print,H2 as prod,gde as profile,K2 as raggedGather,q2 as raggedRange,j2 as raggedTensorToTensor,X2 as rand,g1 as randomGamma,Ud as randomNormal,x1 as randomStandardNormal,dc as randomUniform,y1 as randomUniformInt,mu as range,bde as ready,ci as real,b1 as reciprocal,su as registerBackend,Xce as registerGradient,ri as registerKernel,D5 as registerOp,du as relu,Gd as relu6,wde as removeBackend,W as reshape,mo as reverse,C1 as reverse1d,w1 as reverse2d,S1 as reverse3d,I1 as reverse4d,hc as rfft,Hd as round,v1 as rsqrt,ke as scalar,P6 as scatterND,hu as scatter_util,$l as searchSorted,k1 as selu,N1 as separableConv2d,WN as serialization,yde as setBackend,kde as setPlatform,Qse as setThreadsCount,Xse as setWasmPath,Yse as setWasmPaths,vI as setWebGLContext,T1 as setdiff1dAsync,Tc as shared,$a as sigmoid,_1 as sign,Hj as signal,$1 as sin,E1 as sinh,Xe as slice,R1 as slice1d,D1 as slice2d,A1 as slice3d,F1 as slice4d,ct as slice_util,P1 as softmax,_d as softplus,Od as spaceToBatchND,Xj as sparse,L6 as sparseToDense,Gj as spectral,li as split,Dr as sqrt,er as square,qd as squaredDifference,gc as squeeze,kr as stack,jd as step,O1 as stridedSlice,Yj as string,Te as sub,ot as sum,ni as sumOutType,M1 as tan,Nl as tanh,ur as tensor,tr as tensor1d,fu as tensor2d,Xd as tensor3d,L1 as tensor4d,B1 as tensor5d,z1 as tensor6d,W1 as tensorScatterUpdate,ek as tensor_util,h1 as test_util,De as tidy,cu as tile,xde as time,U1 as topk,TGe as train,yc as transpose,G1 as truncatedNormal,H1 as unique,Qce as unregisterGradient,Yce as unregisterKernel,K1 as unsortedSegmentSum,fo as unstack,dt as upcastType,q1 as upperBound,y as util,PK as valueAndGrad,OK as valueAndGrads,j1 as variable,zw as variableGrads,Ace as version,D8 as version_converter,_X as version_core,cY as version_cpu,Jse as version_wasm,s9 as version_webgl,Cat as webgl,Ac as webgl_util,Yv as webgpu_util,lo as where,Qd as whereAsync,Gr as zeros,Ht as zerosLike};\n", "import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial, config: Partial, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n for (const key of Object.keys(config)) {\n if (typeof config[key] === 'object') {\n validate(defaults[key], config[key], key, msgs);\n } else {\n const defined = defaults && (typeof defaults[key] !== 'undefined');\n if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n const same = defaults && typeof defaults[key] === typeof config[key];\n if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n }\n // ok = ok && defined && same;\n }\n if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: number[]) => data.reduce((acc: number[], val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n await waiting;\n}\n", "/* eslint-disable no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n /** is module enabled? */\n enabled: boolean,\n /** path to model json file (relative to `modelBasePath` */\n modelPath: string,\n /** how many max frames to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipFrames: number,\n /** how many max milliseconds to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n /** is face rotation correction performed after detecting face?\n * used to correctly analyze faces under high angles\n */\n rotation: boolean,\n /** maximum number of detected faces */\n maxDetected: number,\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n /** minimum size in pixels of a detected face box before resutls are discared */\n minSize: number,\n /** minimum overlap between two detected faces before one is discarded */\n iouThreshold: number,\n /** should child models perform on masked image of a face */\n mask: boolean,\n /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n * if enabled it must be manually deallocated to avoid memory leak */\n return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n /** Keep detected faces that cannot be verified using facemesh */\n keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n /** minimum confidence for a detected race before results are discarded */\n minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n detector: Partial,\n mesh: Partial,\n attention: Partial,\n iris: Partial,\n description: Partial,\n emotion: Partial,\n antispoof: Partial,\n liveness: Partial,\n gear: Partial,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n /** maximum number of detected bodies */\n maxDetected: number,\n /** minimum confidence for a detected body before results are discarded */\n minConfidence: number,\n /* experimental\n /** experimental: detector used for body model before actual analysis\n detector?: {\n /** experimental: enable body detector before body landmarks\n enabled: boolean,\n /** experimental: path to optional body detector model json file\n modelPath: string,\n /** experimental: minimum confidence for a detected body before results are discarded\n minConfidence: number,\n /** experimental: minimum overlap between two detected bodies before one is discarded\n iouThreshold: number\n },\n */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n /** should hand rotation correction be performed after hand detection? */\n rotation: boolean,\n /** minimum confidence for a detected hand before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected hands before one is discarded */\n iouThreshold: number,\n /** maximum number of detected hands */\n maxDetected: number,\n /** should hand landmarks be detected or just return detected hand box */\n landmarks: boolean,\n detector: {\n /** path to hand detector model json */\n modelPath?: string,\n },\n skeleton: {\n /** path to hand skeleton model json */\n modelPath?: string,\n },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n /** minimum confidence for a detected objects before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected objects before one is discarded */\n iouThreshold: number,\n /** maximum number of detected objects */\n maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n ratio: number,\n /** possible rvm segmentation mode */\n mode: SegmentationEnum,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n /** are image filters enabled? */\n enabled: boolean,\n /** perform image histogram equalization\n * - equalization is performed on input as a whole and detected face before its passed for further analysis\n */\n equalization: boolean,\n /** resize input width\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n width: number,\n /** resize input height\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n height: number,\n /** return processed canvas imagedata in result */\n return: boolean,\n /** flip input as mirror image */\n flip: boolean,\n /** apply auto-brighness */\n autoBrightness: boolean,\n /** range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** image negative */\n negative: boolean,\n /** image sepia colors */\n sepia: boolean,\n /** image vintage colors */\n vintage: boolean,\n /** image kodachrome colors */\n kodachrome: boolean,\n /** image technicolor colors */\n technicolor: boolean,\n /** image polaroid camera effect */\n polaroid: boolean,\n /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n /** is gesture detection enabled? */\n enabled: boolean,\n}\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n /** Backend used for TFJS operations\n * valid build-in backends are:\n * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n * - NodeJS: `cpu`, `wasm`, `tensorflow`\n * default: `webgl` for browser and `tensorflow` for nodejs\n */\n backend: BackendEnum,\n\n /** Path to *.wasm files if backend is set to `wasm`\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPath: string,\n\n /** Force WASM loader to use platform fetch\n *\n * default: false\n */\n wasmPlatformFetch: boolean,\n\n /** Print debug statements to console\n *\n * default: `true`\n */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially\n *\n * default: `true`\n */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - used by `webgl`, `humangl` and `webgpu` backends\n *\n * default: `full`\n */\n warmup: WarmupEnum,\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n *\n * default: `../models/` for browsers and `file://models/` for nodejs\n */\n modelBasePath: string,\n\n /** Cache models in IndexDB on first sucessfull load\n * default: true if indexdb is available (browsers), false if its not (nodejs)\n */\n cacheModels: boolean,\n\n /** Validate kernel ops used in model during model load\n * default: true\n * any errors will be printed on console but will be treated as non-fatal\n */\n validateModels: boolean,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n *\n * default: 0.7\n */\n cacheSensitivity: number;\n\n /** Explicit flags passed to initialize TFJS */\n flags: Record,\n\n /** Software Kernels\n * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n */\n softwareKernels: boolean,\n\n /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n deallocate: boolean;\n\n /** Internal Variable */\n skipAllowed: boolean;\n\n /** Filter config {@link FilterConfig} */\n filter: Partial,\n\n /** Gesture config {@link GestureConfig} */\n gesture: Partial;\n\n /** Face config {@link FaceConfig} */\n face: Partial,\n\n /** Body config {@link BodyConfig} */\n body: Partial,\n\n /** Hand config {@link HandConfig} */\n hand: Partial,\n\n /** Object config {@link ObjectConfig} */\n object: Partial,\n\n /** Segmentation config {@link SegmentationConfig} */\n segmentation: Partial,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n backend: '',\n modelBasePath: '',\n cacheModels: true,\n validateModels: true,\n wasmPath: '',\n wasmPlatformFetch: false,\n debug: false,\n async: true,\n warmup: 'full',\n cacheSensitivity: 0.70,\n skipAllowed: false,\n deallocate: false,\n flags: {},\n softwareKernels: false,\n filter: {\n enabled: true,\n equalization: false,\n width: 0,\n height: 0,\n flip: false,\n return: true,\n autoBrightness: true,\n brightness: 0,\n contrast: 0,\n sharpness: 0,\n blur: 0,\n saturation: 0,\n hue: 0,\n negative: false,\n sepia: false,\n vintage: false,\n kodachrome: false,\n technicolor: false,\n polaroid: false,\n pixelate: 0,\n },\n gesture: {\n enabled: true,\n },\n face: {\n enabled: true,\n detector: {\n modelPath: 'blazeface.json',\n rotation: false,\n maxDetected: 1,\n skipFrames: 99,\n skipTime: 2500,\n minConfidence: 0.2,\n minSize: 0,\n iouThreshold: 0.1,\n mask: false,\n return: false,\n },\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json',\n keepInvalid: false,\n },\n attention: {\n enabled: false,\n modelPath: 'facemesh-attention.json',\n },\n iris: {\n enabled: true,\n modelPath: 'iris.json',\n },\n emotion: {\n enabled: true,\n minConfidence: 0.1,\n skipFrames: 99,\n skipTime: 1500,\n modelPath: 'emotion.json',\n },\n description: {\n enabled: true,\n modelPath: 'faceres.json',\n skipFrames: 99,\n skipTime: 3000,\n minConfidence: 0.1,\n },\n antispoof: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'antispoof.json',\n },\n liveness: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'liveness.json',\n },\n },\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json',\n maxDetected: -1,\n minConfidence: 0.3,\n skipFrames: 1,\n skipTime: 200,\n },\n hand: {\n enabled: true,\n rotation: true,\n skipFrames: 99,\n skipTime: 1000,\n minConfidence: 0.50,\n iouThreshold: 0.2,\n maxDetected: -1,\n landmarks: true,\n detector: {\n modelPath: 'handtrack.json',\n },\n skeleton: {\n modelPath: 'handlandmark-lite.json',\n },\n },\n object: {\n enabled: false,\n modelPath: 'centernet.json',\n minConfidence: 0.2,\n iouThreshold: 0.4,\n maxDetected: 10,\n skipFrames: 99,\n skipTime: 2000,\n },\n segmentation: {\n enabled: false,\n modelPath: 'rvm.json',\n ratio: 0.5,\n mode: 'default',\n },\n};\n\nexport { config as defaults };\n", "export const vertexIdentity = `\n precision highp float;\n attribute vec2 pos;\n attribute vec2 uv;\n varying vec2 vUv;\n uniform float flipY;\n void main(void) {\n vUv = uv;\n gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n }\n`;\n\nexport const fragmentIdentity = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n void main(void) {\n gl_FragColor = texture2D(texture, vUv);\n }\n`;\n\nexport const colorMatrixWithAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n gl_FragColor.a = c.a;\n }\n`;\n\nexport const pixelate = `\n precision highp float;\n varying vec2 vUv;\n uniform vec2 size;\n uniform sampler2D texture;\n vec2 pixelate(vec2 coord, vec2 size) {\n return floor( coord / size ) * size;\n }\n void main(void) {\n gl_FragColor = vec4(0.0);\n vec2 coord = pixelate(vUv, size);\n gl_FragColor += texture2D(texture, coord);\n }\n`;\n\nexport const blur = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n void main(void) {\n gl_FragColor = vec4(0.0);\n gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv )*0.159576912161;\n gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;\n }\n`;\n\nexport const convolution = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n uniform float m[9];\n void main(void) {\n vec4 c11 = texture2D(texture, vUv - px); // top left\n vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n vec4 c22 = texture2D(texture, vUv); // mid center\n vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n gl_FragColor = \n c11 * m[0] + c12 * m[1] + c22 * m[2] +\n c21 * m[3] + c22 * m[4] + c23 * m[5] +\n c31 * m[6] + c32 * m[7] + c33 * m[8];\n gl_FragColor.a = c22.a;\n }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, collection) => {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n};\n\nclass GLProgram {\n uniform = {};\n attribute = {};\n gl: WebGLRenderingContext;\n id: WebGLProgram;\n\n constructor(gl, vertexSource, fragmentSource) {\n this.gl = gl;\n const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n this.id = this.gl.createProgram() as WebGLProgram;\n if (!vertexShader || !fragmentShader) return;\n if (!this.id) {\n log('filter: could not create webgl program');\n return;\n }\n this.gl.attachShader(this.id, vertexShader);\n this.gl.attachShader(this.id, fragmentShader);\n this.gl.linkProgram(this.id);\n if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || 'unknown'}`);\n return;\n }\n this.gl.useProgram(this.id);\n collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n }\n\n compile = (source, type): WebGLShader | null => {\n const shader = this.gl.createShader(type);\n if (!shader) {\n log('filter: could not create shader');\n return null;\n }\n this.gl.shaderSource(shader, source);\n this.gl.compileShader(shader);\n if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || 'unknown'}`);\n return null;\n }\n return shader;\n };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n let drawCount = 0;\n let sourceTexture: WebGLTexture | null = null;\n let lastInChain = false;\n let currentFramebufferIndex = -1;\n let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n let filterChain: Record[] = [];\n let vertexBuffer: WebGLBuffer | null = null;\n let currentProgram: GLProgram | null = null;\n const fxcanvas = canvas(100, 100) as HTMLCanvasElement;\n const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n const DRAW = { INTERMEDIATE: 1 };\n const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n if (!gl) {\n log('filter: cannot get webgl context');\n return;\n }\n // @ts-ignore used for sanity checks outside of imagefx\n this.gl = gl;\n\n function resize(width, height) {\n if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n fxcanvas.width = width;\n fxcanvas.height = height;\n if (!vertexBuffer) { // Create the context if we don't have it yet\n const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n tempFramebuffers = [null, null]; // Delete old temp framebuffers\n }\n\n function createFramebufferTexture(width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n }\n\n function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n }\n\n function draw(flags = 0) {\n if (!currentProgram) return;\n let source: WebGLTexture | null = null;\n let target: WebGLFramebuffer | null = null;\n let flipY = false;\n if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n drawCount++;\n if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n target = null;\n flipY = drawCount % 2 === 0;\n } else {\n currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n }\n gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n }\n\n function compileShader(fragmentSource): GLProgram | null {\n if (shaderProgramCache[fragmentSource]) {\n currentProgram = shaderProgramCache[fragmentSource];\n gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n return currentProgram;\n }\n currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n if (!currentProgram) {\n log('filter: could not get webgl program');\n return null;\n }\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n shaderProgramCache[fragmentSource] = currentProgram;\n return currentProgram;\n }\n\n const filter = {\n colorMatrix: (matrix: number[]) => { // general color matrix filter\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n ? shaders.colorMatrixWithoutAlpha\n : shaders.colorMatrixWithAlpha;\n const program = compileShader(shader);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n draw();\n },\n\n brightness: (brightness: number) => {\n const b = (brightness || 0) + 1;\n filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n saturation: (amount: number) => {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturate: () => {\n filter.saturation(-1);\n },\n\n contrast: (amount: number) => {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n negative: () => {\n filter.contrast(-2);\n },\n\n hue: (rotation: number) => {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturateLuminance: () => {\n filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n sepia: () => {\n filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n brownie: () => {\n filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n vintagePinhole: () => {\n filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n kodachrome: () => {\n filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n technicolor: () => {\n filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n polaroid: () => {\n filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n shiftToBGR: () => {\n filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n convolution: (matrix: number[]) => { // general convolution Filter\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / fxcanvas.width;\n const pixelSizeY = 1 / fxcanvas.height;\n const program = compileShader(shaders.convolution);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n draw();\n },\n\n detectEdges: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n },\n\n sobelX: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n },\n\n sobelY: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n },\n\n sharpen: (amount) => {\n const a = amount || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n },\n\n emboss: (size: number) => {\n const s = size || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n },\n\n blur: (size: number) => {\n const blurSizeX = (size / 7) / fxcanvas.width;\n const blurSizeY = (size / 7) / fxcanvas.height;\n const program = compileShader(shaders.blur);\n if (!program) return;\n // Vertical\n gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n draw();\n },\n\n pixelate: (size: number) => {\n const blurSizeX = (size) / fxcanvas.width;\n const blurSizeY = (size) / fxcanvas.height;\n const program = compileShader(shaders.pixelate);\n if (!program) return;\n gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n draw();\n },\n };\n\n // @ts-ignore this\n this.add = function (name) {\n const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\n const func = filter[name];\n filterChain.push({ func, args });\n };\n\n // @ts-ignore this\n this.reset = function () {\n filterChain = [];\n };\n\n // @ts-ignore this\n this.get = function () {\n return filterChain;\n };\n\n // @ts-ignore this\n this.apply = function (image) {\n resize(image.width, image.height);\n drawCount = 0;\n if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n for (let i = 0; i < filterChain.length; i++) {\n lastInChain = (i === filterChain.length - 1);\n const f = filterChain[i];\n // @ts-ignore function assigment\n f.func.apply(this, f.args || []);\n }\n return fxcanvas;\n };\n\n // @ts-ignore this\n this.draw = function (image) {\n this.add('brightness', 0);\n return this.apply(image);\n };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise {\n const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n const rgb = tf.split(squeeze, 3, 2);\n const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]\n const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]\n // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]\n // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);\n const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]\n const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n const maxRange = maxValue > 1 ? 255 : 1;\n const factor = maxRange / maxValue;\n let final: Tensor;\n if (factor > 1) {\n const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values\n const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges\n // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between\n const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];\n const stack = tf.stack([enh[0], enh[1], enh[2]], 2);\n final = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);\n tf.dispose([...sub, ...range, ...enh, stack]);\n } else {\n final = tf.expandDims(squeeze, 0);\n }\n tf.dispose([...rgb, ...min, ...max, rgb, squeeze, inputImage]);\n return final;\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Config } from '../exports';\nimport type { Tensor, Tensor3D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // eslint-disable-line @typescript-eslint/no-redundant-type-constituents\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n inputSum: 0,\n cacheDiff: 1,\n sumMethod: 0,\n inputTensor: undefined,\n};\n\nexport function reset() {\n last.inputSum = 0;\n last.cacheDiff = 1;\n last.sumMethod = 0;\n last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n let c: AnyCanvas;\n if (env.browser) { // browser defines canvas object\n if (env.worker) { // if runing in web worker use OffscreenCanvas\n if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n c = new OffscreenCanvas(width, height);\n } else { // otherwise use DOM canvas\n if (typeof document !== 'undefined') {\n c = document.createElement('canvas');\n c.width = width;\n c.height = height;\n } else if (typeof navigator !== 'undefined' && navigator.product === 'ReactNative') {\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n else throw new Error('canvas error: attempted to use canvas in react-native without canvas support installed');\n } else {\n throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n }\n }\n } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n }\n // @ts-ignore its either defined or we already threw an error\n return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n const outputCanvas = output || canvas(input.width, input.height);\n const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctx.drawImage(input, 0, 0);\n return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor4D | null, canvas: AnyCanvas | null }> {\n if (!input) {\n // throw new Error('input is missing');\n if (config.debug) log('input error: input is missing');\n return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n }\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('input error: type not recognized');\n }\n if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n let tensor: Tensor | null = null;\n if (input['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n if (!(input as Tensor).shape) throw new Error('input error: attempted to use tensor without a shape');\n if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n tensor = tf.expandDims(input, 0);\n } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n const rgb = tf.slice3d(input as Tensor3D, [0, 0, 0], [-1, -1, 3]);\n tensor = tf.expandDims(rgb, 0);\n tf.dispose(rgb);\n }\n } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n tensor = tf.clone(input);\n } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n tensor = tf.slice4d(input as Tensor4D, [0, 0, 0, 0], [-1, -1, -1, 3]);\n }\n }\n // at the end shape must be [1, height, width, 3]\n if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${((input as Tensor).shape).toString()}`);\n if ((tensor).dtype === 'int32') {\n const cast = tf.cast(tensor, 'float32');\n tf.dispose(tensor);\n tensor = cast;\n }\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n }\n // check if resizing will be needed\n if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).readyState <= 2) {\n if (config.debug) log('input stream is not ready');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n const originalWidth: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n const originalHeight: number = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n if (!originalWidth || !originalHeight) {\n if (config.debug) log('cannot determine input dimensions');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n let targetWidth: number = originalWidth;\n let targetHeight: number = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n }\n\n // create our canvas and resize it if needed\n if ((config.filter?.width || 0) > 0) targetWidth = config.filter.width as number;\n else if ((config.filter?.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n if ((config.filter.height || 0) > 0) targetHeight = config.filter.height as number;\n else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n // draw input to our canvas\n const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n inCtx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n inCtx.translate(originalWidth, 0);\n inCtx.scale(-1, 1);\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n }\n }\n\n if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n // imagefx transforms using gl from input canvas to output canvas\n if (config.filter.enabled && env.webgl.supported) {\n if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n env.filter = !!fx;\n if (!fx?.add) {\n if (config.debug) log('input process error: cannot initialize filters');\n env.webgl.supported = false;\n config.filter.enabled = false;\n copy(inCanvas, outCanvas); // filter failed to initialize\n // return { tensor: null, canvas: inCanvas };\n } else {\n fx.reset();\n if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n if (config.filter.negative) fx.add('negative');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.vintage) fx.add('brownie');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.kodachrome) fx.add('kodachrome');\n if (config.filter.technicolor) fx.add('technicolor');\n if (config.filter.polaroid) fx.add('polaroid');\n if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n if (fx.get()?.length > 1) outCanvas = fx.apply(inCanvas);\n else outCanvas = fx.draw(inCanvas);\n }\n } else {\n copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n if (fx) fx = null;\n env.filter = !!fx;\n }\n\n if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n // create tensor from image unless input was a tensor already\n let pixels;\n let depth = 3;\n if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || ((input as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n if (env.browser && tf.browser) {\n pixels = tf.browser ? tf.browser.fromPixels(input as ImageData) : null;\n } else {\n depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n // const arr = Uint8Array.from(input['data']);\n const arr = new Uint8Array((input as ImageData).data.buffer);\n pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).width, depth], 'int32');\n }\n } else {\n if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n if (tf.browser && env.browser) {\n if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n pixels = tf.browser.fromPixels(outCanvas as HTMLCanvasElement); // safe to reuse since both backend and context are gl based\n } else {\n tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n pixels = tf.browser.fromPixels(tmpCanvas as HTMLCanvasElement);\n }\n } else {\n const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n depth = tempData.data.length / targetWidth / targetHeight;\n const arr = new Uint8Array(tempData.data.buffer);\n pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n }\n }\n if (depth === 4) { // rgba to rgb\n const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n tf.dispose(pixels);\n pixels = rgb;\n }\n if (!pixels) throw new Error('input error: cannot create tensor');\n const casted: Tensor = tf.cast(pixels, 'float32');\n const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n tf.dispose([pixels, casted]);\n\n if (config.filter.autoBrightness) {\n const max = tf.max(tensor);\n const maxVal = await max.data();\n config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);\n tf.dispose(max);\n }\n\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n}\n\n/*\nconst checksum = async (input: Tensor): Promise => { // use tf sum or js based sum loop depending on which is faster\n const resizeFact = 48;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n const tfSum = async (): Promise => {\n const sumT = tf.sum(reduced);\n const sum0 = await sumT.data();\n tf.dispose(sumT);\n return sum0[0];\n };\n const jsSum = async (): Promise => {\n const reducedData = await reduced.data(); // raw image rgb array\n let sum0 = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n return sum0;\n };\n if (last.sumMethod === 0) {\n const t0 = now();\n await jsSum();\n const t1 = now();\n await tfSum();\n const t2 = now();\n last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n }\n const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n tf.dispose(reduced);\n return res;\n};\n*/\n\nexport async function skip(config: Partial, input: Tensor) {\n let skipFrame = false;\n if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n /*\n const checkSum = await checksum(input);\n const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n last.inputSum = checkSum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n */\n\n if (!last.inputTensor) {\n last.inputTensor = tf.clone(input);\n } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n tf.dispose(last.inputTensor);\n last.inputTensor = tf.clone(input);\n } else {\n const t: Record = {};\n t.diff = tf.sub(input, last.inputTensor);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n last.inputTensor = tf.clone(input);\n skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n }\n return skipFrame;\n}\n\nexport async function compare(config: Partial, input1: Tensor, input2: Tensor): Promise {\n const t: Record = {};\n if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n return 0;\n }\n if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n return 0;\n }\n t.input1 = tf.clone(input1);\n t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2 as Tensor3D, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n t.diff = tf.sub(t.input1, t.input2);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n return diffRelative;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n /** Running in Browser */\n browser: boolean;\n /** Running in NodeJS */\n node: boolean;\n /** Running in WebWorker thread */\n worker: boolean;\n /** Detected platform */\n platform: string = '';\n /** Detected agent */\n agent: string = '';\n /** List of supported backends */\n backends: string[] = [];\n /** Has any work been performed so far */\n initial: boolean;\n /** Are image filters supported? */\n filter: boolean | undefined;\n /** TFJS instance details */\n tfjs: {\n version: undefined | string,\n };\n /** Is offscreenCanvas supported? */\n offscreen: undefined | boolean;\n /** Are performance counter instant values or additive */\n perfadd: boolean = false;\n /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n tensorflow: {\n version: undefined | string,\n gpu: undefined | boolean,\n } = {\n version: undefined,\n gpu: undefined,\n };\n /** WASM detected capabilities */\n wasm: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n simd: undefined | boolean,\n multithread: undefined | boolean,\n } = {\n supported: undefined,\n backend: undefined,\n simd: undefined,\n multithread: undefined,\n };\n /** WebGL detected capabilities */\n webgl: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n version: undefined | string,\n renderer: undefined | string,\n shader: undefined | string,\n vendor: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n version: undefined,\n renderer: undefined,\n shader: undefined,\n vendor: undefined,\n };\n /** WebGPU detected capabilities */\n webgpu: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n adapter: undefined | GPUAdapterInfo,\n } = {\n supported: undefined,\n backend: undefined,\n adapter: undefined,\n };\n /** CPU info */\n cpu: {\n model: undefined | string,\n flags: string[],\n } = {\n model: undefined,\n flags: [],\n };\n /** List of supported kernels for current backend */\n kernels: string[] = [];\n\n /** MonkeyPatch for Canvas/Image/ImageData */\n #canvas: undefined;\n #image: undefined;\n #imageData: undefined;\n\n get Canvas() { return this.#canvas; }\n set Canvas(val) { this.#canvas = val; globalThis.Canvas = val; }\n get Image() { return this.#image; }\n // @ts-ignore monkey-patch;\n set Image(val) { this.#image = val; globalThis.Image = val; }\n get ImageData() { return this.#imageData; }\n // @ts-ignore monkey-patch;\n set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; }\n\n constructor() {\n this.browser = typeof navigator !== 'undefined';\n this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n this.tfjs = { version: tf.version['tfjs-core'] };\n this.offscreen = typeof OffscreenCanvas !== 'undefined';\n this.initial = true;\n\n // @ts-ignore WorkerGlobalScope evaluated in browser only\n this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n if ((typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined')) { // TBD replace with navigator.userAgentData once in mainline\n const agent = navigator.userAgent || '';\n const raw = agent.match(/\\(([^()]+)\\)/g);\n if (raw?.[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n this.agent = agent.replace(raw[0], '');\n if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n this.agent = this.agent.replace(/ /g, ' ');\n }\n } else if (typeof process !== 'undefined') {\n this.platform = `${process.platform} ${process.arch}`;\n this.agent = `NodeJS ${process.version}`;\n }\n }\n\n /** update backend information */\n async updateBackend() {\n // analyze backends\n this.backends = Object.keys(tf.engine().registryFactory);\n try { // backend may not be initialized\n this.tensorflow = {\n version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),\n gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),\n };\n } catch { /**/ }\n this.wasm.supported = typeof WebAssembly !== 'undefined';\n this.wasm.backend = this.backends.includes('wasm');\n if (this.wasm.supported && this.wasm.backend) {\n this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n }\n const c = image.canvas(100, 100);\n const gl = c ? c.getContext('webgl2') as WebGL2RenderingContext : undefined; // causes too many gl contexts\n this.webgl.supported = typeof gl !== 'undefined';\n this.webgl.backend = this.backends.includes('webgl');\n if (this.webgl.supported && this.webgl.backend && gl) {\n this.webgl.version = gl.getParameter(gl.VERSION);\n this.webgl.vendor = gl.getParameter(gl.VENDOR);\n this.webgl.renderer = gl.getParameter(gl.RENDERER);\n this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n }\n this.webgpu.supported = this.browser && typeof navigator !== 'undefined' && typeof navigator.gpu !== 'undefined';\n this.webgpu.backend = this.backends.includes('webgpu');\n try {\n if (this.webgpu.supported) {\n const adapter = await navigator.gpu.requestAdapter();\n this.webgpu.adapter = await adapter?.requestAdapterInfo();\n }\n } catch {\n this.webgpu.supported = false;\n }\n try {\n this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n } catch { /**/ }\n }\n\n /** update cpu information */\n updateCPU() {\n const cpu = { model: '', flags: [] };\n if (this.node && this.platform.startsWith('linux')) {\n /*\n const fs = require('fs');\n try {\n const data = fs.readFileSync('/proc/cpuinfo').toString();\n for (const line of data.split('\\n')) {\n if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n }\n } catch { }\n */\n }\n if (!this.cpu) Object.defineProperty(this, 'cpu', { value: cpu });\n else this.cpu = cpu;\n }\n}\n\nexport const env = new Env();\n", "import { log } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n /**\n * element can be:\n * - string which indicates dom element id\n * - actual HTMLVideo dom element\n * - undefined in which case a new HTMLVideoElement will be created\n */\n element: string | HTMLVideoElement | undefined,\n /** print messages on console */\n debug: boolean,\n /** use front or back camera */\n mode: 'front' | 'back',\n /** camera crop mode */\n crop: boolean,\n /** desired webcam width */\n width: number,\n /** desired webcam height */\n height: number,\n /** deviceId of the video device to use */\n id?: string,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n /** current webcam configuration */\n config: WebCamConfig;\n /** instance of dom element associated with webcam stream */\n element: HTMLVideoElement | undefined;\n /** active webcam stream */\n stream: MediaStream | undefined;\n /** enumerated video devices */\n devices: MediaDeviceInfo[] = [];\n\n constructor() {\n this.config = {\n element: undefined,\n debug: true,\n mode: 'front',\n crop: false,\n width: 0,\n height: 0,\n };\n }\n\n /** get active webcam stream track */\n public get track(): MediaStreamTrack | undefined {\n if (!this.stream) return undefined;\n return this.stream.getVideoTracks()[0];\n }\n\n /** get webcam capabilities */\n public get capabilities(): MediaTrackCapabilities | undefined {\n if (!this.track) return undefined;\n return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n }\n\n /** get webcam constraints */\n public get constraints(): MediaTrackConstraints | undefined {\n if (!this.track) return undefined;\n return this.track.getConstraints ? this.track.getConstraints() : undefined;\n }\n\n /** get webcam settings */\n public get settings(): MediaTrackSettings | undefined {\n if (!this.stream) return undefined;\n const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n return track.getSettings ? track.getSettings() : undefined;\n }\n\n /** get webcam label */\n public get label(): string {\n if (!this.track) return '';\n return this.track.label;\n }\n\n /** is webcam paused */\n public get paused(): boolean {\n return this.element?.paused || false;\n }\n\n /** webcam current width */\n public get width(): number {\n return this.element?.videoWidth || 0;\n }\n\n /** webcam current height */\n public get height(): number {\n return this.element?.videoHeight || 0;\n }\n\n public enumerate = async (): Promise => {\n try {\n const devices = await navigator.mediaDevices.enumerateDevices();\n this.devices = devices.filter((device) => device.kind === 'videoinput');\n } catch {\n this.devices = [];\n }\n return this.devices;\n };\n\n /** start method initializizes webcam stream and associates it with a dom video element */\n public start = async (webcamConfig?: Partial): Promise => {\n // set config\n if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n if (webcamConfig?.id) this.config.id = webcamConfig?.id;\n\n // use or create dom element\n if (webcamConfig?.element) {\n if (typeof webcamConfig.element === 'string') {\n const el = document.getElementById(webcamConfig.element);\n if (el && el instanceof HTMLVideoElement) {\n this.element = el;\n } else {\n if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n return `webcam error: cannot get dom element: ${webcamConfig.element}`;\n }\n } else if (webcamConfig.element instanceof HTMLVideoElement) {\n this.element = webcamConfig.element;\n } else {\n if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n return `webcam error: unknown dom element: ${webcamConfig.element}`;\n }\n } else {\n this.element = document.createElement('video');\n }\n\n // set constraints to use\n const requestedConstraints: MediaStreamConstraints = {\n audio: false,\n video: {\n facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n // @ts-ignore // resizeMode is still not defined in tslib\n resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n },\n };\n if (this.config?.width > 0) (requestedConstraints.video as MediaTrackConstraints).width = { ideal: this.config.width };\n if (this.config?.height > 0) (requestedConstraints.video as MediaTrackConstraints).height = { ideal: this.config.height };\n if (this.config.id) (requestedConstraints.video as MediaTrackConstraintSet).deviceId = this.config.id;\n\n // set default event listeners\n this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n if (!this.element || !this.stream) return;\n if (this.element.paused) await this.element.play();\n else this.element.pause();\n });\n\n // get webcam and set it to run in dom element\n if (!navigator?.mediaDevices) {\n if (this.config.debug) log('webcam error', 'no devices');\n return 'webcam error: no devices';\n }\n try {\n this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n } catch (err) {\n log('webcam', err);\n return `webcam error: ${err}`;\n }\n if (!this.stream) {\n if (this.config.debug) log('webcam error', 'no stream');\n return 'webcam error no stream';\n }\n this.element.srcObject = this.stream; // assign it to dom element\n const ready = new Promise((resolve) => { // wait until stream is ready\n if (!this.element) resolve(false);\n else this.element.onloadeddata = () => resolve(true);\n });\n await ready;\n await this.element.play(); // start playing\n\n if (this.config.debug) {\n log('webcam', {\n width: this.width,\n height: this.height,\n label: this.label,\n stream: this.stream,\n track: this.track,\n settings: this.settings,\n constraints: this.constraints,\n capabilities: this.capabilities,\n });\n }\n return `webcam: ${this.label}`;\n };\n\n /** pause webcam video method */\n public pause = (): void => {\n if (this.element) this.element.pause();\n };\n\n /** play webcam video method */\n public play = async (): Promise => {\n if (this.element) await this.element.play();\n };\n\n /** stop method stops active webcam stream track and disconnects webcam */\n public stop = (): void => {\n if (this.config.debug) log('webcam', 'stop');\n if (this.track) this.track.stop();\n };\n}\n", "{\n \"antispoof\": 853098,\n \"blazeface\": 538928,\n \"centernet\": 4030290,\n \"emotion\": 820516,\n \"facemesh\": 1477958,\n \"faceres\": 6978814,\n \"handlandmark-lite\": 2023432,\n \"handtrack\": 2964837,\n \"iris\": 2599092,\n \"liveness\": 592976,\n \"models\": 0,\n \"movenet-lightning\": 4650216,\n \"affectnet-mobilenet\": 6920630,\n \"age\": 161240,\n \"blazeface-back\": 538928,\n \"blazeface-front\": 402048,\n \"blazepose-detector\": 5928856,\n \"blazepose-full\": 6339202,\n \"blazepose-heavy\": 27502466,\n \"blazepose-lite\": 2726402,\n \"efficientpose\": 5651240,\n \"faceboxes\": 2013002,\n \"facemesh-attention-pinto\": 2387598,\n \"facemesh-attention\": 2382414,\n \"facemesh-detection-full\": 1026192,\n \"facemesh-detection-short\": 201268,\n \"faceres-deep\": 13957620,\n \"gear-e1\": 112438,\n \"gear-e2\": 112438,\n \"gear\": 1498916,\n \"gender-ssrnet-imdb\": 161236,\n \"gender\": 201808,\n \"handdetect\": 3515612,\n \"handlandmark-full\": 5431368,\n \"handlandmark-sparse\": 5286322,\n \"handskeleton\": 5502280,\n \"meet\": 372228,\n \"mobileface\": 2183192,\n \"mobilefacenet\": 5171976,\n \"movenet-multipose\": 9448838,\n \"movenet-thunder\": 12477112,\n \"nanodet\": 7574558,\n \"posenet\": 5032780,\n \"rvm\": 3739355,\n \"selfie\": 212886,\n \"anti-spoofing\": 853098,\n \"efficientpose-i-lite\": 2269064,\n \"efficientpose-ii-lite\": 5651240,\n \"efficientpose-iv\": 25643252,\n \"insightface-efficientnet-b0\": 13013224,\n \"insightface-ghostnet-strides1\": 8093408,\n \"insightface-ghostnet-strides2\": 8049584,\n \"insightface-mobilenet-emore\": 6938536,\n \"insightface-mobilenet-swish\": 12168584,\n \"nanodet-e\": 12319156,\n \"nanodet-g\": 7574558,\n \"nanodet-m\": 1887474,\n \"nanodet-t\": 5294216\n}", "import * as tf from 'dist/tfjs.esm.js';\nimport { log, join } from '../util/util';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n cacheModels: true,\n cacheSupported: true,\n verbose: true,\n debug: false,\n modelBasePath: '',\n};\n\nexport interface ModelInfo {\n name: string,\n inCache: boolean,\n sizeDesired: number,\n sizeFromManifest: number,\n sizeLoadedWeights: number,\n url: string,\n}\n\nexport const modelStats: Record = {};\n\nasync function httpHandler(url: string, init?: RequestInit): Promise {\n if (options.debug) log('load model fetch:', url, init);\n return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n options.cacheModels = config.cacheModels;\n options.verbose = config.debug;\n options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise {\n let modelUrl = join(options.modelBasePath, modelPath || '');\n if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n modelStats[shortModelName] = {\n name: shortModelName,\n sizeFromManifest: 0,\n sizeLoadedWeights: 0,\n sizeDesired: modelsDefs[shortModelName],\n inCache: false,\n url: '',\n };\n options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n let cachedModels = {};\n try {\n cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n } catch {\n options.cacheSupported = false;\n }\n modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;\n const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n let loaded = false;\n try {\n // @ts-ignore private function\n model.findIOHandler(); // decide how to actually load a model\n if (options.debug) log('model load handler:', model['handler']);\n } catch (err) {\n log('error finding model i/o handler:', modelUrl, err);\n }\n try {\n // @ts-ignore private property\n const artifacts = await model.handler?.load() || null; // load manifest\n modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n if (artifacts) model.loadSync(artifacts); // load weights\n else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n // @ts-ignore private property\n modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || 0;\n if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n loaded = true;\n } catch (err) {\n log('error loading model:', modelUrl, err);\n }\n if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n try {\n const saveResult = await model.save(cachedModelName);\n if (options.debug) log('model saved:', cachedModelName, saveResult);\n } catch (err) {\n log('error saving model:', modelUrl, err);\n }\n }\n return model;\n}\n", "{\n \"name\": \"@vladmandic/human\",\n \"version\": \"3.1.2\",\n \"description\": \"Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition\",\n \"sideEffects\": false,\n \"main\": \"dist/human.node.js\",\n \"module\": \"dist/human.esm.js\",\n \"browser\": \"dist/human.esm.js\",\n \"types\": \"types/human.d.ts\",\n \"exports\": {\n \"node\": {\n \"require\": \"./dist/human.node.js\",\n \"import\": \"./dist/human.node.js\",\n \"module\": \"./dist/human.node.js\"\n },\n \"require\": \"./dist/human.node.js\",\n \"import\": \"./dist/human.esm.js\",\n \"script\": \"./dist/human.js\",\n \"module\": \"./dist/human.esm.js\",\n \"types\": \"./types/human.d.ts\",\n \"dist/human\": \"./dist/human.js\",\n \"dist/human.js\": \"./dist/human.js\",\n \"dist/human.esm\": \"./dist/human.esm.js\",\n \"dist/human.esm.js\": \"./dist/human.esm.js\",\n \"dist/human.esm-nobundle\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.esm-nobundle.js\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.node\": \"./dist/human.node.js\",\n \"dist/human.node.js\": \"./dist/human.node.js\",\n \"dist/human.node-wasm\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-wasm.js\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-gpu\": \"./dist/human.node-gpu.js\",\n \"dist/human.node-gpu.js\": \"./dist/human.node-gpu.js\"\n },\n \"author\": \"Vladimir Mandic \",\n \"bugs\": {\n \"url\": \"https://github.com/vladmandic/human/issues\"\n },\n \"homepage\": \"https://vladmandic.github.io/human/demo/index.html\",\n \"license\": \"MIT\",\n \"engines\": {\n \"node\": \">=14.0.0\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/vladmandic/human.git\"\n },\n \"scripts\": {\n \"start\": \"node --no-warnings demo/nodejs/node.js\",\n \"dev\": \"build --profile development\",\n \"clean\": \"build --profile clean\",\n \"build\": \"rimraf test/build.log && node build.js\",\n \"test\": \"node --no-warnings --unhandled-rejections=strict --trace-uncaught test/node.js\",\n \"lint\": \"eslint *.json *.js src demo test models wiki\",\n \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n },\n \"keywords\": [\n \"human\",\n \"human-library\",\n \"face-detection\",\n \"faceid\",\n \"face-geometry\",\n \"face-embedding\",\n \"face-recognition\",\n \"face-description\",\n \"face-matching\",\n \"body-tracking\",\n \"body-segmentation\",\n \"hand-tracking\",\n \"iris-tracking\",\n \"age-estimation\",\n \"emotion-detection\",\n \"gender-prediction\",\n \"gesture-recognition\",\n \"gaze-tracking\",\n \"age-gender\",\n \"tensorflowjs\",\n \"tfjs\",\n \"tensorflow\"\n ],\n \"devDependencies\": {\n \"@html-eslint/eslint-plugin\": \"^0.19.1\",\n \"@html-eslint/parser\": \"^0.19.1\",\n \"@microsoft/api-extractor\": \"^7.37.0\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.11.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.11.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.11.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.11.0\",\n \"@tensorflow/tfjs-converter\": \"^4.11.0\",\n \"@tensorflow/tfjs-core\": \"^4.11.0\",\n \"@tensorflow/tfjs-data\": \"^4.11.0\",\n \"@tensorflow/tfjs-layers\": \"^4.11.0\",\n \"@tensorflow/tfjs-node\": \"^4.11.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.11.0\",\n \"@types/node\": \"^20.6.2\",\n \"@types/offscreencanvas\": \"^2019.7.1\",\n \"@typescript-eslint/eslint-plugin\": \"^6.7.0\",\n \"@typescript-eslint/parser\": \"^6.7.0\",\n \"@vladmandic/build\": \"^0.9.2\",\n \"@vladmandic/pilogger\": \"^0.4.8\",\n \"@vladmandic/tfjs\": \"github:vladmandic/tfjs\",\n \"canvas\": \"^2.11.2\",\n \"esbuild\": \"^0.19.3\",\n \"eslint\": \"8.49.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-html\": \"^7.1.0\",\n \"eslint-plugin-import\": \"^2.28.1\",\n \"eslint-plugin-json\": \"^3.1.0\",\n \"eslint-plugin-markdown\": \"^3.0.1\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^6.1.1\",\n \"rimraf\": \"^5.0.1\",\n \"tslib\": \"^2.6.2\",\n \"typedoc\": \"0.25.1\",\n \"typescript\": \"^5.1.6\"\n }\n}\n", "/** TFJS custom backend registration */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as image from '../image/image';\nimport type { AnyCanvas } from '../exports';\n\nexport const config = {\n name: 'humangl',\n priority: 999,\n canvas: null as null | AnyCanvas,\n gl: null as null | WebGL2RenderingContext,\n extensions: [] as string[] | null,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false, // default=true\n desynchronized: true, // default=undefined\n },\n};\n\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions();\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(instance: Human): void {\n // force backend reload if gl context is not valid\n if (instance.config.backend !== 'humangl') return;\n if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {\n log('humangl error: backend invalid context');\n instance.models.reset();\n /*\n log('resetting humangl backend');\n await tf.removeBackend(config.name);\n await register(instance); // re-register\n */\n }\n if (!tf.findBackend(config.name)) {\n try {\n config.canvas = image.canvas(100, 100);\n } catch (err) {\n log('humangl error: cannot create canvas:', err);\n return;\n }\n try {\n config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n if (!config.gl) {\n log('humangl error: cannot get webgl context');\n return;\n }\n const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n if (!glv2) {\n log('backend override: using fallback webgl backend as webgl 2.0 is not detected');\n instance.config.backend = 'webgl';\n return;\n }\n if (config.canvas) {\n config.canvas.addEventListener('webglcontextlost', (e) => {\n log('humangl error:', e.type);\n log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n instance.emit('error');\n throw new Error('backend error: webgl context lost');\n });\n config.canvas.addEventListener('webglcontextrestored', (e) => {\n log('humangl error: context restored:', e);\n });\n config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n log('humangl error: context create:', e);\n });\n }\n } catch (err) {\n log('humangl error: cannot get webgl context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('humangl error: cannot set webgl context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n // @ts-ignore uncompatible kernelMs timing info\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('humangl error: cannot register webgl backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('humangl error: cannot update webgl backend registration:', err);\n return;\n }\n try {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n } catch (err) {\n log('humangl error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n const backend = tf.backend();\n const current = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;\n if (current) {\n if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n } else {\n log('humangl error: no current gl context:', current, config.gl);\n }\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record = {\n tf255: 255.0,\n tf1: 1.0,\n tf2: 2.0,\n tf05: 0.5,\n tf127: 127.5,\n rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n constants.tf255 = tf.scalar(255.0, 'float32');\n constants.tf1 = tf.scalar(1.0, 'float32');\n constants.tf2 = tf.scalar(2.0, 'float32');\n constants.tf05 = tf.scalar(0.5, 'float32');\n constants.tf127 = tf.scalar(127.5, 'float32');\n constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/** TFJS backend initialization and customization */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human, Config, BackendEnum } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as constants from './constants';\nimport type { TensorInfo } from './types';\n\nexport async function getBestBackend(): Promise {\n await env.updateBackend(); // update env on backend init\n if (env.tensorflow?.version) return 'tensorflow';\n if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';\n if (env.webgl.supported && env.webgl.backend) return 'webgl';\n if (env.wasm.supported && env.wasm.backend) return 'wasm';\n return 'cpu';\n}\n\nfunction registerCustomOps(config: Config) {\n const newKernels: string[] = [];\n if (!env.kernels.includes('mod')) {\n const kernelMod = {\n kernelName: 'Mod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('mod');\n newKernels.push('mod');\n }\n if (!env.kernels.includes('floormod')) {\n const kernelFloorMod = {\n kernelName: 'FloorMod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n };\n tf.registerKernel(kernelFloorMod);\n env.kernels.push('floormod');\n newKernels.push('floormod');\n }\n /*\n if (!env.kernels.includes('atan2') && config.softwareKernels) {\n const kernelAtan2 = {\n kernelName: 'Atan2',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu');\n const t = tf.atan2(op.inputs.a, op.inputs.b);\n tf.setBackend(backend);\n return t;\n }),\n };\n if (config.debug) log('registered kernel:', 'atan2');\n log('registered kernel:', 'atan2');\n tf.registerKernel(kernelAtan2);\n env.kernels.push('atan2');\n newKernels.push('atan2');\n }\n */\n if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n const kernelRotateWithOffset = {\n kernelName: 'RotateWithOffset',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu'); // eslint-disable-line @typescript-eslint/no-floating-promises\n const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n tf.setBackend(backend); // eslint-disable-line @typescript-eslint/no-floating-promises\n return t;\n }),\n };\n tf.registerKernel(kernelRotateWithOffset);\n env.kernels.push('rotatewithoffset');\n newKernels.push('rotatewithoffset');\n }\n if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record = {};\n\nexport async function check(instance: Human, force = false) {\n instance.state = 'backend';\n if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();\n if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n const timeStamp = now();\n\n if (instance.config.backend && instance.config.backend.length > 0) {\n // detect web worker\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n if (instance.config.debug) log('running inside web worker');\n }\n\n if (typeof navigator !== 'undefined' && navigator?.userAgent?.toLowerCase().includes('electron')) {\n if (instance.config.debug) log('running inside electron');\n }\n\n // check available backends\n let available = Object.keys(tf.engine().registryFactory as Record);\n if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n humangl.register(instance);\n available = Object.keys(tf.engine().registryFactory as Record);\n }\n if (instance.config.debug) log('available backends:', available);\n\n // force browser vs node backend\n if (env.browser && !env.node && (instance.config.backend === 'tensorflow') && available.includes('webgl')) {\n if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n instance.config.backend = 'webgl';\n }\n if (env.node && !env.browser && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl') && available.includes('tensorflow')) {\n if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n instance.config.backend = 'tensorflow';\n }\n\n // handle webgpu\n if (env.browser && instance.config.backend === 'webgpu') {\n if (typeof navigator === 'undefined' || typeof navigator.gpu === 'undefined') {\n log('override: backend set to webgpu but browser does not support webgpu');\n instance.config.backend = 'webgl';\n } else {\n const adapter = await navigator.gpu.requestAdapter();\n if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n if (!adapter) {\n log('override: backend set to webgpu but browser reports no available gpu');\n instance.config.backend = 'webgl';\n } else {\n // @ts-ignore requestAdapterInfo is not in tslib\n const adapterInfo = 'requestAdapterInfo' in adapter ? await adapter.requestAdapterInfo() : undefined;\n // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n log('webgpu adapter info:', adapterInfo);\n }\n }\n }\n\n if (!available.includes(instance.config.backend)) {\n log(`error: backend ${instance.config.backend} not found in registry`);\n instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n }\n\n if (instance.config.debug) log('setting backend:', [instance.config.backend]);\n\n // customize wasm\n if (instance.config.backend === 'wasm') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n if (typeof tf.setWasmPaths !== 'undefined') tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n let mt = false;\n let simd = false;\n try {\n mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n if (instance.config.debug) log(`wasm execution: ${simd ? 'simd' : 'no simd'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n } catch {\n log('wasm detection failed');\n }\n }\n\n try {\n await tf.setBackend(instance.config.backend);\n await tf.ready();\n } catch (err) {\n log('error: cannot set backend:', instance.config.backend, err);\n return false;\n }\n // @ts-ignore private property\n if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n }\n\n // customize humangl\n if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false \n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false \n // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV']) tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true \n // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false \n // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (instance.config.debug && typeof instance.config.deallocate !== 'undefined' && instance.config.deallocate) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n }\n\n // customize webgpu\n if (tf.getBackend() === 'webgpu') {\n // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n }\n\n if (instance.config.debug) {\n // @ts-ignore private property\n const newFlags = tf.env().flags;\n const updatedFlags = {};\n for (const key of Object.keys(newFlags)) {\n if (defaultFlags[key] === newFlags[key]) continue;\n updatedFlags[key] = newFlags[key];\n }\n if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n }\n\n if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n if (instance.config.debug) log('flags:', instance.config['flags']);\n for (const [key, val] of Object.entries(instance.config.flags)) {\n tf.env().set(key, val as number | boolean);\n }\n }\n\n tf.enableProdMode();\n constants.init();\n instance.performance.initBackend = Math.trunc(now() - timeStamp);\n instance.config.backend = tf.getBackend() as BackendEnum;\n await env.updateBackend(); // update env on backend init\n registerCustomOps(instance.config);\n // await env.updateBackend(); // update env on backend init\n // env.initial = false;\n }\n return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: string[], config) {\n // if (config.debug) log('registerKernel:', kernelNames);\n for (const kernelName of kernelNames) {\n const kernelConfig = {\n kernelName,\n backendName: config.backend,\n kernelFunc: (param): TensorInfo => {\n if (config.debug) log('kernelFunc', kernelName, config.backend, param);\n return param?.inputs?.info as TensorInfo;\n },\n // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n };\n tf.registerKernel(kernelConfig);\n }\n env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport { defaultLabels } from './labels';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\n/** draw processed canvas */\nexport function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n if (!input || !output) return;\n const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial) {\n if (!result?.performance || !inCanvas) return null;\n const timeStamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n const promise = Promise.all([\n face(inCanvas, result.face, localOptions),\n body(inCanvas, result.body, localOptions),\n hand(inCanvas, result.hand, localOptions),\n object(inCanvas, result.object, localOptions),\n gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n // person(inCanvas, result.persons, localOptions); // already included above\n ]);\n drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n result.performance.draw = drawTime;\n return promise;\n}\n\n/** sets default label templates for face/body/hand/object/gestures */\nexport function init() {\n options.faceLabels = defaultLabels.face;\n options.bodyLabels = defaultLabels.body;\n options.bodyPartLabels = defaultLabels.bodyPart;\n options.handLabels = defaultLabels.hand;\n options.fingerLabels = defaultLabels.finger;\n options.objectLabels = defaultLabels.object;\n options.gestureLabels = defaultLabels.gesture;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n if (!input) log('draw error: invalid canvas');\n else if (!input.getContext) log('draw error: canvas context not defined');\n else {\n const ctx = input.getContext('2d', { willReadFrequently: true });\n if (!ctx) log('draw error: cannot get canvas context');\n else return ctx;\n }\n return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const replace = (str: string, source: string, target: string | number) => str.replace(source, typeof target === 'number' ? target.toFixed(1) : target);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function labels(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, str: string, startX: number, startY: number, localOptions: DrawOptions) {\n const line: string[] = str.replace(/\\[.*\\]/g, '').split('\\n').map((l) => l.trim()); // remove unmatched templates and split into array\n const x = Math.max(0, startX);\n for (let i = line.length - 1; i >= 0; i--) {\n const y = i * localOptions.lineHeight + startY;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(line[i], x + 5, y + 16);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(line[i], x + 4, y + 15);\n }\n}\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n ctx.beginPath();\n ctx.lineWidth = localOptions.lineWidth;\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.lineWidth = localOptions.lineWidth;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n let angle;\n let x;\n let y;\n ctx.beginPath();\n ctx.moveTo(from[0], from[1]);\n ctx.lineTo(to[0], to[1]);\n angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.moveTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n ctx.closePath();\n ctx.stroke();\n ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\n\nexport interface DrawOptions {\n /** draw line color */\n color: string,\n /** alpha value used for lines */\n alpha: number,\n /** label color */\n labelColor: string,\n /** label shadow color */\n shadowColor: string,\n /** label font */\n font: string,\n /** line spacing between labels */\n lineHeight: number,\n /** line width for drawn lines */\n lineWidth: number,\n /** size of drawn points */\n pointSize: number,\n /** draw rounded boxes by n pixels */\n roundRect: number,\n /** should points be drawn? */\n drawPoints: boolean,\n /** should labels be drawn? */\n drawLabels: boolean,\n /** should face attention keypoints be highlighted */\n drawAttention: boolean;\n /** should detected gestures be drawn? */\n drawGestures: boolean,\n /** should draw boxes around detection results? */\n drawBoxes: boolean,\n /** should draw polygons from detection points? */\n drawPolygons: boolean,\n /** should draw gaze arrows? */\n drawGaze: boolean,\n /** should fill polygons? */\n fillPolygons: boolean,\n /** use z-coordinate when available */\n useDepth: boolean,\n /** should lines be curved? */\n useCurves: boolean,\n /** string template for face labels */\n faceLabels: string,\n /** string template for body labels */\n bodyLabels: string,\n /** string template for body part labels */\n bodyPartLabels: string,\n /** string template for hand labels */\n handLabels: string,\n /** string template for hand labels */\n fingerLabels: string,\n /** string template for object labels */\n objectLabels: string,\n /** string template for gesture labels */\n gestureLabels: string,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n shadowColor: 'black' as string,\n alpha: 0.5 as number,\n font: 'small-caps 16px \"Segoe UI\"' as string,\n lineHeight: 18 as number,\n lineWidth: 4 as number,\n pointSize: 2 as number,\n roundRect: 8 as number,\n drawPoints: false as boolean,\n drawLabels: true as boolean,\n drawBoxes: true as boolean,\n drawAttention: true as boolean,\n drawGestures: true as boolean,\n drawPolygons: true as boolean,\n drawGaze: true as boolean,\n fillPolygons: false as boolean,\n useDepth: true as boolean,\n useCurves: false as boolean,\n faceLabels: '' as string,\n bodyLabels: '' as string,\n bodyPartLabels: '' as string,\n objectLabels: '' as string,\n handLabels: '' as string,\n fingerLabels: '' as string,\n gestureLabels: '' as string,\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n rightEyeIris: [473, 474, 475, 476, 477], // 5\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const meshLandmarks: Record = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]],\n};\n\nexport const blazeFaceLandmarks: Record = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nexport const irisIndices: { key: string, indices: number[] }[] = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: number[] = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: number[] = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: number[] = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: number[] = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: number[] = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: number[] = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const pairsLips: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n lips: connectionsToIndices(pairsLips),\n leftEye: connectionsToIndices(pairsLeftEye),\n leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n leftIris: connectionsToIndices(pairsLeftIris),\n rightEye: connectionsToIndices(pairsRightEye),\n rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n rightIris: connectionsToIndices(pairsRightIris),\n faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n lips: connectionsToIndices(LIPS_CONNECTIONS),\n leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n 61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n 185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n 78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n 191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n 76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n 184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n 62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n 183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n 130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n 247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n 226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n 113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n 35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n 143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n 156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n 359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n 467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n 446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n 342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n 265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n 372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n 383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n 468, // Center.\n 469, // Iris right edge.\n 470, // Iris top edge.\n 471, // Iris left edge.\n 472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n 473, // Center.\n 474, // Iris right edge.\n 475, // Iris top edge.\n 476, // Iris left edge.\n 477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow, labels, replace } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet localOptions: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (!localOptions.drawLabels || (localOptions.faceLabels?.length === 0)) return;\n let l = localOptions.faceLabels.slice();\n l = replace(l, '[id]', f.id.toFixed(0));\n if (f.score) l = replace(l, '[score]', 100 * f.score);\n if (f.gender) l = replace(l, '[gender]', f.gender);\n if (f.genderScore) l = replace(l, '[genderScore]', 100 * f.genderScore);\n if (f.age) l = replace(l, '[age]', f.age);\n if (f.distance) l = replace(l, '[distance]', 100 * f.distance);\n if (f.real) l = replace(l, '[real]', 100 * f.real);\n if (f.live) l = replace(l, '[live]', 100 * f.live);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n l = replace(l, '[emotions]', emotion.join(' '));\n }\n if (f.rotation?.angle?.roll) l = replace(l, '[roll]', rad2deg(f.rotation.angle.roll));\n if (f.rotation?.angle?.yaw) l = replace(l, '[yaw]', rad2deg(f.rotation.angle.yaw));\n if (f.rotation?.angle?.pitch) l = replace(l, '[pitch]', rad2deg(f.rotation.angle.pitch));\n if (f.rotation?.gaze?.bearing) l = replace(l, '[gaze]', rad2deg(f.rotation.gaze.bearing));\n labels(ctx, l, f.box[0], f.box[1], localOptions);\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n // iris: array[center, left, top, right, bottom]\n if (f.annotations?.leftEyeIris && f.annotations?.leftEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (f.annotations?.rightEyeIris && f.annotations?.rightEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n ctx.strokeStyle = 'pink';\n const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n const pathV = new Path2D(`\n M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n C\n ${valX} ${f.box[1]},\n ${valX} ${f.box[1] + f.box[3]},\n ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n `);\n const pathH = new Path2D(`\n M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n C \n ${f.box[0]} ${valY},\n ${f.box[0] + f.box[2]} ${valY},\n ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n `);\n ctx.stroke(pathH);\n ctx.stroke(pathV);\n }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.gaze.strength && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {\n ctx.strokeStyle = 'pink';\n ctx.fillStyle = 'pink';\n const leftGaze = [\n f.annotations.leftEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.leftEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);\n const rightGaze = [\n f.annotations.rightEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.rightEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);\n }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPolygons && f.mesh.length >= 468) {\n ctx.lineWidth = 1;\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n lines(ctx, points, localOptions);\n }\n drawIrisElipse(f, ctx);\n }\n /*\n if (localOptions.drawPolygons && f.contours.length > 1) {\n ctx.lineWidth = 5;\n lines(ctx, f.contours, opt);\n }\n ctx.lineWidth = 1;\n */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPoints) {\n if (f?.mesh.length >= 468) {\n for (let i = 0; i < f.mesh.length; i++) {\n point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n if (localOptions.drawAttention) {\n if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n }\n }\n } else {\n for (const [k, v] of Object.entries(f?.annotations || {})) {\n if (!v?.[0]) continue;\n const pt = v[0];\n point(ctx, pt[0], pt[1], 0, localOptions);\n if (localOptions.drawLabels) labels(ctx, k, pt[0], pt[1], localOptions);\n }\n }\n }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n if (localOptions.drawBoxes) {\n rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial) {\n localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n for (const f of result) {\n drawFaceBoxes(f, ctx);\n drawLabels(f, ctx);\n if (f.mesh && f.mesh.length > 0) {\n drawFacePoints(f, ctx);\n drawFacePolygons(f, ctx);\n drawGazeSpheres(f, ctx);\n drawGazeArrows(f, ctx);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.bodyLabels?.length > 0)) {\n let l = localOptions.bodyLabels.slice();\n l = replace(l, '[id]', result[i].id.toFixed(0));\n l = replace(l, '[score]', 100 * result[i].score);\n labels(ctx, l, result[i].box[0], result[i].box[1], localOptions);\n }\n }\n if (localOptions.drawPoints && result[i].keypoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels && (localOptions.bodyPartLabels?.length > 0) && result[i].keypoints) {\n ctx.font = localOptions.font;\n for (const pt of result[i].keypoints) {\n if (!pt.score || (pt.score === 0)) continue;\n let l = localOptions.bodyPartLabels.slice();\n l = replace(l, '[label]', pt.part);\n l = replace(l, '[score]', 100 * pt.score);\n labels(ctx, l, pt.position[0], pt.position[1], localOptions);\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n for (const part of Object.values(result[i].annotations)) {\n for (const connected of part) curves(ctx, connected, localOptions);\n }\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.handLabels?.length > 0)) {\n let l = localOptions.handLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = colorDepth(pt[2], localOptions);\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels && h.annotations && (localOptions.fingerLabels?.length > 0)) {\n for (const [part, pt] of Object.entries(h.annotations)) {\n let l = localOptions.fingerLabels.slice();\n l = replace(l, '[label]', part);\n labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions);\n }\n }\n if (localOptions.drawPolygons && h.annotations) {\n const addHandLine = (part: Point[]) => {\n if (!part || part.length === 0 || !part[0]) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n const z = part[i][2] || 0;\n ctx.strokeStyle = colorDepth(i * z, localOptions);\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations.index);\n addHandLine(h.annotations.middle);\n addHandLine(h.annotations.ring);\n addHandLine(h.annotations.pinky);\n addHandLine(h.annotations.thumb);\n // addPart(h.annotations.palm);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.objectLabels?.length > 0)) {\n let l = localOptions.objectLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (localOptions.drawGestures && (localOptions.gestureLabels?.length > 0)) {\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n const [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n let l = localOptions.gestureLabels.slice();\n l = replace(l, '[where]', where[0]);\n l = replace(l, '[who]', who);\n l = replace(l, '[what]', what[1]);\n labels(ctx, l, 8, 2 + (i * localOptions.lineHeight), localOptions);\n i += 1;\n }\n }\n }\n}\n", "export const defaultLabels = {\n face: `face\n confidence: [score]%\n [gender] [genderScore]%\n age: [age] years\n distance: [distance]cm\n real: [real]%\n live: [live]%\n [emotions]\n roll: [roll]\u00B0 yaw:[yaw]\u00B0 pitch:[pitch]\u00B0\n gaze: [gaze]\u00B0`,\n body: 'body [score]%',\n bodyPart: '[label] [score]%',\n object: '[label] [score]%',\n hand: '[label] [score]%',\n finger: '[label]',\n gesture: '[where] [who]: [what]',\n};\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: string[] = [\n 'nose', // 0\n 'leftEyeInside', // 1\n 'leftEye', // 2\n 'leftEyeOutside', // 3\n 'rightEyeInside', // 4\n 'rightEye', // 5\n 'rightEyeOutside', // 6\n 'leftEar', // 7\n 'rightEar', // 8\n 'leftMouth', // 9\n 'rightMouth', // 10\n 'leftShoulder', // 11\n 'rightShoulder', // 12\n 'leftElbow', // 13\n 'rightElbow', // 14\n 'leftWrist', // 15\n 'rightWrist', // 16\n 'leftPinky', // 17\n 'rightPinky', // 18\n 'leftIndex', // 19\n 'rightIndex', // 20\n 'leftThumb', // 21\n 'rightThumb', // 22\n 'leftHip', // 23\n 'rightHip', // 24\n 'leftKnee', // 25\n 'rightKnee', // 26\n 'leftAnkle', // 27\n 'rightAnkle', // 28\n 'leftHeel', // 29\n 'rightHeel', // 30\n 'leftFoot', // 31\n 'rightFoot', // 32\n 'bodyCenter', // 33\n 'bodyTop', // 34\n 'leftPalm', // 35 // z-coord not ok\n 'leftHand', // 36 // similar to wrist but z-coord not ok\n 'rightPalm', // 37 // z-coord not ok\n 'rightHand', // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record = {\n shoulders: ['leftShoulder', 'rightShoulder'],\n hips: ['rightHip', 'leftHip'],\n mouth: ['leftMouth', 'rightMouth'],\n leftLegUpper: ['leftHip', 'leftKnee'],\n leftLegLower: ['leftKnee', 'leftAnkle'],\n leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n leftTorso: ['leftShoulder', 'leftHip'],\n leftArmUpper: ['leftShoulder', 'leftElbow'],\n leftArmLower: ['leftElbow', 'leftWrist'],\n leftHand: ['leftWrist', 'leftPalm'],\n leftHandPinky: ['leftPalm', 'leftPinky'],\n leftHandIndex: ['leftPalm', 'leftIndex'],\n leftHandThumb: ['leftPalm', 'leftThumb'],\n leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n rightLegUpper: ['rightHip', 'rightKnee'],\n rightLegLower: ['rightKnee', 'rightAnkle'],\n rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n rightTorso: ['rightShoulder', 'rightHip'],\n rightArmUpper: ['rightShoulder', 'rightElbow'],\n rightArmLower: ['rightElbow', 'rightWrist'],\n rightHand: ['rightWrist', 'rightPalm'],\n rightHandPinky: ['rightPalm', 'rightPinky'],\n rightHandIndex: ['rightPalm', 'rightIndex'],\n rightHandThumb: ['rightPalm', 'rightThumb'],\n rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D } from '../tfjs/types';\n\nexport interface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nlet model: GraphModel | null;\nlet inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n const anchors: { x: number, y: number }[] = [];\n let layerId = 0;\n while (layerId < numLayers) {\n let anchorCount = 0;\n let lastSameStrideLayer = layerId;\n while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n anchorCount += 2;\n lastSameStrideLayer++;\n }\n const stride = strides[layerId];\n const featureMapHeight = Math.ceil(inputSize / stride);\n const featureMapWidth = Math.ceil(inputSize / stride);\n for (let y = 0; y < featureMapHeight; ++y) {\n for (let x = 0; x < featureMapWidth; ++x) {\n for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n }\n }\n }\n layerId = lastSameStrideLayer;\n }\n anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nexport async function loadDetector(config: Config): Promise {\n if (env.initial) model = null;\n if (!model && config.body['detector'] && config.body['detector'].modelPath || '') {\n model = await loadModel(config.body['detector'].modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug && model) log('cached model:', model['modelUrl']);\n createAnchors();\n return model as GraphModel;\n}\n\nconst cropFactor = [5.0, 5.0];\nexport function decodeBoxes(boxesTensor, anchor) {\n return tf.tidy(() => {\n const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n let xCenter = tf.squeeze(split[0]);\n let yCenter = tf.squeeze(split[1]);\n let width = tf.squeeze(split[2]);\n let height = tf.squeeze(split[3]);\n xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n const xMin = tf.sub(xCenter, tf.div(width, 2));\n const yMin = tf.sub(yCenter, tf.div(height, 2));\n const xMax = tf.add(xMin, width);\n const yMax = tf.add(yMin, height);\n const boxes = tf.stack([xMin, yMin, xMax, yMax], 1);\n return boxes;\n });\n}\n\nasync function decodeResults(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise {\n const detectedBoxes: DetectedBox[] = [];\n const t: Record = {};\n t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n t.scores = tf.sigmoid(logitsTensor);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, 1, config.body['detector']?.minConfidence || 0.1, config.body['detector']?.iouThreshold || 0.1);\n const nms = await t.nms.data();\n const scores = await t.scores.data();\n const boxes = await t.boxes.array();\n for (const i of Array.from(nms)) {\n const score = scores[i];\n const boxRaw: Box = boxes[i];\n const box: Box = [Math.round(boxRaw[0] * outputSize[0]), Math.round(boxRaw[1] * outputSize[1]), Math.round(boxRaw[2] * outputSize[0]), Math.round(boxRaw[3] * outputSize[1])];\n const detectedBox: DetectedBox = { score, boxRaw, box };\n detectedBoxes.push(detectedBox);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return detectedBoxes;\n}\n\nexport async function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n const t: Record = {};\n t.res = model?.execute(input, ['Identity']) as Tensor; //\n t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n t.logits = tf.squeeze(t.logitsRaw);\n t.boxes = tf.squeeze(t.boxesRaw);\n const boxes = await decodeResults(t.boxes, t.logits, config, outputSize);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function square(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n const dist = [box[2] * scaleFact, box[3] * scaleFact];\n const newBox: Box = [\n box[0] - (dist[0] - box[2]) / 2,\n box[1] - (dist[1] - box[3]) / 2,\n dist[0],\n dist[1],\n ];\n return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport { loadDetector, detectBoxes, DetectedBox } from './blazeposedetector';\nimport * as box from '../util/box';\nimport { env } from '../util/env';\n\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nlet model: GraphModel | null;\nlet inputSize = 256;\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n detector: [],\n};\n\nconst cache: BodyResult[] = [];\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport const loadDetect = (config: Config): Promise => loadDetector(config);\n\nexport async function loadPose(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.body.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nfunction prepareImage(input: Tensor4D, size: number, cropBox?: Box): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n let final: Tensor;\n if (cropBox) {\n t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n }\n if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n const height: [number, number] = [\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n ];\n const width: [number, number] = [\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n ];\n padding = [\n [0, 0], // dont touch batch\n height, // height before&after\n width, // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n t.resize = tf.image.resizeBilinear(t.pad as Tensor4D, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else if (input.shape[1] !== size) { // if input needs resizing\n t.resize = tf.image.resizeBilinear(t.cropped as Tensor4D || input, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else { // if input is already in a correct resolution just normalize it\n final = tf.div(t.cropped || input, constants.tf255);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nfunction rescaleKeypoints(keypoints: BodyKeypoint[], outputSize: [number, number], cropBox?: Box): BodyKeypoint[] {\n for (const kpt of keypoints) { // first rescale due to padding\n kpt.position = [\n Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n kpt.position[2] as number,\n ];\n kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n }\n if (cropBox) { // second rescale due to cropping\n const width = cropBox[2] - cropBox[0];\n const height = cropBox[3] - cropBox[1];\n for (const kpt of keypoints) {\n kpt.positionRaw = [\n kpt.positionRaw[0] / height + cropBox[1], // correct offset due to crop\n kpt.positionRaw[1] / width + cropBox[0], // correct offset due to crop\n kpt.positionRaw[2] as number,\n ];\n kpt.position = [\n Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n kpt.positionRaw[2] as number,\n ];\n }\n }\n return keypoints;\n}\n\nfunction fixKeypoints(keypoints: BodyKeypoint[]) {\n // palm z-coord is incorrect around near-zero so we approximate it\n const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise {\n /**\n * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n * t.segmentation:\n * t.heatmap:\n * t.world: 39 keypoints [x,y,z] normalized to -1..1\n * t.poseflag: body score\n */\n if (!model?.['executor']) return null;\n const t: Record = {};\n [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = model?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n const poseScore = (await t.poseflag.data())[0];\n const points = await t.ld.data();\n const distances = await t.world.data();\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n const keypointsRelative: BodyKeypoint[] = [];\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n const score = sigmoid(points[depth * i + 3]);\n const presence = sigmoid(points[depth * i + 4]);\n const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n const positionRaw: Point = [points[depth * i + 0] / inputSize, points[depth * i + 1] / inputSize, points[depth * i + 2] + 0];\n const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n }\n if (poseScore < (config.body.minConfidence || 0)) return null;\n fixKeypoints(keypointsRelative);\n const keypoints: BodyKeypoint[] = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n const kpts = keypoints.map((k) => k.position);\n const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n return body;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n skipped++;\n } else {\n let boxes: DetectedBox[] = [];\n if (config.body?.['detector']?.['enabled']) {\n const preparedImage = prepareImage(input, 224);\n boxes = await detectBoxes(preparedImage, config, outputSize);\n tf.dispose(preparedImage);\n } else {\n boxes = [{ box: [0, 0, 0, 0] as Box, boxRaw: [0, 0, 1, 1], score: 0 }]; // running without detector\n }\n for (let i = 0; i < boxes.length; i++) {\n const preparedBox = prepareImage(input, 256, boxes[i]?.boxRaw); // padded and resized\n cache.length = 0;\n const bodyResult = await detectLandmarks(preparedBox, config, outputSize);\n tf.dispose(preparedBox);\n if (!bodyResult) continue;\n bodyResult.id = i;\n // bodyResult.score = 0; // TBD\n cache.push(bodyResult);\n }\n /*\n cropBox = [0, 0, 1, 1]; // reset crop coordinates\n if (cache?.boxRaw && config.skipAllowed) {\n const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n size = (size * 1.0) / 2; // enlarge and half it\n if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n const y = 0; // cy - size;\n const x = cx - size;\n cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n }\n }\n */\n lastTime = now();\n skipped = 0;\n }\n return cache;\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n // fakeOps(['floormod'], config);\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n if (!res) return [];\n const t: Record = {};\n const results: ObjectResult[] = [];\n const detections = await res.array() as number[][][];\n t.squeeze = tf.squeeze(res);\n const arr = tf.split(t.squeeze, 6, 1); // x1, y1, x2, y2, score, class\n t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n t.boxes = tf.squeeze(t.stack);\n t.scores = tf.squeeze(arr[4]);\n t.classes = tf.squeeze(arr[5]);\n tf.dispose([res, ...arr]);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, config.object.maxDetected || 0, config.object.iouThreshold, (config.object.minConfidence || 0));\n const nms = await t.nms.data();\n let i = 0;\n for (const id of Array.from(nms)) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n if (Number.isNaN(classVal)) continue;\n const label = labels[classVal].label as ObjectType;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw: Box = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ];\n const box: Box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return results;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n lastTime = now();\n tf.dispose(resize);\n\n const obj = await process(objectT, outputSize, config);\n last = obj;\n\n resolve(obj);\n });\n}\n", "export const kpt: string[] = [\n 'head',\n 'neck',\n 'rightShoulder',\n 'rightElbow',\n 'rightWrist',\n 'chest',\n 'leftShoulder',\n 'leftElbow',\n 'leftWrist',\n 'bodyCenter',\n 'rightHip',\n 'rightKnee',\n 'rightAnkle',\n 'leftHip',\n 'leftKnee',\n 'leftAnkle',\n];\n\nexport const connected: Record = {\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record };\n\n// const keypoints: Array = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore): Promise<[number, number, number]> {\n const [width, height] = inputs.shape;\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const max = tf.max(reshaped, 0);\n const newScore: number = (await max.data())[0]; // get highest score\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coordinates = tf.argMax(reshaped, 0);\n const mod = tf.mod(coordinates, width);\n const x = (await mod.data())[0];\n const div = tf.div(coordinates, width);\n const y: number = (await div.data())[0];\n tf.dispose([reshaped, max, coordinates, mod, div]);\n return [x, y, newScore];\n }\n tf.dispose([reshaped, max]);\n return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs[0].shape) return [];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n skipped++;\n return [cache];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape?.[2] || 0, model?.inputs[0].shape?.[1] || 0], false);\n const enhance = tf.mul(resize, constants.tf2);\n const norm = tf.sub(enhance, constants.tf1);\n return norm;\n });\n let resT;\n if (config.body.enabled) resT = model?.execute(tensor);\n lastTime = now();\n tf.dispose(tensor);\n\n if (resT) {\n cache.keypoints.length = 0;\n const squeeze = tf.squeeze(resT);\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = tf.unstack(squeeze, 2);\n tf.dispose(squeeze);\n\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n if (partScore > (config.body.minConfidence || 0)) {\n cache.keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = cache.keypoints.map((a) => a.position[0]);\n const y = cache.keypoints.map((a) => a.position[1]);\n cache.box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n cache.boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n cache.annotations[name] = pt;\n }\n resolve([cache]);\n });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n Math.trunc(Math.max(0, box.startPoint[0])),\n Math.trunc(Math.max(0, box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n box.startPoint[0] / (input.shape[2] || 0),\n box.startPoint[1] / (input.shape[1] || 0),\n (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor, anchor) => {\n const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n // const centerPoint = [(startPoint[0] + endPoint[0]) / 2, (startPoint[1] + endPoint[1]) / 2];\n const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]);\n /**\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0]) / 2) + (pt[0] * input.shape[2] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1]) / 2) + (pt[1] * input.shape[1] / blazeface.size()),\n ]);\n */\n\n return { startPoint, endPoint, landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n const h = image.shape[1];\n const w = image.shape[2];\n const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n const norm = tf.div(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n return {\n startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point,\n endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size,\n };\n};\n\nexport const squarifyBox = (box) => {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize = Math.max(...size) / 2;\n return {\n startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point,\n endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size: [Math.round(size[0]), Math.round(size[1])] as [number, number],\n };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n const x = landmarks.map((d) => d[0]);\n const y = landmarks.map((d) => d[1]);\n return {\n startPoint: [Math.min(...x), Math.min(...y)] as Point,\n endPoint: [Math.max(...x), Math.max(...y)] as Point,\n landmarks,\n };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n let product = 0;\n for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize: number) {\n const spec = inputSize === 192\n ? { strides: [4], anchors: [1] } // facemesh-detector\n : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n const anchors: [number, number][] = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n }\n }\n }\n return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n const boxSize = getBoxSize(box);\n const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n (coord[2] || 0),\n ]));\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n const boxCenter = getBoxCenter(box);\n const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n return coordsRotated.map((coord) => ([\n Math.trunc(coord[0] + offsets[0]),\n Math.trunc(coord[1] + offsets[1]),\n Math.trunc(coord[2] || 0),\n ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n ? coords.meshLandmarks.symmetryLine\n : coords.blazeFaceLandmarks.symmetryLine;\n let angle = 0; // default\n let rotationMatrix = fixedRotationMatrix; // default\n let face; // default\n\n if (rotate && env.kernels.includes('rotatewithoffset')) {\n angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n if (largeAngle) { // perform rotation only if angle is sufficiently high\n const center: Point = getBoxCenter(box);\n const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n const rotated = tf.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);\n rotationMatrix = buildRotationMatrix(-angle, center);\n face = cutAndResize(box, rotated, [inputSize, inputSize]);\n tf.dispose(rotated);\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n const x = mesh.map((m) => m[0]);\n const y = mesh.map((m) => m[1]);\n // weighted center\n /*\n const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n return [sum(x) / mesh.length, sum(y) / mesh.length];\n */\n // absolute center\n return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n const center = findFaceCenter(mesh);\n const boxSize = getBoxSize(previousBox);\n const calculatedBox = {\n startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n };\n return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\nexport interface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number, size: [number, number] }\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.detector?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n if (!anchors || !inputSizeT) return tf.zeros([0, 0]);\n const t: Record = {};\n t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n t.centers = tf.add(t.boxStarts, anchors);\n t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n t.centersNormalized = tf.div(t.centers, inputSizeT);\n t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n t.startNormalized = tf.mul(t.starts, inputSizeT);\n t.endNormalized = tf.mul(t.ends, inputSizeT);\n const boxes = tf.concat2d([t.startNormalized as Tensor2D, t.endNormalized as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor4D, config: Config): Promise {\n // sanity check on input\n if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n const t: Record = {};\n t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n t.div = tf.div(t.resized, constants.tf127);\n t.normalized = tf.sub(t.div, constants.tf05);\n const res = model?.execute(t.normalized) as Tensor[];\n if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n const sorted = res.sort((a, b) => a.size - b.size);\n t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n t.concat = tf.concat([t.concat512, t.concat384], 1);\n t.batch = tf.squeeze(t.concat, [0]);\n } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n t.batch = tf.squeeze(res[0]);\n } else { // original blazeface tfhub model\n t.batch = tf.squeeze(res);\n }\n tf.dispose(res);\n t.boxes = decodeBoxes(t.batch);\n t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.logits);\n t.scores = tf.squeeze(t.sigmoid);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n const nms = await t.nms.array() as number[];\n const boxes: DetectBox[] = [];\n const scores = await t.scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n\n if (confidence > (config.face.detector?.minConfidence || 0)) {\n const b: Record = {};\n b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n b.squeeze = tf.squeeze(b.slice);\n b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n const points = await b.bbox.data();\n const rawBox = {\n startPoint: [points[0], points[1]] as Point,\n endPoint: [points[2], points[3]] as Point,\n landmarks: (await b.landmarks.array()) as Point[],\n confidence,\n };\n b.anchor = tf.slice(anchors as Tensor, [nms[i], 0], [1, 2]);\n const anchor = await b.anchor.data();\n const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize], anchor);\n const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n const squaredBox = util.squarifyBox(enlargedBox);\n if (squaredBox.size[0] > (config.face.detector?.['minSize'] || 0) && squaredBox.size[1] > (config.face.detector?.['minSize'] || 0)) boxes.push(squaredBox);\n Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations.leftEyeLower0;\nconst rightOutline = coords.meshAnnotations.rightEyeLower0;\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.iris?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize === -1) inputSize = 64;\n return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.irisIndices.length; i++) {\n const { key, indices } = coords.irisIndices[i];\n const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0],\n newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n const boxSize = util.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / meshSize,\n box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n box.endPoint[0] / meshSize,\n ]], [0], [inputSize, inputSize]);\n if (flip && env.kernels.includes('flipleftright')) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\n }\n return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n const eyeRawCoords: Point[] = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n};\n\nexport async function augmentIris(rawCoords, face, meshSize) {\n if (!model?.['executor']) return rawCoords;\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = model.execute(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data();\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\n}\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n const t: Record = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n // flag: results[1], // already processed in parent // conv_faceflag\n lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n };\n for (const val of Object.values(t)) {\n if (!val) return rawCoords; // could not find tensor\n }\n\n // augment iris: adds additional 5 keypoints per eye\n const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n // augment eyes: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n // augment lips: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\nimport type { DetectBox } from './blazeface';\n\nconst cache = {\n boxes: [] as DetectBox[],\n skipped: Number.MAX_SAFE_INTEGER,\n timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n // reset cached boxes\n const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n cache.timestamp = now();\n cache.skipped = 0;\n } else {\n cache.skipped++;\n }\n const faces: FaceResult[] = [];\n const newCache: DetectBox[] = [];\n let id = 0;\n const size = inputSize;\n for (let i = 0; i < cache.boxes.length; i++) {\n const box = cache.boxes[i];\n let angle = 0;\n let rotationMatrix;\n const face: FaceResult = { // init face result\n id: id++,\n mesh: [],\n meshRaw: [],\n box: [0, 0, 0, 0],\n boxRaw: [0, 0, 0, 0],\n score: 0,\n boxScore: 0,\n faceScore: 0,\n size: [0, 0],\n // contoursRaw: [],\n // contours: [],\n annotations: {} as Record,\n };\n\n // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n if (config.filter.equalization) {\n const equilized = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n tf.dispose(face.tensor);\n if (equilized) face.tensor = equilized;\n }\n face.boxScore = Math.round(100 * box.confidence) / 100;\n if (!config.face.mesh?.enabled || !model?.['executor']) { // mesh not enabled or not loaded, return resuts from detector only\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.size = box.size;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n } else if (!model) { // mesh enabled, but not loaded\n if (config.debug) log('face mesh detection requested, but model is not loaded');\n } else { // mesh enabled\n if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n config.face.attention.enabled = false;\n tf.dispose(face.tensor);\n return faces;\n }\n const results = model.execute(face.tensor as Tensor) as Tensor[];\n const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n const faceConfidence = await confidenceT.data();\n face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n box.confidence = face.faceScore; // reset confidence of cached box\n if (config.face.mesh['keepInvalid']) {\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.size = box.size;\n face.score = face.boxScore;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n }\n } else {\n const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n tf.dispose(coordsReshaped);\n if (config.face.attention?.enabled) {\n rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n } else if (config.face.iris?.enabled) {\n rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize); // run iris model and augment results\n }\n face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n face.score = face.faceScore;\n const calculatedBox = {\n ...util.calculateFaceBox(face.mesh, box),\n confidence: box.confidence,\n landmarks: box.landmarks,\n size: box.size,\n };\n face.box = util.clampBox(calculatedBox, input);\n face.boxRaw = util.getRawBox(calculatedBox, input);\n face.size = calculatedBox.size;\n /*\n const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n face.contoursRaw = [];\n for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n */\n newCache.push(calculatedBox);\n }\n tf.dispose(results);\n }\n if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n else tf.dispose(face.tensor);\n }\n cache.boxes = newCache; // reset cache\n return faces;\n}\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (config.face.attention?.enabled && model?.['signature']) {\n if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n }\n if (!model) {\n if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n else model = await loadModel(config.face.mesh?.modelPath);\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nlet annotations: string[] = [];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet rgb = false;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.face.emotion?.modelPath);\n rgb = model?.inputs?.[0].shape?.[3] === 3;\n if (!rgb) annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']; // oarriaga and gear\n else annotations = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']; // affectnet\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ score: number, emotion: Emotion }[]> {\n if (!model) return [];\n const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj: { score: number, emotion: Emotion }[] = [];\n if (config.face.emotion?.enabled) {\n const t: Record = {};\n const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n if (config.face.emotion?.['crop'] > 0) { // optional crop\n const crop = config.face.emotion?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n }\n if (rgb) {\n t.mul = tf.mul(t.resize, 255);\n t.normalize = tf.sub(t.mul, [103.939, 116.779, 123.68]); // affectnet uses specific norm values\n t.emotion = model?.execute(t.normalize) as Tensor; // result is already in range 0..1, no need for additional activation\n } else {\n // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n // t.redNorm = tf.mul(t.red, rgb[0]);\n // t.greenNorm = tf.mul(t.green, rgb[1]);\n // t.blueNorm = tf.mul(t.blue, rgb[2]);\n // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n t.channels = tf.mul(t.resize, constants.rgb);\n t.grayscale = tf.sum(t.channels, 3, true);\n t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n }\n lastTime = now();\n const data = await t.emotion.data();\n for (let i = 0; i < data.length; i++) {\n if (data[i] > (config.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n }\n obj.sort((a, b) => b.score - a.score);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face similarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel, Tensor4D, Tensor1D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.description?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport function enhance(input, config: Config): Tensor {\n const tensor = (input.image || input.tensor || input) as Tensor4D; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n let crop: Tensor;\n if (config.face.description?.['crop'] > 0) { // optional crop\n const cropval = config.face.description?.['crop'];\n const box = [[cropval, cropval, 1 - cropval, 1 - cropval]];\n crop = tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n const norm: Tensor = tf.mul(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n /*\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n */\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n const obj: FaceRes = {\n age: 0 as number,\n gender: 'unknown' as Gender,\n genderScore: 0 as number,\n descriptor: [] as number[],\n };\n if (!model?.['executor']) return obj;\n const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && (last?.[idx]?.age > 0) && (last?.[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (config.face.description?.enabled) {\n const enhanced = enhance(image, config);\n const resT = model?.execute(enhanced) as Tensor[];\n lastTime = now();\n tf.dispose(enhanced);\n const genderT = resT.find((t) => t.shape[1] === 1) as Tensor;\n const gender = await genderT.data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > (config.face.description.minConfidence || 0)) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100) as Tensor1D, 1);\n const ageIdx: number = (await argmax.data())[0];\n tf.dispose(argmax);\n const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n const all = await ageT.data();\n obj.age = Math.round(all[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n const descriptor = desc ? await desc.data() : [] as number[];\n obj.descriptor = Array.from(descriptor);\n resT.forEach((t) => tf.dispose(t));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\n// import * as tf from 'dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: { x: number, y: number }[]): boolean {\n let inside = false;\n let j = polygon.length - 1;\n for (let i = 0; i < polygon.length; j = i++) {\n if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n }\n return inside;\n}\n\nexport async function mask(face: FaceResult): Promise {\n if (!face.tensor) return face.tensor;\n if (!face.mesh || face.mesh.length < 100) return face.tensor;\n const width = face.tensor.shape[2] || 0;\n const height = face.tensor.shape[1] || 0;\n const buffer = await face.tensor.buffer();\n let silhouette: { x: number, y: number }[] = [];\n for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n for (let x = 0; x < width; x++) {\n for (let y = 0; y < height; y++) {\n const inside = insidePoly(x / width, y / width, silhouette);\n if (!inside) {\n buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n }\n }\n }\n const output = buffer.toTensor();\n // tf.dispose(buffer);\n return output;\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.liveness?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: GearType[] = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.gear?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n const skipFrame = skipped < (config.face.gear?.skipFrames || 0);\n const skipTime = (config.face.gear?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n let box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n if (config.face.gear?.['crop'] > 0) { // optional crop config value\n const crop = config.face.gear?.['crop'];\n box = [[crop, crop, 1 - crop, 1 - crop]];\n }\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n const gender = await t.gender.data();\n obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n const race = await t.race.data();\n for (let i = 0; i < race.length; i++) {\n if (race[i] > (config.face.gear?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n }\n obj.race.sort((a, b) => b.score - a.score);\n // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n const ageDistribution = Array.from(await t.age.data());\n const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n let age = ageSorted[0][0]; // pick best starting point\n for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n obj.age = Math.round(10 * age) / 10;\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ age: number }> {\n if (!model) return { age: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.mul(t.resize, constants.tf255);\n const obj = { age: 0 };\n if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;\n if (t.age) {\n const data = await t.age.data();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { gender: Gender, genderScore: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n if (!model) return { gender: 'unknown', genderScore: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.tidy(() => {\n let normalize: Tensor;\n if (model?.inputs?.[0].shape?.[3] === 1) {\n const [red, green, blue] = tf.split(t.resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n } else {\n normalize = tf.mul(tf.sub(t.resize, constants.tf05), 2); // range rgb:-1..1\n }\n return normalize;\n });\n const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;\n const data = await t.gender.data();\n obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * MobileFaceNet model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['mobilefacenet']?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n const skipTime = (config.face['mobilefacenet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['mobilefacenet']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n /*\n // optional normalize outputs with l2 normalization\n const scaled = tf.tidy(() => {\n const l2 = res.norm('euclidean');\n const scale = res.div(l2);\n return scale;\n });\n\n // optional reduce feature vector complexity\n const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n */\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['insightface'].modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n const skipTime = (config.face['insightface']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['insightface']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations.rightEyeIris || !face.annotations.leftEyeIris) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v: Vector): Vector => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\n let thetaX: number;\n let thetaY: number;\n let thetaZ: number;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n thetaZ = Math.asin(r10);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n if (Number.isNaN(thetaX)) thetaX = 0;\n if (Number.isNaN(thetaY)) thetaY = 0;\n if (Number.isNaN(thetaZ)) thetaZ = 0;\n return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n };\n\n /*\n const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n };\n };\n */\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n const yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n const zAxis = normalize(crossVectors(xAxis, yAxis));\n // adjust xAxis to make sure that all axes are perpendicular to each other\n xAxis = crossVectors(yAxis, zAxis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n xAxis[0], xAxis[1], xAxis[2],\n yAxis[0], yAxis[1], yAxis[2],\n zAxis[0], zAxis[1], zAxis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n", "import type { FaceResult } from '../result';\n\nexport function calculateCameraDistance(face: FaceResult, width: number): number {\n // iris points are [center, left, top, right, bottom]\n // average size of human iris is 11.7mm - fairly constant for all ages/genders/races\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // distance of eye from camera in meters\n const cameraDistance = Math.round(1.17 / irisSize) / 100;\n return cameraDistance;\n}\n\nexport function calculateEyesDistance(face: FaceResult, width: number): number {\n // average distance between eyes is 65mm - fairly constant for typical adult male, but varies otherwise\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // pixel x and y distance of centers of left and right iris, you can use edges instead\n const irisDistanceXY = [f.leftEyeIris[0][0] - f.rightEyeIris[0][0], f.leftEyeIris[0][1] - f.rightEyeIris[0][1]];\n // absolute distance bewtween eyes in 0..1 range to account for head pitch (we can ignore yaw)\n const irisDistance = Math.sqrt((irisDistanceXY[0] * irisDistanceXY[0]) + (irisDistanceXY[1] * irisDistanceXY[1])) / width;\n // distance between eyes in meters\n const eyesDistance = Math.round(1.17 * irisDistance / irisSize) / 100;\n return eyesDistance;\n}\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor4D } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\nimport { calculateCameraDistance } from './anthropometry';\n\ninterface DescRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor4D): Promise => {\n // run facemesh, includes blazeface and iris\n let timeStamp: number = now();\n let ageRes: { age: number } | Promise<{ age: number }> | null;\n let gearRes: gear.GearType | Promise | null;\n let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n let mobilefacenetRes: number[] | Promise | null;\n let insightfaceRes: number[] | Promise | null;\n let antispoofRes: number | Promise | null;\n let livenessRes: number | Promise | null;\n let descRes: DescRes | Promise | null;\n\n const faceRes: FaceResult[] = [];\n instance.state = 'run:face';\n const faces: FaceResult[] = await facemesh.predict(input, instance.config);\n instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n instance.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefied\n if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n // optional face mask\n if (instance.config.face.detector?.mask) {\n const masked = await mask.mask(faces[i]);\n tf.dispose(faces[i].tensor);\n if (masked) faces[i].tensor = masked;\n }\n\n // calculate face angles\n const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Emotion:');\n if (instance.config.async) {\n emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n } else {\n instance.state = 'run:emotion';\n timeStamp = now();\n emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Emotion:');\n\n // run antispoof, inherits face from blazeface\n instance.analyze('Start AntiSpoof:');\n if (instance.config.async) {\n antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:antispoof';\n timeStamp = now();\n antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End AntiSpoof:');\n\n // run liveness, inherits face from blazeface\n instance.analyze('Start Liveness:');\n if (instance.config.async) {\n livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:liveness';\n timeStamp = now();\n livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Liveness:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start GEAR:');\n if (instance.config.async) {\n gearRes = instance.config.face.gear?.enabled ? gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:gear';\n timeStamp = now();\n gearRes = instance.config.face.gear?.enabled ? await gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.gear = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End GEAR:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start SSRNet:');\n if (instance.config.async) {\n ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:ssrnet';\n timeStamp = now();\n ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End SSRNet:');\n\n // run mobilefacenet alternative, inherits face from blazeface\n instance.analyze('Start MobileFaceNet:');\n if (instance.config.async) {\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End MobileFaceNet:');\n\n // run insightface alternative, inherits face from blazeface\n instance.analyze('Start InsightFace:');\n if (instance.config.async) {\n insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End InsightFace:');\n\n // run faceres, inherits face from blazeface\n instance.analyze('Start Description:');\n if (instance.config.async) {\n descRes = faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n } else {\n instance.state = 'run:description';\n timeStamp = now();\n descRes = await faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Description:');\n\n // if async wait for results\n if (instance.config.async) {\n [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);\n }\n instance.analyze('Finish Face:');\n\n if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n descRes = {\n ...(descRes as DescRes),\n age: (ageRes as { age: number}).age,\n gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n };\n }\n if (instance.config.face.gear?.enabled && gearRes) { // override age/gender/race if gear model is used\n descRes = {\n ...(descRes as DescRes),\n age: (gearRes as gear.GearType).age,\n gender: (gearRes as gear.GearType).gender,\n genderScore: (gearRes as gear.GearType).genderScore,\n race: (gearRes as gear.GearType).race,\n };\n }\n if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if mobilefacenet model is used\n (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n }\n\n if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n (descRes as DescRes).descriptor = insightfaceRes as number[];\n }\n\n const irisSize = instance.config.face.iris?.enabled ? calculateCameraDistance(faces[i], input.shape[2]) : 0;\n\n // optionally return tensor\n const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor as Tensor4D) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n const res: FaceResult = {\n ...faces[i],\n id: i,\n };\n if ((descRes as DescRes).age) res.age = (descRes as DescRes).age;\n if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n if (antispoofRes) res.real = antispoofRes as number;\n if (livenessRes) res.live = livenessRes as number;\n if (irisSize > 0) res.distance = irisSize;\n if (rotation) res.rotation = rotation;\n if (tensor) res.tensor = tensor;\n faceRes.push(res);\n instance.analyze('End Face');\n }\n instance.analyze('End FaceMesh:');\n if (instance.config.async) {\n if (instance.performance.face) delete instance.performance.face;\n if (instance.performance.age) delete instance.performance.age;\n if (instance.performance.gender) delete instance.performance.gender;\n if (instance.performance.emotion) delete instance.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n thumb: 0,\n index: 1,\n middle: 2,\n ring: 3,\n pinky: 4,\n all: [0, 1, 2, 3, 4], // just for convenience\n nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n // Describes mapping of joints based on the 21 points returned by handpose.\n // [0] Palm\n // [1-4] Thumb\n // [5-8] Index\n // [9-12] Middle\n // [13-16] Ring\n // [17-20] Pinky\n pointsMapping: {\n 0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n 1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n 2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n 3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n 4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n },\n getName: (value) => Finger.nameMapping[value],\n getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n none: 0,\n half: 1,\n full: 2,\n nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n verticalUp: 0,\n verticalDown: 1,\n horizontalLeft: 2,\n horizontalRight: 3,\n diagonalUpRight: 4,\n diagonalUpLeft: 5,\n diagonalDownRight: 6,\n diagonalDownLeft: 7,\n nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n name;\n curls;\n directions;\n weights;\n weightsRelative;\n\n constructor(name) {\n // name (should be unique)\n this.name = name;\n this.curls = {};\n this.directions = {};\n this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n }\n\n curl(finger, curl, confidence) {\n if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n this.curls[finger].push([curl, confidence]);\n }\n\n direction(finger, position, confidence) {\n if (!this.directions[finger]) this.directions[finger] = [];\n this.directions[finger].push([position, confidence]);\n }\n\n weight(finger, weight) {\n this.weights[finger] = weight;\n // recalculate relative weights\n const total = this.weights.reduce((a, b) => a + b, 0);\n this.weightsRelative = this.weights.map((el) => el * 5 / total);\n }\n\n matchAgainst(detectedCurls, detectedDirections) {\n let confidence = 0.0;\n // look at the detected curl of each finger and compare with\n // the expected curl of this finger inside current gesture\n for (const fingerIdx in detectedCurls) {\n const detectedCurl = detectedCurls[fingerIdx];\n const expectedCurls = this.curls[fingerIdx];\n if (typeof expectedCurls === 'undefined') {\n // no curl description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible curl of this specific finger\n for (const [expectedCurl, score] of expectedCurls) {\n if (detectedCurl === expectedCurl) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n // same for detected direction of each finger\n for (const fingerIdx in detectedDirections) {\n const detectedDirection = detectedDirections[fingerIdx];\n const expectedDirections = this.directions[fingerIdx];\n if (typeof expectedDirections === 'undefined') {\n // no direction description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible direction of this specific finger\n for (const [expectedDirection, score] of expectedDirections) {\n if (detectedDirection === expectedDirection) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n return confidence / 10;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n ThumbsUp.curl(finger, full, 1.0);\n ThumbsUp.direction(finger, horizontalLeft, 1.0);\n ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\n/* eslint-disable camelcase */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n // curl estimation\n HALF_CURL_START_LIMIT: 60.0,\n NO_CURL_START_LIMIT: 130.0,\n // direction estimation\n DISTANCE_VOTE_POWER: 1.1,\n SINGLE_ANGLE_VOTE_POWER: 0.9,\n TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n const value = (point1y - point2y) / (point1x - point2x);\n let slope = Math.atan(value) * 180 / Math.PI;\n if (slope <= 0) slope = -slope;\n else if (slope > 0) slope = 180 - slope;\n return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n if (!point1 || !point2) return [0, 0];\n const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n if (point1.length === 2) return slopeXY;\n const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n let isVertical = 0;\n let isDiagonal = 0;\n let isHorizontal = 0;\n if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n else isHorizontal = 1 * weightageAt;\n return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const start_mid_z_dist = startPoint[2] - midPoint[2];\n const start_end_z_dist = startPoint[2] - endPoint[2];\n const mid_end_z_dist = midPoint[2] - endPoint[2];\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n if (cos_in > 1.0) cos_in = 1.0;\n else if (cos_in < -1.0) cos_in = -1.0;\n let angleOfCurve = Math.acos(cos_in);\n angleOfCurve = (57.2958 * angleOfCurve) % 180;\n let fingerCurl;\n if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n else fingerCurl = FingerCurl.full;\n return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n if (max_dist_x === Math.abs(start_end_x_dist)) {\n if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else {\n if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n }\n return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n let estimatedDirection;\n if (max_dist_y === Math.abs(start_end_y_dist)) {\n if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else {\n if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n }\n return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n if (reqd_vertical_direction === FingerDirection.verticalUp) {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n else estimatedDirection = FingerDirection.diagonalUpRight;\n } else {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n else estimatedDirection = FingerDirection.diagonalDownRight;\n }\n return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n let voteVertical = 0.0;\n let voteDiagonal = 0.0;\n let voteHorizontal = 0.0;\n const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n else voteHorizontal += options.DISTANCE_VOTE_POWER;\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n let calc_start_point_x = startPoint[0];\n let calc_start_point_y = startPoint[1];\n let calc_end_point_x = endPoint[0];\n let calc_end_point_y = endPoint[1];\n if (max_dist === start_mid_dist) {\n calc_end_point_x = endPoint[0];\n calc_end_point_y = endPoint[1];\n } else if (max_dist === mid_end_dist) {\n calc_start_point_x = midPoint[0];\n calc_start_point_y = midPoint[1];\n }\n const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n voteVertical += votes[0];\n voteDiagonal += votes[1];\n voteHorizontal += votes[2];\n for (const fingerSlope of fingerSlopes) {\n const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n voteVertical += fingerVotes[0];\n voteDiagonal += fingerVotes[1];\n voteHorizontal += fingerVotes[2];\n }\n // in case of tie, highest preference goes to Vertical,\n // followed by horizontal and then diagonal\n let estimatedDirection;\n if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n } else {\n estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n }\n return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n // step 1: calculate slopes\n const slopesXY: number[][] = [];\n const slopesYZ: number[][] = [];\n const fingerCurls: number[] = [];\n const fingerDirections: number[] = [];\n if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n // step 1: calculate slopes\n for (const finger of Finger.all) {\n const points = Finger.getPoints(finger);\n const slopeAtXY: number[] = [];\n const slopeAtYZ: number[] = [];\n for (const point of points) {\n const point1 = landmarks[point[0]];\n const point2 = landmarks[point[1]];\n // calculate single slope\n const slopes = getSlopes(point1, point2);\n const slopeXY = slopes[0];\n const slopeYZ = slopes[1];\n slopeAtXY.push(slopeXY);\n slopeAtYZ.push(slopeYZ);\n }\n slopesXY.push(slopeAtXY);\n slopesYZ.push(slopeAtYZ);\n }\n\n // step 2: calculate orientations\n for (const finger of Finger.all) {\n // start finger predictions from palm - except for thumb\n const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n const fingerPointsAt = Finger.getPoints(finger);\n const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n const endPoint = landmarks[fingerPointsAt[3][1]];\n // check if finger is curled\n const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n fingerCurls[finger] = fingerCurled;\n fingerDirections[finger] = fingerPosition;\n }\n return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n if (!keypoints || keypoints.length === 0) return null;\n const estimatorRes = estimate(keypoints);\n const landmarks = {};\n for (const fingerIdx of Finger.all) {\n landmarks[Finger.getName(fingerIdx)] = {\n curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n };\n }\n return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n const poses: { name: string, confidence: number }[] = [];\n if (!keypoints || keypoints.length === 0) return poses;\n const estimatorRes = estimate(keypoints);\n for (const gesture of Gestures) {\n const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n }\n return poses;\n}\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n | 'victory'\n | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { body: number, gesture: BodyGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n }\n }\n return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { face: number, gesture: FaceGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 450) {\n const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2] || 0;\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { iris: number, gesture: IrisGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations?.leftEyeIris?.[0] || !res[i].annotations?.rightEyeIris?.[0]) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n if (rightIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking right' });\n } else {\n if (leftIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking left' });\n }\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { hand: number, gesture: HandGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: { name: string, position: Point }[] = [];\n if (res[i].annotations) {\n for (const [finger, pos] of Object.entries(res[i].annotations)) {\n if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n if (res[i].keypoints) {\n const poses = fingerPose.match(res[i].keypoints);\n for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n }\n }\n return gestures;\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 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y: 0.40625 },\n { x: 0.84375, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.84375, y: 0.46875 },\n { x: 0.84375, y: 0.46875 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y: 0.59375 },\n { x: 0.96875, y: 0.59375 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.96875, y: 0.65625 },\n { x: 0.96875, y: 0.65625 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0.15625, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n 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0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 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0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 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},\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, Tensor1D, Tensor2D, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model: GraphModel) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSize = this?.model?.inputs?.[0]?.shape?.[2] || 0;\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n const t: Record = {};\n t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n const res = tf.concat2d([t.startPoints as Tensor2D, t.endPoints as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res as Tensor;\n }\n\n normalizeLandmarks(rawPalmLandmarks, index: number): Tensor {\n const t: Record = {};\n t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n t.div = tf.div(t.reshape, this.inputSizeTensor);\n t.landmarks = tf.add(t.div, this.anchors[index] ? this.anchors[index] : 0);\n const res = tf.mul(t.landmarks, this.inputSizeTensor);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n async predict(input: Tensor4D, config: Config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n t.div = tf.div(t.resize, constants.tf127);\n t.image = tf.sub(t.div, constants.tf1);\n t.batched = this.model.execute(t.image) as Tensor;\n t.predictions = tf.squeeze(t.batched);\n t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.slice);\n t.scores = tf.squeeze(t.sigmoid);\n const scores = await t.scores.data();\n t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n t.norm = this.normalizeBoxes(t.boxes);\n // box detection is flaky so we look for 3x boxes than we need results\n t.nms = await tf.image.nonMaxSuppressionAsync(t.norm as Tensor2D, t.scores as Tensor1D, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n const nms = await t.nms.array() as number[];\n const hands: { startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }[] = [];\n for (const index of nms) {\n const p: Record = {};\n p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n p.norm = this.normalizeLandmarks(p.slice, index);\n p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n const box = await p.box.data();\n const startPoint = box.slice(0, 2) as unknown as Point;\n const endPoint = box.slice(2, 4) as unknown as Point;\n const palmLandmarks = await p.palmLandmarks.array();\n const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n const scaled = util.scaleBoxCoordinates(hand, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);\n hands.push(scaled);\n Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: ({ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null)[];\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n this.inputSize = this.handPoseModel?.inputs?.[0].shape?.[2] || 0;\n this.storedBoxes = [];\n this.skipped = Number.MAX_SAFE_INTEGER;\n this.detectedHands = 0;\n }\n\n calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = util.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...util.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(coord[2]),\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames\n let boxes;\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n this.skipped++;\n } else {\n boxes = await this.handDetector.predict(image, config);\n this.skipped = 0;\n }\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: { landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }[] = [];\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n const palmCenter = util.getBoxCenter(currentBox);\n const palmCenterNormalized: [number, number] = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = tf.div(croppedInput, constants.tf255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Tensor[];\n lastTime = now();\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence / 4) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n const result = {\n landmarks: coords,\n confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n const result = {\n confidence: currentBox.confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: 0,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n landmarks: [],\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport function initPipeline() {\n const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!handPipeline) initPipeline();\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: HandResult[] = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n const keypoints = predictions[i].landmarks as unknown as Point[];\n let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: Box = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n const landmarks = fingerPose.analyze(keypoints);\n hands.push({\n id: i,\n score: Math.round(100 * predictions[i].confidence) / 100,\n boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n label: 'hand',\n box,\n boxRaw,\n keypoints,\n annotations: annotations as HandResult['annotations'],\n landmarks: landmarks as HandResult['landmarks'],\n });\n }\n return hands;\n}\n\nexport async function loadDetect(config: Config): Promise {\n if (env.initial) handDetectorModel = null;\n if (!handDetectorModel) handDetectorModel = await loadModel(config.hand.detector?.modelPath);\n else if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n return handDetectorModel;\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) handPoseModel = null;\n if (!handPoseModel) handPoseModel = await loadModel(config.hand.skeleton?.modelPath);\n else if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n return handPoseModel;\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ninterface HandDetectResult {\n id: number,\n score: number,\n box: Box,\n boxRaw: Box,\n label: HandType,\n}\n\nconst cache: {\n boxes: HandDetectResult[],\n hands: HandResult[];\n} = {\n boxes: [],\n hands: [],\n};\n\nconst fingerMap = {\n /*\n thumb: [0, 1, 2, 3, 4],\n index: [0, 5, 6, 7, 8],\n middle: [0, 9, 10, 11, 12],\n ring: [0, 13, 14, 15, 16],\n pinky: [0, 17, 18, 19, 20],\n palm: [0],\n */\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n base: [0],\n palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise {\n // HandTrack Model: Original: TFJS Port: \n if (env.initial) models[0] = null;\n if (!models[0]) {\n // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n // ideally need to prune the model itself\n fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n models[0] = await loadModel(config.hand.detector?.modelPath);\n const inputs = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) models[1] = null;\n if (!models[1]) {\n models[1] = await loadModel(config.hand.skeleton?.modelPath);\n const inputs = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models[0]) await loadDetect(config);\n if (!models[1]) await loadSkeleton(config);\n return models;\n}\n\nasync function detectHands(input: Tensor4D, config: Config): Promise {\n const hands: HandDetectResult[] = [];\n if (!input || !models[0]) return hands;\n const t: Record = {};\n const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n const width = Math.round(height * ratio / 8) * 8;\n t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n t.cast = tf.cast(t.resize, 'int32');\n [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n t.scores = tf.squeeze(t.rawScores, [0]);\n const classScores: Tensor[] = tf.unstack(t.scores, 1); // unstack scores based on classes\n tf.dispose(classScores[faceIndex]);\n classScores.splice(faceIndex, 1); // remove faces\n t.filtered = tf.stack(classScores, 1); // restack\n tf.dispose(classScores);\n // t.filtered = t.scores;\n t.max = tf.max(t.filtered, 1); // max overall score\n t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n let id = 0;\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.max as Tensor1D, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n const nms = await t.nms.data();\n const scores = await t.max.data();\n const classNum = await t.argmax.data();\n for (const nmsIndex of Array.from(nms)) { // generates results for each class\n const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n const boxYX = await boxSlice.data();\n tf.dispose(boxSlice);\n const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n const score = scores[nmsIndex];\n const label = classes[classNum[nmsIndex]] as HandType;\n const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n hands.push(hand);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n hands.sort((a, b) => b.score - a.score);\n if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n return hands;\n}\n\nasync function detectFingers(input: Tensor4D, h: HandDetectResult, config: Config): Promise {\n const hand: HandResult = { // initial values inherited from hand detect\n id: h.id,\n score: Math.round(100 * h.score) / 100,\n boxScore: Math.round(100 * h.score) / 100,\n fingerScore: 0,\n box: h.box,\n boxRaw: h.boxRaw,\n label: h.label,\n keypoints: [],\n landmarks: {} as HandResult['landmarks'],\n annotations: {} as HandResult['annotations'],\n };\n if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n const t: Record = {};\n const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n t.div = tf.div(t.crop, constants.tf255);\n [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n const rawScore = (await t.score.data())[0];\n const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n if (score >= (config.hand.minConfidence || 0)) {\n hand.fingerScore = score;\n t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n const coordsData: Point[] = await t.reshaped.array() as Point[];\n const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n return hand;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!models[0]?.['executor'] || !models[1]?.['executor'] || !models[0].inputs[0].shape || !models[1].inputs[0].shape) return []; // something is wrong with the model\n outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n skipped++; // increment skip frames\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.hands; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else { // finally rerun detector\n cache.boxes = await detectHands(input, config);\n lastTime = now();\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n skipped = 0;\n }\n\n const oldCache = [...cache.boxes];\n cache.boxes.length = 0; // reset cache\n if (config.cacheSensitivity > 0) {\n for (let i = 0; i < cache.hands.length; i++) {\n const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n const boxScale = box.scale(boxKpt.box, boxExpandFact);\n const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n // const boxCrop = box.crop(boxScaleRaw);\n cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n }\n }\n }\n for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n cache.hands[i].box = bbox.box;\n cache.hands[i].boxRaw = bbox.boxRaw;\n }\n resolve(cache.hands);\n });\n}\n", "/**\n * Type definitions for Human result object\n */\n\nimport type { Tensor } from './tfjs/types';\nimport type { FaceGesture, BodyGesture, HandGesture, IrisGesture } from './gesture/gesture';\nimport type { AnyCanvas } from './exports';\n\n/** generic box as [x, y, width, height] */\nexport type Box = [number, number, number, number];\n/** generic point as [x, y, z?] */\nexport type Point = [number, number, number?];\n\nexport type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';\nexport type Gender = 'male' | 'female' | 'unknown';\nexport type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';\nexport type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette'\n | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner'\n | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris'\n | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris'\n | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';\n\n/** Face results\n * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models\n * - Some values may be null if specific model is not enabled\n */\nexport interface FaceResult {\n /** face id */\n id: number\n /** overall face score */\n score: number,\n /** detection score */\n boxScore: number,\n /** mesh score */\n faceScore: number,\n /** detected face box */\n box: Box,\n /** detected face box normalized to 0..1 */\n boxRaw: Box,\n /** detected face box size */\n size: [number, number],\n /** detected face mesh */\n mesh: Point[]\n /** detected face mesh normalized to 0..1 */\n meshRaw: Point[],\n /** face contours as array of 2d points normalized to 0..1 */\n // contoursRaw: Array<[number, number]>,\n /** face contours as array of 2d points */\n // contours: Array<[number, number]>,\n /** mesh keypoints combined into annotated results */\n annotations: Record,\n /** detected age */\n age?: number,\n /** detected gender */\n gender?: Gender,\n /** gender detection score */\n genderScore?: number,\n /** detected emotions */\n emotion?: { score: number, emotion: Emotion }[],\n /** detected race */\n race?: { score: number, race: Race }[],\n /** face descriptor */\n embedding?: number[],\n /** face distance from camera */\n distance?: number,\n /** face anti-spoofing result confidence */\n real?: number,\n /** face liveness result confidence */\n live?: number,\n /** face rotation details */\n rotation?: {\n angle: { roll: number, yaw: number, pitch: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n } | null,\n /** detected face as tensor that can be used in further pipelines */\n tensor?: Tensor,\n}\n\nexport type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';\nexport type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder'\n | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle'\n | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';\nexport type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;\nexport type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';\nexport type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';\nexport type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;\n\n/** Body Result keypoints */\nexport interface BodyKeypoint {\n /** body part name */\n part: BodyLandmark,\n /** body part position */\n position: Point,\n /** body part position normalized to 0..1 */\n positionRaw: Point,\n /** body part position relative to body center in meters */\n distance?: Point,\n /** body part detection score */\n score: number,\n}\n\n/** Body results */\nexport interface BodyResult {\n /** body id */\n id: number,\n /** body detection score */\n score: number,\n /** detected body box */\n box: Box,\n /** detected body box normalized to 0..1 */\n boxRaw: Box,\n /** detected body keypoints */\n keypoints: BodyKeypoint[]\n /** detected body keypoints combined into annotated parts */\n annotations: Record,\n}\n\nexport type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';\nexport type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';\nexport type FingerCurl = 'none' | 'half' | 'full';\nexport type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';\n\n/** Hand results */\nexport interface HandResult {\n /** hand id */\n id: number,\n /** hand overal score */\n score: number,\n /** hand detection score */\n boxScore: number,\n /** hand skelton score */\n fingerScore: number,\n /** detected hand box */\n box: Box,\n /** detected hand box normalized to 0..1 */\n boxRaw: Box,\n /** detected hand keypoints */\n keypoints: Point[],\n /** detected hand class */\n label: HandType,\n /** detected hand keypoints combined into annotated parts */\n annotations: Record,\n /** detected hand parts annotated with part gestures */\n landmarks: Record,\n}\n\nexport type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter'\n | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee'\n | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork'\n | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant'\n | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book'\n | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';\n\n/** Object results */\nexport interface ObjectResult {\n /** object id */\n id: number,\n /** object detection score */\n score: number,\n /** detected object class id */\n class: number,\n /** detected object class name */\n label: ObjectType,\n /** detected object box */\n box: Box,\n /** detected object box normalized to 0..1 */\n boxRaw: Box,\n}\n\n/** Gesture combined results\n * Each result has:\n * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`\n * - gesture: gesture detected\n */\nexport type GestureResult =\n { 'face': number, gesture: FaceGesture }\n | { 'iris': number, gesture: IrisGesture }\n | { 'body': number, gesture: BodyGesture }\n | { 'hand': number, gesture: HandGesture }\n\n/** Person getter\n* - Triggers combining all individual results into a virtual person object\n*/\nexport interface PersonResult {\n /** person id */\n id: number,\n /** face result that belongs to this person */\n face: FaceResult,\n /** body result that belongs to this person */\n body: BodyResult | null,\n /** left and right hand results that belong to this person */\n hands: { left: HandResult | null, right: HandResult | null },\n /** detected gestures specific to this person */\n gestures: GestureResult[],\n /** box that defines the person */\n box: Box,\n /** box that defines the person normalized to 0..1 */\n boxRaw?: Box,\n}\n\n/**\n * Result interface definition for **Human** library\n *\n * Contains all possible detection results\n */\nexport interface Result {\n /** {@link FaceResult}: detection & analysis results */\n face: FaceResult[],\n /** {@link BodyResult}: detection & analysis results */\n body: BodyResult[],\n /** {@link HandResult}: detection & analysis results */\n hand: HandResult[],\n /** {@link GestureResult}: detection & analysis results */\n gesture: GestureResult[],\n /** {@link ObjectResult}: detection & analysis results */\n object: ObjectResult[]\n /** global performance object with timing values for each operation */\n performance: Record,\n /** optional processed canvas that can be used to draw input on screen */\n canvas?: AnyCanvas | null,\n /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */\n readonly timestamp: number,\n /** getter property that returns unified persons object */\n persons: PersonResult[],\n /** Last known error message */\n error: string | null;\n /** Resolution width */\n width: number,\n /** Resolution height */\n height: number,\n}\n\nexport const empty = (error: string | null = null): Result => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });\n", "export const kpt: string[] = [ // used to create part labels\n 'nose',\n 'leftEye',\n 'rightEye',\n 'leftEar',\n 'rightEar',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n];\n\nexport const horizontal: string[][] = [ // used to fix left vs right\n ['leftEye', 'rightEye'],\n ['leftEar', 'rightEar'],\n ['leftShoulder', 'rightShoulder'],\n ['leftElbow', 'rightElbow'],\n ['leftWrist', 'rightWrist'],\n ['leftHip', 'rightHip'],\n ['leftKnee', 'rightKnee'],\n ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: string[][] = [ // used to remove unlikely keypoint positions\n ['leftKnee', 'leftShoulder'],\n ['rightKnee', 'rightShoulder'],\n ['leftAnkle', 'leftKnee'],\n ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: string[][][] = [ // used to match relative body parts\n [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record = { // used to create body outline in annotations\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation, empty, FaceLandmark } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = empty();\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n const t0 = now();\n if (!newResult) return empty();\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n\n /* curve fitted: buffer = 8 - ln(delay)\n interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n - at 50ms delay buffer = ~4.1 => 28% towards live data\n - at 250ms delay buffer = ~2.5 => 40% towards live data\n - at 500ms delay buffer = ~1.8 => 55% towards live data\n - at 750ms delay buffer = ~1.4 => 71% towards live data\n - at 1sec delay buffer = 1 which means live data is used\n */\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n if (newResult.error) bufferedResult.error = newResult.error;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)) as BodyResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((newKpt, j) => ({\n score: newKpt.score,\n part: newKpt.part,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n ],\n distance: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n ],\n }))) as { score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }[];\n\n const annotations: Record = {} as Record; // recreate annotations\n let coords = { connected: {} };\n if (config.body.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n else if (config.body.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n else if (config.body.modelPath?.includes('movenet')) coords = moveNetCoords;\n for (const [name, indexes] of Object.entries(coords.connected as Record)) {\n const pt: Point[][] = [];\n for (let j = 0; j < indexes.length - 1; j++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n : [];\n let annotations = {};\n if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.hand[i].annotations;\n } else if (newResult.hand[i].annotations) {\n for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n annotations[key] = newResult.hand[i]?.annotations?.[key]?.[0]\n ? newResult.hand[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)) as FaceResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n let annotations: Record = newResult.face[i].annotations;\n if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) {\n bufferedResult.face[i].annotations = newResult.face[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.face[i].annotations;\n } else if (newResult.face[i].annotations) {\n for (const key of Object.keys(newResult.face[i].annotations)) { // update annotations\n annotations[key] = newResult.face[i]?.annotations?.[key]?.[0]\n ? newResult.face[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n if (newResult.face[i].rotation) {\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.bearing || 0) + (newResult.face[i].rotation?.gaze.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.strength || 0) + (newResult.face[i].rotation?.gaze.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw, annotations }; // shallow clone plus updated values\n } else {\n bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations }; // shallow clone plus updated values\n }\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)) as ObjectResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)) as PersonResult[];\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n }\n }\n }\n\n // copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n // copy resolution info\n bufferedResult.width = newResult.width;\n bufferedResult.height = newResult.height;\n\n // append interpolation performance data\n const t1 = now();\n interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n return bufferedResult;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]);\n // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n t.fg = tf.softmax(t.fgRaw);\n t.mul = tf.mul(t.fg, constants.tf255);\n t.expand = tf.expandDims(t.mul, 2);\n t.output = tf.image.resizeBilinear(t.expand as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.output], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = number[]\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n // general minkowski distance, euclidean distance is limited case where order is 2\n if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\n let sum = 0;\n for (let i = 0; i < descriptor1.length; i++) {\n const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n }\n return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n if (dist === 0) return 1; // short circuit for identical inputs\n const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n * default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n const dist = distance(descriptor1, descriptor2, options);\n return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function find(descriptor: Descriptor, descriptors: Descriptor[], options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { // validate input\n return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n }\n let lowestDistance = Number.MAX_SAFE_INTEGER;\n let index = -1;\n for (let i = 0; i < descriptors.length; i++) {\n const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\n if (res < lowestDistance) {\n lowestDistance = res;\n index = i;\n }\n if (lowestDistance < (options.threshold || 0)) break;\n }\n const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n if (!model) return null;\n if (!instance?.config?.validateModels) return null;\n const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n const ops: string[] = [];\n const missing: string[] = [];\n interface Op { name: string, category: string, op: string }\n const url = model['modelUrl'] as string;\n const executor = model['executor'];\n if (executor?.graph?.nodes) {\n for (const kernel of Object.values(executor.graph.nodes)) {\n const op = (kernel as Op).op.toLowerCase();\n if (!ops.includes(op)) ops.push(op);\n }\n } else {\n if (!executor && instance.config.debug) {\n log('model not loaded', name);\n }\n }\n for (const op of ops) {\n if (!simpleOps.includes(op) // exclude simple ops\n && !ignoreOps.includes(op) // exclude specific ops\n && !instance.env.kernels.includes(op) // check actual kernel ops\n && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n missing.push(op);\n }\n }\n if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n numLoadedModels: number,\n numDefinedModels: number,\n percentageLoaded: number,\n totalSizeFromManifest: number,\n totalSizeWeights: number,\n totalSizeLoading: number,\n modelStats: ModelInfo[],\n}\n\n/** Models class used by Human\n * - models: record of all GraphModels\n * - list: returns list of configured models with their stats\n * - loaded: returns array of loaded models\n * - reset: unloads all models\n * - validate: checks loaded models for valid kernel ops vs current backend\n * - stats: live detailed model stats that can be checked during model load phase\n */\nexport class Models {\n private instance: Human;\n models: Record = {};\n\n constructor(currentInstance: Human) {\n this.models = {};\n this.instance = currentInstance;\n }\n\n stats(): ModelStats {\n let totalSizeFromManifest = 0;\n let totalSizeWeights = 0;\n let totalSizeLoading = 0;\n for (const m of Object.values(modelStats)) {\n totalSizeFromManifest += m.sizeFromManifest;\n totalSizeWeights += m.sizeLoadedWeights;\n totalSizeLoading += m.sizeDesired;\n }\n const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n return {\n numLoadedModels: Object.values(modelStats).length,\n numDefinedModels: Object.keys(this.models).length,\n percentageLoaded,\n totalSizeFromManifest,\n totalSizeWeights,\n totalSizeLoading,\n modelStats: Object.values(modelStats),\n };\n }\n\n reset(): void {\n for (const model of Object.keys(this.models)) this.models[model] = null;\n }\n\n async load(instance?: Human): Promise {\n if (env.initial) this.reset();\n if (instance) this.instance = instance;\n const m: Record> = {};\n // face main models\n m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;\n m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;\n m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;\n m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;\n m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;\n m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;\n m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;\n // face alternatives\n m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;\n m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;\n m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;\n m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;\n m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;\n // body alterinatives\n m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;\n m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;\n m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;\n m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;\n m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;\n // hand alternatives\n m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;\n m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;\n // if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];\n if (this.instance.config.hand.enabled && !this.models.handdetect && this.instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n m.handdetect = handpose.loadDetect(this.instance.config);\n m.handskeleton = handpose.loadSkeleton(this.instance.config);\n }\n // object detection alternatives\n m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;\n m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;\n // segmentation alternatives\n m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;\n m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;\n m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;\n\n // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n for (const [model, promise] of Object.entries(m)) {\n if (promise?.['then']) promise['then']((val) => this.models[model] = val);\n }\n await Promise.all(Object.values(m)); // wait so this function does not resolve prematurely\n }\n\n list() {\n const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));\n for (const m of models) {\n const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));\n if (!stats) continue;\n m.size = modelStats[stats].sizeLoadedWeights;\n m.url = modelStats[stats].url;\n }\n return models;\n }\n\n loaded() {\n const list = this.list();\n const loaded = list.filter((model) => model.loaded).map((model) => model.name);\n return loaded;\n }\n\n validate(): { name: string, missing: string[] }[] {\n const missing: KernelOps[] = [];\n for (const defined of Object.keys(this.models)) {\n const model: GraphModel | null = this.models[defined as keyof Models];\n if (!model) continue;\n const res = validateModel(this.instance, model, defined);\n if (res) missing.push(res);\n }\n return missing;\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport type { Tensor, Tensor3D } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n keypoints: BodyKeypoint[],\n padding: [number, number][];\n} = {\n keypoints: [],\n padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n for (const pair of coords.horizontal) { // fix body parts left vs right\n const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n if (body.keypoints[left] && body.keypoints[right]) {\n if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n }\n for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n if (body.keypoints[lower] && body.keypoints[higher]) {\n if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n body.keypoints.splice(lower, 1);\n }\n }\n }\n for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n const distanceLeft = body.keypoints[left] ? [\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n ] : [0, 0];\n const distanceRight = body.keypoints[right] ? [\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n ] : [0, 0];\n if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n}\n\nexport function jitter(keypoints: BodyKeypoint[]): BodyKeypoint[] {\n for (let i = 0; i < keypoints.length; i++) {\n if (keypoints[i] && cache.keypoints[i]) {\n const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n if (diff[0] < maxJitter && diff[1] < maxJitter) {\n keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n } else {\n cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n }\n } else {\n cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n }\n }\n return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n cache.padding = [\n [0, 0], // dont touch batch\n [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(input, cache.padding);\n t.resize = tf.image.resizeBilinear(t.pad as Tensor3D, [inputSize, inputSize]);\n const final = tf.cast(t.resize, 'int32');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n body.keypoints = body.keypoints.filter((kpt) => kpt?.position); // filter invalid keypoints\n for (const kpt of body.keypoints) {\n kpt.position = [\n kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n ];\n kpt.positionRaw = [\n kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n ];\n }\n const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n body.box = rescaledBoxes.box;\n body.boxRaw = rescaledBoxes.boxRaw;\n return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n boxes: Box[], // unused\n bodies: BodyResult[];\n last: number,\n} = {\n boxes: [],\n bodies: [],\n last: 0,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n fakeOps(['size'], config);\n model = await loadModel(config.body.modelPath);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize < 64) inputSize = 256;\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', false); // default=false \n return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n const kpt = res[0][0];\n const keypoints: BodyKeypoint[] = [];\n let score = 0;\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw,\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * positionRaw[0]),\n Math.round((image.shape[1] || 0) * positionRaw[1]),\n ],\n });\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const bodies: BodyResult[] = [];\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n const annotations: Record = {};\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n fix.bodyParts(body);\n bodies.push(body);\n return bodies;\n}\n\nfunction parseMultiPose(res, config, image) {\n const bodies: BodyResult[] = [];\n for (let id = 0; id < res[0].length; id++) {\n const kpt = res[0][id];\n const boxScore = Math.round(100 * kpt[51 + 4]) / 100;\n if (boxScore > config.body.minConfidence) {\n const keypoints: BodyKeypoint[] = [];\n for (let i = 0; i < 17; i++) {\n const score = kpt[3 * i + 2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n keypoints.push({\n part: coords.kpt[i] as BodyLandmark,\n score: Math.round(100 * score) / 100,\n positionRaw,\n position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n });\n }\n }\n // const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n // movenet-multipose has built-in box details\n const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n const boxNorm: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n // const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n const body: BodyResult = { id, score: boxScore, box: boxNorm, boxRaw, keypoints: [...keypoints], annotations };\n fix.bodyParts(body);\n bodies.push(body);\n }\n }\n bodies.sort((a, b) => b.score - a.score);\n if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return []; // something is wrong with the model\n if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n skipped++; // increment skip frames\n const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.bodies; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const t: Record = {};\n skipped = 0;\n // run detection on squared input and no cached boxes\n t.input = fix.padInput(input, inputSize);\n t.res = model?.execute(t.input) as Tensor;\n cache.last = now();\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17)\n ? parseSinglePose(res, config, input)\n : parseMultiPose(res, config, input);\n for (const body of cache.bodies) {\n fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n fix.jitter(body.keypoints);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n resolve(cache.bodies);\n });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) {\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n let id = 0;\n let results: ObjectResult[] = [];\n const size = inputSize;\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)) as Tensor2D);\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n const featuresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)) as Tensor2D);\n const boxesMaxT = tf.reshape(featuresT, [-1, 4, (featuresT.shape?.[1] || 0) / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdxT = tf.argMax(boxesMaxT, 2); // what we need is indexes of features with highest scores, not values itself\n const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < (scoresT.shape?.[1] || 0); j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > (config.object.minConfidence || 0) && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / (size))); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label as ObjectType,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: box.map((a) => Math.trunc(a)) as Box,\n boxRaw,\n };\n results.push(result);\n }\n }\n }\n tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n }\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: number[] = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected || 0, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = Array.from(await nms.data());\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n const resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n const normT = tf.div(resizeT, constants.tf255);\n const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n let objectT;\n if (config.object.enabled) objectT = model.execute(transposeT);\n lastTime = now();\n\n const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n last = obj;\n tf.dispose([resizeT, normT, transposeT, ...objectT]);\n resolve(obj);\n });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): BodyResult[] {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i): BodyResult => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score: score as number,\n part: part as BodyLandmark,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n })),\n annotations: {} as Record,\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: unknown[]; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n // @ts-ignore getter is of unknown type\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n\nexport function getOffsetPoint(y, x, keypoint: number, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + count),\n };\n}\n\nexport function getImageCoords(part, outputStride: number, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a: { x: number, y: number }, b: { x: number, y: number }) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId: number, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: utils.partNames[root.part.id] as BodyLandmark,\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score: number, heatmapY: number, heatmapX: number, scores) {\n const [height, width]: [number, number] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: { keypoints, box: Box, score: number }[] = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n /** posenet is mostly obsolete\n * caching is not implemented\n */\n if (!model?.['executor']) return [];\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Tensor[] = model.execute(normalized, poseNetOutputs) as Tensor[];\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n t.r1i = tf.tensor(0.0);\n t.r2i = tf.tensor(0.0);\n t.r3i = tf.tensor(0.0);\n t.r4i = tf.tensor(0.0);\n ratio = config.segmentation.ratio || 0.5;\n t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n init(config);\n return model;\n}\n\nconst normalize = (r: Tensor): Tensor => tf.tidy(() => {\n const squeeze = tf.squeeze(r, ([0]));\n const mul = tf.mul(squeeze, constants.tf255);\n const cast = tf.cast(mul, 'int32');\n return cast;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n const rgb = fgr\n ? normalize(fgr) // normalize and use value\n : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const a = pha\n ? normalize(pha) // normalize and use value\n : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const rgba = tf.concat([rgb, a], -1);\n tf.dispose([rgb, a]);\n return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n return tf.tidy(() => {\n const r: Record = {};\n r.unstack = tf.unstack(state, -1);\n r.concat = tf.concat(r.unstack, 1);\n r.split = tf.split(r.concat, 4, 1);\n r.stack = tf.concat(r.split, 2);\n r.squeeze = tf.squeeze(r.stack, [0]);\n r.expand = tf.expandDims(r.squeeze, -1);\n r.add = tf.add(r.expand, 1);\n r.mul = tf.mul(r.add, 127.5);\n r.cast = tf.cast(r.mul, 'int32');\n r.tile = tf.tile(r.cast, [1, 1, 3]);\n r.alpha = tf.fill([(r.tile as Tensor).shape[0] || 0, (r.tile as Tensor).shape[1] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-unnecessary-type-assertion\n return tf.concat([r.tile, r.alpha], -1);\n });\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor']) return null;\n // const expand = tf.expandDims(input, 0);\n t.src = tf.div(input, 255);\n if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n rgba = getRGBA(fgr, pha);\n break;\n case 'alpha':\n rgba = getRGBA(null, pha);\n break;\n case 'foreground':\n rgba = getRGBA(fgr, null);\n break;\n case 'state':\n rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n break;\n default:\n rgba = tf.tensor(0);\n }\n tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n t.alpha = tf.image.resizeBilinear(t.squeeze as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]); // model selfie has a single channel that we can use directly\n t.mul = tf.mul(t.alpha, constants.tf255);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.mul], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n let id = 0;\n const persons: PersonResult[] = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.left?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.right?.id) person.gestures.push(gesture);\n }\n\n // create new overarching box from all boxes belonging to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face.box);\n extractXY(person.body?.box);\n extractXY(person.hands.left?.box);\n extractXY(person.hands.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape?.[1] && shape?.[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport { empty, Result } from './result';\nimport type { Config } from './config';\nimport type { Human } from './human';\nimport type { Tensor, DataType } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise {\n const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob: Blob | null;\n let res: Result | undefined;\n switch (instance.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'body':\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await instance.detect(bitmap, instance.config);\n bitmap.close();\n }\n return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise {\n return new Promise((resolve) => {\n let src: string;\n // let size = 0;\n switch (instance.config.warmup) {\n case 'face':\n // size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n // size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = '';\n }\n // src = encodeURI('../assets/human-sample-upper.jpg');\n let img: HTMLImageElement;\n if (typeof Image !== 'undefined') img = new Image();\n // @ts-ignore env.image is an external monkey-patch\n else if (env.Image) img = new env.Image();\n else {\n resolve(undefined);\n return;\n }\n img.onload = async () => {\n const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n if (!canvas) {\n log('Warmup: Canvas not found');\n resolve(undefined);\n } else {\n const ctx = canvas.getContext('2d') as CanvasRenderingContext2D;\n if (ctx) ctx.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const tensor = await instance.image(canvas, true);\n const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\n resolve(res);\n }\n };\n if (src) img.src = src;\n else resolve(undefined);\n });\n}\n\nasync function warmupNode(instance: Human): Promise {\n const atob = (str: string) => Buffer.from(str, 'base64');\n let img;\n if (instance.config.warmup === 'face') img = atob(sample.face);\n else img = atob(sample.body);\n let res: Result;\n if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n // @ts-ignore\n const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n const expanded: Tensor = tf.expandDims(data, 0);\n instance.tf.dispose(data);\n // log('Input:', expanded);\n res = await instance.detect(expanded, instance.config);\n instance.tf.dispose(expanded);\n } else {\n if (instance.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await instance.detect(input, instance.config);\n */\n }\n // @ts-ignore\n return res;\n}\n\nasync function runInference(instance: Human) {\n let res: Result | undefined;\n if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n else if ((typeof Image !== 'undefined') || (env.Canvas !== undefined)) res = await warmupCanvas(instance);\n else res = await warmupNode(instance);\n return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n // @ts-ignore private property\n if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n const backendType = tf.getBackend();\n const webGLBackend = tf.backend();\n if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.['checkCompileCompletion']) {\n // log('compile pass: skip');\n return;\n }\n tf.env().set('ENGINE_COMPILE_ONLY', true);\n const numTensorsStart = tf.engine().state.numTensors;\n const compiledModels: string[] = [];\n for (const [modelName, model] of Object.entries(instance.models.models)) {\n if (!model) continue;\n const shape = (model?.modelSignature && model?.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n const dtype: DataType = (model?.modelSignature && model?.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n for (let dim = 0; dim < shape.length; dim++) {\n if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n }\n const tensor = tf.zeros(shape, dtype);\n try {\n const res = model.execute(tensor);\n compiledModels.push(modelName);\n if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n else tf.dispose(res);\n } catch {\n if (instance.config.debug) log('compile fail model:', modelName);\n }\n tf.dispose(tensor);\n }\n const kernels = await webGLBackend['checkCompileCompletionAsync']();\n webGLBackend['getUniformLocations']();\n if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n tf.env().set('ENGINE_COMPILE_ONLY', false);\n const numTensorsEnd = tf.engine().state.numTensors;\n if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial): Promise {\n await backend.check(instance, false);\n const t0 = now();\n instance.state = 'warmup';\n if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n return empty();\n }\n return new Promise(async (resolve) => {\n await instance.models.load();\n await runCompile(instance);\n const res = await runInference(instance);\n const t1 = now();\n if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n instance.emit('warmup');\n resolve(res);\n });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\n// module imports\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport { Input, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, empty } from './exports';\nimport type { Tensor, Tensor4D } from './tfjs/types';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n\n /** Current configuration\n * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\n config: Config;\n\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n\n /** currenty processed image tensor and canvas */\n process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n /** Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n * [TFJS API](https://js.tensorflow.org/api/latest/)\n */\n tf;\n\n /** Object containing environment information used for diagnostics */\n env: Env = env;\n\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - canvas: draws input to canvas\n * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n */\n // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n draw: typeof draw = draw;\n\n /** Face Matching\n * - similarity: compare two face descriptors and return similarity index\n * - distance: compare two face descriptors and return raw calculated differences\n * - find: compare face descriptor to array of face descriptors and return best match\n */\n match: typeof match = match;\n\n /** Currently loaded models\n * @internal\n * {@link models#Models}\n */\n models: models.Models;\n\n /** Container for events dispatched by Human\n * Possible events:\n * - `create`: triggered when Human object is instantiated\n * - `load`: triggered when models are loaded (explicitly or on-demand)\n * - `image`: triggered when input image is processed\n * - `result`: triggered when detection is complete\n * - `warmup`: triggered when warmup is complete\n * - `error`: triggered on some errors\n */\n events: EventTarget | undefined;\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: number[];\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: [number, number][];\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n // definition end\n\n /** Constructor for **Human** library that is futher used for all operations\n * @param userConfig - user configuration object {@link Config}\n */\n constructor(userConfig?: Partial) {\n /*\n defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n ? 'https://vladmandic.github.io/tfjs/dist/'\n : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n */\n const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n this.version = app.version; // expose version property on instance of class\n Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n this.config = JSON.parse(JSON.stringify(defaults));\n Object.seal(this.config);\n this.config.cacheModels = typeof indexedDB !== 'undefined';\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n setModelLoadOptions(this.config);\n this.tf = tf;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.performance = {};\n this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n // object that contains all initialized models\n this.models = new models.Models(this);\n // reexport draw methods\n draw.init();\n this.result = empty();\n // export access to image processing\n this.process = { tensor: null, canvas: null };\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // init model validation\n models.validateModel(this, null, '');\n // include platform info\n this.emit('create');\n if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n const envTemp = JSON.parse(JSON.stringify(this.env));\n delete envTemp.kernels;\n delete envTemp.initial;\n delete envTemp.perfadd;\n if (this.config.debug) log('environment:', envTemp);\n }\n\n /** internal function to measure tensor leaks */\n analyze = (...msg: string[]) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n };\n\n /** internal function for quick sanity check on inputs @hidden */\n #sanity = (input: Input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n };\n\n /** Reset configuration to default values */\n reset(): void {\n const currentBackend = this.config.backend; // save backend;\n this.config = JSON.parse(JSON.stringify(defaults));\n this.config.backend = currentBackend;\n image.reset();\n env.initial = true;\n }\n\n /** Validate current configuration schema */\n validate(userConfig?: Partial) {\n const msgs = validate(defaults, userConfig || this.config);\n if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n return msgs;\n }\n\n /** Utility wrapper for performance.now() */\n now(): number { // eslint-disable-line class-methods-use-this\n return now();\n }\n\n /** Process input as return canvas and tensor\n *\n * @param input - any input {@link Input}\n * @param getTensor - should image processing also return tensor or just canvas\n * Returns object with `tensor` and `canvas`\n */\n image(input: Input, getTensor: boolean = false) {\n return image.process(input, this.config, getTensor);\n }\n\n /** Segmentation method takes any input and returns RGBA tensor\n * Note: Segmentation is not triggered as part of detect process\n *\n * @param input - {@link Input}\n * Returns tensor which contains image data in RGBA format\n */\n async segmentation(input: Input, userConfig?: Partial): Promise {\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (!this.config.segmentation.enabled) return null;\n const processed = await image.process(input, this.config);\n if (!processed.tensor) return null;\n let tensor: Tensor | null = null;\n if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n tf.dispose(processed.tensor);\n return tensor;\n }\n\n /** Compare two input tensors for pixel similarity\n * - use `human.image` to process any valid input and get a tensor that can be used for compare\n * - when passing manually generated tensors:\n * - both input tensors must be in format [1, height, width, 3]\n * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n * - return value is pixel similarity score normalized by input resolution and rgb channels\n */\n compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise {\n return image.compare(this.config, firstImageTensor, secondImageTensor);\n }\n\n /** Explicit backend initialization\n * - Normally done implicitly during initial load phase\n * - Call to explictly register and initialize TFJS backend without any other operations\n * - Use when changing backend during runtime\n */\n async init(): Promise {\n await backend.check(this, true);\n await this.tf.ready();\n image.reset();\n }\n\n /** WebCam helper methods\n *\n */\n public webcam = new WebCam();\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n *\n * @param userConfig - {@link Config}\n */\n async load(userConfig?: Partial): Promise {\n this.state = 'load';\n const timeStamp = now();\n const count = Object.values(this.models.models).filter((model) => model).length;\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (this.env.initial) { // print version info on first run and check for correct backend setup\n if (!await backend.check(this, false)) log('error: backend check failed');\n await tf.ready();\n if (this.env.browser) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n\n await this.models.load(this); // actually loads models\n if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n this.env.initial = false;\n\n const loaded = Object.values(this.models.models).filter((model) => model).length;\n if (loaded !== count) { // number of loaded models changed\n this.models.validate(); // validate kernel ops used by model against current backend\n this.emit('load');\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n }\n\n /** emit event */\n emit = (event: string) => {\n if (this.events?.dispatchEvent) this.events.dispatchEvent(new Event(event));\n };\n\n /** Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result - {@link Result} optional use specific result set to run interpolation on\n * @returns result - {@link Result}\n */\n next(result: Result = this.result): Result {\n return interpolate.calc(result, this.config);\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async warmup(userConfig?: Partial) {\n const t0 = now();\n const res = await warmups.warmup(this, userConfig);\n const t1 = now();\n this.performance.warmup = Math.trunc(t1 - t0);\n return res;\n }\n\n /** Run detect with tensorflow profiling\n * - result object will contain total exeuction time information for top-20 kernels\n * - actual detection object can be accessed via `human.result`\n */\n async profile(input: Input, userConfig?: Partial): Promise<{ kernel: string, time: number, perc: number }[]> {\n // @ts-ignore profile wraps method return values\n const profile = await this.tf.profile(() => this.detect(input, userConfig));\n const kernels: Record = {};\n let total = 0;\n for (const kernel of profile.kernels) { // sum kernel time values per kernel\n const ms = Number(kernel.kernelTimeMs) || 0;\n if (kernels[kernel.name]) kernels[kernel.name] += ms;\n else kernels[kernel.name] = ms;\n total += ms;\n }\n const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n for (const kernel of kernelArr) {\n kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n kernel.time = Math.round(1000 * kernel.time) / 1000;\n }\n kernelArr.sort((a, b) => b.time - a.time); // sort\n kernelArr.length = 20; // crop\n return kernelArr;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input - {@link Input}\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async detect(input: Input, userConfig?: Partial): Promise {\n // detection happens inside a promise\n this.state = 'detect';\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n // sanity checks\n this.state = 'check';\n const error = this.#sanity(input);\n if (error) {\n log(error, input);\n this.emit('error');\n resolve(empty(error));\n }\n\n const timeStart = now();\n\n // load models if enabled\n await this.load();\n\n timeStamp = now();\n this.state = 'image';\n const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor4D };\n this.process = img;\n this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n if (!img.tensor) {\n if (this.config.debug) log('could not convert input to tensor');\n this.emit('error');\n resolve(empty('could not convert input to tensor'));\n return;\n }\n this.emit('image');\n\n timeStamp = now();\n this.config.skipAllowed = await image.skip(this.config, img.tensor);\n this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change\n if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n (this.performance.totalFrames)++;\n if (this.config.skipAllowed) this.performance.cachedFrames++;\n this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes: FaceResult[] | Promise | never[] = [];\n let bodyRes: BodyResult[] | Promise | never[] = [];\n let handRes: HandResult[] | Promise | never[] = [];\n let objectRes: ObjectResult[] | Promise | never[] = [];\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n this.state = 'detect:face';\n if (this.config.async) {\n faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n\n if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n this.state = 'detect:body';\n const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n if (this.config.async) {\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n timeStamp = now();\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n this.state = 'detect:hand';\n const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n if (this.config.async) {\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n timeStamp = now();\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // run object detection\n this.analyze('Start Object:');\n this.state = 'detect:object';\n if (this.config.async) {\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n timeStamp = now();\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Object:');\n\n // if async wait for results\n this.state = 'detect:await';\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n this.state = 'detect:gesture';\n let gestureRes: GestureResult[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n const shape = this.process.tensor?.shape || [0, 0, 0, 0];\n this.result = {\n face: faceRes as FaceResult[],\n body: bodyRes as BodyResult[],\n hand: handRes as HandResult[],\n gesture: gestureRes,\n object: objectRes as ObjectResult[],\n performance: this.performance,\n canvas: this.process.canvas,\n timestamp: Date.now(),\n error: null,\n width: shape[2],\n height: shape[1],\n get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(img.tensor);\n\n // log('Result:', result);\n this.emit('detect');\n this.state = 'idle';\n resolve(this.result);\n });\n }\n\n /** Helper function\n * @param ms - sleep time in miliseconds\n */\n async sleep(ms: number): Promise { // eslint-disable-line class-methods-use-this\n return new Promise((resolve) => { setTimeout(resolve, ms); });\n }\n\n /** internal structure that keeps track of processed videos @hidden */\n #loops: Record = {};\n /** Continously detect video frames\n * @param element - HTMLVideoElement input\n * @param run - boolean run continously or stop if already running, default true\n * @param delay - number delay detection between frames for number of miliseconds, default 0\n */\n async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n if (run) {\n if (!this.#loops[element.id]) {\n if (this.config.debug) log('video start', element.id);\n this.#loops[element.id] = true;\n }\n if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n if (delay > 0) await this.sleep(delay);\n if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n } else {\n if (this.config.debug) log('video stop', element.id);\n this.#loops[element.id] = false;\n }\n }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"], - "mappings": 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xw;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(xw||(xw={}));var yw;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(yw||(yw={}));var bw;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(bw||(bw={}));var u4={float32:yw,int32:gw,bool:xw,complex64:bw};function dt(r,t){if(r===\"string\"||t===\"string\"){if(r===\"string\"&&t===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${t}`)}return u4[r][t]}function oi(r){return dt(r,\"int32\")}function ed(r){return r!=null&&typeof r==\"object\"&&\"texture\"in r&&r.texture instanceof WebGLTexture}function td(r){return typeof GPUBuffer!=\"undefined\"&&r!=null&&typeof r==\"object\"&&\"buffer\"in r&&r.buffer instanceof GPUBuffer}function Oe(r,t){if(r.dtype===t.dtype)return[r,t];let e=dt(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function Cw(r,t){$(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${t.dtype}) input must match`)}function p4(r,t){return t.some(e=>e.id===r.id)}function yl(r){let t=[];return ek(r,t,new Set),t}function ek(r,t,e){if(r==null)return;if(r instanceof mt){t.push(r);return}if(!c4(r))return;let o=r;for(let n in o){let s=o[n];e.has(s)||(e.add(s),ek(s,t,e))}}function c4(r){return Array.isArray(r)||typeof r==\"object\"}function ww(r){return r.kernelName!=null}var rd=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(t=>t.name)))}}}dispose(){for(let t in this.registeredVariables)this.registeredVariables[t].dispose()}},bl=class r{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new rd}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let e=0;e{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){jm(t).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let o=e.factory();if(o&&!(o instanceof so)&&typeof o.then==\"function\"){let n=++this.pendingBackendInitId,s=o.then(a=>n(nthis.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;ethis.startScope(o),()=>this.endScope(n),()=>(n=e(),n instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),n))}scopedRun(t,e,o){t();try{let n=o();return e(),n}catch(n){throw e(),n}}nextTensorId(){return r.nextTensorId++}nextVariableId(){return r.nextVariableId++}clone(t){let e=T.runKernel(Co,{x:t}),o={x:t},n=a=>({x:()=>{let i=\"float32\",p={x:a},u={dtype:i};return T.runKernel(yo,p,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[e],n,s,{}),e}runKernel(t,e,o){if(this.backendName==null&&this.backend,!(qp(t,this.backendName)!=null))throw new Error(`Kernel '${t}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:t,inputs:e,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(t,e,o){let n=this.backend.numDataIds(),s=0;o.forEach(p=>{s+=p.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-e-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${t}'`)}runKernelFunc(t){let e,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=ww(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(ww(t)){let{kernelName:f,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=qp(f,this.backendName);$(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let S=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,S);o=this.saveTensorsForBackwardMode(k)}return S}}else{let{forwardFunc:f}=t,h=g=>{n&&(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=t,m=ww(t)?null:t.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?e=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),e=d.outputs)}),n&&this.addTapeNode(u,c,e,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:e.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(t,e,o){let n=aw(t);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?($(Array.isArray(e),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(e).map(u=>e[u])):i=s.map(u=>e[u]);let p=o.filter((u,c)=>a[c]);return i.concat(p)}return[]}makeTensor(t,e,o,n){if(t==null)throw new Error(\"Values passed to engine.makeTensor() are null\");o=o||\"float32\",n=n||this.backend;let s=t;o===\"string\"&&zo(t[0])&&(s=t.map(p=>Zi(p)));let a=n.write(s,e,o),i=new mt(e,o,a,this.nextTensorId());if(this.trackTensor(i,n),o===\"string\"){let p=this.state.tensorInfo.get(a),u=rw(s);this.state.numBytes+=u-p.bytes,p.bytes=u}return i}makeTensorFromDataId(t,e,o,n){o=o||\"float32\";let s={dataId:t,shape:e,dtype:o};return this.makeTensorFromTensorInfo(s,n)}makeTensorFromTensorInfo(t,e){let{dataId:o,shape:n,dtype:s}=t,a=new mt(n,s,o,this.nextTensorId());return this.trackTensor(a,e),a}makeVariable(t,e=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==t.dtype&&(t=t.cast(n));let s=new ri(t,e,o,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(t,e){this.state.numTensors++,t.dtype===\"string\"&&this.state.numStringTensors++;let o=0;t.dtype!==\"complex64\"&&t.dtype!==\"string\"&&(o=t.size*zp(t.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:o})),t instanceof ri||this.track(t)}incRef(t,e){this.trackTensor(t,e),this.backend.incRef(t.dataId)}removeDataId(t,e){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===e&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;let e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),t.dtype!==\"complex64\"&&t.dtype!==\"string\"){let o=t.size*zp(t.dtype);this.state.numBytes-=o}e.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,e.backend)}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:o,saved:s},p=aw(t);p!=null&&(n=p.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,l)=>{if(c==null){let m=o[l],d=Wp(m.size,m.dtype);return this.makeTensor(d,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,a))),this.state.activeTape.push(i)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=yl(t),o=new Set(e.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(t,e,o,n=!1){if($(e.length>0,()=>\"gradients() received an empty list of xs.\"),o!=null&&o.dtype!==\"float32\")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",t));$(s instanceof mt,()=>\"The result y returned by f() must be a tensor.\");let a=K0(this.state.activeTape,e,s);if(!n&&a.length===0&&e.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");return this.tidy(\"backward\",()=>{let i={};i[s.id]=o==null?l4(s.shape):o,q0(i,a,u=>this.tidy(u),m4);let p=e.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:p}})}customGrad(t){return $(qs(t),()=>\"The f passed in customGrad(f) must be a function.\"),(...e)=>{$(e.every(i=>i instanceof mt),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let o,n={};e.forEach((i,p)=>{n[p]=i});let s=(i,p)=>(o=t(...e,p),$(o.value instanceof mt,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),$(qs(o.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),o.value),a=(i,p)=>{let u=o.gradFunc(i,p),c=Array.isArray(u)?u:[u];$(c.length===e.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),$(c.every(m=>m instanceof mt),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");let l={};return c.forEach((m,d)=>{l[d]=()=>m}),l};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:n})}}readSync(t){return this.state.tensorInfo.get(t).backend.readSync(t)}read(t){return this.state.tensorInfo.get(t).backend.read(t)}readToGPU(t,e){return this.state.tensorInfo.get(t).backend.readToGPU(t,e)}async time(t){let e=Pu(),o=await this.backend.time(t);return o.wallMs=Pu()-e,o}track(t){return this.state.activeScope!=null&&(t.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(t)),t}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new rd;for(let t in this.registry)this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};bl.nextTensorId=0;bl.nextVariableId=0;function l4(r){let t=cl(He(r),\"float32\");return T.makeTensor(t,r,\"float32\")}function Sw(){let r=sw();if(r._tfengine==null){let t=new ll(r);r._tfengine=new bl(t)}return $0(r._tfengine.ENV),Q0(()=>r._tfengine),r._tfengine}var T=Sw();function m4(r,t){let e={a:r,b:t};return T.runKernel(io,e)}var Ji={};qe(Ji,{isBrowser:()=>vw,isMobile:()=>h4,mockIsMobile:()=>f4});function d4(){return typeof navigator!=\"undefined\"&&navigator!=null}var Iw;function f4(r){Iw=r}function h4(r){if(Iw!==void 0)return Iw;if(r||d4()){if(r||(r=navigator),r.product===\"ReactNative\")return!0;let t=r.userAgent||r.vendor||(typeof window!=\"undefined\"?window.opera:\"\");if(!t){let e=r;return 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this.applyGradients(s);return Ot(s),e?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(t,e){return zw(t,e)}dispose(){this.iterations_!=null&&Ot(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:\"iter\",tensor:ke(this.iterations_,\"int32\")}}async getWeights(){throw new Error(\"getWeights() is not implemented for this optimizer yet.\")}async setWeights(t){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(t){return this.iterations_=(await t[0].tensor.data())[0],t.slice(1)}};Object.defineProperty(kr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var Qu=class extends kr{static get className(){return\"Adadelta\"}constructor(t,e,o=null){super(),this.learningRate=t,this.rho=e,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:De(()=>Gt(s).variable(a))});let i=Array.isArray(t)?t[n].tensor:t[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;De(()=>{let c=Ce(se(p,this.rho),se(Zt(i),1-this.rho)),l=se(je(Rr(Ce(u,this.epsilon)),Rr(Ce(p,this.epsilon))),i),m=Ce(se(u,this.rho),se(Zt(l),1-this.rho));p.assign(c),u.assign(m);let d=Ce(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(t=>t.variable)),Ot(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,o=!1;this.accumulatedGrads=t.slice(0,e).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=t.slice(e,e*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var Zu=class extends kr{static get className(){return\"Adagrad\"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>Ea(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(t)?t[n].tensor:t[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=Ce(i,Zt(a));i.assign(p);let u=Ce(se(je(a,Rr(Ce(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(o=>({originalName:o.name,variable:o.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};var Ju=class extends kr{static get className(){return\"Adam\"}constructor(t,e,o,n=null){super(),this.learningRate=t,this.beta1=e,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(e).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(o=>o.name):Object.keys(t);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);e.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Gt(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Gt(i).variable(p))});let u=Array.isArray(t)?t[a].tensor:t[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=Ce(se(l,this.beta2),se(Zt(u),1-this.beta2)),f=je(m,o),h=je(d,n);c.assign(m),l.assign(d);let g=Ce(se(je(f,Ce(Rr(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(se(this.accBeta1,this.beta1)),this.accBeta2.assign(se(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Ot(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),De(()=>{this.accBeta1.assign(ii(this.beta1,this.iterations_+1)),this.accBeta2.assign(ii(this.beta2,this.iterations_+1))});let e=t.length/2,o=!1;this.accumulatedFirstMoment=t.slice(0,e).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}};var ep=class extends kr{static get className(){return\"Adamax\"}constructor(t,e,o,n=null,s=0){super(),this.learningRate=t,this.beta1=e,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(e).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(o=>o.name):Object.keys(t);De(()=>{let o=Te(1,this.accBeta1),n=je(-this.learningRate,Ce(se(this.iteration,this.decay),1));e.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Gt(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Gt(i).variable(p)});let u=Array.isArray(t)?t[a].tensor:t[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=se(l,this.beta2),f=Qt(u),h=Dd(d,f);c.assign(m),l.assign(h);let g=Ce(se(je(n,o),je(m,Ce(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Ce(this.iteration,1)),this.accBeta1.assign(se(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(t){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};var li=class extends kr{static get className(){return\"SGD\"}constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=Array.isArray(t)?t[n].tensor:t[o];if(s==null)return;let a=T.registeredVariables[o];De(()=>{let i=Ce(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=Er(ke(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(t,e){return new t(e.learningRate)}};var tp=class extends li{static get className(){return\"Momentum\"}constructor(t,e,o=!1){super(t),this.learningRate=t,this.momentum=e,this.useNesterov=o,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:De(()=>Gt(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(t)?t[n].tensor:t[o];i!=null&&De(()=>{let p,u=Ce(se(this.m,a),i);this.useNesterov?p=Ce(se(this.c,Ce(i,se(u,this.m))),s):p=Ce(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ot(this.accumulations.map(t=>t.variable))}setMomentum(t){this.momentum=t}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(o=>({originalName:o.name,variable:o.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var rp=class extends kr{static get className(){return\"RMSProp\"}constructor(t,e=.9,o=0,n=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Gt(s).variable(a))});let i=Array.isArray(t)?t[n].tensor:t[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;De(()=>{let c=Ce(se(p,this.decay),se(Zt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=Ce(se(l,this.decay),se(i,1-this.decay)),d=je(se(i,this.learningRate),Rr(Te(c,Ce(Zt(m),this.epsilon)))),f=Ce(se(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Te(s,f);s.assign(h)}else{let l=Ce(se(p,this.decay),se(Zt(i),1-this.decay)),m=Ce(se(u,this.momentum),je(se(i,this.learningRate),Rr(Ce(l,this.epsilon))));p.assign(l),u.assign(m);let d=Te(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ot(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ot(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Ot(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=this.centered?t.length/3:t.length/2,o=!1;this.accumulatedMeanSquares=t.slice(0,e).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=t.slice(e,e*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}};var Jj=[Qu,Zu,Ju,ep,tp,rp,li];function GN(){for(let r of Jj)tS(r)}var 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browser.\");t.startsWith(r.URL_SCHEME)&&(t=t.slice(r.URL_SCHEME.length)),(t==null||t.length===0)&&(t=eX),this.modelJsonFileName=t+tX,this.weightDataFileName=t+rX}async save(t){if(typeof document==\"undefined\")throw new Error(\"Browser downloads are not supported in this environment since `document` is not present\");let e=ir.join(t.weightData),o=window.URL.createObjectURL(new Blob([e],{type:\"application/octet-stream\"}));if(t.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserDownloads.save() does not support saving model topology in binary formats yet.\");{let n=[{paths:[\"./\"+this.weightDataFileName],weights:t.weightSpecs}],s=sd(t,n),a=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:\"application/json\"})),i=this.modelJsonAnchor==null?document.createElement(\"a\"):this.modelJsonAnchor;if(i.download=this.modelJsonFileName,i.href=a,await HN(()=>i.dispatchEvent(new MouseEvent(\"click\"))),t.weightData!=null){let p=this.weightDataAnchor==null?document.createElement(\"a\"):this.weightDataAnchor;p.download=this.weightDataFileName,p.href=o,await HN(()=>p.dispatchEvent(new MouseEvent(\"click\")))}return{modelArtifactsInfo:va(t)}}}};lc.URL_SCHEME=\"downloads://\";var rS=class{constructor(t){if(t==null||t.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${t}`);this.jsonFile=t[0],this.weightsFiles=t.slice(1)}async load(){return new Promise((t,e)=>{let o=new FileReader;o.onload=n=>{let s=JSON.parse(n.target.result),a=s.modelTopology;if(a==null){e(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));return}if(s.weightsManifest==null){e(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){t({modelTopology:a});return}let p=Jp(s,u=>this.loadWeights(u));t(p)},o.onerror=n=>e(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. 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o=I(\"tensorListId\",r,t,e),n=e.getTensorList(o.id);return[ke(n.size(),\"int32\")]}case\"TensorListResize\":{let o=I(\"tensorListId\",r,t,e),n=I(\"size\",r,t,e),a=e.getTensorList(o.id).resize(n);return e.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function $T(r,t,e){let[o,n]=I(\"fusedOps\",r,t,e),s=o===\"biasadd\",a=!s,i=n===\"prelu\",p=o===\"fusedbatchnorm\",u=I(\"numArgs\",r,t,e);if(s){if(i&&u!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&s&&u!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(p)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,t,e),l=Al(r,t,e),m=I(\"dataFormat\",r,t,e).toUpperCase(),d=I(\"dilations\",r,t,e),[f,h]=I(\"args\",r,t,e);a&&(h=f,f=void 0);let 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n=I(\"outputShape\",r,t,e),s=I(\"strides\",r,t,e),a=Al(r,t,e);return[o.conv2dTranspose(I(\"x\",r,t,e),I(\"filter\",r,t,e),n,[s[1],s[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let n=I(\"strides\",r,t,e),s=Al(r,t,e),a=I(\"dilations\",r,t,e),i=I(\"dataFormat\",r,t,e).toUpperCase();return[o.depthwiseConv2d(I(\"input\",r,t,e),I(\"filter\",r,t,e),[n[1],n[2]],s,i,[a[1],a[2]])]}case\"Conv3D\":{let n=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"dataFormat\",r,t,e).toUpperCase(),i=I(\"dilations\",r,t,e);return[o.conv3d(I(\"x\",r,t,e),I(\"filter\",r,t,e),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let n=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"kernelSize\",r,t,e);return[o.avgPool(I(\"x\",r,t,e),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPool\":{let n=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"kernelSize\",r,t,e);return[o.maxPool(I(\"x\",r,t,e),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPoolWithArgmax\":{let n=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"kernelSize\",r,t,e),i=I(\"includeBatchInIndex\",r,t,e),{result:p,indexes:u}=o.maxPoolWithArgmax(I(\"x\",r,t,e),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case\"AvgPool3D\":{let n=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"kernelSize\",r,t,e);return[o.avgPool3d(I(\"x\",r,t,e),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"MaxPool3D\":{let n=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"kernelSize\",r,t,e);return[o.maxPool3d(I(\"x\",r,t,e),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"Dilation2D\":{let n=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),a=I(\"dilations\",r,t,e),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I(\"x\",r,t,e),I(\"filter\",r,t,e),[i,p],s,[u,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var RT=(r,t,e,o=Je)=>{switch(r.op){case\"Fill\":{let n=I(\"shape\",r,t,e),s=I(\"dtype\",r,t,e),a=I(\"value\",r,t,e);return[o.fill(n,a,s)]}case\"LinSpace\":{let n=I(\"start\",r,t,e),s=I(\"stop\",r,t,e),a=I(\"num\",r,t,e);return[o.linspace(n,s,a)]}case\"Multinomial\":{let n=I(\"logits\",r,t,e),s=I(\"numSamples\",r,t,e),a=I(\"seed\",r,t,e);return[o.multinomial(n,s,a)]}case\"OneHot\":{let n=I(\"indices\",r,t,e),s=I(\"depth\",r,t,e),a=I(\"onValue\",r,t,e),i=I(\"offValue\",r,t,e),p=I(\"dtype\",r,t,e);return[o.oneHot(n,s,a,i,p)]}case\"Ones\":return[o.ones(I(\"shape\",r,t,e),I(\"dtype\",r,t,e))];case\"OnesLike\":return[o.onesLike(I(\"x\",r,t,e))];case\"RandomStandardNormal\":return[o.randomStandardNormal(I(\"shape\",r,t,e),I(\"dtype\",r,t,e),I(\"seed\",r,t,e))];case\"RandomUniform\":return[o.randomUniform(I(\"shape\",r,t,e),I(\"minval\",r,t,e),I(\"maxval\",r,t,e),I(\"dtype\",r,t,e))];case\"RandomUniformInt\":return[o.randomUniformInt(I(\"shape\",r,t,e),I(\"minval\",r,t,e),I(\"maxval\",r,t,e),I(\"seed\",r,t,e))];case\"Range\":{let n=I(\"start\",r,t,e),s=I(\"stop\",r,t,e),a=I(\"step\",r,t,e);return[o.range(n,s,a,I(\"dtype\",r,t,e))]}case\"TruncatedNormal\":{let n=I(\"shape\",r,t,e),s=I(\"mean\",r,t,e),a=I(\"stdDev\",r,t,e),i=I(\"seed\",r,t,e);return[o.truncatedNormal(n,s,a,I(\"dtype\",r,t,e),i)]}case\"Zeros\":return[o.zeros(I(\"shape\",r,t,e),I(\"dtype\",r,t,e))];case\"ZerosLike\":return[o.zerosLike(I(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function PS(r,t,e){let o=I(\"boxes\",r,t,e),n=I(\"scores\",r,t,e),s=I(\"maxOutputSize\",r,t,e),a=I(\"iouThreshold\",r,t,e),i=I(\"scoreThreshold\",r,t,e),p=I(\"softNmsSigma\",r,t,e);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var DT=async(r,t,e,o,n=Je)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=PS(r,t,e),l=await n.image.nonMaxSuppressionWithScoreAsync(s,a,i,p,u,c);return[l.selectedIndices,l.selectedScores]}case\"NonMaxSuppressionV4\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=PS(r,t,e),c=I(\"padToMaxOutputSize\",r,t,e),l=await n.image.nonMaxSuppressionPaddedAsync(s,a,i,p,u,c);return[l.selectedIndices,l.validOutputs]}case\"NonMaxSuppressionV3\":case\"NonMaxSuppressionV2\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=PS(r,t,e);return[await n.image.nonMaxSuppressionAsync(s,a,i,p,u)]}case\"Where\":{let s=n.cast(I(\"condition\",r,t,e),\"bool\"),a=[await n.whereAsync(s)];return s.dispose(),a}case\"ListDiff\":return n.setdiff1dAsync(I(\"x\",r,t,e),I(\"y\",r,t,e));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var AT=(r,t,e,o=Je)=>{switch(r.op){case\"LowerBound\":{let n=I(\"sortedSequence\",r,t,e),s=I(\"values\",r,t,e);return[o.lowerBound(n,s)]}case\"TopKV2\":{let n=I(\"x\",r,t,e),s=I(\"k\",r,t,e),a=I(\"sorted\",r,t,e),i=o.topk(n,s,a);return[i.values,i.indices]}case\"UpperBound\":{let n=I(\"sortedSequence\",r,t,e),s=I(\"values\",r,t,e);return[o.upperBound(n,s)]}case\"Unique\":{let n=I(\"x\",r,t,e),s=o.unique(n);return[s.values,s.indices]}case\"UniqueV2\":{let n=I(\"x\",r,t,e),s=I(\"axis\",r,t,e),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var FT=(r,t,e,o=Je)=>{switch(r.op){case\"Const\":return t[r.name];case\"PlaceholderWithDefault\":let n=I(\"default\",r,t,e);return[Bt(r.name,t,e)||n];case\"Placeholder\":return[Bt(r.name,t,e)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,t,e);return[Bs(c)]}case\"IdentityN\":return I(\"x\",r,t,e).map(c=>Bs(c));case\"Snapshot\":let s=I(\"x\",r,t,e);return[Bs(s)];case\"Shape\":return[o.tensor1d(I(\"x\",r,t,e).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,t,e).map(c=>o.tensor1d(c.shape));case\"Size\":return[o.scalar(I(\"x\",r,t,e).size,\"int32\")];case\"Rank\":return[o.scalar(I(\"x\",r,t,e).rank,\"int32\")];case\"NoOp\":return[o.scalar(1)];case\"Print\":let a=I(\"x\",r,t,e),i=I(\"data\",r,t,e),p=I(\"message\",r,t,e),u=I(\"summarize\",r,t,e);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(p);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ke(this.size(),\"int32\")}async import(t,e){this.checkKeyAndValueTensor(t,e);let o=await t.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),De(()=>{let n=mo(e),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I(\"keyDType\",r,t,e),a=I(\"valueDType\",r,t,e),i=new If(s,a);return o.addHashTable(r.name,i),[i.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let n=I(\"tableHandle\",r,t,e,o),s=I(\"keys\",r,t,e),a=I(\"values\",r,t,e);return[await o.getHashTableById(n.id).import(s,a)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let n=I(\"tableHandle\",r,t,e,o),s=I(\"keys\",r,t,e),a=I(\"defaultValue\",r,t,e);return[await o.getHashTableById(n.id).find(s,a)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let n=I(\"tableHandle\",r,t,e,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var OT=(r,t,e,o=Je)=>{switch(r.op){case\"ResizeBilinear\":{let n=I(\"images\",r,t,e),s=I(\"size\",r,t,e),a=I(\"alignCorners\",r,t,e),i=I(\"halfPixelCenters\",r,t,e);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case\"ResizeNearestNeighbor\":{let n=I(\"images\",r,t,e),s=I(\"size\",r,t,e),a=I(\"alignCorners\",r,t,e),i=I(\"halfPixelCenters\",r,t,e);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case\"CropAndResize\":{let n=I(\"image\",r,t,e),s=I(\"boxes\",r,t,e),a=I(\"boxInd\",r,t,e),i=I(\"cropSize\",r,t,e),p=I(\"method\",r,t,e),u=I(\"extrapolationValue\",r,t,e);return[o.image.cropAndResize(n,s,a,i,p,u)]}case\"ImageProjectiveTransformV3\":{let n=I(\"images\",r,t,e),s=I(\"transforms\",r,t,e),a=I(\"outputShape\",r,t,e),i=I(\"fillValue\",r,t,e),p=I(\"interpolation\",r,t,e),u=I(\"fillMode\",r,t,e);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MT=(r,t,e,o=Je)=>{switch(r.op){case\"Equal\":return[o.equal(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"NotEqual\":return[o.notEqual(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"Greater\":return[o.greater(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"GreaterEqual\":return[o.greaterEqual(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"Less\":return[o.less(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"LessEqual\":return[o.lessEqual(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"LogicalAnd\":return[o.logicalAnd(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"LogicalNot\":return[o.logicalNot(I(\"a\",r,t,e))];case\"LogicalOr\":return[o.logicalOr(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"Select\":case\"SelectV2\":return[o.where(I(\"condition\",r,t,e),I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"BitwiseAnd\":return[o.bitwiseAnd(I(\"a\",r,t,e),I(\"b\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LT=(r,t,e,o=Je)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[o.matMul(I(\"a\",r,t,e),I(\"b\",r,t,e),I(\"transposeA\",r,t,e),I(\"transposeB\",r,t,e))];case\"Einsum\":return[o.einsum(I(\"equation\",r,t,e),...I(\"tensors\",r,t,e))];case\"Transpose\":return[o.transpose(I(\"x\",r,t,e),I(\"perm\",r,t,e))];case\"_FusedMatMul\":let[n,s]=I(\"fusedOps\",r,t,e),a=n===\"biasadd\",i=s===\"prelu\",p=I(\"numArgs\",r,t,e),u=I(\"leakyreluAlpha\",r,t,e);if(a){if(i&&p!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&p!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,l]=I(\"args\",r,t,e);return[o.fused.matMul({a:I(\"a\",r,t,e),b:I(\"b\",r,t,e),transposeA:I(\"transposeA\",r,t,e),transposeB:I(\"transposeB\",r,t,e),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];case\"MatrixBandPart\":return[o.linalg.bandPart(I(\"a\",r,t,e),I(\"numLower\",r,t,e),I(\"numUpper\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var BT=(r,t,e,o=Je)=>{switch(r.op){case\"EuclideanNorm\":return[o.euclideanNorm(I(\"x\",r,t,e),I(\"axis\",r,t,e),I(\"keepDims\",r,t,e))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[o.batchNorm(I(\"x\",r,t,e),I(\"mean\",r,t,e),I(\"variance\",r,t,e),I(\"offset\",r,t,e),I(\"scale\",r,t,e),I(\"epsilon\",r,t,e))];case\"FusedBatchNormV3\":return[o.batchNorm(I(\"x\",r,t,e),I(\"mean\",r,t,e),I(\"variance\",r,t,e),I(\"offset\",r,t,e),I(\"scale\",r,t,e),I(\"epsilon\",r,t,e))];case\"LRN\":return[o.localResponseNormalization(I(\"x\",r,t,e),I(\"radius\",r,t,e),I(\"bias\",r,t,e),I(\"alpha\",r,t,e),I(\"beta\",r,t,e))];case\"Softmax\":return[o.softmax(I(\"x\",r,t,e))];case\"LogSoftmax\":return[o.logSoftmax(I(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var zT=(r,t,e,o=Je)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I(\"paramsNestedSplits\",r,t,e),I(\"paramsDenseValues\",r,t,e),I(\"indices\",r,t,e),I(\"outputRaggedRank\",r,t,e));return n.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I(\"starts\",r,t,e),I(\"limits\",r,t,e),I(\"splits\",r,t,e));return[n,s]}case\"RaggedTensorToTensor\":return[o.raggedTensorToTensor(I(\"shape\",r,t,e),I(\"values\",r,t,e),I(\"defaultValue\",r,t,e),I(\"rowPartitionTensors\",r,t,e),I(\"rowPartitionTypes\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var VT=(r,t,e,o=Je)=>{switch(r.op){case\"Max\":{let i=I(\"axis\",r,t,e),p=I(\"keepDims\",r,t,e);return[o.max(I(\"x\",r,t,e),i,p)]}case\"Mean\":{let i=I(\"axis\",r,t,e),p=I(\"keepDims\",r,t,e);return[o.mean(I(\"x\",r,t,e),i,p)]}case\"Min\":{let i=I(\"axis\",r,t,e),p=I(\"keepDims\",r,t,e);return[o.min(I(\"x\",r,t,e),i,p)]}case\"Sum\":{let i=I(\"axis\",r,t,e),p=I(\"keepDims\",r,t,e);return[o.sum(I(\"x\",r,t,e),i,p)]}case\"All\":{let i=I(\"axis\",r,t,e),p=I(\"keepDims\",r,t,e);return[o.all(I(\"x\",r,t,e),i,p)]}case\"Any\":{let i=I(\"axis\",r,t,e),p=I(\"keepDims\",r,t,e);return[o.any(I(\"x\",r,t,e),i,p)]}case\"ArgMax\":{let i=I(\"axis\",r,t,e);return[o.argMax(I(\"x\",r,t,e),i)]}case\"ArgMin\":{let i=I(\"axis\",r,t,e);return[o.argMin(I(\"x\",r,t,e),i)]}case\"Prod\":{let i=I(\"axis\",r,t,e),p=I(\"keepDims\",r,t,e);return[o.prod(I(\"x\",r,t,e),i,p)]}case\"Cumprod\":{let i=I(\"axis\",r,t,e),p=I(\"exclusive\",r,t,e),u=I(\"reverse\",r,t,e);return[o.cumprod(I(\"x\",r,t,e),i,p,u)]}case\"Cumsum\":{let i=I(\"axis\",r,t,e),p=I(\"exclusive\",r,t,e),u=I(\"reverse\",r,t,e);return[o.cumsum(I(\"x\",r,t,e),i,p,u)]}case\"Bincount\":let n=I(\"x\",r,t,e),s=I(\"weights\",r,t,e),a=I(\"size\",r,t,e);return[o.bincount(n,s,a)];case\"DenseBincount\":{let i=I(\"x\",r,t,e),p=I(\"weights\",r,t,e),u=I(\"size\",r,t,e),c=I(\"binaryOutput\",r,t,e);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var WT=(r,t,e,o=Je)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let n=I(\"n\",r,t,e),s=I(\"axis\",r,t,e),a=I(\"tensors\",r,t,e);return a=a.slice(0,n),[o.concat(a,s)]}case\"Gather\":{let n=I(\"x\",r,t,e),s=I(\"indices\",r,t,e);return[o.gather(n,o.cast(s,\"int32\"),0)]}case\"GatherV2\":{let n=I(\"axis\",r,t,e),s=I(\"batchDims\",r,t,e),a=I(\"x\",r,t,e),i=I(\"indices\",r,t,e);return[o.gather(a,o.cast(i,\"int32\"),n,s)]}case\"Reverse\":{let n=I(\"dims\",r,t,e),s=[];for(let i=0;i{let n=I(\"axis\",r,t,e),s=I(\"tensors\",r,t,e),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error(\"the input tensors shape does not match\");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case\"Unpack\":{let n=I(\"axis\",r,t,e),s=I(\"tensor\",r,t,e);return o.unstack(s,n)}case\"Tile\":{let n=I(\"reps\",r,t,e);return[o.tile(I(\"x\",r,t,e),n)]}case\"Split\":case\"SplitV\":{let n=I(\"axis\",r,t,e),s=I(\"numOrSizeSplits\",r,t,e),a=I(\"x\",r,t,e);return o.split(a,s,n)}case\"ScatterNd\":{let n=I(\"indices\",r,t,e),s=I(\"values\",r,t,e),a=I(\"shape\",r,t,e);return[o.scatterND(n,s,a)]}case\"GatherNd\":{let n=I(\"x\",r,t,e),s=I(\"indices\",r,t,e);return[o.gatherND(n,s)]}case\"SparseToDense\":{let n=I(\"sparseIndices\",r,t,e),s=I(\"outputShape\",r,t,e),a=I(\"sparseValues\",r,t,e),i=I(\"defaultValue\",r,t,e);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}case\"TensorScatterUpdate\":{let n=I(\"indices\",r,t,e),s=I(\"values\",r,t,e),a=I(\"tensor\",r,t,e);return[o.tensorScatterUpdate(a,n,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UT=(r,t,e,o=Je)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I(\"indices\",r,t,e),I(\"values\",r,t,e),I(\"denseShape\",r,t,e),I(\"defaultValue\",r,t,e));return[n,s,a,i]}case\"SparseReshape\":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I(\"inputIndices\",r,t,e),I(\"inputShape\",r,t,e),I(\"newShape\",r,t,e));return[n,s]}case\"SparseSegmentMean\":return[o.sparse.sparseSegmentMean(I(\"data\",r,t,e),I(\"indices\",r,t,e),I(\"segmentIds\",r,t,e))];case\"SparseSegmentSum\":return[o.sparse.sparseSegmentSum(I(\"data\",r,t,e),I(\"indices\",r,t,e),I(\"segmentIds\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GT=(r,t,e,o=Je)=>{switch(r.op){case\"FFT\":return[o.fft(I(\"x\",r,t,e))];case\"IFFT\":return[o.ifft(I(\"x\",r,t,e))];case\"RFFT\":return[o.rfft(I(\"x\",r,t,e))];case\"IRFFT\":return[o.irfft(I(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HT=(r,t,e,o=Je)=>{switch(r.op){case\"StaticRegexReplace\":return[o.string.staticRegexReplace(I(\"input\",r,t,e),I(\"pattern\",r,t,e),I(\"rewrite\",r,t,e),I(\"replaceGlobal\",r,t,e))];case\"StringNGrams\":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I(\"data\",r,t,e),I(\"dataSplits\",r,t,e),I(\"separator\",r,t,e),I(\"nGramWidths\",r,t,e),I(\"leftPad\",r,t,e),I(\"rightPad\",r,t,e),I(\"padWidth\",r,t,e),I(\"preserveShortSequences\",r,t,e));return[n,s]}case\"StringSplit\":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I(\"input\",r,t,e),I(\"delimiter\",r,t,e),I(\"skipEmpty\",r,t,e));return[n,s,a]}case\"StringToHashBucketFast\":return[o.string.stringToHashBucketFast(I(\"input\",r,t,e),I(\"numBuckets\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KT=(r,t,e,o=Je)=>{switch(r.op){case\"Cast\":return[o.cast(I(\"x\",r,t,e),I(\"dtype\",r,t,e))];case\"ExpandDims\":{let n=I(\"axis\",r,t,e);return[o.expandDims(I(\"x\",r,t,e),n)]}case\"Squeeze\":{let n=I(\"axis\",r,t,e);return[o.squeeze(I(\"x\",r,t,e),n)]}case\"Reshape\":return[o.reshape(I(\"x\",r,t,e),I(\"shape\",r,t,e))];case\"EnsureShape\":return[o.ensureShape(I(\"x\",r,t,e),I(\"shape\",r,t,e))];case\"MirrorPad\":return[o.mirrorPad(I(\"x\",r,t,e),I(\"padding\",r,t,e),I(\"mode\",r,t,e))];case\"PadV2\":case\"Pad\":return[o.pad(I(\"x\",r,t,e),I(\"padding\",r,t,e),I(\"constantValue\",r,t,e))];case\"SpaceToBatchND\":{let n=I(\"blockShape\",r,t,e),s=I(\"paddings\",r,t,e);return[o.spaceToBatchND(I(\"x\",r,t,e),n,s)]}case\"BatchToSpaceND\":{let n=I(\"blockShape\",r,t,e),s=I(\"crops\",r,t,e);return[o.batchToSpaceND(I(\"x\",r,t,e),n,s)]}case\"DepthToSpace\":{let n=I(\"blockSize\",r,t,e),s=I(\"dataFormat\",r,t,e).toUpperCase();return[o.depthToSpace(I(\"x\",r,t,e),n,s)]}case\"BroadcastTo\":return[o.broadcastTo(I(\"x\",r,t,e),I(\"shape\",r,t,e))];case\"BroadcastArgs\":return[o.broadcastArgs(I(\"s0\",r,t,e),I(\"s1\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function OS(r,t,e,o,n=De){let s=((a,i,p)=>{switch(a.category){case\"arithmetic\":return n(()=>wT(a,i,p));case\"basic_math\":return n(()=>ST(a,i,p));case\"control\":return _T(a,i,p);case\"convolution\":return n(()=>ET(a,i,p));case\"creation\":return n(()=>RT(a,i,p));case\"dynamic\":return DT(a,i,p);case\"evaluation\":return n(()=>AT(a,i,p));case\"image\":return n(()=>OT(a,i,p));case\"graph\":return n(()=>FT(a,i,p));case\"logical\":return n(()=>MT(a,i,p));case\"matrices\":return n(()=>LT(a,i,p));case\"normalization\":return n(()=>BT(a,i,p));case\"ragged\":return n(()=>zT(a,i,p));case\"reduction\":return n(()=>VT(a,i,p));case\"slice_join\":return n(()=>WT(a,i,p));case\"sparse\":return n(()=>UT(a,i,p));case\"spectral\":return n(()=>GT(a,i,p));case\"string\":return n(()=>HT(a,i,p));case\"transformation\":return n(()=>KT(a,i,p));case\"hash_table\":return PT(a,i,p,o);case\"custom\":let u=uf(a.op);if(u&&u.customExecutor)return u.customExecutor(new Cf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Pl=class{constructor(t={},e={},o={},n={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=o,this.functionMap=n,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?\"\":`${e.frameName}-${e.iterationId}`).join(\"/\"):\"\"}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function MS(r,t,e,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=new Set(Object.keys(r).map(m=>Nr(m)[0]));o=o||[];let c=new Set(o.map(m=>Nr(m.name)[0])),l=[...t];for(;l.length>0;){let m=l.pop();if((mu(m)||k8(m)||N8(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),e[m.name]==null&&!u.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:t,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function qT(r,t){let{usedNodes:e,inputs:o}=t,n=Object.keys(o).map(g=>Nr(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>e.has(typeof g==\"string\"?g:g.name);function i(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let p=i([...n,...r.weights,...s]).filter(a),u=i([...p,...Object.values(r.nodes)]).filter(a),c=new Map(u.map(g=>[g.name,g])),l={};for(let g of u){l[g.name]=l[g.name]||0;for(let x of g.children)a(x)||(l[x.name]=Number.POSITIVE_INFINITY),l[x.name]=(l[x.name]||0)+1}let m=Object.entries(l).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(a))--l[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>c.get(g)),h=C8(f,p);return w8(h,p),h}function C8(r,t){let e=new Map(r.map(a=>[a.name,a])),o=t.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=e.get(a);for(let p of i.children)!e.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var fc=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function w8(r,t){let e=new Map(r.map((i,p)=>[i.name,p])),o=new Set(t.map(i=>i.name)),n=i=>o.has(typeof i==\"string\"?i:i.name),s=new Set(r.map(i=>i.name)),a=i=>s.has(typeof i==\"string\"?i:i.name);for(let i of r){for(let p of i.children.filter(a)){if(!e.has(p.name))throw new fc(`Child ${p.name} of node ${i.name} is unreachable.`);if(e.get(i.name)>e.get(p.name))throw new fc(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!e.has(p.name))throw new fc(`Input ${p.name} of node ${i.name} is unreachable.`);if(e.get(p.name)>e.get(i.name))throw new fc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function jT(r){let t=new Map(r.map((i,p)=>[i.name,p])),e=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>mu(i)?e:p),n=i=>{let p=o[t.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;it[o].map(n=>n.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(o=>{this._functionExecutorMap[o]=new r(t.functions[o],this)})}getCompilationKey(t,e){let o=t.map(s=>s.name).sort(),n=e.map(s=>s.name).sort();return o.join(this.SEPARATOR)+\"--\"+n.join(this.SEPARATOR)}compile(t,e){let o=MS(t,e,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let u=e.map(l=>l.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`)}let i=qT(this.graph,o),p=jT(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return Er(e),e}cloneTensorList(t){return t?t.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,o])=>[e,this.cloneTensorList(o)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let o=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let n=o.map(m=>this.graph.nodes[Nr(m)[0]]),s=e.map(m=>Nr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(t,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=A().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Pl(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,C]=Nr(x,m),S=[];S[C]=t[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=OS(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);d[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,d,m,f,a,g.get(x.name))}return this.parent==null&&m.dispose(f),e.map(x=>Bt(x,d,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(o=>t[o]).map(o=>o.map(n=>n.id)));return new Set(e)}checkTensorForDisposal(t,e,o,n,s,a,i){if(!(mu(e)||a.has(t))){for(let p of o[t])p!=null&&(i[p.id]=(i[p.id]||0)+e.children.length);for(let p of e.inputs){if(mu(p))continue;let u=fS(p.name,o,n);if(u!=null)for(let c of u){if(!c||c.kept||s.has(c.id))continue;let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,o,n,s,a){function i(p){return mu(p)||s.has(p.name)}if(!(mu(t)||a==null))for(let p of a){if(i(p))continue;let u=fS(p.name,e,o);for(let c of u)!c||c.kept||n.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=A().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Pl(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(t,a,e,o),p=e.map(m=>Bt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(t,e,o){let n=t.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,e,o)}async executeWithControlFlow(t,e,o,n){let s=Object.keys(t),a=s.map(S=>this.graph.nodes[Nr(S)[0]]),i=o.map(S=>Nr(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:c,missingInputs:l,dynamicNode:m,syncInputs:d}=MS(t,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(S=>{let[k,_]=Nr(S),E=[];E[_]=t[S],h[k]=E});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,e,h,b,x,p,g,c);await Promise.all(S)}m==null&&!n&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let C=u.filter(S=>!mu(S)&&!Bt(S.name,h,e)).map(S=>S.name);if(C.length>0){let S=\"\";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`)}return h}processStack(t,e,o,n,s,a,i,p,u){let c=[];for(;e.length>0;){let l=e.pop();o.currentContext=l.contexts;let m=\"\";if(l.node.op===\"Enter\"&&I(\"isConstant\",l.node,n,o)&&([m]=Ls(l.node.name,o)),n[l.node.name]==null){let d=OS(l.node,n,o,this._resourceManager);m||([m]=Ls(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,e,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,e,o,n,s,u))}else this.processChildNodes(l.node,e,o,n,s,u)}return c}processChildNodes(t,e,o,n,s,a){t.children.forEach(i=>{let[p]=Ls(i.name,o);s[p]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!Bt(u,n,o))&&(s[p]=!0,e.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Bt(u,n,o))&&(s[p]=!0,e.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let o=t[e],[n]=Nr(e),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(t){var e,o;let n={};for(let s in t){let a=(o=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=t[s]:n[s]=t[s]}return n}checkInputs(t){let e=Object.keys(t).filter(o=>{let[n]=Nr(o);return this.graph.nodes[n]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[o]=Nr(e);if(!this.graph.nodes[o])throw new Error(`The output '${e}' is not found in the graph`)})}};var vf=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in 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s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Ol(Fl.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Fl.Instance.transformGraph(t.modelInitializer);this.initializer=new Ol(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t==\"string\"){let o=this.io.getSaveHandlers(t);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(o.length>1)throw new Error(`Found more than 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WY=Ie(an,r=>Math.cosh(r)),C$={kernelName:an,backendName:\"cpu\",kernelFunc:WY};function UY(r){let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],\"float32\"),b=e.data.get(s.dataId).values,C=e.data.get(a.dataId).values,S=e.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let E=0;E=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-P)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let q=0;q1?P*(m-1)+re*z:.5*(P+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?P*(m-1)+q*z:.5*(P+M)*(m-1);if(Y<0||Y>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. 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Got strides ${a} and dilations '${m}'`);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,C=b.left,S=b.top,k=d.outChannels/d.inChannels,_=new tt(d.outShape,n.dtype),E=e.data.get(n.dataId).values,R=e.data.get(s.dataId).values,D=_.values;for(let P=0;P=d.inHeight)continue;let q=U*l[0],Y=O+j*c[1];for(let J=0;J=d.inWidth)continue;let ie=q+ee*l[1],le=Y+oe*d.inChannels,be=re,_e=ie;for(let ve=0;ve{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=e,p=t,u=p.data.get(o.dataId).values,c=o.shape.length,l=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:C,strideHeight:S,strideWidth:k,filterHeight:_,filterWidth:E,dilationHeight:R,dilationWidth:D,outShape:P}=w.computeDilation2DInfo(o.shape,n.shape,s,a,\"NHWC\",i),O=y.sizeFromShape(P),M=P.length,L=y.getArrayFromDType(o.dtype,O);for(let z=0;z=0&&oe=0&&lere&&(re=ve)}}}let ne=y.locToIndex([z,U,q,J],M,y.computeStrides(P));L[ne]=re}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),P,o.dtype),shape:P,dtype:o.dtype}}};var R$={kernelName:Mi,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=e,u=t,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=w.computeDilation2DInfo(o.shape,n.shape,a,i,\"NHWC\",p);y.assert(s.rank===D.length,()=>`Error in ${Mi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ne,re=oe)}}}O[J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var D$={kernelName:Oi,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=e,u=t,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=w.computeDilation2DInfo(o.shape,n.shape,a,i,\"NHWC\",p);y.assert(s.rank===D.length,()=>`Error in ${Oi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ee,re=ie)}}}O[L][J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function QY(r){let{inputs:t,backend:e,attrs:o}=r,{image:n}=t,{canvas:s,options:a}=o,{contextOptions:i,imageOptions:p}=a||{},u=(p==null?void 0:p.alpha)||1,c=(i==null?void 0:i.contextType)||\"2d\";if(c!==\"2d\")throw new Error(`Context type ${i.contextType} is not supported by the CPU backend.`);let l=s.getContext(c,(i==null?void 0:i.contextAttributes)||{});if(l==null)throw new Error(`Could not get the context with ${c} type.`);let[m,d]=n.shape.slice(0,2),f=n.shape.length===2?1:n.shape[2],h=e.data.get(n.dataId).values,g=n.dtype===\"float32\"?255:1,x=new Uint8ClampedArray(d*m*4);for(let C=0;C1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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Qf(r,t,e){let{newShape:o,keptDims:n}=y.squeezeShape(t),s=t.length,a=r&&s===3&&t[0]===1,i=a?t.slice(1):o,p=!r&&s>1&&!y.arraysEqual(t,e)&&o.lengthr[e]).join(\", \")}function $R(r,t,e,o){let n=e.map((c,l)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[l],shapeInfo:m}}),s=n.map(c=>c.shapeInfo),a={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},i=kR(n,a,t),p=EI(r.gl,i),u=r.createProgram(p);return 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k=y.computeStrides(c);f=`${k[0]===p[1]}_${k[k.length-1]===p[1]}`}let h=a.shape.length,g=c.length===2&&y.arraysEqual(a.shape,p),x=y.sizeFromShape(a.shape)===1,b=w.getBroadcastDims(a.shape,e.shape),C=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(p,e.texData.texShape),S=r.packedInputs||c.length>2?\"\":`${p[0]>1}_${p[1]>1}`;o+=`${h}_${C}_${u?l:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${S}_${i}`}else{let p=a.isUniform?\"uniform\":a.texData.texShape;o+=`${a.shape}_${p}_${i}`}});let n=r.userCode,s=r.constructor.name;return s+=\"_\"+o+\"_\"+n+`${A().getNumber(\"WEBGL_VERSION\")}`,s}function ut(r){return A().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Zf=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=fu.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let e=It();this.outputShape=t,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n 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e=this.gl;ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),ov(e,t,this.vertexBuffer)}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ce(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&jl(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,o=!0){return this.throwIfDisposed(),o?LI(this.gl,t,e):BI(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return 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this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(t,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=HZ(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:o}=this.itemsToPoll[e];o()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let o;\"setTimeoutCustom\"in A().platform&&(o=A().platform.setTimeoutCustom.bind(A().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),Xl(this.gl,t,this.framebuffer),this.debug&&kc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Xl(this.gl,this.outputTexture,this.framebuffer),this.debug&&kc(this.gl)):jf(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let o=e();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(t,e,o){this.throwIfDisposed();let n=this.gl;Xl(n,t,this.framebuffer),this.debug&&kc(n),this.outputTexture=t,ce(n,()=>n.viewport(0,0,e,o)),ce(n,()=>n.scissor(0,0,e,o))}setOutputMatrixWriteRegionDriver(t,e,o,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(t,e,o,n))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function HZ(r){let t=0;for(;t`${r}.${e}`)}function Rt(r,t){return t===1?[r]:mv(r,t)}function ID(r,t){if(r===1)return\"rc\";let e=\"\";for(let o=0;o ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let e=\"\";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o};\n bool rEdge = rp1 >= ${n};\n `}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),\n cEdge ? 0. : getA(${e[1]}),\n rEdge ? 0. : getA(${e[2]}),\n rEdge || cEdge ? 0. : getA(${e[3]})`}};var Pc=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=t,this.enableShapeUniforms=ut(this.outputShape.length);let o=\"\";for(let n=0;n<4;n++){let s=\"thisRC = rc;\";n%2===1&&(s+=\"thisRC.z += 1;\"),n>1&&(s+=\"thisRC.y += 1;\"),o+=`\n ${s}\n ${n>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${n}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${n>0?\"}\":\"\"}\n `}this.userCode=`\n ${KZ(e,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?$c():_c(t)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":t[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":t[2]};\n\n ${o}\n\n setOutput(result);\n }\n `}};function KZ(r,t){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${t?IR([\"r\",\"c\",\"d\"],\"inputShape\"):Ws([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var mh=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,o){let n=kD(e,o),s=ND(t,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=vD(t,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].pop();return this.usedTextures[s].push(p),p}let i;return n===er.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):n===er.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):n===er.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):n===er.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):n===er.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(t,e,o,n){if(this.freeTextures==null)return;let s=kD(o,n),a=ND(e,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=vD(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=A().getNumber(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(t),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(t);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function qZ(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function vD(r,t,e,o,n){let s=jZ(t,o),a;if(n){let[p,u]=Ma(r[0],r[1]);a=p*u}else{let[p,u]=fp(r[0],r[1]);a=p*u}let i=qZ(e,s);return a*i}function jZ(r,t){switch(r){case er.PACKED_2X2_FLOAT32:return ah(t);case er.PACKED_2X2_FLOAT16:return ih(t);case er.UNPACKED_FLOAT32:return oh(t);case er.UNPACKED_FLOAT16:return nh(t);case er.PACKED_4X1_UNSIGNED_BYTE:return sh(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function XZ(r){return A().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?er.PACKED_2X2_FLOAT32:er.UNPACKED_FLOAT32:r?er.PACKED_2X2_FLOAT16:er.UNPACKED_FLOAT16}function kD(r,t){if(r===mr.UPLOAD)return er.PACKED_2X2_FLOAT32;if(r===mr.RENDER||r==null)return XZ(t);if(r===mr.DOWNLOAD||r===mr.PIXELS)return er.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function ND(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var tr=class{constructor(t,e){this.variableNames=[\"A\"],this.outputShape=t,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${e}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},Wt=\"if (isnan(x)) return x;\",TD=\"return x;\",dv=\"return abs(x);\";var _D=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",$D=Wt+`\n return (x < 0.0) ? 0.0 : x;\n`,ED=Wt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,La=\"return x;\",RD=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var AD=\"return x;\",FD=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,PD=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,OD=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,MD=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Ar=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${e}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var dh=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=ut(this.outputShape.length);let e=t.length,o=Rt(\"rc\",e),n=Re(e),s=ID(e,o),a=o.slice(-2),i=e<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n void main() {\n ${n} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${i}));\n }\n `}};var QZ=Vt.whereImpl,ZZ=1e-7,JZ=1e-4,fh={};function e9(r){return r in fh||(fh[r]={}),fh[r]}var t9=A().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),r9=600;function o9(){return A().global.screen==null?1024:A().global.screen.height*A().global.screen.width*window.devicePixelRatio*r9/1024/1024}var Oc=class r extends so{nextDataId(){return r.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!A().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let e;if(t!=null){if(t instanceof xp)e=t;else{let o=Hr(A().getNumber(\"WEBGL_VERSION\"),t);e=new xp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Hr(A().getNumber(\"WEBGL_VERSION\"));e=new xp(o),this.binaryCache=e9(A().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new mh(this.gpgpu),this.numMBBeforeWarning=o9(),this.texData=new Bo(this,ur())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,o,n,s,a){let i=this.makeTensorInfo(e,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:t,texShape:[n,s]},p.texShape=[n,s];let u=Nc(e),c=new Yl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=e,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(t,e,o){if((A().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||A().getBool(\"DEBUG\"))&&this.checkNumericalProblems(t),o===\"complex64\"&&t!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.texData.set(n,{shape:e,dtype:o,values:t,usage:mr.UPLOAD,refCount:1}),n}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,o,n,s){if(A().getBool(\"DEBUG\")&&this.checkNumericalProblems(e),n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(t,{shape:o,dtype:n,values:e,usage:mr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=e;if(a!=null){let m;p?m=new Ar(i,La):m=new tr(i,La);let d=this.runWebGLProgram(m,[{dataId:t,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(t);if(n===\"string\")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n===\"complex64\"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=w.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,l)}async read(t){if(this.pendingRead.has(t)){let f=this.pendingRead.get(t);return new Promise(h=>f.push(h))}let e=this.texData.get(t),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=e;if(s!=null){let f;p?f=new Ar(n,La):f=new tr(n,La);let h=this.runWebGLProgram(f,[{dataId:t,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(t);if(A().getBool(\"DEBUG\")&&!A().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&A().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,c;if(a!==\"complex64\"&&A().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(t);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Kl(n))}this.pendingRead.set(t,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let l;if(a===\"complex64\"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=w.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(t);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(t,l),d=this.pendingRead.get(t);return this.pendingRead.delete(t),d.forEach(f=>f(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&ur().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let o=this.texData.get(t),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;p?d=new Ar(s,La):d=new tr(s,La);let f=this.runWebGLProgram(d,[{dataId:t,shape:s,dtype:i}],i),h=this.readToGPU(f,e);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(t,e.customTexShape),l=ur().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype===\"string\")try{let o=e.map(n=>y.decodeString(n));return me(t.shape,t.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,t();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=e,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:o}=this.texData.get(t);return o!=null&&(this.disposeData(o.real.dataId,e),this.disposeData(o.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(t),p=i&&i.origDataId||t,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),e!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(e,n,s,a)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=t9){return A().getBool(\"WEBGL_CPU_FORWARD\")&&t.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,t,e)}else n=this.write(o,t,e);return this.texData.get(n).usage=null,{dataId:n,shape:t,dtype:e}}makeOutput(t,e,o){return ur().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,o),this)}unpackTensor(t){let e=new dh(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new lh(t.shape),o=!0;return this.runWebGLProgram(e,[t],t.dtype,null,o)}packedReshape(t,e){let o=[hi(t.shape),...gi(t.shape)],n={dtype:t.dtype,shape:o,dataId:t.dataId},s=[hi(e),...gi(e)],a=new Pc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],t.dtype,p,i);return{dataId:u.dataId,shape:e,dtype:u.dtype}}decode(t,e){let o=this.texData.get(t),{isPacked:n,shape:s,dtype:a}=o;if(e!=null){let m=y.sizeFromShape(s),d=e[0]*e[1]*4;y.assert(m<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=Nc(s),p;n?p=new Jf(i):p=new Zf(i);let u=!0,c=[e!=null?e:Kl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:t}],a,c,u,e);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(t,e,o,n,s=!1,a){let i=this.makeTensorInfo(t.outputShape,o),p=this.texData.get(i.dataId);if(t.packedOutput&&(p.isPacked=!0),t.outPackingScheme===fu.DENSE){let x=a!=null?a:Kl(t.outputShape);p.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(p.usage=t.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=e.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=A().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!hu(b.shape,x.shape)){let C=x,S=x.shape;x.shape=b.shape,x=this.packedReshape(x,S),u.push(x),b=this.texData.get(x.dataId),C.shape=S}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=RR(t,c,l),d=this.getAndSaveBinary(m,()=>$R(this.gpgpu,t,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),A().get(\"ENGINE_COMPILE_ONLY\")||ER(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=A().getNumber(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!A().getBool(\"WEBGL_LAZILY_UNPACK\")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(t,e,o,n,s=!1){return o=o||e[0].dtype,this.runWebGLProgram(t,e,o,n,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(A().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!A().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let t=A().getBool(\"DEBUG\");A().set(\"DEBUG\",!1);let e=this.abs(ke(1e-8)).dataSync()[0];if(A().set(\"DEBUG\",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?ZZ:JZ}uploadToGPU(t){let e=this.texData.get(t),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=e;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=e.texShape;if(l==null&&(l=VI(o,p),e.texShape=l),s!=null){let m=Nc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ma(l[0],l[1])),p?d=new rh(m,g):d=new Yl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=mr.PIXELS:C.usage=mr.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let S=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,S,k),E=this.texData.get(_.dataId);e.texShape=E.texShape,e.isPacked=E.isPacked,e.usage=E.usage,A().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(e.texture=E.texture,e.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);e.texture=m}}convertAndCacheOnCPU(t,e){let o=this.texData.get(t),{dtype:n}=o;return e!=null&&(o.values=n9(e,n)),o.values}acquireTexture(t,e,o,n){if(this.numBytesInGPU+=this.computeBytes(t,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,n)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(e),n(!0)}catch(s){throw s}});t.push(o)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await pS(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Kf(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:o,infLoc:n,nanLoc:s,outShapeLocation:a,outShapeStridesLocation:i,outTexShapeLocation:p}=jI(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=o,t.infLoc=n,t.nanLoc=s,t.outShapeLocation=a,t.outShapeStridesLocation=i,t.outTexShapeLocation=p}}createTensorFromGPUData(t,e,o){t.channels=t.channels||\"RGBA\";let{texture:n,height:s,width:a,channels:i}=t,p=ur().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let u=p.writeTexture(n,e,o,s,a,i);return ur().makeTensorFromDataId(u,e,o,p)}};Oc.nextDataId=0;function n9(r,t){if(t===\"float32\"||t===\"complex64\")return r;if(t===\"int32\"||t===\"bool\"){let e=t===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew Oc,2);var Cat={forceHalfFloat:LD};var Mc=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var Fr=class{constructor(t,e,o){this.variableNames=[\"A\",\"B\"],this.outputShape=w.assertAndGetBroadcastShape(e,o),this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${t}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var jr=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var qr=class{constructor(t,e,o,n=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=w.assertAndGetBroadcastShape(e,o);let s=this.outputShape.length;this.enableShapeUniforms=ut(s);let a=\"\";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(a=`\n ${Re(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?a+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:a+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let p=Rt(\"coords\",s);this.enableShapeUniforms?a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${t}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${a}\n\n setOutput(result);\n }\n `}};function Dt(r){let{inputs:t,backend:e}=r,{x:o}=t;return e.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var BD={kernelName:Co,backendName:\"webgl\",kernelFunc:Dt};function Pr(r){let{inputs:t,backend:e}=r,{real:o,imag:n}=t,s=e.makeTensorInfo(o.shape,\"complex64\"),a=e.texData.get(s.dataId),i=Dt({inputs:{x:o},backend:e}),p=Dt({inputs:{x:n},backend:e});return a.complexTensorInfos={real:i,imag:p},s}var zD={kernelName:Ri,backendName:\"webgl\",kernelFunc:Pr};var fv=\"return (a < 0.) ? b * a : a;\",hv=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function a9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{alpha:s}=o,a=e.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),i=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new qr(hv,n.shape,a.shape):new Fr(fv,n.shape,a.shape),p=e.runWebGLProgram(i,[n,a],\"float32\");return e.disposeIntermediateTensorInfo(a),p}var VD={kernelName:En,backendName:\"webgl\",kernelFunc:a9};var gv=\"return (a < 0.) ? b * a : a;\",xv=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function i9(r){let{inputs:t,backend:e}=r,{x:o,alpha:n}=t,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new qr(xv,o.shape,n.shape):new Fr(gv,o.shape,n.shape);return e.runWebGLProgram(s,[o,n],\"float32\")}var WD={kernelName:rs,backendName:\"webgl\",kernelFunc:i9};var Fo=\"if (isnan(x)) return x;\";function xe({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&e!=null){let l=i.texData.get(a.dataId),m=e(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=A().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return u?c=new Ar(a.shape,t):c=new tr(a.shape,r),i.runWebGLProgram(c,[a],p)}}function nt({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype===\"complex64\"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[S,k]=C,_={dataId:S.dataId,dtype:S.dtype,shape:p.shape},E={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new Fr(r,p.shape,u.shape);return c.runWebGLProgram(R,[_,E],dt(S.dtype,k.dtype))}),b=Pr({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype===\"string\"||u.dtype===\"string\"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype===\"string\"?w.fromUint8ToStringArray(f):f,x=p.dtype===\"string\"?w.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),S=c.makeTensorInfo(C,l),k=c.texData.get(S.dataId);return k.values=b,S}let m=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,d;return m?d=new qr(t,p.shape,u.shape,e):d=new Fr(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function xi(r,t=!1){if(r===\"linear\")return t?AD:TD;if(r===\"relu\")return t?PD:$D;if(r===\"elu\")return t?FD:_D;if(r===\"relu6\")return t?OD:ED;if(r===\"prelu\")return t?xv:gv;if(r===\"leakyrelu\")return t?hv:fv;if(r===\"sigmoid\")return t?MD:RD;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Lc=class{constructor(t,e,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=ut(this.outputShape.length);let c=n?t[1]:t[2],l=Math.ceil(c/2),m=n?\"i * 2, rc.y\":\"rc.y, i * 2\",d=s?\"rc.z, i * 2\":\"i * 2, rc.z\",f=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";i&&(p?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${i}\n }`:u?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${i}\n }`:g=`vec4 activation(vec4 x) {\n ${i}\n }`,x=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let C=\"rc.x\",S=\"rc.x\";t[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!hu(n.shape,p)&&!(c.texture!==null&&hu(c.shape,p))?HD(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var KD={kernelName:da,backendName:\"webgl\",kernelFunc:te};var em=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=t;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u=\"sumValue += dot(values, ones);\";if(e!=null){let l=1/e;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c=\"\";s%o>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${u}\n }\n\n int inIdx = inOffset + ${i};\n if (${p===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${u}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${u}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${u}\n }\n setOutput(sumValue);\n }\n `}};var hh=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=t;this.outputShape=[n,a];let i=\"0.0\",p=\"\";e===\"prod\"?i=\"1.0\":e===\"min\"?(i=\"1.0 / 1e-20\",p=\"min\"):e===\"max\"&&(i=\"-1.0 / 1e-20\",p=\"max\");let u=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"sum\"?u=\"sumValue\":e===\"prod\"?u=\"prodValue\":e===\"all\"?u=\"allValue\":e===\"any\"&&(u=\"anyValue\");let c=Math.floor(o/4)*4,l=o%4,m=`\n if (${e===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${e===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${p}(values, minMaxValue);\n if (${e===\"min\"} || ${e===\"max\"}) {\n minMaxValue = ${p}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,d=\"vec4\";e===\"all\"?(i=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,d=\"bvec4\"):e===\"any\"&&(i=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `,d=\"bvec4\");let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${i};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n vec4 minMaxValue = vec4(${i});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===2}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===3}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function p9(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],o=w.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:o,outSize:Math.ceil(e/o)})}return t}function Xr(r,t,e,o){let n=p9(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],o=new Array(t);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Re(this.rank),s=mv(\"rc\",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes 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ne=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));re.push(ne),D.push(ne)}j=n.runWebGLProgram(J,re,Y)}let q=te({inputs:{x:j},backend:n,attrs:{shape:S}});D.push(j);for(let Y of D)n.disposeIntermediateTensorInfo(Y);return q}function l9(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Cp({a:n,b:s,transposeA:p,transposeB:u,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var YD={kernelName:So,backendName:\"webgl\",kernelFunc:l9};var QD=\"return abs(x);\";function m9(r){let{inputs:t,backend:e}=r,{x:o}=t;if(e.shouldExecuteOnCPU([o])&&o.dtype!==\"complex64\"){let s=e.texData.get(o.dataId),a=ph(s.values);return e.makeTensorInfo(o.shape,o.dtype,a)}let n;return A().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?n=new Ar(o.shape,QD):n=new tr(o.shape,QD),e.runWebGLProgram(n,[o],o.dtype)}var ZD={kernelName:Xs,backendName:\"webgl\",kernelFunc:m9};var d9=Wt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,f9=xe({opSnippet:d9}),JD={kernelName:Vo,backendName:\"webgl\",kernelFunc:f9};var h9=Wt+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,g9=xe({opSnippet:h9}),eA={kernelName:Wo,backendName:\"webgl\",kernelFunc:g9};var tA=\"return a + b;\",x9=nt({opSnippet:tA,packedOpSnippet:tA,supportsComplex:!0,cpuKernelImpl:DR}),rA={kernelName:io,backendName:\"webgl\",kernelFunc:x9};var yh=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n float result = ${n};\n setOutput(result);\n }\n `}};var bh=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n vec4 result = ${n};\n setOutput(result);\n }\n `}};function Ch(r){let{inputs:t,backend:e}=r,o=t;if(o.length===1)return Dt({inputs:{x:o[0]},backend:e});if(o.length>A().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let p=Math.floor(o.length/2),u=Ch({inputs:o.slice(0,p),backend:e}),c=Ch({inputs:o.slice(p),backend:e});return Ch({inputs:[u,c],backend:e})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=A().getBool(\"WEBGL_PACK\")?new bh(o[0].shape,s):new yh(o[0].shape,s);return e.runWebGLProgram(i,o,n)}var oA={kernelName:Uo,backendName:\"webgl\",kernelFunc:Ch};function y9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:e,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims(\"all\",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:e,attrs:{shape:[-1,f]}}),g=Xr(h,h.dtype,\"all\",e),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(l),x}var nA={kernelName:Go,backendName:\"webgl\",kernelFunc:y9};function b9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:e,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims(\"any\",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:e,attrs:{shape:[-1,f]}}),g=Xr(h,h.dtype,\"any\",e),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(l),x}var sA={kernelName:Ho,backendName:\"webgl\",kernelFunc:b9};var wh=class{constructor(t,e,o){this.variableNames=[\"A\"];let{windowSize:n,batchSize:s,outSize:a}=t;o||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,a];let i=e===\"max\"?\">\":\"<\",p=o?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${n}; i++) {\n int inIdx = ${p};\n float candidate = getA(batch, inIdx);\n if (candidate ${i} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var Sh=class{constructor(t,e,o,n){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],a=Math.ceil(s/e);this.outputShape=t.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,p=i.length,u=Re(p),c=Rt(\"coords\",p),l,m;if(a===1){m=p+1;let R=Re(m);l=`\n ${R} sourceLocR = ${R}(${c.join()}, 0);\n ++${c[p-1]};\n ${R} sourceLocG = ${R}(${c.join()}, 0);\n ++${c[p-2]};\n ${R} sourceLocA = ${R}(${c.join()}, 0);\n --${c[p-1]};\n ${R} sourceLocB = ${R}(${c.join()}, 0);\n --${c[p-2]};`}else m=p,l=`\n ${u} sourceLocR = coords;\n ++${c[p-1]};\n ${u} sourceLocG = coords;\n ++${c[p-2]};\n ${u} sourceLocA = coords;\n --${c[p-1]};\n ${u} sourceLocB = coords;\n --${c[p-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),f=\".\"+d[m-1],h=d.map(R=>\"int \"+R),g=Rt(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=Rt(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=Rt(\"sourceLocB\",m-1).concat(\"inIdx.b\"),C=Rt(\"sourceLocA\",m-1).concat(\"inIdx.a\"),S=o===\"max\"?\"greaterThan\":\"lessThan\",k=n?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${C.join()})));`,_=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? 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n=[e];if(w.assertAxesAreInnerMostDims(\"arg\"+o.charAt(0).toUpperCase()+o.slice(1),n,t.shape.length),!A().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let s=[],a=r.texData.get(t.dataId),i=a!==null&&a.isPacked,p=t;i&&(p=r.unpackTensor(t),s.push(p));let[u,c]=w.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=aA(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return iA(r,t,o)}function C9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=Ih(e,p,a[0],\"max\");return u.forEach(l=>e.disposeIntermediateTensorInfo(l)),c}var uA={kernelName:Ys,backendName:\"webgl\",kernelFunc:C9};function w9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=Ih(e,p,a[0],\"min\");return u.forEach(l=>e.disposeIntermediateTensorInfo(l)),c}var pA={kernelName:Qs,backendName:\"webgl\",kernelFunc:w9};var S9=Wt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,I9=xe({opSnippet:S9}),cA={kernelName:Ko,backendName:\"webgl\",kernelFunc:I9};var v9=Wt+\"return log(x + sqrt(x * x + 1.0));\",k9=xe({opSnippet:v9}),lA={kernelName:qo,backendName:\"webgl\",kernelFunc:k9};var N9=Wt+`\n return atan(x);\n`,T9=xe({opSnippet:N9}),mA={kernelName:jo,backendName:\"webgl\",kernelFunc:T9};var _9=Mc+`\n return atan(a, b);\n`,$9=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+jr+`\n return result;\n`,E9=nt({opSnippet:_9,packedOpSnippet:$9}),dA={kernelName:Yo,backendName:\"webgl\",kernelFunc:E9};var R9=Wt+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),fA={kernelName:Xo,backendName:\"webgl\",kernelFunc:D9};var Us=class{constructor(t,e,o,n=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=t.filterWidth,i=t.strideHeight,p=t.strideWidth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterHeight,m=t.effectiveFilterWidth,d=t.padInfo.top,f=t.padInfo.left;this.outputShape=t.outShape;let h=e===\"avg\",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),o){let R=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${R} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let C=\"max\",S=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(S=\"avgValue / max(count, 1.0)\");let k=Math.floor(a/4)*4,_=a%4,E=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${C}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${k}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${E}\n }\n\n int xC = xCCorner + ${k};\n if (${_===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${E}\n } else if (${_===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${E}\n } else if (${_===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${E}\n }\n }\n setOutput(${S});\n }\n `}},xu=class{constructor(t,e,o,n=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=t.filterWidth,i=t.strideDepth,p=t.strideHeight,u=t.strideWidth,c=t.dilationDepth,l=t.dilationHeight,m=t.dilationWidth,d=t.effectiveFilterDepth,f=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let C=e===\"avg\",S=\"0.0\";if(C||(S=\"-1.0 / 1e-20\"),o){let P=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${P} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${f} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let k=\"max\",_=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(_=\"avgValue / max(count, 1.0)\");let E=Math.floor(a/4)*4,R=a%4,D=`\n if (${C}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${k}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${S};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${S});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${E}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${D}\n }\n\n int xC = xCCorner + ${E};\n if (${R===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${D}\n }\n }\n }\n setOutput(${_});\n }\n `}};function A9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t;Vs(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:e});let l=new Us(c,\"avg\",!1);return e.runWebGLProgram(l,[n],\"float32\")}var hA={kernelName:Qo,backendName:\"webgl\",kernelFunc:A9};function F9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new xu(l,\"avg\",!1);return e.runWebGLProgram(m,[n],\"float32\")}var gA={kernelName:Zs,backendName:\"webgl\",kernelFunc:F9};var vh=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,p=t.effectiveFilterHeight,u=t.effectiveFilterWidth,c=p-1-t.padInfo.top,l=u-1-t.padInfo.left,m=1/(e*o);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${l});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${i}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},kh=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterDepth,o=t.filterHeight,n=t.filterWidth,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterDepth,m=t.effectiveFilterHeight,d=t.effectiveFilterWidth,f=l-1-t.padInfo.front,h=m-1-t.padInfo.top,g=d-1-t.padInfo.left,x=1/(e*o*n);this.userCode=`\n const ivec3 pads = ivec3(${f}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${l};\n wD += ${p}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${d};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function P9(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new kh(m);return e.runWebGLProgram(d,[n],a.dtype)}var xA={kernelName:Ei,backendName:\"webgl\",kernelFunc:P9};function O9(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;Vs([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new vh(c);return e.runWebGLProgram(l,[n],a.dtype)}var yA={kernelName:$i,backendName:\"webgl\",kernelFunc:O9};function M9(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s}=t,{transposeA:a,transposeB:i}=o;return Cp({a:n,b:s,transposeA:a,transposeB:i,backend:e})}var bA={kernelName:Zo,backendName:\"webgl\",kernelFunc:M9};var Nh=class{constructor(t,e,o,n,s,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],w.assertAndGetBroadcastShape(t,e),w.assertAndGetBroadcastShape(t,o);let i=\"0.0\";n!=null&&(w.assertAndGetBroadcastShape(t,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"1.0\";s!=null&&(w.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${i};\n float scale = ${p};\n float inv = scale * inversesqrt(variance + float(${a}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var Th=class{constructor(t,e,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],w.assertAndGetBroadcastShape(t,e),w.assertAndGetBroadcastShape(t,o);let i=\"vec4(0.0)\";n!=null&&(w.assertAndGetBroadcastShape(t,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"vec4(1.0)\";s!=null&&(w.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n vec4 offset = ${i};\n vec4 scale = ${p};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var L9=({inputs:r,backend:t,attrs:e})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||n.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(i==null||n.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:p}=e;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=A().getBool(\"WEBGL_PACK_NORMALIZATION\")?new Th(o.shape,n.shape,s.shape,c,l,p):new Nh(o.shape,n.shape,s.shape,c,l,p);return t.runWebGLProgram(m,u,u[0].dtype)},CA={kernelName:In,backendName:\"webgl\",kernelFunc:L9};var _h=class{constructor(t){this.variableNames=[\"source\"],this.outputShape=t,this.rank=t.length;let e=Re(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let o=B9(this.rank),n,s=t.map((a,i)=>`sourceLoc.${Cv[i]} = start[${i}] + coords.${Cv[i]};`);n=`\n ${e} sourceLoc;\n ${e} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${n}\n setOutput(getSource(${o}));\n }\n `}},Cv=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function B9(r){if(r===1)return\"sourceLoc\";if(r<=6)return Cv.slice(0,r).map(t=>\"sourceLoc.\"+t).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var $h=class{constructor(t){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let e=Re(this.rank),o=Rt(\"coords\",this.rank),n=Rt(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`\n result.x = ${a};\n if (++${o[this.rank-1]} < ${t[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.y = ${a};\n --${n[this.rank-1]};\n }\n `,p=this.rank===1?\"\":`\n --${o[this.rank-1]};\n if (++${o[this.rank-2]} < ${t[this.rank-2]}) {\n ++${n[this.rank-2]};\n result.z = ${a};\n if (++${o[this.rank-1]} < ${t[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.w = ${a};\n }\n }\n `,u=this.rank<=4?`sourceLoc = coords +\n ${e}(${t.map((c,l)=>`start[${l}]`).join()});`:t.map((c,l)=>`${n[l]} = ${o[l]} + start[${l}];`).join(`\n`);this.userCode=`\n void main() {\n ${e} coords = getOutputCoords();\n ${e} sourceLoc;\n ${u}\n vec4 result = vec4(0.);\n ${i}\n ${p}\n setOutput(result);\n }\n `}};function z9(r,t,e,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(e,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=e,a.dtype=r.dtype;let i=pt.computeFlatOffset(t,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function Gs(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,size:a}=o,[i,p]=pt.parseSliceParams(n,s,a);if(pt.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return e.makeTensorInfo(p,n.dtype,[]);if(e.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=e.texData.get(n.dataId),m=cD(l.values,i,p,n.shape,n.dtype);return e.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=e.texData.get(n.dataId),c=pt.isSliceContinous(n.shape,i,p);if(u||!c){let l=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new $h(p):new _h(p),m=[i];return e.runWebGLProgram(l,[n],n.dtype,m)}return e.uploadToGPU(n.dataId),z9(n,i,p,e)}var wA={kernelName:ha,backendName:\"webgl\",kernelFunc:Gs};var V9=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:e,attrs:{shape:p}}),h=bt({inputs:{x:f},backend:e,attrs:{perm:u}}),g=te({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Gs({inputs:{x:g},backend:e,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},SA={kernelName:Js,backendName:\"webgl\",kernelFunc:V9};function W9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=e.readSync(n.dataId),p=e.readSync(s.dataId),u=uh(i,p,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,u)}var IA={kernelName:Jo,backendName:\"webgl\",kernelFunc:W9};var U9=`\n int r = int(a.r) & int(b.r);\n int g = int(a.g) & int(b.g);\n int rb = int(a.b) & int(b.b);\n int ra = int(a.a) & int(b.a);\n return vec4(r, g, rb, ra);\n`,G9=`\n return float(int(a.r) & int(b.r));\n`;function H9(r){let{inputs:t,backend:e}=r,{a:o,b:n}=t,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"),a=A().getNumber(\"WEBGL_VERSION\");if(e.shouldExecuteOnCPU([o,n])||a===1){let p=e.texData.get(o.dataId).values,u=e.texData.get(n.dataId).values,[c,l]=FR(o.shape,n.shape,p,u,o.dtype),m=e.makeTensorInfo(l,o.dtype),d=e.texData.get(m.dataId);return d.values=c,m}let i;return s?i=new qr(U9,o.shape,n.shape,!1):i=new Fr(G9,o.shape,n.shape),e.runWebGLProgram(i,[o,n],o.dtype)}var vA={kernelName:qa,backendName:\"webgl\",kernelFunc:H9};function K9(r){let{inputs:t,backend:e}=r,{s0:o,s1:n}=t,s=e.readSync(o.dataId),a=e.readSync(n.dataId),i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return e.makeTensorInfo([i.length],\"int32\",Int32Array.from(i))}var kA={kernelName:ea,backendName:\"webgl\",kernelFunc:K9};var q9=\"return float(a != b);\",wv=nt({opSnippet:q9,cpuKernelImpl:tD,dtype:\"bool\"}),NA={kernelName:Yn,backendName:\"webgl\",kernelFunc:wv};function yi(r){let{inputs:t,backend:e}=r,{input:o}=t,n=e.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.real},backend:e})}var TA={kernelName:Gi,backendName:\"webgl\",kernelFunc:yi};var j9=\"return float(int(x));\";function _A(r,t){let e=new tr(r.shape,j9),o=t.runWebGLProgram(e,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Sv(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return Dt({inputs:{x:n},backend:e});let a=Ur(n.shape),i=Sv({inputs:{x:n},backend:e,attrs:{dtype:\"float32\"}}),p=Pr({inputs:{real:i,imag:a},backend:e});return a.dispose(),e.disposeIntermediateTensorInfo(i),p}if(n.dtype===\"complex64\"){let a=yi({inputs:{input:n},backend:e}),i=Sv({inputs:{x:a},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Dt({inputs:{x:n},backend:e});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(e.shouldExecuteOnCPU([n])){let a=e.texData.get(n.dataId).values,[i,p,u]=PR(a,n.shape,n.dtype,s);return e.makeTensorInfo(i,p,u)}if(s===\"int32\")return _A(n,e);if(s===\"bool\"){let a=e.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=wv({inputs:{a:n,b:a},backend:e});return e.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var $A={kernelName:yo,backendName:\"webgl\",kernelFunc:Sv};var EA=\"return ceil(x);\",X9=xe({opSnippet:EA,packedOpSnippet:EA,cpuKernelImpl:OR}),RA={kernelName:en,backendName:\"webgl\",kernelFunc:X9};var Eh=class{constructor(t){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var Rh=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function Y9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{clipValueMin:s,clipValueMax:a}=o,i;A().getBool(\"WEBGL_PACK_CLIP\")?i=new Rh(n.shape):i=new Eh(n.shape);let p=[[s],[a]];return e.runWebGLProgram(i,[n],n.dtype,p)}var DA={kernelName:bo,backendName:\"webgl\",kernelFunc:Y9};var Dh=class{constructor(t){this.variableNames=[\"real\",\"imag\"],this.outputShape=t,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). 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1;\n if (${a[n-2]} < ${o[n-2]} &&\n ${a[n-1]} < ${o[n-1]}) {\n result.b = getValue(${a});\n }\n setOutput(result);\n }\n `}};function Fh(r,t,e){let o=r.indexOf(t);return r.map((s,a)=>a===o?`${s} - ${e}`:s).join()}function wp(r){let{inputs:t,backend:e}=r,{input:o}=t,n=e.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.imag},backend:e})}var PA={kernelName:Vi,backendName:\"webgl\",kernelFunc:wp};function Bc(r,t,e){let o=r[0].dtype;if(o===\"complex64\"){let d=r.map(b=>yi({inputs:{input:b},backend:e})),f=r.map(b=>wp({inputs:{input:b},backend:e})),h=Bc(d,t,e),g=Bc(f,t,e),x=Pr({inputs:{real:h,imag:g},backend:e});return d.forEach(b=>e.disposeIntermediateTensorInfo(b)),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let n=e.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let d=r.map(S=>{let _=[-1,y.sizeFromShape(S.shape.slice(t))];return te({inputs:{x:S},backend:e,attrs:{shape:_}})}),f=d.map(S=>({vals:e.readSync(S.dataId),shape:S.shape})),h=w.computeOutShape(d.map(S=>S.shape),1),g=d[0].shape[0]===1,x=MR(f,h,o,g),b=w.computeOutShape(r.map(S=>S.shape),t),C=e.makeTensorInfo(b,o,x);return d.forEach(S=>e.disposeIntermediateTensorInfo(S)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let d=a?new tr(r[0].shape,La):new Ar(r[0].shape,La);return e.runWebGLProgram(d,r,o)}let i=A().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>i){let d=[];for(let h=0;hf.shape),t);return e.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=Z9(s,t,e),c=new Ah(p.map(d=>d.shape)),l=e.runWebGLProgram(c,p,o);p.forEach(d=>e.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:l},attrs:{shape:u},backend:e});return e.disposeIntermediateTensorInfo(l),m}function Z9(r,t,e){let o=w.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:o}}function Iv(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,t[0].shape)[0],a=t.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(t.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let p=t.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Dt({inputs:{x:p[0]},backend:e}):Bc(p,s,e)}var OA={kernelName:ta,backendName:\"webgl\",kernelFunc:Iv};var zc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let a=t.padInfo.top,i=t.padInfo.left,p=t.strideHeight,u=t.strideWidth,c=t.dilationHeight,l=t.dilationWidth,m=t.filterHeight,d=t.filterWidth,f=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,C=g?3:1,S=\"\",k=\"\";o&&(n?S=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?S=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:S=`\n float activation(float x) {\n ${o}\n }\n `,k=\"result = activation(result);\");let _=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${S}\n\n const ivec2 strides = ivec2(${p}, ${u});\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${C}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${f}) *\n getW(wR, wC, ${f}, d2);\n } else {\n dotProd +=\n getX(batch, ${f}, xR, xC) *\n getW(wR, wC, ${f}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2),\n getW(wR, wC, ${f} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1),\n getX(batch, xR, xC, ${f} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC),\n getX(batch, ${f} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${k}\n setOutput(result);\n }\n `}},Oh=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let e=t.padInfo.front,o=t.padInfo.top,n=t.padInfo.left,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.filterDepth,m=t.filterHeight,d=t.filterWidth,f=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${a}, ${i});\n const ivec3 pads = ivec3(${e}, ${o}, ${n});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${l}; wF++) {\n int xF = xFCorner + wF * ${p};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${f}) *\n getW(wF, wR, wC, ${f}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1),\n getX(batch, xF, xR, xC, ${f} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2),\n getW(wF, wR, wC, ${f} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var Vc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=t.padInfo.left,i=t.strideWidth,p=t.dilationWidth,u=t.filterHeight,c=t.filterWidth,l=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*p};\n `,i===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,p===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,p>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${i}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+l}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+l}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${n.output} = result;\n }\n `}};function Lh(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function Bh({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=e.inChannels,l=p[0]*p[1]*p[2],m=e.outChannels,d=e.dataFormat===\"channelsLast\",f=!1,h=!1,g,x=[];if(s!=null){let S=Lh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Lh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((l===1||m===1)&&c>bv)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let S=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,S,e.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(hu(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let E=te({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(E);let R=Cp({a:k,b:E,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),D=o.texData.get(R.dataId);y.assert(D.isPacked,()=>\"batchMatMul result is expected to be packed\"),u.shape=_,D.shape=e.outShape,g=Dt({inputs:{x:R},backend:o}),g.shape=e.outShape,x.push(R)}else{let S=e.outHeight*e.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[e.batchSize,S,e.inChannels]:[e.batchSize,e.inChannels,S]}}),_=te({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}}),E=Cp({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:E},backend:o,attrs:{shape:e.outShape}}),x.push(k),x.push(_),x.push(E)}for(let S of x)o.disposeIntermediateTensorInfo(S);return g}function zh({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=e,f=d===\"channelsLast\",h=p*u*c,g=m*l,x=[e.batchSize,h,g],b=!0,C=!1,S=[];if(s!=null){let q=Lh(s.shape,f);q!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:q}}),S.push(s))}if(n!=null){let q=Lh(n.shape,f);q!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:q}}),S.push(n))}let k=te({inputs:{x:t},backend:o,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});S.push(k);let _=new Mh(x,e),E=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],R=o.runWebGLProgram(_,[r],\"float32\",E),D=te({inputs:{x:R},backend:o,attrs:{shape:x}});S.push(R),S.push(D);let P=n!=null,O=s!=null,M=i===\"leakyrelu\",L=i?xi(i,!0):null,B=new Lc(f?D.shape:k.shape,f?k.shape:D.shape,f?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,C,P,L,O,M),z=f?[D,k]:[k,D];if(n&&z.push(n),O&&z.push(s),M){let q=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));z.push(q),S.push(q)}let U=o.runWebGLProgram(B,z,\"float32\"),j=te({inputs:{x:U},backend:o,attrs:{shape:e.outShape}});S.push(U);for(let q of S)o.disposeIntermediateTensorInfo(q);return j}function J9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))d=Bh({x:n,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&l===\"channelsLast\"&&A().getBool(\"WEBGL_EXP_CONV\")){let h=new Vc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=e.runWebGLProgram(h,[n,s],\"float32\",g)}else if(A().getBool(\"WEBGL_CONV_IM2COL\"))d=zh({x:n,filter:s,convInfo:m,backend:e});else{let h=new zc(m);d=e.runWebGLProgram(h,[n,s],\"float32\")}let f=te({inputs:{x:d},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(d),f}var MA={kernelName:tn,backendName:\"webgl\",kernelFunc:J9};var Vh=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,o=t.strideWidth,n=t.padInfo.top,s=t.padInfo.left,a=t.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${n};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n ${a?`float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Wh=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=t.dataFormat===\"channelsLast\",i=e-1-t.padInfo.top,p=o-1-t.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${l}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n\n if (${a}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Uh=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideDepth,o=t.strideHeight,n=t.strideWidth,s=t.padInfo.front,a=t.padInfo.top,i=t.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yF = 0; yF < ${t.outDepth}; yF++) {\n int xF = wF + yF * ${e} - ${s};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${o} - ${a};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${i};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Gh=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterDepth,o=t.filterHeight,n=t.filterWidth,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=e-1-t.padInfo.front,u=o-1-t.padInfo.top,c=n-1-t.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${u}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${e}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${e} - 1 - wF;\n\n for (int wR = 0; wR < ${o}; wR++) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${o} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function eJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Vh(m);return e.runWebGLProgram(d,[n,s],\"float32\")}var LA={kernelName:Ai,backendName:\"webgl\",kernelFunc:eJ};var Hh=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=t.inShape,this.enableShapeUniforms=ut(this.outputShape.length);let e=t.filterHeight,o=t.filterWidth,n=e-1-t.padInfo.top,s=o-1-t.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${n}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n vec4 result = vec4(0.);\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n int wCPerm = ${o} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${t.outWidth}.0)\n && (fract(dyC) == 0.0);\n int idyC = int(dyC);\n\n float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${t.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n dySample : getDy(batch, idyR, idyC2, d2);\n\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n\n dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample2.xy : dySample2.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC2, d2);\n vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n }\n }\n }\n setOutput(result);\n }\n `}};function tJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l);if(A().getBool(\"WEBGL_PACK_CONV2DTRANSPOSE\")&&l===\"channelsLast\"){let d=[[m.strideHeight,m.strideWidth]],f=new Hh(m);return e.runWebGLProgram(f,[n,s],\"float32\",d)}else{let d=new Wh(m);return e.runWebGLProgram(d,[n,s],\"float32\")}}var BA={kernelName:rn,backendName:\"webgl\",kernelFunc:tJ};function rJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Oh(u);return e.runWebGLProgram(c,[n,s],\"float32\")}var zA={kernelName:on,backendName:\"webgl\",kernelFunc:rJ};function oJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Uh(u);return e.runWebGLProgram(c,[n,s],\"float32\")}var VA={kernelName:ja,backendName:\"webgl\",kernelFunc:oJ};function nJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{pad:a,strides:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,i,1,a),c=new Gh(u);return e.runWebGLProgram(c,[n,s],\"float32\")}var WA={kernelName:nn,backendName:\"webgl\",kernelFunc:nJ};var sJ=Fo+`\n return cos(x);\n`,aJ=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${jr}\n return result;\n`,iJ=xe({opSnippet:sJ,packedOpSnippet:aJ}),UA={kernelName:sn,backendName:\"webgl\",kernelFunc:iJ};var uJ=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,pJ=xe({opSnippet:uJ}),GA={kernelName:an,backendName:\"webgl\",kernelFunc:pJ};var Kh=class{constructor(t,e,o,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,p,u]=t,[c]=e,[l,m]=o;this.outputShape=[c,l,m,u];let d=n===\"bilinear\"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,\"(y2-y1) * height_ratio\",`y1*${f} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${f}`],[C,S,k]=m>1?[`${(p-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${C});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${a}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${S};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${f} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${k};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${d} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var cJ=r=>{let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Kh(n.shape,s.shape,i,p,u);return e.runWebGLProgram(c,[n,s,a],\"float32\")},HA={kernelName:cn,backendName:\"webgl\",kernelFunc:cJ};var Sp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Sp||(Sp={}));var tm=class{constructor(t,e,o,n){this.op=t,this.outputShape=e,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,a=this.op===Sp.Prod?\"1.0\":\"0.0\",i=o?a:`getX(${KA(s,\"coords\",this.op)})`,p=this.outputShape[this.outputShape.length-1],u=\"\",c=\"\";o?(u=n?`end != ${p-1}`:\"end != 0\",c=n?\"end + 1\":\"end - 1\"):(u=n?`end + pow2 < ${p}`:\"end >= pow2\",c=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${Re(s)} coords = getOutputCoords();\n int end = ${qA(s,\"coords\",this.op)};\n float val = ${i};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${c};\n ${qA(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${KA(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function KA(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function qA(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function qh(r,t,e,o,n,s){let a=t.shape.length,i=w.getAxesPermutation([o],a),p=t;i!=null&&(p=bt({inputs:{x:t},backend:e,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new tm(r,p.shape,!1,s),f=[[m]],h=l;l=e.runWebGLProgram(d,[l],l.dtype,f),e.disposeIntermediateTensorInfo(h)}if(n){let m=new tm(r,p.shape,n,s),d=l;l=e.runWebGLProgram(m,[l],l.dtype),e.disposeIntermediateTensorInfo(d)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=bt({inputs:{x:l},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(p),d}return l}function lJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return qh(Sp.Prod,n,e,s,a,i)}var jA={kernelName:un,backendName:\"webgl\",kernelFunc:lJ};function mJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return qh(Sp.Sum,n,e,s,a,i)}var XA={kernelName:pn,backendName:\"webgl\",kernelFunc:mJ};function dJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=e.readSync(n.dataId),u=e.readSync(s.dataId),c=uh(p,u,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=e.bufferSync(n),u=e.bufferSync(s),c=AR(p,u,a,i);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var YA={kernelName:ra,backendName:\"webgl\",kernelFunc:dJ};var jh=class{constructor(t,e,o){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=o,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${e};\n int offset_h = imod(h, ${e});\n int in_w = w / ${e};\n int offset_w = imod(w, ${e});\n int offset_d = (offset_h * ${e} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function fJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=new jh(f,s,a);return e.runWebGLProgram(h,[n],n.dtype)}var QA={kernelName:ln,backendName:\"webgl\",kernelFunc:fJ};var Wc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=t.filterHeight,i=t.filterWidth,p=t.outChannels/t.inChannels,u=\"\",c=\"\";o&&(n?u=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?u=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:u=`\n float activation(float x) {\n ${o}\n }\n `,c=\"result = activation(result);\");let l=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${p};\n int q = d2 - d1 * ${p};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${a}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${i}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${l}\n ${c}\n setOutput(result);\n }\n `}};var Uc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=t.outChannels/t.inChannels,i=t.padInfo.left,p=t.strideWidth,u=t.dilationWidth,c=t.filterHeight,l=t.filterWidth,m=l,d=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=`\n xC = xCCorner + ${b*u};\n `,p===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,u===1&&b>0?d+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:d+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,u>1?d+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:d+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):C===1?d+=`\n xC${b+1} = xTexelC${b};\n `:d+=`\n xCOffset = xC + ${C};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=w.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;A().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new Uc(l):m=new Wc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return e.runWebGLProgram(m,[n,s],\"float32\",d)}var ZA={kernelName:mn,backendName:\"webgl\",kernelFunc:hJ};var Xh=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,o=t.strideWidth,n=t.padInfo.top,s=t.padInfo.left,a=t.outChannels/t.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${a} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${n};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Yh=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=e-1-t.padInfo.top,i=o-1-t.padInfo.left,p=t.outChannels/t.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${p}; dm++) {\n int d2 = d1 * ${p} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function gJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Xh(l);return e.runWebGLProgram(m,[n,s],\"float32\")}var JA={kernelName:Fi,backendName:\"webgl\",kernelFunc:gJ};function xJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Yh(l);return e.runWebGLProgram(m,[n,s],\"float32\")}var eF={kernelName:Pi,backendName:\"webgl\",kernelFunc:xJ};var Qh=class{constructor(t){this.variableNames=[\"X\"],this.outputShape=[t,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function yJ(r){let{inputs:t,backend:e}=r,{x:o}=t,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:e,attrs:{shape:[s]}}),i=new Qh(s),p=e.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:e,attrs:{shape:n}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(p),u}var tF={kernelName:oa,backendName:\"webgl\",kernelFunc:yJ};var Zh=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let{inHeight:e,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=t,{top:l,left:m}=n;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${a});\n const ivec2 pads = ivec2(${l}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${i}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${e}) {\n for (int w = 0; w < ${p}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${o}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function bJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c,l=new Zh(u);c=e.runWebGLProgram(l,[n,s],\"float32\");let m=te({inputs:{x:c},backend:e,attrs:{shape:u.outShape}});return e.disposeIntermediateTensorInfo(c),m}var rF={kernelName:dn,backendName:\"webgl\",kernelFunc:bJ};function CJ(r){let{inputs:t,backend:e,attrs:o}=r,{equation:n}=o,s=t,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=bp({inputs:{x:m},backend:e,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var oF={kernelName:Li,backendName:\"webgl\",kernelFunc:CJ};var wJ=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",SJ=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,IJ=xe({opSnippet:wJ,packedOpSnippet:SJ}),nF={kernelName:hn,backendName:\"webgl\",kernelFunc:IJ};var vJ=\"return (b >= 0.0) ? a : a * (b + 1.0);\",kJ=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,NJ=r=>{let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new qr(kJ,o.shape,n.shape):new Fr(vJ,o.shape,n.shape);return e.runWebGLProgram(s,[o,n],o.dtype)},sF={kernelName:Xa,backendName:\"webgl\",kernelFunc:NJ};var TJ=`\n return vec4(equal(a, b));\n`,_J=\"return float(a == b);\",$J=nt({opSnippet:_J,packedOpSnippet:TJ,dtype:\"bool\",cpuKernelImpl:LR}),aF={kernelName:xn,backendName:\"webgl\",kernelFunc:$J};var EJ=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${w.ERF_P};\n float a1 = ${w.ERF_A1};\n float a2 = ${w.ERF_A2};\n float a3 = ${w.ERF_A3};\n float a4 = ${w.ERF_A4};\n float a5 = ${w.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,RJ=xe({opSnippet:EJ}),iF={kernelName:gn,backendName:\"webgl\",kernelFunc:RJ};var DJ=Fo+`\n return exp(x);\n`,AJ=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,vv=xe({opSnippet:DJ,packedOpSnippet:AJ,cpuKernelImpl:BR,dtype:\"float32\"}),uF={kernelName:yn,backendName:\"webgl\",kernelFunc:vv};function Jh(r){let{inputs:t,attrs:e,backend:o}=r,{dim:n}=e,{input:s}=t,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var pF={kernelName:na,backendName:\"webgl\",kernelFunc:Jh};var cF=\"return exp(x) - 1.0;\",FJ=xe({opSnippet:cF,packedOpSnippet:cF,cpuKernelImpl:zR}),lF={kernelName:bn,backendName:\"webgl\",kernelFunc:FJ};var rm=class{constructor(t,e,o){this.variableNames=[\"real\",\"imag\"];let n=e[1];this.outputShape=e;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:\"1.0\",i;if(t===\"real\")i=\"return real * expR - imag * expI;\";else if(t===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${t}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${i}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${n});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${n}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${a};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function eg(r,t,e){let o=e.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:e,attrs:{shape:[a,s]}}),p=i.shape,u=new rm(\"real\",p,t),c=new rm(\"imag\",p,t),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=e.runWebGLProgram(u,l,\"float32\"),d=e.runWebGLProgram(c,l,\"float32\"),f=Pr({inputs:{real:m,imag:d},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(f),h}function PJ(r){let{inputs:t,backend:e}=r,{input:o}=t;return eg(o,!1,e)}var mF={kernelName:Bi,backendName:\"webgl\",kernelFunc:PJ};var tg=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=t,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function bi(r){let{backend:t,attrs:e}=r,{shape:o,value:n}=e,{dtype:s}=e;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),t.makeTensorInfo(o,s,a)}else{let a=new tg(o,n),i=[[n]];return t.runWebGLProgram(a,[],s,i)}}var dF={kernelName:sa,backendName:\"webgl\",kernelFunc:bi};var rg=class{constructor(t){this.variableNames=[\"Image\"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${e} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${e}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var fF={kernelName:Cn,backendName:\"webgl\",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,o=t,n=new rg(e.shape);return o.runWebGLProgram(n,[e],e.dtype)}};var hF=\"return floor(x);\",OJ=xe({opSnippet:hF,packedOpSnippet:hF,cpuKernelImpl:VR}),gF={kernelName:wn,backendName:\"webgl\",kernelFunc:OJ};var MJ=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,LJ=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,BJ=nt({opSnippet:MJ,packedOpSnippet:LJ,dtype:\"int32\"}),xF={kernelName:Sn,backendName:\"webgl\",kernelFunc:BJ};var og=class{constructor(t){this.variableNames=[\"A\"];let e=It(),[o,n]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);\n\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var ng=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let e=It(),[o,n]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${n}.0, ${o}.0);\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${e.output} = result;\n }\n `}};var yF={kernelName:Eu,backendName:\"webgl\",kernelFunc:zJ},Gc,kv=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function zJ(r){let{inputs:t,backend:e,attrs:o}=r,{pixels:n}=t,{numChannels:s}=o,a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Gc==null||h!==kv)&&(kv=h,Gc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:kv})),Gc.canvas.width=p,Gc.canvas.height=u,Gc.drawImage(n,0,0,p,u),n=Gc.canvas}let m=e.makeTensorInfo(c,\"int32\");e.texData.get(m.dataId).usage=mr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),n);let d=A().getBool(\"WEBGL_PACK\")?new ng(l):new og(l),f=e.runWebGLProgram(d,[m],\"int32\");return e.disposeData(m.dataId),f}function VJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,S=i!=null,k=d===\"leakyrelu\",_=()=>{let R=[n,s],D=(P,O)=>{if(O===\"NCHW\"&&P.shape.length===1&&P.shape[0]!==1){let M=te({inputs:{x:P},backend:e,attrs:{shape:[P.shape[0],1,1]}});return b.push(M),M}return P};if(C&&R.push(D(a,c)),S&&R.push(D(i,c)),k){let P=e.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));R.push(P),b.push(P)}return R};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=Bh({x:n,filter:s,convInfo:g,backend:e,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h===\"channelsLast\"&&A().getBool(\"WEBGL_EXP_CONV\")){let R=d?xi(d,!0):null,D=new Vc(g,C,R,S,k),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],O=_();x=e.runWebGLProgram(D,O,\"float32\",P)}else if(A().getBool(\"WEBGL_CONV_IM2COL\"))x=zh({x:n,filter:s,convInfo:g,backend:e,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let R=d?xi(d,!1):null,D=new zc(g,C,R,S,k),P=_();x=e.runWebGLProgram(D,P,\"float32\")}let E=te({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach(R=>e.disposeIntermediateTensorInfo(R)),E}var bF={kernelName:Io,backendName:\"webgl\",kernelFunc:VJ};function WJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${h}'`);let g=w.computeConv2DInfo(n.shape,s.shape,p,h,u,l,!0),x=A().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?xi(m,x):null,C=[n,s],S=a!=null,k=i!=null,_=m===\"leakyrelu\";if(S&&C.push(a),k&&C.push(i),_){let P=e.makeTensorInfo([],\"float32\",y.createScalarValue(d,\"float32\"));C.push(P),f.push(P)}let E;x?E=new Uc(g,S,b,k,_):E=new Wc(g,S,b,k,_);let R=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],D=e.runWebGLProgram(E,C,\"float32\",R);return f.forEach(P=>e.disposeIntermediateTensorInfo(P)),D}var CF={kernelName:vo,backendName:\"webgl\",kernelFunc:WJ};var sg=class{constructor(t,e,o,n){this.sliceDim=t,this.strides=e,this.paramsShape=n,this.variableNames=[\"x\",\"indices\"],this.outputShape=o;let s=Re(o.length),a=`\n int index;`;for(let i=0;i= ${this.paramsShape[i]};\n flattenIndex += index * ${this.strides[i]};`;this.userCode=`\n void main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${a}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function UJ(r){let{inputs:t,backend:e}=r,{params:o,indices:n}=t,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=te({inputs:{x:n},backend:e,attrs:{shape:[u,a]}}),d=te({inputs:{x:o},backend:e,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(e.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let x=e.readSync(n.dataId),b=e.bufferSync(o),C=WR(x,b,o.dtype,u,a,c,l,o.shape,i);return e.makeTensorInfo(p,o.dtype,C.values)}let f=new sg(a,l,[u,c],o.shape),h=e.runWebGLProgram(f,[d,m],d.dtype),g=te({inputs:{x:h},backend:e,attrs:{shape:p}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var wF={kernelName:vn,backendName:\"webgl\",kernelFunc:UJ};var ag=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.outputShape=e,this.rank=e.length;let o=Re(this.rank),n=GJ(t,2);this.userCode=`\n void main() {\n ${o} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${n}));\n }\n `}};function GJ(r,t){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=0,()=>`GatherV2: the index value ${k} is not in [0, ${C-1}]`)}}let u=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=te({inputs:{x:n},backend:e,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=te({inputs:{x:s},backend:e,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(e.shouldExecuteOnCPU([n,s])||n.dtype===\"string\"){let b=e.bufferSync(d),C=e.bufferSync(m),S=UR(C,b,f);return l.forEach(k=>e.disposeIntermediateTensorInfo(k)),e.makeTensorInfo(u.outputShape,S.dtype,S.values)}let h=new ag(m.shape,f),g=e.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=te({inputs:{x:g},backend:e,attrs:{shape:u.outputShape}});return l.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var SF={kernelName:aa,backendName:\"webgl\",kernelFunc:Nv};var HJ=\"return float(a > b);\",KJ=`\n return vec4(greaterThan(a, b));\n`,qJ=nt({opSnippet:HJ,packedOpSnippet:KJ,cpuKernelImpl:GR,dtype:\"bool\"}),IF={kernelName:kn,backendName:\"webgl\",kernelFunc:qJ};var jJ=\"return float(a >= b);\",XJ=`\n return vec4(greaterThanEqual(a, b));\n`,YJ=nt({opSnippet:jJ,packedOpSnippet:XJ,dtype:\"bool\",cpuKernelImpl:HR}),vF={kernelName:Nn,backendName:\"webgl\",kernelFunc:YJ};function QJ(r){let{inputs:t,backend:e}=r,{input:o}=t;return eg(o,!0,e)}var kF={kernelName:zi,backendName:\"webgl\",kernelFunc:QJ};var ZJ=\"return float(!isnan(x) && !isinf(x));\",JJ=xe({opSnippet:ZJ,dtype:\"bool\"}),NF={kernelName:Tn,backendName:\"webgl\",kernelFunc:JJ};var eee=\"return float(isinf(x));\",tee=xe({opSnippet:eee,dtype:\"bool\"}),TF={kernelName:_n,backendName:\"webgl\",kernelFunc:tee};var ree=\"return float(isnan(x));\",oee=xe({opSnippet:ree,dtype:\"bool\"}),_F={kernelName:$n,backendName:\"webgl\",kernelFunc:oee};var nee=\"return float(a < b);\",see=`\n return vec4(lessThan(a, b));\n`,aee=nt({opSnippet:nee,packedOpSnippet:see,cpuKernelImpl:KR,dtype:\"bool\"}),$F={kernelName:Rn,backendName:\"webgl\",kernelFunc:aee};var iee=\"return float(a <= b);\",uee=`\n return vec4(lessThanEqual(a, b));\n`,pee=nt({opSnippet:iee,packedOpSnippet:uee,cpuKernelImpl:qR,dtype:\"bool\"}),EF={kernelName:Dn,backendName:\"webgl\",kernelFunc:pee};function cee(r){let{backend:t,attrs:e}=r,{start:o,stop:n,num:s}=e,a=jR(o,n,s);return t.makeTensorInfo([a.length],\"float32\",a)}var RF={kernelName:An,backendName:\"webgl\",kernelFunc:cee};var lee=Fo+`\n return x < 0.0 ? 0./0. : log(x);\n`,mee=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,dee=xe({opSnippet:lee,packedOpSnippet:mee,cpuKernelImpl:XR}),DF={kernelName:Fn,backendName:\"webgl\",kernelFunc:dee};var fee=Fo+`\n return log(1.0 + x);\n`,hee=xe({opSnippet:fee}),AF={kernelName:Pn,backendName:\"webgl\",kernelFunc:hee};var gee=\"return float(a >= 1.0 && b >= 1.0);\",xee=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,yee=nt({opSnippet:gee,packedOpSnippet:xee,dtype:\"bool\"}),FF={kernelName:On,backendName:\"webgl\",kernelFunc:yee};var bee=\"return float(!(x >= 1.0));\",Cee=xe({opSnippet:bee}),PF={kernelName:Mn,backendName:\"webgl\",kernelFunc:Cee};var wee=\"return float(a >= 1.0 || b >= 1.0);\",See=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,Iee=nt({opSnippet:wee,packedOpSnippet:See,dtype:\"bool\"}),OF={kernelName:Ln,backendName:\"webgl\",kernelFunc:Iee};var ig=class{constructor(t,e,o,n,s){this.variableNames=[\"x\"],this.outputShape=[];let a=e,i=t[3]-1;this.outputShape=t;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${a}; j <= ${a}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${i}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${p};\n setOutput(val);\n }\n `}};var ug=class{constructor(t,e,o,n,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=e,i=t[3]-1;this.outputShape=t;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${a};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${a}; j <= ${a}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${p};\n setOutput(result);\n }\n `}};var vee=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=A().getBool(\"WEBGL_PACK_NORMALIZATION\")?new ug(n.shape,s,a,i,p):new ig(n.shape,s,a,i,p);return e.runWebGLProgram(u,[n],n.dtype)},MF={kernelName:Bn,backendName:\"webgl\",kernelFunc:vee};var pg=class{constructor(t,e,o,n,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${e})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${e} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${n}) * norm + float(${o});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${n})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var kee=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new pg(n.shape,i,p,u,c);return e.runWebGLProgram(l,[n,s,a],n.dtype)},LF={kernelName:Ya,backendName:\"webgl\",kernelFunc:kee};function BF(r,t,e,o){let n=y.sizeFromShape(t),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Xr(i,r.dtype,\"max\",o),u=te({inputs:{x:p},attrs:{shape:e},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function Tv(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=e.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=e.texData.get(d.dataId).values,S=new Array(i);for(let E=0;E`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:e});let l=new Us(c,\"max\",!1);return e.runWebGLProgram(l,[n],n.dtype)}var WF={kernelName:Wn,backendName:\"webgl\",kernelFunc:$ee};function Eee(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new xu(l,\"max\",!1);return e.runWebGLProgram(m,[n],n.dtype)}var UF={kernelName:ia,backendName:\"webgl\",kernelFunc:Eee};var cg=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideHeight,o=t.strideWidth,n=t.dilationHeight,s=t.effectiveFilterHeight,a=t.effectiveFilterWidth,i=s-1-t.padInfo.top,p=a-1-t.padInfo.left,u=s*a-1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${n}) {\n float dyR = float(dyRCorner + wR) / ${e}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${a}; wC++) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${a} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},lg=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideDepth,o=t.strideHeight,n=t.strideWidth,s=t.dilationDepth,a=t.dilationHeight,i=t.dilationWidth,p=t.effectiveFilterDepth,u=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=p-1-t.padInfo.front,m=u-1-t.padInfo.top,d=c-1-t.padInfo.left,f=p*u*c-1;this.userCode=`\n const ivec3 pads = ivec3(${l}, ${m}, ${d});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${e}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${i}) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${f} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Ree(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new xu(m,\"max\",!0),f=e.runWebGLProgram(d,[a],a.dtype),h=new lg(m),g=e.runWebGLProgram(h,[n,f],a.dtype);return e.disposeIntermediateTensorInfo(f),g}var GF={kernelName:Ui,backendName:\"webgl\",kernelFunc:Ree};function Dee(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s,output:a}=t,i=s;Vs([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new Us(m,\"max\",d),h=e.runWebGLProgram(f,[i],i.dtype),g=new cg(m),x=e.runWebGLProgram(g,[n,h],i.dtype);return e.disposeIntermediateTensorInfo(h),x}var HF={kernelName:Wi,backendName:\"webgl\",kernelFunc:Dee};function KF(r,t,e,o){let n=new Us(e,\"max\",!1),s=o.runWebGLProgram(n,[r],\"float32\");n=new Us(e,\"max\",!0,!0,t);let a=o.runWebGLProgram(n,[r],\"float32\");return[s,a]}var qF={kernelName:ua,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=t,p=e;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(o.shape,n,s,u,a),[l,m]=KF(o,i,c,p);return[l,m]}};function jF(r,t,e,o){let n=y.sizeFromShape(t),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Xr(i,\"float32\",\"mean\",o),u=te({inputs:{x:p},attrs:{shape:e},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}var XF={kernelName:Un,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o}=r,{keepDims:n,axis:s}=t,a=e,i=o.shape.length,p=y.parseAxisParam(s,o.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=a.shouldExecuteOnCPU([o]),d=[],f=o;if(l){if(m){let S=a.texData.get(f.dataId).values,k=new Array(i);for(let R=0;Rc[0]+t[l]+c[1]);let n=t.length,s=Re(n),a=e.map(c=>c[0]).join(\",\"),i=e.map((c,l)=>c[0]+t[l]).join(\",\"),p=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,n),u=o===\"reflect\"?0:1;if(n===1){this.userCode=`\n int start = ${a};\n int end = ${i};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start) {\n outC = start * 2 - outC - ${u};\n } else if(outC >= end) {\n outC = (end - 1) * 2 - outC + ${u};\n }\n setOutput(getX(outC - start));\n }\n `;return}this.userCode=`\n ${s} start = ${s}(${a});\n ${s} end = ${s}(${i});\n\n void main() {\n ${s} outC = getOutputCoords();\n for (int i = 0; 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hg(u,a,i,p),l=te({inputs:{x:n},backend:e,attrs:{shape:[u]}}),m=e.runWebGLProgram(c,[l],s);e.disposeIntermediateTensorInfo(l);let d=[...n.shape,a],f=te({inputs:{x:m},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(m),f},p3={kernelName:Jn,backendName:\"webgl\",kernelFunc:Jee};function om(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype===\"complex64\"){let n=yi({inputs:{input:o},backend:e}),s=om({inputs:{x:n},backend:e}),a=wp({inputs:{input:o},backend:e}),i=om({inputs:{x:a},backend:e}),p=Pr({inputs:{real:s,imag:i},backend:e});return e.disposeIntermediateTensorInfo(n),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(i),p}else return bi({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:e})}var c3={kernelName:Sa,backendName:\"webgl\",kernelFunc:om};function l3(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string 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xg=class{constructor(t,e,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let n=t.length,s=Re(n),a=e.map(h=>h[0]).join(\",\"),i=e.map((h,g)=>h[0]+t[g]).join(\",\"),p=Rt(\"rc\",n),u=Rt(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1;\n if(${c}) {\n `,n===1?\"\":`}\n rc = outputLoc;\n ${p[n-2]} += 1;\n if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?\"\":` ${p[n-1]} += 1;\n if(${c}) {`],d=n===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",f=\"\";for(let h=0,g=n===1?2:4;h{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return bi({backend:e,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new xg(n.shape,s,a):new gg(n.shape,s,a),p=[[a]];return e.runWebGLProgram(i,[n],n.dtype,p)},f3={kernelName:es,backendName:\"webgl\",kernelFunc:Rv};var tte=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,rte=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = lessThan(floor(b), b);\n bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);\n `+jr+`\n return result;\n`,ote=nt({opSnippet:tte,packedOpSnippet:rte}),h3={kernelName:ts,backendName:\"webgl\",kernelFunc:ote};function nte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o,i=n.shape.length,p=[],u=y.parseAxisParam(s,n.shape),c=u,l=w.getAxesPermutation(c,i),m=n;l!=null&&(m=bt({inputs:{x:n},backend:e,attrs:{perm:l}}),c=w.getInnerMostAxes(c.length,i),p.push(m)),w.assertAxesAreInnerMostDims(\"prod\",c,i);let d;if(e.shouldExecuteOnCPU([m])){let f=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=rD(m.shape,m.dtype,f,c);d=e.makeTensorInfo(g,x,h)}else{let[f,h]=w.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=te({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=oi(n.dtype),C=Xr(x,b,\"prod\",e);d=te({inputs:{x:C},backend:e,attrs:{shape:f}}),p.push(x),p.push(C)}if(a){p.push(d);let f=w.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:e,attrs:{shape:f}})}return p.forEach(f=>e.disposeIntermediateTensorInfo(f)),d}var g3={kernelName:os,backendName:\"webgl\",kernelFunc:nte};function ste(r){let{inputs:t,backend:e,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=t,{outputRaggedRank:i}=o,p=n.map(x=>e.readSync(x.dataId)),u=n.map(x=>x.shape),c=e.readSync(s.dataId),l=e.readSync(a.dataId),[m,d,f]=oD(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>e.makeTensorInfo([x.length],\"int32\",x)),g=e.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var x3={kernelName:Up,backendName:\"webgl\",kernelFunc:ste};function ate(r){let{inputs:t,backend:e}=r,{starts:o,limits:n,deltas:s}=t,a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=nD(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=e.makeTensorInfo([u.length],\"int32\",u),m=e.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var y3={kernelName:Gp,backendName:\"webgl\",kernelFunc:ate};function ite(r){let{inputs:t,backend:e,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=t,{rowPartitionTypes:p}=o,u=e.readSync(n.dataId),c=e.readSync(s.dataId),l=e.readSync(a.dataId),m=i.map(g=>e.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=sD(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return e.makeTensorInfo(f,s.dtype,h)}var b3={kernelName:Hp,backendName:\"webgl\",kernelFunc:ite};var Dv=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=aD(o,n,s,a);return t.makeTensorInfo([i.length],a,i)},C3={kernelName:ma,backendName:\"webgl\",kernelFunc:Dv};var ute=\"return 1.0 / x;\",pte=xe({opSnippet:ute}),w3={kernelName:ns,backendName:\"webgl\",kernelFunc:pte};var cte=Wt+`\n return (x < 0.0) ? 0.0 : x;\n`,lte=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,mte=xe({opSnippet:cte,packedOpSnippet:lte}),S3={kernelName:ss,backendName:\"webgl\",kernelFunc:mte};var dte=Wt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,fte=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,hte=xe({opSnippet:dte,packedOpSnippet:fte}),I3={kernelName:us,backendName:\"webgl\",kernelFunc:hte};var yg=class{constructor(t,e,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var bg=class{constructor(t,e,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function gte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new bg(n.shape,p,u,s,a):new yg(n.shape,p,u,s,a);return e.runWebGLProgram(c,[n],\"float32\")}var v3={kernelName:is,backendName:\"webgl\",kernelFunc:gte};var Cg=class{constructor(t,e,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,n,s]=e,[,a,i]=t,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function xte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=new Cg(s.shape,n.shape,a);return e.runWebGLProgram(i,[s],s.dtype)}var k3={kernelName:Ja,backendName:\"webgl\",kernelFunc:xte};var wg=class{constructor(t,e,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var Sg=class{constructor(t,e,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function yte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new Sg(n.shape,p,u,s,a):new wg(n.shape,p,u,s,a);return e.runWebGLProgram(c,[n],n.dtype)}var N3={kernelName:as,backendName:\"webgl\",kernelFunc:yte};var Ig=class{constructor(t,e,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,n,s]=e,[,a,i]=t,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float sourceFracRow =\n float(${p[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${p[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${n}) - 1),\n ${o} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${o} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function bte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=new Ig(s.shape,n.shape,a);return e.runWebGLProgram(i,[s],s.dtype)}var T3={kernelName:Za,backendName:\"webgl\",kernelFunc:bte};var vg=class{constructor(t,e){this.variableNames=[\"x\"];let o=t.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=t,o===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${t[0]} - coord - 1));\n }\n `;return}let n=i=>e.indexOf(i)!==-1&&t[i]!==1?`${t[i]} - coords[${i}] - 1`:`coords[${i}]`,s=t.map((i,p)=>n(p)).join(\",\"),a=Re(o);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var kg=class{constructor(t,e){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let o=t.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=t;let n=Rt(\"rc\",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Re(o);o===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${t[0]} - rc - 1),\n ${t[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${t[0]} - (rc + 1) - 1),\n ${t[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${i} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${p(n.slice())};\n if(${s}){\n result.g = ${u(n.slice())};\n }\n if(${a}) {\n result.b = ${c(n.slice())};\n if(${s}) {\n result.a = ${l(n.slice())};\n }\n }\n setOutput(result);\n }\n `;function p(f){return m(f)}function u(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",m(f)}function c(f){return f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function l(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function m(f){let h=t.map((b,C)=>d(C,f)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return e.indexOf(f)!==-1&&t[f]!==1?`${t[f]} - ${h[f]} - 1`:`${h[f]}`}}};function Cte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return Dt({inputs:{x:n},backend:e});let p=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new kg(n.shape,i):new vg(n.shape,i);return e.runWebGLProgram(p,[n],n.dtype)}var _3={kernelName:ps,backendName:\"webgl\",kernelFunc:Cte};var Ng=class{constructor(t,e){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let o=t[1],n=t[2];this.outputShape=t;let s=\"\";typeof e==\"number\"?s=`float outputValue = ${e.toFixed(2)};`:s=`\n vec3 fill = vec3(${e.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var $3={kernelName:Ds,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=t,i=e,p=new Ng(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var wte=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`,Ste=xe({opSnippet:wte}),E3={kernelName:cs,backendName:\"webgl\",kernelFunc:Ste};var Ite=\"return inversesqrt(x);\",vte=xe({opSnippet:Ite,cpuKernelImpl:iD}),R3={kernelName:ls,backendName:\"webgl\",kernelFunc:vte};var yu=class{constructor(t,e,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${t}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${e}; j++) {\n int index = round(${m});\n flattenedIndex += index * ${x};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${f};\n found = true;\n }\n }\n setOutput(mix(${g}, sum, float(found)));\n }\n `}};var Tg=class{constructor(t,e,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\",b=e>1?\"strides[j + 1]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${t}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${e}; j+=2) {\n ivec4 index = round(${m});\n flattenedIndex += index.xz * ${x};\n if (j + 1 < ${e}) {\n flattenedIndex += index.yw * ${b};\n }\n }\n if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n vec4 updVals = ${f};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${g}, sum, found));\n }\n `}};function kte(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n,updates:s}=t,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return e.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:e,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:e,attrs:{shape:[p,u]}}),h=e.makeTensorInfo([],\"float32\",new Float32Array([0])),g;A().getBool(\"WEBGL_PACK\")?g=new Tg(p,i,d.shape.length,f.shape.length,c,m):g=new yu(p,i,d.shape.length,f.shape.length,c,m);let x=e.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var D3={kernelName:ms,backendName:\"webgl\",kernelFunc:kte};var _g=class{constructor(t,e,o,n){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[t,o];let s=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,i=A().getNumber(\"WEBGL_VERSION\")===2?s:a,p=n===\"left\"?\"<\":\"<=\";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${i}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${p} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}};function Nte(r){let{inputs:t,backend:e,attrs:o}=r,{sortedSequence:n,values:s}=t,{side:a}=o,i=new _g(n.shape[0],n.shape[1],s.shape[1],a),p=[[n.shape[1]]];return e.runWebGLProgram(i,[n,s],\"int32\",p)}var A3={kernelName:fs,backendName:\"webgl\",kernelFunc:Nte};var $g=class{constructor(t,e,o){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=e;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s=\"resRC\",n=\"resRC\";else{let i=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],p=[],u=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function Tte(r){let{inputs:t,backend:e}=r,{condition:o,t:n,e:s}=t,a=new $g(o.shape.length,n.shape,n.shape.length);return e.runWebGLProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var F3={kernelName:fa,backendName:\"webgl\",kernelFunc:Tte};var _te=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${w.SELU_SCALEALPHA};\n float scale = ${w.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,$te=xe({opSnippet:_te}),P3={kernelName:hs,backendName:\"webgl\",kernelFunc:$te};var Ete=Fo+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,Rte=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Dte=xe({opSnippet:Ete,packedOpSnippet:Rte,cpuKernelImpl:pD}),O3={kernelName:bs,backendName:\"webgl\",kernelFunc:Dte};var Ate=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,Fte=xe({opSnippet:Ate}),M3={kernelName:ys,backendName:\"webgl\",kernelFunc:Fte};var Pte=Fo+`\n return sin(x);\n`,Ote=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${jr}\n return result;\n`,Mte=xe({opSnippet:Pte,packedOpSnippet:Ote}),L3={kernelName:gs,backendName:\"webgl\",kernelFunc:Mte};var Lte=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Bte=xe({opSnippet:Lte}),B3={kernelName:xs,backendName:\"webgl\",kernelFunc:Bte};var zte=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,Vte=xe({opSnippet:zte}),z3={kernelName:Cs,backendName:\"webgl\",kernelFunc:Vte};var Wte=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xe.disposeIntermediateTensorInfo(x)),g},V3={kernelName:ga,backendName:\"webgl\",kernelFunc:Wte};function Ute(r){let{inputs:t,backend:e}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(o.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${o.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${n.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${a.shape}`);let i=e.readSync(o.dataId),p=e.readSync(n.dataId),u=e.readSync(s.dataId),c=e.readSync(a.dataId)[0],[l,m,d,f,h]=lD(i,o.shape,o.dtype,p,n.dtype,u,c);return[e.makeTensorInfo(m,o.dtype,l),e.makeTensorInfo([m[0]],n.dtype,d),e.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),e.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var W3={kernelName:Hi,backendName:\"webgl\",kernelFunc:Ute};function Gte(r){let{inputs:t,backend:e}=r,{inputIndices:o,inputShape:n,newShape:s}=t;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(e.readSync(n.dataId)),i=e.readSync(o.dataId),p=Array.from(e.readSync(s.dataId)),[u,c,l]=mD(i,o.shape,o.dtype,a,p);return[e.makeTensorInfo(c,o.dtype,u),e.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var U3={kernelName:ei,backendName:\"webgl\",kernelFunc:Gte};function Hte(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=ch(a,o.shape,o.dtype,i,p,!0);return e.makeTensorInfo(c,o.dtype,u)}var G3={kernelName:ya,backendName:\"webgl\",kernelFunc:Hte};function Kte(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=ch(a,o.shape,o.dtype,i,p);return e.makeTensorInfo(c,o.dtype,u)}var H3={kernelName:ba,backendName:\"webgl\",kernelFunc:Kte};function qte(r){let{inputs:t,backend:e,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=t,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let x=e.bufferSync(n),b=e.bufferSync(s),C=y.decodeString(e.readSync(a.dataId)[0]),S=uD(x,b,i,m,c,u,p,l,C,d);return e.makeTensorInfo(i,S.dtype,S.values)}let f=new yu(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=e.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(h),g}var K3={kernelName:vs,backendName:\"webgl\",kernelFunc:qte};function jte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Gs({inputs:{x:n},backend:e,attrs:{begin:c,size:d}});return c[i]+=m,f})}var q3={kernelName:xa,backendName:\"webgl\",kernelFunc:jte};var j3=\"return sqrt(x);\",Xte=xe({opSnippet:j3,packedOpSnippet:j3,cpuKernelImpl:dD}),X3={kernelName:ws,backendName:\"webgl\",kernelFunc:Xte};var Yte=\"return x * x;\",Qte=xe({opSnippet:Yte}),Y3={kernelName:Ki,backendName:\"webgl\",kernelFunc:Qte};var Q3=\"return (a - b) * (a - b);\",Zte=nt({opSnippet:Q3,packedOpSnippet:Q3}),Z3={kernelName:ks,backendName:\"webgl\",kernelFunc:Zte};function Jte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t;if(n.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");let s=e.readSync(n.dataId),a=w.fromUint8ToStringArray(s),i=fD(a,\"string\",o);return e.makeTensorInfo(n.shape,\"string\",i)}var J3={kernelName:$u,backendName:\"webgl\",kernelFunc:Jte};function ere({inputs:r,attrs:t,backend:e}){let{x:o}=r,n=Wt+`\n return x > 0.0 ? 1.0 : float(${t.alpha});\n `,s=new tr(o.shape,n);return e.runWebGLProgram(s,[o],o.dtype)}var eP={kernelName:wo,backendName:\"webgl\",kernelFunc:ere};var Eg=class{constructor(t,e,o){this.variableNames=[\"x\"],this.outputShape=o;let n=o.length,s=Re(o.length),a=Re(o.length),i=\"\";if(n===1)i=\"coords * strides + begin\";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${t});\n ${s} strides = ${s}(${e});\n\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${i}));\n }\n `}};function tre(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=pt.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=te({inputs:{x:n},backend:e,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let E=pt.computeOutShape(b,C,S),R=Gs({inputs:{x:n},backend:e,attrs:{begin:b,size:E}});k=te({inputs:{x:R},backend:e,attrs:{shape:f}}),e.disposeIntermediateTensorInfo(R)}else if(e.shouldExecuteOnCPU([n])){let R=e.readSync(n.dataId),D=me(n.shape,n.dtype,R),P=hD(d,D,S,b);k=e.makeTensorInfo(f,n.dtype,P.values)}else{let R=new Eg(b,S,d);k=e.runWebGLProgram(R,[n],n.dtype)}let _=te({inputs:{x:k},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(k),_}var tP={kernelName:Ns,backendName:\"webgl\",kernelFunc:tre};function rre(r){let{inputs:t,backend:e,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=t,m=e.readSync(c.dataId),d=e.readSync(l.dataId),[f,h]=gD(m,d,n,s,a,i,p,u);return[e.makeTensorInfo([f.length],\"string\",f),e.makeTensorInfo(l.shape,\"int32\",h)]}var rP={kernelName:Ca,backendName:\"webgl\",kernelFunc:rre};function ore(r){let{inputs:t,backend:e,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=t;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=e.readSync(s.dataId),p=e.readSync(a.dataId)[0],[u,c,l]=xD(i,p,n),m=c.length;return[e.makeTensorInfo([m,2],\"int32\",u),e.makeTensorInfo([m],\"string\",c),e.makeTensorInfo([2],\"int32\",new Int32Array(l))]}var oP={kernelName:qi,backendName:\"webgl\",kernelFunc:ore};function nre(r){let{inputs:t,backend:e,attrs:o}=r,{numBuckets:n}=o,{input:s}=t;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(n<=0)throw new Error(\"Number of buckets must be at least 1\");let a=e.readSync(s.dataId),i=yD(a,n);return e.makeTensorInfo(s.shape,\"int32\",i)}var nP={kernelName:ji,backendName:\"webgl\",kernelFunc:nre};var sre=\"return tan(x);\",are=xe({opSnippet:sre}),sP={kernelName:_s,backendName:\"webgl\",kernelFunc:are};var ire=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,ure=xe({opSnippet:ire}),aP={kernelName:$s,backendName:\"webgl\",kernelFunc:ure};function pre(r){let{inputs:t,backend:e,attrs:o}=r,{tensor:n,indices:s,updates:a}=t,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return e.makeTensorInfo(n.shape,s.dtype);let d=te({inputs:{x:s},backend:e,attrs:{shape:[p,i]}}),f=te({inputs:{x:a},backend:e,attrs:{shape:[p,u]}}),h=te({inputs:{x:n},backend:e,attrs:{shape:m}}),g=new yu(p,i,d.shape.length,f.shape.length,c,m,!1,!0),x=e.runWebGLProgram(g,[f,d,h],h.dtype),b=te({inputs:{x},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(x),b}var iP={kernelName:ds,backendName:\"webgl\",kernelFunc:pre};var Rg=class{constructor(t,e){this.variableNames=[\"A\"];let o=new Array(t.length);for(let a=0;a5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],o=[];for(let n=0;n5){let p=e.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=CD(c,s);return e.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new Rg(n.shape,s);return e.runWebGLProgram(a,[n],n.dtype)}var uP={kernelName:uo,backendName:\"webgl\",kernelFunc:Av};var Dg=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n `}},Ag=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function Ip(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function pP(r){let t=1;for(;tp){let P=e.readSync(n.dataId),[O,M]=wD(P,u,n.dtype,s,a);return[e.makeTensorInfo(O.shape,O.dtype,O.values),e.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[e.makeTensorInfo(u,n.dtype,[]),e.makeTensorInfo(u,\"int32\",[])];if(c===1)return[n,bi({attrs:{shape:u,dtype:\"int32\",value:0},backend:e})];let l=e.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?e.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=te({inputs:{x:d},attrs:{shape:[h,c]},backend:e});m&&Ip(e,d);let x=pP(s),b=pP(c),C=null,S=()=>C===null?[g,g]:[g,C],k=(P,O,M)=>{let L=S(),B=new Dg(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[P],[O]],j=C;C=e.runWebGLProgram(B,L,\"int32\",U),Ip(e,j)};for(let P=1;P=1;M/=2)k(O,M,[h,b])}for(let P=b;P>x;P/=2){let O=S(),M=new Ag([h,P/2]),B=[[c],[C===null?1:0],[x]],z=C;C=e.runWebGLProgram(M,O,\"int32\",B),Ip(e,z);let U=x/2,j=U*2;for(let q=U;q>=1;q/=2)k(j,q,C.shape)}let _=C;C=Gs({inputs:{x:C},backend:e,attrs:{begin:0,size:[h,s]}}),Ip(e,_);let E=Nv({inputs:{x:g,indices:C},backend:e,attrs:{axis:1,batchDims:1}});Ip(e,g);let R=u.slice(0,-1);R.push(s),_=C,C=te({inputs:{x:C},attrs:{shape:R},backend:e}),Ip(e,_);let D=E;return E=te({inputs:{x:E},attrs:{shape:R},backend:e}),Ip(e,D),[E,C]}var cP={kernelName:Es,backendName:\"webgl\",kernelFunc:lre};var Fg=class{constructor(t,e,o,n,s,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=o===\"nearest\"?1:2,p;switch(n){case\"constant\":p=1;break;case\"reflect\":p=2;break;case\"wrap\":p=3;break;case\"nearest\":p=4;break;default:p=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${p} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = 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valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function mre(r){let{inputs:t,backend:e,attrs:o}=r,{image:n,transforms:s}=t,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Fg(l,m,a,i,p,g);return e.runWebGLProgram(x,[n,s],\"float32\")}var lP={kernelName:Rs,backendName:\"webgl\",kernelFunc:mre};function dre(r){let{inputs:t,attrs:e,backend:o}=r,{axis:n}=e,{x:s}=t;Vs(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let a=o.readSync(s.dataId),{outputValues:i,outputShape:p,indices:u}=SD(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],\"int32\",u)]}var mP={kernelName:Xi,backendName:\"webgl\",kernelFunc:dre};function fre(r){let{inputs:t,backend:e,attrs:o}=r,{value:n}=t,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;he.disposeIntermediateTensorInfo(h)),f}var dP={kernelName:wa,backendName:\"webgl\",kernelFunc:fre};var Pg=class{constructor(t,e){this.variableNames=[\"x\",\"segmentIds\"];let o=t.windowSize,n=t.batchSize,s=t.inSize,a=t.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p=\"0.0\",u=\"sumValue\",c=Math.floor(o/4)*4,l=o%4,m=`\n sumValue += dot(values, segFilter);\n `,d=\"\";s%o>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${p};\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${f}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${a})) * float(${o}));\n int currentSeg = int(mod(float(outIdx), float(${a})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function hre(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,segmentIds:s}=t,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:e,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=te({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});p.push(f);let h=oi(n.dtype),g=(S,k,_,E,R)=>{let D=S.shape[0],P=S.shape[1],O=w.segment_util.segOpComputeOptimalWindowSize(P,R),M={windowSize:O,inSize:P,batchSize:D,numSegments:R},L=new Pg(M,k),B=e.compileAndRun(L,[S,_],E);if(p.push(B),B.shape[1]===R)return B;let z=Dv({backend:e,attrs:{start:0,stop:R,step:1,dtype:\"float32\"}}),U=Av({inputs:{x:z},backend:e,attrs:{reps:[P/O]}});return p.push(z),p.push(U),g(B,k,U,E,R)},x=g(f,\"unsortedSegmentSum\",s,h,a),b=te({inputs:{x},backend:e,attrs:{shape:m}}),C=b;if(c!=null){p.push(b);let S=w.getUndoAxesPermutation(c);C=bt({inputs:{x:C},backend:e,attrs:{perm:S}})}return 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bu;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(bu||(bu={}));var hP;function xre(r){hP=r.wasm.cwrap(So,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function yre(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t;if(n.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o,m=e.dataIdMap.get(n.dataId).id,d=e.dataIdMap.get(s.dataId).id,f=0;if(a!=null){let R=e.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let h=i==null?0:e.dataIdMap.get(i.dataId).id,g=bu[c];if(g==null)throw new Error(`${c} 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goe(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s}=t,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l}=e,m=u==null?[1,1]:u,d=w.computeConv2DInfo(n.shape,s.shape,p,m,c,l,!0),f=d.filterHeight,h=d.filterWidth,g=d.padInfo.top,x=d.padInfo.right,b=d.padInfo.bottom,C=d.padInfo.left,S=d.dilationHeight,k=d.dilationWidth,_=d.strideHeight,E=d.strideWidth,R=d.inChannels,D=d.outChannels,P=d.padInfo.type===\"SAME\"?1:0;if(d.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. 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Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=e.makeOutput(s.shape,s.dtype);return AO(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var FO={kernelName:Mi,backendName:\"wasm\",setupFunc:woe,kernelFunc:Soe};var PO;function Ioe(r){PO=r.wasm.cwrap(Oi,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function voe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=e.makeOutput(n.shape,n.dtype);return PO(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var OO={kernelName:Oi,backendName:\"wasm\",setupFunc:Ioe,kernelFunc:voe};var MO=he(hn);var LO;function koe(r){LO=r.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\"])}function Noe(r){let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=e.makeOutput(n.shape,\"float32\"),a=i=>e.dataIdMap.get(i.dataId).id;return LO(a(n),a(o),a(s)),s}var BO={kernelName:Xa,backendName:\"wasm\",setupFunc:koe,kernelFunc:Noe};var Toe=!1,zO=Ue(xn,Toe,\"bool\");var VO=he(gn);var WO=he(yn,\"float32\");function Mg(r){let{inputs:t,attrs:e,backend:o}=r,{input:n}=t,{dim:s}=e,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),zt({inputs:{x:n},backend:o,attrs:{shape:i}})}var UO={kernelName:na,backendName:\"wasm\",kernelFunc:Mg};var GO=he(bn,\"float32\");function Ov(r){let{attrs:{shape:t,value:e},backend:o}=r,{attrs:{dtype:n}}=r;n=n||y.inferDtype(e);let s=o.makeOutput(t,n);return o.typedArrayFromHeap(s).fill(e),s}var HO={kernelName:sa,backendName:\"wasm\",kernelFunc:Ov};var KO;function _oe(r){KO=r.wasm.cwrap(Cn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function $oe(r){let{inputs:t,backend:e}=r,{image:o}=t,n=e.makeOutput(o.shape,o.dtype),s=e.dataIdMap.get(o.dataId).id,a=e.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return KO(s,i,p,u,c,a),n}var qO={kernelName:Cn,backendName:\"wasm\",kernelFunc:$oe,setupFunc:_oe};var jO=he(wn);var Eoe=!1,XO=Ue(Sn,Eoe);var YO;function Roe(r){YO=r.wasm.cwrap(In,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Doe(r){let{backend:t,inputs:e,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=e,c=t.dataIdMap.get(s.dataId).id,l=t.dataIdMap.get(a.dataId).id,m=t.dataIdMap.get(i.dataId).id,d=p!=null?t.dataIdMap.get(p.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return YO(c,l,m,d,f,n,g),h}var QO={kernelName:In,backendName:\"wasm\",setupFunc:Roe,kernelFunc:Doe};var ZO;function Aoe(r){ZO=r.wasm.cwrap(Io,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Foe(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=e,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=bu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return ZO(x,j,q,Y,b,k,_,S,E,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var JO={kernelName:Io,backendName:\"wasm\",setupFunc:Aoe,kernelFunc:Foe};var eM;function Poe(r){eM=r.wasm.cwrap(vo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ooe(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=e,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m,!0),g=bu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return eM(x,j,q,Y,b,k,_,S,E,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var tM={kernelName:vo,backendName:\"wasm\",setupFunc:Poe,kernelFunc:Ooe};var rM;function Moe(r){rM=r.wasm.cwrap(vn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Loe(r){let{backend:t,inputs:e}=r,{params:o,indices:n}=e,[s,a,i,p]=sf.prepareAndValidate(o,n),u=t.makeOutput(s,o.dtype);if(a===0)return u;let c=n.shape,l=c[c.length-1],d=t.dataIdMap.get(o.dataId).id,h=t.dataIdMap.get(n.dataId).id,g=new Uint8Array(new Int32Array(p).buffer),x=t.dataIdMap.get(u.dataId).id;return rM(d,we[o.dtype],h,a,l,i,g,x),u}var oM={kernelName:vn,backendName:\"wasm\",setupFunc:Moe,kernelFunc:Loe};var nM;function Boe(r){nM=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function zoe(r){let{backend:t,inputs:e,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=t.readSync(s.dataId),c=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let l=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=zt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:t}),d=y.sizeFromShape(s.shape),f=zt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:t}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=t.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,C=t.dataIdMap.get(m.dataId).id,k=t.dataIdMap.get(f.dataId).id,_=t.dataIdMap.get(g.dataId).id,E=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return nM(C,we[n.dtype],E,x,k,l.batchSize,R,_),t.disposeData(m.dataId),t.disposeData(f.dataId),g.shape=l.outputShape,g}var sM={kernelName:aa,backendName:\"wasm\",setupFunc:Boe,kernelFunc:zoe};var Voe=!1,aM=Ue(kn,Voe,\"bool\");var Woe=!1,iM=Ue(Nn,Woe,\"bool\");var uM=he(Tn,\"bool\");var pM=he(_n,\"bool\");var cM=he($n,\"bool\");var lM;function Uoe(r){lM=r.wasm.cwrap(En,null,[\"number\",\"number\",\"number\",\"number\"])}function Goe(r){let{inputs:{x:t},attrs:{alpha:e},backend:o}=r,n=o.dataIdMap.get(t.dataId).id,s=o.makeOutput(t.shape,\"float32\");if(y.sizeFromShape(t.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;lM(n,we[t.dtype],e,a)}return s}var mM={kernelName:En,backendName:\"wasm\",setupFunc:Uoe,kernelFunc:Goe};var Hoe=!1,dM=Ue(Rn,Hoe,\"bool\");var Koe=!1,fM=Ue(Dn,Koe,\"bool\");var hM;function qoe(r){hM=r.wasm.cwrap(An,null,[\"number\",\"number\",\"number\",\"number\"])}function joe(r){let{attrs:t,backend:e}=r,{start:o,stop:n,num:s}=t,a=Math.floor(s),i=e.makeOutput([a],\"float32\");return hM(e.dataIdMap.get(i.dataId).id,o,n,a),i}var gM={kernelName:An,backendName:\"wasm\",setupFunc:qoe,kernelFunc:joe};var xM=he(Fn);var yM=he(Pn);var Xoe=!1,bM=Ue(On,Xoe,\"bool\");var CM=he(Mn);var Yoe=!1,wM=Ue(Ln,Yoe,\"bool\");var Qoe=!1,SM=Ue(E0,Qoe,\"bool\");var IM;function Zoe(r){IM=r.wasm.cwrap(Bn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Joe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o;if(n.dtype!==\"float32\")throw new Error(\"LRN error: x must have dtype float32\");let u=e.makeOutput(n.shape,n.dtype);return IM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var vM={kernelName:Bn,backendName:\"wasm\",setupFunc:Zoe,kernelFunc:Joe};var kM;function ene(r){kM=r.wasm.cwrap(Ya,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function tne(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o;if(n.dtype!==\"float32\"||s.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");let l=e.makeOutput(n.shape,n.dtype);return kM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,a.shape[3],i,p,u,c),l}var NM={kernelName:Ya,backendName:\"wasm\",setupFunc:ene,kernelFunc:tne};var TM;function rne(r){TM=r.wasm.cwrap(zn,null,[\"number\",\"number\",\"number\",\"number\"])}function one(r){let{backend:t,inputs:e,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=e,p=t.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,t);if(d){let C=t.dataIdMap.get(c.dataId).id;u=c,p=C}let f=u.shape.length;w.assertAxesAreInnerMostDims(\"max\",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=t.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let C=t.dataIdMap.get(b.dataId).id;TM(p,we[a.dtype],x,C)}if(d&&t.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var _M={kernelName:zn,backendName:\"wasm\",setupFunc:rne,kernelFunc:one};var nne=!1,$M=Ue(Vn,nne);var EM;function sne(r){EM=r.wasm.cwrap(Wn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ane(r){let{inputs:t,attrs:e,backend:o}=r,n=t.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=e,c=w.computePool2DInfo(n.shape,a,i,1,p,u),l=c.filterHeight,m=c.filterWidth,d=c.padInfo.top,f=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,C=c.strideHeight,S=c.strideWidth,k=c.inChannels,_=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let E=o.makeOutput(c.outShape,\"float32\"),R=o.dataIdMap.get(E.dataId).id;return EM(s,n.shape[0],n.shape[1],n.shape[2],l,m,d,f,h,g,x,b,C,S,k,_,R),E}var RM={kernelName:Wn,backendName:\"wasm\",setupFunc:sne,kernelFunc:ane};var DM;function ine(r){DM=r.wasm.cwrap(\"MaxPool3D\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function une(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=e.makeOutput(c.outShape,n.dtype);return DM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var AM={kernelName:ia,backendName:\"wasm\",setupFunc:ine,kernelFunc:une};var FM;function pne(r){FM=r.wasm.cwrap(\"MaxPool3DGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function cne(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=e.makeOutput(s.shape,s.dtype);return FM(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var PM={kernelName:Ui,backendName:\"wasm\",setupFunc:pne,kernelFunc:cne};var OM;function lne(r){OM=r.wasm.cwrap(\"MaxPoolGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function mne(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool2DInfo(s.shape,a,i,1,p,u),l=e.makeOutput(s.shape,s.dtype);return OM(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),l}var MM={kernelName:Wi,backendName:\"wasm\",setupFunc:lne,kernelFunc:mne};var LM;function dne(r){LM=r.wasm.cwrap(\"MaxPoolWithArgmax\",null,[\"number\",\"number\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function fne(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,includeBatchInIndex:p}=o;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,[1,1],i),l=e.makeOutput(c.outShape,n.dtype),m=e.makeOutput(c.outShape,\"int32\");return LM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,e.dataIdMap.get(m.dataId).id,we[n.dtype],p,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[l,m]}var BM={kernelName:ua,backendName:\"wasm\",setupFunc:dne,kernelFunc:fne};var zM;function hne(r){zM=r.wasm.cwrap(Un,null,[\"number, number, number\"])}function gne(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,t),f=l;if(d){let S=t.dataIdMap.get(c.dataId).id;S!==i&&(u=c,p=S,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"mean\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!==\"float32\"&&(b=Or({backend:t,inputs:{x:u},attrs:{dtype:\"float32\"}}),p=t.dataIdMap.get(b.dataId).id);let C=t.makeOutput(h,\"float32\");if(y.sizeFromShape(u.shape)!==0){let S=t.dataIdMap.get(C.dataId).id;zM(p,x,S)}if(d&&t.disposeData(c.dataId),s){let S=w.expandShapeToKeepDim(C.shape,m);C.shape=S}return u.dtype!==\"float32\"&&t.disposeData(b.dataId),C}var VM={kernelName:Un,backendName:\"wasm\",setupFunc:hne,kernelFunc:gne};var WM;function xne(r){WM=r.wasm.cwrap(Gn,null,[\"number\",\"number\",\"number\",\"number\"])}function yne(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,t);if(d){let 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fL={kernelName:rs,backendName:\"wasm\",setupFunc:zne,kernelFunc:Vne};var hL;function Wne(r){hL=r.wasm.cwrap(os,null,[\"number\",\"number\",\"number\",\"number\"])}function Une(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,t),f=l;if(d){let C=t.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"prod\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=t.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=t.dataIdMap.get(b.dataId).id;hL(p,x,we[b.dtype],C)}if(d&&t.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var gL={kernelName:os,backendName:\"wasm\",setupFunc:Wne,kernelFunc:Une};var Gne=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=ap(o,n,s,a),p=t.makeOutput([i.length],a);return 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$L={kernelName:Za,backendName:\"wasm\",setupFunc:Zne,kernelFunc:Jne};var EL;function ese(r){EL=r.wasm.cwrap(ps,null,[\"number\",\"array\",\"number\",\"array\",\"number\",\"number\"])}function tse(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return vp({inputs:{x:n},backend:e});let i=e.makeOutput(n.shape,n.dtype),p=e.dataIdMap.get(n.dataId).id,u=e.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),l=new Uint8Array(new Int32Array(n.shape).buffer);EL(p,c,a.length,l,n.shape.length,u);let m=zt({inputs:{x:i},attrs:{shape:n.shape},backend:e});return e.disposeData(i.dataId),m}var RL={kernelName:ps,backendName:\"wasm\",kernelFunc:tse,setupFunc:ese};var DL;function rse(r){DL=r.wasm.cwrap(Ds,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\"])}function 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lse(r){let{backend:t,inputs:{x:e}}=r,o=t.dataIdMap.get(e.dataId).id,n=t.makeOutput(e.shape,e.dtype),s=t.dataIdMap.get(n.dataId).id;return y.sizeFromShape(n.shape)===0||UL(o,s),n}var GL={kernelName:\"Sigmoid\",backendName:\"wasm\",setupFunc:cse,kernelFunc:lse};var HL=he(ys);var KL=he(gs);var qL=he(xs);var jL=he(Cs);function mse(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,paddings:a}=o,i=y.sizeFromShape(s),p=[[0,0]];p.push(...a);for(let _=1+s.length;_0?p+1:0;if(c<0)throw new Error(w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let l=n.shape.slice();l[0]=c;let m=e.dataIdMap.get(n.dataId).id,d=e.dataIdMap.get(s.dataId).id,f=e.dataIdMap.get(a.dataId).id,h=e.makeOutput(l,n.dtype),g=e.dataIdMap.get(h.dataId).id,x=e.makeOutput([4],\"int32\"),b=e.dataIdMap.get(x.dataId).id;eB(m,we[n.dtype],n.shape[0],d,f,g,b,t,0);let C=e.readSync(x.dataId),S;switch(C[0]){case 0:{S=w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{S=w.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:S=w.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(C[1],C[2]);break;case 3:S=w.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(C[1],C[2],C[3]);break;default:S=\"\"}if(e.disposeData(x.dataId),S)throw e.disposeData(h.dataId),new Error(S);return h}function xse(r){return zg(r,!0)}var tB={kernelName:ya,backendName:\"wasm\",setupFunc:Bg,kernelFunc:xse};function yse(r){return zg(r,!1)}var rB={kernelName:ba,backendName:\"wasm\",setupFunc:Bg,kernelFunc:yse};var oB;function bse(r){oB=r.wasm.cwrap(vs,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\"])}function Cse(r){let{backend:t,inputs:e,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,p=t.makeOutput(i,a.dtype);if(y.sizeFromShape(i)===0)return 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$se(r){let{backend:t,inputs:e,attrs:o}=r,{input:n}=e,{numBuckets:s}=o,a=t.readSync(n.dataId),i=cp(a,s),p=t.makeOutput(n.shape,\"int32\");return t.typedArrayFromHeap(p).set(i),p}var hB={kernelName:ji,backendName:\"wasm\",kernelFunc:$se};var Ese=!0,gB=Ue(Ts,Ese);var xB;function Rse(r){xB=r.wasm.cwrap(Ss,null,[\"number\",\"number\",\"number\",\"number\"])}function Dse(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,t),f=l;if(d){let C=t.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"sum\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=t.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=t.dataIdMap.get(b.dataId).id;xB(p,x,we[b.dtype],C)}if(d&&t.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var yB={kernelName:Ss,backendName:\"wasm\",setupFunc:Rse,kernelFunc:Dse};var bB=he(_s);var CB=he($s);var wB;function Ase(r){wB=r.wasm.cwrap(ds,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function Fse(r){let{backend:t,inputs:e,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,i=t.makeOutput(n.shape,n.dtype);if(y.sizeFromShape(n.shape)===0)return i;let{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=lu.calculateShapes(a,s,n.shape),f=t.dataIdMap.get(s.dataId).id,g=t.dataIdMap.get(a.dataId).id,b=t.dataIdMap.get(n.dataId).id,C=new Uint8Array(new Int32Array(l).buffer),S=t.dataIdMap.get(i.dataId).id;return wB(f,g,we[a.dtype],p,u,c,C,m,S,b),i}var SB={kernelName:ds,backendName:\"wasm\",setupFunc:Ase,kernelFunc:Fse};var IB;function Pse(r){IB=r.wasm.cwrap(uo,null,[\"number\",\"array\",\"number\",\"array\",\"number\",\"number\"])}function 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e=this.dataIdMap.get(t);e!=null&&e.refCount++}floatPrecision(){return 32}getMemoryOffset(t){return this.dataIdMap.get(t).memoryOffset}dispose(){this.wasm.tfjs.dispose(),\"PThread\"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(t,e,o,n){let s;if(o==null)s=this.write(n!=null?n:null,t,e);else{let a=this.dataIdNextNumber++;s={id:a},this.dataIdMap.set(s,{id:a,memoryOffset:o,shape:t,dtype:e,refCount:1});let i=y.sizeFromShape(t);this.wasm.tfjs.registerTensor(a,i,o)}return{dataId:s,shape:t,dtype:e}}typedArrayFromHeap({shape:t,dtype:e,dataId:o}){let n=this.wasm.HEAPU8.buffer,{memoryOffset:s}=this.dataIdMap.get(o),a=y.sizeFromShape(t);switch(e){case\"float32\":return new Float32Array(n,s,a);case\"int32\":return new Int32Array(n,s,a);case\"bool\":return new Uint8Array(n,s,a);default:throw new Error(`Unknown dtype ${e}`)}}};function Kse(r){return(t,e)=>(y.fetch(r,{credentials:\"same-origin\"}).then(o=>{o.ok||t.env.a(`failed to load wasm binary file at '${r}'`),o.arrayBuffer().then(n=>{WebAssembly.instantiate(n,t).then(s=>{e(s.instance,s.module)})})}),{})}function zB(r,t,e){if(Ug!=null)return Ug;let o=\"tfjs-backend-wasm.wasm\";return r&&t?o=\"tfjs-backend-wasm-threaded-simd.wasm\":r&&(o=\"tfjs-backend-wasm-simd.wasm\"),sm!=null&&sm[o]!=null?sm[o]:e+o}async function WB(){let[r,t]=await Promise.all([A().getAsync(\"WASM_HAS_SIMD_SUPPORT\"),A().getAsync(\"WASM_HAS_MULTITHREAD_SUPPORT\")]);return new Promise((e,o)=>{let n={};n.locateFile=(i,p)=>{if(i.endsWith(\".worker.js\")){let u=VB.wasmWorkerContents.replace(/\\n/g,\"\\\\n\"),c=new Blob([u],{type:\"application/javascript\"});return URL.createObjectURL(c)}return i.endsWith(\".wasm\")?zB(r,t,nm!=null?nm:p):p+i},Xv&&(n.instantiateWasm=Kse(zB(r,t,nm!=null?nm:\"\")));let s=!1;n.onAbort=()=>{if(s||am)return;am=!0,o({message:\"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`\");Ug=r,Xv=t}function Yse(r,t=!1){if(am)throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`\");if(typeof r==\"string\")nm=r;else{sm=r;let e=jse.filter(o=>sm[o]==null);if(e.length>0)throw new Error(`There were no entries found for the following binaries: ${e.join(\",\")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}Xv=t}var UB=-1,Kv=-1;function Qse(r){UB=r}function Zse(){if(Kv===-1)throw new Error(\"WASM backend not initialized.\");return Kv}var Jse=\"4.14.0\";var eae=2;tu(\"wasm\",async()=>{let{wasm:r}=await WB();return new im(r)},eae);var go=A();go.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\",()=>15);go.registerFlag(\"WEBGPU_CPU_FORWARD\",()=>!0);go.registerFlag(\"WEBGPU_MATMUL_PROGRAM_TYPE\",()=>-1);go.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\",()=>!0);go.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\",()=>!1);go.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e3);go.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\",()=>!1);go.registerFlag(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\",()=>!0);go.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\",()=>!1);go.registerFlag(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\",()=>-1);go.registerFlag(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\",()=>!1);go.registerFlag(\"WEBGPU_PRINT_SHADER\",()=>\"\");go.registerFlag(\"WEBGPU_ENGINE_COMPILE_ONLY\",()=>!1);var Gg=class{constructor(t){t&&(this.vendor=t.vendor,this.architecture=t.architecture,this.intelGPUGeneration=this.getIntelGPUGeneration())}getIntelGPUGeneration(){if(this.isIntel()){if(this.architecture.startsWith(\"gen\"))return Number(this.architecture.match(/\\d+/));if(this.architecture.startsWith(\"xe\"))return 12}return 0}isIntel(){return this.vendor===\"intel\"}};var Hg=class{constructor(t){this.device=t,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(t,e,o=!1,n=!0){let s,a=GB(t,e);return n?(this.freeBuffers.has(a)||this.freeBuffers.set(a,[]),this.freeBuffers.get(a).length>0?(s=this.freeBuffers.get(a).pop(),this.numFreeBuffers--):(s=this.device.createBuffer({size:t,usage:e,mappedAtCreation:o}),this.numBytesAllocated+=t)):(s=this.device.createBuffer({size:t,usage:e,mappedAtCreation:o}),this.numBytesAllocated+=t),this.usedBuffers.has(a)||this.usedBuffers.set(a,[]),this.usedBuffers.get(a).push(s),this.numUsedBuffers++,this.numBytesUsed+=t,s}releaseBuffer(t,e=!0){if(this.freeBuffers.size===0)return;let o=t.size,n=t.usage,s=GB(o,n),a=this.usedBuffers.get(s),i=a.indexOf(t);if(i<0)throw new Error(\"Cannot find the buffer in buffer manager\");a[i]=a[a.length-1],a.pop(),this.numUsedBuffers--,this.numBytesUsed-=o,e?(this.freeBuffers.get(s).push(t),this.numFreeBuffers++):(t.destroy(),this.numBytesAllocated-=o)}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((t,e)=>{t.forEach(o=>{o.destroy()})}),this.usedBuffers.forEach((t,e)=>{t.forEach(o=>{o.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function GB(r,t){return`${r}_${t}`}var Kg=class{constructor(t){this.device=t,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(t,e,o,n){let s=KB(o),a=t*e*s,i=HB(t,e,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[t,e],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(t){if(this.freeTextures.size===0)return;let e=t.width,o=t.height,n=t.format,s=t.usage,a=HB(e,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(t),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(t);if(p<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");i.splice(p,1);let u=KB(n),c=e*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((t,e)=>{t.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((t,e)=>{t.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function HB(r,t,e,o){return`${r}_${t}_${e}_${o}`}function KB(r){if(r===\"rgba8unorm\")return 16;throw new Error(`${r} is not supported!`)}function qB(r,t){if(Math.max(...r)>5)throw new Error(\"Cannot symbolically compute strides for rank > 6 tensor.\");let e=r.length,o=\"xyzwuv\",n=r.map(a=>`${t}.${o[a]}`),s=new Array(e-1);s[e-2]=n[e-1];for(let a=e-3;a>=0;--a)s[a]=`(${s[a+1]} * ${n[a+1]})`;return s}var Yr=(r,t,e)=>e===\"int32\"?`atomicAdd(${r}, bitcast(${t}));`:`\n {\n var oldValue = 0;\n loop {\n let newValueF32 = bitcast(oldValue) + (${t});\n let newValue = bitcast(newValueF32);\n let res = atomicCompareExchangeWeak(${r}, oldValue, newValue);\n if res.exchanged {\n break;\n }\n oldValue = res.old_value;\n }\n }`;var Ci;(function(r){r[r.FROM_PIXELS=0]=\"FROM_PIXELS\",r[r.DRAW=1]=\"DRAW\"})(Ci||(Ci={}));var QB=(r,t,e,o,n)=>{let s={dtype:o.dtype,shape:o.shape},a=rae(e,s,t),i=r.createShaderModule({code:a,label:t.constructor.name}),p=A().get(\"WEBGPU_PRINT_SHADER\");if(p!==\"\"){p=p.toLowerCase();let u=p.split(\",\");(p===\"all\"||u.some(c=>t.shaderKey.toLowerCase().includes(c)))&&(console.group(t.shaderKey),console.debug(a),console.groupEnd())}return n?r.createComputePipelineAsync({compute:{module:i,entryPoint:\"_start\"},label:t.constructor.name,layout:\"auto\"}):r.createComputePipeline({compute:{module:i,entryPoint:\"_start\"},label:t.constructor.name,layout:\"auto\"})},Ae=(r,t=\"f32\")=>{switch(r){case 1:return`${t}`;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${r}-component ${t} is not supported.`)}};function ft(r){if(r<=1)return\"i32\";if(r===2)return\"vec2\";if(r===3)return\"vec3\";if(r===4)return\"vec4\";if(r===5)return\"vec5\";if(r===6)return\"vec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Oo(r){if(r===0)return\"x\";if(r===1)return\"y\";if(r===2)return\"z\";if(r===3)return\"w\";if(r===4)return\"u\";if(r===5)return\"v\";throw Error(`Index ${r} is not yet supported`)}function G(...r){let t;switch(r.length){case 0:t=`\n fn main()\n `;break;case 1:t=`\n fn main(${r[0]} : i32)\n `;break;default:throw Error(\"Unreachable\")}return t}function jB(r,t){let e;return e=`\n ${tae(t)}\n fn _start(@builtin(local_invocation_id) LocalId : vec3,\n @builtin(global_invocation_id) GlobalId : vec3,\n @builtin(local_invocation_index) LocalIndex: u32,\n @builtin(workgroup_id) WorkgroupId : vec3,\n @builtin(num_workgroups) NumWorkgroups : vec3) {\n localId = LocalId;\n localIndex = LocalIndex;\n globalId = GlobalId;\n numWorkgroups = NumWorkgroups;\n workgroupId = WorkgroupId;\n ${r?\"main(getGlobalIndex());\":\"main();\"};\n }\n `,e}function tae(r){return`\n @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]})\n`}function rae(r,t,e){let o=[],n=e.workgroupSize[0]*e.workgroupSize[1]*e.workgroupSize[2];if(e.outputComponent=e.outputComponent?e.outputComponent:1,o.push(`\n\n var localId: vec3;\n var localIndex: u32;\n var globalId: vec3;\n var numWorkgroups: vec3;\n var workgroupId: vec3;\n\n // Only used when the y/z dimension of workgroup size is 1.\n fn getGlobalIndex() -> i32 {\n ${JB(e)?\" return i32(globalId.x);\":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +\n workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u +\n localIndex);\n `}\n }\n `),e.pixelsOpType!=null){let f=e.pixelsOpType===Ci.FROM_PIXELS?`@group(0) @binding(0) var result: array<${Cu(t.dtype,e.outputComponent)}>;`:`@group(0) @binding(1) var inBuf : array<${Cu(r[0].dtype,e.outputComponent)}>;`,h=t.shape.length===3?\"vec2\":\"i32\";o.push(`\n struct Uniform {\n outShapeStrides : ${h},\n size : i32,\n numChannels : i32,\n alpha : f32,\n };\n\n ${f}\n @group(0) @binding(2) var uniforms: Uniform;\n `);let g=YB(e);return[XB,o.join(`\n`),um(t.shape),e.getUserCode(),jB(g,e)].join(`\n`)}let s,a,i=\"struct Uniforms { NAN : f32, INFINITY : f32, \";e.variableNames.forEach((f,h)=>{let g=ft(r[h].shape.length);i+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `,s=r[h].shape.length-1,a=ft(s),i+=`${f.charAt(0).toLowerCase()+f.slice(1)}ShapeStrides: ${a}, `});let p=ft(t.shape.length);i+=`outShape : ${p}, `,s=t.shape.length-1,a=ft(s),i+=`\n outShapeStrides: ${a}, `,e.size&&(i+=\"size : i32, \"),e.uniforms&&(i+=e.uniforms),i+=\"};\",i=cae(i),o.push(i),e.atomic?o.push(`\n @group(0) @binding(0) var result: array>;\n `):o.push(`\n @group(0) @binding(0) var result: array<${Cu(t.dtype,e.outputComponent)}>;\n `),e.variableNames.forEach((f,h)=>{o.push(`\n @group(0) @binding(${1+h}) var ${f}: array<${e.variableComponents?Cu(r[h].dtype,e.variableComponents[h]):Cu(r[h].dtype,e.outputComponent)}>;\n `)}),i!==\"\"&&o.push(`\n @group(0) @binding(${1+e.variableNames.length}) var uniforms: Uniforms;\n `);let u=iae(t.shape,e.dispatchLayout),c=[XB,o.join(`\n`)+oae,um(t.shape),u,uae(t.shape.length)];e.atomic||c.push(pae(t.shape,t.dtype,e.outputComponent)),e.variableNames.forEach((f,h)=>{c.push(`${um(r[h].shape,f)}`)});let l=r.map((f,h)=>aae(f,t.shape,e.variableComponents?e.variableComponents[h]:e.outputComponent,e.dispatchLayout.x.length===t.shape.length)).join(`\n`);c.push(l),c.push(e.getUserCode());let m=YB(e);return c.push(jB(m,e)),c.join(`\n`)}function ZB(r,t,e){let o=r.shaderKey;if(r.pixelsOpType!=null)return o;let n=[],s=[];t.forEach(c=>{n.push(c.shape),s.push(c.dtype)}),n.push(e.shape),s.push(e.dtype);let a=t.map(c=>w.getBroadcastDims(c.shape,e.shape)),i=t.map(c=>y.arraysEqual(c.shape,e.shape)).join(\"_\"),p=a.map(c=>c.join(\"_\")).join(\";\"),u=JB(r)?\"flatDispatch\":\"\";return o+=\"_\"+(r.workgroupSize?r.workgroupSize.join(\",\"):\"\")+n.map(c=>c.length).join(\",\")+s.join(\",\")+r.variableNames.join(\",\")+p+i+u,o}var XB=`\n struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n // Checks whether coordinates lie within the bounds of the shape.\n fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool {\n return all(coord >= vec2(0)) && all(coord < shape);\n }\n fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool {\n return all(coord >= vec3(0)) && all(coord < shape);\n }\n fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool {\n return all(coord >= vec4(0)) && all(coord < shape);\n }\n\n fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n return coord;\n }\n fn getIndexFromCoords2D(coords : vec2, shape : vec2) -> i32 {\n return dot(coords, vec2(shape.y, 1));\n }\n fn getIndexFromCoords3D(coords : vec3, shape : vec3) -> i32 {\n return dot(coords, vec3(shape.y * shape.z, shape.z, 1));\n }\n fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n }\n fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n }\n fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n }\n\n // NaN defination in IEEE 754-1985 is :\n // - sign = either 0 or 1.\n // - biased exponent = all 1 bits.\n // - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n fn isnan(val: f32) -> bool {\n let floatToUint: u32 = bitcast(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n fn isnanVec4(val : vec4) -> vec4 {\n let floatToUint: vec4 = bitcast>(val);\n return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u);\n }\n`,oae=`\n fn isinf(val: f32) -> bool {\n return abs(val) == uniforms.INFINITY;\n }\n`;function um(r,t=\"\"){let e=r.length,o=t!==\"\"?`get${t.charAt(0).toUpperCase()+t.slice(1)}CoordsFromIndex`:\"getCoordsFromIndex\",n=t!==\"\"?`${t.charAt(0).toLowerCase()+t.slice(1)}ShapeStrides`:\"outShapeStrides\";if(e<=1)return`fn ${o}(index : i32) -> i32 { return index; }`;let s=y.computeStrides(r),a=ft(e),i=[];for(let u=0;u vec2 {\n let d0 = index / uniforms.${n}; let d1 = index - d0 * uniforms.${n};\n return vec2(d0, d1);\n }`;let p;return p=\"var index2 = index;\"+s.map((u,c)=>{let l=`let ${i[c]} = index2 / uniforms.${n}.${Oo(c)}`,m=c===s.length-1?`let ${i[c+1]} = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`:`index2 = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`;return`${l}; ${m};`}).join(\"\"),`\n fn ${o}(index : i32) -> ${a} {\n ${p}\n return ${a}(${i.join(\",\")});\n }\n `}function nae(r,t){let e=r.name,o=r.shape.length,n=ft(o),s=\"get\"+e.charAt(0).toUpperCase()+e.slice(1),a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=a.map(c=>`${c} : i32`).join(\", \");if(o<1)return`\n fn ${s}() -> ${Ae(t)} {\n return ${Ae(t)}(${e}[0]);\n }\n `;let p=`uniforms.${e.charAt(0).toLowerCase()+e.slice(1)}Shape`,u=`${o}D`;return o===0&&(u=\"1D\"),`\n fn ${s}(${i}) -> ${Ae(t)} {\n return ${Ae(t)}(${e}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p})${t===1?\"\":` / ${t}`}]);\n }\n `}function sae(r,t,e,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+s+\"ByOutput\",i=r.shape.length,p=t.length,u=ft(p);if(y.arraysEqual(r.shape,t)&&o)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(e)} {\n return ${Ae(e)}(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(e)} {\n return ${Ae(e)}(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}${e===1?\"\":` / ${e}`}]);\n }\n `;let c=w.getBroadcastDims(r.shape,t),l=p-i,m=\"\";if(i===0)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(e)}{\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(e)}{\n return get${s}();\n }\n `;p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(g=>`coords.${Oo(g+l)} = 0;`).join(`\n`);let d=\"\";if(p<2&&i>0)d=\"coords\";else if(p>1){let g=ft(i),x=r.shape.map((b,C)=>`coords.${Oo(C+l)}`).join(\", \");d=`${g}(${x})`}else d=\"coords\";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(e)} {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return ${Ae(e)}(${n}[getIndexFromCoords${h}(${d}, ${f})${e===1?\"\":` / ${e}`}]);\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> ${Ae(e)} {\n var coords = coordsIn;\n ${m}\n return ${Ae(e)}(${n}[getIndexFromCoords${h}(${d}, ${f})${e===1?\"\":` / ${e}`}]);\n }\n`}function aae(r,t,e,o){let n=nae(r,e);return r.shape.length<=t.length&&(n+=sae(r,t,e,o)),n}function iae(r,t){let{x:e,y:o=[],z:n=[]}=t,s=r.length,a=e.length+o.length+n.length;if(a!==s)return\"\";if(e.length===s)return`fn getOutputCoords() -> ${ft(s)}{\n let globalIndex = getGlobalIndex();\n return getCoordsFromIndex(globalIndex);\n }\n `;let i=\"\",p=[e,o,n];for(let m=0;m ${c} {\n ${i}\n`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(\",\")}); }`,l}function uae(r){let t=\"\";switch(r){case 0:case 1:t+=`\n fn getOutputIndexFromCoords(coords : i32) -> i32 {\n return coords;\n }\n `;break;case 2:t+=`\n fn getOutputIndexFromCoords(coords : vec2) -> i32 {\n return dot(coords, vec2(uniforms.outShapeStrides, 1));\n }\n `;break;case 3:t+=`\n fn getOutputIndexFromCoords(coords : vec3) -> i32 {\n return dot(coords, vec3(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n }\n `;break;case 4:t+=`\n fn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n }\n `;break;case 5:t+=`\n fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u;\n }\n `;break;case 6:t+=`\n fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u * uniforms.outShapeStrides.u +\n coords.v;\n }\n `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return t}function JB(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function Cu(r,t=1){if(r===\"float32\")return Ae(t,\"f32\");if(r===\"int32\"||r===\"bool\")return Ae(t,\"i32\");throw new Error(`type ${r} is not supported.`)}function pae(r,t,e){let o=r.length,n=Cu(t,e),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Ae(e)}) {\n result[flatIndex] = ${n}(value);\n }\n\n fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(e,\"i32\")}) {\n result[flatIndex] = ${n}(value);\n }\n `;if(o>=2){let a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=ft(o);s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(e)}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex${e===1?\"\":` / ${e}`}, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(e,\"i32\")}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex${e===1?\"\":` / ${e}`}, value);\n }\n `}return s}function cae(r){let t=/(\\w+)\\s*:\\s*vec(5|6)/g;r=r.replace(t,o=>\"@align(16) \"+o);let e=/vec(5|6)\\s*,\\s*(\\w+)/g;return r=r.replace(e,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function YB(r){return!(r.dispatchLayout.hasOwnProperty(\"y\")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty(\"z\")&&r.dispatchLayout.z.length!==0)}var Qv={};qe(Qv,{GPUBytesPerElement:()=>qg,MatMulProgramType:()=>Mo,assertNotComplex:()=>mm,computeDispatch:()=>H,computeWorkPerThreadForConv2d:()=>cm,computeWorkgroupInfoForMatMul:()=>Yv,computeWorkgroupSizeForConv2d:()=>pm,flatDispatchLayout:()=>X,isWebGPUSupported:()=>lm,tilesFitEvenlyIntoShape:()=>mae});var kp=r=>{let t=1;for(let e=0;ee%r[o]===0)}function H(r,t,e=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(kp(r.x.map(i=>t[i]))/(e[0]*o[0])),r.y?Math.ceil(kp(r.y.map(i=>t[i]))/(e[1]*o[1])):1,r.z?Math.ceil(kp(r.z.map(i=>t[i]))/(e[2]*o[2])):1];return[n,s,a]}function Yv(r,t,e,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),t<=16&&e<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function pm(r,t,e=!1){if(e)return[8,8,1];let o=kp(r.x.map(s=>t[s])),n=kp(r.y.map(s=>t[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function cm(r,t,e=!1){if(e)return[4,4,1];let o=kp(r.x.map(s=>t[s])),n=kp(r.y.map(s=>t[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function X(r){return{x:r.map((t,e)=>e)}}function qg(r){if(r===\"float32\"||r===\"int32\"||r===\"bool\"||r===\"string\")return 4;if(r===\"complex64\")return 8;throw new Error(`Unknown dtype ${r}`)}function lm(){return!!(globalThis&&globalThis.navigator&&globalThis.navigator.gpu)}function mm(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!==\"complex64\",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var Mo;(function(r){r[r.MatMulReduceProgram=0]=\"MatMulReduceProgram\",r[r.MatMulSplitKProgram=1]=\"MatMulSplitKProgram\",r[r.MatMulSmallOutputSizeProgram=2]=\"MatMulSmallOutputSizeProgram\",r[r.MatMulPackedProgram=3]=\"MatMulPackedProgram\",r[r.MatMulMax=4]=\"MatMulMax\"})(Mo||(Mo={}));var dae=A().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),fae=(r,t)=>{let e=r.limits.maxComputeWorkgroupsPerDimension,o=t.dispatchLayout,n=t.dispatch;if(n.every(a=>a<=e))return n;y.assert(n[0]>e&&o.y===void 0&&o.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let s=Math.ceil(Math.sqrt(n[0]));return s>e?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=e,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[s,s,s]):[s,s,1]},Kc=class r extends so{nextDataId(){return r.nextDataId++}constructor(t,e){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchCountInPass=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.queryResolveBuffer=null,this.querySet=null,this.querySetCount=2,this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,this.hasReadSyncWarned=!1,this.hasTimestampQueryWarned=!1,!lm())throw new Error(\"WebGPU is not supported on this device\");this.pipelineCache={},this.device=t,this.queue=t.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new Gg(e),this.supportTimestampQuery=this.device.features.has(\"timestamp-query\"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Hg(this.device),this.textureManager=new Kg(this.device),this.tensorMap=new Bo(this,ur()),A().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:t,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(t,e=!1){if(!this.tensorMap.has(t))return!0;let o=this.tensorMap.get(t);return e?o.refCount=0:o.refCount--,o.refCount>0?!1:(o.complexTensorInfos!=null&&(this.disposeData(o.complexTensorInfos.real.dataId),this.disposeData(o.complexTensorInfos.imag.dataId)),this.commandQueueOwnedIds.has(t)?(this.tensorDataPendingDisposal.push(t),!0):(this.releaseResource(t),this.tensorMap.delete(t),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(t){let e=this.tensorMap.get(t);if(!(!e||!e.resource)){if(e.external){e.resource=null;return}e.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(e.resource):e.resource instanceof GPUTexture&&this.textureManager.releaseTexture(e.resource),e.resource=null}}refCount(t){return this.tensorMap.has(t)?this.tensorMap.get(t).refCount:0}incRef(t){let e=this.tensorMap.get(t);e.refCount++}decRef(t){if(this.tensorMap.has(t)){let e=this.tensorMap.get(t);e.refCount--}}write(t,e,o){if(o===\"complex64\"&&t!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:e,values:t,refCount:1}),n}move(t,e,o,n,s){if(n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.tensorMap.set(t,{dtype:n,shape:o,values:e,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(t=>{this.releaseResource(t),this.tensorMap.delete(t)}),this.uniformPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t)),this.stagingPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let t;try{t=await Promise.all(Object.values(this.pipelineCache))}catch(e){throw new Error(e.message)}Object.keys(this.pipelineCache).map((e,o)=>{this.pipelineCache[e]=t[o]})}async getBufferData(t){if(A().getBool(\"WEBGPU_ENGINE_COMPILE_ONLY\"))return console.warn(\"The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false\"),null;let e=t.size,o=this.bufferManager.acquireBuffer(e,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,o,0,e),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o),A().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(y.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(t,e){let o=this.tensorMap.get(t);return o.values=e,o.values}readSync(t){let e=this.tensorMap.get(t),{values:o,complexTensorInfos:n}=e;if(o!=null||e.dtype===\"string\")return o;if(e.dtype===\"complex64\"){let h=this.readSync(n.real.dataId),g=this.readSync(n.imag.dataId),x=y.convertBackendValuesAndArrayBuffer(w.mergeRealAndImagArrays(h,g).buffer,\"float32\");return this.convertAndCacheOnCPU(t,x),x}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn(\"The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead.\"));let s=[\"opaque\",\"premultiplied\"],a=e.resource,i=a.size;y.assert(i%4===0,()=>\"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.\");let p=i/4,u=new ArrayBuffer(i),c=256,l=256,m=s.map(h=>new OffscreenCanvas(c,l)),d=new OffscreenCanvas(c,l);this.endComputePassEncoder(),m.map((h,g)=>{let x=h.getContext(\"webgpu\");return x.configure({device:this.device,format:\"bgra8unorm\",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),x.getCurrentTexture()}).map((h,g)=>{let x=c*4,b=(R,D,P)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:a,bytesPerRow:x,offset:P},{texture:h},{width:R,height:D}),this.submitQueue();let O=d.getContext(\"2d\",{willReadFrequently:!0});O.clearRect(0,0,R,D),O.drawImage(m[g],0,0);let M=O.getImageData(0,0,R,D).data,L=s[g],B=new Uint8ClampedArray(u,P,R*D*4);for(let z=0;z0&&(b(S,k,_),_+=k*(c*4)),S=E%c,S>0&&b(S,1,_)});let f=y.convertBackendValuesAndArrayBuffer(u,e.dtype);return this.convertAndCacheOnCPU(t,f),f}async read(t){if(!this.tensorMap.has(t))throw new Error(`Tensor ${t} was not registered!`);let e=this.tensorMap.get(t),{values:o}=e;if(o!=null)return o;let n;if(e.dtype===\"complex64\"){let s=await Promise.all([this.read(e.complexTensorInfos.real.dataId),this.read(e.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=w.mergeRealAndImagArrays(a,i)}else{let s=await this.getBufferData(e.resource);n=y.convertBackendValuesAndArrayBuffer(s,e.dtype)}return this.convertAndCacheOnCPU(t,n),n}copyBuffer(t){let e=t.size,o=t.usage,n=this.bufferManager.acquireBuffer(e,o);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,n,0,e),this.submitQueue(),n}createTensorFromGPUData(t,e,o){let n=t.buffer;if(o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. \");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:e,values:null,refCount:1,external:t.zeroCopy});let a=this.tensorMap.get(s),i=qg(a.dtype)*y.sizeFromShape(a.shape);if(t.buffer.sizey.decodeString(n));return me(t.shape,t.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(t.shape,t.dtype,e)}async time(t){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --enable-dawn-features=allow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled.\"),this.hasTimestampQueryWarned=!0);let e=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(t,e,o){return e===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,t,e),shape:t,dtype:e}}tensorToBinding(t){if(!t)return null;let o=this.tensorMap.get(t.dataId).resource;return o instanceof GPUBuffer?{buffer:o}:o instanceof GPUTexture?o.createView():o}uploadToGPU(t){let e=this.tensorMap.get(t);if(e.resource!=null)return;let o=qg(e.dtype)*y.sizeFromShape(e.shape),n,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(e.values){if(n=this.bufferManager.acquireBuffer(o,s,!0),n.mapState===\"unmapped\"){let a=this.bufferManager.acquireBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),i=a.getMappedRange();e.dtype===\"int32\"||e.dtype===\"bool\"?new Int32Array(i).set(e.values):new Float32Array(i).set(e.values),a.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(a,0,n,0,o),this.stagingPendingDisposal.push(a)}else{let a=n.getMappedRange();e.dtype===\"int32\"||e.dtype===\"bool\"?new Int32Array(a).set(e.values):new Float32Array(a).set(e.values),n.unmap()}e.values=null}else n=this.bufferManager.acquireBuffer(o,s);e.resource=n}makeUniforms(t){let e=0,o=0,n=[],s=1;t.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),u>s&&(s=u),e=Math.ceil(e/u)*u,o=p.data.length,n.push(e),e+=p.data.length*4}),e=Math.ceil(e/s)*s;let a=new ArrayBuffer(e);t.forEach((p,u)=>{let c=n[u];p.type===\"int32\"?new Int32Array(a,c,p.data.length).set(p.data):p.type===\"uint32\"?new Uint32Array(a,c,p.data.length).set(p.data):new Float32Array(a,c,p.data.length).set(p.data)});let i=this.bufferManager.acquireBuffer(e,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(i,0,a,0,e),this.uniformPendingDisposal.push(i),{offset:0,size:e,buffer:i}}runWebGPUProgram(t,e,o,n,s){if(s||(s=this.makeTensorInfo(t.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),t.dispatch=fae(this.device,t);let a=e.map((p,u)=>{if(p.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(p.dataId),{dtype:this.tensorMap.get(p.dataId).dtype,shape:p.shape,name:t.variableNames[u]}});t.shaderKey=ZB(t,a,s);let i=A().getBool(\"WEBGPU_ENGINE_COMPILE_ONLY\");return t.shaderKey in this.pipelineCache||(this.pipelineCache[t.shaderKey]=QB(this.device,t,a,s,i)),t.pipeline=this.pipelineCache[t.shaderKey],i||this.recordAndSubmit(t,s,e,n),s}recordAndSubmit(t,e,o,n){if(t.pipeline instanceof Promise)throw new Error(\"Please call checkCompileCompletionAsync to ensure parallel compilation is done!\");let s=[],a=[],i=\"int32\";if(t.pixelsOpType==null){s.push({type:\"float32\",data:[NaN]},{type:\"float32\",data:[1/0]}),a=o.concat(e).map(d=>d.shape);let m=\"int32\";a.map(d=>{s.push({type:m,data:d});let f=y.computeStrides(d);s.push({type:m,data:f})})}else{let m=y.computeStrides(e.shape);s.push({type:i,data:m})}if(t.size){let m=y.sizeFromShape(t.outputShape);s.push({type:i,data:[t.outputComponent?m/t.outputComponent:m]})}n&&(s=[...s,...n]);let p=[this.tensorToBinding(e),...o.map(m=>this.tensorToBinding(m)),this.makeUniforms(s)];o.forEach(m=>{this.commandQueueOwnedIds.add(m.dataId)}),this.commandQueueOwnedIds.add(e.dataId);let u=this.device.createBindGroup({layout:t.pipeline.getBindGroupLayout(0),entries:p.map((m,d)=>({binding:d,resource:m}))}),c=this.activeTimers!=null;this.ensureCommandEncoderReady();let l={};c&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:this.querySetCount})),l.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:0,endOfPassWriteIndex:1},this.computePassEncoder=this.commandEncoder.beginComputePass(l)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(l)),this.computePassEncoder.setPipeline(t.pipeline),this.computePassEncoder.setBindGroup(0,u),this.computePassEncoder.dispatchWorkgroups(t.dispatch[0],t.dispatch[1],t.dispatch[2]),this.dispatchCountInPass++,(c||A().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchCountInPass||t.pixelsOpType===Ci.DRAW)&&(this.endComputePassEncoder(),c?this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let t=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.querySetCount*8),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let e=new BigUint64Array(t.getMappedRange()),o=Number(e[1]-e[0])/1e6;return t.unmap(),this.bufferManager.releaseBuffer(t),o}shouldExecuteOnCPU(t,e=dae){return A().getBool(\"WEBGPU_CPU_FORWARD\")&&t.every(o=>this.tensorMap.get(o.dataId).resource==null&&y.sizeFromShape(o.shape){let r={powerPreference:A().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},t=await navigator.gpu.requestAdapter(r),e={},o=[];t.features.has(\"timestamp-query\")&&o.push(\"timestamp-query\"),t.features.has(\"bgra8unorm-storage\")&&o.push([\"bgra8unorm-storage\"]),e.requiredFeatures=o;let n=t.limits;e.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize,maxBufferSize:n.maxBufferSize,maxComputeWorkgroupSizeX:n.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:n.maxComputeInvocationsPerWorkgroup};let s=await t.requestDevice(e),a=await t.requestAdapterInfo();return new Kc(s,a)},3);var fe;(function(r){r[r.ADD=0]=\"ADD\",r[r.ATAN2=1]=\"ATAN2\",r[r.COMPLEX_MULTIPLY_IMAG=2]=\"COMPLEX_MULTIPLY_IMAG\",r[r.COMPLEX_MULTIPLY_REAL=3]=\"COMPLEX_MULTIPLY_REAL\",r[r.DIV=4]=\"DIV\",r[r.ELU_DER=5]=\"ELU_DER\",r[r.EQUAL=6]=\"EQUAL\",r[r.FLOOR_DIV=7]=\"FLOOR_DIV\",r[r.GREATER=8]=\"GREATER\",r[r.GREATER_EQUAL=9]=\"GREATER_EQUAL\",r[r.LESS=10]=\"LESS\",r[r.LESS_EQUAL=11]=\"LESS_EQUAL\",r[r.LOGICAL_AND=12]=\"LOGICAL_AND\",r[r.LOGICAL_OR=13]=\"LOGICAL_OR\",r[r.MAX=14]=\"MAX\",r[r.MIN=15]=\"MIN\",r[r.MOD=16]=\"MOD\",r[r.MUL=17]=\"MUL\",r[r.NOT_EQUAL=18]=\"NOT_EQUAL\",r[r.POW=19]=\"POW\",r[r.PRELU=20]=\"PRELU\",r[r.SQUARED_DIFFERENCE=21]=\"SQUARED_DIFFERENCE\",r[r.SUB=22]=\"SUB\"})(fe||(fe={}));var hae=\"let resultTemp = a + b;\",gae=\"let resultTemp = atan2(a, b);\",xae=\"let resultTemp = areal * breal - aimag * bimag;\",yae=\"let resultTemp = areal * bimag + aimag * breal;\",bae=\"let resultTemp = a / b;\",Cae=\"let resultTemp = select(a * (b + 1.0), a, b >= b - b);\",wae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a == b);\n`,Sae=`\n let remainder =\n select(a % b, round(a % b), (round(a) == a) & (round(b) == b));\n let quotient = (a - remainder) / b;\n let resultTemp =\n round(select(quotient, quotient - 1, sign(remainder) == -sign(b)));\n`,Iae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a > b);\n`,vae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a >= b);\n`,kae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a < b);\n`,Nae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a <= b);\n`,Tae=\"return f32(a >= 1.0 && b >= 1.0);\",_ae=`return (vec4(a >= vec4(1.0)) *\n vec4(b >= vec4(1.0)));`,$ae=\"return f32(a >= 1.0 || b >= 1.0);\",Eae=`return min(vec4(a >= vec4(1.0)) +\n vec4(b >= vec4(1.0)), vec4(1.0));`,Rae=\"let resultTemp = max(a, b);\",Dae=\"let resultTemp = min(a, b);\",Aae=`\n let isNaN = b == 0.;\n var resultTemp = a % b;\n resultTemp = select((resultTemp + b) % b, resultTemp,\n (a < 0. && b < 0.) || (a >= 0. && b > 0.));\n`,Fae=`\n let isNaN = !vec4(b);\n var resultTemp = vec4(a % b);\n if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {\n resultTemp[0] = (resultTemp[0] + b[0]) % b[0];\n }\n if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {\n resultTemp[1] = (resultTemp[1] + b[1]) % b[1];\n }\n if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {\n resultTemp[2] = (resultTemp[2] + b[2]) % b[2];\n }\n if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {\n resultTemp[3] = (resultTemp[3] + b[3]) % b[3];\n }\n`,Pae=\"let resultTemp = a * b;\",Oae=`\n var resultTemp = f32(a != b);\n let valueForNaN = 1.0;\n`,Mae=`\n var resultTemp = vec4(a != b);\n let valueForNaN = 1.0;\n`,Lae=`\n let isNaN = a < 0.0 && floor(b) < b;\n if (b == 0.0) {\n return 1.0;\n }\n var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b),\n round(abs(b) % 2.0) != 1.0);\n`,Bae=`\n let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1);\n let isModRound1 = vec4(isModRound1Bool);\n let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n var resultTemp = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n let isExpZero = b == vec4(0.0);\n if (isExpZero.r) {\n resultTemp.r = 1.0;\n }\n if (isExpZero.g) {\n resultTemp.g = 1.0;\n }\n if (isExpZero.b) {\n resultTemp.b = 1.0;\n }\n if (isExpZero.a) {\n resultTemp.a = 1.0;\n }\n let isNaN = (a < vec4(0.0)) & (floor(b) < b);\n`,zae=\"if (a < 0.0) { return b * a; } return a;\",Vae=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,Wae=\"let resultTemp = (a - b) * (a - b);\",Uae=\"let resultTemp = a - b;\";function qc(r,t){let e;do{switch(r){case fe.ATAN2:e=gae;break;case fe.MAX:e=Rae;break;case fe.MIN:e=Dae;break;case fe.MOD:e=t?Fae:Aae;break;case fe.NOT_EQUAL:e=t?Mae:Oae;break;case fe.POW:e=t?Bae:Lae;break;default:continue}let o,n,s;return t?(o=\"isnanVec4\",n=\"vec4\",s=\"vec4\"):(o=\"isnan\",n=\"f32\",s=\"bool\"),`\n let aIsNaN = ${o}(a);\n let aPostLegalization = select(a, ${n}(42), aIsNaN);\n let bIsNaN = ${o}(b);\n let bPostLegalization = select(b, ${n}(42), bIsNaN);\n let isNaN = false;\n let valueForNaN = uniforms.NAN;\n {\n let a = aPostLegalization;\n let b = bPostLegalization;\n ${e}\n return select(\n resultTemp, ${n}(valueForNaN),\n ${s}(isNaN) | aIsNaN | bIsNaN);\n }\n `}while(!1);switch(r){case fe.ADD:e=hae;break;case fe.COMPLEX_MULTIPLY_IMAG:e=yae;break;case fe.COMPLEX_MULTIPLY_REAL:e=xae;break;case fe.DIV:e=bae;break;case fe.ELU_DER:e=Cae;break;case fe.EQUAL:e=wae;break;case fe.FLOOR_DIV:e=Sae;break;case fe.GREATER:e=Iae;break;case fe.GREATER_EQUAL:e=vae;break;case fe.LESS:e=kae;break;case fe.LESS_EQUAL:e=Nae;break;case fe.LOGICAL_AND:return t?_ae:Tae;case fe.LOGICAL_OR:return t?Eae:$ae;case fe.MUL:e=Pae;break;case fe.PRELU:return t?Vae:zae;case fe.SQUARED_DIFFERENCE:e=Wae;break;case fe.SUB:e=Uae;break;default:}return`\n ${e}\n return resultTemp;\n `}var Z;(function(r){r[r.ABS=0]=\"ABS\",r[r.ACOS=1]=\"ACOS\",r[r.ACOSH=2]=\"ACOSH\",r[r.ASIN=3]=\"ASIN\",r[r.ASINH=4]=\"ASINH\",r[r.ATAN=5]=\"ATAN\",r[r.ATANH=6]=\"ATANH\",r[r.CEIL=7]=\"CEIL\",r[r.COS=8]=\"COS\",r[r.COSH=9]=\"COSH\",r[r.ELU=10]=\"ELU\",r[r.ERF=11]=\"ERF\",r[r.EXP=12]=\"EXP\",r[r.EXPM1=13]=\"EXPM1\",r[r.FLOOR=14]=\"FLOOR\",r[r.IS_FINITE=15]=\"IS_FINITE\",r[r.IS_INF=16]=\"IS_INF\",r[r.IS_NAN=17]=\"IS_NAN\",r[r.LINEAR=18]=\"LINEAR\",r[r.LOG=19]=\"LOG\",r[r.LOG1P=20]=\"LOG1P\",r[r.LOGICAL_NOT=21]=\"LOGICAL_NOT\",r[r.NEG=22]=\"NEG\",r[r.RELU=23]=\"RELU\",r[r.RELU6=24]=\"RELU6\",r[r.LEAKYRELU=25]=\"LEAKYRELU\",r[r.RECIPROCAL=26]=\"RECIPROCAL\",r[r.ROUND=27]=\"ROUND\",r[r.RSQRT=28]=\"RSQRT\",r[r.SELU=29]=\"SELU\",r[r.SIGMOID=30]=\"SIGMOID\",r[r.SIGN=31]=\"SIGN\",r[r.SIN=32]=\"SIN\",r[r.SINH=33]=\"SINH\",r[r.SOFTPLUS=34]=\"SOFTPLUS\",r[r.SQRT=35]=\"SQRT\",r[r.SQUARE=36]=\"SQUARE\",r[r.STEP=37]=\"STEP\",r[r.TAN=38]=\"TAN\",r[r.TANH=39]=\"TANH\",r[r.TO_INT=40]=\"TO_INT\"})(Z||(Z={}));var Gae=\"return abs(a);\",Hae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return acos(a);\n`,Kae=`\n if (a < 1.) {\n return uniforms.NAN;\n }\n return acosh(a);\n`,qae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return asin(a);\n`,jae=\"return asinh(a);\",Xae=`\n if (isnan(a)) {\n return uniforms.NAN;\n }\n return atan(a);\n`,Yae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n if (a == 1.) {\n return uniforms.INFINITY;\n }\n if (a == -1.) {\n return -uniforms.INFINITY;\n }\n return atanh(a);\n`,Qae=\"return ceil(a);\",Zae=\"return cos(a);\",Jae=`\n let e2x = exp(-a);\n return (e2x + 1.0 / e2x) / 2.0;\n`,eie=\"return exp(a) - 1.0;\",tie=\"if (a >= 0.0) { return a; } return (exp(a) - 1.0);\",rie=`\n var resFloat = exp(a) - vec4(1.0);\n if (a.r >= 0.0) {\n resFloat.r = a.r;\n }\n if (a.g >= 0.0) {\n resFloat.g = a.g;\n }\n if (a.b >= 0.0) {\n resFloat.b = a.b;\n }\n if (a.a >= 0.0) {\n resFloat.a = a.a;\n }\n return resFloat;\n`,oie=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n let p = ${w.ERF_P};\n let a1 = ${w.ERF_A1};\n let a2 = ${w.ERF_A2};\n let a3 = ${w.ERF_A3};\n let a4 = ${w.ERF_A4};\n let a5 = ${w.ERF_A5};\n\n let sign = sign(a);\n let absA = abs(a);\n let t = 1.0 / (1.0 + p * absA);\n return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));\n`,nie=\"return exp(a);\",sie=\"return floor(a);\",aie=\"return f32(!isnan(a) && !isinf(a));\",iie=\"return f32(isinf(a));\",uie=\"return f32(isnan(a));\",pie=\"return a;\",cie=`if (a < 0.0) { return uniforms.NAN; }\n return log(a);`,lie=`\n if (isnan(a)) { return a; }\n return log(1.0 + a);\n`,mie=\"return f32(!(a >= 1.0));\",die=\"return -a;\",fie=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",hie=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,gie=\"return 1.0 / a;\",xie=\"return select(a, 0.0, a < 0.0);\",yie=\"return clamp(a, 0.0, 6.0);\",bie=\"return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));\",Cie=`\n return select(a, vec4(0.0), a < vec4(0.0));\n`,wie=\"return round(a);\",Sie=\"return inverseSqrt(a);\",Iie=`\n if (a >= 0.0) {\n return ${w.SELU_SCALE} * a;\n } else {\n return ${w.SELU_SCALEALPHA} * (exp(a) - 1.0);\n }\n`,vie=\"return 1.0 / (1.0 + exp(-1.0 * a));\",kie=\"return sign(a);\",Nie=\"return sin(a);\",Tie=`\n let e2x = exp(a);\n return (e2x - 1.0 / e2x) / 2.0;\n`,_ie=`\n let epsilon = 1.1920928955078125e-7;\n let threshold = log(epsilon) + 2.0;\n\n let too_large = a > -threshold;\n let too_small = a < threshold;\n let exp_a = exp(a);\n\n if (too_large) {\n return a;\n } else if (too_small) {\n return exp_a;\n } else {\n return log(exp_a + 1.0);\n }\n`,$ie=\"return sqrt(a);\",Eie=\"return a * a;\",Rie=`\n if (isnan(a)) {\n return a;\n }\n\n return select(uniforms.stepAlpha, 1.0, a > 0.0);\n`,Die=\"return tan(a);\",Aie=`\n let e2x = exp(-2.0 * abs(a));\n return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,Fie=\"return f32(i32((a)));\";function wi(r,t){switch(r){case Z.ABS:return Gae;case Z.ACOS:return Hae;case Z.ACOSH:return Kae;case Z.ASIN:return qae;case Z.ASINH:return jae;case Z.ATAN:return Xae;case Z.ATANH:return Yae;case Z.COS:return Zae;case Z.COSH:return Jae;case Z.CEIL:return Qae;case Z.ELU:return t?rie:tie;case Z.ERF:return oie;case Z.EXP:return nie;case Z.EXPM1:return eie;case Z.FLOOR:return sie;case Z.IS_FINITE:return aie;case Z.IS_INF:return iie;case Z.IS_NAN:return uie;case Z.LINEAR:return pie;case Z.LOG:return cie;case Z.LOG1P:return lie;case Z.LOGICAL_NOT:return mie;case Z.NEG:return die;case Z.LEAKYRELU:return t?hie:fie;case Z.RECIPROCAL:return gie;case Z.RELU:return t?Cie:xie;case Z.RELU6:return t?bie:yie;case Z.ROUND:return wie;case Z.RSQRT:return Sie;case Z.SELU:return Iie;case Z.SIGMOID:return vie;case Z.SIGN:return kie;case Z.SIN:return Nie;case Z.SINH:return Tie;case Z.SOFTPLUS:return _ie;case Z.SQRT:return $ie;case Z.SQUARE:return Eie;case Z.STEP:return Rie;case Z.TAN:return Die;case Z.TANH:return Aie;case Z.TO_INT:return Fie;default:throw new Error(`BinaryType ${r} is not implemented!`)}}function dr(r,t=!1,e=!1,o=3){if(r===null)return\"\";let n=\"\";if(r===\"linear\")n=wi(Z.LINEAR);else if(r===\"relu\")n=wi(Z.RELU,e);else if(r===\"elu\")n=wi(Z.ELU,e);else if(r===\"relu6\")n=wi(Z.RELU6,e);else if(r===\"prelu\")n=qc(fe.PRELU,e);else if(r===\"sigmoid\")n=wi(Z.SIGMOID,e);else if(r===\"leakyrelu\")n=wi(Z.LEAKYRELU,e);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=Ae(e?4:1),i=\"\";return t?i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n let b = getPreluActivationWeightsByOutputCoords(coords);\n ${n}\n }`:i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n ${n}\n }`,i}function Qr(r,t){return`\n ${r?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n ${t?\"value = activation(value, coords);\":\"\"}\n `}function Zv(r,t,e=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=`\n ${r?\"value = getA(batch, col, row);\":\"value = getA(batch, row, col);\"}\n\n `,i=t?\"value = getB(batch, col, row);\":\"value = getB(batch, row, col);\";return`\n fn mm_readA(batch: i32, row: i32, col: i32) -> ${Ae(s)} {\n var value = ${Ae(s)}(0.0);\n ${e&&n?a:`\n ${r?\"if(row < uniforms.dimAOuter && col < uniforms.dimInner)\":\"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])\"}\n {\n ${a}\n }\n `}\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, col: i32) -> ${Ae(s)} {\n var value = ${Ae(s)}(0.0);\n ${i}\n return value;\n }\n `}function dm(r,t,e,o,n=!1,s=!1,a=!1,i=1){return`\n ${Zv(e,o,n,s,a,i)}\n fn mm_write(batch: i32, row: i32, col: i32, valueIn: ${Ae(i)}) {\n ${n&&s?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n {\n var value = valueIn;\n let coords = vec3(batch, row, col);\n ${Qr(r,t)}\n setOutputAtCoords(coords[0], coords[1], coords[2], value);\n }\n }\n `}var Pie=(r,t)=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol * ${t});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRow + innerRow,\n kStart + inputCol * ${t});\n `,Oie=(r,t,e,o)=>{if(r)return`\n for (var k = 0; k < ${o}; k++) {\n let BCached0 = mm_Bsub[k][tileCol];\n let ACached0 = mm_Asub[k][localRow];\n for (var i = 0; i < ${e}; i++) {\n acc[i] = fma(BCached0, vec4(ACached0[i]), acc[i]);\n }\n }`;{let n=\"\",s=\"\";for(let a=0;a(ACached[${a}]), acc[i]);`;return`\n for (var k = 0; k < ${o/t}; k++) {\n ${n}\n for (var i = 0; i < ${e}; i++) {\n let ACached = mm_Asub[tileRow + i][k];\n ${s}\n }\n }`}};function Np(r,t,e=!1,o=32,n=!1,s=32,a=!1){let i=t[1]*r[1],p=t[0]*r[0],u=e?i:o,c=e?o:i,l=u/t[0],m=o/t[1],d=r[1],f=r[0];return y.assert((e&&l===4&&r[1]===4||!e&&(l===3||l===4))&&u%t[0]===0&&o%t[1]===0&&r[0]===4,()=>`If transposeA ${e} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4.\n Otherwise, innerElementSize ${l} must be 3 or 4.\n tileAWidth ${u} must be divisible by workgroupSize[0]${t[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${r[0]} must be 4.`),`\n var mm_Asub : array, ${u/l}>, ${c}>;\n var mm_Bsub : array, ${p/r[0]}>, ${o}>;\n\n ${G()} {\n let localRow = i32(localId.y);\n let tileRow = localRow * ${d};\n let tileCol = i32(localId.x);\n\n let globalRow = i32(globalId.y) * ${d};\n let globalCol = i32(globalId.x) * ${f};\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!a?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!a?\"batch\":\"batch % uniforms.bShape[0]\"};\n let globalRowStart = i32(workgroupId.y) * ${i};\n\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc: array, ${d}>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${m};\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${Pie(e,l)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n ${Oie(e,l,d,o)}\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n }`}var ez=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRowStart + inputRow,\n kStart + inputCol);\n `,Mie=r=>r?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function Tp(r,t,e=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*t[1],u=r[0]*t[0],c=e?p:o,l=e?o:p;y.assert(l%t[1]===0&&c%t[0]===0&&o%t[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${t[0]}, tileInner ${o} must be divisible by workgroupSize[1]${t[1]}`);let m=l/t[1],d=c/t[0],f=o/t[1],h=r[1],g=r[0],x=a?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${p};\n let globalColStart = i32(workgroupId.x) * ${u};\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${t[1]}) {\n for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${t[0]}) {\n ${ez(e)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${t[1]}) {\n for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${t[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalColStart + inputCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let ACached = ${e?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let gRow = globalRowStart + localRow + innerRow * ${t[1]};\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let gCol = globalColStart + localCol + innerCol * ${t[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\n let tileRow = i32(localId.y) * ${h};\n let tileCol = i32(localId.x) * ${g};\n\n let globalRow = i32(globalId.y) * ${h};\n let globalCol = i32(globalId.x) * ${g};\n let globalRowStart = i32(workgroupId.y) * ${p};\n\n let tileRowA = i32(localId.y) * ${m};\n let tileColA = i32(localId.x) * ${d};\n let tileRowB = i32(localId.y) * ${f};\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n for (var innerCol = 0; innerCol < ${d}; innerCol++) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${ez(e)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalCol + innerCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n ${Mie(e)}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n }\n `;return`\n var mm_Asub : array, ${l}>;\n var mm_Bsub : array, ${o}>;\n\n ${G()} {\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!i?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!i?\"batch\":\"batch % uniforms.bShape[0]\"};\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc : array, ${h}>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${x}\n }\n `}var Lie=r=>r?`\n mm_readA(batchA, colA, globalRow),\n mm_readA(batchA, colA + 1, globalRow),\n mm_readA(batchA, colA + 2, globalRow),\n mm_readA(batchA, colA + 3, globalRow)\n `:`\n mm_readA(batchA, globalRow, colA),\n mm_readA(batchA, globalRow, colA + 1),\n mm_readA(batchA, globalRow, colA + 2),\n mm_readA(batchA, globalRow, colA + 3)\n `;function Bie(r,t=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let e=r[0]*4;return`\n var mm_Asub : array, ${r[0]}>;\n\n ${G()} {\n let tileCol = i32(localId.x);\n let globalCol = i32(globalId.x);\n let globalRow = i32(globalId.y);\n\n let numTiles = (uniforms.dimInner - 1) / ${e} + 1;\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n // Without this initialization strange values show up in acc.\n var acc = 0.0;\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n let colA = t * ${e} + tileCol * 4;\n mm_Asub[tileCol] = vec4(${Lie(t)});\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < ${e/4}; k++) {\n let rowB = t * ${e} + k * 4;\n let BCached = vec4(mm_readB(batchB, rowB, globalCol),\n mm_readB(batchB, rowB + 1, globalCol),\n mm_readB(batchB, rowB + 2, globalCol),\n mm_readB(batchB, rowB + 3, globalCol));\n\n let ACached = mm_Asub[k];\n acc = acc + dot(ACached, BCached);\n }\n\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var jg=class{constructor(t,e,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?t[1]:t[2];if(this.isVec4=(u%4===0&&!o||e[1]%4===0&&o)&&e[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=e[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=Yv(e[1],u,e[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(e[1],e[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(t,e,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=t%n===0,i=e%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights,this.isVec4)}\n ${dm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}\n ${this.isVec4?Np(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?Bie(this.workgroupSize,this.transposeA):Tp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)}\n `}};function zie(r){return`\n var sumValues : array;\n ${G()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n let row = coords[1];\n let col = coords[2];\n var sum = 0.0;\n let Length = uniforms.dimInner;\n for (var k = i32(localId.x); k < Length; k = k + ${r}) {\n let dataA = mm_readA(batchA, row, k);\n let dataB = mm_readB(batchB, k, col);\n sum = sum + dataA * dataB;\n }\n sumValues[localId.x] = sum;\n workgroupBarrier();\n\n for(var currentSize = ${r/2}u; currentSize > 1u;\n currentSize = currentSize / 2u) {\n if (localId.x < currentSize)\n {\n sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n }\n workgroupBarrier();\n }\n\n if (localId.x == 0u) {\n sum = sumValues[0] + sumValues[1];\n mm_write(batch, row, col, sum);\n }\n }\n `}var Xg=class{constructor(t,e=!1,o=!1,n=null,s=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[256,1,1],this.outputShape=t,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=e,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${e}_${o}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${dm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${zie(this.workgroupSize[0])}\n `}};function Vie(r){let t=r[1],e=r[0],o=t>e?t:e;return`\n var mm_Asub : array, ${t}>;\n var mm_Bsub : array, ${o}>;\n\n // If the output size is small for matrix multiplication, avoid to use vec4\n // and handle some elements per thread to optimally utilize the ALU.\n // Read data from global memory to registers firstly, then store them into\n // shared memory, so it is instruction-Level parallelism for arithmetic\n // operations and others handle IO operations between barrier api, makes ALU\n // and load/store units work simultaneously, could improves the performance.\n ${G()} {\n let tileRow = i32(localId.y);\n let tileCol = i32(localId.x);\n let globalRow = i32(globalId.y);\n let globalCol = i32(globalId.x);\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n\n // uniforms.dimInner should be greater than 0.\n let numTiles = (uniforms.dimInner - 1) / ${o} + 1;\n var acc = 0.0;\n\n var globalColA = tileCol;\n var globalRowB = 0;\n var regA = mm_readA(batchA, globalRow, globalColA);\n var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var t = 0; t < numTiles; t = t + 1) {\n mm_Asub[tileRow][tileCol] = regA;\n mm_Bsub[2 * tileRow][tileCol] = regB0;\n mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n workgroupBarrier();\n\n regA = mm_readA(batchA, globalRow, globalColA);\n regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var k = 0; k < ${o}; k = k + 1) {\n acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n }\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Yg=class{constructor(t,e,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push(\"bias\");let c=p!=null;c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${dm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${Vie(this.workgroupSize)}\n `}};var Qg=class{constructor(t,e,o=!1,n=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(t[0]===1,()=>\"MatMulSplitKProgram only supports batch = 1.\"),this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&e%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=H(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],e],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let t=this.outputComponent;return`\n ${Zv(!1,this.transposeB,!1,!1,!1,t)}\n fn mm_write(batch: i32, row : i32, col : i32, value : ${Ae(t)}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n let coords = vec3(batch, row, col);\n let flatIndex = getOutputIndexFromCoords(coords);\n // The problem is that we should initialize output to zero before using.\n // Otherwise, the original value will be added to the result.\n for (var i = 0; i < ${t}; i = i + 1) {\n ${Yr(\"&result[flatIndex + i]\",`${t>1?\"value[i]\":\"value\"}`,\"float32\")}\n }\n }\n }\n ${t===4?Np(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Tp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}\n `}},Zg=class{constructor(t,e=null,o=null,n=null){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=e!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var value = getXByOutputIndex(index);\n ${Qr(this.addBias,this.activation)}\n setOutputAtIndex(index, value);\n }\n }\n `}};var Jg=class{constructor(t){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.value);\n }\n }\n `}};function vt(r){let{backend:t,attrs:e}=r,{shape:o,value:n}=e,{dtype:s}=e;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),t.makeTensorInfo(o,s,a)}else{let a=new Jg(o),i=[{type:\"float32\",data:[n]}];return t.runWebGPUProgram(a,[],s,i)}}var tz={kernelName:sa,backendName:\"webgpu\",kernelFunc:vt};function pe(r){let{inputs:t,attrs:e}=r,{x:o}=t,{shape:n}=e,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var rz={kernelName:da,backendName:\"webgpu\",kernelFunc:pe};function _p({a:r,b:t,transposeA:e,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=t.shape.length,l=e?r.shape[u-2]:r.shape[u-1],m=o?t.shape[c-1]:t.shape[c-2],d=e?r.shape[u-1]:r.shape[u-2],f=o?t.shape[c-2]:t.shape[c-1],h=r.shape.slice(0,-2),g=t.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=Sr.assertAndGetBroadcastShape(r.shape.slice(0,-2),t.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${o} must match.`);let k=e?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=pe({inputs:{x:r},backend:n,attrs:{shape:k}}),R=pe({inputs:{x:t},backend:n,attrs:{shape:_}}),D=[E,R],P=Math.max(x,b),O=[E,R],M=[{type:\"int32\",data:[d]},{type:\"int32\",data:[f]},{type:\"int32\",data:[l]}],L,B,z=[P,d,f],U=A().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");if(U<0){let q=A().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),Y=q>0?q:n.thresholdToIncreaseWorkgroups,J=P*Math.ceil(d/32)*Math.ceil(f/32);J<=Y||d<=8&&J<=Y*2?P*d*f<=128?U=Mo.MatMulReduceProgram:P===1&&m>=2e3?U=Mo.MatMulSplitKProgram:U=Mo.MatMulSmallOutputSizeProgram:U=Mo.MatMulPackedProgram}switch(U){case Mo.MatMulReduceProgram:L=new Xg(z,e,o,s,p,a);break;case Mo.MatMulSplitKProgram:{if(B=vt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Qg(z,m,e,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let Y=new Zg(B.shape,s,p,a),J=null,re=[B];s&&re.push(s),a&&re.push(a),p===\"leakyrelu\"&&(J=[{type:\"float32\",data:[i]}],Y.uniforms+=\" alpha : f32,\");let ne=n.runWebGPUProgram(Y,re,B.dtype,J);D.push(B);let ee=pe({inputs:{x:ne},backend:n,attrs:{shape:S}});D.push(ne);for(let oe of D)n.disposeData(oe.dataId);return ee}break}case Mo.MatMulSmallOutputSizeProgram:L=new Yg(k,_,z,e,o,s,p,a);break;case Mo.MatMulPackedProgram:let q=n.adapterInfo.isIntel();L=new jg(k,z,e,o,s,p,a,q);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p===\"leakyrelu\"&&(M.push({type:\"float32\",data:[i]}),L.uniforms+=\" alpha : f32,\"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=pe({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let q of D)n.disposeData(q.dataId);return j}function Wie(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return _p({a:n,b:s,transposeA:p,transposeB:u,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var oz={kernelName:So,backendName:\"webgpu\",kernelFunc:Wie};var fm=class{constructor(t,e,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=w.assertAndGetBroadcastShape(e,o),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${t}`,this.op=t}getUserCode(){return`\n fn binaryOpComplex(\n areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n ${qc(this.op,!1)}\n }\n\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let areal = getARealByOutputIndex(index);\n let aimag = getAImagByOutputIndex(index);\n let breal = getBRealByOutputIndex(index);\n let bimag = getBImagByOutputIndex(index);\n setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n }\n }\n `}};var Si=class{constructor(t,e,o){if(this.size=!0,this.variableNames=[\"A\",\"B\"],this.outputShape=w.assertAndGetBroadcastShape(e,o),this.dispatchLayout=X(this.outputShape),this.op=t,this.useSharedMemoryWithA=e.length<=1&&o.length>1&&e[0]<128,this.useSharedMemoryWithB=o.length<=1&&e.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:e[0],this.shaderKey=`binary_${t}_${this.lastDimensionSize}`,this.type=\"shared\",this.workgroupSize=[256,1,1];else{let n=e.length>0&&e[e.length-1]%4===0,s=o.length>0&&o[o.length-1]%4===0;n&&s?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(y.isScalarShape(o)||o[o.length-1]===1)||s&&(y.isScalarShape(e)||e[e.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type=\"nonshared\",this.shaderKey=`binary_${t}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let t,e=this.outputComponent===4?\"vec4\":\"f32\",o=`\n fn binaryOperation(a : ${e}, b : ${e}) -> ${e} {\n ${qc(this.op,this.outputComponent===4)}\n };\n `;if(this.type===\"shared\"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);\n let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];\n let b = getBByOutputIndex(index);`;t=`\n ${o}\n var sharedBuf : array;\n ${G(\"index\")} {\n // Fill in the shared memory buffer.\n let localIndex = i32(localId.x);\n if(localIndex < ${this.lastDimensionSize}) {\n sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n }\n workgroupBarrier();\n\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n ${s}\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `}else t=`\n ${o}\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index * ${this.outputComponent});\n let a = ${e}(getAByOutputCoords(coords));\n let b = ${e}(getBByOutputCoords(coords));\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `;return t}};function At(r){let{inputs:t}=r,{x:e}=t;return r.backend.incRef(e.dataId),{dataId:e.dataId,shape:e.shape,dtype:e.dtype}}var nz={kernelName:Co,backendName:\"webgpu\",kernelFunc:At};function xo(r){let{inputs:t,backend:e}=r,{real:o,imag:n}=t,s=e.makeTensorInfo(o.shape,\"complex64\"),a=e.tensorMap.get(s.dataId),i=At({inputs:{x:o},backend:e}),p=At({inputs:{x:n},backend:e});return a.complexTensorInfos={real:i,imag:p},s}var sz={kernelName:Ri,backendName:\"webgpu\",kernelFunc:xo};var Zr=class{constructor(t,e,o=\"\"){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=e,o!==\"\"&&(this.uniforms=o),this.shaderKey=`unary_${e}`}getUserCode(){return`\n fn unaryOperation(a : f32) -> f32 {\n ${wi(this.op,!1)}\n }\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n setOutputAtIndex(index, unaryOperation(a));\n }\n }\n `}};function ye({opType:r,cpuKernelImpl:t,dtype:e}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=e||s.dtype;if(a.shouldExecuteOnCPU([s])&&t!=null){let u=a.tensorMap.get(s.dataId),c=t(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Zr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function et({opType:r,cpuKernelImpl:t,supportsComplex:e=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(e&&a.dtype===\"complex64\"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Si(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,S],dt(x.dtype,b.dtype))});else{let g=new fm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new fm(fe.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,\"float32\"),f=p.runWebGPUProgram(x,b,\"float32\")}let h=xo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype===\"string\"||i.dtype===\"string\"||p.shouldExecuteOnCPU([a,i]))&&t!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype===\"string\"?w.fromUint8ToStringArray(l):l,f=a.dtype===\"string\"?w.fromUint8ToStringArray(m):m,[h,g]=t(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Si(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:az,castImpl:iz,ceilImpl:uz,concatImpl:pz,equalImpl:cz,expImpl:lz,expm1Impl:mz,floorImpl:dz,floorDivImpl:fz,gatherNdImpl:hz,gatherV2Impl:gz,greaterEqualImpl:xz,greaterImpl:yz,lessEqualImpl:bz,lessImpl:Cz,logImpl:wz,maxImpl:Sz,maximumImpl:Iz,minimumImpl:vz,multiplyImpl:kz,negImpl:Nz,notEqualImpl:Tz,prodImpl:_z,rangeImpl:$z,rsqrtImpl:Ez,scatterImpl:Rz,simpleAbsImpl:Dz,sliceImpl:Az,stridedSliceImpl:Fz,stringNGramsImpl:Pz,subImpl:Oz,tileImpl:Mz,topKImpl:Lz,transposeImpl:Bz,uniqueImpl:KPt}=wc;var Uie=ye({opType:Z.ABS,cpuKernelImpl:Dz}),zz={kernelName:Xs,backendName:\"webgpu\",kernelFunc:Uie};var Gie=ye({opType:Z.ACOS}),Vz={kernelName:Vo,backendName:\"webgpu\",kernelFunc:Gie};var Hie=ye({opType:Z.ACOSH}),Wz={kernelName:Wo,backendName:\"webgpu\",kernelFunc:Hie};var Kie=et({opType:fe.ADD,cpuKernelImpl:az,supportsComplex:!0}),Uz={kernelName:io,backendName:\"webgpu\",kernelFunc:Kie};var ex=class{constructor(t){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t[0],this.variableNames=t.map((e,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let t=[];this.variableNames.forEach(n=>{t.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let e=this.variableNames.map(n=>`v${n}`).join(\" + \");return`\n ${G(\"index\")} {\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let flatIndex = index * ${this.workPerThread} + i;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n ${t.join(`\n `)}\n setOutputAtIndex(flatIndex, ${e});\n }\n }\n }\n `}};function qie(r){let{inputs:t,backend:e}=r,o=t;if(o.length===1)return At({inputs:{x:o[0]},backend:e});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new ex(s);return e.runWebGPUProgram(a,o,n)}var Gz={kernelName:Uo,backendName:\"webgpu\",kernelFunc:qie};var tx=class{constructor(t,e){this.variableNames=[\"A\"],this.workgroupSize=[16,16,1];let o=new Array(t.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let t=this.workgroupSize[0];return`\n var tile : array, ${this.workgroupSize[0]}>;\n ${G()} {\n var x = i32(workgroupId.x) * ${t} + i32(localId.x);\n var y = i32(workgroupId.y) * ${t} + i32(localId.y);\n let width = uniforms.outShape[0];\n let height = uniforms.outShape[1];\n if (x < width && y < height) {\n tile[localId.y][localId.x] = f32(A[y * width + x]);\n }\n workgroupBarrier();\n\n x = i32(workgroupId.y) * ${t} + i32(localId.x);\n y = i32(workgroupId.x) * ${t} + i32(localId.y);\n if (x < height && y < width) {\n setOutputAtIndex((y * height + x), tile[localId.x]\n [localId.y]);\n }\n }\n `}};var rx=class{constructor(t,e){this.variableNames=[\"A\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(t.length);for(let n=0;n6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=new Array(t);for(let o=0;o=32768&&o>=512?this.workgroupSize=[512,1,1]:t.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=e,this.shaderKey=`reduce_${e}`}getUserCode(){let t=\"\",e=\"0.0\",o=this.workgroupSize[0];this.reduceType===\"min\"||this.reduceType===\"max\"?(t=`\n if (isnan(candidate)) {\n bestValue = uniforms.NAN;\n } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n { bestValue = candidate; }`,e=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?t=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"?(t=\" bestValue = bestValue * candidate; \",e=\"1.0\"):this.reduceType===\"all\"?(t=\" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); \",e=\"1.0\"):this.reduceType===\"any\"&&(t=\" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); \",e=\"0.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n fn DIV_CEIL(a : u32, b : u32) -> u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestValues : array;\n `}\n fn getOffset(outputIndex : i32) -> i32 {\n let outputCoords = getCoordsFromIndex(outputIndex);\n let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n return offset;\n }\n ${G(\"index\")} {\n let outputIndex = index / ${o};\n let offset = getOffset(outputIndex);\n var bestValue = ${e};\n let Length = uniforms.reduceSize;\n let WorkPerThread = DIV_CEIL(u32(Length), ${o}u);\n for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n k = k + ${o}) {\n let candidate = f32(x[offset + k]);\n ${t}\n }\n xBestValues[localId.x] = bestValue;\n workgroupBarrier();\n\n var reduceSize = min(u32(Length), ${o}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n ${t}\n xBestValues[localId.x] = bestValue;\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n ${n}\n }\n }\n `}};var jie={mean:\"float32\",all:\"bool\",any:\"bool\"};function Jr(r,t,e,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(t,r.shape),p=i,u=w.getAxesPermutation(p,s),c=r;u!=null&&(c=xr({inputs:{x:r},attrs:{perm:u},backend:n}),p=w.getInnerMostAxes(p.length,s),a.push(c)),w.assertAxesAreInnerMostDims(o,p,s);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=l;e&&(d=w.expandShapeToKeepDim(l,i));let f;if((o===\"max\"||o===\"prod\")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case\"max\":let g=Sz(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case\"prod\":let{outVals:x,outShape:b,outDtype:C}=_z(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=jie[o]||oi(r.dtype),S=[{type:\"int32\",data:[h]}],k=new ox(b,o,n.device.limits.maxComputeWorkgroupSizeX),_=n.runWebGPUProgram(k,[c],C,S);a.push(_),f=pe({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function Xie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return Jr(n,a,s,\"all\",e)}var Kz={kernelName:Go,backendName:\"webgpu\",kernelFunc:Xie};function Yie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return Jr(n,a,s,\"any\",e)}var qz={kernelName:Ho,backendName:\"webgpu\",kernelFunc:Yie};var jc=class{constructor(t,e,o){this.workgroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let n=[e];this.op=o===\"min\"?\"<\":\">\";let[s,a]=w.computeOutAndReduceShapes(t,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=X(this.outputShape),y.sizeFromShape(a)<32?(this.type=\"plain\",this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type=\"shared\",this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=t,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let t=this.workgroupSize[0],e=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${Oo(this.inputShape.length-1)}`,o=()=>{let n=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+=\"outputCoords,\");else for(let s=0;s u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestIndices : array;\n var xBestValues : array;\n `}\n\n ${G(\"index\")} {\n let outputIndex = index / ${t};\n let reduceLength = ${e()};\n\n var bestIndex = i32(localId.x);\n var bestValue = uniforms.infinityValue;\n let outputCoords = getCoordsFromIndex(outputIndex);\n for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n k = k + ${t}) {\n let candidate = getX(${o()} k);\n if (!isnan(candidate) && candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = k;\n }\n }\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = bestIndex;\n workgroupBarrier();\n\n var reduceSize = min(u32(reduceLength), ${t}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n }\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n }\n }\n `:`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outputCoords = getCoordsFromIndex(index);\n var bestIndex = 0;\n var bestValue = getX(${o()} 0);\n let reduceLength = ${e()};\n for (var i = 1; i < reduceLength; i++) {\n let candidate = getX(${o()} i);\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = i;\n }\n }\n setOutputAtIndexI32(index, bestIndex);\n }\n }\n `}};function Qie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xr({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=new jc(p.shape,a[0],\"max\"),l=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],m=e.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>e.disposeData(d.dataId)),m}var jz={kernelName:Ys,backendName:\"webgpu\",kernelFunc:Qie};function Zie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xr({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=new jc(p.shape,a[0],\"min\"),l=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],m=e.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>e.disposeData(d.dataId)),m}var Xz={kernelName:Qs,backendName:\"webgpu\",kernelFunc:Zie};var Jie=ye({opType:Z.ASIN}),Yz={kernelName:Ko,backendName:\"webgpu\",kernelFunc:Jie};var eue=ye({opType:Z.ASINH}),Qz={kernelName:qo,backendName:\"webgpu\",kernelFunc:eue};var tue=ye({opType:Z.ATAN}),Zz={kernelName:jo,backendName:\"webgpu\",kernelFunc:tue};var rue=et({opType:fe.ATAN2}),Jz={kernelName:Yo,backendName:\"webgpu\",kernelFunc:rue};var oue=ye({opType:Z.ATANH}),eV={kernelName:Xo,backendName:\"webgpu\",kernelFunc:oue};var nx=class{constructor(t){this.variableNames=[\"x\"],this.uniforms=\"strides : vec2,\",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let xRCCorner = coords.yz * uniforms.strides;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n let value = getX(batch, xRCorner, xCCorner, d);\n setOutputAtIndex(index, value);\n }\n }\n `}};var Ba=class{constructor(t,e,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,\",this.workgroupSize=[128,1,1],this.size=!0,e===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=e,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${e}_${o}_${n}_${s}`}getUserCode(){let t;this.poolType===\"avg\"?t=\"resultValue = resultValue + value; count = count + 1.0;\":this.computePositions?t=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"((batch * uniforms.xShape[1] + xR) * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"(xR * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"wR * uniforms.filterDims.y + wC\"};\n }`:t=\"resultValue = max(value, resultValue);\";let e=\"resultValue\";return this.poolType===\"avg\"&&(e=\"resultValue / max(count, 1.0)\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n let xRCCorner = vec2(coords.yz) * uniforms.strides - uniforms.pads;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilations.x) {\n let xR = xRCorner + wR;\n\n if (xR < 0 || xR >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilations.y) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.y) {\n continue;\n }\n\n let value = getX(batch, xR, xC, d);\n ${t}\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${e});`}\n }\n }\n `}},wu=class{constructor(t,e,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,\",this.workgroupSize=[128,1,1],this.size=!0,e===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=e,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${e}_${o}_${n}_${s}`}getUserCode(){let t;this.poolType===\"avg\"?t=\"resultValue += value; count += 1.0;\":this.computePositions?t=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"(((batch * uniforms.xShape.y + xD) * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"((xD * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"wD * uniforms.filterDims.y * uniforms.filterDims.y + wR * uniforms.filterDims.z + wC\"};\n }`:t=\"resultValue = max(value, resultValue);\";let e=\"resultValue\";return this.poolType===\"avg\"&&(e=\"resultValue / max(count, 1.0)\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let xCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xDCorner = xCorner.x;\n let xRCorner = xCorner.y;\n let xCCorner = xCorner.z;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wD = 0; wD < uniforms.filterDims.x; wD++) {\n let xD = xDCorner + wD;\n if (xD < 0 || xD >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims.y; wR++) {\n let xR = xRCorner + wR;\n if (xR < 0 || xR >= uniforms.convDims.y) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.z; wC++) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.z) {\n continue;\n }\n\n let value = getX(batch, xD, xR, xC, ch);\n ${t}\n }\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${e});`}\n }\n }\n `}};function e0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{reductionIndices:s,keepDims:a}=o;return Jr(n,s,a,\"max\",e)}var tV={kernelName:zn,backendName:\"webgpu\",kernelFunc:e0};function t0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return Jr(n,a,s,\"mean\",e)}var rV={kernelName:Un,backendName:\"webgpu\",kernelFunc:t0};function sx(r,t,e,o){if(t.filterWidth===1&&t.filterHeight===1&&y.arraysEqual(t.inShape,t.outShape))return At({inputs:{x:r},backend:o});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type===\"VALID\"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;e===\"avg\"?p=t0({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(e===\"max\",()=>`Invalid pool type ${e}`),p=e0({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:t.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:\"int32\",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?n=new nx(t):(e===\"avg\"?n=new Ba(t,\"avg\"):(y.assert(e===\"max\",()=>`Invalid pool type ${e}`),n=new Ba(t,\"max\")),s.push({type:\"int32\",data:[t.padInfo.top,t.padInfo.left]},{type:\"int32\",data:[t.dilationHeight,t.dilationWidth]},{type:\"int32\",data:[t.inHeight,t.inWidth]},{type:\"int32\",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function nue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=w.computePool2DInfo(n.shape,s,a,u,i,p);return sx(n,c,\"avg\",e)}var oV={kernelName:Qo,backendName:\"webgpu\",kernelFunc:nue};function sue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new wu(l,\"avg\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return e.runWebGPUProgram(m,[n],n.dtype,d)}var nV={kernelName:Zs,backendName:\"webgpu\",kernelFunc:sue};var ax=class{constructor(t){this.variableNames=[\"dy\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool2DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n\n dotProd = dotProd + dyValue * uniforms.avgMultiplier;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},ix=class{constructor(t){this.variableNames=[\"dy\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool3DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n dotProd += dyValue * uniforms.avgMultiplier;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function aue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=w.computePool3DInfo(a.shape,i,p,1,u,c),m=new ix(l),d=1/(l.filterDepth*l.filterHeight*l.filterWidth),f=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.effectiveFilterDepth-1-l.padInfo.front,l.effectiveFilterHeight-1-l.padInfo.top,l.effectiveFilterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]},{type:\"int32\",data:[l.outDepth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"float32\",data:[d]}];return e.runWebGPUProgram(m,[n],a.dtype,f)}var sV={kernelName:Ei,backendName:\"webgpu\",kernelFunc:aue};function iue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;mm([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new ax(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:\"int32\",data:[c.outHeight]},{type:\"int32\",data:[c.outWidth]},{type:\"float32\",data:[m]}];return e.runWebGPUProgram(l,[n],a.dtype,d)}var aV={kernelName:$i,backendName:\"webgpu\",kernelFunc:iue};function uue(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s}=t,{transposeA:a,transposeB:i}=o;return _p({a:n,b:s,transposeA:a,transposeB:i,backend:e})}var iV={kernelName:Zo,backendName:\"webgpu\",kernelFunc:uue};var ux=class{constructor(t,e){this.variableNames=[\"source\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.rank=e.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=t,this.uniforms=`start : ${ft(t.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let t=ft(this.rank),e=pue(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>\"sourceLoc = uniforms.start + coords;\"):o=this.outputShape.map((s,a)=>`sourceLoc.${r0[a]} = uniforms.start.${Oo(a)} + coords.${r0[a]};`),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var sourceLoc : ${t};\n let coords = getCoordsFromIndex(index);\n ${o.join(`\n`)}\n setOutputAtIndex(index, getSource(${e}));\n }\n }\n `}},r0=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function pue(r){if(r===1)return\"sourceLoc\";if(r<=6)return r0.slice(0,r).map(t=>`sourceLoc.${t}`).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}function Hs(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,size:a}=o,[i,p]=pt.parseSliceParams(n,s,a);if(pt.assertParamsValid(n,i,p),e.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=e.tensorMap.get(n.dataId),m=Az(l.values,i,p,n.shape,n.dtype);return e.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return e.makeTensorInfo(p,n.dtype,[]);let u=new ux(i,p),c=[{type:\"int32\",data:i}];return e.runWebGPUProgram(u,[n],n.dtype,c)}var uV={kernelName:ha,backendName:\"webgpu\",kernelFunc:Hs};var cue=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=pe({inputs:{x:n},backend:e,attrs:{shape:p}}),h=xr({inputs:{x:f},backend:e,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Hs({inputs:{x:g},backend:e,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>e.disposeData(b.dataId)),x},pV={kernelName:Js,backendName:\"webgpu\",kernelFunc:cue};var lue=`\n fn bincount_write(index: i32, value: f32) {\n ${Yr(\"&result[index]\",\"value\",\"float32\")}\n }\n`,mue=`\n fn bincount_write(index: i32, value: f32) {\n atomicStore(&result[index], bitcast(value));\n }\n`,Xc=class{constructor(t,e,o=!1){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"binCountSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=t,this.rank=t.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=e,this.hasWeights&&this.variableNames.push(\"w\"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`\n ${this.binaryOutput?mue:lue}\n ${G(\"index\")} {\n ${this.rank===1?`if (index < uniforms.xShape) {\n let indexVal = i32(getX(index));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(index)\":\"1.\"};\n bincount_write(indexVal, value);\n }\n }`:`let coord = getCoordsFromIndex(index);\n if (coordsInBounds2D(coord, uniforms.xShape)) {\n let indexVal = i32(getX(coord[0], coord[1]));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(coord[0], coord[1])\":\"1.\"};\n bincount_write(coord.x * uniforms.binCountSize + indexVal, value);\n }\n }`}\n }\n `}};function due(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=vt({backend:e,attrs:{shape:c,value:0,dtype:l}}),d=new Xc([i],u),f=[{type:\"int32\",data:[a]}],h=u?[n,s]:[n];return e.runWebGPUProgram(d,h,l,f,m)}var cV={kernelName:Jo,backendName:\"webgpu\",kernelFunc:due};var px=class{constructor(t){this.outputShape=[],this.variableNames=[\"s0\",\"s1\"],this.uniforms=\"s0Size : i32, s1Size : i32, \",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"broadcastArgs\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var s0 = 1.0;\n var s1 = 1.0;\n let indexS0 = index - uniforms.size + uniforms.s0Size;\n let indexS1 = index - uniforms.size + uniforms.s1Size;\n if (indexS0 >= 0) {\n s0 = getS0(indexS0);\n }\n if (indexS1 >= 0) {\n s1 = getS1(indexS1);\n }\n\n if (s0 == 1.0) {\n setOutputAtIndex(index, s1);\n } else if (s1 == 1.0) {\n setOutputAtIndex(index, s0);\n } else if (s0 != s1) {\n setOutputAtIndex(index, uniforms.NAN);\n } else {\n setOutputAtIndex(index, s0);\n }\n }\n }\n `}};function fue(r){let{inputs:t,backend:e}=r,{s0:o,s1:n}=t;if(e.shouldExecuteOnCPU([o,n])){let c=e.tensorMap.get(o.dataId),l=e.tensorMap.get(n.dataId),m=c.values,d=l.values,f=w.assertAndGetBroadcastShape(Array.from(m),Array.from(d));return e.makeTensorInfo([f.length],\"int32\",Int32Array.from(f))}let s=y.sizeFromShape(o.shape),a=y.sizeFromShape(n.shape),i=Math.max(s,a),p=new px(i),u=[{type:\"int32\",data:[s]},{type:\"int32\",data:[a]}];return e.runWebGPUProgram(p,[o,n],\"int32\",u)}var lV={kernelName:ea,backendName:\"webgpu\",kernelFunc:fue};var o0=et({opType:fe.NOT_EQUAL,dtype:\"bool\",cpuKernelImpl:Tz}),mV={kernelName:Yn,backendName:\"webgpu\",kernelFunc:o0};function Ii(r){let{inputs:t,backend:e}=r,{input:o}=t,n=e.tensorMap.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.real},backend:e})}var dV={kernelName:Gi,backendName:\"webgpu\",kernelFunc:Ii};function fV(r,t){let e=new Zr(r.shape,Z.TO_INT),o=t.runWebGPUProgram(e,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function n0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return At({inputs:{x:n},backend:e});let a=Ur(n.shape),i=n0({inputs:{x:n},backend:e,attrs:{dtype:\"float32\"}}),p=xo({inputs:{real:i,imag:a},backend:e});return a.dispose(),e.disposeData(i.dataId),p}if(n.dtype===\"complex64\"){let a=Ii({inputs:{input:n},backend:e}),i=n0({inputs:{x:a},backend:e,attrs:{dtype:s}});return e.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:e});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(e.shouldExecuteOnCPU([n])){let a=e.tensorMap.get(n.dataId).values,[i,p,u]=iz(a,n.shape,n.dtype,s);return e.makeTensorInfo(i,p,u)}if(s===\"int32\")return fV(n,e);if(s===\"bool\"){let a=e.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=o0({inputs:{a:n,b:a},backend:e});return e.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var hV={kernelName:yo,backendName:\"webgpu\",kernelFunc:n0};var hue=ye({opType:Z.CEIL,cpuKernelImpl:uz}),gV={kernelName:en,backendName:\"webgpu\",kernelFunc:hue};var cx=class{constructor(t){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n var clampedValue = clamp(\n value, vec4(uniforms.minVal), vec4(uniforms.maxVal));\n clampedValue = select(clampedValue, value, isnanVec4(value));\n setOutputAtIndex(index, clampedValue);\n }\n }\n `}};var lx=class{constructor(t){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n if (isnan(value)) {\n setOutputAtIndex(index, value);\n return;\n }\n setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n }\n }\n `}};function gue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:\"float32\",data:[s]},{type:\"float32\",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new cx(n.shape):i=new lx(n.shape),e.runWebGPUProgram(i,[n],n.dtype,p)}var xV={kernelName:bo,backendName:\"webgpu\",kernelFunc:gue};var mx=class{constructor(t){this.outputShape=[],this.variableNames=[\"real\",\"imag\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"complexAbs\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let re = abs(getRealByOutputIndex(index));\n let im = abs(getImagByOutputIndex(index));\n let mx = max(re, im);\n\n // The length function in wgsl may be not underflow-safe on some GPUs.\n // So the safe solution is to ensure underflow-safety in all cases.\n setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0));\n }\n }\n `}};function yV(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function xue(r){let{inputs:t,backend:e}=r,{x:o}=t,n=e.tensorMap.get(o.dataId),s=new mx(o.shape),a=[yV(o,n.complexTensorInfos.real),yV(o,n.complexTensorInfos.imag)];return e.runWebGPUProgram(s,a,a[0].dtype)}var bV={kernelName:Di,backendName:\"webgpu\",kernelFunc:xue};var dx=class{constructor(t){this.uniforms=\"\",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=w.computeOutShape(t,1),this.variableNames=t.map((e,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=t.length-1;for(let e=0;e0){t.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let s=1;sIi({inputs:{input:C},backend:e})),h=r.map(C=>$p({inputs:{input:C},backend:e})),g=Yc(f,t,e),x=Yc(h,t,e),b=xo({inputs:{real:g,imag:x},backend:e});return f.forEach(C=>e.disposeData(C.dataId)),h.forEach(C=>e.disposeData(C.dataId)),e.disposeData(g.dataId),e.disposeData(x.dataId),b}let n=e.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let f=r.map(k=>{let E=[-1,y.sizeFromShape(k.shape.slice(t))];return pe({inputs:{x:k},backend:e,attrs:{shape:E}})}),h=f.map(k=>({vals:e.readSync(k.dataId),shape:k.shape})),g=w.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=pz(h,g,o,x),C=w.computeOutShape(r.map(k=>k.shape),t),S=e.makeTensorInfo(C,o,b);return f.forEach(k=>e.disposeData(k.dataId)),S}let s=e.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new dx(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:\"int32\",data:[l[0]]});for(let f=1;fe.disposeData(f.dataId));let d=pe({inputs:{x:m},backend:e,attrs:{shape:i}});return e.disposeData(m.dataId),d}function yue(r,t,e){let o=w.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>pe({inputs:{x:s},backend:e,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,t)),y.sizeFromShape(s.shape.slice(t))]}})),outShape:o}}function s0(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,t[0].shape)[0],a=t.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(t.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let p=t.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:e}):Yc(p,s,e)}var wV={kernelName:ta,backendName:\"webgpu\",kernelFunc:s0};function bue(r,t,e,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=D=>{switch(D){case 1:return\"resData = f32(x[xIndex]);\";case 3:return\"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";case 4:return\"resData = vec4(x[xIndex / 4]);\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},l=D=>{switch(D){case 1:return\"return f32(W[row * uniforms.wShape[3] + col]);\";case 4:return\"return vec4(W[(row * uniforms.wShape[3] + col) / 4]);\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=r?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,d=r?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,f=r?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",h=r?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=r?\"row\":\"col\",x=r?\"col\":\"row\",b=`\n let inChannels = uniforms.wShape[2];\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n let outRow = ${g} / outWidth;\n let outCol = ${g} % outWidth;\n\n let WRow = ${x} / (uniforms.filterDims[1] * inChannels);\n let WCol = ${x} / inChannels % uniforms.filterDims[1];\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * WRow - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * WCol - uniforms.pads[1];\n let xCh = ${x} % inChannels;\n var resData = ${Ae(i)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the 'same' padding type.\n if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n ${m}\n let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n ${c(i)}\n }\n return resData;`,C=r?t&&o?`\n ${b}`:`\n if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${b}\n }\n return ${Ae(i)}(0.0);`:o&&e?`\n ${b}`:`\n if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n ${b}\n }\n return ${Ae(i)}(0.0);`,S=`${l(p)}`,k=Ae(u),_=r?Ae(i):Ae(p),E=r?Ae(p):Ae(i);return`\n ${dr(s,a,u===4,4)}\n fn mm_readA(batch: i32, row : i32, col : i32) -> ${_} {\n ${r?C:S}\n }\n\n fn mm_readB(batch: i32, row : i32, col : i32) -> ${E} {\n ${r?S:C}\n }\n\n fn mm_write(batch: i32, row : i32, col : i32, valueIn : ${k}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n {\n var value = valueIn;\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n ${d}\n ${Qr(n,s)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`}var fx=class{constructor(t,e,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t.outShape,this.isChannelsLast=t.dataFormat===\"channelsLast\",this.isVec4=((t.inChannels%4===0||t.inChannels%3===0)&&this.isChannelsLast||t.outWidth%4===0&&!this.isChannelsLast)&&t.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=pm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=cm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&t.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push(\"bias\"),this.variableComponents.push(4)),i&&(this.variableNames.push(\"preluActivationWeights\"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=e%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let t=this.isVec4?Np(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Tp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),e=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`\n ${bue(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,e[0],e[1],e[2])}\n ${t}\n `}};var hx=class{constructor(t,e=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,\",this.workgroupSize=[4,4,8],this.outputShape=t.outShape,this.isChannelsLast=t.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=e,this.activation=o,this.hasPreluActivationWeights=n,e&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights,!1,4)}\n fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{\n let coords = vec4(batch, row, col, chan);\n if (coordsInBounds4D(coords, uniforms.xShape)) {\n return getX(batch, row, col, chan);\n } else {\n return 0.0;\n }\n }\n fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n let coords = vec4(row, col, xChannel, outChannel);\n if(coordsInBounds4D(coords, uniforms.wShape)) {\n return getW(row, col, xChannel, outChannel);\n } else {\n return 0.0;\n }\n }\n fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {\n let coords = ${this.isChannelsLast?\"vec4(batch, row, col, chan);\":\"vec4(batch, chan, row, col);\"}\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = valueIn;\n ${Qr(this.addBias,this.activation)}\n setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);\n }\n }\n ${G(\"index\")} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let outChannel = ${this.isChannelsLast?\"coords[3];\":\"coords[1];\"}\n let outRow = ${this.isChannelsLast?\"coords[1];\":\"coords[2];\"}\n let outCol = ${this.isChannelsLast?\"coords[2];\":\"coords[3];\"}\n var acc : f32 = 0.0;\n for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * row - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * col - uniforms.pads[1];\n for (var xChannel = 0; xChannel < ${this.isChannelsLast?\"uniforms.xShape[3];\":\"uniforms.xShape[1];\"} xChannel = xChannel + 1) {\n ${this.isChannelsLast?\"let v = readInp(batch, xRow, xCol, xChannel);\":\"let v = readInp(batch, xChannel, xRow, xCol);\"}\n let f = readFilt(row, col, xChannel, outChannel);\n acc = acc + v * f;\n }\n }\n }\n writeResult(batch, outRow, outCol, outChannel, acc);\n }\n `}};var gx=class{constructor(t,e){this.variableNames=[\"x\"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32,\n inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let t=this.isChannelsLast?1:2,e=this.isChannelsLast?2:3,o=this.isChannelsLast?\"coords[1]\":\"coords[2]\",n=this.isChannelsLast?\"coords[2]\":\"coords[1]\",s=this.isChannelsLast?\"getX(batch, xRow, xCol, ch)\":\"getX(batch, ch, xRow, xCol)\";return`\n ${G(\"index\")} {\n let coords = getCoordsFromIndex(index);\n if(index < uniforms.size) {\n let batch = coords[0];\n let row = ${o};\n let col = ${n};\n let offsetY = (row / uniforms.outWidth) * uniforms.strides[0] - uniforms.pads[0];\n let xRow = offsetY + uniforms.dilations[0] * (col / uniforms.itemsPerBlockRow);\n var value = 0.0;\n if(xRow < uniforms.xShape[${t}] && xRow >= 0) {\n let offsetX = (row % uniforms.outWidth) * uniforms.strides[1] -\n uniforms.pads[1];\n let xCol = offsetX + uniforms.dilations[1] * ((col %\n uniforms.itemsPerBlockRow) / uniforms.inChannels);\n let ch = col % uniforms.inChannels;\n if(xCol < uniforms.xShape[${e}] && xCol >= 0) {\n value = ${s};\n }\n }\n setOutputAtIndex(index, value);\n }\n }\n `}};function xx(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function Cue({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=e.dataFormat===\"channelsLast\",u=!p,c=!1,l=p&&e.filterHeight===e.inHeight&&e.filterWidth===e.inWidth&&e.padInfo.type===\"VALID\",m=[],d,f;if(l){let x=e.inHeight*e.inWidth*e.inChannels;d=pe({inputs:{x:r},backend:o,attrs:{shape:[1,e.batchSize,x]}}),f=pe({inputs:{x:t},backend:o,attrs:{shape:[1,x,e.outChannels]}})}else d=pe({inputs:{x:r},backend:o,attrs:{shape:p?[e.batchSize,e.inHeight*e.inWidth,e.inChannels]:[e.batchSize,e.inChannels,e.inHeight*e.inWidth]}}),f=pe({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=xx(s.shape,p);x!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=xx(n.shape,p);x!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=_p({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=pe({inputs:{x:h},backend:o,attrs:{shape:e.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function wue({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=e,C=b===\"channelsLast\",S=p*u*c,k=h*f,_=C?[e.batchSize,k,S]:[e.batchSize,S,k],E=new gx(_,C),R=[{type:\"int32\",data:[d.top,d.left]},{type:\"int32\",data:[m,l]},{type:\"int32\",data:[x,g]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*p]},{type:\"int32\",data:[c]}],D=o.runWebGPUProgram(E,[r],r.dtype,R),P=[];P.push(D);let O=pe({inputs:{x:t},backend:o,attrs:{shape:[1,S,-1]}});if(P.push(O),s!=null){let U=xx(s.shape,C);U!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:U}}),P.push(s))}if(n!=null){let U=xx(n.shape,C);U!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:U}}),P.push(n))}let B=_p({a:C?D:O,b:C?O:D,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=pe({inputs:{x:B},backend:o,attrs:{shape:e.outShape}});P.push(B);for(let U of P)o.disposeData(U.dataId);return z}function yx({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=e.dataFormat===\"channelsLast\",l=c&&e.filterHeight===e.inHeight&&e.filterWidth===e.inWidth&&e.padInfo.type===\"VALID\",m=A().getBool(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\");if(!m&&(l||e.filterHeight===1&&e.filterWidth===1&&e.dilationHeight===1&&e.dilationWidth===1&&e.strideHeight===1&&e.strideWidth===1&&(e.padInfo.type===\"SAME\"||e.padInfo.type===\"VALID\")))return Cue({x:r,filter:t,convInfo:e,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=A().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),f=d>-1?d:o.thresholdToIncreaseWorkgroups,h=e.batchSize*Math.ceil(e.outHeight*e.outWidth/32)*Math.ceil(e.outChannels/32);if(A().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")||h<=f)return wue({x:r,filter:t,convInfo:e,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[e.padInfo.top,e.padInfo.left],b=[{type:\"int32\",data:[e.filterHeight,e.filterWidth]},{type:\"int32\",data:[...x]},{type:\"int32\",data:[e.strideHeight,e.strideWidth]},{type:\"int32\",data:[e.dilationHeight,e.dilationWidth]}];if(m)g=new hx(e,p,i,u);else{let _=c?e.outHeight*e.outWidth:e.outChannels,E=c?e.outChannels:e.outHeight*e.outWidth,R=e.filterHeight*e.filterWidth*e.inChannels;b.push({type:\"int32\",data:[_]},{type:\"int32\",data:[E]},{type:\"int32\",data:[R]});let D=o.adapterInfo.isIntel();g=new fx(e,_,E,R,p,i,u,D)}let C=[],S=[r,t];p&&(!c&&n.shape.length===1&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),S.push(n)),u&&(!c&&s.shape.length===1&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),S.push(s)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[a]}),g.uniforms+=\" alpha : f32,\");let k=o.runWebGPUProgram(g,S,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function Sue(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=e,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return yx({x:n,filter:s,convInfo:m,backend:o})}var SV={kernelName:tn,backendName:\"webgpu\",kernelFunc:Sue};var bx=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,\",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=t.inShape,this.isChannelsLast=t.dataFormat===\"channelsLast\",this.isVec4=this.isChannelsLast&&t.outChannels%4===0&&t.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let t=this.isChannelsLast?1:2,e=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1,n=`\n ${G()} {\n let batch = i32(globalId.z) / uniforms.outShape[1];\n let r = i32(globalId.z) % uniforms.outShape[1];\n let c = i32(globalId.y) * ${this.workPerThread};\n let d1 = i32(globalId.x) * 4;\n\n let dyCorner = vec2(r, c) - uniforms.pads;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd: array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = f32(dyCorner.x + wR) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) ||\n fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = f32(dyCorner.y + wC) / f32(uniforms.strides.y);\n let dyC2 = f32(dyCorner.y + 1 + wC) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n var bDyCVal = true;\n var bDyCVal2 = true;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0) {\n bDyCVal = false;\n }\n if (dyC2 < 0.0 || dyC2 >= f32(uniforms.outBackprop[2]) ||\n fract(dyC2) > 0.0) {\n bDyCVal2 = false;\n }\n\n let idyC = i32(dyC);\n let idyC2 = i32(dyC2);\n if (bDyCVal && bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n xValue = getDy(batch, idyR, idyC2, d2);\n dotProd[1] = dotProd[1] + vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n }\n } else if (bDyCVal) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n }\n } else if (bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC2, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[1] = dotProd[1] + tmpval;\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n }\n }\n }\n `;return this.isVec4?`\n ${n}\n `:`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[${o}];\n\n let dyCorner = vec2(coords[${t}], coords[${e}]) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC = i32(dyC);\n\n for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n let xValue = ${this.isChannelsLast?\"getDy(batch, idyR, idyC, d2)\":\"getDy(batch, d2, idyR, idyC)\"};\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Cx=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.uniforms=\"pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let d2 = coords[3];\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b = b + 1) {\n for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n if (${this.isChannelsLast}) {\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd = dotProd + xValue * dyValue;\n } else {\n let dyValue = getDy(b, d2, yR, yC);\n let xValue = getX(b, d1, xR, xC);\n dotProd = dotProd + xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},wx=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32,\n outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerFilter\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wF = coords.x;\n let wR = coords.y;\n let wC = coords.z;\n let d1 = coords.w;\n let d2 = coords.u;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yF = 0; yF < uniforms.outDepth; yF++) {\n let xF = wF + yF * uniforms.strides[0] - uniforms.pads[0];\n if (xF < 0 || xF >= uniforms.inDepth) {\n continue;\n }\n\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[1] - uniforms.pads[1];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[2] - uniforms.pads[2];\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yF, yR, yC, d2);\n let xValue = getX(b, xF, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Sx=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerInput\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let d1 = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyFCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let dyF = f32(dyFCorner + wF) / f32(uniforms.strides[0]);\n if (dyF < 0.0 || dyF >= f32(uniforms.outDepth) || fract(dyF) > 0.0) {\n continue;\n }\n let idyF = i32(dyF);\n\n let wFPerm = uniforms.filterDims[0] - 1 - wF;\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n let wRPerm = uniforms.filterDims[1] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let wCPerm = uniforms.filterDims[2] - 1 - wC;\n\n for (var d2 = 0; d2 < uniforms.outChannels; d2++) {\n let xValue = getDy(batch, idyF, idyR, idyC, d2);\n let wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Iue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Cx(m),f=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]},{type:\"int32\",data:[m.inHeight]},{type:\"int32\",data:[m.inWidth]}];return e.runWebGPUProgram(d,[n,s],n.dtype,f)}var IV={kernelName:Ai,backendName:\"webgpu\",kernelFunc:Iue};function vue(r=4){let t=s=>{switch(s){case 1:return\"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n return vec4(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${`\n let outRow = row / uniforms.outShape[2];\n let outCol = row % uniforms.outShape[2];\n\n let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.strides[0]);\n let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.strides[1]);\n if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n let coord = vec4(\n batch,\n i32(xR),\n i32(xC),\n col % uniforms.outBackprop[3]);\n return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}\n }\n return ${Ae(r)}(0.0);`;return`\n fn mm_readA(batch: i32, row : i32, col : i32) -> ${Ae(r)} {\n ${o}\n }\n\n fn mm_readB(batch: i32, row : i32, col : i32) -> ${Ae(r)} {\n let coordX = uniforms.filterDims.x - 1 -\n row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let coordY = uniforms.filterDims.y - 1 -\n (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n coordX >= 0 && coordY >= 0) {\n let rowInner = row % uniforms.outBackprop[3];\n let coord = vec4(coordX, coordY, col, rowInner);\n ${t(r)}\n }\n return ${Ae(r)}(0.0);\n }\n\n fn mm_write(batch: i32, row : i32, col : i32, valueInput : ${Ae(r)}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n var value = valueInput;\n let outCoord = vec4(\n batch,\n row / uniforms.outShape[2],\n row % uniforms.outShape[2],\n col);\n result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;\n }\n }`}var Ix=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t.inShape,y.assert(t.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.isVec4=t.inChannels%4===0&&t.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=pm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=cm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let t=this.isVec4?Np(this.elementsPerThread,this.workgroupSize):Tp(this.elementsPerThread,this.workgroupSize);return`\n ${vue(this.isVec4?4:1)}\n ${t}\n `}};function kue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:\"int32\",data:[m.filterHeight,m.filterWidth]},{type:\"int32\",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(A().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")||m.dataFormat!==\"channelsLast\")f=new bx(m);else{f=new Ix(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:\"uint32\",data:[h]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[x]})}return e.runWebGPUProgram(f,[n,s],\"float32\",d)}var vV={kernelName:rn,backendName:\"webgpu\",kernelFunc:kue};var vx=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3dnaive\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords.x;\n let d2 = coords.u;\n\n let xFRCCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xFCorner = xFRCCorner.x;\n let xRCorner = xFRCCorner.y;\n let xCCorner = xFRCCorner.z;\n\n let inputDepthNearestVec4 = (uniforms.xShape.u / 4) * 4;\n let inputDepthVec4Remainder = uniforms.xShape.u % 4;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let xF = xFCorner + wF * uniforms.dilations[0];\n if (xF < 0 || xF >= uniforms.xShape.y) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let xR = xRCorner + wR * uniforms.dilations[1];\n if (xR < 0 || xR >= uniforms.xShape.z) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let xC = xCCorner + wC * uniforms.dilations[2];\n if (xC < 0 || xC >= uniforms.xShape.w) {\n continue;\n }\n\n for (var d1 = 0; d1 < inputDepthNearestVec4; d1 += 4) {\n let xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n let wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (inputDepthVec4Remainder == 1) {\n dotProd += getX(batch, xF, xR, xC, inputDepthNearestVec4) *\n getW(wF, wR, wC, inputDepthNearestVec4, d2);\n } else if (inputDepthVec4Remainder == 2) {\n let xValues = vec2(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)\n );\n let wValues = vec2(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (inputDepthVec4Remainder == 3) {\n let xValues = vec3(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)\n );\n let wValues = vec3(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }`}};function Nue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new vx(u),d=dt(n.dtype,s.dtype);return e.runWebGPUProgram(m,[n,s],d,l)}var kV={kernelName:on,backendName:\"webgpu\",kernelFunc:Nue};function Tue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new wx(u),l=[{type:\"int32\",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.batchSize]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.inDepth]},{type:\"int32\",data:[u.inHeight]},{type:\"int32\",data:[u.inWidth]}];return e.runWebGPUProgram(c,[n,s],s.dtype,l)}var NV={kernelName:ja,backendName:\"webgpu\",kernelFunc:Tue};function _ue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,pad:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,a,1,i),c=new Sx(u),l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.outChannels]}];return e.runWebGPUProgram(c,[n,s],n.dtype,l)}var TV={kernelName:nn,backendName:\"webgpu\",kernelFunc:_ue};var $ue=ye({opType:Z.COS}),_V={kernelName:sn,backendName:\"webgpu\",kernelFunc:$ue};var Eue=ye({opType:Z.COSH}),$V={kernelName:an,backendName:\"webgpu\",kernelFunc:Eue};var kx=class{constructor(t,e,o,n){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0;let[s]=e;this.outputShape=[s,o[0],o[1],t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[t,e]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[o,n,s]=this.cropHeightBiggerThan1?[`(${t} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${t} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${t}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${e} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${e} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${e}`];return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let height_ratio = f32(${o});\n let width_ratio = f32(${a});\n let b = coords[0];\n let y = coords[1];\n let x = coords[2];\n let d = coords[3];\n // get box vals\n let y1 = getBoxes(b, 0);\n let x1 = getBoxes(b, 1);\n let y2 = getBoxes(b, 2);\n let x2 = getBoxes(b, 3);\n // get image in batch index\n let bInd = i32(round(getBoxInd(b)));\n if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n return;\n }\n let height_scale = ${n};\n let width_scale = ${i};\n let in_y = ${s};\n if( in_y < 0.0 || in_y > ${t} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let in_x = ${p};\n if( in_x < 0.0 || in_x > ${e} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let sourceFracIndexCR = vec2(in_x,in_y);\n if(${this.methodId} == 1) {\n // Compute the four integer indices.\n let sourceFloorCR = vec2(sourceFracIndexCR);\n let sourceCeilCR = vec2(ceil(sourceFracIndexCR));\n let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n let fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n let top = topLeft + (topRight - topLeft) * fracCR.x;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n let newValue = top + (bottom - top) * fracCR.y;\n setOutputAtIndex(index, newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n let sourceNearestCR = vec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n let newValue = getImage(\n bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutputAtIndex(index, newValue);\n }\n }\n }\n `}};var Rue=r=>{let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,c=new kx(n.shape[3],s.shape,i,p),l=[{type:\"float32\",data:[u]}];return e.runWebGPUProgram(c,[n,s,a],\"float32\",l)},EV={kernelName:cn,backendName:\"webgpu\",kernelFunc:Rue};var Ep;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Ep||(Ep={}));var hm=class{constructor(t,e,o,n){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=t,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let t=this.outputShape.length,e=this.op===Ep.Prod?\"1.0\":\"0.0\",o=this.exclusive?e:`getX(${RV(t,\"coords\",this.op)})`,n=this.outputShape[this.outputShape.length-1],s=\"\",a=\"\";return this.exclusive?(s=this.reverse?`end != ${n-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(s=this.reverse?`end + pow2 < ${n}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var coords = getCoordsFromIndex(index);\n\n let end = ${DV(t,\"coords\",this.op)};\n var val = ${o};\n let pow2 = i32(pow(2.0, uniforms.index));\n if (${s}) {\n let idx = ${a};\n ${DV(t,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${RV(t,\"coords\",this.op)});\n }\n setOutputAtIndex(index, val);\n }\n }\n `}};function RV(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function DV(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function Nx(r,t,e,o,n,s){let a=t.shape.length,i=w.getAxesPermutation([o],a),p=t;i!=null&&(p=xr({inputs:{x:t},backend:e,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new hm(r,p.shape,!1,s),f=l,h=[{type:\"float32\",data:[m]}];l=e.runWebGPUProgram(d,[l],l.dtype,h),e.disposeData(f.dataId)}if(n){let m=new hm(r,p.shape,n,s),d=l,f=[{type:\"float32\",data:[0]}];l=e.runWebGPUProgram(m,[l],l.dtype,f),e.disposeData(d.dataId)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=xr({inputs:{x:l},backend:e,attrs:{perm:m}});return e.disposeData(l.dataId),e.disposeData(p.dataId),d}return l}function Due(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return Nx(Ep.Prod,n,e,s,a,i)}var AV={kernelName:un,backendName:\"webgpu\",kernelFunc:Due};function Aue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return Nx(Ep.Sum,n,e,s,a,i)}var FV={kernelName:pn,backendName:\"webgpu\",kernelFunc:Aue};function Fue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=vt({backend:e,attrs:{shape:d,value:0,dtype:l}}),h=new Xc(m,c,i),g=[{type:\"int32\",data:[a]}],x=c?[n,s]:[n];return e.runWebGPUProgram(h,x,l,g,f)}var PV={kernelName:ra,backendName:\"webgpu\",kernelFunc:Fue};var Tx=class{constructor(t,e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${e}`,this.dataFormat=e}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let h = ${this.getHeightCoordString()};\n let w = ${this.getWidthCoordString()};\n let d = ${this.getDepthCoordString()};\n\n let in_h = h / uniforms.blockSize;\n let offset_h = h % uniforms.blockSize;\n let in_w = w / uniforms.blockSize;\n let offset_w = w % uniforms.blockSize;\n let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n ${this.getOutputDepthSize()};\n let in_d = d + offset_d;\n\n let rlt = ${this.getInputSamplingString()};\n setOutputAtIndex(index, rlt);\n }\n }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Pue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=[{type:\"int32\",data:[s]}],g=new Tx(f,a);return e.runWebGPUProgram(g,[n],n.dtype,h)}var OV={kernelName:ln,backendName:\"webgpu\",kernelFunc:Pue};var _x=class{constructor(t,e,o,n=!1,s=null,a=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pads : vec2, inDims : vec2,\",this.workgroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push(\"bias\"),a&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=e,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let t=this.filterWidth*this.filterHeight,e=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`\n ${dr(this.activation,this.hasPreluActivation,!1,4)}\n\n var mm_Asub : array, ${o}>;\n var mm_Bsub : array, ${this.filterHeight}>;\n fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n var value = 0.0;\n if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n {\n value = getX(batch, channel, row, col);\n }\n return value;\n }\n\n ${G()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.zw) - uniforms.pads;\n let channelMul = uniforms.wShape[3];\n let d1 = coords[1] / channelMul;\n let q = coords[1] % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n\n // Load one tile of X into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {\n for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {\n let rowOffset = inputRow - localRow;\n let colOffset = inputCol - localCol;\n mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n }\n }\n\n // Load one tile of W into local memory.\n var wIndex = i32(localIndex);\n ${t, inDims : vec2, virtualWidth : i32,\",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=t.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let s=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=X(s),this.dispatch=H(this.dispatchLayout,s,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),y.assert(t.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),e&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=t,this.addBias=e,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let t=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,e=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return`\n ${dr(this.activation,this.hasPreluActivation,!0,4)}\n fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 {\n var value = vec4(0.0);\n if (col >=0 && col < uniforms.inDims[1]) {\n value = getX(batch, row, col, channel);\n }\n return value;\n }\n\n ${G(\"index\")} {\n let width0 = uniforms.outShape[3] / ${this.outputComponent};\n let d1 = (index % width0) * ${this.outputComponent};\n var index1 = index / width0;\n let width1 = uniforms.virtualWidth / ${this.workPerThread};\n let c = (index1 % width1) * ${this.workPerThread};\n index1 = index1 / width1;\n let r = index1 % uniforms.outShape[1];\n let batch = index1 / uniforms.outShape[1];\n\n let xRCCorner = vec2(r, c) * vec2(${e}, ${o}) - uniforms.pads;\n\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n var xVals : array, ${t}>;\n var dotProd : array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n\n // Use constant instead of uniform can give better performance.\n for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n let xR = xRCorner + wR;\n if (xR >=0 && xR < uniforms.inDims[0]) {\n for (var i = 0; i < ${t}; i++) {\n xVals[i] = readX(batch, xR, xCCorner + i, d1);\n }\n for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n let wValue = getW(wR, wC, d1, 0);\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]);\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = dotProd[i];\n ${Qr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n }\n `}};var Zc=class{constructor(t,e=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32,\n filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t.dataFormat===\"channelsLast\",e&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=t,this.addBias=e,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let t=this.isChannelsLast?\"getX(batch, xR, xC, d1);\":\"getX(batch, d1, xR, xC);\";return`\n ${dr(this.activation,this.hasPreluActivation,!1,4)}\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.${this.isChannelsLast?\"yz\":\"zw\"}) * uniforms.strides - uniforms.pads;\n let d2 = coords[${this.isChannelsLast?3:1}];\n let channelMul = uniforms.wShape[3];\n let d1 = d2 / channelMul;\n let q = d2 % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n let inputRowEnd = inputRowStart + uniforms.filterHeight *\n uniforms.dilations[0];\n let inputColEnd = inputColStart + uniforms.filterWidth *\n uniforms.dilations[1];\n\n // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get\n // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n var value = 0.0;\n\n // Extract if checking out of for loop for performance.\n if (inputRowStart >= 0 && inputColStart >= 0 &&\n inputRowEnd < uniforms.inDims[0] &&\n inputColEnd < uniforms.inDims[1]) {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n let xVal = ${t};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n } else {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n if (xR < 0 || xR >= uniforms.inDims[0]) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n if (xC < 0 || xC >= uniforms.inDims[1]) {\n continue;\n }\n\n let xVal = ${t};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n }\n ${Qr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n `}};function Oue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:\"int32\",data:[d.padInfo.top,d.padInfo.left]},{type:\"int32\",data:[d.inHeight,d.inWidth]}],h=d.dataFormat===\"channelsLast\",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new _x(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?(g=new Qc(d),f.push({type:\"int32\",data:[g.virtualWidth]})):(g=new Zc(d),f.push({type:\"int32\",data:[d.filterHeight]},{type:\"int32\",data:[d.filterWidth]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.dilationHeight,d.dilationWidth]})),e.runWebGPUProgram(g,[n,s],n.dtype,f)}var MV={kernelName:mn,backendName:\"webgpu\",kernelFunc:Oue};var $x=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32,\n outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_filter\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let dm = coords[3];\n let d2 = d1 * uniforms.channelMul + dm;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Ex=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_input\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[3];\n let dyCorner = coords.yz - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n\n let idyR = i32(dyR);\n let wRPerm = uniforms.filterDims[0] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n\n let idyC = i32(dyC);\n let wCPerm = uniforms.filterDims[1] - 1 - wC;\n\n for (var dm = 0; dm < uniforms.channelMul; dm++) {\n let d2 = d1 * uniforms.channelMul + dm;\n let xValue = getDy(batch, idyR, idyC, d2);\n let wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Mue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new $x(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.inHeight]},{type:\"int32\",data:[l.inWidth]},{type:\"int32\",data:[l.batchSize]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return e.runWebGPUProgram(m,[n,s],\"float32\",d)}var LV={kernelName:Fi,backendName:\"webgpu\",kernelFunc:Mue};function Lue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Ex(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.filterHeight-1-l.padInfo.top,l.filterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return e.runWebGPUProgram(m,[n,s],n.dtype,d)}var BV={kernelName:Pi,backendName:\"webgpu\",kernelFunc:Lue};var Rx=class{constructor(t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"diag\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);\n setOutputAtIndex(index, value);\n }\n }\n `}};function Bue(r){let{inputs:t,backend:e}=r,{x:o}=t,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:e,attrs:{shape:[s]}}),i=new Rx(s),p=e.runWebGPUProgram(i,[a],a.dtype),u=pe({inputs:{x:p},backend:e,attrs:{shape:n}});return e.disposeData(a.dataId),e.disposeData(p.dataId),u}var zV={kernelName:oa,backendName:\"webgpu\",kernelFunc:Bue};var Dx=class{constructor(t){this.variableNames=[\"x\",\"w\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"dilation2d\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let neg_infinity = -3.4e38;\n let coords = getOutputCoords();\n let batch = coords.x;\n let d1 = coords.w;\n let outTopLeftCorner = coords.yz * uniforms.strides - uniforms.pads;\n let hBeg = outTopLeftCorner.x;\n let wBeg = outTopLeftCorner.y;\n\n var curVal = neg_infinity;\n for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) {\n let hIn = hBeg + h * uniforms.dilations[0];\n\n if (hIn >= 0 && hIn < uniforms.xShape[1]) {\n for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {\n let wIn = wBeg + w * uniforms.dilations[1];\n\n if (wIn >= 0 && wIn < uniforms.xShape[2]) {\n let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1);\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n setOutputAtIndex(index, curVal);\n }\n }\n `}};function zue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationHeight,u.dilationWidth]}],m=new Dx(u);return e.runWebGPUProgram(m,[n,s],n.dtype,l)}var VV={kernelName:dn,backendName:\"webgpu\",kernelFunc:zue};var Ax=class{constructor(t,e){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t.inShape,this.dispatchLayout=X(t.outShape),this.dispatch=H(this.dispatchLayout,t.outShape,this.workgroupSize),e!==\"float32\"&&e!==\"int32\")throw new Error(`Dilation2DBackpropInput only supports float32 and int32\n types, does not support ${e} type.`);this.type=e,this.shaderKey=\"dilation2DBackpropInput\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var xRMax = 0;\n var xCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n xRMax = xR;\n xCMax = xC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.xShape[3] *\n (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b));\n let value = getDy(b, r, c, d);\n ${Yr(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}},Fx=class{constructor(t,e,o){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(t.outShape),this.dispatch=H(this.dispatchLayout,t.outShape,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"dilation2DBackpropFilter\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var wRMax = 0;\n var wCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n wRMax = wR;\n wCMax = wC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]);\n let value = getDy(b, r, c, d);\n ${Yr(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}};function Vue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=s.dtype,m=new Fx(c,s.shape,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=vt({backend:e,attrs:{shape:s.shape,value:0,dtype:l}});return e.runWebGPUProgram(m,[n,s,a],l,d,f)}var WV={kernelName:Mi,backendName:\"webgpu\",kernelFunc:Vue};function Wue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=n.dtype,m=new Ax(c,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=vt({backend:e,attrs:{shape:c.inShape,value:0,dtype:l}});return e.runWebGPUProgram(m,[n,s,a],l,d,f)}var UV={kernelName:Oi,backendName:\"webgpu\",kernelFunc:Wue};var Px=class{constructor(t,e,o){this.variableNames=[\"Image\"],this.uniforms=\"alpha: f32,\",this.workgroupSize=[64,1,1],this.pixelsOpType=Ci.DRAW,this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=e,this.textureFormat=o,this.shaderKey=`draw_${e}_${o}`}getUserCode(){let t,e=this.type===\"float32\"?\"value\":\"value / 255.0\";return t=`\n if (uniforms.numChannels == 1) {\n rgba[0] = ${e};\n rgba[1] = ${e};\n rgba[2] = ${e};\n } else {\n rgba[d] = ${e};\n }`,`\n @group(0) @binding(0) var outImage : texture_storage_2d<${this.textureFormat}, write>;\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var rgba = vec4(0.0, 0.0, 0.0, uniforms.alpha);\n for (var d = 0; d < uniforms.numChannels; d = d + 1) {\n let value = f32(inBuf[index * uniforms.numChannels + d]);\n ${t}\n }\n rgba.x = rgba.x * rgba.w;\n rgba.y = rgba.y * rgba.w;\n rgba.z = rgba.z * rgba.w;\n let coords = getCoordsFromIndex(index);\n textureStore(outImage, vec2(coords.yx), rgba);\n }\n }\n `}};function Uue(r){let{inputs:t,backend:e,attrs:o}=r,{image:n}=t,{canvas:s,options:a}=o,[i,p]=n.shape.slice(0,2),{imageOptions:u}=a||{},c=(u==null?void 0:u.alpha)||1,l=e.device.features.has(\"bgra8unorm-storage\")?\"bgra8unorm\":\"rgba8unorm\",m=[i,p],d=new Px(m,n.dtype,l);s.width=p,s.height=i;let f=\"webgpu\",h=s.getContext(f),g;h||(g=new OffscreenCanvas(p,i),h=g.getContext(f));let x=n.shape.length===3?n.shape[2]:1;h.configure({device:e.device,format:l,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:\"premultiplied\"});let b=\"int32\",C=e.makeTensorInfo(m,b),S=e.tensorMap.get(C.dataId);S.resource=h.getCurrentTexture(),S.external=!0;let k=[{type:\"uint32\",data:[x]},{type:\"float32\",data:[c]}];if(e.runWebGPUProgram(d,[n],b,k,C),g){let _=s.getContext(\"2d\");if(!_)throw new Error(\"Please make sure this canvas has only been used for 2d or webgpu context!\");_.drawImage(g,0,0)}return e.disposeData(C.dataId),n}var GV={kernelName:_u,backendName:\"webgpu\",kernelFunc:Uue};var a0=et({opType:fe.MUL,cpuKernelImpl:kz,supportsComplex:!0}),HV={kernelName:Xn,backendName:\"webgpu\",kernelFunc:a0};function i0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return Jr(n,s,a,\"sum\",e)}var KV={kernelName:Ss,backendName:\"webgpu\",kernelFunc:i0};function Gue(r){let{inputs:t,backend:e,attrs:o}=r,{equation:n}=o,s=t,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=i0({inputs:{x:m},backend:e,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&e.disposeData(h.dataId);return m}var qV={kernelName:Li,backendName:\"webgpu\",kernelFunc:Gue};var Hue=ye({opType:Z.ELU}),jV={kernelName:hn,backendName:\"webgpu\",kernelFunc:Hue};var Kue=r=>{let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=new Si(fe.ELU_DER,o.shape,n.shape);return e.runWebGPUProgram(s,[o,n],o.dtype)},XV={kernelName:Xa,backendName:\"webgpu\",kernelFunc:Kue};var que=et({opType:fe.EQUAL,dtype:\"bool\",cpuKernelImpl:cz}),YV={kernelName:xn,backendName:\"webgpu\",kernelFunc:que};var jue=ye({opType:Z.ERF}),QV={kernelName:gn,backendName:\"webgpu\",kernelFunc:jue};var Xue=ye({opType:Z.EXP,cpuKernelImpl:lz,dtype:\"float32\"}),ZV={kernelName:yn,backendName:\"webgpu\",kernelFunc:Xue};function Ox(r){let{inputs:t,attrs:e,backend:o}=r,{dim:n}=e,{input:s}=t,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var JV={kernelName:na,backendName:\"webgpu\",kernelFunc:Ox};var Yue=ye({opType:Z.EXPM1,cpuKernelImpl:mz}),eW={kernelName:bn,backendName:\"webgpu\",kernelFunc:Yue};var gm=class{constructor(t,e){this.variableNames=[\"real\",\"imag\"],this.outputShape=[],this.uniforms=\"exponentMultiplier : f32, denominator: f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=t,this.shaderKey=`fft_${t}`}getUserCode(){return`\n fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {\n ${this.component===\"real\"?\"return real * expR - imag * expI;\":\"return real * expI + imag * expR;\"}\n }\n\n fn mulMatDFT(batch: i32, index: i32) -> f32 {\n let indexRatio = f32(index) / f32(uniforms.realShape[1]);\n let exponentMultiplierTimesIndexRatio =\n uniforms.exponentMultiplier * indexRatio;\n\n var result = 0.0;\n\n for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {\n // x = (-2|2 * PI / N) * index * i;\n let x = exponentMultiplierTimesIndexRatio * f32(i);\n let expR = cos(x);\n let expI = sin(x);\n let real = getReal(batch, i);\n let imag = getImag(batch, i);\n\n result = result +\n unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;\n }\n\n return result;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));\n }\n }\n `}};function Mx(r,t,e){let o=e.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:e,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new gm(\"real\",u),l=new gm(\"imag\",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=t?2*Math.PI:-2*Math.PI,f=t?u[1]:1,h=[{type:\"float32\",data:[d]},{type:\"float32\",data:[f]}],g=e.runWebGPUProgram(c,m,\"float32\",h);i.push(g);let x=e.runWebGPUProgram(l,m,\"float32\",h);i.push(x);let b=xo({inputs:{real:g,imag:x},backend:e});i.push(b);let C=pe({inputs:{x:b},backend:e,attrs:{shape:r.shape}});return i.forEach(S=>e.disposeData(S.dataId)),C}function Que(r){let{inputs:t,backend:e}=r,{input:o}=t;return Mx(o,!1,e)}var tW={kernelName:Bi,backendName:\"webgpu\",kernelFunc:Que};var Lx=class{constructor(t){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordX = uniforms.xShape[2] - coords[2] - 1;\n let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var rW={kernelName:Cn,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,o=t,n=new Lx(e.shape);return o.runWebGPUProgram(n,[e],e.dtype)}};var Zue=ye({opType:Z.FLOOR,cpuKernelImpl:dz}),oW={kernelName:wn,backendName:\"webgpu\",kernelFunc:Zue};var Jue=et({opType:fe.FLOOR_DIV,cpuKernelImpl:fz,dtype:\"int32\"}),nW={kernelName:Sn,backendName:\"webgpu\",kernelFunc:Jue};var Bx=class{constructor(t,e,o=!1){this.pixelsOpType=Ci.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[e,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let t=this.importVideo?\"textureLoad(src, vec2(coords.yx));\":\"textureLoad(src, vec2(coords.yx), 0)\";return`\n @binding(1) @group(0) var src: ${this.importVideo?\"texture_external\":\"texture_2d\"};\n ${G(\"index\")} {\n let flatIndex = index * uniforms.numChannels;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n let values = ${t};\n for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n result[flatIndex + i] = i32(floor(255.0 * values[i]));\n }\n }\n }\n `}};var sW={kernelName:Eu,backendName:\"webgpu\",kernelFunc:epe},Jc,u0=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function epe(r){let{inputs:t,backend:e,attrs:o}=r,{pixels:n}=t,{numChannels:s}=o;if(n==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!=\"undefined\"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!=\"undefined\"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=A().getBool(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\")&&a,f=a||i;if(u||p||f){let b;if(d)b=e.device.importExternalTexture({source:n});else{if(f){let L=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Jc==null||L!==u0)&&(u0=L,Jc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:u0})),Jc.canvas.width=c,Jc.canvas.height=l,Jc.drawImage(n,0,0,c,l),n=Jc.canvas}let P=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,O=\"rgba8unorm\",M=e.textureManager.acquireTexture(m[1],m[0],O,P);e.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b=M}let C=y.sizeFromShape(m),S=y.computeStrides(m),k=new Bx(m,s,d),_=[{type:\"uint32\",data:[C]},{type:\"uint32\",data:[s]},{type:\"uint32\",data:[...S]}],E=e.makeTensorInfo([l,c],\"int32\"),R=e.tensorMap.get(E.dataId);R.resource=b;let D=e.runWebGPUProgram(k,[E],\"int32\",_);return e.disposeData(E.dataId),D}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,C=0;for(let S=0;S(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0)));\n }\n }\n `}};var aW={kernelName:In,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=t,u=e,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new zx(o.shape,a.shape,i.shape,l,m),f=[{type:\"float32\",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function tpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return yx({x:n,filter:s,convInfo:g,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var iW={kernelName:Io,backendName:\"webgpu\",kernelFunc:tpe};function rpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=w.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:\"int32\",data:[h.padInfo.top,h.padInfo.left]},{type:\"int32\",data:[h.inHeight,h.inWidth]}],S;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(S=new Qc(h,x,m,b),C.push({type:\"int32\",data:[S.virtualWidth]})):(S=new Zc(h,x,m,b),C.push({type:\"int32\",data:[h.filterHeight]},{type:\"int32\",data:[h.filterWidth]},{type:\"int32\",data:[h.strideHeight,h.strideWidth]},{type:\"int32\",data:[h.dilationHeight,h.dilationWidth]})),m===\"leakyrelu\"&&(C.push({type:\"float32\",data:[d]}),S.uniforms+=\" alpha : f32,\"),e.runWebGPUProgram(S,g,\"float32\",C)}var uW={kernelName:vo,backendName:\"webgpu\",kernelFunc:rpe};var Vx=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${t}`,this.sliceDim=t,this.uniforms=`sliceDim : i32, strides : ${ft(t)},`}getUserCode(){let t;return this.sliceDim>1?t=\"uniforms.strides[j]\":t=\"uniforms.strides\",`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var flattenIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexTemp = i32(round(getIndices(coords[0], j)));\n let strideNum = ${t};\n flattenIndex = flattenIndex + indexTemp * strideNum;\n }\n\n setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n }\n }\n `}};function ope(r){let{inputs:t,backend:e}=r,{params:o,indices:n}=t,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=pe({inputs:{x:n},backend:e,attrs:{shape:[u,a]}}),d=pe({inputs:{x:o},backend:e,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(e.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let b=e.readSync(n.dataId),C=e.bufferSync(o),S=hz(b,C,o.dtype,u,a,c,l,o.shape,i);return e.makeTensorInfo(p,o.dtype,S.values)}let f=new Vx(a,[u,c]),h=[{type:\"int32\",data:[a]},{type:\"int32\",data:l}],g=e.runWebGPUProgram(f,[d,m],d.dtype,h),x=pe({inputs:{x:g},backend:e,attrs:{shape:p}});return e.disposeData(m.dataId),e.disposeData(d.dataId),e.disposeData(g.dataId),x}var pW={kernelName:vn,backendName:\"webgpu\",kernelFunc:ope};var Wx=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.slice(),this.aShape=t,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"gather\"}getUserCode(){let t=npe(this.aShape);return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let resRC = getCoordsFromIndex(index);\n let indexZ = i32(getIndices(resRC.x, resRC.z));\n let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n setOutputAtIndex(index, inBounds * getA(${t}));\n }\n }\n `}};function npe(r){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],e=[];for(let o=0;oe.disposeData(D.dataId)),e.makeTensorInfo(u.outputShape,R.dtype,R.values)}let h=new Wx(m.shape,f),g=e.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=pe({inputs:{x:g},backend:e,attrs:{shape:u.outputShape}});return l.forEach(b=>e.disposeData(b.dataId)),x}var cW={kernelName:aa,backendName:\"webgpu\",kernelFunc:p0};var spe=et({opType:fe.GREATER,cpuKernelImpl:yz,dtype:\"bool\"}),lW={kernelName:kn,backendName:\"webgpu\",kernelFunc:spe};var ape=et({opType:fe.GREATER_EQUAL,dtype:\"bool\",cpuKernelImpl:xz}),mW={kernelName:Nn,backendName:\"webgpu\",kernelFunc:ape};function ipe(r){let{inputs:t,backend:e}=r,{input:o}=t;return Mx(o,!0,e)}var dW={kernelName:zi,backendName:\"webgpu\",kernelFunc:ipe};var upe=ye({opType:Z.IS_FINITE,dtype:\"bool\"}),fW={kernelName:Tn,backendName:\"webgpu\",kernelFunc:upe};var ppe=ye({opType:Z.IS_INF,dtype:\"bool\"}),hW={kernelName:_n,backendName:\"webgpu\",kernelFunc:ppe};var cpe=ye({opType:Z.IS_NAN,dtype:\"bool\"}),gW={kernelName:$n,backendName:\"webgpu\",kernelFunc:cpe};function lpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{alpha:s}=o,a=[{type:\"float32\",data:[s]}],i=new Zr(n.shape,Z.LEAKYRELU,\"alpha : f32,\");return e.runWebGPUProgram(i,[n],\"float32\",a)}var xW={kernelName:En,backendName:\"webgpu\",kernelFunc:lpe};var mpe=et({opType:fe.LESS,dtype:\"bool\",cpuKernelImpl:Cz}),yW={kernelName:Rn,backendName:\"webgpu\",kernelFunc:mpe};var dpe=et({opType:fe.LESS_EQUAL,dtype:\"bool\",cpuKernelImpl:bz}),bW={kernelName:Dn,backendName:\"webgpu\",kernelFunc:dpe};var Ux=class{constructor(t){this.variableNames=[],this.outputShape=[],this.uniforms=\"start : f32, step : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"linSpace\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);\n }\n }\n `}};function fpe(r){let{backend:t,attrs:e}=r,{start:o,stop:n,num:s}=e,a=(n-o)/(s-1),i=new Ux(s),p=[{type:\"float32\",data:[o]},{type:\"float32\",data:[a]}];return t.runWebGPUProgram(i,[],\"float32\",p)}var CW={kernelName:An,backendName:\"webgpu\",kernelFunc:fpe};var hpe=ye({opType:Z.LOG,cpuKernelImpl:wz}),wW={kernelName:Fn,backendName:\"webgpu\",kernelFunc:hpe};var gpe=ye({opType:Z.LOG1P}),SW={kernelName:Pn,backendName:\"webgpu\",kernelFunc:gpe};var xpe=et({opType:fe.LOGICAL_AND,dtype:\"bool\"}),IW={kernelName:On,backendName:\"webgpu\",kernelFunc:xpe};var ype=ye({opType:Z.LOGICAL_NOT}),vW={kernelName:Mn,backendName:\"webgpu\",kernelFunc:ype};var bpe=et({opType:fe.LOGICAL_OR}),kW={kernelName:Ln,backendName:\"webgpu\",kernelFunc:bpe};var NW=`\n var powValue = 0.0;\n let basis = uniforms.bias + uniforms.alpha * sum;\n if (uniforms.beta == 0.5) {\n powValue = inverseSqrt(basis);\n } else if (uniforms.beta == 1.0) {\n powValue = 1.0 / basis;\n } else {\n powValue = exp(log(basis) * (-uniforms.beta));\n }\n`,Gx=class{constructor(t){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let x = getX(b, r, c, d);\n var sum = 0.0;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let idx = d + i;\n if (idx >= 0 && idx < uniforms.xShape[3]) {\n let z = getX(b, r, c, idx);\n sum = sum + z * z;\n }\n }\n ${NW}\n\n setOutputAtIndex(index, x * powValue);\n }\n }\n `}},Hx=class{constructor(t,e){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(e<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${e}`),this.outputShape=t,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey=\"lrn_shared\"}getUserCode(){return`\n var lrnSub: array;\n const elementsPerWorkgroup = ${this.elementsPerWorkgroup};\n const maxAllowRadius = ${this.maxAllowRadius};\n\n ${G()} {\n let localDepth = i32(localId.x);\n let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup;\n let xDepth = workgroupDepth + localDepth - maxAllowRadius;\n let b = i32(globalId.z) / uniforms.xShape[1];\n let r = i32(globalId.z) - b * uniforms.xShape[1];\n let c = i32(globalId.y);\n let d = workgroupDepth + localDepth;\n\n var x = 0.0;\n if (xDepth >= 0 && xDepth < uniforms.xShape[3]) {\n x = getX(b, r, c, xDepth);\n }\n lrnSub[localDepth] = x;\n workgroupBarrier();\n\n if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) {\n var sum = 0.0;\n let index = localDepth + maxAllowRadius;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let z = lrnSub[index + i];\n sum = sum + z * z;\n }\n ${NW}\n\n setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);\n }\n } `}};function Cpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Gx(n.shape):u=new Hx(n.shape,s);let c=[{type:\"int32\",data:[s]},{type:\"float32\",data:[a]},{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}];return e.runWebGPUProgram(u,[n],n.dtype,c)}var TW={kernelName:Bn,backendName:\"webgpu\",kernelFunc:Cpe};var Kx=class{constructor(t){this.outputShape=[],this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.uniforms=\"depthRadius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn_grad\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n\n let MIN_DEPTH_BEGIN = 0;\n let MAX_DEPTH_END = uniforms.outShape[3];\n var result = 0.0;\n for (var d = MIN_DEPTH_BEGIN; d < MAX_DEPTH_END; d++) {\n let depthBegin = max(MIN_DEPTH_BEGIN, d - uniforms.depthRadius);\n let depthEnd = min(MAX_DEPTH_END, d + uniforms.depthRadius + 1);\n\n var norm = 0.0;\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n } else {\n break;\n }\n }\n\n norm = uniforms.alpha * norm + uniforms.bias;\n\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n var dyi = -2.0 * uniforms.alpha * uniforms.beta\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * uniforms.beta);\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n } else {\n break;\n }\n }\n }\n\n setOutputAtIndex(index, result);\n }\n }\n `}};function wpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new Kx(n.shape),m=[{type:\"int32\",data:[i]},{type:\"float32\",data:[p]},{type:\"float32\",data:[u]},{type:\"float32\",data:[c]}];return e.runWebGPUProgram(l,[n,s,a],n.dtype,m)}var _W={kernelName:Ya,backendName:\"webgpu\",kernelFunc:wpe};var Spe=et({opType:fe.MAX,cpuKernelImpl:Iz}),$W={kernelName:Vn,backendName:\"webgpu\",kernelFunc:Spe};function Ipe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=w.computePool2DInfo(n.shape,s,a,u,i,p);return sx(n,c,\"max\",e)}var EW={kernelName:Wn,backendName:\"webgpu\",kernelFunc:Ipe};function vpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new wu(l,\"max\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return e.runWebGPUProgram(m,[n],n.dtype,d)}var RW={kernelName:ia,backendName:\"webgpu\",kernelFunc:vpe};var qx=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool2DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] - 1;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR += uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC += uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wR * uniforms.filterDims[1] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},jx=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool3DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] * uniforms.filterDims[2] - 1;\n\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wD * uniforms.filterDims[1] * uniforms.filterDims[2] + wR * uniforms.filterDims[2] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n }\n\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function kpe(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new wu(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.inDepth,m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=e.runWebGPUProgram(d,[a],\"int32\",f),g=new jx(m);f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outDepth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=e.runWebGPUProgram(g,[n,h],a.dtype,f);return e.disposeData(h.dataId),x}var DW={kernelName:Ui,backendName:\"webgpu\",kernelFunc:kpe};function Npe(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s,output:a}=t,i=s;mm([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=new Ba(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=e.runWebGPUProgram(d,[i],\"int32\",f),g=new qx(m);f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=e.runWebGPUProgram(g,[n,h],i.dtype,f);return e.disposeData(h.dataId),x}var AW={kernelName:Wi,backendName:\"webgpu\",kernelFunc:Npe};function Tpe(r){let{inputs:t,backend:e,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=t;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(p.shape,n,s,u,a),l=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new Ba(c,\"max\",!1),d=e.runWebGPUProgram(m,[p],p.dtype,l);m=new Ba(c,\"max\",!0,!0,i);let f=e.runWebGPUProgram(m,[p],\"int32\",l);return[d,f]}var FW={kernelName:ua,backendName:\"webgpu\",kernelFunc:Tpe};function _pe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return Jr(n,s,a,\"min\",e)}var PW={kernelName:Gn,backendName:\"webgpu\",kernelFunc:_pe};var $pe=et({opType:fe.MIN,cpuKernelImpl:vz}),OW={kernelName:Hn,backendName:\"webgpu\",kernelFunc:$pe};var Xx=class{constructor(t,e,o){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.map((n,s)=>n[0]+t[s]+n[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=t,e.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let t=this.xShape.length,e=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(\",\"),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${t>1?`[${c}]`:\"\"}`).join(\",\"),n=t===1?\"start\":\"start[i]\",s=t===1?\"end\":\"end[i]\",a=t===1?\"outC\":\"outC[i]\",i=ft(t),p=t>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,t):\"coords\";return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let start = ${i}(${e});\n let end = ${i}(${o});\n var outC = getCoordsFromIndex(index);\n for (var i = 0; i < ${t}; i = i + 1) {\n if (${a} < ${n}) {\n ${a} = ${n} * 2 - ${a} - ${this.offset};\n } else if(${a} >= ${s}) {\n ${a} = (${s} - 1) * 2 - ${a} + ${this.offset};\n }\n }\n let coords = outC - start;\n setOutputAtIndex(index, getX(${p}));\n }\n }\n `}};var MW={kernelName:Kn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=e,i=n.map(c=>({type:\"int32\",data:[c[0],c[1]]})),p=new Xx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var Epe=et({opType:fe.MOD}),LW={kernelName:qn,backendName:\"webgpu\",kernelFunc:Epe};var Yx=class{constructor(t,e){this.variableNames=[\"probs\"],this.outputShape=[],this.uniforms=\"seed : f32, numOutcomes: i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"multinomial\"}getUserCode(){return`\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n fn random (seed : f32, resultUV : vec2) -> f32 {\n let HASHSCALE1 = 443.8975;\n let p = resultUV * seed;\n var p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 = p3 + dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n\n let resUV = vec2(f32(coords[1]) / f32(uniforms.outShape[1]),\n f32(coords[0]) / f32(uniforms.outShape[0]));\n let r = random(uniforms.seed, resUV);\n var cdf = 0.0;\n for (var i = 0; i < uniforms.numOutcomes - 1; i = i + 1) {\n cdf = cdf + getProbs(batch, i);\n\n if (r < cdf) {\n setOutputAtIndexI32(index, i);\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutputAtIndexI32(index, uniforms.numOutcomes - 1);\n }\n }\n `}};var Qx=class{constructor(t){this.variableNames=[\"logits\"],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey=\"softmax\"}getUserCode(){return`\n var buf : array;\n var rowMaxShared : f32;\n var rowSumShared : f32;\n const blockSize = ${this.workgroupSize[0]};\n ${G(\"index\")} {\n let row = index / blockSize;\n let tid = i32(localId.x);\n let cols = uniforms.outShape[1];\n\n var threadMax = -3.402823e+38f;\n for (var col = tid; col < cols; col += blockSize) {\n let value = getLogits(row, col);\n threadMax = max(threadMax, value);\n }\n if (tid < cols) {\n buf[tid] = threadMax;\n }\n workgroupBarrier();\n\n var reduceSize = min(cols, blockSize);\n for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) {\n reduceSize = currSize + (reduceSize & 1);\n if (tid < currSize) {\n buf[tid] = max(buf[tid], buf[tid + reduceSize]);\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowMaxShared = buf[0];\n }\n workgroupBarrier();\n\n var threadSum = 0.0;\n for (var col = tid; col < cols; col += blockSize) {\n let subExp = exp(getLogits(row, col) - rowMaxShared);\n threadSum += subExp;\n }\n buf[tid] = threadSum;\n workgroupBarrier();\n\n for (var currSize = blockSize >> 1; currSize > 0; currSize = currSize >> 1) {\n if (tid < currSize) {\n buf[tid] = buf[tid] + buf[tid + currSize];\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowSumShared = buf[0];\n }\n workgroupBarrier();\n\n for (var col = tid; col < cols; col += blockSize) {\n let value = exp(getLogits(row, col) - rowMaxShared) / rowSumShared;\n setOutputAtCoords(row, col, value);\n }\n }\n `}};function c0(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{dim:s}=o,a=pe({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new Qx(a.shape),p=e.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:e,attrs:{shape:n.shape}});return e.disposeData(a.dataId),e.disposeData(p.dataId),u}var BW={kernelName:Is,backendName:\"webgpu\",kernelFunc:c0};function Rpe(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{numSamples:s,seed:a,normalized:i}=o,p=i?n:c0({inputs:{logits:n},backend:e,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Yx(u,s),m=[{type:\"float32\",data:[a]},{type:\"int32\",data:[c]}],d=e.runWebGPUProgram(l,[p],\"int32\",m);return i||e.disposeData(p.dataId),d}var zW={kernelName:jn,backendName:\"webgpu\",kernelFunc:Rpe};function Dpe(r){let{inputs:t,backend:e}=r,{x:o}=t;if(e.shouldExecuteOnCPU([o])){let s=e.tensorMap.get(o.dataId),[a,i]=Nz(s.values,o.shape,o.dtype);return e.makeTensorInfo(i,o.dtype,a)}let n=new Zr(o.shape,Z.NEG);return e.runWebGPUProgram(n,[o],o.dtype)}var VW={kernelName:pa,backendName:\"webgpu\",kernelFunc:Dpe};function Ape(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=e.readSync(n.dataId),c=e.readSync(s.dataId),{selectedIndices:l}=Vt.nonMaxSuppressionV3Impl(u,c,a,i,p);return e.makeTensorInfo([l.length],\"int32\",new Int32Array(l))}var WW={kernelName:Qn,backendName:\"webgpu\",kernelFunc:Ape};function Fpe(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=e.readSync(n.dataId),l=e.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Vt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[e.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),e.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var UW={kernelName:Zn,backendName:\"webgpu\",kernelFunc:Fpe};var Zx=class{constructor(t,e){this.variableNames=[\"x\"],this.uniforms=\"onValue : f32, offValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"onehot\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,\n f32(i32(round(getX(coords.x))) == coords.y)));\n }\n }\n `}};function Ppe(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n}=t,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Zx(u,a),l=pe({inputs:{x:n},backend:e,attrs:{shape:[u]}}),m=[{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}],d=e.runWebGPUProgram(c,[l],s,m);e.disposeData(l.dataId);let f=[...n.shape,a],h=pe({inputs:{x:d},backend:e,attrs:{shape:f}});return e.disposeData(d.dataId),h}var GW={kernelName:Jn,backendName:\"webgpu\",kernelFunc:Ppe};function xm(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype===\"complex64\"){let n=Ii({inputs:{input:o},backend:e}),s=xm({inputs:{x:n},backend:e}),a=$p({inputs:{input:o},backend:e}),i=xm({inputs:{x:a},backend:e}),p=xo({inputs:{real:s,imag:i},backend:e});return e.disposeData(n.dataId),e.disposeData(s.dataId),e.disposeData(a.dataId),e.disposeData(i.dataId),p}else return vt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:e})}var HW={kernelName:Sa,backendName:\"webgpu\",kernelFunc:xm};function KW(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(o.dtype===\"complex64\"){let n=Ii({inputs:{input:o},backend:e}),s=KW({inputs:{x:n},backend:e}),a=$p({inputs:{input:o},backend:e}),i=xm({inputs:{x:a},backend:e}),p=xo({inputs:{real:s,imag:i},backend:e});return e.disposeData(n.dataId),e.disposeData(s.dataId),e.disposeData(a.dataId),e.disposeData(i.dataId),p}else return vt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:e})}var qW={kernelName:ca,backendName:\"webgpu\",kernelFunc:KW};function Ope(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o;if(t.length===1)return Ox({inputs:{input:t[0]},backend:e,attrs:{dim:n}});let s=t[0].shape,a=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=t.map(c=>{let l=Ox({inputs:{input:c},backend:e,attrs:{dim:n}});return i.push(l),l}),u=s0({inputs:p,backend:e,attrs:{axis:n}});return i.forEach(c=>e.disposeData(c.dataId)),u}var jW={kernelName:la,backendName:\"webgpu\",kernelFunc:Ope};function l0(r,t=!1){let e=r.length,o=ft(e),n=r.map((l,m)=>`uniforms.pad${m}[0]`).join(\",\"),s=r.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:\"\"}`).join(\",\"),a=e>1?`${o}(${n})`:`${n}`,i=e>1?`${o}(${s})`:`${s}`,p=e>1?\"any(paddedCoords < start)\":\"paddedCoords < start\",u=e>1?\"any(paddedCoords >= end)\":\"paddedCoords >= end\",c=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n let start = ${a};\n let end = ${i};\n if (${p} || ${u}) {\n setOutputAtIndex(index, ${t?0:\"uniforms.constantValue\"});\n } else {\n let coords = paddedCoords - start;\n setOutputAtIndex(index, getX(${c}));\n }\n `}var Jx=class{constructor(t,e){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.map((o,n)=>o[0]+t[n]+o[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),e.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=t,this.shaderKey=\"pad\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let paddedCoords = getCoordsFromIndex(index);\n ${l0(this.xShape)}\n }\n }\n `}};var Mpe=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return At({inputs:{x:n},backend:e});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return vt({backend:e,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:\"float32\",data:[a]}];s.map(u=>i.push({type:\"int32\",data:[u[0],u[1]]}));let p=new Jx(n.shape,s);return e.runWebGPUProgram(p,[n],n.dtype,i)},XW={kernelName:es,backendName:\"webgpu\",kernelFunc:Mpe};var Lpe=et({opType:fe.POW}),YW={kernelName:ts,backendName:\"webgpu\",kernelFunc:Lpe};function Bpe(r){let{inputs:t,backend:e}=r,{x:o,alpha:n}=t,s=new Si(fe.PRELU,o.shape,n.shape);return e.runWebGPUProgram(s,[o,n],\"float32\")}var QW={kernelName:rs,backendName:\"webgpu\",kernelFunc:Bpe};function zpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return Jr(n,s,a,\"prod\",e)}var ZW={kernelName:os,backendName:\"webgpu\",kernelFunc:zpe};var Vpe=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=$z(o,n,s,a);return t.makeTensorInfo([i.length],a,i)},JW={kernelName:ma,backendName:\"webgpu\",kernelFunc:Vpe};var Wpe=et({opType:fe.DIV}),eU={kernelName:fn,backendName:\"webgpu\",kernelFunc:Wpe};var Upe=ye({opType:Z.RECIPROCAL}),tU={kernelName:ns,backendName:\"webgpu\",kernelFunc:Upe};var Gpe=ye({opType:Z.RELU}),rU={kernelName:ss,backendName:\"webgpu\",kernelFunc:Gpe};var Hpe=ye({opType:Z.RELU6}),oU={kernelName:us,backendName:\"webgpu\",kernelFunc:Hpe};var ey=class{constructor(t,e,o){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, halfPixelCenters : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t[0],e,o,t[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC =\n (vec2(rc) + vec2(uniforms.halfPixelCenters)) *\n effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters);\n\n // Compute the four integer indices.\n let sourceFloorRC = vec2(sourceFracIndexRC);\n let sourceCeilRC = vec2(\n min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC)));\n\n let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n let fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n let top = topLeft + (topRight - topLeft) * fracRC.y;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n let newValue = top + (bottom - top) * fracRC.x;\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function Kpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[i?.5:0]}],f=new ey(n.shape,p,u);return e.runWebGPUProgram(f,[n],\"float32\",d)}var nU={kernelName:is,backendName:\"webgpu\",kernelFunc:Kpe};var ty=class{constructor(t,e){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32,\n invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=e,this.shaderKey=`resizeBilinearBackprop_${e}`}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(startRLerp - f32(uniforms.winHeight / 2));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(startCLerp - f32(uniforms.winWidth / 2));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let dxR = f32(dyR) * uniforms.heightScale;\n let topDxRIndex = i32(floor(dxR));\n let bottomDxRIndex = i32(min(ceil(dxR), f32(uniforms.outShape[1] - 1)));\n let dxRLerp = dxR - f32(topDxRIndex);\n let inverseDxRLerp = 1.0 - dxRLerp;\n\n let dxC = f32(dyC) * uniforms.widthScale;\n let leftDxCIndex = i32(floor(dxC));\n let rightDxCIndex = i32(min(ceil(dxC), f32(uniforms.outShape[2] - 1)));\n let dxCLerp = dxC - f32(leftDxCIndex);\n let inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function qpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ty(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[d]},{type:\"float32\",data:[f]},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return e.runWebGPUProgram(C,[s],s.dtype,S)}var sU={kernelName:Ja,backendName:\"webgpu\",kernelFunc:qpe};var ry=class{constructor(t,e,o,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, roundBase : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t[0],e,o,t[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let t;return this.halfPixelCenters?t=\"max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":t=\"vec2(rc) * effectiveInputOverOutputRatioRC\",`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC = ${t};\n\n // Compute the coordinators of nearest neighbor point.\n let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n let sourceNearestRC = vec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function jpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[s?.5:0]}],f=new ry(n.shape,p,u,a);return e.runWebGPUProgram(f,[n],n.dtype,d)}var aU={kernelName:as,backendName:\"webgpu\",kernelFunc:jpe};var oy=class{constructor(t,e){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32,\n winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=e,this.shaderKey=`resizeNearestNeigborBackprop_${e}`}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(floor(startRLerp - f32(uniforms.winHeight / 2)));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(floor(startCLerp - f32(uniforms.winWidth / 2)));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let sourceFracRow = f32(uniforms.effectiveXSize[0]) *\n (f32(dyR) / f32(uniforms.effectiveYSize[0]));\n\n let sourceFracCol = f32(uniforms.effectiveXSize[1]) *\n (f32(dyC) / f32(uniforms.effectiveYSize[1]));\n\n let sourceNearestRow =\n i32(min(f32(uniforms.outShape[1] - 1),\n ${this.alignCorners?\"floor(sourceFracRow + 0.5)\":\"floor(sourceFracRow)\"}));\n\n let sourceNearestCol =\n i32(min(f32(uniforms.outShape[2] - 1),\n ${this.alignCorners?\"floor(sourceFracCol + 0.5)\":\"floor(sourceFracCol)\"}));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function Xpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new oy(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return e.runWebGPUProgram(C,[s],s.dtype,S)}var iU={kernelName:Za,backendName:\"webgpu\",kernelFunc:Xpe};var ny=class{constructor(t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\" axis : vec4,\",this.shaderKey=\"reverse\"}getUserCode(){return`\n \n // Using uniform variables as judging conditions, so the function has\n // coherent execution within all threads.\n fn getReverseCoords(coords : vec4) -> vec4 {\n var reverseCoords = coords;\n if (uniforms.axis[0] == 1) {\n reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;\n }\n if (uniforms.axis[1] == 1) {\n reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;\n }\n if (uniforms.axis[2] == 1) {\n reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;\n }\n if (uniforms.axis[3] == 1) {\n reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;\n }\n\n return reverseCoords;\n }\n \n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let reverseCoords = getReverseCoords(coords);\n setOutputAtIndex(index, getX(reverseCoords[0],\n reverseCoords[1], reverseCoords[2], reverseCoords[3]));\n }\n }\n `}};function Ype(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=n.shape.length;if(a===0)return At({inputs:{x:n},backend:e});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:\"int32\",data:c}],m=pe({inputs:{x:n},backend:e,attrs:{shape:p}}),d=new ny(p),f=e.runWebGPUProgram(d,[m],m.dtype,l);e.disposeData(m.dataId);let h=pe({inputs:{x:f},backend:e,attrs:{shape:i}});return e.disposeData(f.dataId),h}var uU={kernelName:ps,backendName:\"webgpu\",kernelFunc:Ype};var sy=class{constructor(t,e){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=t,typeof e==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n uniforms.sinRadians;\n let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n uniforms.cosRadians;\n let coordX = i32(round(coordXFloat + uniforms.centerX));\n let coordY = i32(round(coordYFloat + uniforms.centerY));\n ${this.fillSnippet}\n if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n coordY < uniforms.xShape[1]) {\n outputValue = getX(coords[0], coordY, coordX, coords[3]);\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var pU={kernelName:Ds,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=t,i=e,p=new sy(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:\"float32\",data:[u]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(n)]},{type:\"float32\",data:[Math.cos(n)]}];return typeof s==\"number\"?l.push({type:\"float32\",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:\"float32\",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Qpe=ye({opType:Z.ROUND}),cU={kernelName:cs,backendName:\"webgpu\",kernelFunc:Qpe};var Zpe=ye({opType:Z.RSQRT,cpuKernelImpl:Ez}),lU={kernelName:ls,backendName:\"webgpu\",kernelFunc:Zpe};var za=class{constructor(t,e,o,n,s,a,i,p=!0){this.variableNames=[\"updates\",\"indices\"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.sliceDimGreaterThanOne=e>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}_${s.length}`;let u=ft(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let t=\"\";this.indicesRank===1?t=\"coords[0]\":this.indicesRank===2&&(t=\"coords[0], j\");let e=`getIndices(${t})`,o=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",n=\"\",s=\"\";this.dispatchLayout.x.length===1?(n=\"flattenedIndex\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n return index;\n }\n `):this.dispatchLayout.x.length===2&&(n=\"vec2(flattenedIndex, coords[1])\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 {\n // N.B. |updates| could be a scalar tensor, conceptually representing a\n // 2D tensor with all values equal to that. By design, its size must be\n // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n // gives the other.\n let sliceSize = uniforms.outShape[1];\n let d0 = index / sliceSize;\n let d1 = index - d0 * sliceSize;\n return vec2(d0, d1);\n }\n `);let i=`getUpdates(${Array.from({length:this.updatesRank},(u,c)=>`coords[${c}]`).join(\", \")})`;return`\n ${s}\n ${G(\"index\")} {\n if (index < uniforms.updatesSize) {\n let coords = getUpdatesCoordsFromFlatIndex(index);\n var flattenedIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexInside = i32(round(${e}));\n flattenedIndex = flattenedIndex + indexInside * ${o};\n }\n let updateValue =\n ${Cu(this.type)}(${i});\n let flatIndex = getOutputIndexFromCoords(${n});\n\n ${this.sumDupeIndices?Yr(\"&result[flatIndex]\",\"updateValue\",this.type):\"atomicStore(&result[flatIndex], bitcast(updateValue));\"}\n }\n }`}};function Jpe(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n,updates:s}=t,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return e.makeTensorInfo(a,n.dtype);let d=pe({inputs:{x:n},backend:e,attrs:{shape:[p,i]}}),f=pe({inputs:{x:s},backend:e,attrs:{shape:[p,u]}}),h=f.dtype,g=vt({backend:e,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[x]}],C=new za(f.shape,i,d.shape.length,f.shape.length,c,m,h),S=e.runWebGPUProgram(C,[f,d],h,b,g),k=pe({inputs:{x:S},backend:e,attrs:{shape:a}});return e.disposeData(d.dataId),e.disposeData(f.dataId),e.disposeData(S.dataId),k}var mU={kernelName:ms,backendName:\"webgpu\",kernelFunc:Jpe};var ay=class{constructor(t,e){this.outputShape=[],this.variableNames=[\"sortedSequence\",\"values\"],this.uniforms=\"numInputs : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=e,this.shaderKey=`search_sorted_${e}`}getUserCode(){return`\n fn findBound(batch: i32, value: f32) -> i32 {\n var left = i32(0);\n var right = uniforms.numInputs;\n while (left < right) {\n var mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${this.side===\"left\"?\"<\":\"<=\"} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let value = getValuesByOutputIndex(index);\n setOutputAtIndexI32(index, findBound(coords[0], value));\n }\n }\n `}};function ece(r){let{inputs:t,backend:e,attrs:o}=r,{sortedSequence:n,values:s}=t,{side:a}=o,i=new ay([s.shape[0],s.shape[1]],a),p=[{type:\"int32\",data:[n.shape[1]]}];return e.runWebGPUProgram(i,[n,s],\"int32\",p)}var dU={kernelName:fs,backendName:\"webgpu\",kernelFunc:ece};var iy=class{constructor(t,e,o){this.variableNames=[\"c\",\"a\",\"b\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=t,this.rank=o,this.shaderKey=\"select\"}getUserCode(){let t,e;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)e=\"resRC\",t=\"resRC\";else{let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[],a=[];for(let i=0;i= 1.0) {\n setOutputAtIndex(index, getA(${e}));\n } else {\n setOutputAtIndex(index, getB(${e}));\n }\n }\n }\n `}};function tce(r){let{inputs:t,backend:e}=r,{condition:o,t:n,e:s}=t,a=new iy(o.shape.length,n.shape,n.shape.length);return e.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var fU={kernelName:fa,backendName:\"webgpu\",kernelFunc:tce};var rce=ye({opType:Z.SELU}),hU={kernelName:hs,backendName:\"webgpu\",kernelFunc:rce};var oce=ye({opType:Z.SIGMOID}),gU={kernelName:bs,backendName:\"webgpu\",kernelFunc:oce};var nce=ye({opType:Z.SIGN}),xU={kernelName:ys,backendName:\"webgpu\",kernelFunc:nce};var sce=ye({opType:Z.SIN}),yU={kernelName:gs,backendName:\"webgpu\",kernelFunc:sce};var ace=ye({opType:Z.SINH}),bU={kernelName:xs,backendName:\"webgpu\",kernelFunc:ace};var ice=ye({opType:Z.SOFTPLUS}),CU={kernelName:Cs,backendName:\"webgpu\",kernelFunc:ice};var uy=class{constructor(t,e,o,n,s,a){this.variableNames=[\"x\"],this.outputShape=[],this.uniforms=\"\",this.workgroupSize=[64,1,1],this.size=!0;let i=new Array(n.length);for(let p=0;p{this.uniforms+=` pad${u} : vec2,`}),this.shaderKey=`spaceToBatchND_${s}`}getUserCode(){let t=ft(this.outputShape.length),e=Jv(this.newDim);return`\n ${um(this.paddedXShape,\"PaddedX\")}\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${t}(${e}), uniforms.reshapedPaddedXShape);\n let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex);\n ${l0(this.xShape,!0)}\n }\n }\n `}};var uce=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=[[0,0]];p.push(...a);for(let b=1+s.length;bb[0]+n.shape[C]+b[1]),c=w.getReshaped(u,s,i,!1),l=w.getPermuted(c.length,s.length,!1),m=w.getReshapedPermuted(u,s,i,!1),d=y.computeStrides(u),f=new uy(n.shape,u,p,c,l,d.length),h=[{type:\"int32\",data:c},{type:\"int32\",data:d}];p.map(b=>h.push({type:\"int32\",data:[b[0],b[1]]}));let g=e.runWebGPUProgram(f,[n],n.dtype,h),x=pe({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeData(g.dataId),x},wU={kernelName:ga,backendName:\"webgpu\",kernelFunc:uce};var py=class{constructor(t,e,o){this.variableNames=[\"input\",\"indices\",\"segmentIds\"],this.outputShape=[],this.uniforms=\"segmentSize : i32, sparseSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.type=o,this.dispatchLayout=X([e]),this.dispatch=H(this.dispatchLayout,[e],this.workgroupSize),this.shaderKey=\"sparseSegmentSum\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.sparseSize) {\n let indexInSegmentIds = index / uniforms.segmentSize;\n let indexInSegment = index % uniforms.segmentSize;\n let indexInInput = indices[indexInSegmentIds];\n let segmentId = segmentIds[indexInSegmentIds];\n\n let value = input[indexInInput * uniforms.segmentSize + indexInSegment];\n let outIndex = segmentId * uniforms.segmentSize + indexInSegment;\n ${Yr(\"&result[outIndex]\",\"value\",this.type)}\n }\n }\n `}},cy=class{constructor(t,e){this.variableNames=[\"segmentIds\"],this.outputShape=[],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=[t],this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),this.shaderKey=\"sparseSegmentIdCountProgram\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.segmentIdsShape) {\n let segmentId = segmentIds[index];\n ${Yr(\"&result[segmentId]\",\"1\",\"int32\")}\n }\n }\n `}},ly=class{constructor(t,e){this.variableNames=[\"segmentSum\",\"sameSegmentIdCount\"],this.outputShape=[],this.uniforms=\"segmentSize : i32\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.type=e,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.shaderKey=\"sparseSegmentMean\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let segmentId = index / uniforms.segmentSize;\n let count = sameSegmentIdCount[segmentId];\n if (count != 0) {\n ${this.type===\"float32\"?\"setOutputAtIndex(index, segmentSum[index] / f32(count));\":\"setOutputAtIndexI32(index, segmentSum[index] / count);\"}\n }\n }\n }\n `}};function my(r,t,e,o=!1,n){let a=y.sizeFromShape(r.shape)/r.shape[0],i=r.dtype,p=y.sizeFromShape(t.shape),u=n.readSync(e.dataId),l=p>0?u[p-1]+1:0,m,d=r.shape.slice();d[0]=l;let f=p*a,h=vt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new py(d,f,i);let g=[{type:\"int32\",data:[a]},{type:\"int32\",data:[f]}],x=n.runWebGPUProgram(m,[r,t,e],i,g,h);if(o)return x;let b=vt({backend:n,attrs:{shape:[l],value:0,dtype:\"int32\"}});m=new cy(l,e.shape);let C=n.runWebGPUProgram(m,[e],\"int32\",null,b),S=vt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new ly(d,i),g=[{type:\"int32\",data:[a]}];let k=n.runWebGPUProgram(m,[x,C],i,g,S);return n.disposeData(x.dataId),n.disposeData(C.dataId),k}function pce(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;return my(o,n,s,!1,e)}var SU={kernelName:ya,backendName:\"webgpu\",kernelFunc:pce};function cce(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;return my(o,n,s,!0,e)}var IU={kernelName:ba,backendName:\"webgpu\",kernelFunc:cce};var dy=class{constructor(t,e){this.variableNames=[\"A\"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(t.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${t}aShape)`;let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=5){let p=e.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=Mz(c,s);return e.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new dy(n.shape,s);return e.runWebGPUProgram(a,[n],n.dtype)}var vU={kernelName:uo,backendName:\"webgpu\",kernelFunc:ym};function mce(r){let{inputs:t,backend:e,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=t,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let R=e.bufferSync(n),D=e.bufferSync(s),P=y.decodeString(e.readSync(a.dataId)[0]),O=Rz(R,D,i,m,c,u,p,l,P,d);return e.makeTensorInfo(i,O.dtype,O.values)}let f=[m/c,c],h=pe({inputs:{x:n},backend:e,attrs:{shape:[u,p]}}),g=s.shape.length?pe({inputs:{x:s},backend:e,attrs:{shape:[u,c]}}):At({inputs:{x:s},backend:e}),x=g.dtype,b=e.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=pe({inputs:{x:a},backend:e,attrs:{shape:Array(f.length).fill(1)}}),S=ym({inputs:{x:C},backend:e,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:\"int32\",data:[p]},{type:\"int32\",data:l},{type:\"int32\",data:[k]}];switch(u){case 0:break;case 1:{let R=new za([u,c],p,h.shape.length,g.shape.length,l,f,x,d);e.runWebGPUProgram(R,[g,h],x,_,S)}break;default:{let R=new za([u,c],p,h.shape.length,b.shape.length,l,f,x,d);e.runWebGPUProgram(R,[b,h],x,_,S)}{let R=new za([u,c],p,h.shape.length,g.shape.length,l,f,x);e.runWebGPUProgram(R,[g,h],x,_,S)}}let E=pe({inputs:{x:S},backend:e,attrs:{shape:i}});return e.disposeData(h.dataId),e.disposeData(g.dataId),e.disposeData(C.dataId),e.disposeData(b.dataId),e.disposeData(S.dataId),E}var kU={kernelName:vs,backendName:\"webgpu\",kernelFunc:mce};function dce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Hs({inputs:{x:n},backend:e,attrs:{begin:c,size:d}});return c[i]+=m,f})}var NU={kernelName:xa,backendName:\"webgpu\",kernelFunc:dce};var fce=ye({opType:Z.SQRT}),TU={kernelName:ws,backendName:\"webgpu\",kernelFunc:fce};var _U={kernelName:Ki,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:t})=>{let{x:e}=r,o=t,n=new Zr(e.shape,Z.SQUARE);return o.runWebGPUProgram(n,[e],e.dtype)}};var hce=et({opType:fe.SQUARED_DIFFERENCE}),$U={kernelName:ks,backendName:\"webgpu\",kernelFunc:hce};function gce({inputs:r,attrs:t,backend:e}){let{x:o}=r,n=new Zr(o.shape,Z.STEP,\"stepAlpha : f32,\"),s=[{type:\"float32\",data:[t.alpha]}];return e.runWebGPUProgram(n,[o],o.dtype,s)}var EU={kernelName:wo,backendName:\"webgpu\",kernelFunc:gce};var fy=class{constructor(t){this.variableNames=[\"x\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let e=ft(this.outputShape.length);this.uniforms=`begin : ${e}, strides : ${e}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let t=this.outputShape.length,e=\"\";if(t===1)e=\"coords * uniforms.strides + uniforms.begin\";else{let n=0;e=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, getX(${e}));\n }\n }\n `}};function xce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=pt.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=pe({inputs:{x:n},backend:e,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=pt.computeOutShape(b,C,S),E=Hs({inputs:{x:n},backend:e,attrs:{begin:b,size:_}});k=pe({inputs:{x:E},backend:e,attrs:{shape:f}}),e.disposeData(E.dataId)}else if(e.shouldExecuteOnCPU([n])){let E=e.readSync(n.dataId),R=me(n.shape,n.dtype,E),D=Fz(d,R,S,b);k=e.makeTensorInfo(f,n.dtype,D.values)}else{let E=new fy(d),R=[{type:\"int32\",data:b},{type:\"int32\",data:S}],D=e.runWebGPUProgram(E,[n],n.dtype,R);k=pe({inputs:{x:D},backend:e,attrs:{shape:f}}),e.disposeData(D.dataId)}return k}var RU={kernelName:Ns,backendName:\"webgpu\",kernelFunc:xce};function yce(r){let{inputs:t,backend:e,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=t,m=e.readSync(c.dataId),d=e.readSync(l.dataId),[f,h]=Pz(m,d,n,s,a,i,p,u);return[e.makeTensorInfo([f.length],\"string\",f),e.makeTensorInfo(l.shape,\"int32\",h)]}var DU={kernelName:Ca,backendName:\"webgpu\",kernelFunc:yce};var bce=et({opType:fe.SUB,cpuKernelImpl:Oz,supportsComplex:!0}),AU={kernelName:Ts,backendName:\"webgpu\",kernelFunc:bce};var Cce=ye({opType:Z.TAN}),FU={kernelName:_s,backendName:\"webgpu\",kernelFunc:Cce};var wce=ye({opType:Z.TANH}),PU={kernelName:$s,backendName:\"webgpu\",kernelFunc:wce};function Sce(r){let{inputs:t,backend:e,attrs:o}=r,{tensor:n,indices:s,updates:a}=t,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return e.makeTensorInfo(n.shape,s.dtype);let d=[],f=pe({inputs:{x:s},backend:e,attrs:{shape:[p,i]}});d.push(f);let h=pe({inputs:{x:a},backend:e,attrs:{shape:[p,u]}});d.push(h);let g=pe({inputs:{x:n},backend:e,attrs:{shape:m}});d.push(g);let x=ym({inputs:{x:g},backend:e,attrs:{reps:Array(m.length).fill(1)}}),b=new za([p,u],i,f.shape.length,h.shape.length,c,m,n.dtype,!1),C=y.sizeFromShape([p,u]),S=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[C]}],k=e.runWebGPUProgram(b,[h,f],g.dtype,S,x);d.push(k);let _=pe({inputs:{x:k},backend:e,attrs:{shape:n.shape}});return d.forEach(E=>e.disposeData(E.dataId)),_}var OU={kernelName:ds,backendName:\"webgpu\",kernelFunc:Sce};var hy=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced\n // above, Figure5(a) shows that element[1] is in the second half of\n // the group when group size is 2, but it is in the first half of\n // the group when group size is 4.\n let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n var i = 0;\n if (isFirstInPair) {\n i = elemIdx;\n } else {\n i = elemIdx - uniforms.inc;\n }\n\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.inc;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.inc));\n }\n\n var x0 = f32(0.0);\n var x1 = f32(0.0);\n if (i0 < uniforms.inputSize) {\n x0 = getX(batch, i0);\n } else {\n x0 = uniforms.negativeInf;\n }\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = uniforms.negativeInf;\n }\n\n let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) {\n // Elements in opposite order of direction\n let iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}},gy=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _\n // (k=4), we only need to output the indices at positions |, the\n // indices at positions _ can be thrown away, see Figure5(b) After\n // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n // above.\n // For example, the paper shows we only need to output the orange\n // bars. The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back to\n // the previous sequence to find the corresponding value, we need\n // to double the index. When we double the index, we basically\n // interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n // position of each 2k positions by - elemIdx % k. E.g. for output\n // at index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n var i = 0;\n if (elemIdx < uniforms.k) {\n i = elemIdx;\n } else {\n i = elemIdx * 2 - elemIdx % uniforms.k;\n }\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.k;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.k));\n }\n\n let x0 = getX(batch, i0);\n var x1 = f32(0.0);\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = x0;\n }\n\n if (x0 >= x1) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}};function el(r,t){t!==null&&r.disposeData(t.dataId)}function MU(r){let t=1;for(;tf===null?[l,l]:[l,f],g=(k,_,E)=>{let R=h(),D=new hy(E),O=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[_]}],M=f;f=e.runWebGPUProgram(D,R,\"int32\",O),el(e,M)};for(let k=1;k=1;E/=2)g(_,E,[c,d])}for(let k=d;k>m;k/=2){let _=h(),E=new gy([c,k/2]),D=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[m]}],P=f;f=e.runWebGPUProgram(E,_,\"int32\",D),el(e,P);let O=m/2,M=O*2;for(let L=O;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=Hs({inputs:{x:f},backend:e,attrs:{begin:0,size:[c,s]}}),el(e,x);let b=p0({inputs:{x:l,indices:f},backend:e,attrs:{axis:1,batchDims:1}});el(e,l);let C=i.slice(0,-1);C.push(s),x=f,f=pe({inputs:{x:f},attrs:{shape:C},backend:e}),el(e,x);let S=b;return b=pe({inputs:{x:b},attrs:{shape:C},backend:e}),el(e,S),[b,f]}var LU={kernelName:Es,backendName:\"webgpu\",kernelFunc:Ice};var xy=class{constructor(t){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n fn mapCoord(outCoord : f32, len : f32) -> f32{\n var inCoord = outCoord;\n if(uniforms.fillModeId == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n inCoord;\n }\n if (inCoord < -len) {\n inCoord = inCoord + sz2;\n } else {\n inCoord = -inCoord - 1.0;\n }\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n }\n return outCoord;\n }\n fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n channel : i32) -> f32 {\n var outputValue : f32;\n if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = uniforms.fillValue;\n }\n return outputValue;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var outputValue : f32;\n let batch = coords[0];\n let x = coords[2];\n let y = coords[1];\n let channel = coords[3];\n let xf = f32(x);\n let yf = f32(y);\n let a1 = getTransforms(batch, 0);\n let a2 = getTransforms(batch, 1);\n let a3 = getTransforms(batch, 2);\n let b1 = getTransforms(batch, 3);\n let b2 = getTransforms(batch, 4);\n let b3 = getTransforms(batch, 5);\n let c1 = getTransforms(batch, 6);\n let c2 = getTransforms(batch, 7);\n let projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = uniforms.fillValue;\n } else {\n let inX = (a1 * xf + a2 * yf + a3) / projection;\n let inY = (b1 * xf + b2 * yf + b3) / projection;\n let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n if (uniforms.interpolationModeId == 1) {\n let coordY = i32(round(mapY));\n let coordX = i32(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n let yFloor = floor(mapY);\n let xFloor = floor(mapX);\n let yCeil = yFloor + 1.0;\n let xCeil = xFloor + 1.0;\n let valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n let valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};function vce(r){let{inputs:t,backend:e,attrs:o}=r,{image:n,transforms:s}=t,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new xy(g),b=a===\"nearest\"?1:2,C;switch(i){case\"constant\":C=1;break;case\"reflect\":C=2;break;case\"wrap\":C=3;break;case\"nearest\":C=4;break;default:C=1;break}let S=[{type:\"int32\",data:[b]},{type:\"int32\",data:[C]},{type:\"float32\",data:[p]}];return e.runWebGPUProgram(x,[n,s],\"float32\",S)}var BU={kernelName:Rs,backendName:\"webgpu\",kernelFunc:vce};function kce(r){let{inputs:t,backend:e,attrs:o}=r,{value:n}=t,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;he.disposeData(h.dataId)),f}var zU={kernelName:wa,backendName:\"webgpu\",kernelFunc:kce};var yy=class{constructor(t,e,o){if(this.outputShape=[],this.variableNames=[\"x\",\"segmentIds\"],this.uniforms=\"numSegments : i32, xSize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`UnsortedSegmentSum only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"unsortedSegmentSum\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.xSize) {\n let coords = getXCoordsFromIndex(index);\n let b = coords[0];\n let inCol = coords[1];\n\n let segmentId = i32(getSegmentIds(inCol));\n if (segmentId >= 0) {\n let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments;\n let value = getX(b, inCol);\n\n ${Yr(\"&result[flatIndex]\",\"value\",this.type)}\n }\n }\n }\n `}};function Nce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,segmentIds:s}=t,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=xr({inputs:{x:n},backend:e,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=pe({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=vt({backend:e,attrs:{shape:g,value:0,dtype:h}}),b=new yy(f.shape,g,h),C=[{type:\"int32\",data:[a]},{type:\"int32\",data:[y.sizeFromShape(f.shape)]}],S=e.runWebGPUProgram(b,[f,s],h,C,x),k=pe({inputs:{x:S},backend:e,attrs:{shape:m}});p.push(S);let _=k;if(c!=null){p.push(k);let E=w.getUndoAxesPermutation(c);_=xr({inputs:{x:_},backend:e,attrs:{perm:E}})}return p.forEach(E=>e.disposeData(E.dataId)),_}var VU={kernelName:Yi,backendName:\"webgpu\",kernelFunc:Nce};var Tce=[oz,zz,Vz,Wz,Uz,Gz,Kz,qz,jz,Xz,Yz,Qz,Zz,Jz,eV,oV,nV,sV,aV,iV,pV,cV,lV,hV,gV,xV,sz,bV,wV,SV,IV,vV,kV,NV,TV,_V,$V,EV,AV,FV,PV,OV,LV,BV,MV,zV,VV,WV,UV,GV,qV,jV,XV,YV,QV,ZV,JV,eW,tW,tz,rW,sW,oW,nW,aW,iW,uW,pW,cW,lW,mW,nz,dW,CV,fW,hW,gW,xW,yW,bW,CW,SW,wW,IW,vW,kW,TW,_W,tV,$W,EW,AW,RW,DW,FW,rV,PW,OW,MW,LW,zW,HV,VW,WW,UW,mV,GW,qW,jW,XW,YW,QW,ZW,JW,dV,eU,tU,rU,oU,rz,nU,sU,aU,iU,uU,pU,cU,lU,mU,dU,fU,hU,gU,xU,yU,bU,uV,EU,RU,DU,BW,CU,wU,SU,IU,kU,NU,TU,_U,$U,AU,KV,FU,PU,OU,vU,LU,BU,Hz,zU,VU,HW];for(let r of Tce)ti(r);var WU=\"4.14.0\",_ce=\"4.14.0\",$ce=\"4.14.0\",Ece=\"4.14.0\",Rce=\"4.14.0\",Dce=\"4.14.0\",Ace={tfjs:WU,\"tfjs-core\":WU,\"tfjs-converter\":_ce,\"tfjs-backend-cpu\":$ce,\"tfjs-backend-webgl\":Ece,\"tfjs-backend-wasm\":Rce,\"tfjs-backend-webgpu\":Dce};var bQt=void 0;export{Xs as Abs,Vo as Acos,Wo as Acosh,Qu as AdadeltaOptimizer,Zu as AdagradOptimizer,Ju as AdamOptimizer,ep as AdamaxOptimizer,io as Add,Uo as AddN,Go as All,Ho as Any,Ys as ArgMax,Qs as ArgMin,Ko as Asin,qo as Asinh,jo as Atan,Yo as Atan2,Xo as Atanh,Qo as AvgPool,Zs as AvgPool3D,Ei as AvgPool3DGrad,$i as AvgPoolGrad,im as BackendWasm,Zo as BatchMatMul,Js as BatchToSpaceND,Jo as Bincount,qa as BitwiseAnd,ea as BroadcastArgs,Bce as BroadcastTo,yo as Cast,en as Ceil,bo as ClipByValue,Ri as Complex,Di as ComplexAbs,ta as Concat,tn as Conv2D,Ai as Conv2DBackpropFilter,rn as Conv2DBackpropInput,on as Conv3D,ja as Conv3DBackpropFilterV2,nn as Conv3DBackpropInputV2,sn as Cos,an as Cosh,cn as CropAndResize,un as Cumprod,pn as Cumsum,Bo as DataStorage,ra as DenseBincount,ln as DepthToSpace,mn as DepthwiseConv2dNative,Fi as DepthwiseConv2dNativeBackpropFilter,Pi as DepthwiseConv2dNativeBackpropInput,oa as Diag,dn as Dilation2D,Mi as Dilation2DBackpropFilter,Oi as Dilation2DBackpropInput,_u as Draw,ow as ENV,Li as Einsum,hn as Elu,Xa as EluGrad,ll as Environment,xn as Equal,gn as Erf,yn as Exp,na as ExpandDims,bn as Expm1,Bi as FFT,sa as Fill,Cn as FlipLeftRight,wn as Floor,Sn as FloorDiv,Eu as FromPixels,In as FusedBatchNorm,Io as FusedConv2D,vo as FusedDepthwiseConv2D,xp as GPGPUContext,vn as GatherNd,aa as GatherV2,Ml as GraphModel,kn as Greater,Nn as GreaterEqual,zi as IFFT,Co as Identity,Vi as Imag,Tn as IsFinite,_n as IsInf,$n as IsNan,so as KernelBackend,Bn as LRN,Ya as LRNGrad,En as LeakyRelu,Rn as Less,Dn as LessEqual,An as LinSpace,Fn as Log,Pn as Log1p,zce as LogSoftmax,On as LogicalAnd,Mn as LogicalNot,Ln as LogicalOr,E0 as LogicalXor,Vce as LowerBound,hc as MathBackendCPU,Oc as MathBackendWebGL,Wce as MatrixBandPart,zn as Max,Wn as MaxPool,ia as MaxPool3D,Ui as MaxPool3DGrad,Wi as MaxPoolGrad,ua as MaxPoolWithArgmax,Vn as Maximum,Un as Mean,Gn as Min,Hn as Minimum,Kn as MirrorPad,qn as Mod,tp as MomentumOptimizer,jn as Multinomial,Xn as Multiply,pa as Neg,Qn as NonMaxSuppressionV3,Qa as NonMaxSuppressionV4,Zn as NonMaxSuppressionV5,Yn as NotEqual,kw as OP_SCOPE_SUFFIX,Jn as OneHot,ca as OnesLike,kr as Optimizer,Dl as OptimizerConstructors,la as Pack,es as PadV2,Uce as Pool,ts as Pow,rs as Prelu,os as Prod,rp as RMSPropOptimizer,Up as RaggedGather,Gp as RaggedRange,Hp as RaggedTensorToTensor,ma as Range,hw as Rank,Gi as Real,fn as RealDiv,ns as Reciprocal,Et as Reduction,ss as Relu,us as Relu6,da as Reshape,is as ResizeBilinear,Ja as ResizeBilinearGrad,as as ResizeNearestNeighbor,Za as ResizeNearestNeighborGrad,ps as Reverse,Ds as RotateWithOffset,cs as Round,ls as Rsqrt,li as SGDOptimizer,ms as ScatterNd,fs as SearchSorted,fa as Select,hs as Selu,bs as Sigmoid,ys as Sign,gs as Sin,xs as Sinh,ha as Slice,Is as Softmax,Cs as Softplus,ga as SpaceToBatchND,Hi as SparseFillEmptyRows,ei as SparseReshape,ya as SparseSegmentMean,ba as SparseSegmentSum,vs as SparseToDense,xa as SplitV,ws as Sqrt,Ki as Square,ks as SquaredDifference,$u as StaticRegexReplace,wo as Step,Ns as StridedSlice,Ca as StringNGrams,qi as StringSplit,ji as StringToHashBucketFast,Ts as Sub,Ss as Sum,_s as Tan,$s as Tanh,mt as Tensor,tt as TensorBuffer,ds as TensorScatterUpdate,uo as Tile,Es as TopK,Rs as Transform,po as Transpose,Xi as Unique,wa as Unpack,Yi as UnsortedSegmentSum,Gce as UpperBound,ri as Variable,Kc as WebGPUBackend,Sa as ZerosLike,So as _FusedMatMul,Qt as abs,Nk as acos,Tk as acosh,Ce as add,_k as addN,$k as all,Ek as any,Rk as argMax,Dk as argMin,Ak as asin,Fk as asinh,Pk as atan,Ok as atan2,Mk as atanh,md as avgPool,zk as avgPool3d,vde as backend,w as backend_util,Vk as basicLSTMCell,ru as batchNorm,Uk as batchNorm2d,Gk as batchNorm3d,Hk as batchNorm4d,dd as batchToSpaceND,fd as bincount,Kk as bitwiseAnd,E6 as booleanMaskAsync,qk as broadcastArgs,ou as broadcastTo,Sr as broadcast_util,nT as browser,me as buffer,We as cast,jk as ceil,Xk as clipByValue,Wr as clone,$r as complex,yt as concat,Yk as concat1d,Qk as concat2d,Zk as concat3d,Jk as concat4d,e2 as conv1d,nu as conv2d,t2 as conv2dTranspose,r2 as conv3d,n2 as conv3dTranspose,Zce as copyRegisteredKernels,s2 as cos,a2 as cosh,_l as cosineWindow,i2 as cumprod,u2 as cumsum,Ir as customGrad,p2 as denseBincount,Pw as deprecationWarn,c2 as depthToSpace,oc as depthwiseConv2d,A5 as deregisterOp,Ji as device_util,l2 as diag,m2 as dilation2d,dde as disableDeprecationWarnings,Ot as dispose,fde as disposeVariables,je as div,f2 as divNoNan,h2 as dot,W6 as dropout,su as einsum,yd as elu,mde as enableDebugMode,lde as enableProdMode,Qw as enclosingPowerOfTwo,ur as engine,g2 as ensureShape,A as env,xd as equal,x2 as erf,C2 as euclideanNorm,_o as exp,Ms as expandDims,w2 as expm1,bd as eye,ac as fft,Ea as fill,Sde as findBackend,Ide as findBackendFactory,Cd as floor,ld as floorDiv,LD as forceHalfFloat,Zw as fused,wd as gather,z6 as gatherND,sf as gather_util,Cde as getBackend,aw as getGradient,qp as getKernel,jm as getKernelsForBackend,Zse as getThreadsCount,lv as gpgpu_util,AK as grad,FK as grads,zu as greater,Sd as greaterEqual,Ku as ifft,iu as imag,Kj as image,G6 as inTopKAsync,mi as io,Gd as irfft,S2 as isFinite,I2 as isInf,v2 as isNaN,Er as keep,Vt as kernel_impls,Id as leakyRelu,kl as less,nc as lessEqual,qj as linalg,k2 as linspace,$8 as loadGraphModel,E8 as loadGraphModelSync,N2 as localResponseNormalization,ui as log,vd as log1p,T2 as logSigmoid,_2 as logSoftmax,Td as logSumExp,Vu as logicalAnd,_d as logicalNot,$d as logicalOr,$2 as logicalXor,jj as losses,E2 as lowerBound,Ze as matMul,eT as math,Ra as max,Rd as maxPool,R2 as maxPool3d,D2 as maxPoolWithArgmax,Dd as maximum,Wu as mean,hde as memory,A2 as meshgrid,vl as min,Uu as minimum,F2 as mirrorPad,P2 as mod,O2 as moments,A6 as movingAverage,se as mul,M2 as multiRNNCell,L2 as multinomial,pr as neg,pS as nextFrame,bQt as node,Bu as norm,Ad as notEqual,Tl as oneHot,Da as ones,B2 as onesLike,N as op,z2 as outerProduct,Aa as pad,V2 as pad1d,W2 as pad2d,U2 as pad3d,G2 as pad4d,H2 as pool,ii as pow,Pd as prelu,cd as print,K2 as prod,gde as profile,q2 as raggedGather,j2 as raggedRange,X2 as raggedTensorToTensor,Y2 as rand,x1 as randomGamma,Vd as randomNormal,y1 as randomStandardNormal,sc as randomUniform,b1 as randomUniformInt,uu as range,bde as ready,pi as real,C1 as reciprocal,tu as registerBackend,Xce as registerGradient,ti as registerKernel,D5 as registerOp,pu as relu,Wd as relu6,wde as removeBackend,W as reshape,lo as reverse,w1 as reverse1d,S1 as reverse2d,I1 as reverse3d,v1 as reverse4d,ic as rfft,Ud as round,k1 as rsqrt,ke as scalar,P6 as scatterND,lu as scatter_util,Nl as searchSorted,N1 as selu,T1 as separableConv2d,UN as serialization,yde as setBackend,kde as setPlatform,Qse as setThreadsCount,Xse as setWasmPath,Yse as setWasmPaths,kI as setWebGLContext,_1 as setdiff1dAsync,wc as shared,$a as sigmoid,$1 as sign,Hj as signal,E1 as sin,R1 as sinh,Xe as slice,D1 as slice1d,A1 as slice2d,F1 as slice3d,P1 as slice4d,pt as slice_util,O1 as softmax,Nd as softplus,Fd as spaceToBatchND,Xj as sparse,L6 as sparseToDense,Gj as spectral,ci as split,Rr as sqrt,Zt as square,Hd as squaredDifference,uc as squeeze,vr as stack,Kd as step,M1 as stridedSlice,Yj as string,Te as sub,ot as sum,oi as sumOutType,L1 as tan,Il as tanh,ar as tensor,Jt as tensor1d,cu as tensor2d,qd as tensor3d,B1 as tensor4d,z1 as tensor5d,V1 as tensor6d,U1 as tensorScatterUpdate,tk as tensor_util,g1 as test_util,De as tidy,au as tile,xde as time,G1 as topk,TGe as train,cc as transpose,H1 as truncatedNormal,K1 as unique,Qce as unregisterGradient,Yce as unregisterKernel,q1 as unsortedSegmentSum,mo as unstack,dt as upcastType,j1 as upperBound,y as util,PK as valueAndGrad,OK as valueAndGrads,X1 as variable,zw as variableGrads,Ace as version,D8 as version_converter,_X as version_core,cY as version_cpu,Jse as version_wasm,s9 as version_webgl,Cat as webgl,Tc as webgl_util,Qv as webgpu_util,co as where,Xd as whereAsync,Ur as zeros,Gt as zerosLike};\n", "import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial, config: Partial, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n for (const key of Object.keys(config)) {\n if (typeof config[key] === 'object') {\n validate(defaults[key], config[key], key, msgs);\n } else {\n const defined = defaults && (typeof defaults[key] !== 'undefined');\n if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n const same = defaults && typeof defaults[key] === typeof config[key];\n if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n }\n // ok = ok && defined && same;\n }\n if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: number[]) => data.reduce((acc: number[], val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n await waiting;\n}\n", "/* eslint-disable no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n /** is module enabled? */\n enabled: boolean,\n /** path to model json file (relative to `modelBasePath` */\n modelPath: string,\n /** how many max frames to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipFrames: number,\n /** how many max milliseconds to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n /** is face rotation correction performed after detecting face?\n * used to correctly analyze faces under high angles\n */\n rotation: boolean,\n /** maximum number of detected faces */\n maxDetected: number,\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n /** minimum size in pixels of a detected face box before resutls are discared */\n minSize: number,\n /** minimum overlap between two detected faces before one is discarded */\n iouThreshold: number,\n /** should child models perform on masked image of a face */\n mask: boolean,\n /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n * if enabled it must be manually deallocated to avoid memory leak */\n return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n /** Keep detected faces that cannot be verified using facemesh */\n keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n /** minimum confidence for a detected race before results are discarded */\n minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n detector: Partial,\n mesh: Partial,\n attention: Partial,\n iris: Partial,\n description: Partial,\n emotion: Partial,\n antispoof: Partial,\n liveness: Partial,\n gear: Partial,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n /** maximum number of detected bodies */\n maxDetected: number,\n /** minimum confidence for a detected body before results are discarded */\n minConfidence: number,\n /* experimental\n /** experimental: detector used for body model before actual analysis\n detector?: {\n /** experimental: enable body detector before body landmarks\n enabled: boolean,\n /** experimental: path to optional body detector model json file\n modelPath: string,\n /** experimental: minimum confidence for a detected body before results are discarded\n minConfidence: number,\n /** experimental: minimum overlap between two detected bodies before one is discarded\n iouThreshold: number\n },\n */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n /** should hand rotation correction be performed after hand detection? */\n rotation: boolean,\n /** minimum confidence for a detected hand before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected hands before one is discarded */\n iouThreshold: number,\n /** maximum number of detected hands */\n maxDetected: number,\n /** should hand landmarks be detected or just return detected hand box */\n landmarks: boolean,\n detector: {\n /** path to hand detector model json */\n modelPath?: string,\n },\n skeleton: {\n /** path to hand skeleton model json */\n modelPath?: string,\n },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n /** minimum confidence for a detected objects before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected objects before one is discarded */\n iouThreshold: number,\n /** maximum number of detected objects */\n maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n ratio: number,\n /** possible rvm segmentation mode */\n mode: SegmentationEnum,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n /** are image filters enabled? */\n enabled: boolean,\n /** perform image histogram equalization\n * - equalization is performed on input as a whole and detected face before its passed for further analysis\n */\n equalization: boolean,\n /** resize input width\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n width: number,\n /** resize input height\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n height: number,\n /** return processed canvas imagedata in result */\n return: boolean,\n /** flip input as mirror image */\n flip: boolean,\n /** apply auto-brighness */\n autoBrightness: boolean,\n /** range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** image negative */\n negative: boolean,\n /** image sepia colors */\n sepia: boolean,\n /** image vintage colors */\n vintage: boolean,\n /** image kodachrome colors */\n kodachrome: boolean,\n /** image technicolor colors */\n technicolor: boolean,\n /** image polaroid camera effect */\n polaroid: boolean,\n /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n /** is gesture detection enabled? */\n enabled: boolean,\n}\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n /** Backend used for TFJS operations\n * valid build-in backends are:\n * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n * - NodeJS: `cpu`, `wasm`, `tensorflow`\n * default: `webgl` for browser and `tensorflow` for nodejs\n */\n backend: BackendEnum,\n\n /** Path to *.wasm files if backend is set to `wasm`\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPath: string,\n\n /** Force WASM loader to use platform fetch\n *\n * default: false\n */\n wasmPlatformFetch: boolean,\n\n /** Print debug statements to console\n *\n * default: `true`\n */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially\n *\n * default: `true`\n */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - used by `webgl`, `humangl` and `webgpu` backends\n *\n * default: `full`\n */\n warmup: WarmupEnum,\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n *\n * default: `../models/` for browsers and `file://models/` for nodejs\n */\n modelBasePath: string,\n\n /** Cache models in IndexDB on first sucessfull load\n * default: true if indexdb is available (browsers), false if its not (nodejs)\n */\n cacheModels: boolean,\n\n /** Validate kernel ops used in model during model load\n * default: true\n * any errors will be printed on console but will be treated as non-fatal\n */\n validateModels: boolean,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n *\n * default: 0.7\n */\n cacheSensitivity: number;\n\n /** Explicit flags passed to initialize TFJS */\n flags: Record,\n\n /** Software Kernels\n * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n */\n softwareKernels: boolean,\n\n /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n deallocate: boolean;\n\n /** Internal Variable */\n skipAllowed: boolean;\n\n /** Filter config {@link FilterConfig} */\n filter: Partial,\n\n /** Gesture config {@link GestureConfig} */\n gesture: Partial;\n\n /** Face config {@link FaceConfig} */\n face: Partial,\n\n /** Body config {@link BodyConfig} */\n body: Partial,\n\n /** Hand config {@link HandConfig} */\n hand: Partial,\n\n /** Object config {@link ObjectConfig} */\n object: Partial,\n\n /** Segmentation config {@link SegmentationConfig} */\n segmentation: Partial,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n backend: '',\n modelBasePath: '',\n cacheModels: true,\n validateModels: true,\n wasmPath: '',\n wasmPlatformFetch: false,\n debug: false,\n async: true,\n warmup: 'full',\n cacheSensitivity: 0.70,\n skipAllowed: false,\n deallocate: false,\n flags: {},\n softwareKernels: false,\n filter: {\n enabled: true,\n equalization: false,\n width: 0,\n height: 0,\n flip: false,\n return: true,\n autoBrightness: true,\n brightness: 0,\n contrast: 0,\n sharpness: 0,\n blur: 0,\n saturation: 0,\n hue: 0,\n negative: false,\n sepia: false,\n vintage: false,\n kodachrome: false,\n technicolor: false,\n polaroid: false,\n pixelate: 0,\n },\n gesture: {\n enabled: true,\n },\n face: {\n enabled: true,\n detector: {\n modelPath: 'blazeface.json',\n rotation: false,\n maxDetected: 1,\n skipFrames: 99,\n skipTime: 2500,\n minConfidence: 0.2,\n minSize: 0,\n iouThreshold: 0.1,\n mask: false,\n return: false,\n },\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json',\n keepInvalid: false,\n },\n attention: {\n enabled: false,\n modelPath: 'facemesh-attention.json',\n },\n iris: {\n enabled: true,\n modelPath: 'iris.json',\n },\n emotion: {\n enabled: true,\n minConfidence: 0.1,\n skipFrames: 99,\n skipTime: 1500,\n modelPath: 'emotion.json',\n },\n description: {\n enabled: true,\n modelPath: 'faceres.json',\n skipFrames: 99,\n skipTime: 3000,\n minConfidence: 0.1,\n },\n antispoof: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'antispoof.json',\n },\n liveness: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'liveness.json',\n },\n },\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json',\n maxDetected: -1,\n minConfidence: 0.3,\n skipFrames: 1,\n skipTime: 200,\n },\n hand: {\n enabled: true,\n rotation: true,\n skipFrames: 99,\n skipTime: 1000,\n minConfidence: 0.50,\n iouThreshold: 0.2,\n maxDetected: -1,\n landmarks: true,\n detector: {\n modelPath: 'handtrack.json',\n },\n skeleton: {\n modelPath: 'handlandmark-lite.json',\n },\n },\n object: {\n enabled: false,\n modelPath: 'centernet.json',\n minConfidence: 0.2,\n iouThreshold: 0.4,\n maxDetected: 10,\n skipFrames: 99,\n skipTime: 2000,\n },\n segmentation: {\n enabled: false,\n modelPath: 'rvm.json',\n ratio: 0.5,\n mode: 'default',\n },\n};\n\nexport { config as defaults };\n", "export const vertexIdentity = `\n precision highp float;\n attribute vec2 pos;\n attribute vec2 uv;\n varying vec2 vUv;\n uniform float flipY;\n void main(void) {\n vUv = uv;\n gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n }\n`;\n\nexport const fragmentIdentity = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n void main(void) {\n gl_FragColor = texture2D(texture, vUv);\n }\n`;\n\nexport const colorMatrixWithAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n gl_FragColor.a = c.a;\n }\n`;\n\nexport const pixelate = `\n precision highp float;\n varying vec2 vUv;\n uniform vec2 size;\n uniform sampler2D texture;\n vec2 pixelate(vec2 coord, vec2 size) {\n return floor( coord / size ) * size;\n }\n void main(void) {\n gl_FragColor = vec4(0.0);\n vec2 coord = pixelate(vUv, size);\n gl_FragColor += texture2D(texture, coord);\n }\n`;\n\nexport const blur = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n void main(void) {\n gl_FragColor = vec4(0.0);\n gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv )*0.159576912161;\n gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;\n }\n`;\n\nexport const convolution = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n uniform float m[9];\n void main(void) {\n vec4 c11 = texture2D(texture, vUv - px); // top left\n vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n vec4 c22 = texture2D(texture, vUv); // mid center\n vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n gl_FragColor = \n c11 * m[0] + c12 * m[1] + c22 * m[2] +\n c21 * m[3] + c22 * m[4] + c23 * m[5] +\n c31 * m[6] + c32 * m[7] + c33 * m[8];\n gl_FragColor.a = c22.a;\n }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, collection) => {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n};\n\nclass GLProgram {\n uniform = {};\n attribute = {};\n gl: WebGLRenderingContext;\n id: WebGLProgram;\n\n constructor(gl, vertexSource, fragmentSource) {\n this.gl = gl;\n const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n this.id = this.gl.createProgram() as WebGLProgram;\n if (!vertexShader || !fragmentShader) return;\n if (!this.id) {\n log('filter: could not create webgl program');\n return;\n }\n this.gl.attachShader(this.id, vertexShader);\n this.gl.attachShader(this.id, fragmentShader);\n this.gl.linkProgram(this.id);\n if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || 'unknown'}`);\n return;\n }\n this.gl.useProgram(this.id);\n collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n }\n\n compile = (source, type): WebGLShader | null => {\n const shader = this.gl.createShader(type);\n if (!shader) {\n log('filter: could not create shader');\n return null;\n }\n this.gl.shaderSource(shader, source);\n this.gl.compileShader(shader);\n if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || 'unknown'}`);\n return null;\n }\n return shader;\n };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n let drawCount = 0;\n let sourceTexture: WebGLTexture | null = null;\n let lastInChain = false;\n let currentFramebufferIndex = -1;\n let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n let filterChain: Record[] = [];\n let vertexBuffer: WebGLBuffer | null = null;\n let currentProgram: GLProgram | null = null;\n const fxcanvas = canvas(100, 100) as HTMLCanvasElement;\n const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n const DRAW = { INTERMEDIATE: 1 };\n const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n if (!gl) {\n log('filter: cannot get webgl context');\n return;\n }\n // @ts-ignore used for sanity checks outside of imagefx\n this.gl = gl;\n\n function resize(width, height) {\n if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n fxcanvas.width = width;\n fxcanvas.height = height;\n if (!vertexBuffer) { // Create the context if we don't have it yet\n const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n tempFramebuffers = [null, null]; // Delete old temp framebuffers\n }\n\n function createFramebufferTexture(width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n }\n\n function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n }\n\n function draw(flags = 0) {\n if (!currentProgram) return;\n let source: WebGLTexture | null = null;\n let target: WebGLFramebuffer | null = null;\n let flipY = false;\n if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n drawCount++;\n if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n target = null;\n flipY = drawCount % 2 === 0;\n } else {\n currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n }\n gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n }\n\n function compileShader(fragmentSource): GLProgram | null {\n if (shaderProgramCache[fragmentSource]) {\n currentProgram = shaderProgramCache[fragmentSource];\n gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n return currentProgram;\n }\n currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n if (!currentProgram) {\n log('filter: could not get webgl program');\n return null;\n }\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n shaderProgramCache[fragmentSource] = currentProgram;\n return currentProgram;\n }\n\n const filter = {\n colorMatrix: (matrix: number[]) => { // general color matrix filter\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n ? shaders.colorMatrixWithoutAlpha\n : shaders.colorMatrixWithAlpha;\n const program = compileShader(shader);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n draw();\n },\n\n brightness: (brightness: number) => {\n const b = (brightness || 0) + 1;\n filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n saturation: (amount: number) => {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturate: () => {\n filter.saturation(-1);\n },\n\n contrast: (amount: number) => {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n negative: () => {\n filter.contrast(-2);\n },\n\n hue: (rotation: number) => {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturateLuminance: () => {\n filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n sepia: () => {\n filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n brownie: () => {\n filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n vintagePinhole: () => {\n filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n kodachrome: () => {\n filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n technicolor: () => {\n filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n polaroid: () => {\n filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n shiftToBGR: () => {\n filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n convolution: (matrix: number[]) => { // general convolution Filter\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / fxcanvas.width;\n const pixelSizeY = 1 / fxcanvas.height;\n const program = compileShader(shaders.convolution);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n draw();\n },\n\n detectEdges: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n },\n\n sobelX: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n },\n\n sobelY: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n },\n\n sharpen: (amount) => {\n const a = amount || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n },\n\n emboss: (size: number) => {\n const s = size || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n },\n\n blur: (size: number) => {\n const blurSizeX = (size / 7) / fxcanvas.width;\n const blurSizeY = (size / 7) / fxcanvas.height;\n const program = compileShader(shaders.blur);\n if (!program) return;\n // Vertical\n gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n draw();\n },\n\n pixelate: (size: number) => {\n const blurSizeX = (size) / fxcanvas.width;\n const blurSizeY = (size) / fxcanvas.height;\n const program = compileShader(shaders.pixelate);\n if (!program) return;\n gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n draw();\n },\n };\n\n // @ts-ignore this\n this.add = function (name) {\n const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\n const func = filter[name];\n filterChain.push({ func, args });\n };\n\n // @ts-ignore this\n this.reset = function () {\n filterChain = [];\n };\n\n // @ts-ignore this\n this.get = function () {\n return filterChain;\n };\n\n // @ts-ignore this\n this.apply = function (image) {\n resize(image.width, image.height);\n drawCount = 0;\n if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n for (let i = 0; i < filterChain.length; i++) {\n lastInChain = (i === filterChain.length - 1);\n const f = filterChain[i];\n // @ts-ignore function assigment\n f.func.apply(this, f.args || []);\n }\n return fxcanvas;\n };\n\n // @ts-ignore this\n this.draw = function (image) {\n this.add('brightness', 0);\n return this.apply(image);\n };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise {\n const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n const rgb = tf.split(squeeze, 3, 2);\n const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]\n const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]\n // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]\n // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);\n const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]\n const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n const maxRange = maxValue > 1 ? 255 : 1;\n const factor = maxRange / maxValue;\n let final: Tensor;\n if (factor > 1) {\n const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values\n const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges\n // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between\n const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];\n const stack = tf.stack([enh[0], enh[1], enh[2]], 2);\n final = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);\n tf.dispose([...sub, ...range, ...enh, stack]);\n } else {\n final = tf.expandDims(squeeze, 0);\n }\n tf.dispose([...rgb, ...min, ...max, rgb, squeeze, inputImage]);\n return final;\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Config } from '../exports';\nimport type { Tensor, Tensor3D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // eslint-disable-line @typescript-eslint/no-redundant-type-constituents\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n inputSum: 0,\n cacheDiff: 1,\n sumMethod: 0,\n inputTensor: undefined,\n};\n\nexport function reset() {\n last.inputSum = 0;\n last.cacheDiff = 1;\n last.sumMethod = 0;\n last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n let c: AnyCanvas;\n if (env.browser) { // browser defines canvas object\n if (env.worker) { // if runing in web worker use OffscreenCanvas\n if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n c = new OffscreenCanvas(width, height);\n } else { // otherwise use DOM canvas\n if (typeof document !== 'undefined') {\n c = document.createElement('canvas');\n c.width = width;\n c.height = height;\n } else if (typeof navigator !== 'undefined' && navigator.product === 'ReactNative') {\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n else throw new Error('canvas error: attempted to use canvas in react-native without canvas support installed');\n } else {\n throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n }\n }\n } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n }\n // @ts-ignore its either defined or we already threw an error\n return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n const outputCanvas = output || canvas(input.width, input.height);\n const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctx.drawImage(input, 0, 0);\n return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor4D | null, canvas: AnyCanvas | null }> {\n if (!input) {\n // throw new Error('input is missing');\n if (config.debug) log('input error: input is missing');\n return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n }\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('input error: type not recognized');\n }\n if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n let tensor: Tensor | null = null;\n if (input['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n if (!(input as Tensor).shape) throw new Error('input error: attempted to use tensor without a shape');\n if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n tensor = tf.expandDims(input, 0);\n } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n const rgb = tf.slice3d(input as Tensor3D, [0, 0, 0], [-1, -1, 3]);\n tensor = tf.expandDims(rgb, 0);\n tf.dispose(rgb);\n }\n } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n tensor = tf.clone(input);\n } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n tensor = tf.slice4d(input as Tensor4D, [0, 0, 0, 0], [-1, -1, -1, 3]);\n }\n }\n // at the end shape must be [1, height, width, 3]\n if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${((input as Tensor).shape).toString()}`);\n if ((tensor).dtype === 'int32') {\n const cast = tf.cast(tensor, 'float32');\n tf.dispose(tensor);\n tensor = cast;\n }\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n }\n // check if resizing will be needed\n if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).readyState <= 2) {\n if (config.debug) log('input stream is not ready');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n const originalWidth: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n const originalHeight: number = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n if (!originalWidth || !originalHeight) {\n if (config.debug) log('cannot determine input dimensions');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n let targetWidth: number = originalWidth;\n let targetHeight: number = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n }\n\n // create our canvas and resize it if needed\n if ((config.filter?.width || 0) > 0) targetWidth = config.filter.width as number;\n else if ((config.filter?.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n if ((config.filter.height || 0) > 0) targetHeight = config.filter.height as number;\n else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n // draw input to our canvas\n const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n inCtx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n inCtx.translate(originalWidth, 0);\n inCtx.scale(-1, 1);\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n }\n }\n\n if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n // imagefx transforms using gl from input canvas to output canvas\n if (config.filter.enabled && env.webgl.supported) {\n if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n env.filter = !!fx;\n if (!fx?.add) {\n if (config.debug) log('input process error: cannot initialize filters');\n env.webgl.supported = false;\n config.filter.enabled = false;\n copy(inCanvas, outCanvas); // filter failed to initialize\n // return { tensor: null, canvas: inCanvas };\n } else {\n fx.reset();\n if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n if (config.filter.negative) fx.add('negative');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.vintage) fx.add('brownie');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.kodachrome) fx.add('kodachrome');\n if (config.filter.technicolor) fx.add('technicolor');\n if (config.filter.polaroid) fx.add('polaroid');\n if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n if (fx.get()?.length > 1) outCanvas = fx.apply(inCanvas);\n else outCanvas = fx.draw(inCanvas);\n }\n } else {\n copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n if (fx) fx = null;\n env.filter = !!fx;\n }\n\n if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n // create tensor from image unless input was a tensor already\n let pixels;\n let depth = 3;\n if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || ((input as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n if (env.browser && tf.browser) {\n pixels = tf.browser ? tf.browser.fromPixels(input as ImageData) : null;\n } else {\n depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n // const arr = Uint8Array.from(input['data']);\n const arr = new Uint8Array((input as ImageData).data.buffer);\n pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).width, depth], 'int32');\n }\n } else {\n if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n if (tf.browser && env.browser) {\n if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n pixels = tf.browser.fromPixels(outCanvas as HTMLCanvasElement); // safe to reuse since both backend and context are gl based\n } else {\n tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n pixels = tf.browser.fromPixels(tmpCanvas as HTMLCanvasElement);\n }\n } else {\n const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n depth = tempData.data.length / targetWidth / targetHeight;\n const arr = new Uint8Array(tempData.data.buffer);\n pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n }\n }\n if (depth === 4) { // rgba to rgb\n const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n tf.dispose(pixels);\n pixels = rgb;\n }\n if (!pixels) throw new Error('input error: cannot create tensor');\n const casted: Tensor = tf.cast(pixels, 'float32');\n const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n tf.dispose([pixels, casted]);\n\n if (config.filter.autoBrightness) {\n const max = tf.max(tensor);\n const maxVal = await max.data();\n config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);\n tf.dispose(max);\n }\n\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n}\n\n/*\nconst checksum = async (input: Tensor): Promise => { // use tf sum or js based sum loop depending on which is faster\n const resizeFact = 48;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n const tfSum = async (): Promise => {\n const sumT = tf.sum(reduced);\n const sum0 = await sumT.data();\n tf.dispose(sumT);\n return sum0[0];\n };\n const jsSum = async (): Promise => {\n const reducedData = await reduced.data(); // raw image rgb array\n let sum0 = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n return sum0;\n };\n if (last.sumMethod === 0) {\n const t0 = now();\n await jsSum();\n const t1 = now();\n await tfSum();\n const t2 = now();\n last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n }\n const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n tf.dispose(reduced);\n return res;\n};\n*/\n\nexport async function skip(config: Partial, input: Tensor) {\n let skipFrame = false;\n if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n /*\n const checkSum = await checksum(input);\n const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n last.inputSum = checkSum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n */\n\n if (!last.inputTensor) {\n last.inputTensor = tf.clone(input);\n } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n tf.dispose(last.inputTensor);\n last.inputTensor = tf.clone(input);\n } else {\n const t: Record = {};\n t.diff = tf.sub(input, last.inputTensor);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n last.inputTensor = tf.clone(input);\n skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n }\n return skipFrame;\n}\n\nexport async function compare(config: Partial, input1: Tensor, input2: Tensor): Promise {\n const t: Record = {};\n if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n return 0;\n }\n if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n return 0;\n }\n t.input1 = tf.clone(input1);\n t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2 as Tensor3D, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n t.diff = tf.sub(t.input1, t.input2);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n return diffRelative;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n /** Running in Browser */\n browser: boolean;\n /** Running in NodeJS */\n node: boolean;\n /** Running in WebWorker thread */\n worker: boolean;\n /** Detected platform */\n platform: string = '';\n /** Detected agent */\n agent: string = '';\n /** List of supported backends */\n backends: string[] = [];\n /** Has any work been performed so far */\n initial: boolean;\n /** Are image filters supported? */\n filter: boolean | undefined;\n /** TFJS instance details */\n tfjs: {\n version: undefined | string,\n };\n /** Is offscreenCanvas supported? */\n offscreen: undefined | boolean;\n /** Are performance counter instant values or additive */\n perfadd: boolean = false;\n /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n tensorflow: {\n version: undefined | string,\n gpu: undefined | boolean,\n } = {\n version: undefined,\n gpu: undefined,\n };\n /** WASM detected capabilities */\n wasm: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n simd: undefined | boolean,\n multithread: undefined | boolean,\n } = {\n supported: undefined,\n backend: undefined,\n simd: undefined,\n multithread: undefined,\n };\n /** WebGL detected capabilities */\n webgl: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n version: undefined | string,\n renderer: undefined | string,\n shader: undefined | string,\n vendor: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n version: undefined,\n renderer: undefined,\n shader: undefined,\n vendor: undefined,\n };\n /** WebGPU detected capabilities */\n webgpu: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n adapter: undefined | GPUAdapterInfo,\n } = {\n supported: undefined,\n backend: undefined,\n adapter: undefined,\n };\n /** CPU info */\n cpu: {\n model: undefined | string,\n flags: string[],\n } = {\n model: undefined,\n flags: [],\n };\n /** List of supported kernels for current backend */\n kernels: string[] = [];\n\n /** MonkeyPatch for Canvas/Image/ImageData */\n #canvas: undefined;\n #image: undefined;\n #imageData: undefined;\n\n get Canvas() { return this.#canvas; }\n set Canvas(val) { this.#canvas = val; globalThis.Canvas = val; }\n get Image() { return this.#image; }\n // @ts-ignore monkey-patch;\n set Image(val) { this.#image = val; globalThis.Image = val; }\n get ImageData() { return this.#imageData; }\n // @ts-ignore monkey-patch;\n set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; }\n\n constructor() {\n this.browser = (typeof navigator !== 'undefined') && (typeof navigator.appVersion !== 'undefined');\n this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n this.tfjs = { version: tf.version['tfjs-core'] };\n this.offscreen = typeof OffscreenCanvas !== 'undefined';\n this.initial = true;\n\n // @ts-ignore WorkerGlobalScope evaluated in browser only\n this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n if ((typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined')) { // TBD replace with navigator.userAgentData once in mainline\n const agent = navigator.userAgent || '';\n const raw = agent.match(/\\(([^()]+)\\)/g);\n if (raw?.[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n this.agent = agent.replace(raw[0], '');\n if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n this.agent = this.agent.replace(/ /g, ' ');\n }\n } else if (typeof process !== 'undefined') {\n this.platform = `${process.platform} ${process.arch}`;\n this.agent = `NodeJS ${process.version}`;\n }\n }\n\n /** update backend information */\n async updateBackend() {\n // analyze backends\n this.backends = Object.keys(tf.engine().registryFactory);\n try { // backend may not be initialized\n this.tensorflow = {\n version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),\n gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),\n };\n } catch { /**/ }\n this.wasm.supported = typeof WebAssembly !== 'undefined';\n this.wasm.backend = this.backends.includes('wasm');\n if (this.wasm.supported && this.wasm.backend) {\n this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n }\n const c = image.canvas(100, 100);\n const gl = c ? c.getContext('webgl2') as WebGL2RenderingContext : undefined; // causes too many gl contexts\n this.webgl.supported = typeof gl !== 'undefined';\n this.webgl.backend = this.backends.includes('webgl');\n if (this.webgl.supported && this.webgl.backend && gl) {\n this.webgl.version = gl.getParameter(gl.VERSION);\n this.webgl.vendor = gl.getParameter(gl.VENDOR);\n this.webgl.renderer = gl.getParameter(gl.RENDERER);\n this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n }\n this.webgpu.supported = this.browser && typeof navigator !== 'undefined' && typeof navigator.gpu !== 'undefined';\n this.webgpu.backend = this.backends.includes('webgpu');\n try {\n if (this.webgpu.supported) {\n const adapter = await navigator.gpu.requestAdapter();\n this.webgpu.adapter = await adapter?.requestAdapterInfo();\n }\n } catch {\n this.webgpu.supported = false;\n }\n try {\n this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n } catch { /**/ }\n }\n\n /** update cpu information */\n updateCPU() {\n const cpu = { model: '', flags: [] };\n if (this.node && this.platform.startsWith('linux')) {\n /*\n const fs = require('fs');\n try {\n const data = fs.readFileSync('/proc/cpuinfo').toString();\n for (const line of data.split('\\n')) {\n if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n }\n } catch { }\n */\n }\n if (!this.cpu) Object.defineProperty(this, 'cpu', { value: cpu });\n else this.cpu = cpu;\n }\n}\n\nexport const env = new Env();\n", "import { log } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n /**\n * element can be:\n * - string which indicates dom element id\n * - actual HTMLVideo dom element\n * - undefined in which case a new HTMLVideoElement will be created\n */\n element: string | HTMLVideoElement | undefined,\n /** print messages on console */\n debug: boolean,\n /** use front or back camera */\n mode: 'front' | 'back',\n /** camera crop mode */\n crop: boolean,\n /** desired webcam width */\n width: number,\n /** desired webcam height */\n height: number,\n /** deviceId of the video device to use */\n id?: string,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n /** current webcam configuration */\n config: WebCamConfig;\n /** instance of dom element associated with webcam stream */\n element: HTMLVideoElement | undefined;\n /** active webcam stream */\n stream: MediaStream | undefined;\n /** enumerated video devices */\n devices: MediaDeviceInfo[] = [];\n\n constructor() {\n this.config = {\n element: undefined,\n debug: true,\n mode: 'front',\n crop: false,\n width: 0,\n height: 0,\n };\n }\n\n /** get active webcam stream track */\n public get track(): MediaStreamTrack | undefined {\n if (!this.stream) return undefined;\n return this.stream.getVideoTracks()[0];\n }\n\n /** get webcam capabilities */\n public get capabilities(): MediaTrackCapabilities | undefined {\n if (!this.track) return undefined;\n return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n }\n\n /** get webcam constraints */\n public get constraints(): MediaTrackConstraints | undefined {\n if (!this.track) return undefined;\n return this.track.getConstraints ? this.track.getConstraints() : undefined;\n }\n\n /** get webcam settings */\n public get settings(): MediaTrackSettings | undefined {\n if (!this.stream) return undefined;\n const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n return track.getSettings ? track.getSettings() : undefined;\n }\n\n /** get webcam label */\n public get label(): string {\n if (!this.track) return '';\n return this.track.label;\n }\n\n /** is webcam paused */\n public get paused(): boolean {\n return this.element?.paused || false;\n }\n\n /** webcam current width */\n public get width(): number {\n return this.element?.videoWidth || 0;\n }\n\n /** webcam current height */\n public get height(): number {\n return this.element?.videoHeight || 0;\n }\n\n public enumerate = async (): Promise => {\n try {\n const devices = await navigator.mediaDevices.enumerateDevices();\n this.devices = devices.filter((device) => device.kind === 'videoinput');\n } catch {\n this.devices = [];\n }\n return this.devices;\n };\n\n /** start method initializizes webcam stream and associates it with a dom video element */\n public start = async (webcamConfig?: Partial): Promise => {\n // set config\n if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n if (webcamConfig?.id) this.config.id = webcamConfig?.id;\n\n // use or create dom element\n if (webcamConfig?.element) {\n if (typeof webcamConfig.element === 'string') {\n const el = document.getElementById(webcamConfig.element);\n if (el && el instanceof HTMLVideoElement) {\n this.element = el;\n } else {\n if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n return `webcam error: cannot get dom element: ${webcamConfig.element}`;\n }\n } else if (webcamConfig.element instanceof HTMLVideoElement) {\n this.element = webcamConfig.element;\n } else {\n if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n return `webcam error: unknown dom element: ${webcamConfig.element}`;\n }\n } else {\n this.element = document.createElement('video');\n }\n\n // set constraints to use\n const requestedConstraints: MediaStreamConstraints = {\n audio: false,\n video: {\n facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n // @ts-ignore // resizeMode is still not defined in tslib\n resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n },\n };\n if (this.config?.width > 0) (requestedConstraints.video as MediaTrackConstraints).width = { ideal: this.config.width };\n if (this.config?.height > 0) (requestedConstraints.video as MediaTrackConstraints).height = { ideal: this.config.height };\n if (this.config.id) (requestedConstraints.video as MediaTrackConstraintSet).deviceId = this.config.id;\n\n // set default event listeners\n this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n if (!this.element || !this.stream) return;\n if (this.element.paused) await this.element.play();\n else this.element.pause();\n });\n\n // get webcam and set it to run in dom element\n if (!navigator?.mediaDevices) {\n if (this.config.debug) log('webcam error', 'no devices');\n return 'webcam error: no devices';\n }\n try {\n this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n } catch (err) {\n log('webcam', err);\n return `webcam error: ${err}`;\n }\n if (!this.stream) {\n if (this.config.debug) log('webcam error', 'no stream');\n return 'webcam error no stream';\n }\n this.element.srcObject = this.stream; // assign it to dom element\n const ready = new Promise((resolve) => { // wait until stream is ready\n if (!this.element) resolve(false);\n else this.element.onloadeddata = () => resolve(true);\n });\n await ready;\n await this.element.play(); // start playing\n\n if (this.config.debug) {\n log('webcam', {\n width: this.width,\n height: this.height,\n label: this.label,\n stream: this.stream,\n track: this.track,\n settings: this.settings,\n constraints: this.constraints,\n capabilities: this.capabilities,\n });\n }\n return `webcam: ${this.label}`;\n };\n\n /** pause webcam video method */\n public pause = (): void => {\n if (this.element) this.element.pause();\n };\n\n /** play webcam video method */\n public play = async (): Promise => {\n if (this.element) await this.element.play();\n };\n\n /** stop method stops active webcam stream track and disconnects webcam */\n public stop = (): void => {\n if (this.config.debug) log('webcam', 'stop');\n if (this.track) this.track.stop();\n };\n}\n", "{\n \"antispoof\": 853098,\n \"blazeface\": 538928,\n \"centernet\": 4030290,\n \"emotion\": 820516,\n \"facemesh\": 1477958,\n \"faceres\": 6978814,\n \"handlandmark-lite\": 2023432,\n \"handtrack\": 2964837,\n \"iris\": 2599092,\n \"liveness\": 592976,\n \"models\": 0,\n \"movenet-lightning\": 4650216,\n \"affectnet-mobilenet\": 6920630,\n \"age\": 161240,\n \"blazeface-back\": 538928,\n \"blazeface-front\": 402048,\n \"blazepose-detector\": 5928856,\n \"blazepose-full\": 6339202,\n \"blazepose-heavy\": 27502466,\n \"blazepose-lite\": 2726402,\n \"efficientpose\": 5651240,\n \"faceboxes\": 2013002,\n \"facemesh-attention-pinto\": 2387598,\n \"facemesh-attention\": 2382414,\n \"facemesh-detection-full\": 1026192,\n \"facemesh-detection-short\": 201268,\n \"faceres-deep\": 13957620,\n \"gear-e1\": 112438,\n \"gear-e2\": 112438,\n \"gear\": 1498916,\n \"gender-ssrnet-imdb\": 161236,\n \"gender\": 201808,\n \"handdetect\": 3515612,\n \"handlandmark-full\": 5431368,\n \"handlandmark-sparse\": 5286322,\n \"handskeleton\": 5502280,\n \"meet\": 372228,\n \"mobileface\": 2183192,\n \"mobilefacenet\": 5171976,\n \"movenet-multipose\": 9448838,\n \"movenet-thunder\": 12477112,\n \"nanodet\": 7574558,\n \"posenet\": 5032780,\n \"rvm\": 3739355,\n \"selfie\": 212886,\n \"anti-spoofing\": 853098,\n \"efficientpose-i-lite\": 2269064,\n \"efficientpose-ii-lite\": 5651240,\n \"efficientpose-iv\": 25643252,\n \"insightface-efficientnet-b0\": 13013224,\n \"insightface-ghostnet-strides1\": 8093408,\n \"insightface-ghostnet-strides2\": 8049584,\n \"insightface-mobilenet-emore\": 6938536,\n \"insightface-mobilenet-swish\": 12168584,\n \"nanodet-e\": 12319156,\n \"nanodet-g\": 7574558,\n \"nanodet-m\": 1887474,\n \"nanodet-t\": 5294216\n}", "import * as tf from 'dist/tfjs.esm.js';\nimport { log, join } from '../util/util';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n cacheModels: true,\n cacheSupported: true,\n verbose: true,\n debug: false,\n modelBasePath: '',\n};\n\nexport interface ModelInfo {\n name: string,\n inCache: boolean,\n sizeDesired: number,\n sizeFromManifest: number,\n sizeLoadedWeights: number,\n url: string,\n}\n\nexport const modelStats: Record = {};\n\nasync function httpHandler(url: string, init?: RequestInit): Promise {\n if (options.debug) log('load model fetch:', url, init);\n return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n options.cacheModels = config.cacheModels;\n options.verbose = config.debug;\n options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise {\n let modelUrl = join(options.modelBasePath, modelPath || '');\n if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n modelStats[shortModelName] = {\n name: shortModelName,\n sizeFromManifest: 0,\n sizeLoadedWeights: 0,\n sizeDesired: modelsDefs[shortModelName],\n inCache: false,\n url: '',\n };\n options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n let cachedModels = {};\n try {\n cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n } catch {\n options.cacheSupported = false;\n }\n modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;\n const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n let loaded = false;\n try {\n // @ts-ignore private function\n model.findIOHandler(); // decide how to actually load a model\n if (options.debug) log('model load handler:', model['handler']);\n } catch (err) {\n log('error finding model i/o handler:', modelUrl, err);\n }\n try {\n // @ts-ignore private property\n const artifacts = await model.handler?.load() || null; // load manifest\n modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n if (artifacts) model.loadSync(artifacts); // load weights\n else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n // @ts-ignore private property\n modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || 0;\n if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n loaded = true;\n } catch (err) {\n log('error loading model:', modelUrl, err);\n }\n if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n try {\n const saveResult = await model.save(cachedModelName);\n if (options.debug) log('model saved:', cachedModelName, saveResult);\n } catch (err) {\n log('error saving model:', modelUrl, err);\n }\n }\n return model;\n}\n", "{\n \"name\": \"@vladmandic/human\",\n \"version\": \"3.2.0\",\n \"description\": \"Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition\",\n \"sideEffects\": false,\n \"main\": \"dist/human.node.js\",\n \"module\": \"dist/human.esm.js\",\n \"browser\": \"dist/human.esm.js\",\n \"types\": \"types/human.d.ts\",\n \"exports\": {\n \"node\": {\n \"require\": \"./dist/human.node.js\",\n \"import\": \"./dist/human.node.js\",\n \"module\": \"./dist/human.node.js\"\n },\n \"require\": \"./dist/human.node.js\",\n \"import\": \"./dist/human.esm.js\",\n \"script\": \"./dist/human.js\",\n \"module\": \"./dist/human.esm.js\",\n \"types\": \"./types/human.d.ts\",\n \"dist/human\": \"./dist/human.js\",\n \"dist/human.js\": \"./dist/human.js\",\n \"dist/human.esm\": \"./dist/human.esm.js\",\n \"dist/human.esm.js\": \"./dist/human.esm.js\",\n \"dist/human.esm-nobundle\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.esm-nobundle.js\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.node\": \"./dist/human.node.js\",\n \"dist/human.node.js\": \"./dist/human.node.js\",\n \"dist/human.node-wasm\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-wasm.js\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-gpu\": \"./dist/human.node-gpu.js\",\n \"dist/human.node-gpu.js\": \"./dist/human.node-gpu.js\"\n },\n \"author\": \"Vladimir Mandic \",\n \"bugs\": {\n \"url\": \"https://github.com/vladmandic/human/issues\"\n },\n \"homepage\": \"https://vladmandic.github.io/human/demo/index.html\",\n \"license\": \"MIT\",\n \"engines\": {\n \"node\": \">=14.0.0\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/vladmandic/human.git\"\n },\n \"scripts\": {\n \"start\": \"node --no-warnings demo/nodejs/node.js\",\n \"dev\": \"build --profile development\",\n \"clean\": \"build --profile clean\",\n \"build\": \"rimraf test/build.log && node build.js\",\n \"test\": \"node --no-warnings --unhandled-rejections=strict --trace-uncaught test/node.js\",\n \"lint\": \"eslint *.json *.js src demo test models wiki\",\n \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n },\n \"keywords\": [\n \"human\",\n \"human-library\",\n \"face-detection\",\n \"faceid\",\n \"face-geometry\",\n \"face-embedding\",\n \"face-recognition\",\n \"face-description\",\n \"face-matching\",\n \"body-tracking\",\n \"body-segmentation\",\n \"hand-tracking\",\n \"iris-tracking\",\n \"age-estimation\",\n \"emotion-detection\",\n \"gender-prediction\",\n \"gesture-recognition\",\n \"gaze-tracking\",\n \"age-gender\",\n \"tensorflowjs\",\n \"tfjs\",\n \"tensorflow\"\n ],\n \"devDependencies\": {\n \"@html-eslint/eslint-plugin\": \"^0.21.0\",\n \"@html-eslint/parser\": \"^0.21.0\",\n \"@microsoft/api-extractor\": \"^7.38.4\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.14.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.14.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.14.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.14.0\",\n \"@tensorflow/tfjs-converter\": \"^4.14.0\",\n \"@tensorflow/tfjs-core\": \"^4.14.0\",\n \"@tensorflow/tfjs-data\": \"^4.14.0\",\n \"@tensorflow/tfjs-layers\": \"^4.14.0\",\n \"@tensorflow/tfjs-node\": \"^4.14.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.14.0\",\n \"@types/node\": \"^20.10.3\",\n \"@types/offscreencanvas\": \"^2019.7.3\",\n \"@typescript-eslint/eslint-plugin\": \"^6.13.2\",\n \"@typescript-eslint/parser\": \"^6.13.2\",\n \"@vladmandic/build\": \"^0.9.3\",\n \"@vladmandic/pilogger\": \"^0.4.9\",\n \"@vladmandic/tfjs\": \"github:vladmandic/tfjs\",\n \"canvas\": \"^2.11.2\",\n \"esbuild\": \"^0.19.8\",\n \"eslint\": \"8.55.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-html\": \"^7.1.0\",\n \"eslint-plugin-import\": \"^2.29.0\",\n \"eslint-plugin-json\": \"^3.1.0\",\n \"eslint-plugin-markdown\": \"^3.0.1\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^6.1.1\",\n \"rimraf\": \"^5.0.5\",\n \"tslib\": \"^2.6.2\",\n \"typedoc\": \"0.25.4\",\n \"typescript\": \"~5.3.0\"\n }\n}\n", "/** TFJS custom backend registration */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as image from '../image/image';\nimport type { AnyCanvas } from '../exports';\n\nexport const config = {\n name: 'humangl',\n priority: 999,\n canvas: null as null | AnyCanvas,\n gl: null as null | WebGL2RenderingContext,\n extensions: [] as string[] | null,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false, // default=true\n desynchronized: true, // default=undefined\n },\n};\n\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions();\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(instance: Human): void {\n // force backend reload if gl context is not valid\n if (instance.config.backend !== 'humangl') return;\n if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {\n log('humangl error: backend invalid context');\n instance.models.reset();\n /*\n log('resetting humangl backend');\n await tf.removeBackend(config.name);\n await register(instance); // re-register\n */\n }\n if (!tf.findBackend(config.name)) {\n try {\n config.canvas = image.canvas(100, 100);\n } catch (err) {\n log('humangl error: cannot create canvas:', err);\n return;\n }\n try {\n config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n if (!config.gl) {\n log('humangl error: cannot get webgl context');\n return;\n }\n const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n if (!glv2) {\n log('backend override: using fallback webgl backend as webgl 2.0 is not detected');\n instance.config.backend = 'webgl';\n return;\n }\n if (config.canvas) {\n config.canvas.addEventListener('webglcontextlost', (e) => {\n log('humangl error:', e.type);\n log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n instance.emit('error');\n throw new Error('backend error: webgl context lost');\n });\n config.canvas.addEventListener('webglcontextrestored', (e) => {\n log('humangl error: context restored:', e);\n });\n config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n log('humangl error: context create:', e);\n });\n }\n } catch (err) {\n log('humangl error: cannot get webgl context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('humangl error: cannot set webgl context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n // @ts-ignore uncompatible kernelMs timing info\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('humangl error: cannot register webgl backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('humangl error: cannot update webgl backend registration:', err);\n return;\n }\n try {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n } catch (err) {\n log('humangl error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n const backend = tf.backend();\n const current = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;\n if (current) {\n if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n } else {\n log('humangl error: no current gl context:', current, config.gl);\n }\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record = {\n tf255: 255.0,\n tf1: 1.0,\n tf2: 2.0,\n tf05: 0.5,\n tf127: 127.5,\n rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n constants.tf255 = tf.scalar(255.0, 'float32');\n constants.tf1 = tf.scalar(1.0, 'float32');\n constants.tf2 = tf.scalar(2.0, 'float32');\n constants.tf05 = tf.scalar(0.5, 'float32');\n constants.tf127 = tf.scalar(127.5, 'float32');\n constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/** TFJS backend initialization and customization */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human, Config, BackendEnum } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as constants from './constants';\nimport type { TensorInfo } from './types';\n\nexport async function getBestBackend(): Promise {\n await env.updateBackend(); // update env on backend init\n if (env.tensorflow?.version) return 'tensorflow';\n if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';\n if (env.webgl.supported && env.webgl.backend) return 'webgl';\n if (env.wasm.supported && env.wasm.backend) return 'wasm';\n return 'cpu';\n}\n\nfunction registerCustomOps(config: Config) {\n const newKernels: string[] = [];\n if (!env.kernels.includes('mod')) {\n const kernelMod = {\n kernelName: 'Mod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('mod');\n newKernels.push('mod');\n }\n if (!env.kernels.includes('floormod')) {\n const kernelFloorMod = {\n kernelName: 'FloorMod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n };\n tf.registerKernel(kernelFloorMod);\n env.kernels.push('floormod');\n newKernels.push('floormod');\n }\n /*\n if (!env.kernels.includes('atan2') && config.softwareKernels) {\n const kernelAtan2 = {\n kernelName: 'Atan2',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu');\n const t = tf.atan2(op.inputs.a, op.inputs.b);\n tf.setBackend(backend);\n return t;\n }),\n };\n if (config.debug) log('registered kernel:', 'atan2');\n log('registered kernel:', 'atan2');\n tf.registerKernel(kernelAtan2);\n env.kernels.push('atan2');\n newKernels.push('atan2');\n }\n */\n if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n const kernelRotateWithOffset = {\n kernelName: 'RotateWithOffset',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu'); // eslint-disable-line @typescript-eslint/no-floating-promises\n const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n tf.setBackend(backend); // eslint-disable-line @typescript-eslint/no-floating-promises\n return t;\n }),\n };\n tf.registerKernel(kernelRotateWithOffset);\n env.kernels.push('rotatewithoffset');\n newKernels.push('rotatewithoffset');\n }\n if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record = {};\n\nexport async function check(instance: Human, force = false) {\n instance.state = 'backend';\n if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();\n if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n const timeStamp = now();\n\n if (instance.config.backend && instance.config.backend.length > 0) {\n // detect web worker\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n if (instance.config.debug) log('running inside web worker');\n }\n\n if (typeof navigator !== 'undefined' && navigator?.userAgent?.toLowerCase().includes('electron')) {\n if (instance.config.debug) log('running inside electron');\n }\n\n // check available backends\n let available = Object.keys(tf.engine().registryFactory as Record);\n if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n humangl.register(instance);\n available = Object.keys(tf.engine().registryFactory as Record);\n }\n if (instance.config.debug) log('available backends:', available);\n\n // force browser vs node backend\n if (env.browser && !env.node && (instance.config.backend === 'tensorflow') && available.includes('webgl')) {\n if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n instance.config.backend = 'webgl';\n }\n if (env.node && !env.browser && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl') && available.includes('tensorflow')) {\n if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n instance.config.backend = 'tensorflow';\n }\n\n // handle webgpu\n if (env.browser && instance.config.backend === 'webgpu') {\n if (typeof navigator === 'undefined' || typeof navigator.gpu === 'undefined') {\n log('override: backend set to webgpu but browser does not support webgpu');\n instance.config.backend = 'webgl';\n } else {\n const adapter = await navigator.gpu.requestAdapter();\n if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n if (!adapter) {\n log('override: backend set to webgpu but browser reports no available gpu');\n instance.config.backend = 'webgl';\n } else {\n // @ts-ignore requestAdapterInfo is not in tslib\n const adapterInfo = 'requestAdapterInfo' in adapter ? await adapter.requestAdapterInfo() : undefined;\n // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n log('webgpu adapter info:', adapterInfo);\n }\n }\n }\n\n if (!available.includes(instance.config.backend)) {\n log(`error: backend ${instance.config.backend} not found in registry`);\n instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n }\n\n if (instance.config.debug) log('setting backend:', [instance.config.backend]);\n\n // customize wasm\n if (instance.config.backend === 'wasm') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n if (typeof tf.setWasmPaths !== 'undefined') tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n let mt = false;\n let simd = false;\n try {\n mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n if (instance.config.debug) log(`wasm execution: ${simd ? 'simd' : 'no simd'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n } catch {\n log('wasm detection failed');\n }\n }\n\n try {\n await tf.setBackend(instance.config.backend);\n await tf.ready();\n } catch (err) {\n log('error: cannot set backend:', instance.config.backend, err);\n return false;\n }\n // @ts-ignore private property\n if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n }\n\n // customize humangl\n if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false \n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false \n // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV']) tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true \n // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false \n // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (instance.config.debug && typeof instance.config.deallocate !== 'undefined' && instance.config.deallocate) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n }\n\n // customize webgpu\n if (tf.getBackend() === 'webgpu') {\n // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n }\n\n if (instance.config.debug) {\n // @ts-ignore private property\n const newFlags = tf.env().flags;\n const updatedFlags = {};\n for (const key of Object.keys(newFlags)) {\n if (defaultFlags[key] === newFlags[key]) continue;\n updatedFlags[key] = newFlags[key];\n }\n if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n }\n\n if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n if (instance.config.debug) log('flags:', instance.config['flags']);\n for (const [key, val] of Object.entries(instance.config.flags)) {\n tf.env().set(key, val as number | boolean);\n }\n }\n\n tf.enableProdMode();\n constants.init();\n instance.performance.initBackend = Math.trunc(now() - timeStamp);\n instance.config.backend = tf.getBackend() as BackendEnum;\n await env.updateBackend(); // update env on backend init\n registerCustomOps(instance.config);\n // await env.updateBackend(); // update env on backend init\n // env.initial = false;\n }\n return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: string[], config) {\n // if (config.debug) log('registerKernel:', kernelNames);\n for (const kernelName of kernelNames) {\n const kernelConfig = {\n kernelName,\n backendName: config.backend,\n kernelFunc: (param): TensorInfo => {\n if (config.debug) log('kernelFunc', kernelName, config.backend, param);\n return param?.inputs?.info as TensorInfo;\n },\n // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n };\n tf.registerKernel(kernelConfig);\n }\n env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport { defaultLabels } from './labels';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\n/** draw processed canvas */\nexport function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n if (!input || !output) return;\n const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial) {\n if (!result?.performance || !inCanvas) return null;\n const timeStamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n const promise = Promise.all([\n face(inCanvas, result.face, localOptions),\n body(inCanvas, result.body, localOptions),\n hand(inCanvas, result.hand, localOptions),\n object(inCanvas, result.object, localOptions),\n gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n // person(inCanvas, result.persons, localOptions); // already included above\n ]);\n drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n result.performance.draw = drawTime;\n return promise;\n}\n\n/** sets default label templates for face/body/hand/object/gestures */\nexport function init() {\n options.faceLabels = defaultLabels.face;\n options.bodyLabels = defaultLabels.body;\n options.bodyPartLabels = defaultLabels.bodyPart;\n options.handLabels = defaultLabels.hand;\n options.fingerLabels = defaultLabels.finger;\n options.objectLabels = defaultLabels.object;\n options.gestureLabels = defaultLabels.gesture;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n if (!input) log('draw error: invalid canvas');\n else if (!input.getContext) log('draw error: canvas context not defined');\n else {\n const ctx = input.getContext('2d', { willReadFrequently: true });\n if (!ctx) log('draw error: cannot get canvas context');\n else return ctx;\n }\n return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const replace = (str: string, source: string, target: string | number) => str.replace(source, typeof target === 'number' ? target.toFixed(1) : target);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function labels(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, str: string, startX: number, startY: number, localOptions: DrawOptions) {\n const line: string[] = str.replace(/\\[.*\\]/g, '').split('\\n').map((l) => l.trim()); // remove unmatched templates and split into array\n const x = Math.max(0, startX);\n for (let i = line.length - 1; i >= 0; i--) {\n const y = i * localOptions.lineHeight + startY;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(line[i], x + 5, y + 16);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(line[i], x + 4, y + 15);\n }\n}\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n ctx.beginPath();\n ctx.lineWidth = localOptions.lineWidth;\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.lineWidth = localOptions.lineWidth;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n let angle;\n let x;\n let y;\n ctx.beginPath();\n ctx.moveTo(from[0], from[1]);\n ctx.lineTo(to[0], to[1]);\n angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.moveTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n ctx.closePath();\n ctx.stroke();\n ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\n\nexport interface DrawOptions {\n /** draw line color */\n color: string,\n /** alpha value used for lines */\n alpha: number,\n /** label color */\n labelColor: string,\n /** label shadow color */\n shadowColor: string,\n /** label font */\n font: string,\n /** line spacing between labels */\n lineHeight: number,\n /** line width for drawn lines */\n lineWidth: number,\n /** size of drawn points */\n pointSize: number,\n /** draw rounded boxes by n pixels */\n roundRect: number,\n /** should points be drawn? */\n drawPoints: boolean,\n /** should labels be drawn? */\n drawLabels: boolean,\n /** should face attention keypoints be highlighted */\n drawAttention: boolean;\n /** should detected gestures be drawn? */\n drawGestures: boolean,\n /** should draw boxes around detection results? */\n drawBoxes: boolean,\n /** should draw polygons from detection points? */\n drawPolygons: boolean,\n /** should draw gaze arrows? */\n drawGaze: boolean,\n /** should fill polygons? */\n fillPolygons: boolean,\n /** use z-coordinate when available */\n useDepth: boolean,\n /** should lines be curved? */\n useCurves: boolean,\n /** string template for face labels */\n faceLabels: string,\n /** string template for body labels */\n bodyLabels: string,\n /** string template for body part labels */\n bodyPartLabels: string,\n /** string template for hand labels */\n handLabels: string,\n /** string template for hand labels */\n fingerLabels: string,\n /** string template for object labels */\n objectLabels: string,\n /** string template for gesture labels */\n gestureLabels: string,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n shadowColor: 'black' as string,\n alpha: 0.5 as number,\n font: 'small-caps 16px \"Segoe UI\"' as string,\n lineHeight: 18 as number,\n lineWidth: 4 as number,\n pointSize: 2 as number,\n roundRect: 8 as number,\n drawPoints: false as boolean,\n drawLabels: true as boolean,\n drawBoxes: true as boolean,\n drawAttention: true as boolean,\n drawGestures: true as boolean,\n drawPolygons: true as boolean,\n drawGaze: true as boolean,\n fillPolygons: false as boolean,\n useDepth: true as boolean,\n useCurves: false as boolean,\n faceLabels: '' as string,\n bodyLabels: '' as string,\n bodyPartLabels: '' as string,\n objectLabels: '' as string,\n handLabels: '' as string,\n fingerLabels: '' as string,\n gestureLabels: '' as string,\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n rightEyeIris: [473, 474, 475, 476, 477], // 5\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const meshLandmarks: Record = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]],\n};\n\nexport const blazeFaceLandmarks: Record = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nexport const irisIndices: { key: string, indices: number[] }[] = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: number[] = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: number[] = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: number[] = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: number[] = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: number[] = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: number[] = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const pairsLips: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n lips: connectionsToIndices(pairsLips),\n leftEye: connectionsToIndices(pairsLeftEye),\n leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n leftIris: connectionsToIndices(pairsLeftIris),\n rightEye: connectionsToIndices(pairsRightEye),\n rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n rightIris: connectionsToIndices(pairsRightIris),\n faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n lips: connectionsToIndices(LIPS_CONNECTIONS),\n leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n 61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n 185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n 78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n 191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n 76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n 184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n 62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n 183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n 130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n 247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n 226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n 113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n 35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n 143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n 156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n 359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n 467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n 446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n 342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n 265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n 372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n 383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n 468, // Center.\n 469, // Iris right edge.\n 470, // Iris top edge.\n 471, // Iris left edge.\n 472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n 473, // Center.\n 474, // Iris right edge.\n 475, // Iris top edge.\n 476, // Iris left edge.\n 477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow, labels, replace } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet localOptions: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (!localOptions.drawLabels || (localOptions.faceLabels?.length === 0)) return;\n let l = localOptions.faceLabels.slice();\n l = replace(l, '[id]', f.id.toFixed(0));\n if (f.score) l = replace(l, '[score]', 100 * f.score);\n if (f.gender) l = replace(l, '[gender]', f.gender);\n if (f.genderScore) l = replace(l, '[genderScore]', 100 * f.genderScore);\n if (f.age) l = replace(l, '[age]', f.age);\n if (f.distance) l = replace(l, '[distance]', 100 * f.distance);\n if (f.real) l = replace(l, '[real]', 100 * f.real);\n if (f.live) l = replace(l, '[live]', 100 * f.live);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n l = replace(l, '[emotions]', emotion.join(' '));\n }\n if (f.rotation?.angle?.roll) l = replace(l, '[roll]', rad2deg(f.rotation.angle.roll));\n if (f.rotation?.angle?.yaw) l = replace(l, '[yaw]', rad2deg(f.rotation.angle.yaw));\n if (f.rotation?.angle?.pitch) l = replace(l, '[pitch]', rad2deg(f.rotation.angle.pitch));\n if (f.rotation?.gaze?.bearing) l = replace(l, '[gaze]', rad2deg(f.rotation.gaze.bearing));\n labels(ctx, l, f.box[0], f.box[1], localOptions);\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n // iris: array[center, left, top, right, bottom]\n if (f.annotations?.leftEyeIris && f.annotations?.leftEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (f.annotations?.rightEyeIris && f.annotations?.rightEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n ctx.strokeStyle = 'pink';\n const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n const pathV = new Path2D(`\n M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n C\n ${valX} ${f.box[1]},\n ${valX} ${f.box[1] + f.box[3]},\n ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n `);\n const pathH = new Path2D(`\n M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n C \n ${f.box[0]} ${valY},\n ${f.box[0] + f.box[2]} ${valY},\n ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n `);\n ctx.stroke(pathH);\n ctx.stroke(pathV);\n }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.gaze.strength && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {\n ctx.strokeStyle = 'pink';\n ctx.fillStyle = 'pink';\n const leftGaze = [\n f.annotations.leftEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.leftEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);\n const rightGaze = [\n f.annotations.rightEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.rightEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);\n }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPolygons && f.mesh.length >= 468) {\n ctx.lineWidth = 1;\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n lines(ctx, points, localOptions);\n }\n drawIrisElipse(f, ctx);\n }\n /*\n if (localOptions.drawPolygons && f.contours.length > 1) {\n ctx.lineWidth = 5;\n lines(ctx, f.contours, opt);\n }\n ctx.lineWidth = 1;\n */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPoints) {\n if (f?.mesh.length >= 468) {\n for (let i = 0; i < f.mesh.length; i++) {\n point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n if (localOptions.drawAttention) {\n if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n }\n }\n } else {\n for (const [k, v] of Object.entries(f?.annotations || {})) {\n if (!v?.[0]) continue;\n const pt = v[0];\n point(ctx, pt[0], pt[1], 0, localOptions);\n if (localOptions.drawLabels) labels(ctx, k, pt[0], pt[1], localOptions);\n }\n }\n }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n if (localOptions.drawBoxes) {\n rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial) {\n localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n for (const f of result) {\n drawFaceBoxes(f, ctx);\n drawLabels(f, ctx);\n if (f.mesh && f.mesh.length > 0) {\n drawFacePoints(f, ctx);\n drawFacePolygons(f, ctx);\n drawGazeSpheres(f, ctx);\n drawGazeArrows(f, ctx);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.bodyLabels?.length > 0)) {\n let l = localOptions.bodyLabels.slice();\n l = replace(l, '[id]', result[i].id.toFixed(0));\n l = replace(l, '[score]', 100 * result[i].score);\n labels(ctx, l, result[i].box[0], result[i].box[1], localOptions);\n }\n }\n if (localOptions.drawPoints && result[i].keypoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels && (localOptions.bodyPartLabels?.length > 0) && result[i].keypoints) {\n ctx.font = localOptions.font;\n for (const pt of result[i].keypoints) {\n if (!pt.score || (pt.score === 0)) continue;\n let l = localOptions.bodyPartLabels.slice();\n l = replace(l, '[label]', pt.part);\n l = replace(l, '[score]', 100 * pt.score);\n labels(ctx, l, pt.position[0], pt.position[1], localOptions);\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n for (const part of Object.values(result[i].annotations)) {\n for (const connected of part) curves(ctx, connected, localOptions);\n }\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.handLabels?.length > 0)) {\n let l = localOptions.handLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = colorDepth(pt[2], localOptions);\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels && h.annotations && (localOptions.fingerLabels?.length > 0)) {\n for (const [part, pt] of Object.entries(h.annotations)) {\n let l = localOptions.fingerLabels.slice();\n l = replace(l, '[label]', part);\n labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions);\n }\n }\n if (localOptions.drawPolygons && h.annotations) {\n const addHandLine = (part: Point[]) => {\n if (!part || part.length === 0 || !part[0]) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n const z = part[i][2] || 0;\n ctx.strokeStyle = colorDepth(i * z, localOptions);\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations.index);\n addHandLine(h.annotations.middle);\n addHandLine(h.annotations.ring);\n addHandLine(h.annotations.pinky);\n addHandLine(h.annotations.thumb);\n // addPart(h.annotations.palm);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.objectLabels?.length > 0)) {\n let l = localOptions.objectLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (localOptions.drawGestures && (localOptions.gestureLabels?.length > 0)) {\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n const [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n let l = localOptions.gestureLabels.slice();\n l = replace(l, '[where]', where[0]);\n l = replace(l, '[who]', who);\n l = replace(l, '[what]', what[1]);\n labels(ctx, l, 8, 2 + (i * localOptions.lineHeight), localOptions);\n i += 1;\n }\n }\n }\n}\n", "export const defaultLabels = {\n face: `face\n confidence: [score]%\n [gender] [genderScore]%\n age: [age] years\n distance: [distance]cm\n real: [real]%\n live: [live]%\n [emotions]\n roll: [roll]\u00B0 yaw:[yaw]\u00B0 pitch:[pitch]\u00B0\n gaze: [gaze]\u00B0`,\n body: 'body [score]%',\n bodyPart: '[label] [score]%',\n object: '[label] [score]%',\n hand: '[label] [score]%',\n finger: '[label]',\n gesture: '[where] [who]: [what]',\n};\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: string[] = [\n 'nose', // 0\n 'leftEyeInside', // 1\n 'leftEye', // 2\n 'leftEyeOutside', // 3\n 'rightEyeInside', // 4\n 'rightEye', // 5\n 'rightEyeOutside', // 6\n 'leftEar', // 7\n 'rightEar', // 8\n 'leftMouth', // 9\n 'rightMouth', // 10\n 'leftShoulder', // 11\n 'rightShoulder', // 12\n 'leftElbow', // 13\n 'rightElbow', // 14\n 'leftWrist', // 15\n 'rightWrist', // 16\n 'leftPinky', // 17\n 'rightPinky', // 18\n 'leftIndex', // 19\n 'rightIndex', // 20\n 'leftThumb', // 21\n 'rightThumb', // 22\n 'leftHip', // 23\n 'rightHip', // 24\n 'leftKnee', // 25\n 'rightKnee', // 26\n 'leftAnkle', // 27\n 'rightAnkle', // 28\n 'leftHeel', // 29\n 'rightHeel', // 30\n 'leftFoot', // 31\n 'rightFoot', // 32\n 'bodyCenter', // 33\n 'bodyTop', // 34\n 'leftPalm', // 35 // z-coord not ok\n 'leftHand', // 36 // similar to wrist but z-coord not ok\n 'rightPalm', // 37 // z-coord not ok\n 'rightHand', // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record = {\n shoulders: ['leftShoulder', 'rightShoulder'],\n hips: ['rightHip', 'leftHip'],\n mouth: ['leftMouth', 'rightMouth'],\n leftLegUpper: ['leftHip', 'leftKnee'],\n leftLegLower: ['leftKnee', 'leftAnkle'],\n leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n leftTorso: ['leftShoulder', 'leftHip'],\n leftArmUpper: ['leftShoulder', 'leftElbow'],\n leftArmLower: ['leftElbow', 'leftWrist'],\n leftHand: ['leftWrist', 'leftPalm'],\n leftHandPinky: ['leftPalm', 'leftPinky'],\n leftHandIndex: ['leftPalm', 'leftIndex'],\n leftHandThumb: ['leftPalm', 'leftThumb'],\n leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n rightLegUpper: ['rightHip', 'rightKnee'],\n rightLegLower: ['rightKnee', 'rightAnkle'],\n rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n rightTorso: ['rightShoulder', 'rightHip'],\n rightArmUpper: ['rightShoulder', 'rightElbow'],\n rightArmLower: ['rightElbow', 'rightWrist'],\n rightHand: ['rightWrist', 'rightPalm'],\n rightHandPinky: ['rightPalm', 'rightPinky'],\n rightHandIndex: ['rightPalm', 'rightIndex'],\n rightHandThumb: ['rightPalm', 'rightThumb'],\n rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D } from '../tfjs/types';\n\nexport interface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nlet model: GraphModel | null;\nlet inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n const anchors: { x: number, y: number }[] = [];\n let layerId = 0;\n while (layerId < numLayers) {\n let anchorCount = 0;\n let lastSameStrideLayer = layerId;\n while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n anchorCount += 2;\n lastSameStrideLayer++;\n }\n const stride = strides[layerId];\n const featureMapHeight = Math.ceil(inputSize / stride);\n const featureMapWidth = Math.ceil(inputSize / stride);\n for (let y = 0; y < featureMapHeight; ++y) {\n for (let x = 0; x < featureMapWidth; ++x) {\n for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n }\n }\n }\n layerId = lastSameStrideLayer;\n }\n anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nexport async function loadDetector(config: Config): Promise {\n if (env.initial) model = null;\n if (!model && config.body['detector'] && config.body['detector'].modelPath || '') {\n model = await loadModel(config.body['detector'].modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug && model) log('cached model:', model['modelUrl']);\n createAnchors();\n return model as GraphModel;\n}\n\nconst cropFactor = [5.0, 5.0];\nexport function decodeBoxes(boxesTensor, anchor) {\n return tf.tidy(() => {\n const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n let xCenter = tf.squeeze(split[0]);\n let yCenter = tf.squeeze(split[1]);\n let width = tf.squeeze(split[2]);\n let height = tf.squeeze(split[3]);\n xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n const xMin = tf.sub(xCenter, tf.div(width, 2));\n const yMin = tf.sub(yCenter, tf.div(height, 2));\n const xMax = tf.add(xMin, width);\n const yMax = tf.add(yMin, height);\n const boxes = tf.stack([xMin, yMin, xMax, yMax], 1);\n return boxes;\n });\n}\n\nasync function decodeResults(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise {\n const detectedBoxes: DetectedBox[] = [];\n const t: Record = {};\n t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n t.scores = tf.sigmoid(logitsTensor);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, 1, config.body['detector']?.minConfidence || 0.1, config.body['detector']?.iouThreshold || 0.1);\n const nms = await t.nms.data();\n const scores = await t.scores.data();\n const boxes = await t.boxes.array();\n for (const i of Array.from(nms)) {\n const score = scores[i];\n const boxRaw: Box = boxes[i];\n const box: Box = [Math.round(boxRaw[0] * outputSize[0]), Math.round(boxRaw[1] * outputSize[1]), Math.round(boxRaw[2] * outputSize[0]), Math.round(boxRaw[3] * outputSize[1])];\n const detectedBox: DetectedBox = { score, boxRaw, box };\n detectedBoxes.push(detectedBox);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return detectedBoxes;\n}\n\nexport async function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n const t: Record = {};\n t.res = model?.execute(input, ['Identity']) as Tensor; //\n t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n t.logits = tf.squeeze(t.logitsRaw);\n t.boxes = tf.squeeze(t.boxesRaw);\n const boxes = await decodeResults(t.boxes, t.logits, config, outputSize);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function square(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n const dist = [box[2] * scaleFact, box[3] * scaleFact];\n const newBox: Box = [\n box[0] - (dist[0] - box[2]) / 2,\n box[1] - (dist[1] - box[3]) / 2,\n dist[0],\n dist[1],\n ];\n return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport { loadDetector, detectBoxes, DetectedBox } from './blazeposedetector';\nimport * as box from '../util/box';\nimport { env } from '../util/env';\n\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nlet model: GraphModel | null;\nlet inputSize = 256;\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n detector: [],\n};\n\nconst cache: BodyResult[] = [];\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport const loadDetect = (config: Config): Promise => loadDetector(config);\n\nexport async function loadPose(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.body.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nfunction prepareImage(input: Tensor4D, size: number, cropBox?: Box): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n let final: Tensor;\n if (cropBox) {\n t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n }\n if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n const height: [number, number] = [\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n ];\n const width: [number, number] = [\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n ];\n padding = [\n [0, 0], // dont touch batch\n height, // height before&after\n width, // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n t.resize = tf.image.resizeBilinear(t.pad as Tensor4D, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else if (input.shape[1] !== size) { // if input needs resizing\n t.resize = tf.image.resizeBilinear(t.cropped as Tensor4D || input, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else { // if input is already in a correct resolution just normalize it\n final = tf.div(t.cropped || input, constants.tf255);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nfunction rescaleKeypoints(keypoints: BodyKeypoint[], outputSize: [number, number], cropBox?: Box): BodyKeypoint[] {\n for (const kpt of keypoints) { // first rescale due to padding\n kpt.position = [\n Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n kpt.position[2] as number,\n ];\n kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n }\n if (cropBox) { // second rescale due to cropping\n const width = cropBox[2] - cropBox[0];\n const height = cropBox[3] - cropBox[1];\n for (const kpt of keypoints) {\n kpt.positionRaw = [\n kpt.positionRaw[0] / height + cropBox[1], // correct offset due to crop\n kpt.positionRaw[1] / width + cropBox[0], // correct offset due to crop\n kpt.positionRaw[2] as number,\n ];\n kpt.position = [\n Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n kpt.positionRaw[2] as number,\n ];\n }\n }\n return keypoints;\n}\n\nfunction fixKeypoints(keypoints: BodyKeypoint[]) {\n // palm z-coord is incorrect around near-zero so we approximate it\n const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise {\n /**\n * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n * t.segmentation:\n * t.heatmap:\n * t.world: 39 keypoints [x,y,z] normalized to -1..1\n * t.poseflag: body score\n */\n if (!model?.['executor']) return null;\n const t: Record = {};\n [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = model?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n const poseScore = (await t.poseflag.data())[0];\n const points = await t.ld.data();\n const distances = await t.world.data();\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n const keypointsRelative: BodyKeypoint[] = [];\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n const score = sigmoid(points[depth * i + 3]);\n const presence = sigmoid(points[depth * i + 4]);\n const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n const positionRaw: Point = [points[depth * i + 0] / inputSize, points[depth * i + 1] / inputSize, points[depth * i + 2] + 0];\n const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n }\n if (poseScore < (config.body.minConfidence || 0)) return null;\n fixKeypoints(keypointsRelative);\n const keypoints: BodyKeypoint[] = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n const kpts = keypoints.map((k) => k.position);\n const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n return body;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n skipped++;\n } else {\n let boxes: DetectedBox[] = [];\n if (config.body?.['detector']?.['enabled']) {\n const preparedImage = prepareImage(input, 224);\n boxes = await detectBoxes(preparedImage, config, outputSize);\n tf.dispose(preparedImage);\n } else {\n boxes = [{ box: [0, 0, 0, 0] as Box, boxRaw: [0, 0, 1, 1], score: 0 }]; // running without detector\n }\n for (let i = 0; i < boxes.length; i++) {\n const preparedBox = prepareImage(input, 256, boxes[i]?.boxRaw); // padded and resized\n cache.length = 0;\n const bodyResult = await detectLandmarks(preparedBox, config, outputSize);\n tf.dispose(preparedBox);\n if (!bodyResult) continue;\n bodyResult.id = i;\n // bodyResult.score = 0; // TBD\n cache.push(bodyResult);\n }\n /*\n cropBox = [0, 0, 1, 1]; // reset crop coordinates\n if (cache?.boxRaw && config.skipAllowed) {\n const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n size = (size * 1.0) / 2; // enlarge and half it\n if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n const y = 0; // cy - size;\n const x = cx - size;\n cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n }\n }\n */\n lastTime = now();\n skipped = 0;\n }\n return cache;\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n // fakeOps(['floormod'], config);\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n if (!res) return [];\n const t: Record = {};\n const results: ObjectResult[] = [];\n const detections = await res.array() as number[][][];\n t.squeeze = tf.squeeze(res);\n const arr = tf.split(t.squeeze, 6, 1); // x1, y1, x2, y2, score, class\n t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n t.boxes = tf.squeeze(t.stack);\n t.scores = tf.squeeze(arr[4]);\n t.classes = tf.squeeze(arr[5]);\n tf.dispose([res, ...arr]);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, config.object.maxDetected || 0, config.object.iouThreshold, (config.object.minConfidence || 0));\n const nms = await t.nms.data();\n let i = 0;\n for (const id of Array.from(nms)) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n if (Number.isNaN(classVal)) continue;\n const label = labels[classVal].label as ObjectType;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw: Box = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ];\n const box: Box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return results;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n lastTime = now();\n tf.dispose(resize);\n\n const obj = await process(objectT, outputSize, config);\n last = obj;\n\n resolve(obj);\n });\n}\n", "export const kpt: string[] = [\n 'head',\n 'neck',\n 'rightShoulder',\n 'rightElbow',\n 'rightWrist',\n 'chest',\n 'leftShoulder',\n 'leftElbow',\n 'leftWrist',\n 'bodyCenter',\n 'rightHip',\n 'rightKnee',\n 'rightAnkle',\n 'leftHip',\n 'leftKnee',\n 'leftAnkle',\n];\n\nexport const connected: Record = {\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record };\n\n// const keypoints: Array = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore): Promise<[number, number, number]> {\n const [width, height] = inputs.shape;\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const max = tf.max(reshaped, 0);\n const newScore: number = (await max.data())[0]; // get highest score\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coordinates = tf.argMax(reshaped, 0);\n const mod = tf.mod(coordinates, width);\n const x = (await mod.data())[0];\n const div = tf.div(coordinates, width);\n const y: number = (await div.data())[0];\n tf.dispose([reshaped, max, coordinates, mod, div]);\n return [x, y, newScore];\n }\n tf.dispose([reshaped, max]);\n return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs[0].shape) return [];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n skipped++;\n return [cache];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape?.[2] || 0, model?.inputs[0].shape?.[1] || 0], false);\n const enhance = tf.mul(resize, constants.tf2);\n const norm = tf.sub(enhance, constants.tf1);\n return norm;\n });\n let resT;\n if (config.body.enabled) resT = model?.execute(tensor);\n lastTime = now();\n tf.dispose(tensor);\n\n if (resT) {\n cache.keypoints.length = 0;\n const squeeze = tf.squeeze(resT);\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = tf.unstack(squeeze, 2);\n tf.dispose(squeeze);\n\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n if (partScore > (config.body.minConfidence || 0)) {\n cache.keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = cache.keypoints.map((a) => a.position[0]);\n const y = cache.keypoints.map((a) => a.position[1]);\n cache.box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n cache.boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n cache.annotations[name] = pt;\n }\n resolve([cache]);\n });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n Math.trunc(Math.max(0, box.startPoint[0])),\n Math.trunc(Math.max(0, box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n box.startPoint[0] / (input.shape[2] || 0),\n box.startPoint[1] / (input.shape[1] || 0),\n (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor, anchor) => {\n const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n // const centerPoint = [(startPoint[0] + endPoint[0]) / 2, (startPoint[1] + endPoint[1]) / 2];\n const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]);\n /**\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0]) / 2) + (pt[0] * input.shape[2] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1]) / 2) + (pt[1] * input.shape[1] / blazeface.size()),\n ]);\n */\n\n return { startPoint, endPoint, landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n const h = image.shape[1];\n const w = image.shape[2];\n const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n const norm = tf.div(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n return {\n startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point,\n endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size,\n };\n};\n\nexport const squarifyBox = (box) => {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize = Math.max(...size) / 2;\n return {\n startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point,\n endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size: [Math.round(size[0]), Math.round(size[1])] as [number, number],\n };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n const x = landmarks.map((d) => d[0]);\n const y = landmarks.map((d) => d[1]);\n return {\n startPoint: [Math.min(...x), Math.min(...y)] as Point,\n endPoint: [Math.max(...x), Math.max(...y)] as Point,\n landmarks,\n };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n let product = 0;\n for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize: number) {\n const spec = inputSize === 192\n ? { strides: [4], anchors: [1] } // facemesh-detector\n : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n const anchors: [number, number][] = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n }\n }\n }\n return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n const boxSize = getBoxSize(box);\n const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n (coord[2] || 0),\n ]));\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n const boxCenter = getBoxCenter(box);\n const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n return coordsRotated.map((coord) => ([\n Math.trunc(coord[0] + offsets[0]),\n Math.trunc(coord[1] + offsets[1]),\n Math.trunc(coord[2] || 0),\n ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n ? coords.meshLandmarks.symmetryLine\n : coords.blazeFaceLandmarks.symmetryLine;\n let angle = 0; // default\n let rotationMatrix = fixedRotationMatrix; // default\n let face; // default\n\n if (rotate && env.kernels.includes('rotatewithoffset')) {\n angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n if (largeAngle) { // perform rotation only if angle is sufficiently high\n const center: Point = getBoxCenter(box);\n const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n const rotated = tf.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);\n rotationMatrix = buildRotationMatrix(-angle, center);\n face = cutAndResize(box, rotated, [inputSize, inputSize]);\n tf.dispose(rotated);\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n const x = mesh.map((m) => m[0]);\n const y = mesh.map((m) => m[1]);\n // weighted center\n /*\n const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n return [sum(x) / mesh.length, sum(y) / mesh.length];\n */\n // absolute center\n return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n const center = findFaceCenter(mesh);\n const boxSize = getBoxSize(previousBox);\n const calculatedBox = {\n startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n };\n return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\nexport interface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number, size: [number, number] }\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.detector?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n if (!anchors || !inputSizeT) return tf.zeros([0, 0]);\n const t: Record = {};\n t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n t.centers = tf.add(t.boxStarts, anchors);\n t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n t.centersNormalized = tf.div(t.centers, inputSizeT);\n t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n t.startNormalized = tf.mul(t.starts, inputSizeT);\n t.endNormalized = tf.mul(t.ends, inputSizeT);\n const boxes = tf.concat2d([t.startNormalized as Tensor2D, t.endNormalized as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor4D, config: Config): Promise {\n // sanity check on input\n if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n const t: Record = {};\n t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n t.div = tf.div(t.resized, constants.tf127);\n t.normalized = tf.sub(t.div, constants.tf05);\n const res = model?.execute(t.normalized) as Tensor[];\n if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n const sorted = res.sort((a, b) => a.size - b.size);\n t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n t.concat = tf.concat([t.concat512, t.concat384], 1);\n t.batch = tf.squeeze(t.concat, [0]);\n } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n t.batch = tf.squeeze(res[0]);\n } else { // original blazeface tfhub model\n t.batch = tf.squeeze(res);\n }\n tf.dispose(res);\n t.boxes = decodeBoxes(t.batch);\n t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.logits);\n t.scores = tf.squeeze(t.sigmoid);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n const nms = await t.nms.array() as number[];\n const boxes: DetectBox[] = [];\n const scores = await t.scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n\n if (confidence > (config.face.detector?.minConfidence || 0)) {\n const b: Record = {};\n b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n b.squeeze = tf.squeeze(b.slice);\n b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n const points = await b.bbox.data();\n const rawBox = {\n startPoint: [points[0], points[1]] as Point,\n endPoint: [points[2], points[3]] as Point,\n landmarks: (await b.landmarks.array()) as Point[],\n confidence,\n };\n b.anchor = tf.slice(anchors as Tensor, [nms[i], 0], [1, 2]);\n const anchor = await b.anchor.data();\n const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize], anchor);\n const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n const squaredBox = util.squarifyBox(enlargedBox);\n if (squaredBox.size[0] > (config.face.detector?.['minSize'] || 0) && squaredBox.size[1] > (config.face.detector?.['minSize'] || 0)) boxes.push(squaredBox);\n Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations.leftEyeLower0;\nconst rightOutline = coords.meshAnnotations.rightEyeLower0;\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.iris?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize === -1) inputSize = 64;\n return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.irisIndices.length; i++) {\n const { key, indices } = coords.irisIndices[i];\n const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0],\n newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n const boxSize = util.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / meshSize,\n box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n box.endPoint[0] / meshSize,\n ]], [0], [inputSize, inputSize]);\n if (flip && env.kernels.includes('flipleftright')) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\n }\n return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n const eyeRawCoords: Point[] = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n};\n\nexport async function augmentIris(rawCoords, face, meshSize) {\n if (!model?.['executor']) return rawCoords;\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = model.execute(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data();\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\n}\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n const t: Record = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n // flag: results[1], // already processed in parent // conv_faceflag\n lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n };\n for (const val of Object.values(t)) {\n if (!val) return rawCoords; // could not find tensor\n }\n\n // augment iris: adds additional 5 keypoints per eye\n const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n // augment eyes: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n // augment lips: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\nimport type { DetectBox } from './blazeface';\n\nconst cache = {\n boxes: [] as DetectBox[],\n skipped: Number.MAX_SAFE_INTEGER,\n timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n // reset cached boxes\n const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n cache.timestamp = now();\n cache.skipped = 0;\n } else {\n cache.skipped++;\n }\n const faces: FaceResult[] = [];\n const newCache: DetectBox[] = [];\n let id = 0;\n const size = inputSize;\n for (let i = 0; i < cache.boxes.length; i++) {\n const box = cache.boxes[i];\n let angle = 0;\n let rotationMatrix;\n const face: FaceResult = { // init face result\n id: id++,\n mesh: [],\n meshRaw: [],\n box: [0, 0, 0, 0],\n boxRaw: [0, 0, 0, 0],\n score: 0,\n boxScore: 0,\n faceScore: 0,\n size: [0, 0],\n // contoursRaw: [],\n // contours: [],\n annotations: {} as Record,\n };\n\n // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n if (config.filter.equalization) {\n const equilized = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n tf.dispose(face.tensor);\n if (equilized) face.tensor = equilized;\n }\n face.boxScore = Math.round(100 * box.confidence) / 100;\n if (!config.face.mesh?.enabled || !model?.['executor']) { // mesh not enabled or not loaded, return resuts from detector only\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.size = box.size;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n } else if (!model) { // mesh enabled, but not loaded\n if (config.debug) log('face mesh detection requested, but model is not loaded');\n } else { // mesh enabled\n if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n config.face.attention.enabled = false;\n tf.dispose(face.tensor);\n return faces;\n }\n const results = model.execute(face.tensor as Tensor) as Tensor[];\n const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n const faceConfidence = await confidenceT.data();\n face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n box.confidence = face.faceScore; // reset confidence of cached box\n if (config.face.mesh['keepInvalid']) {\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.size = box.size;\n face.score = face.boxScore;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n }\n } else {\n const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n tf.dispose(coordsReshaped);\n if (config.face.attention?.enabled) {\n rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n } else if (config.face.iris?.enabled) {\n rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize); // run iris model and augment results\n }\n face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n face.score = face.faceScore;\n const calculatedBox = {\n ...util.calculateFaceBox(face.mesh, box),\n confidence: box.confidence,\n landmarks: box.landmarks,\n size: box.size,\n };\n face.box = util.clampBox(calculatedBox, input);\n face.boxRaw = util.getRawBox(calculatedBox, input);\n face.size = calculatedBox.size;\n /*\n const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n face.contoursRaw = [];\n for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n */\n newCache.push(calculatedBox);\n }\n tf.dispose(results);\n }\n if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n else tf.dispose(face.tensor);\n }\n cache.boxes = newCache; // reset cache\n return faces;\n}\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (config.face.attention?.enabled && model?.['signature']) {\n if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n }\n if (!model) {\n if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n else model = await loadModel(config.face.mesh?.modelPath);\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nlet annotations: string[] = [];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet rgb = false;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.face.emotion?.modelPath);\n rgb = model?.inputs?.[0].shape?.[3] === 3;\n if (!rgb) annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']; // oarriaga and gear\n else annotations = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']; // affectnet\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ score: number, emotion: Emotion }[]> {\n if (!model) return [];\n const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj: { score: number, emotion: Emotion }[] = [];\n if (config.face.emotion?.enabled) {\n const t: Record = {};\n const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n if (config.face.emotion?.['crop'] > 0) { // optional crop\n const crop = config.face.emotion?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n }\n if (rgb) {\n t.mul = tf.mul(t.resize, 255);\n t.normalize = tf.sub(t.mul, [103.939, 116.779, 123.68]); // affectnet uses specific norm values\n t.emotion = model?.execute(t.normalize) as Tensor; // result is already in range 0..1, no need for additional activation\n } else {\n // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n // t.redNorm = tf.mul(t.red, rgb[0]);\n // t.greenNorm = tf.mul(t.green, rgb[1]);\n // t.blueNorm = tf.mul(t.blue, rgb[2]);\n // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n t.channels = tf.mul(t.resize, constants.rgb);\n t.grayscale = tf.sum(t.channels, 3, true);\n t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n }\n lastTime = now();\n const data = await t.emotion.data();\n for (let i = 0; i < data.length; i++) {\n if (data[i] > (config.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n }\n obj.sort((a, b) => b.score - a.score);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face similarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel, Tensor4D, Tensor1D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.description?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport function enhance(input, config: Config): Tensor {\n const tensor = (input.image || input.tensor || input) as Tensor4D; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n let crop: Tensor;\n if (config.face.description?.['crop'] > 0) { // optional crop\n const cropval = config.face.description?.['crop'];\n const box = [[cropval, cropval, 1 - cropval, 1 - cropval]];\n crop = tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n const norm: Tensor = tf.mul(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n /*\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n */\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n const obj: FaceRes = {\n age: 0 as number,\n gender: 'unknown' as Gender,\n genderScore: 0 as number,\n descriptor: [] as number[],\n };\n if (!model?.['executor']) return obj;\n const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && (last?.[idx]?.age > 0) && (last?.[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (config.face.description?.enabled) {\n const enhanced = enhance(image, config);\n const resT = model?.execute(enhanced) as Tensor[];\n lastTime = now();\n tf.dispose(enhanced);\n const genderT = resT.find((t) => t.shape[1] === 1) as Tensor;\n const gender = await genderT.data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > (config.face.description.minConfidence || 0)) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100) as Tensor1D, 1);\n const ageIdx: number = (await argmax.data())[0];\n tf.dispose(argmax);\n const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n const all = await ageT.data();\n obj.age = Math.round(all[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n const descriptor = desc ? await desc.data() : [] as number[];\n obj.descriptor = Array.from(descriptor);\n resT.forEach((t) => tf.dispose(t));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\n// import * as tf from 'dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: { x: number, y: number }[]): boolean {\n let inside = false;\n let j = polygon.length - 1;\n for (let i = 0; i < polygon.length; j = i++) {\n if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n }\n return inside;\n}\n\nexport async function mask(face: FaceResult): Promise {\n if (!face.tensor) return face.tensor;\n if (!face.mesh || face.mesh.length < 100) return face.tensor;\n const width = face.tensor.shape[2] || 0;\n const height = face.tensor.shape[1] || 0;\n const buffer = await face.tensor.buffer();\n let silhouette: { x: number, y: number }[] = [];\n for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n for (let x = 0; x < width; x++) {\n for (let y = 0; y < height; y++) {\n const inside = insidePoly(x / width, y / width, silhouette);\n if (!inside) {\n buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n }\n }\n }\n const output = buffer.toTensor();\n // tf.dispose(buffer);\n return output;\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.liveness?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: GearType[] = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.gear?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n const skipFrame = skipped < (config.face.gear?.skipFrames || 0);\n const skipTime = (config.face.gear?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n let box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n if (config.face.gear?.['crop'] > 0) { // optional crop config value\n const crop = config.face.gear?.['crop'];\n box = [[crop, crop, 1 - crop, 1 - crop]];\n }\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n const gender = await t.gender.data();\n obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n const race = await t.race.data();\n for (let i = 0; i < race.length; i++) {\n if (race[i] > (config.face.gear?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n }\n obj.race.sort((a, b) => b.score - a.score);\n // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n const ageDistribution = Array.from(await t.age.data());\n const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n let age = ageSorted[0][0]; // pick best starting point\n for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n obj.age = Math.round(10 * age) / 10;\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ age: number }> {\n if (!model) return { age: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.mul(t.resize, constants.tf255);\n const obj = { age: 0 };\n if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;\n if (t.age) {\n const data = await t.age.data();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { gender: Gender, genderScore: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n if (!model) return { gender: 'unknown', genderScore: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.tidy(() => {\n let normalize: Tensor;\n if (model?.inputs?.[0].shape?.[3] === 1) {\n const [red, green, blue] = tf.split(t.resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n } else {\n normalize = tf.mul(tf.sub(t.resize, constants.tf05), 2); // range rgb:-1..1\n }\n return normalize;\n });\n const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;\n const data = await t.gender.data();\n obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * MobileFaceNet model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['mobilefacenet']?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n const skipTime = (config.face['mobilefacenet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['mobilefacenet']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n /*\n // optional normalize outputs with l2 normalization\n const scaled = tf.tidy(() => {\n const l2 = res.norm('euclidean');\n const scale = res.div(l2);\n return scale;\n });\n\n // optional reduce feature vector complexity\n const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n */\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['insightface'].modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n const skipTime = (config.face['insightface']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['insightface']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations.rightEyeIris || !face.annotations.leftEyeIris) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v: Vector): Vector => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\n let thetaX: number;\n let thetaY: number;\n let thetaZ: number;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n thetaZ = Math.asin(r10);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n if (Number.isNaN(thetaX)) thetaX = 0;\n if (Number.isNaN(thetaY)) thetaY = 0;\n if (Number.isNaN(thetaZ)) thetaZ = 0;\n return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n };\n\n /*\n const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n };\n };\n */\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n const yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n const zAxis = normalize(crossVectors(xAxis, yAxis));\n // adjust xAxis to make sure that all axes are perpendicular to each other\n xAxis = crossVectors(yAxis, zAxis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n xAxis[0], xAxis[1], xAxis[2],\n yAxis[0], yAxis[1], yAxis[2],\n zAxis[0], zAxis[1], zAxis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n", "import type { FaceResult } from '../result';\n\nexport function calculateCameraDistance(face: FaceResult, width: number): number {\n // iris points are [center, left, top, right, bottom]\n // average size of human iris is 11.7mm - fairly constant for all ages/genders/races\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // distance of eye from camera in meters\n const cameraDistance = Math.round(1.17 / irisSize) / 100;\n return cameraDistance;\n}\n\nexport function calculateEyesDistance(face: FaceResult, width: number): number {\n // average distance between eyes is 65mm - fairly constant for typical adult male, but varies otherwise\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // pixel x and y distance of centers of left and right iris, you can use edges instead\n const irisDistanceXY = [f.leftEyeIris[0][0] - f.rightEyeIris[0][0], f.leftEyeIris[0][1] - f.rightEyeIris[0][1]];\n // absolute distance bewtween eyes in 0..1 range to account for head pitch (we can ignore yaw)\n const irisDistance = Math.sqrt((irisDistanceXY[0] * irisDistanceXY[0]) + (irisDistanceXY[1] * irisDistanceXY[1])) / width;\n // distance between eyes in meters\n const eyesDistance = Math.round(1.17 * irisDistance / irisSize) / 100;\n return eyesDistance;\n}\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor4D } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\nimport { calculateCameraDistance } from './anthropometry';\n\ninterface DescRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor4D): Promise => {\n // run facemesh, includes blazeface and iris\n let timeStamp: number = now();\n let ageRes: { age: number } | Promise<{ age: number }> | null;\n let gearRes: gear.GearType | Promise | null;\n let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n let mobilefacenetRes: number[] | Promise | null;\n let insightfaceRes: number[] | Promise | null;\n let antispoofRes: number | Promise | null;\n let livenessRes: number | Promise | null;\n let descRes: DescRes | Promise | null;\n\n const faceRes: FaceResult[] = [];\n instance.state = 'run:face';\n const faces: FaceResult[] = await facemesh.predict(input, instance.config);\n instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n instance.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefied\n if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n // optional face mask\n if (instance.config.face.detector?.mask) {\n const masked = await mask.mask(faces[i]);\n tf.dispose(faces[i].tensor);\n if (masked) faces[i].tensor = masked;\n }\n\n // calculate face angles\n const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Emotion:');\n if (instance.config.async) {\n emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n } else {\n instance.state = 'run:emotion';\n timeStamp = now();\n emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Emotion:');\n\n // run antispoof, inherits face from blazeface\n instance.analyze('Start AntiSpoof:');\n if (instance.config.async) {\n antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:antispoof';\n timeStamp = now();\n antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End AntiSpoof:');\n\n // run liveness, inherits face from blazeface\n instance.analyze('Start Liveness:');\n if (instance.config.async) {\n livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:liveness';\n timeStamp = now();\n livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Liveness:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start GEAR:');\n if (instance.config.async) {\n gearRes = instance.config.face.gear?.enabled ? gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:gear';\n timeStamp = now();\n gearRes = instance.config.face.gear?.enabled ? await gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.gear = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End GEAR:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start SSRNet:');\n if (instance.config.async) {\n ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:ssrnet';\n timeStamp = now();\n ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End SSRNet:');\n\n // run mobilefacenet alternative, inherits face from blazeface\n instance.analyze('Start MobileFaceNet:');\n if (instance.config.async) {\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End MobileFaceNet:');\n\n // run insightface alternative, inherits face from blazeface\n instance.analyze('Start InsightFace:');\n if (instance.config.async) {\n insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End InsightFace:');\n\n // run faceres, inherits face from blazeface\n instance.analyze('Start Description:');\n if (instance.config.async) {\n descRes = faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n } else {\n instance.state = 'run:description';\n timeStamp = now();\n descRes = await faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Description:');\n\n // if async wait for results\n if (instance.config.async) {\n [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);\n }\n instance.analyze('Finish Face:');\n\n if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n descRes = {\n ...(descRes as DescRes),\n age: (ageRes as { age: number}).age,\n gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n };\n }\n if (instance.config.face.gear?.enabled && gearRes) { // override age/gender/race if gear model is used\n descRes = {\n ...(descRes as DescRes),\n age: (gearRes as gear.GearType).age,\n gender: (gearRes as gear.GearType).gender,\n genderScore: (gearRes as gear.GearType).genderScore,\n race: (gearRes as gear.GearType).race,\n };\n }\n if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if mobilefacenet model is used\n (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n }\n\n if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n (descRes as DescRes).descriptor = insightfaceRes as number[];\n }\n\n const irisSize = instance.config.face.iris?.enabled ? calculateCameraDistance(faces[i], input.shape[2]) : 0;\n\n // optionally return tensor\n const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor as Tensor4D) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n const res: FaceResult = {\n ...faces[i],\n id: i,\n };\n if ((descRes as DescRes).age) res.age = (descRes as DescRes).age;\n if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n if (antispoofRes) res.real = antispoofRes as number;\n if (livenessRes) res.live = livenessRes as number;\n if (irisSize > 0) res.distance = irisSize;\n if (rotation) res.rotation = rotation;\n if (tensor) res.tensor = tensor;\n faceRes.push(res);\n instance.analyze('End Face');\n }\n instance.analyze('End FaceMesh:');\n if (instance.config.async) {\n if (instance.performance.face) delete instance.performance.face;\n if (instance.performance.age) delete instance.performance.age;\n if (instance.performance.gender) delete instance.performance.gender;\n if (instance.performance.emotion) delete instance.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n thumb: 0,\n index: 1,\n middle: 2,\n ring: 3,\n pinky: 4,\n all: [0, 1, 2, 3, 4], // just for convenience\n nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n // Describes mapping of joints based on the 21 points returned by handpose.\n // [0] Palm\n // [1-4] Thumb\n // [5-8] Index\n // [9-12] Middle\n // [13-16] Ring\n // [17-20] Pinky\n pointsMapping: {\n 0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n 1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n 2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n 3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n 4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n },\n getName: (value) => Finger.nameMapping[value],\n getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n none: 0,\n half: 1,\n full: 2,\n nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n verticalUp: 0,\n verticalDown: 1,\n horizontalLeft: 2,\n horizontalRight: 3,\n diagonalUpRight: 4,\n diagonalUpLeft: 5,\n diagonalDownRight: 6,\n diagonalDownLeft: 7,\n nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n name;\n curls;\n directions;\n weights;\n weightsRelative;\n\n constructor(name) {\n // name (should be unique)\n this.name = name;\n this.curls = {};\n this.directions = {};\n this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n }\n\n curl(finger, curl, confidence) {\n if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n this.curls[finger].push([curl, confidence]);\n }\n\n direction(finger, position, confidence) {\n if (!this.directions[finger]) this.directions[finger] = [];\n this.directions[finger].push([position, confidence]);\n }\n\n weight(finger, weight) {\n this.weights[finger] = weight;\n // recalculate relative weights\n const total = this.weights.reduce((a, b) => a + b, 0);\n this.weightsRelative = this.weights.map((el) => el * 5 / total);\n }\n\n matchAgainst(detectedCurls, detectedDirections) {\n let confidence = 0.0;\n // look at the detected curl of each finger and compare with\n // the expected curl of this finger inside current gesture\n for (const fingerIdx in detectedCurls) {\n const detectedCurl = detectedCurls[fingerIdx];\n const expectedCurls = this.curls[fingerIdx];\n if (typeof expectedCurls === 'undefined') {\n // no curl description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible curl of this specific finger\n for (const [expectedCurl, score] of expectedCurls) {\n if (detectedCurl === expectedCurl) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n // same for detected direction of each finger\n for (const fingerIdx in detectedDirections) {\n const detectedDirection = detectedDirections[fingerIdx];\n const expectedDirections = this.directions[fingerIdx];\n if (typeof expectedDirections === 'undefined') {\n // no direction description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible direction of this specific finger\n for (const [expectedDirection, score] of expectedDirections) {\n if (detectedDirection === expectedDirection) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n return confidence / 10;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n ThumbsUp.curl(finger, full, 1.0);\n ThumbsUp.direction(finger, horizontalLeft, 1.0);\n ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\n/* eslint-disable camelcase */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n // curl estimation\n HALF_CURL_START_LIMIT: 60.0,\n NO_CURL_START_LIMIT: 130.0,\n // direction estimation\n DISTANCE_VOTE_POWER: 1.1,\n SINGLE_ANGLE_VOTE_POWER: 0.9,\n TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n const value = (point1y - point2y) / (point1x - point2x);\n let slope = Math.atan(value) * 180 / Math.PI;\n if (slope <= 0) slope = -slope;\n else if (slope > 0) slope = 180 - slope;\n return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n if (!point1 || !point2) return [0, 0];\n const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n if (point1.length === 2) return slopeXY;\n const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n let isVertical = 0;\n let isDiagonal = 0;\n let isHorizontal = 0;\n if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n else isHorizontal = 1 * weightageAt;\n return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const start_mid_z_dist = startPoint[2] - midPoint[2];\n const start_end_z_dist = startPoint[2] - endPoint[2];\n const mid_end_z_dist = midPoint[2] - endPoint[2];\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n if (cos_in > 1.0) cos_in = 1.0;\n else if (cos_in < -1.0) cos_in = -1.0;\n let angleOfCurve = Math.acos(cos_in);\n angleOfCurve = (57.2958 * angleOfCurve) % 180;\n let fingerCurl;\n if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n else fingerCurl = FingerCurl.full;\n return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n if (max_dist_x === Math.abs(start_end_x_dist)) {\n if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else {\n if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n }\n return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n let estimatedDirection;\n if (max_dist_y === Math.abs(start_end_y_dist)) {\n if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else {\n if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n }\n return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n if (reqd_vertical_direction === FingerDirection.verticalUp) {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n else estimatedDirection = FingerDirection.diagonalUpRight;\n } else {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n else estimatedDirection = FingerDirection.diagonalDownRight;\n }\n return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n let voteVertical = 0.0;\n let voteDiagonal = 0.0;\n let voteHorizontal = 0.0;\n const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n else voteHorizontal += options.DISTANCE_VOTE_POWER;\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n let calc_start_point_x = startPoint[0];\n let calc_start_point_y = startPoint[1];\n let calc_end_point_x = endPoint[0];\n let calc_end_point_y = endPoint[1];\n if (max_dist === start_mid_dist) {\n calc_end_point_x = endPoint[0];\n calc_end_point_y = endPoint[1];\n } else if (max_dist === mid_end_dist) {\n calc_start_point_x = midPoint[0];\n calc_start_point_y = midPoint[1];\n }\n const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n voteVertical += votes[0];\n voteDiagonal += votes[1];\n voteHorizontal += votes[2];\n for (const fingerSlope of fingerSlopes) {\n const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n voteVertical += fingerVotes[0];\n voteDiagonal += fingerVotes[1];\n voteHorizontal += fingerVotes[2];\n }\n // in case of tie, highest preference goes to Vertical,\n // followed by horizontal and then diagonal\n let estimatedDirection;\n if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n } else {\n estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n }\n return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n // step 1: calculate slopes\n const slopesXY: number[][] = [];\n const slopesYZ: number[][] = [];\n const fingerCurls: number[] = [];\n const fingerDirections: number[] = [];\n if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n // step 1: calculate slopes\n for (const finger of Finger.all) {\n const points = Finger.getPoints(finger);\n const slopeAtXY: number[] = [];\n const slopeAtYZ: number[] = [];\n for (const point of points) {\n const point1 = landmarks[point[0]];\n const point2 = landmarks[point[1]];\n // calculate single slope\n const slopes = getSlopes(point1, point2);\n const slopeXY = slopes[0];\n const slopeYZ = slopes[1];\n slopeAtXY.push(slopeXY);\n slopeAtYZ.push(slopeYZ);\n }\n slopesXY.push(slopeAtXY);\n slopesYZ.push(slopeAtYZ);\n }\n\n // step 2: calculate orientations\n for (const finger of Finger.all) {\n // start finger predictions from palm - except for thumb\n const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n const fingerPointsAt = Finger.getPoints(finger);\n const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n const endPoint = landmarks[fingerPointsAt[3][1]];\n // check if finger is curled\n const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n fingerCurls[finger] = fingerCurled;\n fingerDirections[finger] = fingerPosition;\n }\n return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n if (!keypoints || keypoints.length === 0) return null;\n const estimatorRes = estimate(keypoints);\n const landmarks = {};\n for (const fingerIdx of Finger.all) {\n landmarks[Finger.getName(fingerIdx)] = {\n curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n };\n }\n return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n const poses: { name: string, confidence: number }[] = [];\n if (!keypoints || keypoints.length === 0) return poses;\n const estimatorRes = estimate(keypoints);\n for (const gesture of Gestures) {\n const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n }\n return poses;\n}\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n | 'victory'\n | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { body: number, gesture: BodyGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n }\n }\n return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { face: number, gesture: FaceGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 450) {\n const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2] || 0;\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { iris: number, gesture: IrisGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations?.leftEyeIris?.[0] || !res[i].annotations?.rightEyeIris?.[0]) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n if (rightIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking right' });\n } else {\n if (leftIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking left' });\n }\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { hand: number, gesture: HandGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: { name: string, position: Point }[] = [];\n if (res[i].annotations) {\n for (const [finger, pos] of Object.entries(res[i].annotations)) {\n if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n if (res[i].keypoints) {\n const poses = fingerPose.match(res[i].keypoints);\n for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n }\n }\n return gestures;\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 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y: 0.40625 },\n { x: 0.84375, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.84375, y: 0.46875 },\n { x: 0.84375, y: 0.46875 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y: 0.59375 },\n { x: 0.96875, y: 0.59375 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.96875, y: 0.65625 },\n { x: 0.96875, y: 0.65625 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0.15625, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n 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0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 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0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 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},\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, Tensor1D, Tensor2D, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model: GraphModel) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSize = this?.model?.inputs?.[0]?.shape?.[2] || 0;\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n const t: Record = {};\n t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n const res = tf.concat2d([t.startPoints as Tensor2D, t.endPoints as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res as Tensor;\n }\n\n normalizeLandmarks(rawPalmLandmarks, index: number): Tensor {\n const t: Record = {};\n t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n t.div = tf.div(t.reshape, this.inputSizeTensor);\n t.landmarks = tf.add(t.div, this.anchors[index] ? this.anchors[index] : 0);\n const res = tf.mul(t.landmarks, this.inputSizeTensor);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n async predict(input: Tensor4D, config: Config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n t.div = tf.div(t.resize, constants.tf127);\n t.image = tf.sub(t.div, constants.tf1);\n t.batched = this.model.execute(t.image) as Tensor;\n t.predictions = tf.squeeze(t.batched);\n t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.slice);\n t.scores = tf.squeeze(t.sigmoid);\n const scores = await t.scores.data();\n t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n t.norm = this.normalizeBoxes(t.boxes);\n // box detection is flaky so we look for 3x boxes than we need results\n t.nms = await tf.image.nonMaxSuppressionAsync(t.norm as Tensor2D, t.scores as Tensor1D, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n const nms = await t.nms.array() as number[];\n const hands: { startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }[] = [];\n for (const index of nms) {\n const p: Record = {};\n p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n p.norm = this.normalizeLandmarks(p.slice, index);\n p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n const box = await p.box.data();\n const startPoint = box.slice(0, 2) as unknown as Point;\n const endPoint = box.slice(2, 4) as unknown as Point;\n const palmLandmarks = await p.palmLandmarks.array();\n const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n const scaled = util.scaleBoxCoordinates(hand, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);\n hands.push(scaled);\n Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: ({ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null)[];\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n this.inputSize = this.handPoseModel?.inputs?.[0].shape?.[2] || 0;\n this.storedBoxes = [];\n this.skipped = Number.MAX_SAFE_INTEGER;\n this.detectedHands = 0;\n }\n\n calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = util.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...util.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(coord[2]),\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames\n let boxes;\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n this.skipped++;\n } else {\n boxes = await this.handDetector.predict(image, config);\n this.skipped = 0;\n }\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: { landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }[] = [];\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n const palmCenter = util.getBoxCenter(currentBox);\n const palmCenterNormalized: [number, number] = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = tf.div(croppedInput, constants.tf255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Tensor[];\n lastTime = now();\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence / 4) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n const result = {\n landmarks: coords,\n confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n const result = {\n confidence: currentBox.confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: 0,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n landmarks: [],\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport function initPipeline() {\n const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!handPipeline) initPipeline();\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: HandResult[] = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n const keypoints = predictions[i].landmarks as unknown as Point[];\n let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: Box = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n const landmarks = fingerPose.analyze(keypoints);\n hands.push({\n id: i,\n score: Math.round(100 * predictions[i].confidence) / 100,\n boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n label: 'hand',\n box,\n boxRaw,\n keypoints,\n annotations: annotations as HandResult['annotations'],\n landmarks: landmarks as HandResult['landmarks'],\n });\n }\n return hands;\n}\n\nexport async function loadDetect(config: Config): Promise {\n if (env.initial) handDetectorModel = null;\n if (!handDetectorModel) handDetectorModel = await loadModel(config.hand.detector?.modelPath);\n else if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n return handDetectorModel;\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) handPoseModel = null;\n if (!handPoseModel) handPoseModel = await loadModel(config.hand.skeleton?.modelPath);\n else if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n return handPoseModel;\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ninterface HandDetectResult {\n id: number,\n score: number,\n box: Box,\n boxRaw: Box,\n label: HandType,\n}\n\nconst cache: {\n boxes: HandDetectResult[],\n hands: HandResult[];\n} = {\n boxes: [],\n hands: [],\n};\n\nconst fingerMap = {\n /*\n thumb: [0, 1, 2, 3, 4],\n index: [0, 5, 6, 7, 8],\n middle: [0, 9, 10, 11, 12],\n ring: [0, 13, 14, 15, 16],\n pinky: [0, 17, 18, 19, 20],\n palm: [0],\n */\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n base: [0],\n palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise {\n // HandTrack Model: Original: TFJS Port: \n if (env.initial) models[0] = null;\n if (!models[0]) {\n // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n // ideally need to prune the model itself\n fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n models[0] = await loadModel(config.hand.detector?.modelPath);\n const inputs = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) models[1] = null;\n if (!models[1]) {\n models[1] = await loadModel(config.hand.skeleton?.modelPath);\n const inputs = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models[0]) await loadDetect(config);\n if (!models[1]) await loadSkeleton(config);\n return models;\n}\n\nasync function detectHands(input: Tensor4D, config: Config): Promise {\n const hands: HandDetectResult[] = [];\n if (!input || !models[0]) return hands;\n const t: Record = {};\n const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n const width = Math.round(height * ratio / 8) * 8;\n t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n t.cast = tf.cast(t.resize, 'int32');\n [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n t.scores = tf.squeeze(t.rawScores, [0]);\n const classScores: Tensor[] = tf.unstack(t.scores, 1); // unstack scores based on classes\n tf.dispose(classScores[faceIndex]);\n classScores.splice(faceIndex, 1); // remove faces\n t.filtered = tf.stack(classScores, 1); // restack\n tf.dispose(classScores);\n // t.filtered = t.scores;\n t.max = tf.max(t.filtered, 1); // max overall score\n t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n let id = 0;\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.max as Tensor1D, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n const nms = await t.nms.data();\n const scores = await t.max.data();\n const classNum = await t.argmax.data();\n for (const nmsIndex of Array.from(nms)) { // generates results for each class\n const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n const boxYX = await boxSlice.data();\n tf.dispose(boxSlice);\n const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n const score = scores[nmsIndex];\n const label = classes[classNum[nmsIndex]] as HandType;\n const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n hands.push(hand);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n hands.sort((a, b) => b.score - a.score);\n if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n return hands;\n}\n\nasync function detectFingers(input: Tensor4D, h: HandDetectResult, config: Config): Promise {\n const hand: HandResult = { // initial values inherited from hand detect\n id: h.id,\n score: Math.round(100 * h.score) / 100,\n boxScore: Math.round(100 * h.score) / 100,\n fingerScore: 0,\n box: h.box,\n boxRaw: h.boxRaw,\n label: h.label,\n keypoints: [],\n landmarks: {} as HandResult['landmarks'],\n annotations: {} as HandResult['annotations'],\n };\n if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n const t: Record = {};\n const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n t.div = tf.div(t.crop, constants.tf255);\n [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n const rawScore = (await t.score.data())[0];\n const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n if (score >= (config.hand.minConfidence || 0)) {\n hand.fingerScore = score;\n t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n const coordsData: Point[] = await t.reshaped.array() as Point[];\n const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n return hand;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!models[0]?.['executor'] || !models[1]?.['executor'] || !models[0].inputs[0].shape || !models[1].inputs[0].shape) return []; // something is wrong with the model\n outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n skipped++; // increment skip frames\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.hands; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else { // finally rerun detector\n cache.boxes = await detectHands(input, config);\n lastTime = now();\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n skipped = 0;\n }\n\n const oldCache = [...cache.boxes];\n cache.boxes.length = 0; // reset cache\n if (config.cacheSensitivity > 0) {\n for (let i = 0; i < cache.hands.length; i++) {\n const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n const boxScale = box.scale(boxKpt.box, boxExpandFact);\n const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n // const boxCrop = box.crop(boxScaleRaw);\n cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n }\n }\n }\n for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n cache.hands[i].box = bbox.box;\n cache.hands[i].boxRaw = bbox.boxRaw;\n }\n resolve(cache.hands);\n });\n}\n", "/**\n * Type definitions for Human result object\n */\n\nimport type { Tensor } from './tfjs/types';\nimport type { FaceGesture, BodyGesture, HandGesture, IrisGesture } from './gesture/gesture';\nimport type { AnyCanvas } from './exports';\n\n/** generic box as [x, y, width, height] */\nexport type Box = [number, number, number, number];\n/** generic point as [x, y, z?] */\nexport type Point = [number, number, number?];\n\nexport type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';\nexport type Gender = 'male' | 'female' | 'unknown';\nexport type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';\nexport type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette'\n | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner'\n | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris'\n | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris'\n | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';\n\n/** Face results\n * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models\n * - Some values may be null if specific model is not enabled\n */\nexport interface FaceResult {\n /** face id */\n id: number\n /** overall face score */\n score: number,\n /** detection score */\n boxScore: number,\n /** mesh score */\n faceScore: number,\n /** detected face box */\n box: Box,\n /** detected face box normalized to 0..1 */\n boxRaw: Box,\n /** detected face box size */\n size: [number, number],\n /** detected face mesh */\n mesh: Point[]\n /** detected face mesh normalized to 0..1 */\n meshRaw: Point[],\n /** face contours as array of 2d points normalized to 0..1 */\n // contoursRaw: Array<[number, number]>,\n /** face contours as array of 2d points */\n // contours: Array<[number, number]>,\n /** mesh keypoints combined into annotated results */\n annotations: Record,\n /** detected age */\n age?: number,\n /** detected gender */\n gender?: Gender,\n /** gender detection score */\n genderScore?: number,\n /** detected emotions */\n emotion?: { score: number, emotion: Emotion }[],\n /** detected race */\n race?: { score: number, race: Race }[],\n /** face descriptor */\n embedding?: number[],\n /** face distance from camera */\n distance?: number,\n /** face anti-spoofing result confidence */\n real?: number,\n /** face liveness result confidence */\n live?: number,\n /** face rotation details */\n rotation?: {\n angle: { roll: number, yaw: number, pitch: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n } | null,\n /** detected face as tensor that can be used in further pipelines */\n tensor?: Tensor,\n}\n\nexport type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';\nexport type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder'\n | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle'\n | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';\nexport type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;\nexport type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';\nexport type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';\nexport type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;\n\n/** Body Result keypoints */\nexport interface BodyKeypoint {\n /** body part name */\n part: BodyLandmark,\n /** body part position */\n position: Point,\n /** body part position normalized to 0..1 */\n positionRaw: Point,\n /** body part position relative to body center in meters */\n distance?: Point,\n /** body part detection score */\n score: number,\n}\n\n/** Body results */\nexport interface BodyResult {\n /** body id */\n id: number,\n /** body detection score */\n score: number,\n /** detected body box */\n box: Box,\n /** detected body box normalized to 0..1 */\n boxRaw: Box,\n /** detected body keypoints */\n keypoints: BodyKeypoint[]\n /** detected body keypoints combined into annotated parts */\n annotations: Record,\n}\n\nexport type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';\nexport type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';\nexport type FingerCurl = 'none' | 'half' | 'full';\nexport type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';\n\n/** Hand results */\nexport interface HandResult {\n /** hand id */\n id: number,\n /** hand overal score */\n score: number,\n /** hand detection score */\n boxScore: number,\n /** hand skelton score */\n fingerScore: number,\n /** detected hand box */\n box: Box,\n /** detected hand box normalized to 0..1 */\n boxRaw: Box,\n /** detected hand keypoints */\n keypoints: Point[],\n /** detected hand class */\n label: HandType,\n /** detected hand keypoints combined into annotated parts */\n annotations: Record,\n /** detected hand parts annotated with part gestures */\n landmarks: Record,\n}\n\nexport type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter'\n | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee'\n | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork'\n | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant'\n | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book'\n | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';\n\n/** Object results */\nexport interface ObjectResult {\n /** object id */\n id: number,\n /** object detection score */\n score: number,\n /** detected object class id */\n class: number,\n /** detected object class name */\n label: ObjectType,\n /** detected object box */\n box: Box,\n /** detected object box normalized to 0..1 */\n boxRaw: Box,\n}\n\n/** Gesture combined results\n * Each result has:\n * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`\n * - gesture: gesture detected\n */\nexport type GestureResult =\n { 'face': number, gesture: FaceGesture }\n | { 'iris': number, gesture: IrisGesture }\n | { 'body': number, gesture: BodyGesture }\n | { 'hand': number, gesture: HandGesture }\n\n/** Person getter\n* - Triggers combining all individual results into a virtual person object\n*/\nexport interface PersonResult {\n /** person id */\n id: number,\n /** face result that belongs to this person */\n face: FaceResult,\n /** body result that belongs to this person */\n body: BodyResult | null,\n /** left and right hand results that belong to this person */\n hands: { left: HandResult | null, right: HandResult | null },\n /** detected gestures specific to this person */\n gestures: GestureResult[],\n /** box that defines the person */\n box: Box,\n /** box that defines the person normalized to 0..1 */\n boxRaw?: Box,\n}\n\n/**\n * Result interface definition for **Human** library\n *\n * Contains all possible detection results\n */\nexport interface Result {\n /** {@link FaceResult}: detection & analysis results */\n face: FaceResult[],\n /** {@link BodyResult}: detection & analysis results */\n body: BodyResult[],\n /** {@link HandResult}: detection & analysis results */\n hand: HandResult[],\n /** {@link GestureResult}: detection & analysis results */\n gesture: GestureResult[],\n /** {@link ObjectResult}: detection & analysis results */\n object: ObjectResult[]\n /** global performance object with timing values for each operation */\n performance: Record,\n /** optional processed canvas that can be used to draw input on screen */\n canvas?: AnyCanvas | null,\n /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */\n readonly timestamp: number,\n /** getter property that returns unified persons object */\n persons: PersonResult[],\n /** Last known error message */\n error: string | null;\n /** Resolution width */\n width: number,\n /** Resolution height */\n height: number,\n}\n\nexport const empty = (error: string | null = null): Result => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });\n", "export const kpt: string[] = [ // used to create part labels\n 'nose',\n 'leftEye',\n 'rightEye',\n 'leftEar',\n 'rightEar',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n];\n\nexport const horizontal: string[][] = [ // used to fix left vs right\n ['leftEye', 'rightEye'],\n ['leftEar', 'rightEar'],\n ['leftShoulder', 'rightShoulder'],\n ['leftElbow', 'rightElbow'],\n ['leftWrist', 'rightWrist'],\n ['leftHip', 'rightHip'],\n ['leftKnee', 'rightKnee'],\n ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: string[][] = [ // used to remove unlikely keypoint positions\n ['leftKnee', 'leftShoulder'],\n ['rightKnee', 'rightShoulder'],\n ['leftAnkle', 'leftKnee'],\n ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: string[][][] = [ // used to match relative body parts\n [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record = { // used to create body outline in annotations\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation, empty, FaceLandmark } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = empty();\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n const t0 = now();\n if (!newResult) return empty();\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n\n /* curve fitted: buffer = 8 - ln(delay)\n interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n - at 50ms delay buffer = ~4.1 => 28% towards live data\n - at 250ms delay buffer = ~2.5 => 40% towards live data\n - at 500ms delay buffer = ~1.8 => 55% towards live data\n - at 750ms delay buffer = ~1.4 => 71% towards live data\n - at 1sec delay buffer = 1 which means live data is used\n */\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n if (newResult.error) bufferedResult.error = newResult.error;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)) as BodyResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((newKpt, j) => ({\n score: newKpt.score,\n part: newKpt.part,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n ],\n distance: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n ],\n }))) as { score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }[];\n\n const annotations: Record = {} as Record; // recreate annotations\n let coords = { connected: {} };\n if (config.body.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n else if (config.body.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n else if (config.body.modelPath?.includes('movenet')) coords = moveNetCoords;\n for (const [name, indexes] of Object.entries(coords.connected as Record)) {\n const pt: Point[][] = [];\n for (let j = 0; j < indexes.length - 1; j++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n : [];\n let annotations = {};\n if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.hand[i].annotations;\n } else if (newResult.hand[i].annotations) {\n for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n annotations[key] = newResult.hand[i]?.annotations?.[key]?.[0]\n ? newResult.hand[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)) as FaceResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n let annotations: Record = newResult.face[i].annotations;\n if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) {\n bufferedResult.face[i].annotations = newResult.face[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.face[i].annotations;\n } else if (newResult.face[i].annotations) {\n for (const key of Object.keys(newResult.face[i].annotations)) { // update annotations\n annotations[key] = newResult.face[i]?.annotations?.[key]?.[0]\n ? newResult.face[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n if (newResult.face[i].rotation) {\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.bearing || 0) + (newResult.face[i].rotation?.gaze.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.strength || 0) + (newResult.face[i].rotation?.gaze.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw, annotations }; // shallow clone plus updated values\n } else {\n bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations }; // shallow clone plus updated values\n }\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)) as ObjectResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)) as PersonResult[];\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n }\n }\n }\n\n // copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n // copy resolution info\n bufferedResult.width = newResult.width;\n bufferedResult.height = newResult.height;\n\n // append interpolation performance data\n const t1 = now();\n interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n return bufferedResult;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]);\n // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n t.fg = tf.softmax(t.fgRaw);\n t.mul = tf.mul(t.fg, constants.tf255);\n t.expand = tf.expandDims(t.mul, 2);\n t.output = tf.image.resizeBilinear(t.expand as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.output], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = number[]\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n // general minkowski distance, euclidean distance is limited case where order is 2\n if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\n let sum = 0;\n for (let i = 0; i < descriptor1.length; i++) {\n const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n }\n return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n if (dist === 0) return 1; // short circuit for identical inputs\n const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n * default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n const dist = distance(descriptor1, descriptor2, options);\n return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function find(descriptor: Descriptor, descriptors: Descriptor[], options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { // validate input\n return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n }\n let lowestDistance = Number.MAX_SAFE_INTEGER;\n let index = -1;\n for (let i = 0; i < descriptors.length; i++) {\n const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\n if (res < lowestDistance) {\n lowestDistance = res;\n index = i;\n }\n if (lowestDistance < (options.threshold || 0)) break;\n }\n const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n if (!model) return null;\n if (!instance?.config?.validateModels) return null;\n const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n const ops: string[] = [];\n const missing: string[] = [];\n interface Op { name: string, category: string, op: string }\n const url = model['modelUrl'] as string;\n const executor = model['executor'];\n if (executor?.graph?.nodes) {\n for (const kernel of Object.values(executor.graph.nodes)) {\n const op = (kernel as Op).op.toLowerCase();\n if (!ops.includes(op)) ops.push(op);\n }\n } else {\n if (!executor && instance.config.debug) {\n log('model not loaded', name);\n }\n }\n for (const op of ops) {\n if (!simpleOps.includes(op) // exclude simple ops\n && !ignoreOps.includes(op) // exclude specific ops\n && !instance.env.kernels.includes(op) // check actual kernel ops\n && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n missing.push(op);\n }\n }\n if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n numLoadedModels: number,\n numDefinedModels: number,\n percentageLoaded: number,\n totalSizeFromManifest: number,\n totalSizeWeights: number,\n totalSizeLoading: number,\n modelStats: ModelInfo[],\n}\n\n/** Models class used by Human\n * - models: record of all GraphModels\n * - list: returns list of configured models with their stats\n * - loaded: returns array of loaded models\n * - reset: unloads all models\n * - validate: checks loaded models for valid kernel ops vs current backend\n * - stats: live detailed model stats that can be checked during model load phase\n */\nexport class Models {\n private instance: Human;\n models: Record = {};\n\n constructor(currentInstance: Human) {\n this.models = {};\n this.instance = currentInstance;\n }\n\n stats(): ModelStats {\n let totalSizeFromManifest = 0;\n let totalSizeWeights = 0;\n let totalSizeLoading = 0;\n for (const m of Object.values(modelStats)) {\n totalSizeFromManifest += m.sizeFromManifest;\n totalSizeWeights += m.sizeLoadedWeights;\n totalSizeLoading += m.sizeDesired;\n }\n const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n return {\n numLoadedModels: Object.values(modelStats).length,\n numDefinedModels: Object.keys(this.models).length,\n percentageLoaded,\n totalSizeFromManifest,\n totalSizeWeights,\n totalSizeLoading,\n modelStats: Object.values(modelStats),\n };\n }\n\n reset(): void {\n for (const model of Object.keys(this.models)) this.models[model] = null;\n }\n\n async load(instance?: Human): Promise {\n if (env.initial) this.reset();\n if (instance) this.instance = instance;\n const m: Record> = {};\n // face main models\n m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;\n m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;\n m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;\n m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;\n m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;\n m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;\n m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;\n // face alternatives\n m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;\n m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;\n m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;\n m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;\n m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;\n // body alterinatives\n m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;\n m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;\n m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;\n m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;\n m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;\n // hand alternatives\n m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;\n m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;\n // if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];\n if (this.instance.config.hand.enabled && !this.models.handdetect && this.instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n m.handdetect = handpose.loadDetect(this.instance.config);\n m.handskeleton = handpose.loadSkeleton(this.instance.config);\n }\n // object detection alternatives\n m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;\n m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;\n // segmentation alternatives\n m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;\n m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;\n m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;\n\n // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n for (const [model, promise] of Object.entries(m)) {\n if (promise?.['then']) promise['then']((val) => this.models[model] = val);\n }\n await Promise.all(Object.values(m)); // wait so this function does not resolve prematurely\n }\n\n list() {\n const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));\n for (const m of models) {\n const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));\n if (!stats) continue;\n m.size = modelStats[stats].sizeLoadedWeights;\n m.url = modelStats[stats].url;\n }\n return models;\n }\n\n loaded() {\n const list = this.list();\n const loaded = list.filter((model) => model.loaded).map((model) => model.name);\n return loaded;\n }\n\n validate(): { name: string, missing: string[] }[] {\n const missing: KernelOps[] = [];\n for (const defined of Object.keys(this.models)) {\n const model: GraphModel | null = this.models[defined as keyof Models];\n if (!model) continue;\n const res = validateModel(this.instance, model, defined);\n if (res) missing.push(res);\n }\n return missing;\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport type { Tensor, Tensor3D } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n keypoints: BodyKeypoint[],\n padding: [number, number][];\n} = {\n keypoints: [],\n padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n for (const pair of coords.horizontal) { // fix body parts left vs right\n const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n if (body.keypoints[left] && body.keypoints[right]) {\n if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n }\n for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n if (body.keypoints[lower] && body.keypoints[higher]) {\n if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n body.keypoints.splice(lower, 1);\n }\n }\n }\n for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n const distanceLeft = body.keypoints[left] ? [\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n ] : [0, 0];\n const distanceRight = body.keypoints[right] ? [\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n ] : [0, 0];\n if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n}\n\nexport function jitter(keypoints: BodyKeypoint[]): BodyKeypoint[] {\n for (let i = 0; i < keypoints.length; i++) {\n if (keypoints[i] && cache.keypoints[i]) {\n const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n if (diff[0] < maxJitter && diff[1] < maxJitter) {\n keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n } else {\n cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n }\n } else {\n cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n }\n }\n return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n cache.padding = [\n [0, 0], // dont touch batch\n [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(input, cache.padding);\n t.resize = tf.image.resizeBilinear(t.pad as Tensor3D, [inputSize, inputSize]);\n const final = tf.cast(t.resize, 'int32');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n body.keypoints = body.keypoints.filter((kpt) => kpt?.position); // filter invalid keypoints\n for (const kpt of body.keypoints) {\n kpt.position = [\n kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n ];\n kpt.positionRaw = [\n kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n ];\n }\n const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n body.box = rescaledBoxes.box;\n body.boxRaw = rescaledBoxes.boxRaw;\n return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n boxes: Box[], // unused\n bodies: BodyResult[];\n last: number,\n} = {\n boxes: [],\n bodies: [],\n last: 0,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n fakeOps(['size'], config);\n model = await loadModel(config.body.modelPath);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize < 64) inputSize = 256;\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', false); // default=false \n return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n const kpt = res[0][0];\n const keypoints: BodyKeypoint[] = [];\n let score = 0;\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw,\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * positionRaw[0]),\n Math.round((image.shape[1] || 0) * positionRaw[1]),\n ],\n });\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const bodies: BodyResult[] = [];\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n const annotations: Record = {};\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n fix.bodyParts(body);\n bodies.push(body);\n return bodies;\n}\n\nfunction parseMultiPose(res, config, image) {\n const bodies: BodyResult[] = [];\n for (let id = 0; id < res[0].length; id++) {\n const kpt = res[0][id];\n const boxScore = Math.round(100 * kpt[51 + 4]) / 100;\n if (boxScore > config.body.minConfidence) {\n const keypoints: BodyKeypoint[] = [];\n for (let i = 0; i < 17; i++) {\n const score = kpt[3 * i + 2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n keypoints.push({\n part: coords.kpt[i] as BodyLandmark,\n score: Math.round(100 * score) / 100,\n positionRaw,\n position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n });\n }\n }\n // const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n // movenet-multipose has built-in box details\n const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n const boxNorm: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n // const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n const body: BodyResult = { id, score: boxScore, box: boxNorm, boxRaw, keypoints: [...keypoints], annotations };\n fix.bodyParts(body);\n bodies.push(body);\n }\n }\n bodies.sort((a, b) => b.score - a.score);\n if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return []; // something is wrong with the model\n if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n skipped++; // increment skip frames\n const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.bodies; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const t: Record = {};\n skipped = 0;\n // run detection on squared input and no cached boxes\n t.input = fix.padInput(input, inputSize);\n t.res = model?.execute(t.input) as Tensor;\n cache.last = now();\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17)\n ? parseSinglePose(res, config, input)\n : parseMultiPose(res, config, input);\n for (const body of cache.bodies) {\n fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n fix.jitter(body.keypoints);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n resolve(cache.bodies);\n });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) {\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n let id = 0;\n let results: ObjectResult[] = [];\n const size = inputSize;\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)) as Tensor2D);\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n const featuresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)) as Tensor2D);\n const boxesMaxT = tf.reshape(featuresT, [-1, 4, (featuresT.shape?.[1] || 0) / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdxT = tf.argMax(boxesMaxT, 2); // what we need is indexes of features with highest scores, not values itself\n const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < (scoresT.shape?.[1] || 0); j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > (config.object.minConfidence || 0) && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / (size))); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label as ObjectType,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: box.map((a) => Math.trunc(a)) as Box,\n boxRaw,\n };\n results.push(result);\n }\n }\n }\n tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n }\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: number[] = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected || 0, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = Array.from(await nms.data());\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n const resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n const normT = tf.div(resizeT, constants.tf255);\n const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n let objectT;\n if (config.object.enabled) objectT = model.execute(transposeT);\n lastTime = now();\n\n const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n last = obj;\n tf.dispose([resizeT, normT, transposeT, ...objectT]);\n resolve(obj);\n });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): BodyResult[] {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i): BodyResult => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score: score as number,\n part: part as BodyLandmark,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n })),\n annotations: {} as Record,\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: unknown[]; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n // @ts-ignore getter is of unknown type\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n\nexport function getOffsetPoint(y, x, keypoint: number, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + count),\n };\n}\n\nexport function getImageCoords(part, outputStride: number, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a: { x: number, y: number }, b: { x: number, y: number }) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId: number, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: utils.partNames[root.part.id] as BodyLandmark,\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score: number, heatmapY: number, heatmapX: number, scores) {\n const [height, width]: [number, number] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: { keypoints, box: Box, score: number }[] = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n /** posenet is mostly obsolete\n * caching is not implemented\n */\n if (!model?.['executor']) return [];\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Tensor[] = model.execute(normalized, poseNetOutputs) as Tensor[];\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n t.r1i = tf.tensor(0.0);\n t.r2i = tf.tensor(0.0);\n t.r3i = tf.tensor(0.0);\n t.r4i = tf.tensor(0.0);\n ratio = config.segmentation.ratio || 0.5;\n t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n init(config);\n return model;\n}\n\nconst normalize = (r: Tensor): Tensor => tf.tidy(() => {\n const squeeze = tf.squeeze(r, ([0]));\n const mul = tf.mul(squeeze, constants.tf255);\n const cast = tf.cast(mul, 'int32');\n return cast;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n const rgb = fgr\n ? normalize(fgr) // normalize and use value\n : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const a = pha\n ? normalize(pha) // normalize and use value\n : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const rgba = tf.concat([rgb, a], -1);\n tf.dispose([rgb, a]);\n return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n return tf.tidy(() => {\n const r: Record = {};\n r.unstack = tf.unstack(state, -1);\n r.concat = tf.concat(r.unstack, 1);\n r.split = tf.split(r.concat, 4, 1);\n r.stack = tf.concat(r.split, 2);\n r.squeeze = tf.squeeze(r.stack, [0]);\n r.expand = tf.expandDims(r.squeeze, -1);\n r.add = tf.add(r.expand, 1);\n r.mul = tf.mul(r.add, 127.5);\n r.cast = tf.cast(r.mul, 'int32');\n r.tile = tf.tile(r.cast, [1, 1, 3]);\n r.alpha = tf.fill([(r.tile as Tensor).shape[0] || 0, (r.tile as Tensor).shape[1] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-unnecessary-type-assertion\n return tf.concat([r.tile, r.alpha], -1);\n });\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor']) return null;\n // const expand = tf.expandDims(input, 0);\n t.src = tf.div(input, 255);\n if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n rgba = getRGBA(fgr, pha);\n break;\n case 'alpha':\n rgba = getRGBA(null, pha);\n break;\n case 'foreground':\n rgba = getRGBA(fgr, null);\n break;\n case 'state':\n rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n break;\n default:\n rgba = tf.tensor(0);\n }\n tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n t.alpha = tf.image.resizeBilinear(t.squeeze as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]); // model selfie has a single channel that we can use directly\n t.mul = tf.mul(t.alpha, constants.tf255);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.mul], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n let id = 0;\n const persons: PersonResult[] = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.left?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.right?.id) person.gestures.push(gesture);\n }\n\n // create new overarching box from all boxes belonging to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face.box);\n extractXY(person.body?.box);\n extractXY(person.hands.left?.box);\n extractXY(person.hands.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape?.[1] && shape?.[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport { empty, Result } from './result';\nimport type { Config } from './config';\nimport type { Human } from './human';\nimport type { Tensor, DataType } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise {\n const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob: Blob | null;\n let res: Result | undefined;\n switch (instance.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'body':\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await instance.detect(bitmap, instance.config);\n bitmap.close();\n }\n return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise {\n return new Promise((resolve) => {\n let src: string;\n // let size = 0;\n switch (instance.config.warmup) {\n case 'face':\n // size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n // size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = '';\n }\n // src = encodeURI('../assets/human-sample-upper.jpg');\n let img: HTMLImageElement;\n if (typeof Image !== 'undefined') img = new Image();\n // @ts-ignore env.image is an external monkey-patch\n else if (env.Image) img = new env.Image();\n else {\n resolve(undefined);\n return;\n }\n img.onload = async () => {\n const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n if (!canvas) {\n log('Warmup: Canvas not found');\n resolve(undefined);\n } else {\n const ctx = canvas.getContext('2d') as CanvasRenderingContext2D;\n if (ctx) ctx.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const tensor = await instance.image(canvas, true);\n const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\n resolve(res);\n }\n };\n if (src) img.src = src;\n else resolve(undefined);\n });\n}\n\nasync function warmupNode(instance: Human): Promise {\n const atob = (str: string) => Buffer.from(str, 'base64');\n let img;\n if (instance.config.warmup === 'face') img = atob(sample.face);\n else img = atob(sample.body);\n let res: Result;\n if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n // @ts-ignore\n const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n const expanded: Tensor = tf.expandDims(data, 0);\n instance.tf.dispose(data);\n // log('Input:', expanded);\n res = await instance.detect(expanded, instance.config);\n instance.tf.dispose(expanded);\n } else {\n if (instance.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await instance.detect(input, instance.config);\n */\n }\n // @ts-ignore\n return res;\n}\n\nasync function runInference(instance: Human) {\n let res: Result | undefined;\n if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n else if ((typeof Image !== 'undefined') || (env.Canvas !== undefined)) res = await warmupCanvas(instance);\n else res = await warmupNode(instance);\n return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n // @ts-ignore private property\n if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n const backendType = tf.getBackend();\n const webGLBackend = tf.backend();\n if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.['checkCompileCompletion']) {\n // log('compile pass: skip');\n return;\n }\n tf.env().set('ENGINE_COMPILE_ONLY', true);\n const numTensorsStart = tf.engine().state.numTensors;\n const compiledModels: string[] = [];\n for (const [modelName, model] of Object.entries(instance.models.models)) {\n if (!model) continue;\n const shape = (model?.modelSignature && model?.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n const dtype: DataType = (model?.modelSignature && model?.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n for (let dim = 0; dim < shape.length; dim++) {\n if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n }\n const tensor = tf.zeros(shape, dtype);\n try {\n const res = model.execute(tensor);\n compiledModels.push(modelName);\n if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n else tf.dispose(res);\n } catch {\n if (instance.config.debug) log('compile fail model:', modelName);\n }\n tf.dispose(tensor);\n }\n const kernels = await webGLBackend['checkCompileCompletionAsync']();\n webGLBackend['getUniformLocations']();\n if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n tf.env().set('ENGINE_COMPILE_ONLY', false);\n const numTensorsEnd = tf.engine().state.numTensors;\n if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial): Promise {\n await backend.check(instance, false);\n const t0 = now();\n instance.state = 'warmup';\n if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n return empty();\n }\n return new Promise(async (resolve) => {\n await instance.models.load();\n await runCompile(instance);\n const res = await runInference(instance);\n const t1 = now();\n if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n instance.emit('warmup');\n resolve(res);\n });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\n// module imports\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport { Input, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, empty } from './exports';\nimport type { Tensor, Tensor4D } from './tfjs/types';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n\n /** Current configuration\n * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\n config: Config;\n\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n\n /** currenty processed image tensor and canvas */\n process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n /** Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n * [TFJS API](https://js.tensorflow.org/api/latest/)\n */\n tf;\n\n /** Object containing environment information used for diagnostics */\n env: Env = env;\n\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - canvas: draws input to canvas\n * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n */\n // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n draw: typeof draw = draw;\n\n /** Face Matching\n * - similarity: compare two face descriptors and return similarity index\n * - distance: compare two face descriptors and return raw calculated differences\n * - find: compare face descriptor to array of face descriptors and return best match\n */\n match: typeof match = match;\n\n /** Currently loaded models\n * @internal\n * {@link models#Models}\n */\n models: models.Models;\n\n /** Container for events dispatched by Human\n * Possible events:\n * - `create`: triggered when Human object is instantiated\n * - `load`: triggered when models are loaded (explicitly or on-demand)\n * - `image`: triggered when input image is processed\n * - `result`: triggered when detection is complete\n * - `warmup`: triggered when warmup is complete\n * - `error`: triggered on some errors\n */\n events: EventTarget | undefined;\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: number[];\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: [number, number][];\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n // definition end\n\n /** Constructor for **Human** library that is futher used for all operations\n * @param userConfig - user configuration object {@link Config}\n */\n constructor(userConfig?: Partial) {\n /*\n defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n ? 'https://vladmandic.github.io/tfjs/dist/'\n : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n */\n const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n this.version = app.version; // expose version property on instance of class\n Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n this.config = JSON.parse(JSON.stringify(defaults));\n Object.seal(this.config);\n this.config.cacheModels = typeof indexedDB !== 'undefined';\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n setModelLoadOptions(this.config);\n this.tf = tf;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.performance = {};\n this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n // object that contains all initialized models\n this.models = new models.Models(this);\n // reexport draw methods\n draw.init();\n this.result = empty();\n // export access to image processing\n this.process = { tensor: null, canvas: null };\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // init model validation\n models.validateModel(this, null, '');\n // include platform info\n this.emit('create');\n if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n const envTemp = JSON.parse(JSON.stringify(this.env));\n delete envTemp.kernels;\n delete envTemp.initial;\n delete envTemp.perfadd;\n if (this.config.debug) log('environment:', envTemp);\n }\n\n /** internal function to measure tensor leaks */\n analyze = (...msg: string[]) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n };\n\n /** internal function for quick sanity check on inputs @hidden */\n #sanity = (input: Input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n };\n\n /** Reset configuration to default values */\n reset(): void {\n const currentBackend = this.config.backend; // save backend;\n this.config = JSON.parse(JSON.stringify(defaults));\n this.config.backend = currentBackend;\n image.reset();\n env.initial = true;\n }\n\n /** Validate current configuration schema */\n validate(userConfig?: Partial) {\n const msgs = validate(defaults, userConfig || this.config);\n if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n return msgs;\n }\n\n /** Utility wrapper for performance.now() */\n now(): number { // eslint-disable-line class-methods-use-this\n return now();\n }\n\n /** Process input as return canvas and tensor\n *\n * @param input - any input {@link Input}\n * @param getTensor - should image processing also return tensor or just canvas\n * Returns object with `tensor` and `canvas`\n */\n image(input: Input, getTensor: boolean = false) {\n return image.process(input, this.config, getTensor);\n }\n\n /** Segmentation method takes any input and returns RGBA tensor\n * Note: Segmentation is not triggered as part of detect process\n *\n * @param input - {@link Input}\n * Returns tensor which contains image data in RGBA format\n */\n async segmentation(input: Input, userConfig?: Partial): Promise {\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (!this.config.segmentation.enabled) return null;\n const processed = await image.process(input, this.config);\n if (!processed.tensor) return null;\n let tensor: Tensor | null = null;\n if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n tf.dispose(processed.tensor);\n return tensor;\n }\n\n /** Compare two input tensors for pixel similarity\n * - use `human.image` to process any valid input and get a tensor that can be used for compare\n * - when passing manually generated tensors:\n * - both input tensors must be in format [1, height, width, 3]\n * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n * - return value is pixel similarity score normalized by input resolution and rgb channels\n */\n compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise {\n return image.compare(this.config, firstImageTensor, secondImageTensor);\n }\n\n /** Explicit backend initialization\n * - Normally done implicitly during initial load phase\n * - Call to explictly register and initialize TFJS backend without any other operations\n * - Use when changing backend during runtime\n */\n async init(): Promise {\n await backend.check(this, true);\n await this.tf.ready();\n image.reset();\n }\n\n /** WebCam helper methods\n *\n */\n public webcam = new WebCam();\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n *\n * @param userConfig - {@link Config}\n */\n async load(userConfig?: Partial): Promise {\n this.state = 'load';\n const timeStamp = now();\n const count = Object.values(this.models.models).filter((model) => model).length;\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (this.env.initial) { // print version info on first run and check for correct backend setup\n if (!await backend.check(this, false)) log('error: backend check failed');\n await tf.ready();\n if (this.env.browser) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n\n await this.models.load(this); // actually loads models\n if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n this.env.initial = false;\n\n const loaded = Object.values(this.models.models).filter((model) => model).length;\n if (loaded !== count) { // number of loaded models changed\n this.models.validate(); // validate kernel ops used by model against current backend\n this.emit('load');\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n }\n\n /** emit event */\n emit = (event: string) => {\n if (this.events?.dispatchEvent) this.events.dispatchEvent(new Event(event));\n };\n\n /** Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result - {@link Result} optional use specific result set to run interpolation on\n * @returns result - {@link Result}\n */\n next(result: Result = this.result): Result {\n return interpolate.calc(result, this.config);\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async warmup(userConfig?: Partial) {\n const t0 = now();\n const res = await warmups.warmup(this, userConfig);\n const t1 = now();\n this.performance.warmup = Math.trunc(t1 - t0);\n return res;\n }\n\n /** Run detect with tensorflow profiling\n * - result object will contain total exeuction time information for top-20 kernels\n * - actual detection object can be accessed via `human.result`\n */\n async profile(input: Input, userConfig?: Partial): Promise<{ kernel: string, time: number, perc: number }[]> {\n // @ts-ignore profile wraps method return values\n const profile = await this.tf.profile(() => this.detect(input, userConfig));\n const kernels: Record = {};\n let total = 0;\n for (const kernel of profile.kernels) { // sum kernel time values per kernel\n const ms = Number(kernel.kernelTimeMs) || 0;\n if (kernels[kernel.name]) kernels[kernel.name] += ms;\n else kernels[kernel.name] = ms;\n total += ms;\n }\n const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n for (const kernel of kernelArr) {\n kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n kernel.time = Math.round(1000 * kernel.time) / 1000;\n }\n kernelArr.sort((a, b) => b.time - a.time); // sort\n kernelArr.length = 20; // crop\n return kernelArr;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input - {@link Input}\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async detect(input: Input, userConfig?: Partial): Promise {\n // detection happens inside a promise\n this.state = 'detect';\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n // sanity checks\n this.state = 'check';\n const error = this.#sanity(input);\n if (error) {\n log(error, input);\n this.emit('error');\n resolve(empty(error));\n }\n\n const timeStart = now();\n\n // load models if enabled\n await this.load();\n\n timeStamp = now();\n this.state = 'image';\n const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor4D };\n this.process = img;\n this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n if (!img.tensor) {\n if (this.config.debug) log('could not convert input to tensor');\n this.emit('error');\n resolve(empty('could not convert input to tensor'));\n return;\n }\n this.emit('image');\n\n timeStamp = now();\n this.config.skipAllowed = await image.skip(this.config, img.tensor);\n this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change\n if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n (this.performance.totalFrames)++;\n if (this.config.skipAllowed) this.performance.cachedFrames++;\n this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes: FaceResult[] | Promise | never[] = [];\n let bodyRes: BodyResult[] | Promise | never[] = [];\n let handRes: HandResult[] | Promise | never[] = [];\n let objectRes: ObjectResult[] | Promise | never[] = [];\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n this.state = 'detect:face';\n if (this.config.async) {\n faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n\n if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n this.state = 'detect:body';\n const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n if (this.config.async) {\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n timeStamp = now();\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n this.state = 'detect:hand';\n const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n if (this.config.async) {\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n timeStamp = now();\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // run object detection\n this.analyze('Start Object:');\n this.state = 'detect:object';\n if (this.config.async) {\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n timeStamp = now();\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Object:');\n\n // if async wait for results\n this.state = 'detect:await';\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n this.state = 'detect:gesture';\n let gestureRes: GestureResult[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n const shape = this.process.tensor?.shape || [0, 0, 0, 0];\n this.result = {\n face: faceRes as FaceResult[],\n body: bodyRes as BodyResult[],\n hand: handRes as HandResult[],\n gesture: gestureRes,\n object: objectRes as ObjectResult[],\n performance: this.performance,\n canvas: this.process.canvas,\n timestamp: Date.now(),\n error: null,\n width: shape[2],\n height: shape[1],\n get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(img.tensor);\n\n // log('Result:', result);\n this.emit('detect');\n this.state = 'idle';\n resolve(this.result);\n });\n }\n\n /** Helper function\n * @param ms - sleep time in miliseconds\n */\n async sleep(ms: number): Promise { // eslint-disable-line class-methods-use-this\n return new Promise((resolve) => { setTimeout(resolve, ms); });\n }\n\n /** internal structure that keeps track of processed videos @hidden */\n #loops: Record = {};\n /** Continously detect video frames\n * @param element - HTMLVideoElement input\n * @param run - boolean run continously or stop if already running, default true\n * @param delay - number delay detection between frames for number of miliseconds, default 0\n */\n async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n if (run) {\n if (!this.#loops[element.id]) {\n if (this.config.debug) log('video start', element.id);\n this.#loops[element.id] = true;\n }\n if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n if (delay > 0) await this.sleep(delay);\n if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n } else {\n if (this.config.debug) log('video stop', element.id);\n this.#loops[element.id] = false;\n }\n }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"], + "mappings": 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className(){return"Adadelta"}constructor(e,t,a=null){super(),this.learningRate=e,this.rho=t,this.epsilon=a,this.accumulatedGrads=[],this.accumulatedUpdates=[],a==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t],r=!1;this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accum_grad`,variable:De(()=>en(n).variable(r))}),this.accumulatedUpdates[a]==null&&(this.accumulatedUpdates[a]={originalName:`${t}/accum_var`,variable:De(()=>en(n).variable(r))});let s=Array.isArray(e)?e[a].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[a].variable,o=this.accumulatedUpdates[a].variable;De(()=>{let l=we(te(i,this.rho),te(Nn(s),1-this.rho)),u=te(ve(Qn(we(o,this.epsilon)),Qn(we(i,this.epsilon))),s),p=we(te(o,this.rho),te(Nn(u),1-this.rho));i.assign(l),o.assign(p);let c=we(te(u,-this.learningRate),n);n.assign(c)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(J(this.accumulatedGrads.map(e=>e.variable)),J(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,a=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},Kg=class extends us{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t];this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accumulator`,variable:De(()=>ir(n.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[a].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[a].variable;De(()=>{let i=we(s,Nn(r));s.assign(i);let o=we(te(ve(r,Qn(we(i,L.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&J(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(a=>({originalName:a.name,variable:a.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},Yg=class extends us{static get className(){return"Adam"}constructor(e,t,a,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=Ge(t).variable(),this.accBeta2=Ge(a).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);De(()=>{let a=xe(1,this.accBeta1),n=xe(1,this.accBeta2);t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:De(()=>en(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:De(()=>en(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,c=we(te(u,this.beta1),te(l,1-this.beta1)),d=we(te(p,this.beta2),te(Nn(l),1-this.beta2)),h=ve(c,a),m=ve(d,n);u.assign(c),p.assign(d);let f=we(te(ve(h,we(Qn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(te(this.accBeta1,this.beta1)),this.accBeta2.assign(te(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&J(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&J(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),De(()=>{this.accBeta1.assign(Hl(this.beta1,this.iterations_+1)),this.accBeta2.assign(Hl(this.beta2,this.iterations_+1))});let t=e.length/2,a=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}},Zg=class extends us{static get className(){return"Adamax"}constructor(e,t,a,n=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=Ge(0).variable(),this.accBeta1=Ge(t).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);De(()=>{let a=xe(1,this.accBeta1),n=ve(-this.learningRate,we(te(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:en(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:en(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedWeightedInfNorm[s].variable,c=we(te(u,this.beta1),te(l,1-this.beta1)),d=te(p,this.beta2),h=Ja(l),m=Ng(d,h);u.assign(c),p.assign(m);let f=we(te(ve(n,a),ve(c,we(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(we(this.iteration,1)),this.accBeta1.assign(te(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&J(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&J(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}},Jh=class extends us{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=Array.isArray(e)?e[a].tensor:e[t];if(n==null)return;let r=L.registeredVariables[t];De(()=>{let s=we(te(this.c,n),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=zn(Ge(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}},Jg=class extends Jh{static get className(){return"Momentum"}constructor(e,t,a=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=a,this.accumulations=[],this.m=Ge(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t];this.accumulations[a]==null&&(this.accumulations[a]={originalName:`${t}/momentum`,variable:De(()=>en(n).variable(!1))});let r=this.accumulations[a].variable,s=Array.isArray(e)?e[a].tensor:e[t];s!=null&&De(()=>{let i,o=we(te(this.m,r),s);this.useNesterov?i=we(te(this.c,we(s,te(o,this.m))),n):i=we(te(this.c,o),n),r.assign(o),n.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&J(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(a=>({originalName:a.name,variable:a.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}},Qg=class extends us{static get className(){return"RMSProp"}constructor(e,t=.9,a=0,n=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=a,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,n==null&&(this.epsilon=L.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t],r=!1;this.accumulatedMeanSquares[a]==null&&(this.accumulatedMeanSquares[a]={originalName:`${t}/rms`,variable:De(()=>en(n).variable(r))}),this.accumulatedMoments[a]==null&&(this.accumulatedMoments[a]={originalName:`${t}/momentum`,variable:De(()=>en(n).variable(r))}),this.accumulatedMeanGrads[a]==null&&this.centered&&(this.accumulatedMeanGrads[a]={originalName:`${t}/mg`,variable:De(()=>en(n).variable(r))});let s=Array.isArray(e)?e[a].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[a].variable,o=this.accumulatedMoments[a].variable;De(()=>{let l=we(te(i,this.decay),te(Nn(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[a].variable,p=we(te(u,this.decay),te(s,1-this.decay)),c=ve(te(s,this.learningRate),Qn(xe(l,we(Nn(p),this.epsilon)))),d=we(te(o,this.momentum),c);i.assign(l),u.assign(p),o.assign(d);let h=xe(n,d);n.assign(h)}else{let u=we(te(i,this.decay),te(Nn(s),1-this.decay)),p=we(te(o,this.momentum),ve(te(s,this.learningRate),Qn(we(u,this.epsilon))));i.assign(u),o.assign(p);let c=xe(n,p);n.assign(c)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&J(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&J(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&J(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,a=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}},M$=[Xg,Kg,Yg,Zg,Jg,Qg,Jh];function _$(){for(let e of M$)c7(e)}var Xn={};Ze(Xn,{CompositeArrayBuffer:()=>nr,browserFiles:()=>L$,browserHTTPRequest:()=>G$,concatenateArrayBuffers:()=>tN,copyModel:()=>kN,decodeWeights:()=>PA,encodeWeights:()=>ZT,fromMemory:()=>j$,fromMemorySync:()=>y7,getLoadHandlers:()=>uN,getModelArtifactsForJSON:()=>rg,getModelArtifactsForJSONSync:()=>FA,getModelArtifactsInfoForJSON:()=>Fp,getSaveHandlers:()=>lN,getWeightSpecs:()=>DA,http:()=>t3,isHTTPScheme:()=>g1,listModels:()=>vN,loadWeights:()=>W$,moveModel:()=>IN,registerLoadRouter:()=>oN,registerSaveRouter:()=>iN,removeModel:()=>wN,weightsLoaderFactory:()=>m7,withSaveHandler:()=>q$,withSaveHandlerSync:()=>X$});var P$="model",$$=".json",F$=".weights.bin";function Kx(e){return new Promise(t=>setTimeout(t)).then(e)}var Kl=class{constructor(e){if(!W().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a 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W$=z({staticRegexReplace_:L$}),c7={fft:Uh,ifft:Kd,rfft:Gh,irfft:Bg},h7={hammingWindow:c_,hannWindow:o7,frame:l7,stft:g_},fe={flipLeftRight:b_,grayscaleToRGB:w_,resizeNearestNeighbor:X_,resizeBilinear:j_,rgbToGrayscale:I_,rotateWithOffset:C_,cropAndResize:x_,nonMaxSuppression:N_,nonMaxSuppressionAsync:D_,nonMaxSuppressionWithScore:z_,nonMaxSuppressionWithScoreAsync:W_,nonMaxSuppressionPadded:V_,nonMaxSuppressionPaddedAsync:G_,threshold:Z_,transform:Q_},m7={bandPart:t$,gramSchmidt:n$,qr:s$},f7={absoluteDifference:l$,computeWeightedLoss:Rr,cosineDistance:d$,hingeLoss:c$,huberLoss:m$,logLoss:g$,meanSquaredError:x$,sigmoidCrossEntropy:v$,softmaxCrossEntropy:I$},g7={sparseFillEmptyRows:C$,sparseReshape:N$,sparseSegmentMean:E$,sparseSegmentSum:P$},y7={stringNGrams:$$,stringSplit:D$,stringToHashBucketFast:z$,staticRegexReplace:W$},x7={};Ze(x7,{Serializable:()=>A7,SerializationMap:()=>b7,getRegisteredName:()=>V$,registerClass:()=>v7});var B$=new Map,f1=new Map,A7=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},b7=class Ol{constructor(){this.classNameMap={}}static getMap(){return Ol.instance==null&&(Ol.instance=new Ol),Ol.instance}static register(t){Ol.getMap().classNameMap[t.className]=[t,t.fromConfig]}};function v7(e,t,a){F(e.className!=null,()=>"Class being registered does not have the static className property defined."),F(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),F(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),typeof t=="undefined"&&(t="Custom"),typeof a=="undefined"&&(a=e.className);let n=a,r=t+">"+n;return b7.register(e),B$.set(r,e),f1.set(e,r),e}function V$(e){return f1.has(e)?f1.get(e):e.className}var ps=class extends A7{minimize(e,t=!1,a){let{value:n,grads:r}=this.computeGradients(e,a);if(a!=null){let s=a.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else 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className(){return"Adadelta"}constructor(e,t,a=null){super(),this.learningRate=e,this.rho=t,this.epsilon=a,this.accumulatedGrads=[],this.accumulatedUpdates=[],a==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t],r=!1;this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accum_grad`,variable:De(()=>Ja(n).variable(r))}),this.accumulatedUpdates[a]==null&&(this.accumulatedUpdates[a]={originalName:`${t}/accum_var`,variable:De(()=>Ja(n).variable(r))});let s=Array.isArray(e)?e[a].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[a].variable,o=this.accumulatedUpdates[a].variable;De(()=>{let l=we(te(i,this.rho),te(Tn(s),1-this.rho)),u=te(ve(Qn(we(o,this.epsilon)),Qn(we(i,this.epsilon))),s),p=we(te(o,this.rho),te(Tn(u),1-this.rho));i.assign(l),o.assign(p);let c=we(te(u,-this.learningRate),n);n.assign(c)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(J(this.accumulatedGrads.map(e=>e.variable)),J(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,a=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},Zg=class extends ps{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t];this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accumulator`,variable:De(()=>sr(n.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[a].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[a].variable;De(()=>{let i=we(s,Tn(r));s.assign(i);let o=we(te(ve(r,Qn(we(i,L.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&J(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(a=>({originalName:a.name,variable:a.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},Jg=class extends ps{static get className(){return"Adam"}constructor(e,t,a,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=Ge(t).variable(),this.accBeta2=Ge(a).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);De(()=>{let a=xe(1,this.accBeta1),n=xe(1,this.accBeta2);t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:De(()=>Ja(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:De(()=>Ja(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,c=we(te(u,this.beta1),te(l,1-this.beta1)),d=we(te(p,this.beta2),te(Tn(l),1-this.beta2)),h=ve(c,a),m=ve(d,n);u.assign(c),p.assign(d);let f=we(te(ve(h,we(Qn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(te(this.accBeta1,this.beta1)),this.accBeta2.assign(te(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&J(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&J(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),De(()=>{this.accBeta1.assign(ql(this.beta1,this.iterations_+1)),this.accBeta2.assign(ql(this.beta2,this.iterations_+1))});let t=e.length/2,a=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}},Qg=class extends ps{static get className(){return"Adamax"}constructor(e,t,a,n=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=Ge(0).variable(),this.accBeta1=Ge(t).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);De(()=>{let a=xe(1,this.accBeta1),n=ve(-this.learningRate,we(te(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Ja(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Ja(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedWeightedInfNorm[s].variable,c=we(te(u,this.beta1),te(l,1-this.beta1)),d=te(p,this.beta2),h=Ya(l),m=Eg(d,h);u.assign(c),p.assign(m);let f=we(te(ve(n,a),ve(c,we(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(we(this.iteration,1)),this.accBeta1.assign(te(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&J(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&J(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}},Jh=class extends ps{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=Array.isArray(e)?e[a].tensor:e[t];if(n==null)return;let r=L.registeredVariables[t];De(()=>{let s=we(te(this.c,n),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=zn(Ge(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}},e3=class extends Jh{static get className(){return"Momentum"}constructor(e,t,a=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=a,this.accumulations=[],this.m=Ge(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t];this.accumulations[a]==null&&(this.accumulations[a]={originalName:`${t}/momentum`,variable:De(()=>Ja(n).variable(!1))});let r=this.accumulations[a].variable,s=Array.isArray(e)?e[a].tensor:e[t];s!=null&&De(()=>{let i,o=we(te(this.m,r),s);this.useNesterov?i=we(te(this.c,we(s,te(o,this.m))),n):i=we(te(this.c,o),n),r.assign(o),n.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&J(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await 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n=k("tensor",e,t,a),r=k("elementShape",e,t,a),s=k("elementDType",e,t,a),i=HD(n,r,s);return a.addTensorList(i),[i.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let n=k("tensorListId",e,t,a),r=a.getTensorList(n.id),s=k("dtype",e,t,a),i=k("elementShape",e,t,a);return[r.concat(s,i)]}case"TensorListPushBack":{let n=k("tensorListId",e,t,a),r=k("tensor",e,t,a),s=a.getTensorList(n.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let n=k("tensorListId",e,t,a),r=k("elementShape",e,t,a),s=k("elementDType",e,t,a);return[a.getTensorList(n.id).popBack(r,s)]}case"TensorListSplit":{let n=k("tensor",e,t,a),r=k("elementShape",e,t,a),s=k("lengths",e,t,a),i=XD(n,s,r);return a.addTensorList(i),[i.idTensor]}case"TensorListLength":{let n=k("tensorListId",e,t,a),r=a.getTensorList(n.id);return[Ge(r.size(),"int32")]}case"TensorListResize":{let n=k("tensorListId",e,t,a),r=k("size",e,t,a),s=a.getTensorList(n.id).resize(r);return a.addTensorList(s),[s.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function i5(e,t,a){let[n,r]=k("fusedOps",e,t,a),s=n==="biasadd",i=!s,o=r==="prelu",l=n==="fusedbatchnorm",u=k("numArgs",e,t,a);if(s){if(o&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let p=k("strides",e,t,a),c=th(e,t,a),d=k("dataFormat",e,t,a).toUpperCase(),h=k("dilations",e,t,a),[m,f]=k("args",e,t,a);i&&(f=m,m=void 0);let g=k("leakyreluAlpha",e,t,a);return{stride:p,pad:c,dataFormat:d,dilations:h,biasArg:m,preluArg:f,activationFunc:r,leakyreluAlpha:g}}var YD=(e,t,a,n=ea)=>{switch(e.op){case"Conv1D":{let 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r=k("logits",e,t,a),s=k("numSamples",e,t,a),i=k("seed",e,t,a);return[n.multinomial(r,s,i)]}case"OneHot":{let r=k("indices",e,t,a),s=k("depth",e,t,a),i=k("onValue",e,t,a),o=k("offValue",e,t,a),l=k("dtype",e,t,a);return[n.oneHot(r,s,i,o,l)]}case"Ones":return[n.ones(k("shape",e,t,a),k("dtype",e,t,a))];case"OnesLike":return[n.onesLike(k("x",e,t,a))];case"RandomStandardNormal":return[n.randomStandardNormal(k("shape",e,t,a),k("dtype",e,t,a),k("seed",e,t,a))];case"RandomUniform":return[n.randomUniform(k("shape",e,t,a),k("minval",e,t,a),k("maxval",e,t,a),k("dtype",e,t,a))];case"RandomUniformInt":return[n.randomUniformInt(k("shape",e,t,a),k("minval",e,t,a),k("maxval",e,t,a),k("seed",e,t,a))];case"Range":{let r=k("start",e,t,a),s=k("stop",e,t,a),i=k("step",e,t,a);return[n.range(r,s,i,k("dtype",e,t,a))]}case"TruncatedNormal":{let 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performance."),console.log(l);for(let p=0;pe.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Ge(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let a=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),De(()=>{let n=Ra(t),r=a.length,s=n.length;v.assert(r===s,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`);for(let i=0;i{let n=[];for(let r=0;r{switch(e.op){case"HashTable":case"HashTableV2":{let r=n.getHashTableHandleByName(e.name);if(r!=null)return[r];{let s=k("keyDType",e,t,a),i=k("valueDType",e,t,a),o=new tO(s,i);return n.addHashTable(e.name,o),[o.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let r=k("tableHandle",e,t,a,n),s=k("keys",e,t,a),i=k("values",e,t,a);return[await n.getHashTableById(r.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let r=k("tableHandle",e,t,a,n),s=k("keys",e,t,a),i=k("defaultValue",e,t,a);return[await n.getHashTableById(r.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let r=k("tableHandle",e,t,a,n);return[n.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},nO=(e,t,a,n=ea)=>{switch(e.op){case"ResizeBilinear":{let r=k("images",e,t,a),s=k("size",e,t,a),i=k("alignCorners",e,t,a),o=k("halfPixelCenters",e,t,a);return[n.image.resizeBilinear(r,[s[0],s[1]],i,o)]}case"ResizeNearestNeighbor":{let r=k("images",e,t,a),s=k("size",e,t,a),i=k("alignCorners",e,t,a),o=k("halfPixelCenters",e,t,a);return[n.image.resizeNearestNeighbor(r,[s[0],s[1]],i,o)]}case"CropAndResize":{let r=k("image",e,t,a),s=k("boxes",e,t,a),i=k("boxInd",e,t,a),o=k("cropSize",e,t,a),l=k("method",e,t,a),u=k("extrapolationValue",e,t,a);return[n.image.cropAndResize(r,s,i,o,l,u)]}case"ImageProjectiveTransformV3":{let r=k("images",e,t,a),s=k("transforms",e,t,a),i=k("outputShape",e,t,a),o=k("fillValue",e,t,a),l=k("interpolation",e,t,a),u=k("fillMode",e,t,a);return[n.image.transform(r,s,l.toLowerCase(),u.toLowerCase(),o,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},rO=(e,t,a,n=ea)=>{switch(e.op){case"Equal":return[n.equal(k("a",e,t,a),k("b",e,t,a))];case"NotEqual":return[n.notEqual(k("a",e,t,a),k("b",e,t,a))];case"Greater":return[n.greater(k("a",e,t,a),k("b",e,t,a))];case"GreaterEqual":return[n.greaterEqual(k("a",e,t,a),k("b",e,t,a))];case"Less":return[n.less(k("a",e,t,a),k("b",e,t,a))];case"LessEqual":return[n.lessEqual(k("a",e,t,a),k("b",e,t,a))];case"LogicalAnd":return[n.logicalAnd(k("a",e,t,a),k("b",e,t,a))];case"LogicalNot":return[n.logicalNot(k("a",e,t,a))];case"LogicalOr":return[n.logicalOr(k("a",e,t,a),k("b",e,t,a))];case"Select":case"SelectV2":return[n.where(k("condition",e,t,a),k("a",e,t,a),k("b",e,t,a))];case"BitwiseAnd":return[n.bitwiseAnd(k("a",e,t,a),k("b",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},sO=(e,t,a,n=ea)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(k("a",e,t,a),k("b",e,t,a),k("transposeA",e,t,a),k("transposeB",e,t,a))];case"Einsum":return[n.einsum(k("equation",e,t,a),...k("tensors",e,t,a))];case"Transpose":return[n.transpose(k("x",e,t,a),k("perm",e,t,a))];case"_FusedMatMul":let[r,s]=k("fusedOps",e,t,a),i=r==="biasadd",o=s==="prelu",l=k("numArgs",e,t,a),u=k("leakyreluAlpha",e,t,a);if(i){if(o&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,c]=k("args",e,t,a);return[n.fused.matMul({a:k("a",e,t,a),b:k("b",e,t,a),transposeA:k("transposeA",e,t,a),transposeB:k("transposeB",e,t,a),bias:p,activation:s,preluActivationWeights:c,leakyreluAlpha:u})];case"MatrixBandPart":return[n.linalg.bandPart(k("a",e,t,a),k("numLower",e,t,a),k("numUpper",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},iO=(e,t,a,n=ea)=>{switch(e.op){case"EuclideanNorm":return[n.euclideanNorm(k("x",e,t,a),k("axis",e,t,a),k("keepDims",e,t,a))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(k("x",e,t,a),k("mean",e,t,a),k("variance",e,t,a),k("offset",e,t,a),k("scale",e,t,a),k("epsilon",e,t,a))];case"FusedBatchNormV3":return[n.batchNorm(k("x",e,t,a),k("mean",e,t,a),k("variance",e,t,a),k("offset",e,t,a),k("scale",e,t,a),k("epsilon",e,t,a))];case"LRN":return[n.localResponseNormalization(k("x",e,t,a),k("radius",e,t,a),k("bias",e,t,a),k("alpha",e,t,a),k("beta",e,t,a))];case"Softmax":return[n.softmax(k("x",e,t,a))];case"LogSoftmax":return[n.logSoftmax(k("x",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},oO=(e,t,a,n=ea)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:r,outputDenseValues:s}=n.raggedGather(k("paramsNestedSplits",e,t,a),k("paramsDenseValues",e,t,a),k("indices",e,t,a),k("outputRaggedRank",e,t,a));return r.concat(s)}case"RaggedRange":{let{rtNestedSplits:r,rtDenseValues:s}=n.raggedRange(k("starts",e,t,a),k("limits",e,t,a),k("splits",e,t,a));return[r,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(k("shape",e,t,a),k("values",e,t,a),k("defaultValue",e,t,a),k("rowPartitionTensors",e,t,a),k("rowPartitionTypes",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},lO=(e,t,a,n=ea)=>{switch(e.op){case"Max":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.max(k("x",e,t,a),o,l)]}case"Mean":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.mean(k("x",e,t,a),o,l)]}case"Min":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.min(k("x",e,t,a),o,l)]}case"Sum":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.sum(k("x",e,t,a),o,l)]}case"All":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.all(k("x",e,t,a),o,l)]}case"Any":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.any(k("x",e,t,a),o,l)]}case"ArgMax":{let o=k("axis",e,t,a);return[n.argMax(k("x",e,t,a),o)]}case"ArgMin":{let o=k("axis",e,t,a);return[n.argMin(k("x",e,t,a),o)]}case"Prod":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.prod(k("x",e,t,a),o,l)]}case"Cumprod":{let o=k("axis",e,t,a),l=k("exclusive",e,t,a),u=k("reverse",e,t,a);return[n.cumprod(k("x",e,t,a),o,l,u)]}case"Cumsum":{let o=k("axis",e,t,a),l=k("exclusive",e,t,a),u=k("reverse",e,t,a);return[n.cumsum(k("x",e,t,a),o,l,u)]}case"Bincount":let r=k("x",e,t,a),s=k("weights",e,t,a),i=k("size",e,t,a);return[n.bincount(r,s,i)];case"DenseBincount":{let o=k("x",e,t,a),l=k("weights",e,t,a),u=k("size",e,t,a),p=k("binaryOutput",e,t,a);return[n.denseBincount(o,l,u,p)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},uO=(e,t,a,n=ea)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,a),s=k("axis",e,t,a),i=k("tensors",e,t,a);return i=i.slice(0,r),[n.concat(i,s)]}case"Gather":{let r=k("x",e,t,a),s=k("indices",e,t,a);return[n.gather(r,n.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,a),s=k("batchDims",e,t,a),i=k("x",e,t,a),o=k("indices",e,t,a);return[n.gather(i,n.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,a),s=[];for(let o=0;o{let r=k("axis",e,t,a),s=k("tensors",e,t,a),i=s[0].shape,o=n.squeeze(s[0]).shape,l=s.map(u=>{let p=v.arraysEqual(u.shape,i);if(!p&&!v.arraysEqual(n.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:n.reshape(u,i)});return[n.stack(l,r)]});case"Unpack":{let r=k("axis",e,t,a),s=k("tensor",e,t,a);return n.unstack(s,r)}case"Tile":{let r=k("reps",e,t,a);return[n.tile(k("x",e,t,a),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,a),s=k("numOrSizeSplits",e,t,a),i=k("x",e,t,a);return n.split(i,s,r)}case"ScatterNd":{let r=k("indices",e,t,a),s=k("values",e,t,a),i=k("shape",e,t,a);return[n.scatterND(r,s,i)]}case"GatherNd":{let r=k("x",e,t,a),s=k("indices",e,t,a);return[n.gatherND(r,s)]}case"SparseToDense":{let r=k("sparseIndices",e,t,a),s=k("outputShape",e,t,a),i=k("sparseValues",e,t,a),o=k("defaultValue",e,t,a);return[n.sparseToDense(r,i,s,i.dtype===o.dtype?o:n.cast(o,i.dtype))]}case"TensorScatterUpdate":{let r=k("indices",e,t,a),s=k("values",e,t,a),i=k("tensor",e,t,a);return[n.tensorScatterUpdate(i,r,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},dO=(e,t,a,n=ea)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=n.sparse.sparseFillEmptyRows(k("indices",e,t,a),k("values",e,t,a),k("denseShape",e,t,a),k("defaultValue",e,t,a));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=n.sparse.sparseReshape(k("inputIndices",e,t,a),k("inputShape",e,t,a),k("newShape",e,t,a));return[r,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(k("data",e,t,a),k("indices",e,t,a),k("segmentIds",e,t,a))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(k("data",e,t,a),k("indices",e,t,a),k("segmentIds",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},pO=(e,t,a,n=ea)=>{switch(e.op){case"FFT":return[n.fft(k("x",e,t,a))];case"IFFT":return[n.ifft(k("x",e,t,a))];case"RFFT":return[n.rfft(k("x",e,t,a))];case"IRFFT":return[n.irfft(k("x",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},cO=(e,t,a,n=ea)=>{switch(e.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(k("input",e,t,a),k("pattern",e,t,a),k("rewrite",e,t,a),k("replaceGlobal",e,t,a))];case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=n.string.stringNGrams(k("data",e,t,a),k("dataSplits",e,t,a),k("separator",e,t,a),k("nGramWidths",e,t,a),k("leftPad",e,t,a),k("rightPad",e,t,a),k("padWidth",e,t,a),k("preserveShortSequences",e,t,a));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=n.string.stringSplit(k("input",e,t,a),k("delimiter",e,t,a),k("skipEmpty",e,t,a));return[r,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(k("input",e,t,a),k("numBuckets",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},hO=(e,t,a,n=ea)=>{switch(e.op){case"Cast":return[n.cast(k("x",e,t,a),k("dtype",e,t,a))];case"ExpandDims":{let r=k("axis",e,t,a);return[n.expandDims(k("x",e,t,a),r)]}case"Squeeze":{let r=k("axis",e,t,a);return[n.squeeze(k("x",e,t,a),r)]}case"Reshape":return[n.reshape(k("x",e,t,a),k("shape",e,t,a))];case"EnsureShape":return[n.ensureShape(k("x",e,t,a),k("shape",e,t,a))];case"MirrorPad":return[n.mirrorPad(k("x",e,t,a),k("padding",e,t,a),k("mode",e,t,a))];case"PadV2":case"Pad":return[n.pad(k("x",e,t,a),k("padding",e,t,a),k("constantValue",e,t,a))];case"SpaceToBatchND":{let r=k("blockShape",e,t,a),s=k("paddings",e,t,a);return[n.spaceToBatchND(k("x",e,t,a),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,a),s=k("crops",e,t,a);return[n.batchToSpaceND(k("x",e,t,a),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,a),s=k("dataFormat",e,t,a).toUpperCase();return[n.depthToSpace(k("x",e,t,a),r,s)]}case"BroadcastTo":return[n.broadcastTo(k("x",e,t,a),k("shape",e,t,a))];case"BroadcastArgs":return[n.broadcastArgs(k("s0",e,t,a),k("s1",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function o5(e,t,a,n,r=De){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>VD(i,o,l));case"basic_math":return r(()=>UD(i,o,l));case"control":return KD(i,o,l);case"convolution":return r(()=>YD(i,o,l));case"creation":return r(()=>ZD(i,o,l));case"dynamic":return JD(i,o,l);case"evaluation":return r(()=>QD(i,o,l));case"image":return r(()=>nO(i,o,l));case"graph":return r(()=>eO(i,o,l));case"logical":return r(()=>rO(i,o,l));case"matrices":return r(()=>sO(i,o,l));case"normalization":return r(()=>iO(i,o,l));case"ragged":return r(()=>oO(i,o,l));case"reduction":return r(()=>lO(i,o,l));case"slice_join":return r(()=>uO(i,o,l));case"sparse":return r(()=>dO(i,o,l));case"spectral":return r(()=>pO(i,o,l));case"string":return r(()=>cO(i,o,l));case"transformation":return r(()=>hO(i,o,l));case"hash_table":return aO(i,o,l,n);case"custom":let u=P7(i.op);if(u&&u.customExecutor)return u.customExecutor(new BD(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,a);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var l5=class{constructor(e={},t={},a={},n={},r){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=a,this.functionMap=n,this.parseNodeNameCache=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function u5(e,t,a,n){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(d=>Za(d)[0]));n=n||[];let p=new Set(n.map(d=>Za(d.name)[0])),c=[...t];for(;c.length>0;){let d=c.pop();if((Bs(d)||vO(d)||wO(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),a[d.name]==null&&!u.has(d.name)&&!p.has(d.name)){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),c.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function mO(e,t){let{usedNodes:a,inputs:n}=t,r=Object.keys(n).map(g=>Za(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>a.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(y=>[y.name,y])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),c={};for(let g of u){c[g.name]=c[g.name]||0;for(let y of g.children)i(y)||(c[y.name]=Number.POSITIVE_INFINITY),c[y.name]=(c[y.name]||0)+1}let d=Object.entries(c).filter(([,g])=>g===0).map(([g])=>g),h=[...d];for(;d.length>0;){let g=d.pop(),y=p.get(g);for(let x of y.children.filter(i))--c[x.name]===0&&(h.push(x.name),d.push(x.name))}let m=h.map(g=>p.get(g)),f=fO(m,l);return gO(f,l),f}function fO(e,t){let a=new Map(e.map(s=>[s.name,s])),n=t.map(s=>s.name),r=new Set(n);for(;n.length>0;){let s=n.pop(),i=a.get(s);for(let o of i.children)!a.has(o.name)||r.has(o.name)||(r.add(o.name),n.push(o.name))}return e.filter(s=>r.has(s.name))}var Xc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function gO(e,t){let a=new Map(e.map((o,l)=>[o.name,l])),n=new Set(t.map(o=>o.name)),r=o=>n.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!a.has(l.name))throw new Xc(`Child ${l.name} of node ${o.name} is unreachable.`);if(a.get(o.name)>a.get(l.name))throw new Xc(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!a.has(l.name))throw new Xc(`Input ${l.name} of node ${o.name} is unreachable.`);if(a.get(l.name)>a.get(o.name))throw new Xc(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function yO(e){let t=new Map(e.map((o,l)=>[o.name,l])),a=Number.MAX_SAFE_INTEGER,n=e.map((o,l)=>Bs(o)?a:l),r=o=>{let l=n[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),n[l])),i=new Map;for(let o=0;oe[a].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(a=>{this._functionExecutorMap[a]=new R1(e.functions[a],this)})}getCompilationKey(e,t){let a=e.map(r=>r.name).sort(),n=t.map(r=>r.name).sort();return a.join(this.SEPARATOR)+"--"+n.join(this.SEPARATOR)}compile(e,t){let a=u5(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:r,syncInputs:s}=a;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(n.length>0){let l=t.map(p=>p.name),u=Object.keys(e);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${u}]. Missing the following inputs: [${n}]`)}let i=mO(this.graph,a),o=yO(i);return{orderedNodes:i,nodeLiveUntilMap:o}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return zn(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,a])=>[t,this.cloneTensorList(a)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let a=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=a.map(c=>this.graph.nodes[Za(c)[0]]),r=t.map(c=>Za(c)[0]),s=new Set(r),i=r.map(c=>this.graph.nodes[c]);i.length===0&&(i=this._outputs);let o=this.getCompilationKey(n,i),l=this.compiledMap.get(o);l==null&&(l=this.compile(e,i),this.compiledMap.set(o,l));try{this.keepIntermediateTensors=W().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let u={},p={};return De(()=>{let c=new l5(this.weightMap,u,p,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(g=>{let[y,x]=Za(g,c),A=[];A[x]=e[g],d[y]=A,this.keepIntermediateTensors&&(this.clonedTensorsMap[y]=this.cloneTensorList(A))});let h=this.getFrozenTensorIds(d),{orderedNodes:m,nodeLiveUntilMap:f}=l;for(let g of m){if(d[g.name])continue;let y=o5(g,d,c,this._resourceManager);if(v.isPromise(y))throw new Error(`The execution of the op '${g.op}' returned a promise. Please use model.executeAsync() instead.`);d[g.name]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[g.name]=this.cloneTensorList(y)),this.checkTensorForDisposalWithNodeLiveUntilInfo(g,d,c,h,s,f.get(g.name))}return this.parent==null&&c.dispose(h),t.map(g=>ua(g,d,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(a=>e[a]).map(a=>a.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,a,n,r,s,i){if(!(Bs(t)||s.has(e))){for(let o of a[e])o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length);for(let o of t.inputs){if(Bs(o))continue;let l=a5(o.name,a,n);if(l!=null)for(let u of l){if(!u||u.kept||r.has(u.id))continue;let p=i[u.id];p===1?(u.dispose(),delete i[u.id]):p!=null&&i[u.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,a,n,r,s){function i(o){return Bs(o)||r.has(o.name)}if(!(Bs(e)||s==null))for(let o of s){if(i(o))continue;let l=a5(o.name,t,a);for(let u of l)!u||u.kept||n.has(u.id)||u.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,a=!1,n={},r={}){this.disposeIntermediateTensors(),a||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=W().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let s=new l5(this.weightMap,n,r,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,a),o=t.map(c=>ua(c,i,s)),l=o.map(c=>c.id),u=Object.keys(e).map(c=>e[c].id),p=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach(c=>{c.forEach(d=>{d&&!d.isDisposed&&!p.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(p),o}async executeFunctionAsync(e,t,a){let n=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(n,this.outputNodes,!0,t,a)}async executeWithControlFlow(e,t,a,n){let r=Object.keys(e),s=r.map(A=>this.graph.nodes[Za(A)[0]]),i=a.map(A=>Za(A)[0]),o=new Set(i),l=i.map(A=>this.graph.nodes[A]);l.length===0&&(l=this._outputs);let{usedNodes:u,missingInputs:p,dynamicNode:c,syncInputs:d}=u5(e,l,this.weightMap,this._initNodes),h=[...s,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),m=Object.assign({},this.weightMap);Object.keys(e).forEach(A=>{let[b,w]=Za(A),I=[];I[w]=e[A],m[b]=I});let f={},g=this.getFrozenTensorIds(m),y={};for(;h.length>0;){let A=this.processStack(s,h,t,m,y,g,o,f,u);await Promise.all(A)}c==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let x=l.filter(A=>!Bs(A)&&!ua(A.name,m,t)).map(A=>A.name);if(x.length>0){let A="";throw c!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${x}] from the provided inputs [${r}]. Consider providing the following inputs: [${p}]. ${A}`)}return m}processStack(e,t,a,n,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();a.currentContext=p.contexts;let c="";if(p.node.op==="Enter"&&k("isConstant",p.node,n,a)&&([c]=br(p.node.name,a)),n[p.node.name]==null){let d=o5(p.node,n,a,this._resourceManager);c||([c]=br(p.node.name,a));let h=a.currentContext;v.isPromise(d)?u.push(d.then(m=>(n[c]=m,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(m)),a.currentContext=h,this.checkTensorForDisposal(c,p.node,n,a,s,i,o),this.processChildNodes(p.node,t,a,n,r,l),m))):(n[c]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(d)),this.checkTensorForDisposal(c,p.node,n,a,s,i,o),this.processChildNodes(p.node,t,a,n,r,l))}else this.processChildNodes(p.node,t,a,n,r,l)}return u}processChildNodes(e,t,a,n,r,s){e.children.forEach(i=>{let[o]=br(i.name,a);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!ua(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})):i.inputNames.every(l=>!!ua(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let a=e[t],[n]=Za(t),r=this.graph.nodes[n];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===a.shape.length&&a.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${a.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(a.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(e){var t,a;let n={};for(let r in e){let s=(a=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||a===void 0?void 0:a[r];s!=null?n[s.name]=e[r]:n[r]=e[r]}return n}checkInputs(e){let t=Object.keys(e).filter(a=>{let[n]=Za(a);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var a,n;let r=(n=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||n===void 0?void 0:n[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[a]=Za(t);if(!this.graph.nodes[a])throw new Error(`The output '${t}' is not found in the graph`)})}},kO=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},IO="?tfjs-format=file",SO="model.json",Xp=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},a=Xn){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=a,t==null&&(this.loadOptions={}),this.resourceManager=new kO}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,a=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(a=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=a,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new R1(n5.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=n5.Instance.transformGraph(e.modelInitializer);this.initializer=new R1(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let a=this.io.getSaveHandlers(e);if(a.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(a.length>1)throw new Error(`Found more than one (${a.length}) save handlers for URL '${e}'`);e=a[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof mt?[e]:e,a={};return t.forEach((n,r)=>a[this.structuredOutputKeys[r]]=n),a}return e}predict(e,t){let a=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(a)}async predictAsync(e,t){let a=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(a)}normalizeInputs(e){var t;if(!(e instanceof mt)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let a=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+a!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-a} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[n++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,a=Object.keys(t);for(let n=0;n1?a:a[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let a=await this.executor.executeAsync(e,t);return a.length>1?a:a[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,a)=>(t[a]=[e[a]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&J(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function o3(e,t={},a=Xn){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof e=="string"&&(e=TO(e));let n=new Xp(e,t,a);return await n.load(),n}function CO(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(e instanceof Array){let[n,r]=e;if(!n)throw new Error("modelJSON must be the first element of the array");if(!r||!(r instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in n))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in n))throw new Error("Model JSON is missing 'weightsManifest'");let s=Xn.getWeightSpecs(n.weightsManifest),i=Xn.getModelArtifactsForJSONSync(n,s,r);t=Xn.fromMemorySync(i)}else if("load"in e)t=e;else if("modelTopology"in e&&"weightSpecs"in e&&"weightData"in e)t=Xn.fromMemorySync(e);else throw new Error("Unknown model format");let a=new Xp(t);return a.load(),a}function TO(e){return e.endsWith("/")||(e=e+"/"),`${e}${SO}${IO}`}var NO="4.11.0";function Ie(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var RO=En.whereImpl,Qh=class extends au{nextDataId(){return Qh.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new ip(this,It())}write(e,t,a){this.firstUse&&(this.firstUse=!1,W().get("IS_NODE")&&S.warn(` + ${n}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=R1(s,a),o=n===0?0:e.size/n,l=De(()=>{let p=[];e=Q(e,[1,n,o]);for(let c=0;c{switch(e.op){case"If":case"StatelessIf":{let n=k("thenBranch",e,t,a),r=k("elseBranch",e,t,a),s=k("cond",e,t,a),i=k("args",e,t,a);return(await s.data())[0]?a.functionMap[n].executeFunctionAsync(i,a.tensorArrayMap,a.tensorListMap):a.functionMap[r].executeFunctionAsync(i,a.tensorArrayMap,a.tensorListMap)}case"While":case"StatelessWhile":{let n=k("body",e,t,a),r=k("cond",e,t,a),s=k("args",e,t,a),i=await a.functionMap[r].executeFunctionAsync(s,a.tensorArrayMap,a.tensorListMap),o=s.map(p=>p.id),l=await i[0].data();i.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&p.dispose()});let u=s;for(;l[0];){let p=u;u=await a.functionMap[n].executeFunctionAsync(u,a.tensorArrayMap,a.tensorListMap);let c=u.map(h=>h.id);p.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&c.indexOf(h.id)===-1&&h.dispose()});let d=await a.functionMap[r].executeFunctionAsync(u,a.tensorArrayMap,a.tensorListMap);l=await d[0].data(),d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&c.indexOf(h.id)===-1&&h.dispose()})}return u}case"LoopCond":{let n=k("pred",e,t,a);return[br(n)]}case"Switch":{let n=k("pred",e,t,a),r=k("data",e,t,a);return r.kept||(r=br(r)),(await n.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let n=e.inputNames.find(r=>la(r,t,a)!==void 0);if(n){let r=la(n,t,a);return[br(r)]}return}case"Enter":{let n=k("frameName",e,t,a),r=k("tensor",e,t,a);return a.enterFrame(n),[br(r)]}case"Exit":{let n=k("tensor",e,t,a);return a.exitFrame(),[br(n)]}case"NextIteration":{let n=k("tensor",e,t,a);return a.nextIteration(),[br(n)]}case"TensorArrayV3":{let n=k("size",e,t,a),r=k("dtype",e,t,a),s=k("elementShape",e,t,a),i=k("dynamicSize",e,t,a),o=k("clearAfterRead",e,t,a),l=k("identicalElementShapes",e,t,a),u=k("name",e,t,a),p=new aO(u,r,n,s,l,i,o);return a.addTensorArray(p),[p.idTensor,Ge(1)]}case"TensorArrayWriteV3":{let n=k("tensorArrayId",e,t,a),r=k("index",e,t,a),s=k("tensor",e,t,a),i=a.getTensorArray(n.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let n=k("tensorArrayId",e,t,a),r=k("index",e,t,a);return[a.getTensorArray(n.id).read(r)]}case"TensorArrayGatherV3":{let n=k("tensorArrayId",e,t,a),r=k("indices",e,t,a),s=k("dtype",e,t,a);return[a.getTensorArray(n.id).gather(r,s)]}case"TensorArrayScatterV3":{let n=k("tensorArrayId",e,t,a),r=k("indices",e,t,a),s=k("tensor",e,t,a),i=a.getTensorArray(n.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let n=k("tensorArrayId",e,t,a),r=a.getTensorArray(n.id),s=k("dtype",e,t,a);return[r.concat(s)]}case"TensorArraySplitV3":{let 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n=k("elementShape",e,t,a),r=k("elementDType",e,t,a),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,a),o=e.op==="TensorListReserve"?-1:i,l=rO(n,r,i,o);return a.addTensorList(l),[l.idTensor]}case"TensorListGather":{let n=k("tensorListId",e,t,a),r=k("indices",e,t,a),s=k("elementShape",e,t,a),i=k("elementDType",e,t,a);return[a.getTensorList(n.id).gather(r,i,s)]}case"TensorListStack":{let n=k("tensorListId",e,t,a),r=k("elementShape",e,t,a),s=k("elementDType",e,t,a),i=k("numElements",e,t,a);return[a.getTensorList(n.id).stack(r,s,i)]}case"TensorListFromTensor":{let n=k("tensor",e,t,a),r=k("elementShape",e,t,a),s=k("elementDType",e,t,a),i=nO(n,r,s);return a.addTensorList(i),[i.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let n=k("tensorListId",e,t,a),r=a.getTensorList(n.id),s=k("dtype",e,t,a),i=k("elementShape",e,t,a);return[r.concat(s,i)]}case"TensorListPushBack":{let n=k("tensorListId",e,t,a),r=k("tensor",e,t,a),s=a.getTensorList(n.id);return 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r=k("logits",e,t,a),s=k("numSamples",e,t,a),i=k("seed",e,t,a);return[n.multinomial(r,s,i)]}case"OneHot":{let r=k("indices",e,t,a),s=k("depth",e,t,a),i=k("onValue",e,t,a),o=k("offValue",e,t,a),l=k("dtype",e,t,a);return[n.oneHot(r,s,i,o,l)]}case"Ones":return[n.ones(k("shape",e,t,a),k("dtype",e,t,a))];case"OnesLike":return[n.onesLike(k("x",e,t,a))];case"RandomStandardNormal":return[n.randomStandardNormal(k("shape",e,t,a),k("dtype",e,t,a),k("seed",e,t,a))];case"RandomUniform":return[n.randomUniform(k("shape",e,t,a),k("minval",e,t,a),k("maxval",e,t,a),k("dtype",e,t,a))];case"RandomUniformInt":return[n.randomUniformInt(k("shape",e,t,a),k("minval",e,t,a),k("maxval",e,t,a),k("seed",e,t,a))];case"Range":{let r=k("start",e,t,a),s=k("stop",e,t,a),i=k("step",e,t,a);return[n.range(r,s,i,k("dtype",e,t,a))]}case"TruncatedNormal":{let 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r=k("images",e,t,a),s=k("transforms",e,t,a),i=k("outputShape",e,t,a),o=k("fillValue",e,t,a),l=k("interpolation",e,t,a),u=k("fillMode",e,t,a);return[n.image.transform(r,s,l.toLowerCase(),u.toLowerCase(),o,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},gO=(e,t,a,n=Qt)=>{switch(e.op){case"Equal":return[n.equal(k("a",e,t,a),k("b",e,t,a))];case"NotEqual":return[n.notEqual(k("a",e,t,a),k("b",e,t,a))];case"Greater":return[n.greater(k("a",e,t,a),k("b",e,t,a))];case"GreaterEqual":return[n.greaterEqual(k("a",e,t,a),k("b",e,t,a))];case"Less":return[n.less(k("a",e,t,a),k("b",e,t,a))];case"LessEqual":return[n.lessEqual(k("a",e,t,a),k("b",e,t,a))];case"LogicalAnd":return[n.logicalAnd(k("a",e,t,a),k("b",e,t,a))];case"LogicalNot":return[n.logicalNot(k("a",e,t,a))];case"LogicalOr":return[n.logicalOr(k("a",e,t,a),k("b",e,t,a))];case"Select":case"SelectV2":return[n.where(k("condition",e,t,a),k("a",e,t,a),k("b",e,t,a))];case"BitwiseAnd":return[n.bitwiseAnd(k("a",e,t,a),k("b",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},yO=(e,t,a,n=Qt)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(k("a",e,t,a),k("b",e,t,a),k("transposeA",e,t,a),k("transposeB",e,t,a))];case"Einsum":return[n.einsum(k("equation",e,t,a),...k("tensors",e,t,a))];case"Transpose":return[n.transpose(k("x",e,t,a),k("perm",e,t,a))];case"_FusedMatMul":let[r,s]=k("fusedOps",e,t,a),i=r==="biasadd",o=s==="prelu",l=k("numArgs",e,t,a),u=k("leakyreluAlpha",e,t,a);if(i){if(o&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,c]=k("args",e,t,a);return[n.fused.matMul({a:k("a",e,t,a),b:k("b",e,t,a),transposeA:k("transposeA",e,t,a),transposeB:k("transposeB",e,t,a),bias:p,activation:s,preluActivationWeights:c,leakyreluAlpha:u})];case"MatrixBandPart":return[n.linalg.bandPart(k("a",e,t,a),k("numLower",e,t,a),k("numUpper",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},xO=(e,t,a,n=Qt)=>{switch(e.op){case"EuclideanNorm":return[n.euclideanNorm(k("x",e,t,a),k("axis",e,t,a),k("keepDims",e,t,a))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(k("x",e,t,a),k("mean",e,t,a),k("variance",e,t,a),k("offset",e,t,a),k("scale",e,t,a),k("epsilon",e,t,a))];case"FusedBatchNormV3":return[n.batchNorm(k("x",e,t,a),k("mean",e,t,a),k("variance",e,t,a),k("offset",e,t,a),k("scale",e,t,a),k("epsilon",e,t,a))];case"LRN":return[n.localResponseNormalization(k("x",e,t,a),k("radius",e,t,a),k("bias",e,t,a),k("alpha",e,t,a),k("beta",e,t,a))];case"Softmax":return[n.softmax(k("x",e,t,a))];case"LogSoftmax":return[n.logSoftmax(k("x",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},AO=(e,t,a,n=Qt)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:r,outputDenseValues:s}=n.raggedGather(k("paramsNestedSplits",e,t,a),k("paramsDenseValues",e,t,a),k("indices",e,t,a),k("outputRaggedRank",e,t,a));return r.concat(s)}case"RaggedRange":{let{rtNestedSplits:r,rtDenseValues:s}=n.raggedRange(k("starts",e,t,a),k("limits",e,t,a),k("splits",e,t,a));return[r,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(k("shape",e,t,a),k("values",e,t,a),k("defaultValue",e,t,a),k("rowPartitionTensors",e,t,a),k("rowPartitionTypes",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},bO=(e,t,a,n=Qt)=>{switch(e.op){case"Max":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.max(k("x",e,t,a),o,l)]}case"Mean":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.mean(k("x",e,t,a),o,l)]}case"Min":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.min(k("x",e,t,a),o,l)]}case"Sum":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.sum(k("x",e,t,a),o,l)]}case"All":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.all(k("x",e,t,a),o,l)]}case"Any":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.any(k("x",e,t,a),o,l)]}case"ArgMax":{let o=k("axis",e,t,a);return[n.argMax(k("x",e,t,a),o)]}case"ArgMin":{let o=k("axis",e,t,a);return[n.argMin(k("x",e,t,a),o)]}case"Prod":{let o=k("axis",e,t,a),l=k("keepDims",e,t,a);return[n.prod(k("x",e,t,a),o,l)]}case"Cumprod":{let o=k("axis",e,t,a),l=k("exclusive",e,t,a),u=k("reverse",e,t,a);return[n.cumprod(k("x",e,t,a),o,l,u)]}case"Cumsum":{let o=k("axis",e,t,a),l=k("exclusive",e,t,a),u=k("reverse",e,t,a);return[n.cumsum(k("x",e,t,a),o,l,u)]}case"Bincount":let r=k("x",e,t,a),s=k("weights",e,t,a),i=k("size",e,t,a);return[n.bincount(r,s,i)];case"DenseBincount":{let o=k("x",e,t,a),l=k("weights",e,t,a),u=k("size",e,t,a),p=k("binaryOutput",e,t,a);return[n.denseBincount(o,l,u,p)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},vO=(e,t,a,n=Qt)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,a),s=k("axis",e,t,a),i=k("tensors",e,t,a);return i=i.slice(0,r),[n.concat(i,s)]}case"Gather":{let r=k("x",e,t,a),s=k("indices",e,t,a);return[n.gather(r,n.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,a),s=k("batchDims",e,t,a),i=k("x",e,t,a),o=k("indices",e,t,a);return[n.gather(i,n.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,a),s=[];for(let o=0;o{let r=k("axis",e,t,a),s=k("tensors",e,t,a),i=s[0].shape,o=n.squeeze(s[0]).shape,l=s.map(u=>{let p=v.arraysEqual(u.shape,i);if(!p&&!v.arraysEqual(n.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:n.reshape(u,i)});return[n.stack(l,r)]});case"Unpack":{let r=k("axis",e,t,a),s=k("tensor",e,t,a);return n.unstack(s,r)}case"Tile":{let r=k("reps",e,t,a);return[n.tile(k("x",e,t,a),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,a),s=k("numOrSizeSplits",e,t,a),i=k("x",e,t,a);return n.split(i,s,r)}case"ScatterNd":{let r=k("indices",e,t,a),s=k("values",e,t,a),i=k("shape",e,t,a);return[n.scatterND(r,s,i)]}case"GatherNd":{let r=k("x",e,t,a),s=k("indices",e,t,a);return[n.gatherND(r,s)]}case"SparseToDense":{let r=k("sparseIndices",e,t,a),s=k("outputShape",e,t,a),i=k("sparseValues",e,t,a),o=k("defaultValue",e,t,a);return[n.sparseToDense(r,i,s,i.dtype===o.dtype?o:n.cast(o,i.dtype))]}case"TensorScatterUpdate":{let r=k("indices",e,t,a),s=k("values",e,t,a),i=k("tensor",e,t,a);return[n.tensorScatterUpdate(i,r,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},wO=(e,t,a,n=Qt)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=n.sparse.sparseFillEmptyRows(k("indices",e,t,a),k("values",e,t,a),k("denseShape",e,t,a),k("defaultValue",e,t,a));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=n.sparse.sparseReshape(k("inputIndices",e,t,a),k("inputShape",e,t,a),k("newShape",e,t,a));return[r,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(k("data",e,t,a),k("indices",e,t,a),k("segmentIds",e,t,a))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(k("data",e,t,a),k("indices",e,t,a),k("segmentIds",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},kO=(e,t,a,n=Qt)=>{switch(e.op){case"FFT":return[n.fft(k("x",e,t,a))];case"IFFT":return[n.ifft(k("x",e,t,a))];case"RFFT":return[n.rfft(k("x",e,t,a))];case"IRFFT":return[n.irfft(k("x",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},IO=(e,t,a,n=Qt)=>{switch(e.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(k("input",e,t,a),k("pattern",e,t,a),k("rewrite",e,t,a),k("replaceGlobal",e,t,a))];case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=n.string.stringNGrams(k("data",e,t,a),k("dataSplits",e,t,a),k("separator",e,t,a),k("nGramWidths",e,t,a),k("leftPad",e,t,a),k("rightPad",e,t,a),k("padWidth",e,t,a),k("preserveShortSequences",e,t,a));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=n.string.stringSplit(k("input",e,t,a),k("delimiter",e,t,a),k("skipEmpty",e,t,a));return[r,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(k("input",e,t,a),k("numBuckets",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},SO=(e,t,a,n=Qt)=>{switch(e.op){case"Cast":return[n.cast(k("x",e,t,a),k("dtype",e,t,a))];case"ExpandDims":{let r=k("axis",e,t,a);return[n.expandDims(k("x",e,t,a),r)]}case"Squeeze":{let r=k("axis",e,t,a);return[n.squeeze(k("x",e,t,a),r)]}case"Reshape":return[n.reshape(k("x",e,t,a),k("shape",e,t,a))];case"EnsureShape":return[n.ensureShape(k("x",e,t,a),k("shape",e,t,a))];case"MirrorPad":return[n.mirrorPad(k("x",e,t,a),k("padding",e,t,a),k("mode",e,t,a))];case"PadV2":case"Pad":return[n.pad(k("x",e,t,a),k("padding",e,t,a),k("constantValue",e,t,a))];case"SpaceToBatchND":{let r=k("blockShape",e,t,a),s=k("paddings",e,t,a);return[n.spaceToBatchND(k("x",e,t,a),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,a),s=k("crops",e,t,a);return[n.batchToSpaceND(k("x",e,t,a),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,a),s=k("dataFormat",e,t,a).toUpperCase();return[n.depthToSpace(k("x",e,t,a),r,s)]}case"BroadcastTo":return[n.broadcastTo(k("x",e,t,a),k("shape",e,t,a))];case"BroadcastArgs":return[n.broadcastArgs(k("s0",e,t,a),k("s1",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function c5(e,t,a,n,r=De){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>eO(i,o,l));case"basic_math":return r(()=>tO(i,o,l));case"control":return oO(i,o,l);case"convolution":return r(()=>lO(i,o,l));case"creation":return r(()=>uO(i,o,l));case"dynamic":return dO(i,o,l);case"evaluation":return r(()=>pO(i,o,l));case"image":return r(()=>fO(i,o,l));case"graph":return r(()=>cO(i,o,l));case"logical":return r(()=>gO(i,o,l));case"matrices":return r(()=>yO(i,o,l));case"normalization":return r(()=>xO(i,o,l));case"ragged":return r(()=>AO(i,o,l));case"reduction":return r(()=>bO(i,o,l));case"slice_join":return r(()=>vO(i,o,l));case"sparse":return r(()=>wO(i,o,l));case"spectral":return r(()=>kO(i,o,l));case"string":return r(()=>IO(i,o,l));case"transformation":return r(()=>SO(i,o,l));case"hash_table":return mO(i,o,l,n);case"custom":let u=V7(i.op);if(u&&u.customExecutor)return u.customExecutor(new QD(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,a);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var h5=class{constructor(e={},t={},a={},n={},r){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=a,this.functionMap=n,this.parseNodeNameCache=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function m5(e,t,a,n){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(d=>Ka(d)[0]));n=n||[];let p=new Set(n.map(d=>Ka(d.name)[0])),c=[...t];for(;c.length>0;){let d=c.pop();if((Vs(d)||_O(d)||$O(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),a[d.name]==null&&!u.has(d.name)&&!p.has(d.name)){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),c.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function CO(e,t){let{usedNodes:a,inputs:n}=t,r=Object.keys(n).map(g=>Ka(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>a.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(y=>[y.name,y])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),c={};for(let g of u){c[g.name]=c[g.name]||0;for(let y of g.children)i(y)||(c[y.name]=Number.POSITIVE_INFINITY),c[y.name]=(c[y.name]||0)+1}let d=Object.entries(c).filter(([,g])=>g===0).map(([g])=>g),h=[...d];for(;d.length>0;){let g=d.pop(),y=p.get(g);for(let x of y.children.filter(i))--c[x.name]===0&&(h.push(x.name),d.push(x.name))}let m=h.map(g=>p.get(g)),f=TO(m,l);return NO(f,l),f}function TO(e,t){let a=new Map(e.map(s=>[s.name,s])),n=t.map(s=>s.name),r=new Set(n);for(;n.length>0;){let s=n.pop(),i=a.get(s);for(let o of i.children)!a.has(o.name)||r.has(o.name)||(r.add(o.name),n.push(o.name))}return e.filter(s=>r.has(s.name))}var qc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function NO(e,t){let a=new Map(e.map((o,l)=>[o.name,l])),n=new Set(t.map(o=>o.name)),r=o=>n.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!a.has(l.name))throw new qc(`Child ${l.name} of node ${o.name} is unreachable.`);if(a.get(o.name)>a.get(l.name))throw new qc(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!a.has(l.name))throw new qc(`Input ${l.name} of node ${o.name} is unreachable.`);if(a.get(l.name)>a.get(o.name))throw new qc(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function RO(e){let t=new Map(e.map((o,l)=>[o.name,l])),a=Number.MAX_SAFE_INTEGER,n=e.map((o,l)=>Vs(o)?a:l),r=o=>{let l=n[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),n[l])),i=new Map;for(let o=0;ot[n].map(r=>r.id));this._weightIds=[].concat(...a),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let a=t.signatureKey||t.name;return t.defaultOutput?`${a}:${t.defaultOutput}`:a})}get functions(){return Object.keys(this._functions).reduce((t,a)=>(t[a]=this._functions[a].signature,t),{})}constructor(t,a){this.graph=t,this.parent=a,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new d6(t.functions[n],this)})}getCompilationKey(t,a){let n=t.map(s=>s.name).sort(),r=a.map(s=>s.name).sort();return n.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(t,a){let n=m5(t,a,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(r.length>0){let u=a.map(c=>c.name),p=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${p}]. Missing the following inputs: [${r}]`)}let o=CO(this.graph,n),l=RO(o);return{orderedNodes:o,nodeLiveUntilMap:l}}cloneAndKeepTensor(t){if(t==null)return null;let a=t.clone();return zn(a),a}cloneTensorList(t){return t?t.map(a=>this.cloneAndKeepTensor(a)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([a,n])=>[a,this.cloneTensorList(n)]))}execute(t,a){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),a=this.mapOutputs(a),this.checkOutputs(a);let r=n.map(d=>this.graph.nodes[Ka(d)[0]]),s=a.map(d=>Ka(d)[0]),i=new Set(s),o=s.map(d=>this.graph.nodes[d]);o.length===0&&(o=this._outputs);let l=this.getCompilationKey(r,o),u=this.compiledMap.get(l);u==null&&(u=this.compile(t,o),this.compiledMap.set(l,u));try{this.keepIntermediateTensors=B().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let p={},c={};return De(()=>{let d=new h5(this.weightMap,p,c,this.functionExecutorMap,this.parseNodeNameCache),h=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(y=>{let[x,A]=Ka(y,d),b=[];b[A]=t[y],h[x]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x]=this.cloneTensorList(b))});let m=this.getFrozenTensorIds(h),{orderedNodes:f,nodeLiveUntilMap:g}=u;for(let y of f){if(h[y.name])continue;let x=c5(y,h,d,this._resourceManager);if(v.isPromise(x))throw new Error(`The execution of the op '${y.op}' returned a promise. Please use model.executeAsync() instead.`);h[y.name]=x,this.keepIntermediateTensors&&(this.clonedTensorsMap[y.name]=this.cloneTensorList(x)),this.checkTensorForDisposalWithNodeLiveUntilInfo(y,h,d,m,i,g.get(y.name))}return this.parent==null&&d.dispose(m),a.map(y=>la(y,h,d))})}getFrozenTensorIds(t){let a=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(r=>r.id)));return new Set(a)}checkTensorForDisposal(t,a,n,r,s,i,o){if(!(Vs(a)||i.has(t))){for(let l of n[t])l!=null&&(o[l.id]=(o[l.id]||0)+a.children.length);for(let l of a.inputs){if(Vs(l))continue;let u=o5(l.name,n,r);if(u!=null)for(let p of u){if(!p||p.kept||s.has(p.id))continue;let c=o[p.id];c===1?(p.dispose(),delete o[p.id]):c!=null&&o[p.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,a,n,r,s,i){function o(l){return Vs(l)||s.has(l.name)}if(!(Vs(t)||i==null))for(let l of i){if(o(l))continue;let u=o5(l.name,a,n);for(let p of u)!p||p.kept||r.has(p.id)||p.dispose()}}async executeAsync(t,a){return this._executeAsync(t,a)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let a of t)a&&!a.isDisposed&&a.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,a,n=!1,r={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),a=this.mapOutputs(a),this.checkOutputs(a));try{this.keepIntermediateTensors=B().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let i=new h5(this.weightMap,r,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let o=await this.executeWithControlFlow(t,i,a,n),l=a.map(d=>la(d,o,i)),u=l.map(d=>d.id),p=Object.keys(t).map(d=>t[d].id),c=new Set([...u,...p,...this.weightIds]);return Object.values(o).forEach(d=>{d.forEach(h=>{h&&!h.isDisposed&&!c.has(h.id)&&h.dispose()})}),this.parent==null&&i.dispose(c),l}async executeFunctionAsync(t,a,n){let r=t.reduce((s,i,o)=>(s[this.inputs[o].name]=i,s),{});return this._executeAsync(r,this.outputNodes,!0,a,n)}async executeWithControlFlow(t,a,n,r){let s=Object.keys(t),i=s.map(b=>this.graph.nodes[Ka(b)[0]]),o=n.map(b=>Ka(b)[0]),l=new Set(o),u=o.map(b=>this.graph.nodes[b]);u.length===0&&(u=this._outputs);let{usedNodes:p,missingInputs:c,dynamicNode:d,syncInputs:h}=m5(t,u,this.weightMap,this._initNodes),m=[...i,...this.graph.weights,...this._initNodes||[]].map(b=>({node:b,contexts:a.currentContext})),f=Object.assign({},this.weightMap);Object.keys(t).forEach(b=>{let[w,I]=Ka(b),T=[];T[I]=t[b],f[w]=T});let g={},y=this.getFrozenTensorIds(f),x={};for(;m.length>0;){let b=this.processStack(i,m,a,f,x,y,l,g,p);await Promise.all(b)}d==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let A=u.filter(b=>!Vs(b)&&!la(b.name,f,a)).map(b=>b.name);if(A.length>0){let b="";throw d!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${b}`)}return f}processStack(t,a,n,r,s,i,o,l,u){let p=[];for(;a.length>0;){let c=a.pop();n.currentContext=c.contexts;let d="";if(c.node.op==="Enter"&&k("isConstant",c.node,r,n)&&([d]=Ar(c.node.name,n)),r[c.node.name]==null){let h=c5(c.node,r,n,this._resourceManager);d||([d]=Ar(c.node.name,n));let m=n.currentContext;v.isPromise(h)?p.push(h.then(f=>(r[d]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(f)),n.currentContext=m,this.checkTensorForDisposal(d,c.node,r,n,i,o,l),this.processChildNodes(c.node,a,n,r,s,u),f))):(r[d]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(h)),this.checkTensorForDisposal(d,c.node,r,n,i,o,l),this.processChildNodes(c.node,a,n,r,s,u))}else this.processChildNodes(c.node,a,n,r,s,u)}return p}processChildNodes(t,a,n,r,s,i){t.children.forEach(o=>{let[l]=Ar(o.name,n);s[l]||!i.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!la(u,r,n))&&(s[l]=!0,a.push({contexts:n.currentContext,node:o})):o.inputNames.every(u=>!!la(u,r,n))&&(s[l]=!0,a.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(a=>a.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(a=>{let n=t[a],[r]=Ka(a),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,o=i.length===n.shape.length&&n.shape.every((l,u)=>i[u]===-1||i[u]===l);v.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&v.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var a,n;let r={};for(let s in t){let i=(n=(a=this._signature)===null||a===void 0?void 0:a.inputs)===null||n===void 0?void 0:n[s];i!=null?r[i.name]=t[s]:r[s]=t[s]}return r}checkInputs(t){let a=Object.keys(t).filter(n=>{let[r]=Ka(n);return this.graph.nodes[r]==null});if(a.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${a}] that are not part of graph`)}mapOutputs(t){return t.map(a=>{var n,r;let s=(r=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||r===void 0?void 0:r[a];return s!=null?s.name:a},{})}checkOutputs(t){t.forEach(a=>{let[n]=Ka(a);if(!this.graph.nodes[n])throw new Error(`The output '${a}' is not found in the graph`)})}},FO=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,a=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(a=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=a,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new f5(l5.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=l5.Instance.transformGraph(e.modelInitializer);this.initializer=new f5(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let a=this.io.getSaveHandlers(e);if(a.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(a.length>1)throw new Error(`Found more than one (${a.length}) save handlers for URL '${e}'`);e=a[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof gt?[e]:e,a={};return t.forEach((n,r)=>a[this.structuredOutputKeys[r]]=n),a}return e}predict(e,t){let a=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(a)}async predictAsync(e,t){let a=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(a)}normalizeInputs(e){var t;if(!(e instanceof gt)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let a=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+a!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-a} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let 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EL(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Ie([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=S.computePool2DInfo(i.shape,o,l,1,u),c=p.strideHeight,d=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.effectiveFilterHeight,x=p.effectiveFilterWidth,A=x-1-p.padInfo.left,b=y-1-p.padInfo.top,w=$e(i.shape,"float32"),I=1/(h*m),T=a.data.get(r.dataId).values,N=$e(r.shape,"float32",T);for(let M=0;M=p.outHeight||Math.floor(U)!==U))for(let G=0;G=p.outWidth||Math.floor(q)!==q)continue;let H=N.get(M,U,q,P);B+=H}}w.set(B*I,M,E,C,P)}return a.makeTensorInfo(w.shape,w.dtype,w.values)}var ML={kernelName:up,backendName:"cpu",kernelFunc:EL};function _L(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((y,x)=>y*x),l=S.getReshaped(r.shape,s,o),u=S.getPermuted(l.length,s.length),p=S.getReshapedPermuted(r.shape,s,o),c=S.getSliceBeginCoords(i,s.length),d=S.getSliceSize(p,i,s.length),h=At({inputs:{x:r},backend:a,attrs:{shape:l}}),m=Ga({inputs:{x:h},backend:a,attrs:{perm:u}}),f=At({inputs:{x:m},backend:a,attrs:{shape:p}}),g=Js({inputs:{x:f},backend:a,attrs:{begin:c,size:d}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),g}var FL={kernelName:lu,backendName:"cpu",kernelFunc:$L};function DL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,u=u3(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var OL={kernelName:gi,backendName:"cpu",kernelFunc:DL};function zL(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.data.get(n.dataId).values,i=a.data.get(r.dataId).values,o=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var LL={kernelName:du,backendName:"cpu",kernelFunc:zL},WL=ct(ss,(e,t)=>{let a=t;return e>a.clipValueMax?a.clipValueMax:e{let{x:t}=e.inputs,a=e.backend,n=new Float32Array(v.sizeFromShape(t.shape)),r=a.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=a.data.get(s.dataId).values,l=a.data.get(i.dataId).values;for(let u=0;uf.shape);S.assertParamsConsistent(i,s);let o=S.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>v.sizeFromShape(f.shape)>0);if(l.length===1)return tr({inputs:{x:l[0]},backend:a});if(l[0].dtype==="complex64"){let f=l.map(b=>Zs({inputs:{input:b},backend:a})),g=l.map(b=>Jl({inputs:{input:b},backend:a})),y=Ql({inputs:f,backend:a,attrs:{axis:s}}),x=Ql({inputs:g,backend:a,attrs:{axis:s}}),A=Qa({inputs:{real:y,imag:x},backend:a});return f.forEach(b=>a.disposeIntermediateTensorInfo(b)),g.forEach(b=>a.disposeIntermediateTensorInfo(b)),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(x),A}let u=l.map(f=>{let g=[-1,v.sizeFromShape(f.shape.slice(s))];return At({inputs:{x:f},backend:a,attrs:{shape:g}})}),p=u.map(f=>({vals:a.data.get(f.dataId).values,shape:f.shape}));o=S.computeOutShape(u.map(f=>f.shape),1);let c=u[0].shape[0]===1,d=d3(p,o,t[0].dtype,c),h=S.computeOutShape(l.map(f=>f.shape),s),m=a.makeTensorInfo(h,t[0].dtype,d);return u.forEach(f=>a.disposeIntermediateTensorInfo(f)),m}var HL={kernelName:pu,backendName:"cpu",kernelFunc:Ql};function Y6(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n;Ie([r,s],"conv2d");let c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.padInfo.left,x=d.padInfo.top,A=d.dataFormat==="channelsLast",b=new Ut(d.outShape,r.dtype),w=v.computeStrides(r.shape),I=v.computeStrides(s.shape),T=w[0],N=A?w[1]:w[2],M=A?w[2]:1,P=A?1:w[1],E=b.strides[0],C=A?b.strides[1]:b.strides[2],_=A?b.strides[2]:1,O=A?1:b.strides[1],B=a.data.get(r.dataId).values,F=a.data.get(s.dataId).values,U=b.values;for(let G=0;G=d.inHeight)continue;let ge=re*I[0],ie=q+ee*N;for(let be=0;be=d.inWidth)continue;let gt=ge+Le*I[1],dt=ie+Xe*M,st=gt;for(let it=0;it=u.inDepth)continue;let G=F*M[0],q=E+U*N[1];for(let H=0;H=u.inHeight)continue;let ee=G+X*M[1],ge=q+re*N[2];for(let ie=0;ie=u.inWidth)continue;let Xe=ee+Re*M[2],gt=ge+Le*u.inChannels,dt=Xe;for(let st=0;stMath.cos(e)),rW={kernelName:ki,backendName:"cpu",kernelFunc:nW},sW=ct(Ii,e=>Math.cosh(e)),iW={kernelName:Ii,backendName:"cpu",kernelFunc:sW};function oW(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[p,c,d,h]=r.shape,m=s.shape[0],[f,g]=o,y=$e([m,f,g,h],"float32"),x=a.data.get(s.dataId).values,A=a.data.get(i.dataId).values,b=a.data.get(r.dataId).values,w=v.computeStrides(r.shape),I=v.computeStrides(y.shape);for(let T=0;T=p)continue;let O=f>1?(E-M)*(c-1)/(f-1):0,B=g>1?(C-P)*(d-1)/(g-1):0;for(let F=0;F1?M*(c-1)+F*O:.5*(M+E)*(c-1);if(U<0||U>c-1){for(let G=0;G1?P*(d-1)+V*B:.5*(P+C)*(d-1);if(Z<0||Z>d-1){for(let ge=0;ge1?P*(d-1)+G*B:.5*(P+C)*(d-1);if(q<0||q>d-1){for(let Z=0;Zy+m-x-1:(y,x)=>y+x;for(let y=0;yy+m-x-1:(y,x)=>y+x;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],c=l*s,d=u*s,h=p/(s*s),m=a.data.get(r.dataId).values,f=new Float32Array(o*c*d*h),g=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=S.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:x}=h,A=x.left,b=x.top,w=h.outChannels/h.inChannels,I=new Ut(h.outShape,r.dtype),T=a.data.get(r.dataId).values,N=a.data.get(s.dataId).values,M=I.values;for(let P=0;P=h.inHeight)continue;let G=F*c[0],q=E+U*p[1];for(let H=0;H=h.inWidth)continue;let ee=G+X*c[1],ge=q+re*h.inChannels,ie=V,be=ee;for(let Ce=0;Ce{let{x:n,filter:r}=e,{strides:s,pad:i,dilations:o}=a,l=t,u=l.data.get(n.dataId).values,p=n.shape.length,c=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:x,padInfo:A,strideHeight:b,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:N,dilationWidth:M,outShape:P}=S.computeDilation2DInfo(n.shape,r.shape,s,i,"NHWC",o),E=v.sizeFromShape(P),C=P.length,_=v.getArrayFromDType(n.dtype,E);for(let O=0;O=0&&X=0&&eeH&&(H=be)}}}let V=v.locToIndex([O,B,U,q],C,v.computeStrides(P));_[V]=H}}}return{dataId:l.write(v.toTypedArray(_,n.dtype),P,n.dtype),shape:P,dtype:n.dtype}}},SW={kernelName:Ul,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:x,strideHeight:A,strideWidth:b,filterHeight:w,filterWidth:I,dilationHeight:T,dilationWidth:N,outShape:M}=S.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===M.length,()=>`Error in ${Ul}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let P=v.toNestedArray(M,u.data.get(s.dataId).values),E=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let C=0;C=0&&Z=0&&reG&&(G=ee,q=V,H=X)}}}E[q][H][U]+=P[C][_][B][U]}}}return{dataId:u.write(v.toTypedArray(E,n.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},CW={kernelName:Vl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:x,strideHeight:A,strideWidth:b,filterHeight:w,filterWidth:I,dilationHeight:T,dilationWidth:N,outShape:M}=S.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===M.length,()=>`Error in ${Vl}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let P=v.toNestedArray(M,u.data.get(s.dataId).values),E=v.makeZerosNestedTypedArray(n.shape,n.dtype);for(let C=0;C=0&&Z=0&&reG&&(G=ee,q=Z,H=re)}}}E[C][q][H][U]+=P[C][_][B][U]}}}return{dataId:u.write(v.toTypedArray(E,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function TW(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{canvas:s,options:i}=n,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let c=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(c==null)throw new Error(`Could not get the context with ${p} type.`);let[d,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=a.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,y=new Uint8ClampedArray(h*d*4);for(let A=0;A1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${T}.`)}else if(r.dtype==="int32"&&(T<0||T>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${T}.`);m===1?(b[0]=T*g,b[1]=T*g,b[2]=T*g):b[I]=T*g}let w=A*4;y[w+0]=Math.round(b[0]),y[w+1]=Math.round(b[1]),y[w+2]=Math.round(b[2]),y[w+3]=Math.round(b[3])}s.width=h,s.height=d;let x=new ImageData(y,h,d);return c.putImageData(x,0,0),r}var NW={kernelName:gp,backendName:"cpu",kernelFunc:TW};function Kp(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;Ie(r,"sum");let o;r.dtype==="bool"?o=as({inputs:{x:r},backend:a,attrs:{dtype:"int32"}}):o=tr({inputs:{x:r},backend:a});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=S.getAxesPermutation(u,l),c=u,d=o;p!=null&&(d=Ga({inputs:{x:o},backend:a,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,l)),S.assertAxesAreInnerMostDims("sum",c,d.shape.length);let[h,m]=S.computeOutAndReduceShapes(d.shape,c),f=S.upcastType(d.dtype,"int32"),g=yh(a,h,f),y=v.sizeFromShape(m),x=a.data.get(g.dataId).values,A=a.data.get(d.dataId).values;for(let b=0;b=0&&(d=Kp({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeIntermediateTensorInfo(f);return d}var MW={kernelName:yp,backendName:"cpu",kernelFunc:EW};function _W(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t;Ie([n,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=a.data.get(r.dataId).values,o=a.data.get(n.dataId).values;for(let l=0;l=0?s[l]=o[l]:s[l]=o[l]*(u+1)}return a.makeTensorInfo(r.shape,"float32",s)}var PW={kernelName:fu,backendName:"cpu",kernelFunc:_W},$W=S.ERF_P,FW=S.ERF_A1,DW=S.ERF_A2,OW=S.ERF_A3,zW=S.ERF_A4,LW=S.ERF_A5,WW=ct(Pi,e=>{let t=Math.sign(e),a=Math.abs(e),n=1/(1+$W*a);return t*(1-((((LW*n+zW)*n+OW)*n+DW)*n+FW)*n*Math.exp(-a*a))}),BW={kernelName:Pi,backendName:"cpu",kernelFunc:WW};function bh(e){let{inputs:t,backend:a,attrs:n}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),At({inputs:{x:r},backend:a,attrs:{shape:o}})}var VW={kernelName:gu,backendName:"cpu",kernelFunc:bh},UW=Ft((e,t)=>e/t),v3=Yt(Mi,UW),M1={kernelName:Mi,backendName:"cpu",kernelFunc:v3};function J6(e,t,a){let n=e.shape,r=n[0],s=n[1],i=a.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),c=v.getTypedArrayFromDType("float32",p),d=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:n}=e,r=a,s=v.getTypedArrayFromDType(n.dtype,v.sizeFromShape(n.shape)),[i,o,l,u]=n.shape,p=r.data.get(n.dataId).values;for(let c=0;c=0&&x=0,()=>`GatherV2: the index value ${w} is not in [0, ${p-1}]`)}let c=o;o==null&&(c=0);let d=v.sizeFromShape(s.shape),h=S.segment_util.collectGatherOpShapeInfo(r,s,l,c),m=At({inputs:{x:r},backend:a,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=At({inputs:{x:s},backend:a,attrs:{shape:[h.batchSize,d/h.batchSize]}}),g=[h.batchSize,h.outerSize,d/h.batchSize,h.sliceSize],y=a.bufferSync(f),x=a.bufferSync(m),A=f6(x,y,g);return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),a.makeTensorInfo(h.outputShape,A.dtype,A.values)}var sB={kernelName:xu,backendName:"cpu",kernelFunc:rB};function iB(e){let{inputs:t,backend:a}=e,{input:n}=t,r=v.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=r/s,o=At({inputs:{x:n},backend:a,attrs:{shape:[i,s]}}),l=J6(o,!0,a),u=At({inputs:{x:l},backend:a,attrs:{shape:n.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(l),u}var oB={kernelName:Ap,backendName:"cpu",kernelFunc:iB},lB=ct(Hi,e=>Number.isFinite(e)?1:0,"bool"),uB={kernelName:Hi,backendName:"cpu",kernelFunc:lB},dB=ct(ji,e=>Math.abs(e)===1/0?1:0,"bool"),pB={kernelName:ji,backendName:"cpu",kernelFunc:dB},cB=ct(qi,e=>Number.isNaN(e)?1:0,"bool"),hB={kernelName:qi,backendName:"cpu",kernelFunc:cB};function mB(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=b6(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var fB={kernelName:Zi,backendName:"cpu",kernelFunc:mB},gB=ct(Qi,e=>Math.log1p(e)),yB={kernelName:Qi,backendName:"cpu",kernelFunc:gB},xB=Ft((e,t)=>e&&t),AB=Yt(eo,xB,null,"bool"),bB={kernelName:eo,backendName:"cpu",kernelFunc:AB},vB=ct(to,e=>e?0:1,"bool"),wB={kernelName:to,backendName:"cpu",kernelFunc:vB},kB=Ft((e,t)=>e||t),IB=Yt(ao,kB,null,"bool"),SB={kernelName:ao,backendName:"cpu",kernelFunc:IB};function CB(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;Ie(r,"LRN");let u=r.shape[3],p=u-1,c=a.data.get(r.dataId).values,d=v.sizeFromShape(r.shape),h=new Float32Array(d);function m(f){let g=f%u,y=f-g+Math.max(0,g-s),x=f-g+Math.min(g+s,p),A=0;for(;y<=x;y++){let b=c[y];A+=b*b}return A}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=S.computePool2DInfo(r.shape,s,i,u,o,l),c;if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))c=tr({inputs:{x:r},backend:a});else{let d=a.data.get(r.dataId).values,h=v.computeStrides(r.shape),m=b3(d,r.shape,r.dtype,h,p,"max");c=a.makeTensorInfo(p.outShape,r.dtype,m.values)}return c}var _B={kernelName:io,backendName:"cpu",kernelFunc:MB};function PB(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n;Ie(r,"maxPool3d");let p=S.computePool3DInfo(r.shape,s,i,1,o,l,u),c=a.data.get(r.dataId).values,d=K6(c,r.shape,r.dtype,v.computeStrides(r.shape),p,"max");return a.makeTensorInfo(d.shape,"float32",d.values)}var $B={kernelName:bu,backendName:"cpu",kernelFunc:PB};function FB(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n;Ie([r,s],"maxPool3DGrad");let p=S.computePool3DInfo(s.shape,i,o,1,l,u),c=a.bufferSync(s),d=kL(c,p),h=p.strideDepth,m=p.strideHeight,f=p.strideWidth,g=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,A=p.effectiveFilterDepth,b=p.effectiveFilterHeight,w=p.effectiveFilterWidth,I=A-1-p.padInfo.front,T=w-1-p.padInfo.left,N=b-1-p.padInfo.top,M=$e(s.shape,"float32"),P=a.bufferSync(r);for(let E=0;E=p.outDepth||Math.floor(V)!==V))for(let Z=0;Z=p.outHeight||Math.floor(X)!==X))for(let re=0;re=p.outWidth||Math.floor(ee)!==ee)continue;let ge=A*b*w-1-d.get(E,V,X,ee,C),ie=H*b*w+Z*w+re,be=ge===ie?1:0;if(be===0)continue;let Ce=P.get(E,V,X,ee,C);q+=Ce*be}}}M.set(q,E,_,O,B,C)}return a.makeTensorInfo(M.shape,M.dtype,M.values)}var DB={kernelName:wp,backendName:"cpu",kernelFunc:FB};function OB(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;Ie([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=S.computePool2DInfo(o.shape,l,u,1,p,c),h=a.data.get(o.dataId).values,m=$e(d.outShape,o.dtype,X6(h,o.shape,o.dtype,d).values),f=d.strideHeight,g=d.strideWidth,y=d.dilationHeight,x=d.dilationWidth,A=d.effectiveFilterHeight,b=d.effectiveFilterWidth,w=b-1-d.padInfo.left,I=A-1-d.padInfo.top,T=$e(o.shape,"float32"),N=a.data.get(r.dataId).values,M=$e(r.shape,"float32",N);for(let P=0;P=d.outHeight||Math.floor(G)!==G))for(let q=0;q=d.outWidth||Math.floor(H)!==H)continue;let V=A*b-1-m.get(P,G,H,E),Z=U*b+q,X=V===Z?1:0;if(X===0)continue;let re=M.get(P,G,H,E);F+=re*X}}T.set(F,P,C,_,E)}return a.makeTensorInfo(T.shape,T.dtype,T.values)}var zB={kernelName:vp,backendName:"cpu",kernelFunc:OB};function LB(e,t,a,n,r){let s=v.computeStrides(t),i=b3(e,t,a,s,r,"max"),o=X6(e,t,a,r,!0,n);return[i.values,o.values]}var WB={kernelName:vu,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;Ie(n,"MaxPoolWithArgmax");let u=l.data.get(n.dataId).values,p=S.computePool2DInfo(n.shape,r,s,[1,1],i),[c,d]=LB(u,n.shape,n.dtype,o,p),h=l.write(c,p.outShape,n.dtype),m=l.write(d,p.outShape,n.dtype);return[{dataId:h,shape:p.outShape,dtype:n.dtype},{dataId:m,shape:p.outShape,dtype:"int32"}]}};function BB(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=v.parseAxisParam(s,r.shape),l=S.computeOutAndReduceShapes(r.shape,o)[1],u=v.sizeFromShape(l),p=[],c=a.makeTensorInfo([],"float32",new Float32Array([u]));p.push(c);let d=as({inputs:{x:r},backend:a,attrs:{dtype:"float32"}});p.push(d);let h=v3({inputs:{a:d,b:c},backend:a});p.push(h);let m=Kp({inputs:{x:h},backend:a,attrs:{axis:s,keepDims:i}});return p.forEach(f=>a.disposeIntermediateTensorInfo(f)),m}var VB={kernelName:oo,backendName:"cpu",kernelFunc:BB};function UB(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;Ie(r,"min");let o=v.parseAxisParam(s,r.shape),l=o,u=S.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=Ga({inputs:{x:r},backend:a,attrs:{perm:u}}),l=S.getInnerMostAxes(l.length,r.shape.length)),S.assertAxesAreInnerMostDims("min",l,p.shape.length);let[c,d]=S.computeOutAndReduceShapes(p.shape,l),h=v.sizeFromShape(d),m=v.makeZerosTypedArray(v.sizeFromShape(c),p.dtype),f=a.data.get(p.dataId).values;for(let y=0;yx[0]+r.shape[A]+x[1]),l=s.map(x=>x[0]),u=s.map((x,A)=>x[0]+r.shape[A]),p=i==="reflect"?0:1,c=a.data.get(r.dataId).values,d=r.shape.length,h=v.computeStrides(r.shape),m=v.sizeFromShape(o),f=o.length,g=v.computeStrides(o),y=v.getTypedArrayFromDType(r.dtype,m);for(let x=0;x=u[w]&&(A[w]=(u[w]-1)*2-A[w]+p);A=A.map((w,I)=>w-l[I]);let b=v.locToIndex(A,d,h);y[x]=c[b]}return{dataId:a.write(y,o,r.dtype),shape:o,dtype:r.dtype}}var jB={kernelName:po,backendName:"cpu",kernelFunc:HB},qB=Ft((e,t)=>{let a=e%t;return e<0&&t<0||e>=0&&t>=0?a:(a+t)%t}),XB=Yt(co,qB),KB={kernelName:co,backendName:"cpu",kernelFunc:XB},YB=tu(sA());function ev(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=r.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);let l=v.parseAxisParam([o],r.shape),u=Q6({inputs:{x:r},backend:a,attrs:{reductionIndices:l,keepDims:!1}}),p=S.expandShapeToKeepDim(u.shape,l),c=At({inputs:{x:u},backend:a,attrs:{shape:p}}),d=x3({inputs:{a:r,b:c},backend:a}),h=d6({inputs:{x:d},backend:a}),m=Kp({inputs:{x:h},backend:a,attrs:{axis:l,keepDims:!1}}),f=At({inputs:{x:m},backend:a,attrs:{shape:p}}),g=v3({inputs:{a:h,b:f},backend:a});return a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),g}var ZB={kernelName:Vo,backendName:"cpu",kernelFunc:ev};function JB(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n;Ie(r,"multinomial");let l=o?r:ev({inputs:{logits:r},backend:a,attrs:{dim:-1}}),u=l.shape[0],p=l.shape[1],c=a.data.get(l.dataId).values,d=[u,s],h=v.makeZerosTypedArray(v.sizeFromShape(d),"int32");for(let m=0;m=0&&c[d]{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=bh({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=Ql({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeIntermediateTensorInfo(p)),u}var hV={kernelName:Su,backendName:"cpu",kernelFunc:av};function mV(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;Ie(r,"pad");let o=s.map((y,x)=>y[0]+r.shape[x]+y[1]),l=s.map(y=>y[0]),u=a.data.get(r.dataId).values,p=v.sizeFromShape(r.shape),c=r.shape.length,d=v.computeStrides(r.shape),h=v.sizeFromShape(o),m=o.length,f=v.computeStrides(o),g=v.getTypedArrayFromDType(r.dtype,h);i!==0&&g.fill(i);for(let 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i}calculateOutputIndexValueRowID(t,a,n,r){let s=t.length,i=[];if(s===0)return[];let o=0,l=t[0];if(l>=a.length)throw new Error(`Got currentValueRowId=${l}, which is not less than ${a.length}`);let u=a[l];i.push(u);for(let p=1;p=0&&(++o,o=a.length)throw new Error(`Got nextValueRowId=${c} which is not less than ${a.length}`);u=a[c]}i.push(u)}if(i.length!==t.length)throw new Error("Invalid row ids.");return i}calculateOutputIndex(t,a,n,r){let s=this.getRowPartitionTensor(t),i=this.getRowPartitionTypeByDimension(t);switch(i){case In.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(s,a,n,r);case In.ROW_SPLITS:if(s.length-1>a.length)throw new Error(`Row partition size is greater than output size: ${s.length-1} > ${a.length}`);return this.calculateOutputIndexRowSplit(s,a,n,r);default:throw new Error(`Unsupported partition type: ${In[i]}`)}}getFirstDimensionSize(){let t=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");let a=this.rowPartitionTypes[0];switch(a){case In.FIRST_DIM_SIZE:return t[0];case In.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case In.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${In[a]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. 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A=i?[g,p,d]:[g,d,p],b=o?[y,h,c]:[y,c,h],w=At({inputs:{x:r},backend:a,attrs:{shape:A}}),I=At({inputs:{x:s},backend:a,attrs:{shape:b}}),T=i?w.shape[1]:w.shape[2],N=i?w.shape[2]:w.shape[1],M=o?I.shape[1]:I.shape[2],P=Math.max(g,y),E=a.data.get(w.dataId).values,S=a.data.get(I.dataId).values,_=v.computeStrides(w.shape),O=v.computeStrides(I.shape),[W,$,U]=i?[_[0],1,_[1]]:[_[0],_[1],1],[G,q,H]=o?[1,O[1],O[0]]:[O[1],1,O[0]],V=N*M,Z=$e([P,N,M],w.dtype),X=Z.values,re=a.blockSize;for(let ee=0;eeMath.acos(e)),cL={kernelName:si,backendName:"cpu",kernelFunc:pL},hL=ct(ii,e=>Math.acosh(e)),mL={kernelName:ii,backendName:"cpu",kernelFunc:hL};function fL(e){let{inputs:t,backend:a}=e,n=t;Ie(t,"addN");let r=n.map(o=>a.data.get(o.dataId).values),s=$e(n[0].shape,n[0].dtype),i=s.values;for(let o=0;ox&&(x=w,A=b)}h[g]=A}return u.forEach(g=>a.disposeIntermediateTensorInfo(g)),a.makeTensorInfo(p,"int32",h)}var wL={kernelName:su,backendName:"cpu",kernelFunc:vL};function kL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n;Ie(r,"argMin");let i=v.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Va({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[p,c]=C.computeOutAndReduceShapes(l.shape,i),d=v.sizeFromShape(p),h=v.makeZerosTypedArray(d,"int32"),m=v.sizeFromShape(c),f=a.data.get(l.dataId).values;for(let g=0;ga.disposeIntermediateTensorInfo(g)),a.makeTensorInfo(p,"int32",h)}var IL={kernelName:iu,backendName:"cpu",kernelFunc:kL},SL=ct(di,e=>Math.asin(e)),CL={kernelName:di,backendName:"cpu",kernelFunc:SL},TL=ct(pi,e=>Math.asinh(e)),NL={kernelName:pi,backendName:"cpu",kernelFunc:TL},RL=ct(ci,e=>Math.atan(e)),EL={kernelName:ci,backendName:"cpu",kernelFunc:RL},ML=$t((e,t)=>Math.atan2(e,t)),PL=Kt(mi,ML),_L={kernelName:mi,backendName:"cpu",kernelFunc:PL},$L=ct(hi,e=>Math.atanh(e)),FL={kernelName:hi,backendName:"cpu",kernelFunc:$L};function k3(e,t,a,n,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,p=r.effectiveFilterHeight,c=r.effectiveFilterWidth,d=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=$e(r.outShape,a),g=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],x=r.outShape[2]*r.outShape[3],A=r.outShape[3];for(let b=0;bU?U=ee:s==="avg"&&(G+=ee,q++)}if(isNaN(U))break}let H=S+_*A+T;g[H]=s==="avg"?G/q:U}}}return f}function sv(e,t,a,n,r=!1,s=!1){let i=$e(n.outShape,"int32"),o=n.strideHeight,l=n.strideWidth,u=n.dilationHeight,p=n.dilationWidth,c=n.effectiveFilterHeight,d=n.effectiveFilterWidth,h=n.padInfo.top,m=n.padInfo.left,f=$e(t,a,e);for(let g=0;gP&&(P=$,r?E=s?((g*n.inHeight+S)*n.inWidth+O)*n.inChannels+y:(S*n.inWidth+O)*n.inChannels+y:E=_*d+W)}}i.set(E,g,x,I,y)}}return i}function iv(e,t,a,n,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,p=r.dilationHeight,c=r.dilationWidth,d=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,x=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,A=$e(r.outShape,a),b=A.values,w=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],I=r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[3]*r.outShape[4],N=r.outShape[4];for(let M=0;Mbe?be=yt:s==="avg"&&(Ce+=yt,Re++),isNaN(be))break}if(isNaN(be))break}if(isNaN(be))break}let Le=ie+S;b[Le]=s==="avg"?Ce/Math.max(Re,1):be}}}}return A}function DL(e,t){let a=$e(t.outShape,"int32"),n=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,p=t.effectiveFilterHeight,c=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f=_&&(_=V,O=$*p*c+G*p+H)}}}a.set(O,f,y,w,M,g)}}}return a}function OL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;Ie(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,u,o,l),c;if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))c=tr({inputs:{x:r},backend:a});else{let d=a.data.get(r.dataId).values,h=v.computeStrides(r.shape),m=k3(d,r.shape,r.dtype,h,p,"avg");c=a.makeTensorInfo(p.outShape,r.dtype,m.values)}return c}var zL={kernelName:fi,backendName:"cpu",kernelFunc:OL};function LL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n;Ie(r,"avgPool3d");let p=C.computePool3DInfo(r.shape,s,i,1,o,l,u),c=a.data.get(r.dataId).values,d=iv(c,r.shape,r.dtype,v.computeStrides(r.shape),p,"avg");return a.makeTensorInfo(d.shape,"float32",d.values)}var WL={kernelName:ou,backendName:"cpu",kernelFunc:LL};function BL(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n;Ie([r,s],"avgPool3DGrad");let p=C.computePool3DInfo(s.shape,i,o,1,l,u),c=p.strideDepth,d=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,y=p.dilationDepth,x=p.dilationHeight,A=p.dilationWidth,b=p.effectiveFilterDepth,w=p.effectiveFilterHeight,I=p.effectiveFilterWidth,T=b-1-p.padInfo.front,N=I-1-p.padInfo.left,M=w-1-p.padInfo.top,P=$e(s.shape,"float32"),E=1/(m*f*g),S=a.bufferSync(r);for(let _=0;_=p.outDepth||Math.floor(X)!==X))for(let re=0;re=p.outHeight||Math.floor(ee)!==ee))for(let ge=0;ge=p.outWidth||Math.floor(ie)!==ie)continue;let be=S.get(_,X,ee,ie,O);V+=be}}}P.set(V*E,_,W,$,U,O)}return a.makeTensorInfo(P.shape,P.dtype,P.values)}var VL={kernelName:lp,backendName:"cpu",kernelFunc:BL};function UL(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Ie([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=C.computePool2DInfo(i.shape,o,l,1,u),c=p.strideHeight,d=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.effectiveFilterHeight,x=p.effectiveFilterWidth,A=x-1-p.padInfo.left,b=y-1-p.padInfo.top,w=$e(i.shape,"float32"),I=1/(h*m),T=a.data.get(r.dataId).values,N=$e(r.shape,"float32",T);for(let M=0;M=p.outHeight||Math.floor(U)!==U))for(let G=0;G=p.outWidth||Math.floor(q)!==q)continue;let H=N.get(M,U,q,P);W+=H}}w.set(W*I,M,E,S,P)}return a.makeTensorInfo(w.shape,w.dtype,w.values)}var GL={kernelName:op,backendName:"cpu",kernelFunc:UL};function HL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ie([r,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=n;u==null&&(u=.001);let p=a.data.get(r.dataId).values,c=a.data.get(o.dataId).values,d=a.data.get(l.dataId).values,h=s?a.data.get(s.dataId).values:new Float32Array([1]),m=i?a.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(p.length),g=m.length,y=h.length,x=d.length,A=c.length,b=0,w=0,I=0,T=0;for(let N=0;N=g&&(b=0),w>=A&&(w=0),I>=y&&(I=0),T>=x&&(T=0);return a.makeTensorInfo(r.shape,r.dtype,f)}var jL={kernelName:Bi,backendName:"cpu",kernelFunc:HL};function qL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;Ie([r],"batchToSpaceND");let o=s.reduce((y,x)=>y*x),l=C.getReshaped(r.shape,s,o),u=C.getPermuted(l.length,s.length),p=C.getReshapedPermuted(r.shape,s,o),c=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(p,i,s.length),h=At({inputs:{x:r},backend:a,attrs:{shape:l}}),m=Va({inputs:{x:h},backend:a,attrs:{perm:u}}),f=At({inputs:{x:m},backend:a,attrs:{shape:p}}),g=Qs({inputs:{x:f},backend:a,attrs:{begin:c,size:d}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),g}var XL={kernelName:lu,backendName:"cpu",kernelFunc:qL};function KL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,u=c3(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var YL={kernelName:yi,backendName:"cpu",kernelFunc:KL};function ZL(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.data.get(n.dataId).values,i=a.data.get(r.dataId).values,o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var JL={kernelName:du,backendName:"cpu",kernelFunc:ZL},QL=ct(os,(e,t)=>{let a=t;return e>a.clipValueMax?a.clipValueMax:e{let{x:t}=e.inputs,a=e.backend,n=new Float32Array(v.sizeFromShape(t.shape)),r=a.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=a.data.get(s.dataId).values,l=a.data.get(i.dataId).values;for(let u=0;uf.shape);C.assertParamsConsistent(i,s);let o=C.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>v.sizeFromShape(f.shape)>0);if(l.length===1)return tr({inputs:{x:l[0]},backend:a});if(l[0].dtype==="complex64"){let f=l.map(b=>Js({inputs:{input:b},backend:a})),g=l.map(b=>Jl({inputs:{input:b},backend:a})),y=Ql({inputs:f,backend:a,attrs:{axis:s}}),x=Ql({inputs:g,backend:a,attrs:{axis:s}}),A=Za({inputs:{real:y,imag:x},backend:a});return f.forEach(b=>a.disposeIntermediateTensorInfo(b)),g.forEach(b=>a.disposeIntermediateTensorInfo(b)),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(x),A}let u=l.map(f=>{let g=[-1,v.sizeFromShape(f.shape.slice(s))];return At({inputs:{x:f},backend:a,attrs:{shape:g}})}),p=u.map(f=>({vals:a.data.get(f.dataId).values,shape:f.shape}));o=C.computeOutShape(u.map(f=>f.shape),1);let c=u[0].shape[0]===1,d=h3(p,o,t[0].dtype,c),h=C.computeOutShape(l.map(f=>f.shape),s),m=a.makeTensorInfo(h,t[0].dtype,d);return u.forEach(f=>a.disposeIntermediateTensorInfo(f)),m}var rW={kernelName:pu,backendName:"cpu",kernelFunc:Ql};function ov(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n;Ie([r,s],"conv2d");let c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.padInfo.left,x=d.padInfo.top,A=d.dataFormat==="channelsLast",b=new Vt(d.outShape,r.dtype),w=v.computeStrides(r.shape),I=v.computeStrides(s.shape),T=w[0],N=A?w[1]:w[2],M=A?w[2]:1,P=A?1:w[1],E=b.strides[0],S=A?b.strides[1]:b.strides[2],_=A?b.strides[2]:1,O=A?1:b.strides[1],W=a.data.get(r.dataId).values,$=a.data.get(s.dataId).values,U=b.values;for(let G=0;G=d.inHeight)continue;let ge=re*I[0],ie=q+ee*N;for(let be=0;be=d.inWidth)continue;let ft=ge+Le*I[1],dt=ie+qe*M,st=ft;for(let it=0;it=u.inDepth)continue;let G=$*M[0],q=E+U*N[1];for(let H=0;H=u.inHeight)continue;let ee=G+X*M[1],ge=q+re*N[2];for(let ie=0;ie=u.inWidth)continue;let qe=ee+Re*M[2],ft=ge+Le*u.inChannels,dt=qe;for(let st=0;stMath.cos(e)),yW={kernelName:Ii,backendName:"cpu",kernelFunc:gW},xW=ct(Si,e=>Math.cosh(e)),AW={kernelName:Si,backendName:"cpu",kernelFunc:xW};function bW(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[p,c,d,h]=r.shape,m=s.shape[0],[f,g]=o,y=$e([m,f,g,h],"float32"),x=a.data.get(s.dataId).values,A=a.data.get(i.dataId).values,b=a.data.get(r.dataId).values,w=v.computeStrides(r.shape),I=v.computeStrides(y.shape);for(let T=0;T=p)continue;let O=f>1?(E-M)*(c-1)/(f-1):0,W=g>1?(S-P)*(d-1)/(g-1):0;for(let $=0;$1?M*(c-1)+$*O:.5*(M+E)*(c-1);if(U<0||U>c-1){for(let G=0;G1?P*(d-1)+V*W:.5*(P+S)*(d-1);if(Z<0||Z>d-1){for(let ge=0;ge1?P*(d-1)+G*W:.5*(P+S)*(d-1);if(q<0||q>d-1){for(let Z=0;Zy+m-x-1:(y,x)=>y+x;for(let y=0;yy+m-x-1:(y,x)=>y+x;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],c=l*s,d=u*s,h=p/(s*s),m=a.data.get(r.dataId).values,f=new Float32Array(o*c*d*h),g=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=C.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:x}=h,A=x.left,b=x.top,w=h.outChannels/h.inChannels,I=new Vt(h.outShape,r.dtype),T=a.data.get(r.dataId).values,N=a.data.get(s.dataId).values,M=I.values;for(let P=0;P=h.inHeight)continue;let G=$*c[0],q=E+U*p[1];for(let H=0;H=h.inWidth)continue;let ee=G+X*c[1],ge=q+re*h.inChannels,ie=V,be=ee;for(let Ce=0;Ce{let{x:n,filter:r}=e,{strides:s,pad:i,dilations:o}=a,l=t,u=l.data.get(n.dataId).values,p=n.shape.length,c=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:x,padInfo:A,strideHeight:b,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:N,dilationWidth:M,outShape:P}=C.computeDilation2DInfo(n.shape,r.shape,s,i,"NHWC",o),E=v.sizeFromShape(P),S=P.length,_=v.getArrayFromDType(n.dtype,E);for(let O=0;O=0&&X=0&&eeH&&(H=be)}}}let V=v.locToIndex([O,W,U,q],S,v.computeStrides(P));_[V]=H}}}return{dataId:l.write(v.toTypedArray(_,n.dtype),P,n.dtype),shape:P,dtype:n.dtype}}},zW={kernelName:Hl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:x,strideHeight:A,strideWidth:b,filterHeight:w,filterWidth:I,dilationHeight:T,dilationWidth:N,outShape:M}=C.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===M.length,()=>`Error in ${Hl}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let P=v.toNestedArray(M,u.data.get(s.dataId).values),E=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&Z=0&&reG&&(G=ee,q=V,H=X)}}}E[q][H][U]+=P[S][_][W][U]}}}return{dataId:u.write(v.toTypedArray(E,n.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},LW={kernelName:Gl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:x,strideHeight:A,strideWidth:b,filterHeight:w,filterWidth:I,dilationHeight:T,dilationWidth:N,outShape:M}=C.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===M.length,()=>`Error in ${Gl}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let P=v.toNestedArray(M,u.data.get(s.dataId).values),E=v.makeZerosNestedTypedArray(n.shape,n.dtype);for(let S=0;S=0&&Z=0&&reG&&(G=ee,q=Z,H=re)}}}E[S][q][H][U]+=P[S][_][W][U]}}}return{dataId:u.write(v.toTypedArray(E,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function WW(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{canvas:s,options:i}=n,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let c=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(c==null)throw new Error(`Could not get the context with ${p} type.`);let[d,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=a.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,y=new Uint8ClampedArray(h*d*4);for(let A=0;A1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${T}.`)}else if(r.dtype==="int32"&&(T<0||T>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${T}.`);m===1?(b[0]=T*g,b[1]=T*g,b[2]=T*g):b[I]=T*g}let w=A*4;y[w+0]=Math.round(b[0]),y[w+1]=Math.round(b[1]),y[w+2]=Math.round(b[2]),y[w+3]=Math.round(b[3])}s.width=h,s.height=d;let x=new ImageData(y,h,d);return c.putImageData(x,0,0),r}var BW={kernelName:mp,backendName:"cpu",kernelFunc:WW};function qp(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;Ie(r,"sum");let o;r.dtype==="bool"?o=rs({inputs:{x:r},backend:a,attrs:{dtype:"int32"}}):o=tr({inputs:{x:r},backend:a});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=C.getAxesPermutation(u,l),c=u,d=o;p!=null&&(d=Va({inputs:{x:o},backend:a,attrs:{perm:p}}),c=C.getInnerMostAxes(c.length,l)),C.assertAxesAreInnerMostDims("sum",c,d.shape.length);let[h,m]=C.computeOutAndReduceShapes(d.shape,c),f=C.upcastType(d.dtype,"int32"),g=yh(a,h,f),y=v.sizeFromShape(m),x=a.data.get(g.dataId).values,A=a.data.get(d.dataId).values;for(let b=0;b=0&&(d=qp({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeIntermediateTensorInfo(f);return d}var GW={kernelName:fp,backendName:"cpu",kernelFunc:UW};function HW(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t;Ie([n,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=a.data.get(r.dataId).values,o=a.data.get(n.dataId).values;for(let l=0;l=0?s[l]=o[l]:s[l]=o[l]*(u+1)}return a.makeTensorInfo(r.shape,"float32",s)}var jW={kernelName:fu,backendName:"cpu",kernelFunc:HW},qW=C.ERF_P,XW=C.ERF_A1,KW=C.ERF_A2,YW=C.ERF_A3,ZW=C.ERF_A4,JW=C.ERF_A5,QW=ct($i,e=>{let t=Math.sign(e),a=Math.abs(e),n=1/(1+qW*a);return t*(1-((((JW*n+ZW)*n+YW)*n+KW)*n+XW)*n*Math.exp(-a*a))}),eB={kernelName:$i,backendName:"cpu",kernelFunc:QW};function bh(e){let{inputs:t,backend:a,attrs:n}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),At({inputs:{x:r},backend:a,attrs:{shape:o}})}var tB={kernelName:gu,backendName:"cpu",kernelFunc:bh},aB=$t((e,t)=>e/t),I3=Kt(Pi,aB),P1={kernelName:Pi,backendName:"cpu",kernelFunc:I3};function uv(e,t,a){let n=e.shape,r=n[0],s=n[1],i=a.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),c=v.getTypedArrayFromDType("float32",p),d=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:n}=e,r=a,s=v.getTypedArrayFromDType(n.dtype,v.sizeFromShape(n.shape)),[i,o,l,u]=n.shape,p=r.data.get(n.dataId).values;for(let c=0;c=0&&x=0,()=>`GatherV2: the index value ${w} is not in [0, ${p-1}]`)}let c=o;o==null&&(c=0);let d=v.sizeFromShape(s.shape),h=C.segment_util.collectGatherOpShapeInfo(r,s,l,c),m=At({inputs:{x:r},backend:a,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=At({inputs:{x:s},backend:a,attrs:{shape:[h.batchSize,d/h.batchSize]}}),g=[h.batchSize,h.outerSize,d/h.batchSize,h.sliceSize],y=a.bufferSync(f),x=a.bufferSync(m),A=C6(x,y,g);return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),a.makeTensorInfo(h.outputShape,A.dtype,A.values)}var xB={kernelName:xu,backendName:"cpu",kernelFunc:yB};function AB(e){let{inputs:t,backend:a}=e,{input:n}=t,r=v.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=r/s,o=At({inputs:{x:n},backend:a,attrs:{shape:[i,s]}}),l=uv(o,!0,a),u=At({inputs:{x:l},backend:a,attrs:{shape:n.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(l),u}var bB={kernelName:yp,backendName:"cpu",kernelFunc:AB},vB=ct(ji,e=>Number.isFinite(e)?1:0,"bool"),wB={kernelName:ji,backendName:"cpu",kernelFunc:vB},kB=ct(qi,e=>Math.abs(e)===1/0?1:0,"bool"),IB={kernelName:qi,backendName:"cpu",kernelFunc:kB},SB=ct(Xi,e=>Number.isNaN(e)?1:0,"bool"),CB={kernelName:Xi,backendName:"cpu",kernelFunc:SB};function TB(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=M6(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var NB={kernelName:Ji,backendName:"cpu",kernelFunc:TB},RB=ct(eo,e=>Math.log1p(e)),EB={kernelName:eo,backendName:"cpu",kernelFunc:RB},MB=$t((e,t)=>e&&t),PB=Kt(to,MB,null,"bool"),_B={kernelName:to,backendName:"cpu",kernelFunc:PB},$B=ct(ao,e=>e?0:1,"bool"),FB={kernelName:ao,backendName:"cpu",kernelFunc:$B},DB=$t((e,t)=>e||t),OB=Kt(no,DB,null,"bool"),zB={kernelName:no,backendName:"cpu",kernelFunc:OB};function LB(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;Ie(r,"LRN");let u=r.shape[3],p=u-1,c=a.data.get(r.dataId).values,d=v.sizeFromShape(r.shape),h=new Float32Array(d);function m(f){let g=f%u,y=f-g+Math.max(0,g-s),x=f-g+Math.min(g+s,p),A=0;for(;y<=x;y++){let b=c[y];A+=b*b}return A}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,u,o,l),c;if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))c=tr({inputs:{x:r},backend:a});else{let d=a.data.get(r.dataId).values,h=v.computeStrides(r.shape),m=k3(d,r.shape,r.dtype,h,p,"max");c=a.makeTensorInfo(p.outShape,r.dtype,m.values)}return c}var HB={kernelName:oo,backendName:"cpu",kernelFunc:GB};function jB(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n;Ie(r,"maxPool3d");let p=C.computePool3DInfo(r.shape,s,i,1,o,l,u),c=a.data.get(r.dataId).values,d=iv(c,r.shape,r.dtype,v.computeStrides(r.shape),p,"max");return a.makeTensorInfo(d.shape,"float32",d.values)}var qB={kernelName:bu,backendName:"cpu",kernelFunc:jB};function XB(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n;Ie([r,s],"maxPool3DGrad");let p=C.computePool3DInfo(s.shape,i,o,1,l,u),c=a.bufferSync(s),d=DL(c,p),h=p.strideDepth,m=p.strideHeight,f=p.strideWidth,g=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,A=p.effectiveFilterDepth,b=p.effectiveFilterHeight,w=p.effectiveFilterWidth,I=A-1-p.padInfo.front,T=w-1-p.padInfo.left,N=b-1-p.padInfo.top,M=$e(s.shape,"float32"),P=a.bufferSync(r);for(let E=0;E=p.outDepth||Math.floor(V)!==V))for(let Z=0;Z=p.outHeight||Math.floor(X)!==X))for(let re=0;re=p.outWidth||Math.floor(ee)!==ee)continue;let ge=A*b*w-1-d.get(E,V,X,ee,S),ie=H*b*w+Z*w+re,be=ge===ie?1:0;if(be===0)continue;let Ce=P.get(E,V,X,ee,S);q+=Ce*be}}}M.set(q,E,_,O,W,S)}return a.makeTensorInfo(M.shape,M.dtype,M.values)}var KB={kernelName:bp,backendName:"cpu",kernelFunc:XB};function YB(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;Ie([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=C.computePool2DInfo(o.shape,l,u,1,p,c),h=a.data.get(o.dataId).values,m=$e(d.outShape,o.dtype,sv(h,o.shape,o.dtype,d).values),f=d.strideHeight,g=d.strideWidth,y=d.dilationHeight,x=d.dilationWidth,A=d.effectiveFilterHeight,b=d.effectiveFilterWidth,w=b-1-d.padInfo.left,I=A-1-d.padInfo.top,T=$e(o.shape,"float32"),N=a.data.get(r.dataId).values,M=$e(r.shape,"float32",N);for(let P=0;P=d.outHeight||Math.floor(G)!==G))for(let q=0;q=d.outWidth||Math.floor(H)!==H)continue;let V=A*b-1-m.get(P,G,H,E),Z=U*b+q,X=V===Z?1:0;if(X===0)continue;let re=M.get(P,G,H,E);$+=re*X}}T.set($,P,S,_,E)}return a.makeTensorInfo(T.shape,T.dtype,T.values)}var ZB={kernelName:Ap,backendName:"cpu",kernelFunc:YB};function JB(e,t,a,n,r){let s=v.computeStrides(t),i=k3(e,t,a,s,r,"max"),o=sv(e,t,a,r,!0,n);return[i.values,o.values]}var QB={kernelName:vu,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;Ie(n,"MaxPoolWithArgmax");let u=l.data.get(n.dataId).values,p=C.computePool2DInfo(n.shape,r,s,[1,1],i),[c,d]=JB(u,n.shape,n.dtype,o,p),h=l.write(c,p.outShape,n.dtype),m=l.write(d,p.outShape,n.dtype);return[{dataId:h,shape:p.outShape,dtype:n.dtype},{dataId:m,shape:p.outShape,dtype:"int32"}]}};function eV(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=v.parseAxisParam(s,r.shape),l=C.computeOutAndReduceShapes(r.shape,o)[1],u=v.sizeFromShape(l),p=[],c=a.makeTensorInfo([],"float32",new Float32Array([u]));p.push(c);let d=rs({inputs:{x:r},backend:a,attrs:{dtype:"float32"}});p.push(d);let h=I3({inputs:{a:d,b:c},backend:a});p.push(h);let m=qp({inputs:{x:h},backend:a,attrs:{axis:s,keepDims:i}});return p.forEach(f=>a.disposeIntermediateTensorInfo(f)),m}var tV={kernelName:lo,backendName:"cpu",kernelFunc:eV};function aV(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;Ie(r,"min");let o=v.parseAxisParam(s,r.shape),l=o,u=C.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=Va({inputs:{x:r},backend:a,attrs:{perm:u}}),l=C.getInnerMostAxes(l.length,r.shape.length)),C.assertAxesAreInnerMostDims("min",l,p.shape.length);let[c,d]=C.computeOutAndReduceShapes(p.shape,l),h=v.sizeFromShape(d),m=v.makeZerosTypedArray(v.sizeFromShape(c),p.dtype),f=a.data.get(p.dataId).values;for(let y=0;yx[0]+r.shape[A]+x[1]),l=s.map(x=>x[0]),u=s.map((x,A)=>x[0]+r.shape[A]),p=i==="reflect"?0:1,c=a.data.get(r.dataId).values,d=r.shape.length,h=v.computeStrides(r.shape),m=v.sizeFromShape(o),f=o.length,g=v.computeStrides(o),y=v.getTypedArrayFromDType(r.dtype,m);for(let x=0;x=u[w]&&(A[w]=(u[w]-1)*2-A[w]+p);A=A.map((w,I)=>w-l[I]);let b=v.locToIndex(A,d,h);y[x]=c[b]}return{dataId:a.write(y,o,r.dtype),shape:o,dtype:r.dtype}}var sV={kernelName:co,backendName:"cpu",kernelFunc:rV},iV=$t((e,t)=>{let a=e%t;return e<0&&t<0||e>=0&&t>=0?a:(a+t)%t}),oV=Kt(ho,iV),lV={kernelName:ho,backendName:"cpu",kernelFunc:oV},uV=tu(pA());function pv(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=r.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);let l=v.parseAxisParam([o],r.shape),u=dv({inputs:{x:r},backend:a,attrs:{reductionIndices:l,keepDims:!1}}),p=C.expandShapeToKeepDim(u.shape,l),c=At({inputs:{x:u},backend:a,attrs:{shape:p}}),d=v3({inputs:{a:r,b:c},backend:a}),h=v6({inputs:{x:d},backend:a}),m=qp({inputs:{x:h},backend:a,attrs:{axis:l,keepDims:!1}}),f=At({inputs:{x:m},backend:a,attrs:{shape:p}}),g=I3({inputs:{a:h,b:f},backend:a});return a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),g}var dV={kernelName:Uo,backendName:"cpu",kernelFunc:pv};function pV(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n;Ie(r,"multinomial");let l=o?r:pv({inputs:{logits:r},backend:a,attrs:{dim:-1}}),u=l.shape[0],p=l.shape[1],c=a.data.get(l.dataId).values,d=[u,s],h=v.makeZerosTypedArray(v.sizeFromShape(d),"int32");for(let m=0;m=0&&c[d]{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=bh({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=Ql({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeIntermediateTensorInfo(p)),u}var CV={kernelName:Su,backendName:"cpu",kernelFunc:hv};function TV(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;Ie(r,"pad");let o=s.map((y,x)=>y[0]+r.shape[x]+y[1]),l=s.map(y=>y[0]),u=a.data.get(r.dataId).values,p=v.sizeFromShape(r.shape),c=r.shape.length,d=v.computeStrides(r.shape),h=v.sizeFromShape(o),m=o.length,f=v.computeStrides(o),g=v.getTypedArrayFromDType(r.dtype,h);i!==0&&g.fill(i);for(let 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r=1,s=1;e.texImage2D(e.TEXTURE_2D,0,a.internalFormatHalfFloat,r,s,0,a.textureFormatFloat,a.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(i),o}function Lv(e){return e!==2?!1:Wn(e).fenceSync!=null}function Gu(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Se=B();Se.registerFlag("HAS_WEBGL",()=>Se.getNumber("WEBGL_VERSION")>0);Se.registerFlag("WEBGL_VERSION",()=>D1(2)?2:D1(1)?1:0);Se.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Se.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Se.get("WEBGL_VERSION")===2);Se.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Se.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Se.registerFlag("WEBGL_PACK",()=>Se.getBool("HAS_WEBGL"));Se.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_CLIP",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_REDUCE",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_LAZILY_UNPACK",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_CONV_IM2COL",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_CONV2DTRANSPOSE",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>$v(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>Fv(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Se.getNumber("WEBGL_VERSION");return e===0?0:Dv(e)});Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Se.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Pp.isMobile());Se.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>Ov(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Se.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Se.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Se.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>zv(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Lv(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Se.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Se.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(typeof e!="number")throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${e}.`);if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Se.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Pp.isMobile()?1:-1,e=>{if(typeof e!="number")throw new Error(`WEBGL_FLUSH_THRESHOLD must be a number but got ${e}.`);if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Se.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Se.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Se.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Se.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);Se.registerFlag("WEBGL_EXP_CONV",()=>!1);Se.registerFlag("SOFTWARE_WEBGL_ENABLED",()=>Se.getBool("IS_TEST"));Se.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE",()=>1/0);Se.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE",()=>!1);Se.registerFlag("WEBGL2_ISNAN_CUSTOM",()=>!1);Se.registerFlag("ENGINE_COMPILE_ONLY",()=>!1);function Ra(){let e,t,a,n,r,s,i,o,l,u;return B().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",a="out",n="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=B().getBool("WEBGL2_ISNAN_CUSTOM")?` bool isnan_custom(float val) { uint floatToUint = floatBitsToUint(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; @@ -100,15 +100,15 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `),{version:e,attribute:t,varyingVs:a,varyingFs:n,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function el(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / ${r}`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function n0(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function AG(e,t){let a=e.length,n=e.map(s=>`${t}[${s}]`),r=new Array(a-1);r[a-2]=n[a-1];for(let s=a-3;s>=0;--s)r[s]=`(${r[s+1]} * ${n[s+1]})`;return r}function bG(e,t,a="index"){let n=e.map((s,i)=>i),r=AG(n,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${a} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${a} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function S3(e){let t=v.computeStrides(e).map(a=>a.toString());return` + `),{version:e,attribute:t,varyingVs:a,varyingFs:n,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function tl(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / ${r}`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function n0(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function PG(e,t){let a=e.length,n=e.map(s=>`${t}[${s}]`),r=new Array(a-1);r[a-2]=n[a-1];for(let s=a-3;s>=0;--s)r[s]=`(${r[s+1]} * ${n[s+1]})`;return r}function _G(e,t,a="index"){let n=e.map((s,i)=>i),r=PG(n,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${a} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${a} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function N3(e){let t=v.computeStrides(e).map(a=>a.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } -`}function C3(){return` +`}function R3(){return` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } -`}var Rv=` +`}var Wv=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -147,22 +147,22 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return c / 255.0; } -`,{getBroadcastDims:Ev}=S;function vG(e,t,a){let n=[];if(e.forEach(d=>{let h=v.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`)),a.enableShapeUniforms){let{uniformShape:m}=T3(a.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(m.length){case 1:n.push(`uniform int ${d.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${d.name}TexShape;`)}}),a.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}a.customUniforms&&a.customUniforms.forEach(d=>{n.push(`uniform ${d.type} ${d.name}${d.arrayIndex?`[${d.arrayIndex}]`:""};`)});let r=n.join(` -`),s=e.map(d=>wG(d,t,a.packedInputs,a.enableShapeUniforms)).join(` -`),i=t.texShape,o=Ea(),l=SG(o),u,p,c=NG(o);return t.isPacked?(u=kG(t.logicalShape,i,a.enableShapeUniforms),p=TG(o)):(u=IG(t.logicalShape,i,a.enableShapeUniforms),p=CG(o)),a.packedInputs&&(c+=_G),[c,l,p,r,u,s,a.userCode].join(` -`)}function Hu(e,t=!1){let a=e.shapeInfo.logicalShape;switch(a.length){case 0:return GG(e,t);case 1:return jG(e,t);case 2:return XG(e,t);case 3:return YG(e,t);case 4:return JG(e,t);case 5:return QG(e);case 6:return eH(e);default:throw new Error(`${a.length}-D input sampling is not yet supported`)}}function Mv(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return UG(e);case 1:return HG(e,t);case 2:return qG(e,t);case 3:return KG(e,t);default:return ZG(e,t)}}function wG(e,t,a=!1,n){let r="";a?r+=Mv(e,n):r+=Hu(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(a?r+=tH(e,t):r+=aH(e,t)),r}function kG(e,t,a){switch(e.length){case 0:return _v();case 1:return PG(e,t,a);case 2:return BG(e,t,a);case 3:return FG(e,t,a);default:return OG(e,t,a)}}function IG(e,t,a){switch(e.length){case 0:return _v();case 1:return $G(e,t,a);case 2:return VG(e,t,a);case 3:return DG(e,t,a);case 4:return zG(e,t,a);case 5:return LG(e,t);case 6:return WG(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function SG(e){return` +`,{getBroadcastDims:Bv}=C;function $G(e,t,a){let n=[];if(e.forEach(d=>{let h=v.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`)),a.enableShapeUniforms){let{uniformShape:m}=E3(a.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(m.length){case 1:n.push(`uniform int ${d.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${d.name}TexShape;`)}}),a.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}a.customUniforms&&a.customUniforms.forEach(d=>{n.push(`uniform ${d.type} ${d.name}${d.arrayIndex?`[${d.arrayIndex}]`:""};`)});let r=n.join(` +`),s=e.map(d=>FG(d,t,a.packedInputs,a.enableShapeUniforms)).join(` +`),i=t.texShape,o=Ra(),l=zG(o),u,p,c=BG(o);return t.isPacked?(u=DG(t.logicalShape,i,a.enableShapeUniforms),p=WG(o)):(u=OG(t.logicalShape,i,a.enableShapeUniforms),p=LG(o)),a.packedInputs&&(c+=HG),[c,l,p,r,u,s,a.userCode].join(` +`)}function Hu(e,t=!1){let a=e.shapeInfo.logicalShape;switch(a.length){case 0:return nH(e,t);case 1:return sH(e,t);case 2:return oH(e,t);case 3:return uH(e,t);case 4:return pH(e,t);case 5:return cH(e);case 6:return hH(e);default:throw new Error(`${a.length}-D input sampling is not yet supported`)}}function Vv(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return aH(e);case 1:return rH(e,t);case 2:return iH(e,t);case 3:return lH(e,t);default:return dH(e,t)}}function FG(e,t,a=!1,n){let r="";a?r+=Vv(e,n):r+=Hu(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(a?r+=mH(e,t):r+=fH(e,t)),r}function DG(e,t,a){switch(e.length){case 0:return Uv();case 1:return jG(e,t,a);case 2:return eH(e,t,a);case 3:return XG(e,t,a);default:return YG(e,t,a)}}function OG(e,t,a){switch(e.length){case 0:return Uv();case 1:return qG(e,t,a);case 2:return tH(e,t,a);case 3:return KG(e,t,a);case 4:return ZG(e,t,a);case 5:return JG(e,t);case 6:return QG(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function zG(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } - `}function CG(e){return` + `}function LG(e){return` void setOutput(float val) { ${e.output} = vec4(val, 0, 0, 0); } - `}function TG(e){return` + `}function WG(e){return` void setOutput(vec4 val) { ${e.output} = val; } - `}function NG(e){return`${e.version} + `}function BG(e){return`${e.version} precision highp float; precision highp int; precision highp sampler2D; @@ -217,10 +217,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return fract((p3.x + p3.y) * p3.z); } - ${RG} - ${EG} - ${MG} - `}var RG=` + ${VG} + ${UG} + ${GG} + `}var VG=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -232,7 +232,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,EG=` +`,UG=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -240,7 +240,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,MG=` +`,GG=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -249,7 +249,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,_G=` +`,HG=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -260,11 +260,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function _v(){return` +`;function Uv(){return` int getOutputCoords() { return 0; } - `}function PG(e,t,a){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?a?` + `}function jG(e,t,a){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?a?` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } @@ -293,7 +293,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${n[0]}, ${n[1]})); return 2 * (resTexRC.x * ${n[1]} + resTexRC.y); } - `}function $G(e,t,a){return t[0]===1?a?` + `}function qG(e,t,a){return t[0]===1?a?` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } @@ -321,7 +321,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${t[0]}, ${t[1]})); return resTexRC.x * ${t[1]} + resTexRC.y; } - `}function FG(e,t,a){if(a)return` + `}function XG(e,t,a){if(a)return` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); @@ -352,7 +352,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `}function DG(e,t,a){if(a)return` + `}function KG(e,t,a){if(a)return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -360,7 +360,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n0(["r","c","d"],e)} return ivec3(r, c, d); } -`;let n=el(["r","c","d"],e);return` +`;let n=tl(["r","c","d"],e);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -368,7 +368,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n} return ivec3(r, c, d); } - `}function OG(e,t,a){if(a)return` + `}function YG(e,t,a){if(a)return` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -409,7 +409,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec${e.length}(${l}); } - `}function zG(e,t,a){if(a)return` + `}function ZG(e,t,a){if(a)return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -417,7 +417,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n0(["r","c","d","d2"],e)} return ivec4(r, c, d, d2); } - `;let n=el(["r","c","d","d2"],e);return` + `;let n=tl(["r","c","d","d2"],e);return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -425,7 +425,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n} return ivec4(r, c, d, d2); } - `}function LG(e,t){let a=el(["r","c","d","d2","d3"],e);return` + `}function JG(e,t){let a=tl(["r","c","d","d2","d3"],e);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -437,7 +437,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function WG(e,t){let a=el(["r","c","d","d2","d3","d4"],e);return` + `}function QG(e,t){let a=tl(["r","c","d","d2","d3","d4"],e);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -448,7 +448,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec6 result = ivec6(r, c, d, d2, d3, d4); return result; } - `}function BG(e,t,a){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return a?` + `}function eH(e,t,a){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return a?` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); @@ -481,7 +481,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec2(r, c); } - `}function VG(e,t,a){return v.arraysEqual(e,t)?a?` + `}function tH(e,t,a){return v.arraysEqual(e,t)?a?` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -535,15 +535,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${e[1]}; return ivec2(r, c); } - `}function tl(e){return`offset${e}`}function UG(e){let t=e.name,a="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ea();return` + `}function al(e){return`offset${e}`}function aH(e){let t=e.name,a="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ra();return` vec4 ${a}() { return ${n.texture2D}(${t}, halfCR); } - `}function GG(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${a};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return` + `}function nH(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${a};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return` float ${n}() { return sampleTexture(${a}, halfCR); } - `;let i=tl(a);if(t)return` + `;let i=al(a);if(t)return` float ${n}() { vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], ${i}); return sampleTexture(${a}, uv); @@ -553,7 +553,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${o}, ${l}, ${i}); return sampleTexture(${a}, uv); } - `}function HG(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1),r=e.shapeInfo.texShape,s=Ea();if(t)return` + `}function rH(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1),r=e.shapeInfo.texShape,s=Ra();if(t)return` vec4 ${n}(int index) { ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( @@ -566,7 +566,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${i[0]}, ${i[1]}, index); return ${s.texture2D}(${a}, uv); } - `}function jG(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1);if(e.shapeInfo.isUniform)return` + `}function sH(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1);if(e.shapeInfo.isUniform)return` float ${n}(int index) { ${ju(e)} } @@ -574,7 +574,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float ${n}(int index) { return sampleTexture(${a}, halfCR); } - `;let o=tl(a);return i===1?t?` + `;let o=al(a);return i===1?t?` float ${n}(int index) { vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${a}TexShape[0])); return sampleTexture(${a}, uv); @@ -604,7 +604,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${s}, ${i}, index + ${o}); return sampleTexture(${a}, uv); } - `}function qG(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Ea();if(s!=null&&v.arraysEqual(a,s))return t?` + `}function iH(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Ra();if(s!=null&&v.arraysEqual(a,s))return t?` vec4 ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]); @@ -628,7 +628,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = packedUVfrom2D(${p}, ${u[0]}, ${u[1]}, row, col); return ${l.texture2D}(${n}, uv); } - `}function XG(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape;if(s!=null&&v.arraysEqual(a,s)){if(t)return` + `}function oH(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape;if(s!=null&&v.arraysEqual(a,s)){if(t)return` float ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]); return sampleTexture(${n}, uv); @@ -648,7 +648,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int index = round(dot(vec2(row, col), vec2(${a[1]}, 1))); ${ju(e)} } - `;let u=s[0],p=s[1],c=tl(n);return p===1?t?` + `;let u=s[0],p=s[1],c=al(n);return p===1?t?` float ${r}(int row, int col) { float index = dot(vec3(row, col, ${c}), vec3(${n}Shape[1], 1, 1)); vec2 uv = vec2(0.5, (index + 0.5) / float(${n}TexShape[0])); @@ -686,12 +686,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${u}, ${p}, index); return sampleTexture(${n}, uv); } -`}function KG(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(a[0]===1){let d=a.slice(1),h=[1,2],m=qu(e,d),f=["b","row","col"];return` - ${Mv(m,t)} +`}function lH(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(a[0]===1){let d=a.slice(1),h=[1,2],m=qu(e,d),f=["b","row","col"];return` + ${Vv(m,t)} vec4 ${r}(int b, int row, int col) { return ${r}(${Xu(f,h)}); } - `}let o=Ea();if(t)return` + `}let o=Ra();if(t)return` vec4 ${r}(int b, int row, int col) { ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0)); int valuesPerRow = int(ceil(float(${n}Shape[2]) / 2.0)); @@ -706,7 +706,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${l}, ${u}, ${c}, ${p}, b, row, col); return ${o.texture2D}(${n}, uv); } - `}function YG(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=a[1]*a[2],i=a[2],{newShape:o,keptDims:l}=v.squeezeShape(a),u=o;if(u.length=1?p="coords = 0;":p=o.map(g=>`coords.${c[g+u]} = 0;`).join(` + `}function mH(e,t){let a=e.name,n=a.charAt(0).toUpperCase()+a.slice(1),r="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=Bv(e.shapeInfo.logicalShape,t.logicalShape),l=mt(i),u=i-s,p,c=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${c[g+u]} = 0;`).join(` `);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((g,y)=>`coords.${c[y+u]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,f=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=` return vec4(outputValue.xy, outputValue.xy); `;else if(m&&!f)i===1?h=` @@ -965,20 +965,20 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 outputValue = get${n}(${d}); ${h} } - `}function aH(e,t){let a=e.name,n=a.charAt(0).toUpperCase()+a.slice(1),r="get"+n+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return` + `}function fH(e,t){let a=e.name,n=a.charAt(0).toUpperCase()+a.slice(1),r="get"+n+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return` float ${r}() { return sampleTexture(${a}, resultUV); } - `;let u=ft(l),p=Ev(e.shapeInfo.logicalShape,t.logicalShape),c=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&p.length>=1?d="coords = 0;":d=p.map(f=>`coords.${h[f+c]} = 0;`).join(` + `;let u=mt(l),p=Bv(e.shapeInfo.logicalShape,t.logicalShape),c=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&p.length>=1?d="coords = 0;":d=p.map(f=>`coords.${h[f+c]} = 0;`).join(` `);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+c]}`).join(", "),` float ${r}() { ${u} coords = getOutputCoords(); ${d} return get${n}(${m}); } - `}function ft(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function T3(e,t,a){let{newShape:n,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!v.arraysEqual(t,a)&&n.lengthe[a]).join(", ")}function nH(e,t,a,n){let r=a.map((p,c)=>{let d={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(d.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[c],shapeInfo:d}}),s=r.map(p=>p.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=vG(r,i,t),l=lv(e.gl,o),u=e.createProgram(l);return W().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(e.buildVao(u),Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},Pv(e,t,u)))}function Pv(e,t,a){let n=[],r=[],s,i,o,l=null,u=null;u=e.getUniformLocation(a,"NAN",!1),W().getNumber("WEBGL_VERSION")===1&&(l=e.getUniformLocation(a,"INFINITY",!1));let p=!1;for(let c of t.variableNames){let d={name:c,uniform:e.getUniformLocation(a,c,p),offset:e.getUniformLocation(a,`offset${c}`,p)};t.enableShapeUniforms&&(d.shape=e.getUniformLocation(a,`${c}Shape`,p),d.texShape=e.getUniformLocation(a,`${c}TexShape`,p)),n.push(d)}if(t.enableShapeUniforms&&(s=e.getUniformLocation(a,"outShape",p),o=e.getUniformLocation(a,"outShapeStrides",p),i=e.getUniformLocation(a,"outTexShape",p)),t.customUniforms)for(let c of t.customUniforms)r.push(e.getUniformLocation(a,c.name,p));return{variablesLocations:n,customUniformLocations:r,infLoc:l,nanLoc:u,outShapeLocation:s,outShapeStridesLocation:o,outTexShapeLocation:i}}function g5(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((a,n)=>{let r=a.logicalShape,s=t[n],i=s.shape;if(!v.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(a.isUniform&&s.isUniform)return;let o=a.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function rH(e,t,a,n,r){t.program.enableShapeUniforms||(g5(t.inShapeInfos,a),g5([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),W().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN);for(let l=0;l{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:c}=T3(e.packedInputs,i.shape,l),d="",h="",m="";if(p.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${w[0]>1}_${w[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let w=v.computeStrides(p);m=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,x=S.getBroadcastDims(i.shape,a.shape),A=!e.packedInputs&&f===a.shape.length&&v.arraysEqual(l,a.texData.texShape),b=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${f}_${A}_${u?c:""}_${p.length}_${y}_${x}_${g}_${d}_${h}_${m}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+r+`${W().getNumber("WEBGL_VERSION")}`,s}function ya(e){return W().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var iH=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Jd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ea();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` + `}function mt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function E3(e,t,a){let{newShape:n,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!v.arraysEqual(t,a)&&n.lengthe[a]).join(", ")}function gH(e,t,a,n){let r=a.map((p,c)=>{let d={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(d.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[c],shapeInfo:d}}),s=r.map(p=>p.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=$G(r,i,t),l=Av(e.gl,o),u=e.createProgram(l);return B().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(e.buildVao(u),Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},Gv(e,t,u)))}function Gv(e,t,a){let n=[],r=[],s,i,o,l=null,u=null;u=e.getUniformLocation(a,"NAN",!1),B().getNumber("WEBGL_VERSION")===1&&(l=e.getUniformLocation(a,"INFINITY",!1));let p=!1;for(let c of t.variableNames){let d={name:c,uniform:e.getUniformLocation(a,c,p),offset:e.getUniformLocation(a,`offset${c}`,p)};t.enableShapeUniforms&&(d.shape=e.getUniformLocation(a,`${c}Shape`,p),d.texShape=e.getUniformLocation(a,`${c}TexShape`,p)),n.push(d)}if(t.enableShapeUniforms&&(s=e.getUniformLocation(a,"outShape",p),o=e.getUniformLocation(a,"outShapeStrides",p),i=e.getUniformLocation(a,"outTexShape",p)),t.customUniforms)for(let c of t.customUniforms)r.push(e.getUniformLocation(a,c.name,p));return{variablesLocations:n,customUniformLocations:r,infLoc:l,nanLoc:u,outShapeLocation:s,outShapeStridesLocation:o,outTexShapeLocation:i}}function w5(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((a,n)=>{let r=a.logicalShape,s=t[n],i=s.shape;if(!v.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(a.isUniform&&s.isUniform)return;let o=a.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function yH(e,t,a,n,r){t.program.enableShapeUniforms||(w5(t.inShapeInfos,a),w5([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),B().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN);for(let l=0;l{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:c}=E3(e.packedInputs,i.shape,l),d="",h="",m="";if(p.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${w[0]>1}_${w[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let w=v.computeStrides(p);m=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,x=C.getBroadcastDims(i.shape,a.shape),A=!e.packedInputs&&f===a.shape.length&&v.arraysEqual(l,a.texData.texShape),b=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${f}_${A}_${u?c:""}_${p.length}_${y}_${x}_${g}_${d}_${h}_${m}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+r+`${B().getNumber("WEBGL_VERSION")}`,s}function ga(e){return B().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var AH=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Yd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ra();this.outputShape=e,this.enableShapeUniforms=ga(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?n0(["r","c","d"],e):el(["r","c","d"],e)} + ${this.enableShapeUniforms?n0(["r","c","d"],e):tl(["r","c","d"],e)} return ivec3(r, c, d); } @@ -996,9 +996,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},oH=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Jd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ea();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` + `}},bH=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Yd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ra();this.outputShape=e,this.enableShapeUniforms=ga(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?n0(["r","c","d"],e):el(["r","c","d"],e)} + ${this.enableShapeUniforms?n0(["r","c","d"],e):tl(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1016,26 +1016,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},lH=class{constructor(e){this.variableNames=["A"],this.outTexUsage=fn.DOWNLOAD;let t=Ea();this.outputShape=e,this.userCode=` - ${Rv} + `}},vH=class{constructor(e){this.variableNames=["A"],this.outTexUsage=hn.DOWNLOAD;let t=Ra();this.outputShape=e,this.userCode=` + ${Wv} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } - `}},uH=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=fn.DOWNLOAD;let t=Ea();this.outputShape=e,this.userCode=` - ${Rv} + `}},wH=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=hn.DOWNLOAD;let t=Ra();this.outputShape=e,this.userCode=` + ${Wv} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - 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r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Td(this.gl,r),gn(this.gl,s))this.textureHalfFloatExtension=Td(this.gl,s);else if(W().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(a),gn(this.gl,n))this.colorBufferHalfFloatExtension=Td(this.gl,n);else if(W().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(a="EXT_color_buffer_float",gn(this.gl,a))this.colorBufferFloatExtension=this.gl.getExtension(a);else if(gn(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable 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This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),zv(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Lv(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Wv(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Hv(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,a,n){this.throwIfDisposed(),Gv(this.gl,e,t,a,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Vv(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Bv(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&($1(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,a){return this.downloadMatrixDriver(e,()=>Xv(this.gl,t,a,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,a,n,r,s){return Kv(this.gl,e,t,a,n,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return qv(this.gl,e,t)}createBufferFromTexture(e,t,a){this.bindTextureToFrameBuffer(e);let n=jv(this.gl,t,a,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,a;if(W().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,r=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),a=()=>{let s=n.clientWaitSync(r,0,0);return s===n.ALREADY_SIGNALED||s===n.CONDITION_SATISFIED},t=r}else W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),a=()=>this.isQueryAvailable(t,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):a=()=>!0;return{query:t,isFencePassed:a}}downloadMatrixFromPackedTexture(e,t,a){return this.downloadMatrixDriver(e,()=>Yv(this.gl,t,a))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=Fv(t));let a=uv(t);ce(t,()=>t.attachShader(a,this.vertexShader)),ce(t,()=>t.attachShader(a,e)),dv(t,a);let n=Object.assign(a,{vao:this.createVertexArray()});return this.debug&&ah(t,n),n}buildVao(e){this.setProgram(e),this.bindVertexArray(e.vao);let t=this.gl;ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),Uv(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&ah(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,a=!0){return this.throwIfDisposed(),a?yv(this.gl,e,t):xv(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,a){this.throwIfDisposed(),this.throwIfNoProgram(),Av(this.gl,e,t,a)}setOutputMatrixTexture(e,t,a){this.setOutputMatrixTextureDriver(e,a,t)}setOutputPackedMatrixTexture(e,t,a){this.throwIfDisposed();let[n,r]=Uu(t,a);this.setOutputMatrixTextureDriver(e,n,r)}setOutputMatrixWriteRegion(e,t,a,n){this.setOutputMatrixWriteRegionDriver(a,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,a,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&ah(this.gl,this.program),Nd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and 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t=this.gl,a=this.getQueryTimerExtensionWebGL2();t.endQuery(a.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let a=this.gl;return a.getQueryParameter(e,a.QUERY_RESULT)/1e6}else{let a=this.getQueryTimerExtensionWebGL1();return a.getQueryObjectEXT(e,a.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let a=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=a.getQueryParameter(e,a.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let a=this.getQueryTimerExtensionWebGL1(),n=a.getQueryObjectEXT(e,a.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=cH(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:a}=this.itemsToPoll[t];a()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let a;"setTimeoutCustom"in W().platform&&(a=W().platform.setTimeoutCustom.bind(W().platform)),v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,a)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),nh(this.gl,e,this.framebuffer),this.debug&&Nd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(nh(this.gl,this.outputTexture,this.framebuffer),this.debug&&Nd(this.gl)):$1(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let a=t();return this.unbindTextureToFrameBuffer(),a}setOutputMatrixTextureDriver(e,t,a){this.throwIfDisposed();let n=this.gl;nh(n,e,this.framebuffer),this.debug&&Nd(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,a)),ce(n,()=>n.scissor(0,0,t,a))}setOutputMatrixWriteRegionDriver(e,t,a,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,a,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function cH(e){let t=0;for(;t`${e}.${a}`)}function Ia(e,t){return t===1?[e]:e8(e,t)}function rj(e,t){if(e===1)return"rc";let a="";for(let n=0;ne.bindTexture(o,i)),ce(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ce(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ce(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ce(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),B().getNumber("WEBGL_VERSION")===1?ce(e,()=>e.texImage2D(o,0,n,t,a,0,r,s,null)):ce(e,()=>e.texStorage2D(o,1,n,t,a)),ce(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[a,t]}}function M3(e){return e.internalFormatFloat}function Kv(e,t,a,n){let[r,s]=Xp(t,a);return Kp(e,r,s,M3(n),n.textureFormatFloat,e.FLOAT)}function P3(e){return e.internalFormatHalfFloat}function Yv(e,t,a,n){let[r,s]=Xp(t,a);return Kp(e,r,s,P3(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function _3(e){return e.downloadTextureFormat}function Zv(e,t,a,n){let[r,s]=Xp(t,a);return Kp(e,r,s,_3(n),e.RGBA,e.UNSIGNED_BYTE)}function $3(e){return e.internalFormatPackedFloat}function Jv(e,t,a,n){let[r,s]=Uu(t,a);return Kp(e,r,s,$3(n),e.RGBA,e.FLOAT)}function F3(e){return e.internalFormatPackedHalfFloat}function Qv(e,t,a,n){let[r,s]=Uu(t,a);return Kp(e,r,s,F3(n),e.RGBA,n.textureTypeHalfFloat)}function e8(e,t,a){return ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,a)),$1(e,t,"clipSpacePos",a,3,20,0)&&$1(e,t,"uv",a,2,20,12)}function t8(e,t,a,n,r,s){ce(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(a*n*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(a*n*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),B().getNumber("WEBGL_VERSION")===2?ce(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,a,n,e.RGBA,o,i)):ce(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,a,n,0,e.RGBA,o,i)),ce(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function a8(e,t,a){ce(e,()=>e.bindTexture(e.TEXTURE_2D,t)),a.data instanceof Uint8Array?B().getNumber("WEBGL_VERSION")===2?ce(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,a.width,a.height,e.RGBA,e.UNSIGNED_BYTE,a.data)):ce(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,a.width,a.height,0,e.RGBA,e.UNSIGNED_BYTE,a.data)):B().getNumber("WEBGL_VERSION")===2?ce(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,a)):ce(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,a)),ce(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function n8(e,t,a,n){let r=e.createBuffer();ce(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*a;return ce(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),ce(e,()=>e.readPixels(0,0,a,t,e.RGBA,e.FLOAT,0)),ce(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function r8(e,t,a){let n=e,r=new Float32Array(a);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,r),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),r}function s8(e,t,a,n){let[r,s]=Xp(t,a),i=4,o=new Uint8Array(bG(t*a,i));return ce(e,()=>e.readPixels(0,0,r,s,n.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function i8(e,t,a,n,r,s,i,o){let l=e,u=new Float32Array(vG(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function o8(e,t,a){let n=new Float32Array(t*a*4);return ce(e,()=>e.readPixels(0,0,a,t,e.RGBA,e.FLOAT,n)),n}var Vl=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=B().getNumber("WEBGL_VERSION");if(e!=null?(this.gl=e,a0(t,e)):this.gl=Wn(t),e=this.gl,B().getNumber("WEBGL_VERSION")===2){let r=e;this.createVertexArray=()=>ce(r,()=>r.createVertexArray()),this.bindVertexArray=s=>ce(r,()=>r.bindVertexArray(s)),this.deleteVertexArray=s=>ce(r,()=>r.deleteVertexArray(s)),this.getVertexArray=()=>ce(r,()=>r.getParameter(r.VERTEX_ARRAY_BINDING))}else if(e!=null){let r=e.getExtension("OES_vertex_array_object");if(r==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ce(e,()=>r.createVertexArrayOES()),this.bindVertexArray=s=>ce(e,()=>r.bindVertexArrayOES(s)),this.deleteVertexArray=s=>ce(e,()=>r.deleteVertexArrayOES(s)),this.getVertexArray=()=>ce(e,()=>e.getParameter(r.VERTEX_ARRAY_BINDING_OES))}let a="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),B().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Cd(this.gl,r),mn(this.gl,s))this.textureHalfFloatExtension=Cd(this.gl,s);else if(B().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(a),mn(this.gl,n))this.colorBufferHalfFloatExtension=Cd(this.gl,n);else if(B().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(a="EXT_color_buffer_float",mn(this.gl,a))this.colorBufferFloatExtension=this.gl.getExtension(a);else if(mn(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=qv(this.gl),this.indexBuffer=Xv(this.gl),this.framebuffer=Cv(this.gl),this.textureConfig=C3(this.gl,this.textureHalfFloatExtension)}get debug(){return B().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Kv(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Yv(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Zv(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),a8(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,a,n){this.throwIfDisposed(),t8(this.gl,e,t,a,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Qv(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Jv(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(F1(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,a){return this.downloadMatrixDriver(e,()=>s8(this.gl,t,a,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,a,n,r,s){return i8(this.gl,e,t,a,n,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return r8(this.gl,e,t)}createBufferFromTexture(e,t,a){this.bindTextureToFrameBuffer(e);let n=n8(this.gl,t,a,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,a;if(B().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,r=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),a=()=>{let s=n.clientWaitSync(r,0,0);return s===n.ALREADY_SIGNALED||s===n.CONDITION_SATISFIED},t=r}else B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),a=()=>this.isQueryAvailable(t,B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):a=()=>!0;return{query:t,isFencePassed:a}}downloadMatrixFromPackedTexture(e,t,a){return this.downloadMatrixDriver(e,()=>o8(this.gl,t,a))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=jv(t));let a=bv(t);ce(t,()=>t.attachShader(a,this.vertexShader)),ce(t,()=>t.attachShader(a,e)),vv(t,a);let n=Object.assign(a,{vao:this.createVertexArray()});return this.debug&&th(t,n),n}buildVao(e){this.setProgram(e),this.bindVertexArray(e.vao);let t=this.gl;ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),e8(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&th(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,a=!0){return this.throwIfDisposed(),a?Nv(this.gl,e,t):Rv(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,a){this.throwIfDisposed(),this.throwIfNoProgram(),Ev(this.gl,e,t,a)}setOutputMatrixTexture(e,t,a){this.setOutputMatrixTextureDriver(e,a,t)}setOutputPackedMatrixTexture(e,t,a){this.throwIfDisposed();let[n,r]=Uu(t,a);this.setOutputMatrixTextureDriver(e,n,r)}setOutputMatrixWriteRegion(e,t,a,n){this.setOutputMatrixWriteRegionDriver(a,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,a,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&th(this.gl,this.program),Td(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}ce(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Cd(this.gl,B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let a=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=a.createQuery();return a.beginQuery(n.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,a=this.getQueryTimerExtensionWebGL2();t.endQuery(a.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let a=this.gl;return a.getQueryParameter(e,a.QUERY_RESULT)/1e6}else{let a=this.getQueryTimerExtensionWebGL1();return a.getQueryObjectEXT(e,a.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let a=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=a.getQueryParameter(e,a.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let a=this.getQueryTimerExtensionWebGL1(),n=a.getQueryObjectEXT(e,a.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=SH(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:a}=this.itemsToPoll[t];a()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let a;"setTimeoutCustom"in B().platform&&(a=B().platform.setTimeoutCustom.bind(B().platform)),v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,a)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),ah(this.gl,e,this.framebuffer),this.debug&&Td(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ah(this.gl,this.outputTexture,this.framebuffer),this.debug&&Td(this.gl)):F1(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let a=t();return this.unbindTextureToFrameBuffer(),a}setOutputMatrixTextureDriver(e,t,a){this.throwIfDisposed();let n=this.gl;ah(n,e,this.framebuffer),this.debug&&Td(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,a)),ce(n,()=>n.scissor(0,0,t,a))}setOutputMatrixWriteRegionDriver(e,t,a,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,a,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function SH(e){let t=0;for(;t`${e}.${a}`)}function ka(e,t){return t===1?[e]:p8(e,t)}function yj(e,t){if(e===1)return"rc";let a="";for(let n=0;n= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. : getA(${t[2]}), - rEdge || cEdge ? 0. : getA(${t[3]})`}},t8=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length);let a="";for(let n=0;n<4;n++){let r="thisRC = rc;";n%2===1&&(r+="thisRC.z += 1;"),n>1&&(r+="thisRC.y += 1;"),a+=` + rEdge || cEdge ? 0. : getA(${t[3]})`}},c8=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ga(this.outputShape.length);let a="";for(let n=0;n<4;n++){let r="thisRC = rc;";n%2===1&&(r+="thisRC.z += 1;"),n>1&&(r+="thisRC.y += 1;"),a+=` ${r} ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); @@ -1146,8 +1146,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${n>0?"}":""} `}this.userCode=` - ${ij(t,this.enableShapeUniforms)} - ${this.enableShapeUniforms?C3():S3(e)} + ${Aj(t,this.enableShapeUniforms)} + ${this.enableShapeUniforms?R3():N3(e)} void main() { ivec3 rc = getOutputCoords(); @@ -1162,12 +1162,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function ij(e,t){return` + `}};function Aj(e,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?bG(["r","c","d"],"inputShape"):el(["r","c","d"],e)} + ${t?_G(["r","c","d"],"inputShape"):tl(["r","c","d"],e)} return ivec3(r, c, d); } - `}var oj=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,a){let n=A5(t,a),r=b5(e,n,a);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=x5(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,a);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return n===da.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===da.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===da.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===da.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===da.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,a,n){if(this.freeTextures==null)return;let r=A5(a,n),s=b5(t,r,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=x5(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=W().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function lj(e,t){let a=e;if(t===a.R32F)return 4;if(t===a.R16F)return 2;if(t===a.RGBA32F||t===e.RGBA)return 16;if(t===a.RGBA16F)return 8;if(t===a.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function x5(e,t,a,n,r){let s=uj(t,n),i;if(r){let[l,u]=Uu(e[0],e[1]);i=l*u}else{let[l,u]=Yp(e[0],e[1]);i=l*u}let o=lj(a,s);return i*o}function uj(e,t){switch(e){case da.PACKED_2X2_FLOAT32:return M3(t);case da.PACKED_2X2_FLOAT16:return _3(t);case da.UNPACKED_FLOAT32:return N3(t);case da.UNPACKED_FLOAT16:return R3(t);case da.PACKED_4X1_UNSIGNED_BYTE:return E3(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function dj(e){return W().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?da.PACKED_2X2_FLOAT32:da.UNPACKED_FLOAT32:e?da.PACKED_2X2_FLOAT16:da.UNPACKED_FLOAT16}function A5(e,t){if(e===fn.UPLOAD)return da.PACKED_2X2_FLOAT32;if(e===fn.RENDER||e==null)return dj(t);if(e===fn.DOWNLOAD||e===fn.PIXELS)return da.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function b5(e,t,a){return`${e[0]}_${e[1]}_${t}_${a}`}var Kn=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` + `}var bj=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,a){let n=S5(t,a),r=C5(e,n,a);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=I5(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,a);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return n===ua.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===ua.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===ua.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===ua.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===ua.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,a,n){if(this.freeTextures==null)return;let r=S5(a,n),s=C5(t,r,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=I5(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=B().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function vj(e,t){let a=e;if(t===a.R32F)return 4;if(t===a.R16F)return 2;if(t===a.RGBA32F||t===e.RGBA)return 16;if(t===a.RGBA16F)return 8;if(t===a.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function I5(e,t,a,n,r){let s=wj(t,n),i;if(r){let[l,u]=Uu(e[0],e[1]);i=l*u}else{let[l,u]=Xp(e[0],e[1]);i=l*u}let o=vj(a,s);return i*o}function wj(e,t){switch(e){case ua.PACKED_2X2_FLOAT32:return $3(t);case ua.PACKED_2X2_FLOAT16:return F3(t);case ua.UNPACKED_FLOAT32:return M3(t);case ua.UNPACKED_FLOAT16:return P3(t);case ua.PACKED_4X1_UNSIGNED_BYTE:return _3(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function kj(e){return B().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?ua.PACKED_2X2_FLOAT32:ua.UNPACKED_FLOAT32:e?ua.PACKED_2X2_FLOAT16:ua.UNPACKED_FLOAT16}function S5(e,t){if(e===hn.UPLOAD)return ua.PACKED_2X2_FLOAT32;if(e===hn.RENDER||e==null)return kj(t);if(e===hn.DOWNLOAD||e===hn.PIXELS)return ua.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function C5(e,t,a){return`${e[0]}_${e[1]}_${t}_${a}`}var Kn=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ga(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } @@ -1178,11 +1178,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Mn="if (isnan(x)) return x;",pj="return x;",v5="return abs(x);",cj="return (x >= 0.0) ? x : (exp(x) - 1.0);",hj=Mn+` + `}},En="if (isnan(x)) return x;",Ij="return x;",T5="return abs(x);",Sj="return (x >= 0.0) ? x : (exp(x) - 1.0);",Cj=En+` return (x < 0.0) ? 0.0 : x; -`,mj=Mn+` +`,Tj=En+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Wr="return x;",fj="return 1.0 / (1.0 + exp(-1.0 * x));",gj="return x;",yj=` +`,Br="return x;",Nj="return 1.0 / (1.0 + exp(-1.0 * x));",Rj="return x;",Ej=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1191,7 +1191,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,xj=` +`,Mj=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1201,7 +1201,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,Aj=` +`,Pj=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1211,7 +1211,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,bj="return 1.0 / (1.0 + exp(-1.0 * x));",Hr=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` +`,_j="return 1.0 / (1.0 + exp(-1.0 * x));",jr=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ga(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -1222,17 +1222,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},vj=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length);let t=e.length,a=Ia("rc",t),n=ft(t),r=rj(t,a),s=a.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` + `}},$j=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ga(this.outputShape.length);let t=e.length,a=ka("rc",t),n=mt(t),r=yj(t,a),s=a.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` void main() { ${n} rc = getOutputCoords(); vec4 packedInput = getA(${r}); setOutput(getChannel(packedInput, ${i})); } - `}},wj=En.whereImpl,kj=1e-7,Ij=1e-4,J2={};function Sj(e){return e in J2||(J2[e]={}),J2[e]}var Cj=W().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Tj=600;function Nj(){return W().global.screen==null?1024:W().global.screen.height*W().global.screen.width*window.devicePixelRatio*Tj/1024/1024}var Ku=class extends au{nextDataId(){return Ku.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!W().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Wl)t=e;else{let a=Wn(W().getNumber("WEBGL_VERSION"),e);t=new Wl(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=Wn(W().getNumber("WEBGL_VERSION"));t=new Wl(a),this.binaryCache=Sj(W().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new oj(this.gpgpu),this.numMBBeforeWarning=Nj(),this.texData=new ip(this,It())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,a,n,r,s){let i=this.makeTensorInfo(t,a),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[n,r]},o.texShape=[n,r];let l=Rd(t),u=new y5(l,!1,s),p=this.runWebGLProgram(u,[i],a,[[n,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,a){if((W().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||W().getBool("DEBUG"))&&this.checkNumericalProblems(e),a==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:a,values:e,usage:fn.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,a,n,r){if(W().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:a,dtype:n,values:t,usage:fn.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:a,dtype:n,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let c;o?c=new Hr(i,Wr):c=new Kn(i,Wr);let d=this.runWebGLProgram(c,[{dataId:e,shape:i,dtype:n}],n),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(a!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return a;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(n==="complex64"){let c=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);p=S.mergeRealAndImagArrays(c,d)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:a,shape:n,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Hr(n,Wr):h=new Kn(n,Wr);let m=this.runWebGLProgram(h,[{dataId:e,shape:n,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(a!=null)return this.convertAndCacheOnCPU(e);if(W().getBool("DEBUG")&&!W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&W().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&W().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...Yc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];p=S.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;ce(h,()=>h.deleteBuffer(l))}let c=this.convertAndCacheOnCPU(e,p),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(c)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&It().removeDataId(e,this),this.pendingDeletes--),c}readToGPU(e,t={}){let a=this.texData.get(e),{values:n,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=a;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let d;o?d=new Hr(r,Wr):d=new Kn(r,Wr);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),m=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),m}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=It().makeTensorFromTensorInfo(u),c=this.texData.get(u.dataId);return Object.assign({tensorRef:p},c.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return $e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return $e(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:a}=this.texData.get(e);return a!=null&&(this.disposeData(a.real.dataId,t),this.disposeData(a.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:a,texShape:n,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,a),this.textureManager.releaseTexture(t,n,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=Cj){return W().getBool("WEBGL_CPU_FORWARD")&&e.every(a=>this.texData.get(a.dataId).texture==null&&v.sizeFromShape(a.shape)0&&v.isString(a[0])){let r=a.map(s=>v.encodeString(s));n=this.write(r,e,t)}else n=this.write(a,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,a){return It().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,a),this)}unpackTensor(e){let t=new vj(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new sj(e.shape),a=!0;return this.runWebGLProgram(t,[e],e.dtype,null,a)}packedReshape(e,t){let a=[Qs(e.shape),...ei(e.shape)],n={dtype:e.dtype,shape:a,dataId:e.dataId},r=[Qs(t),...ei(t)],s=new t8(r,a),i=!0,o=[a],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let a=this.texData.get(e),{isPacked:n,shape:r,dtype:s}=a;if(t!=null){let c=v.sizeFromShape(r),d=t[0]*t[1]*4;v.assert(c<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Rd(r),o;n?o=new oH(i):o=new iH(i);let l=!0,u=[t!=null?t:Yc(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,a,n,r=!1,s){let i=this.makeTensorInfo(e.outputShape,a),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===Jd.DENSE){let g=s!=null?s:Yc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=W().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!Qd(y.shape,g.shape)){let x=g,A=g.shape;g.shape=y.shape,g=this.packedReshape(g,A),l.push(g),y=this.texData.get(g.dataId),x.shape=A}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},c=sH(e,u,p),d=this.getAndSaveBinary(c,()=>nH(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),W().get("ENGINE_COMPILE_ONLY")||rH(this.gpgpu,d,u,p,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=W().getNumber("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=v.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!W().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,a,n,r=!1){return a=a||t[0].dtype,this.runWebGLProgram(e,t,a,n,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(W().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!W().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=W().getBool("DEBUG");W().set("DEBUG",!1);let t=this.abs(Ge(1e-8)).dataSync()[0];if(W().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?kj:Ij}uploadToGPU(e){let t=this.texData.get(e),{shape:a,dtype:n,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=wv(a,o),t.texShape=p),r!=null){let c=Rd(a),d,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=Uu(p[0],p[1])),o?d=new pH(c,f):d=new y5(c,f);let g=f?[m,h]:p,y=this.makeTensorInfo(g,n),x=this.texData.get(y.dataId);f?x.usage=fn.PIXELS:x.usage=fn.UPLOAD,x.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,m,r);let A=[[m,h]],b=!0,w=this.runWebGLProgram(d,[y],n,A,b),I=this.texData.get(w.dataId);t.texShape=I.texShape,t.isPacked=I.isPacked,t.usage=I.usage,W().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(t.texture=I.texture,t.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let c=this.acquireTexture(p,i,n,o);t.texture=c}}convertAndCacheOnCPU(e,t){let a=this.texData.get(e),{dtype:n}=a;return t!=null&&(a.values=Rj(t,n)),a.values}acquireTexture(e,t,a,n){if(this.numBytesInGPU+=this.computeBytes(e,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let a=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(r){throw r}});e.push(a)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await M7(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(I3(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);let{variablesLocations:t,customUniformLocations:a,infLoc:n,nanLoc:r,outShapeLocation:s,outShapeStridesLocation:i,outTexShapeLocation:o}=Pv(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=a,e.infLoc=n,e.nanLoc=r,e.outShapeLocation=s,e.outShapeStridesLocation=i,e.outTexShapeLocation=o}}createTensorFromGPUData(e,t,a){e.channels=e.channels||"RGBA";let{texture:n,height:r,width:s,channels:i}=e,o=It().backend;if(!o.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(n,t,a,r,s,i);return It().makeTensorFromDataId(l,t,a,o)}};Ku.nextDataId=0;function Rj(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let a=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;nnew Ku,2);var Mj={forceHalfFloat:a8},$3=` + `}},Fj=Rn.whereImpl,Dj=1e-7,Oj=1e-4,Z2={};function zj(e){return e in Z2||(Z2[e]={}),Z2[e]}var Lj=B().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Wj=600;function Bj(){return B().global.screen==null?1024:B().global.screen.height*B().global.screen.width*window.devicePixelRatio*Wj/1024/1024}var Yp=class h8 extends au{nextDataId(){return h8.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!B().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let a;if(t!=null){if(t instanceof Vl)a=t;else{let n=Wn(B().getNumber("WEBGL_VERSION"),t);a=new Vl(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Wn(B().getNumber("WEBGL_VERSION"));a=new Vl(n),this.binaryCache=zj(B().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=a,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new bj(this.gpgpu),this.numMBBeforeWarning=Bj(),this.texData=new rp(this,It())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,a,n,r,s,i){let o=this.makeTensorInfo(a,n),l=this.texData.get(o.dataId);l.isPacked=!1,l.texture={texture:t,texShape:[r,s]},l.texShape=[r,s];let u=Nd(a),p=new k5(u,!1,i),c=this.runWebGLProgram(p,[o],n,[[r,s]]);return c.shape=a,l.texture=null,this.disposeIntermediateTensorInfo(o),c.dataId}write(t,a,n){if((B().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||B().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:a,dtype:n,values:t,usage:hn.UPLOAD,refCount:1}),r}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let a=this.texData.get(t);a.refCount++}decRef(t){if(this.texData.has(t)){let a=this.texData.get(t);a.refCount--}}move(t,a,n,r,s){if(B().getBool("DEBUG")&&this.checkNumericalProblems(a),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:r,values:a,usage:hn.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let a=this.texData.get(t),{values:n,dtype:r,complexTensorInfos:s,slice:i,shape:o,isPacked:l}=a;if(i!=null){let d;l?d=new jr(o,Br):d=new Kn(o,Br);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:r}],r),m=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),m}if(n!=null)return this.convertAndCacheOnCPU(t);if(r==="string")return n;let u=this.activeTimers!=null,p;u&&(p=v.now());let c;if(r==="complex64"){let d=this.readSync(s.real.dataId),h=this.readSync(s.imag.dataId);c=C.mergeRealAndImagArrays(d,h)}else c=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=v.now()-p),this.convertAndCacheOnCPU(t,c)}async read(t){if(this.pendingRead.has(t)){let m=this.pendingRead.get(t);return new Promise(f=>m.push(f))}let a=this.texData.get(t),{values:n,shape:r,slice:s,dtype:i,complexTensorInfos:o,isPacked:l}=a;if(s!=null){let m;l?m=new jr(r,Br):m=new Kn(r,Br);let f=this.runWebGLProgram(m,[{dataId:t,shape:r,dtype:i}],i),g=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(B().getBool("DEBUG")&&!B().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&B().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,p;if(i!=="complex64"&&B().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(t);let m=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(m.texture.texture,...Kc(r))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(i==="complex64"){let m=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=m[0],g=m[1];c=C.mergeRealAndImagArrays(f,g)}else if(u==null)c=this.getValuesFromTexture(t);else{let m=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,m)}if(p!=null&&this.disposeIntermediateTensorInfo(p),u!=null){let m=this.gpgpu.gl;ce(m,()=>m.deleteBuffer(u))}let d=this.convertAndCacheOnCPU(t,c),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(m=>m(d)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&It().removeDataId(t,this),this.pendingDeletes--),d}readToGPU(t,a={}){let n=this.texData.get(t),{values:r,shape:s,slice:i,dtype:o,isPacked:l,texture:u}=n;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let h;l?h=new jr(s,Br):h=new Kn(s,Br);let m=this.runWebGLProgram(h,[{dataId:t,shape:s,dtype:o}],o),f=this.readToGPU(m,a);return this.disposeIntermediateTensorInfo(m),f}if(u==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let p=this.decode(t,a.customTexShape),c=It().makeTensorFromTensorInfo(p),d=this.texData.get(p.dataId);return Object.assign({tensorRef:c},d.texture)}bufferSync(t){let a=this.readSync(t.dataId);if(t.dtype==="string")try{let n=a.map(r=>v.decodeString(r));return $e(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return $e(t.shape,t.dtype,a)}checkNumericalProblems(t){if(t!=null)for(let a=0;a0}time(t){let a=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),i=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=a,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);o.kernelMs=v.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(t){return B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=v.now(),t)}async getQueryTime(t){if(B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let a=t;return a.endMs-a.startMs}disposeData(t,a=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(a?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!a&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,a),this.disposeData(n.imag.dataId,a)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:a,dtype:n,texShape:r,usage:s,isPacked:i,slice:o}=this.texData.get(t),l=o&&o.origDataId||t,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),a!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(a,r,s,i)));let p=this.texData.get(t);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,a=Lj){return B().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)0&&v.isString(n[0])){let s=n.map(i=>v.encodeString(i));r=this.write(s,t,a)}else r=this.write(n,t,a);return this.texData.get(r).usage=null,{dataId:r,shape:t,dtype:a}}makeOutput(t,a,n){return It().makeTensorFromTensorInfo(this.makeTensorInfo(t,a,n),this)}unpackTensor(t){let a=new $j(t.shape);return this.runWebGLProgram(a,[t],t.dtype)}packTensor(t){let a=new xj(t.shape),n=!0;return this.runWebGLProgram(a,[t],t.dtype,null,n)}packedReshape(t,a){let n=[ei(t.shape),...ti(t.shape)],r={dtype:t.dtype,shape:n,dataId:t.dataId},s=[ei(a),...ti(a)],i=new c8(s,n),o=!0,l=[n],u=this.runWebGLProgram(i,[r],t.dtype,l,o);return{dataId:u.dataId,shape:a,dtype:u.dtype}}decode(t,a){let n=this.texData.get(t),{isPacked:r,shape:s,dtype:i}=n;if(a!=null){let d=v.sizeFromShape(s),h=a[0]*a[1]*4;v.assert(d<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=Nd(s),l;r?l=new bH(o):l=new AH(o);let u=!0,p=[a!=null?a:Kc(o)],c=this.runWebGLProgram(l,[{shape:o,dtype:i,dataId:t}],i,p,u,a);return{dtype:i,shape:s,dataId:c.dataId}}runWebGLProgram(t,a,n,r,s=!1,i){let o=this.makeTensorInfo(t.outputShape,n),l=this.texData.get(o.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===Yd.DENSE){let y=i!=null?i:Kc(t.outputShape);l.texShape=y.map(x=>x*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),v.sizeFromShape(o.shape)===0)return l.values=v.getTypedArrayFromDType(o.dtype,0),o;let u=[],p=a.map(y=>{if(y.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let x=this.texData.get(y.dataId);if(x.texture==null){if(!t.packedInputs&&v.sizeFromShape(y.shape)<=B().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:y.shape,texData:null,isUniform:!0,uniformValues:x.values};t.packedInputs&&(x.isPacked=!0,x.shape=y.shape)}if(this.uploadToGPU(y.dataId),!!x.isPacked!=!!t.packedInputs)y=x.isPacked?this.unpackTensor(y):this.packTensor(y),u.push(y),x=this.texData.get(y.dataId);else if(x.isPacked&&!Zd(x.shape,y.shape)){let A=y,b=y.shape;y.shape=x.shape,y=this.packedReshape(y,b),u.push(y),x=this.texData.get(y.dataId),A.shape=b}return{shape:y.shape,texData:x,isUniform:!1}});this.uploadToGPU(o.dataId);let c={shape:o.shape,texData:l,isUniform:!1},d=xH(t,p,c),h=this.getAndSaveBinary(d,()=>gH(this.gpgpu,t,p,c)),m=this.activeTimers!=null,f;m&&(f=this.startTimer()),B().get("ENGINE_COMPILE_ONLY")||yH(this.gpgpu,h,p,c,r),u.forEach(y=>this.disposeIntermediateTensorInfo(y)),m&&(f=this.endTimer(f),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(f)}));let g=B().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let y=v.now();y-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=y)}if(!B().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let y=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),y}return o}compileAndRun(t,a,n,r,s=!1){return n=n||a[0].dtype,this.runWebGLProgram(t,a,n,r,s)}getAndSaveBinary(t,a){return t in this.binaryCache||(this.binaryCache[t]=a()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(B().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!B().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=B().getBool("DEBUG");B().set("DEBUG",!1);let a=this.abs(Ge(1e-8)).dataSync()[0];if(B().set("DEBUG",t),a>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Dj:Oj}uploadToGPU(t){let a=this.texData.get(t),{shape:n,dtype:r,values:s,texture:i,usage:o,isPacked:l}=a;if(i!=null)return;let u=this.activeTimers!=null,p;u&&(p=v.now());let c=a.texShape;if(c==null&&(c=_v(n,l),a.texShape=c),s!=null){let d=Nd(n),h,m=c[1],f=c[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(l||!g)&&([m,f]=Uu(c[0],c[1])),l?h=new IH(d,g):h=new k5(d,g);let y=g?[f,m]:c,x=this.makeTensorInfo(y,r),A=this.texData.get(x.dataId);g?A.usage=hn.PIXELS:A.usage=hn.UPLOAD,A.texShape=y,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(x.dataId),m,f,s);let b=[[f,m]],w=!0,I=this.runWebGLProgram(h,[x],r,b,w),T=this.texData.get(I.dataId);a.texShape=T.texShape,a.isPacked=T.isPacked,a.usage=T.usage,B().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(a.texture=T.texture,a.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(x),u&&(this.uploadWaitMs+=v.now()-p)}else{let d=this.acquireTexture(c,o,r,l);a.texture=d}}convertAndCacheOnCPU(t,a){let n=this.texData.get(t),{dtype:r}=n;return a!=null&&(n.values=Vj(a,r)),n.values}acquireTexture(t,a,n,r){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,a,r)}computeBytes(t,a){return t[0]*t[1]*v.bytesPerElement(a)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,a]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(a));return Promise.all(t)}else{for(let[,a]of Object.entries(this.binaryCache)){let n=new Promise(r=>{try{this.checkCompletion_(a),r(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await W7(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(T3(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:a,customUniformLocations:n,infLoc:r,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:o,outTexShapeLocation:l}=Gv(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=a,t.customUniformLocations=n,t.infLoc=r,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=o,t.outTexShapeLocation=l}}createTensorFromGPUData(t,a,n){t.channels=t.channels||"RGBA";let{texture:r,height:s,width:i,channels:o}=t,l=It().backend;if(!l.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=l.writeTexture(r,a,n,s,i,o);return It().makeTensorFromDataId(u,a,n,l)}};Yp.nextDataId=0;function Vj(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let a=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;nnew Yp,2);var Gj={forceHalfFloat:m8},O3=` if (isnan(a)) return a; if (isnan(b)) return b; -`,ti=class{constructor(e,t,a){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,a),this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` +`,ai=class{constructor(e,t,a){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,a),this.enableShapeUniforms=ga(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -1242,17 +1242,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}},al=` + `}},nl=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; -`,Yu=class{constructor(e,t,a,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,a);let r=this.outputShape.length;this.enableShapeUniforms=ya(r);let s="";if(n)if(r===0||v.sizeFromShape(this.outputShape)===1)s=` +`,Ku=class{constructor(e,t,a,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,a);let r=this.outputShape.length;this.enableShapeUniforms=ga(r);let s="";if(n)if(r===0||v.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(s=` - ${ft(r)} coords = getOutputCoords(); + ${mt(r)} coords = getOutputCoords(); `,r===1)this.enableShapeUniforms?s+=` result.y = (coords + 1) >= outShape ? 0. : result.y; result.z = 0.; @@ -1261,7 +1261,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; - `;else{let i=Ia("coords",r);this.enableShapeUniforms?s+=` + `;else{let i=ka("coords",r);this.enableShapeUniforms?s+=` bool nextRowOutOfBounds = (${i[r-2]} + 1) >= outShape[${r} - 2]; bool nextColOutOfBounds = @@ -1291,13 +1291,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function tn(e){let{inputs:t,backend:a}=e,{x:n}=t;return a.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var _j={kernelName:Gi,backendName:"webgl",kernelFunc:tn};function ps(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.texData.get(s.dataId),o=tn({inputs:{x:n},backend:a}),l=tn({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var Pj={kernelName:pp,backendName:"webgl",kernelFunc:ps},n8="return (a < 0.) ? b * a : a;",r8=` + `}};function Qa(e){let{inputs:t,backend:a}=e,{x:n}=t;return a.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Hj={kernelName:Hi,backendName:"webgl",kernelFunc:Qa};function hs(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.texData.get(s.dataId),o=Qa({inputs:{x:n},backend:a}),l=Qa({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var jj={kernelName:up,backendName:"webgl",kernelFunc:hs},f8="return (a < 0.) ? b * a : a;",g8=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function $j(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=a.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Yu(r8,r.shape,i.shape):new ti(n8,r.shape,i.shape),l=a.runWebGLProgram(o,[r,i],"float32");return a.disposeIntermediateTensorInfo(i),l}var Fj={kernelName:Xi,backendName:"webgl",kernelFunc:$j},s8="return (a < 0.) ? b * a : a;",i8=` +`;function qj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=a.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ku(g8,r.shape,i.shape):new ai(f8,r.shape,i.shape),l=a.runWebGLProgram(o,[r,i],"float32");return a.disposeIntermediateTensorInfo(i),l}var Xj={kernelName:Ki,backendName:"webgl",kernelFunc:qj},y8="return (a < 0.) ? b * a : a;",x8=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function Dj(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Yu(i8,n.shape,r.shape):new ti(s8,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],"float32")}var Oj={kernelName:vo,backendName:"webgl",kernelFunc:Dj},Zu="if (isnan(x)) return x;";function tt({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:a,dtype:n}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&a!=null){let c=o.texData.get(i.dataId),d=a(c.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=W().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new Hr(i.shape,t):p=new Kn(i.shape,e),o.runWebGLProgram(p,[i],l)}}function ma({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:a=!1,supportsComplex:n=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(n&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(A=>{let[b,w]=A,I={dataId:b.dataId,dtype:b.dtype,shape:l.shape},T={dataId:w.dataId,dtype:w.dtype,shape:u.shape},N=new ti(e,l.shape,u.shape);return p.runWebGLProgram(N,[I,T],pa(b.dtype,w.dtype))}),x=ps({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),x}let c=s||pa(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?S.fromUint8ToStringArray(m):m,y=l.dtype==="string"?S.fromUint8ToStringArray(f):f,[x,A]=r(l.shape,u.shape,g,y,c),b=p.makeTensorInfo(A,c),w=p.texData.get(b.dataId);return w.values=x,b}let d=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Yu(t,l.shape,u.shape,a):h=new ti(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],c)}}function ep(e,t=!1){if(e==="linear")return t?gj:pj;if(e==="relu")return t?xj:hj;if(e==="elu")return t?yj:cj;if(e==="relu6")return t?Aj:mj;if(e==="prelu")return t?i8:s8;if(e==="leakyrelu")return t?r8:n8;if(e==="sigmoid")return t?bj:fj;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var o8=class{constructor(e,t,a,n=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a,this.enableShapeUniforms=ya(this.outputShape.length);let u=n?e[1]:e[2],p=Math.ceil(u/2),c=n?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) { +`;function Kj(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ku(x8,n.shape,r.shape):new ai(y8,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],"float32")}var Yj={kernelName:wo,backendName:"webgl",kernelFunc:Kj},Yu="if (isnan(x)) return x;";function tt({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:a,dtype:n}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&a!=null){let c=o.texData.get(i.dataId),d=a(c.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=B().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new jr(i.shape,t):p=new Kn(i.shape,e),o.runWebGLProgram(p,[i],l)}}function ha({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:a=!1,supportsComplex:n=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(n&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(A=>{let[b,w]=A,I={dataId:b.dataId,dtype:b.dtype,shape:l.shape},T={dataId:w.dataId,dtype:w.dtype,shape:u.shape},N=new ai(e,l.shape,u.shape);return p.runWebGLProgram(N,[I,T],da(b.dtype,w.dtype))}),x=hs({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),x}let c=s||da(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?C.fromUint8ToStringArray(m):m,y=l.dtype==="string"?C.fromUint8ToStringArray(f):f,[x,A]=r(l.shape,u.shape,g,y,c),b=p.makeTensorInfo(A,c),w=p.texData.get(b.dataId);return w.values=x,b}let d=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Ku(t,l.shape,u.shape,a):h=new ai(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],c)}}function Jd(e,t=!1){if(e==="linear")return t?Rj:Ij;if(e==="relu")return t?Mj:Cj;if(e==="elu")return t?Ej:Sj;if(e==="relu6")return t?Pj:Tj;if(e==="prelu")return t?x8:y8;if(e==="leakyrelu")return t?g8:f8;if(e==="sigmoid")return t?_j:Nj;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var A8=class{constructor(e,t,a,n=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a,this.enableShapeUniforms=ga(this.outputShape.length);let u=n?e[1]:e[2],p=Math.ceil(u/2),c=n?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:l?f=`vec4 activation(vec4 a) { @@ -1336,7 +1336,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}},w5={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},k5=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(t,a),this.userCode=` + `}},N5={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},R5=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,a),this.userCode=` float binaryOpComplex( float areal, float aimag, float breal, float bimag) { ${e} @@ -1349,7 +1349,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float bimag = getBImagAtOutCoords(); setOutput(binaryOpComplex(areal, aimag, breal, bimag)); } - `}},I5="return a * b;";function F3(e){let{inputs:t,backend:a}=e,{a:n,b:r}=t,s=S.upcastType(n.dtype,r.dtype);if(n.dtype==="complex64"){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),u=new k5(w5.REAL,n.shape,r.shape),p=new k5(w5.IMAG,n.shape,r.shape),c=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),m=ps({inputs:{real:d,imag:h},backend:a});return a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),m}if(a.shouldExecuteOnCPU([n,r])){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),[u,p]=$H(n.shape,r.shape,o.values,l.values,s),c=a.makeTensorInfo(p,s),d=a.texData.get(c.dataId);return d.values=u,c}let i;return W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Yu(I5,n.shape,r.shape):i=new ti(I5,n.shape,r.shape),a.runWebGLProgram(i,[n,r],s)}var zj={kernelName:mo,backendName:"webgl",kernelFunc:F3};function Lj(e,t,a){let n=[Qs(e.shape),...ei(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Qs(t),...ei(t)],i=new t8(s,n),o=!0,l=[n],u=a.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function pe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{shape:s}=n,i=a,o=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. 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The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!Zd(r.shape,l)&&!(p.texture!==null&&Zd(p.shape,l))?Jj(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var Qj={kernelName:Tu,backendName:"webgl",kernelFunc:pe},M5=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(a/4)*4,o=a%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%a>0&&(u=` if (inIdx < 0 || inIdx >= ${r}) { return 0.0; } @@ -1402,7 +1402,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}},Bj=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(a/4)*4,p=a%4,c=` + `}},eq=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(a/4)*4,p=a%4,c=` if (${t==="sum"}) { sumValue += dot(values, ones); } else if (${t==="prod"}) { @@ -1494,12 +1494,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function Vj(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let a=t.length?t[t.length-1].outSize:e[1],n=S.computeOptimalWindowSize(a);t.push({inSize:a,windowSize:n,outSize:Math.ceil(a/n)})}return t}function nl(e,t,a,n){let r=Vj(e.shape),s=e;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=ft(this.rank),r=e8("rc",this.rank),s=new Array(this.rank);for(let u=0;u6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=mt(this.rank),r=p8("rc",this.rank),s=new Array(this.rank);for(let u=0;u`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],w=n?[x,m,d]:[x,d,m],I=pe({inputs:{x:e},backend:r,attrs:{shape:b}}),T=pe({inputs:{x:t},backend:r,attrs:{shape:w}}),N=[I,T],M=Math.max(y,x),P=a?I.shape[1]:I.shape[2],E=s!=null,C=i!=null,_=l==="leakyrelu",O=l!=null?ep(l,!0):null,B=E||C||_||O!=null,F;if((h===1||m===1)&&P>l8&&B===!1){let G=I,q=T;a&&(G=Ta({inputs:{x:I},backend:r,attrs:{perm:[0,2,1]}}),N.push(G)),n&&(q=Ta({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),N.push(q));let H=m!==1,V=m===1,Z=G;H&&(Z=pe({inputs:{x:G},backend:r,attrs:{shape:[M,P,1]}}),N.push(Z));let X=m===1?2:1,re=q;V&&(re=pe({inputs:{x:q},backend:r,attrs:{shape:[M,1,P]}}),N.push(re));let ee=F3({inputs:{a:Z,b:re},backend:r});F=s0({inputs:{x:ee},backend:r,attrs:{axis:X,keepDims:!0}}),N.push(ee)}else{let G=pa(e.dtype,t.dtype),q=new o8(b,w,[M,h,m],a,n,E,O,C,_),H=[I,T];if(s!=null&&H.push(s),C&&H.push(i),_){let V=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));H.push(V),N.push(V)}F=r.runWebGLProgram(q,H,G)}let U=pe({inputs:{x:F},backend:r,attrs:{shape:A}});N.push(F);for(let G of N)r.disposeIntermediateTensorInfo(G);return U}function Kj(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return wh({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var Yj={kernelName:Kr,backendName:"webgl",kernelFunc:Kj},C5="return abs(x);";function Zj(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=a.texData.get(n.dataId),i=Jv(s.values);return a.makeTensorInfo(n.shape,n.dtype,i)}let r;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Hr(n.shape,C5):r=new Kn(n.shape,C5),a.runWebGLProgram(r,[n],n.dtype)}var Jj={kernelName:ru,backendName:"webgl",kernelFunc:Zj},Qj=Mn+` + `}};function r0(e,t,a){let n=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rq(e.shape,t):new aq(e.shape,t);return a.runWebGLProgram(n,[e],e.dtype)}function sq(e,t,a,n){let r=t,s=e.shape.length,i=v.parseAxisParam(r,e.shape),o=i,l=C.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=r0(e,l,n),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[c,d]=C.computeOutAndReduceShapes(p.shape,o),h=c;a&&(h=C.expandShapeToKeepDim(c,i));let m=v.sizeFromShape(d),f=v.sizeFromShape(e.shape)/m,g=pe({inputs:{x:p},attrs:{shape:[f,m]},backend:n}),y=Mp(e.dtype),x=rl(g,y,"sum",n),A=pe({inputs:{x},attrs:{shape:h},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(x),u&&n.disposeIntermediateTensorInfo(p),A}function s0(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return sq(r,s,i,a)}var iq={kernelName:Vo,backendName:"webgl",kernelFunc:s0};function Ca(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{perm:s}=n,i=a,o=r.shape.length,l=new Array(o);for(let p=0;p`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} 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V=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));H.push(V),N.push(V)}$=r.runWebGLProgram(q,H,G)}let U=pe({inputs:{x:$},backend:r,attrs:{shape:A}});N.push($);for(let G of N)r.disposeIntermediateTensorInfo(G);return U}function lq(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return wh({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var uq={kernelName:Yr,backendName:"webgl",kernelFunc:lq},P5="return abs(x);";function dq(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=a.texData.get(n.dataId),i=u8(s.values);return a.makeTensorInfo(n.shape,n.dtype,i)}let r;return B().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new jr(n.shape,P5):r=new Kn(n.shape,P5),a.runWebGLProgram(r,[n],n.dtype)}var pq={kernelName:ru,backendName:"webgl",kernelFunc:dq},cq=En+` if (abs(x) > 1.) { return NAN; } 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b;",xq=ha({opSnippet:_5,packedOpSnippet:_5,supportsComplex:!0,cpuKernelImpl:CH}),Aq={kernelName:is,backendName:"webgl",kernelFunc:xq},bq=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`float v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` void main() { ${a.join(` `)} @@ -1531,7 +1531,7 @@ return log(x + sqrt(x * x - 1.0));`,nq=tt({opSnippet:aq}),rq={kernelName:si,back float result = ${n}; setOutput(result); } - `}},lq=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`vec4 v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` + `}},vq=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`vec4 v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` void main() { ${a.join(` `)} @@ -1539,7 +1539,7 @@ return log(x + sqrt(x * x - 1.0));`,nq=tt({opSnippet:aq}),rq={kernelName:si,back vec4 result = ${n}; setOutput(result); } - `}};function ih(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return tn({inputs:{x:n[0]},backend:a});if(n.length>W().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=ih({inputs:n.slice(0,o),backend:a}),u=ih({inputs:n.slice(o),backend:a});return ih({inputs:[l,u],backend:a})}let r=n.map(o=>o.dtype).reduce((o,l)=>pa(o,l)),s=n.map(o=>o.shape),i=W().getBool("WEBGL_PACK")?new lq(n[0].shape,s):new oq(n[0].shape,s);return a.runWebGLProgram(i,n,r)}var uq={kernelName:ii,backendName:"webgl",kernelFunc:ih};function dq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=S.getAxesPermutation(u,o),c=r;p!=null&&(c=Ta({inputs:{x:r},backend:a,attrs:{perm:p}}),u=S.getInnerMostAxes(u.length,o)),S.assertAxesAreInnerMostDims("all",u,o);let[d,h]=S.computeOutAndReduceShapes(c.shape,u),m=v.sizeFromShape(h),f=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,m]}}),g=nl(f,f.dtype,"all",a),y;if(i){let x=S.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var pq={kernelName:oi,backendName:"webgl",kernelFunc:dq};function cq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=S.getAxesPermutation(u,o),c=r;p!=null&&(c=Ta({inputs:{x:r},backend:a,attrs:{perm:p}}),u=S.getInnerMostAxes(u.length,o)),S.assertAxesAreInnerMostDims("any",u,o);let[d,h]=S.computeOutAndReduceShapes(c.shape,u),m=v.sizeFromShape(h),f=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,m]}}),g=nl(f,f.dtype,"any",a),y;if(i){let x=S.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var hq={kernelName:li,backendName:"webgl",kernelFunc:cq},mq=class{constructor(e,t,a){this.variableNames=["A"];let{windowSize:n,batchSize:r,outSize:s}=e;a||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=a?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function sh(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return Qa({inputs:{x:n[0]},backend:a});if(n.length>B().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=sh({inputs:n.slice(0,o),backend:a}),u=sh({inputs:n.slice(o),backend:a});return sh({inputs:[l,u],backend:a})}let r=n.map(o=>o.dtype).reduce((o,l)=>da(o,l)),s=n.map(o=>o.shape),i=B().getBool("WEBGL_PACK")?new vq(n[0].shape,s):new bq(n[0].shape,s);return a.runWebGLProgram(i,n,r)}var wq={kernelName:oi,backendName:"webgl",kernelFunc:sh};function kq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=C.getAxesPermutation(u,o),c=r;p!=null&&(c=Ca({inputs:{x:r},backend:a,attrs:{perm:p}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("all",u,o);let[d,h]=C.computeOutAndReduceShapes(c.shape,u),m=v.sizeFromShape(h),f=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,m]}}),g=rl(f,f.dtype,"all",a),y;if(i){let x=C.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var Iq={kernelName:li,backendName:"webgl",kernelFunc:kq};function Sq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=C.getAxesPermutation(u,o),c=r;p!=null&&(c=Ca({inputs:{x:r},backend:a,attrs:{perm:p}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("any",u,o);let[d,h]=C.computeOutAndReduceShapes(c.shape,u),m=v.sizeFromShape(h),f=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,m]}}),g=rl(f,f.dtype,"any",a),y;if(i){let x=C.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var Cq={kernelName:ui,backendName:"webgl",kernelFunc:Sq},Tq=class{constructor(e,t,a){this.variableNames=["A"];let{windowSize:n,batchSize:r,outSize:s}=e;a||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=a?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1559,7 +1559,7 @@ return log(x + sqrt(x * x - 1.0));`,nq=tt({opSnippet:aq}),rq={kernelName:si,back } setOutput(float(bestIndex)); } - `}},fq=class{constructor(e,t,a,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${a.charAt(0).toUpperCase()+a.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ft(o),u=Ia("coords",o),p,c;if(s===1){c=o+1;let T=ft(c);p=` + `}},Nq=class{constructor(e,t,a,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${a.charAt(0).toUpperCase()+a.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=mt(o),u=ka("coords",o),p,c;if(s===1){c=o+1;let T=mt(c);p=` ${T} sourceLocR = ${T}(${u.join()}, 0); ++${u[o-1]}; ${T} sourceLocG = ${T}(${u.join()}, 0); @@ -1575,7 +1575,7 @@ return log(x + sqrt(x * x - 1.0));`,nq=tt({opSnippet:aq}),rq={kernelName:si,back ${l} sourceLocA = coords; --${u[o-1]}; ${l} sourceLocB = coords; - --${u[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,c),h="."+d[c-1],m=d.map(T=>"int "+T),f=Ia("sourceLocR",c-1).concat("inIdx.r"),g=Ia("sourceLocG",c-1).concat("inIdx.g"),y=Ia("sourceLocB",c-1).concat("inIdx.b"),x=Ia("sourceLocA",c-1).concat("inIdx.a"),A=a==="max"?"greaterThan":"lessThan",b=n?"":` + --${u[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,c),h="."+d[c-1],m=d.map(T=>"int "+T),f=ka("sourceLocR",c-1).concat("inIdx.r"),g=ka("sourceLocG",c-1).concat("inIdx.g"),y=ka("sourceLocB",c-1).concat("inIdx.b"),x=ka("sourceLocA",c-1).concat("inIdx.a"),A=a==="max"?"greaterThan":"lessThan",b=n?"":` inIdx = round(vec4(getBestIndicesAChannel(${f.join()}), getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${y.join()}), @@ -1621,25 +1621,25 @@ return log(x + sqrt(x * x - 1.0));`,nq=tt({opSnippet:aq}),rq={kernelName:si,back } setOutput(bestIndex); } - `}};function u8(e,t,a,n=null){let r=t.shape[0],s=t.shape[1];n!=null&&(r=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new mq(o,a,n==null),u=[t];n!=null&&u.push(n);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let c=u8(e,t,a,p);return e.disposeIntermediateTensorInfo(p),c}function d8(e,t,a,n=null){let r=n!=null?n.shape:t.shape,s=r[r.length-1],i=S.computeOptimalWindowSize(s),o=new fq(r,i,a,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=d8(e,t,a,u);return e.disposeIntermediateTensorInfo(u),p}return u}function p8(e,t,a,n){let r=[a];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,t.shape.length),!W().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=S.computeOutAndReduceShapes(l.shape,r),c=v.sizeFromShape(p),d=pe({inputs:{x:l},backend:e,attrs:{shape:[-1,c]}});s.push(d);let h=u8(e,d,n);s.push(h);let 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Aq={kernelName:iu,backendName:"webgl",kernelFunc:xq},bq=Mn+` + `}};function v8(e,t,a,n=null){let r=t.shape[0],s=t.shape[1];n!=null&&(r=n.shape[0],s=n.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new Tq(o,a,n==null),u=[t];n!=null&&u.push(n);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let c=v8(e,t,a,p);return e.disposeIntermediateTensorInfo(p),c}function w8(e,t,a,n=null){let r=n!=null?n.shape:t.shape,s=r[r.length-1],i=C.computeOptimalWindowSize(s),o=new Nq(r,i,a,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=w8(e,t,a,u);return e.disposeIntermediateTensorInfo(u),p}return u}function k8(e,t,a,n){let r=[a];if(C.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,t.shape.length),!B().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let 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-`,Tq=tt({opSnippet:Cq}),Nq={kernelName:pi,backendName:"webgl",kernelFunc:Tq},Rq=$3+` +`,Wq=tt({opSnippet:Lq}),Bq={kernelName:ci,backendName:"webgl",kernelFunc:Wq},Vq=O3+` return atan(a, b); -`,Eq=` +`,Uq=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - `+al+` + `+nl+` return result; -`,Mq=ma({opSnippet:Rq,packedOpSnippet:Eq}),_q={kernelName:hi,backendName:"webgl",kernelFunc:Mq},Pq=Mn+` +`,Gq=ha({opSnippet:Vq,packedOpSnippet:Uq}),Hq={kernelName:mi,backendName:"webgl",kernelFunc:Gq},jq=En+` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelName:ci,backendName:"webgl",kernelFunc:$q},tp=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),a){let T=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,qq=tt({opSnippet:jq}),Xq={kernelName:hi,backendName:"webgl",kernelFunc:qq},Qd=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),a){let T=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${d}, ${h}); @@ -1780,7 +1780,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(${A}); } - `}},D3=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,c=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",A="0.0";if(x||(A="-1.0 / 1e-20"),a){let M=">=";this.userCode=` + `}},L3=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,c=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",A="0.0";if(x||(A="-1.0 / 1e-20"),a){let M=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${g}, ${y}); @@ -1943,7 +1943,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(${w}); } - `}};function Dq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;Gu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(S.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=S.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return tn({inputs:{x:r},backend:a});let c=new tp(p,"avg",!1);return a.runWebGLProgram(c,[r],"float32")}var Oq={kernelName:mi,backendName:"webgl",kernelFunc:Dq};function zq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=[1,1,1],c=S.computePool3DInfo(r.shape,s,i,p,o,l,u),d=new D3(c,"avg",!1);return a.runWebGLProgram(d,[r],"float32")}var Lq={kernelName:ou,backendName:"webgl",kernelFunc:zq},Wq=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,c=1/(t*a);this.userCode=` + `}};function Kq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;Gu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return Qa({inputs:{x:r},backend:a});let c=new Qd(p,"avg",!1);return a.runWebGLProgram(c,[r],"float32")}var Yq={kernelName:fi,backendName:"webgl",kernelFunc:Kq};function Zq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=[1,1,1],c=C.computePool3DInfo(r.shape,s,i,p,o,l,u),d=new L3(c,"avg",!1);return a.runWebGLProgram(d,[r],"float32")}var Jq={kernelName:ou,backendName:"webgl",kernelFunc:Zq},Qq=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,c=1/(t*a);this.userCode=` const ivec2 pads = ivec2(${u}, ${p}); const float avgMultiplier = float(${c}); @@ -1985,7 +1985,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(dotProd); } - `}},Bq=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,a=e.filterHeight,n=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=c-1-e.padInfo.top,f=d-1-e.padInfo.left,g=1/(t*a*n);this.userCode=` + `}},eX=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,a=e.filterHeight,n=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=c-1-e.padInfo.top,f=d-1-e.padInfo.left,g=1/(t*a*n);this.userCode=` const ivec3 pads = ivec3(${h}, ${m}, ${f}); const float avgMultiplier = float(${g}); @@ -2041,7 +2041,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(dotProd); } - `}};function Vq(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=S.computePool3DInfo(i.shape,o,l,c,u,p),h=new Bq(d);return a.runWebGLProgram(h,[r],i.dtype)}var Uq={kernelName:dp,backendName:"webgl",kernelFunc:Vq};function Gq(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Gu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=S.computePool2DInfo(i.shape,o,l,1,u),c=new Wq(p);return a.runWebGLProgram(c,[r],i.dtype)}var Hq={kernelName:up,backendName:"webgl",kernelFunc:Gq};function jq(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return wh({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var qq={kernelName:fi,backendName:"webgl",kernelFunc:jq},Xq=class{constructor(e,t,a,n,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,a);let i="0.0";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function tX(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,c,u,p),h=new eX(d);return a.runWebGLProgram(h,[r],i.dtype)}var aX={kernelName:lp,backendName:"webgl",kernelFunc:tX};function nX(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Gu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=C.computePool2DInfo(i.shape,o,l,1,u),c=new Qq(p);return a.runWebGLProgram(c,[r],i.dtype)}var rX={kernelName:op,backendName:"webgl",kernelFunc:nX};function sX(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return wh({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var iX={kernelName:gi,backendName:"webgl",kernelFunc:sX},oX=class{constructor(e,t,a,n,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,a);let i="0.0";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2051,7 +2051,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}},Kq=class{constructor(e,t,a,n,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,a);let i="vec4(0.0)";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}},lX=class{constructor(e,t,a,n,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,a);let i="vec4(0.0)";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; @@ -2064,7 +2064,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam setOutput((x - mean) * inv + offset); } - `}},Yq=({inputs:e,backend:t,attrs:a})=>{let{x:n,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=a;l==null&&(l=.001);let u=[n,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let c=null;o!=null&&(c=o.shape,u.push(o));let d=W().getBool("WEBGL_PACK_NORMALIZATION")?new Kq(n.shape,r.shape,s.shape,p,c,l):new Xq(n.shape,r.shape,s.shape,p,c,l);return t.runWebGLProgram(d,u,u[0].dtype)},Zq={kernelName:Wi,backendName:"webgl",kernelFunc:Yq},Jq=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ft(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let a=Qq(this.rank),n,r=e.map((s,i)=>`sourceLoc.${O1[i]} = start[${i}] + coords.${O1[i]};`);n=` + `}},uX=({inputs:e,backend:t,attrs:a})=>{let{x:n,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=a;l==null&&(l=.001);let u=[n,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let c=null;o!=null&&(c=o.shape,u.push(o));let d=B().getBool("WEBGL_PACK_NORMALIZATION")?new lX(n.shape,r.shape,s.shape,p,c,l):new oX(n.shape,r.shape,s.shape,p,c,l);return t.runWebGLProgram(d,u,u[0].dtype)},dX={kernelName:Bi,backendName:"webgl",kernelFunc:uX},pX=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=mt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let a=cX(this.rank),n,r=e.map((s,i)=>`sourceLoc.${z1[i]} = start[${i}] + coords.${z1[i]};`);n=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${r.join(` @@ -2074,7 +2074,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam ${n} setOutput(getSource(${a})); } - `}},O1=["x","y","z","w","u","v"];function Qq(e){if(e===1)return"sourceLoc";if(e<=6)return O1.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var eX=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ft(this.rank),a=Ia("coords",this.rank),n=Ia("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${r})`,i=` + `}},z1=["x","y","z","w","u","v"];function cX(e){if(e===1)return"sourceLoc";if(e<=6)return z1.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var hX=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=mt(this.rank),a=ka("coords",this.rank),n=ka("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${r})`,i=` result.x = ${s}; if (++${a[this.rank-1]} < ${e[this.rank-1]}) { ++${n[this.rank-1]}; @@ -2103,15 +2103,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam ${o} setOutput(result); } - `}};function tX(e,t,a,n){let r=n.texData.get(e.dataId),s=n.makeTensorInfo(a,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=a,i.dtype=e.dtype;let o=Nt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function Ju(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=Nt.parseSliceParams(r,s,i);if(Nt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);if(a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.texData.get(r.dataId),d=HH(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=a.texData.get(r.dataId),p=Nt.isSliceContinous(r.shape,o,l);if(u||!p){let c=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eX(l):new Jq(l),d=[o];return a.runWebGLProgram(c,[r],r.dtype,d)}return a.uploadToGPU(r.dataId),tX(r,o,l,a)}var aX={kernelName:Mu,backendName:"webgl",kernelFunc:Ju},nX=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=S.getReshaped(r.shape,s,o),u=S.getPermuted(l.length,s.length),p=S.getReshapedPermuted(r.shape,s,o),c=S.getSliceBeginCoords(i,s.length),d=S.getSliceSize(p,i,s.length),h=[],m=pe({inputs:{x:r},backend:a,attrs:{shape:l}}),f=Ta({inputs:{x:m},backend:a,attrs:{perm:u}}),g=pe({inputs:{x:f},backend:a,attrs:{shape:p}}),y=Ju({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(x=>a.disposeIntermediateTensorInfo(x)),y},rX={kernelName:lu,backendName:"webgl",kernelFunc:nX};function sX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.readSync(r.dataId),l=a.readSync(s.dataId),u=Zv(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var iX={kernelName:gi,backendName:"webgl",kernelFunc:sX},oX=` + `}};function mX(e,t,a,n){let r=n.texData.get(e.dataId),s=n.makeTensorInfo(a,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=a,i.dtype=e.dtype;let o=Nt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function Zu(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=Nt.parseSliceParams(r,s,i);if(Nt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);if(a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.texData.get(r.dataId),d=rj(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=a.texData.get(r.dataId),p=Nt.isSliceContinous(r.shape,o,l);if(u||!p){let c=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new hX(l):new pX(l),d=[o];return a.runWebGLProgram(c,[r],r.dtype,d)}return a.uploadToGPU(r.dataId),mX(r,o,l,a)}var fX={kernelName:Mu,backendName:"webgl",kernelFunc:Zu},gX=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let 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uX(e){let{inputs:t,backend:a}=e,{a:n,b:r}=t,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=W().getNumber("WEBGL_VERSION");if(a.shouldExecuteOnCPU([n,r])||i===1){let l=a.texData.get(n.dataId).values,u=a.texData.get(r.dataId).values,[p,c]=fH(n.shape,r.shape,l,u,n.dtype),d=a.makeTensorInfo(c,n.dtype),h=a.texData.get(d.dataId);return h.values=p,d}let o;return s?o=new Yu(oX,n.shape,r.shape,!1):o=new ti(lX,n.shape,r.shape),a.runWebGLProgram(o,[n,r],n.dtype)}var dX={kernelName:uu,backendName:"webgl",kernelFunc:uX};function pX(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.readSync(n.dataId),i=a.readSync(r.dataId),o=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var cX={kernelName:du,backendName:"webgl",kernelFunc:pX},hX="return float(a != b);",c8=ma({opSnippet:hX,cpuKernelImpl:DH,dtype:"bool"}),mX={kernelName:fo,backendName:"webgl",kernelFunc:c8};function Jp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return tn({inputs:{x:r.complexTensorInfos.real},backend:a})}var fX={kernelName:kp,backendName:"webgl",kernelFunc:Jp},gX="return float(int(x));";function yX(e,t){let a=new Kn(e.shape,gX),n=t.runWebGLProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function z1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return tn({inputs:{x:r},backend:a});let i=yn(r.shape),o=z1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=ps({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Jp({inputs:{input:r},backend:a}),o=z1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=tn({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.texData.get(r.dataId).values,[o,l,u]=gH(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return yX(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=c8({inputs:{a:r,b:i},backend:a});return a.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var xX={kernelName:yi,backendName:"webgl",kernelFunc:z1},N5="return ceil(x);",AX=tt({opSnippet:N5,packedOpSnippet:N5,cpuKernelImpl:yH}),bX={kernelName:xi,backendName:"webgl",kernelFunc:AX},vX=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` +`;function wX(e){let{inputs:t,backend:a}=e,{a:n,b:r}=t,s=B().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=B().getNumber("WEBGL_VERSION");if(a.shouldExecuteOnCPU([n,r])||i===1){let l=a.texData.get(n.dataId).values,u=a.texData.get(r.dataId).values,[p,c]=NH(n.shape,r.shape,l,u,n.dtype),d=a.makeTensorInfo(c,n.dtype),h=a.texData.get(d.dataId);return h.values=p,d}let o;return s?o=new Ku(bX,n.shape,r.shape,!1):o=new ai(vX,n.shape,r.shape),a.runWebGLProgram(o,[n,r],n.dtype)}var kX={kernelName:uu,backendName:"webgl",kernelFunc:wX};function IX(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.readSync(n.dataId),i=a.readSync(r.dataId),o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var SX={kernelName:du,backendName:"webgl",kernelFunc:IX},CX="return float(a != b);",I8=ha({opSnippet:CX,cpuKernelImpl:KH,dtype:"bool"}),TX={kernelName:go,backendName:"webgl",kernelFunc:I8};function Zp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return Qa({inputs:{x:r.complexTensorInfos.real},backend:a})}var NX={kernelName:vp,backendName:"webgl",kernelFunc:Zp},RX="return float(int(x));";function EX(e,t){let a=new Kn(e.shape,RX),n=t.runWebGLProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function L1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return Qa({inputs:{x:r},backend:a});let i=gn(r.shape),o=L1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=hs({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Zp({inputs:{input:r},backend:a}),o=L1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=Qa({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.texData.get(r.dataId).values,[o,l,u]=RH(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return EX(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=I8({inputs:{a:r,b:i},backend:a});return a.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var MX={kernelName:xi,backendName:"webgl",kernelFunc:L1},$5="return ceil(x);",PX=tt({opSnippet:$5,packedOpSnippet:$5,cpuKernelImpl:EH}),_X={kernelName:Ai,backendName:"webgl",kernelFunc:PX},$X=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2122,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam setOutput(clamp(value, minVal, maxVal)); } - `}},wX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}},FX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { vec4 value = getAAtOutCoords(); @@ -2133,7 +2133,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function kX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o;W().getBool("WEBGL_PACK_CLIP")?o=new wX(r.shape):o=new vX(r.shape);let l=[[s],[i]];return a.runWebGLProgram(o,[r],r.dtype,l)}var IX={kernelName:ss,backendName:"webgl",kernelFunc:kX},SX=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `}};function DX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o;B().getBool("WEBGL_PACK_CLIP")?o=new FX(r.shape):o=new $X(r.shape);let l=[[s],[i]];return a.runWebGLProgram(o,[r],r.dtype,l)}var OX={kernelName:os,backendName:"webgl",kernelFunc:DX},zX=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2146,7 +2146,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function R5(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function CX(e){let{inputs:t,backend:a}=e,{x:n}=t,r=a.texData.get(n.dataId),s=new SX(n.shape),i=[R5(n,r.complexTensorInfos.real),R5(n,r.complexTensorInfos.imag)];return a.runWebGLProgram(s,i,i[0].dtype)}var TX={kernelName:cp,backendName:"webgl",kernelFunc:CX},NX=class{constructor(e){this.outputShape=[],this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m= ${o[m-1]}) { return getChannel( - getT${m}(${Jc(i,l,f)}), - vec2(${Jc(u,l,f)})); + getT${m}(${Zc(i,l,f)}), + vec2(${Zc(u,l,f)})); }`}let d=o.length,h=o[o.length-1];c+=` return getChannel( - getT${d}(${Jc(i,l,h)}), - vec2(${Jc(u,l,h)}));`,this.userCode=` + getT${d}(${Zc(i,l,h)}), + vec2(${Zc(u,l,h)}));`,this.userCode=` float getValue(${i.map(m=>"int "+m)}) { ${c} } @@ -2192,7 +2192,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(result); } - `}};function Jc(e,t,a){let n=e.indexOf(t);return e.map((r,s)=>s===n?`${r} - ${a}`:r).join()}function i0(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return tn({inputs:{x:r.complexTensorInfos.imag},backend:a})}var EX={kernelName:bp,backendName:"webgl",kernelFunc:i0};function Ed(e,t,a){let n=e[0].dtype;if(n==="complex64"){let h=e.map(x=>Jp({inputs:{input:x},backend:a})),m=e.map(x=>i0({inputs:{input:x},backend:a})),f=Ed(h,t,a),g=Ed(m,t,a),y=ps({inputs:{real:f,imag:g},backend:a});return h.forEach(x=>a.disposeIntermediateTensorInfo(x)),m.forEach(x=>a.disposeIntermediateTensorInfo(x)),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),y}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let h=e.map(b=>{let w=[-1,v.sizeFromShape(b.shape.slice(t))];return pe({inputs:{x:b},backend:a,attrs:{shape:w}})}),m=h.map(b=>({vals:a.readSync(b.dataId),shape:b.shape})),f=S.computeOutShape(h.map(b=>b.shape),1),g=h[0].shape[0]===1,y=xH(m,f,n,g),x=S.computeOutShape(e.map(b=>b.shape),t),A=a.makeTensorInfo(x,n,y);return h.forEach(b=>a.disposeIntermediateTensorInfo(b)),A}let s=e.filter(h=>v.sizeFromShape(h.shape)>0),i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new Kn(e[0].shape,Wr):new Hr(e[0].shape,Wr);return a.runWebGLProgram(h,e,n)}let o=W().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let f=0;fm.shape),t);return a.runWebGLProgram(h,s,n)}let{tensors2D:l,outShape:u}=MX(s,t,a),p=new NX(l.map(h=>h.shape)),c=a.runWebGLProgram(p,l,n);l.forEach(h=>a.disposeIntermediateTensorInfo(h));let d=pe({inputs:{x:c},attrs:{shape:u},backend:a});return a.disposeIntermediateTensorInfo(c),d}function MX(e,t,a){let n=S.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>pe({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:a})),outShape:n}}function h8(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);S.assertParamsConsistent(i,s);let o=S.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?tn({inputs:{x:l[0]},backend:a}):Ed(l,s,a)}var _X={kernelName:pu,backendName:"webgl",kernelFunc:h8},m8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,x=f?3:1,A="",b="";a&&(n?A=`float activation(float a) { + `}};function Zc(e,t,a){let n=e.indexOf(t);return e.map((r,s)=>s===n?`${r} - ${a}`:r).join()}function i0(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return Qa({inputs:{x:r.complexTensorInfos.imag},backend:a})}var UX={kernelName:xp,backendName:"webgl",kernelFunc:i0};function Rd(e,t,a){let n=e[0].dtype;if(n==="complex64"){let h=e.map(x=>Zp({inputs:{input:x},backend:a})),m=e.map(x=>i0({inputs:{input:x},backend:a})),f=Rd(h,t,a),g=Rd(m,t,a),y=hs({inputs:{real:f,imag:g},backend:a});return h.forEach(x=>a.disposeIntermediateTensorInfo(x)),m.forEach(x=>a.disposeIntermediateTensorInfo(x)),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),y}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let h=e.map(b=>{let w=[-1,v.sizeFromShape(b.shape.slice(t))];return pe({inputs:{x:b},backend:a,attrs:{shape:w}})}),m=h.map(b=>({vals:a.readSync(b.dataId),shape:b.shape})),f=C.computeOutShape(h.map(b=>b.shape),1),g=h[0].shape[0]===1,y=MH(m,f,n,g),x=C.computeOutShape(e.map(b=>b.shape),t),A=a.makeTensorInfo(x,n,y);return h.forEach(b=>a.disposeIntermediateTensorInfo(b)),A}let s=e.filter(h=>v.sizeFromShape(h.shape)>0),i=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new Kn(e[0].shape,Br):new jr(e[0].shape,Br);return a.runWebGLProgram(h,e,n)}let o=B().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let f=0;fm.shape),t);return a.runWebGLProgram(h,s,n)}let{tensors2D:l,outShape:u}=GX(s,t,a),p=new BX(l.map(h=>h.shape)),c=a.runWebGLProgram(p,l,n);l.forEach(h=>a.disposeIntermediateTensorInfo(h));let d=pe({inputs:{x:c},attrs:{shape:u},backend:a});return a.disposeIntermediateTensorInfo(c),d}function GX(e,t,a){let n=C.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>pe({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:a})),outShape:n}}function S8(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);C.assertParamsConsistent(i,s);let o=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?Qa({inputs:{x:l[0]},backend:a}):Rd(l,s,a)}var HX={kernelName:pu,backendName:"webgl",kernelFunc:S8},C8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,x=f?3:1,A="",b="";a&&(n?A=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${a} }`:r?A=`float activation(float a) { @@ -2326,7 +2326,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam ${b} setOutput(result); } - `}},PX=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,a=e.padInfo.top,n=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` + `}},jX=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,a=e.padInfo.top,n=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${r}, ${s}, ${i}); const ivec3 pads = ivec3(${t}, ${a}, ${n}); @@ -2414,7 +2414,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(dotProd); } - `}},f8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,c=` + `}},T8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ga(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,c=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let f=0;f=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function g8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),p=a.inChannels,c=l[0]*l[1]*l[2],d=a.outChannels,h=a.dataFormat==="channelsLast",m=!1,f=!1,g,y=[];if(s!=null){let x=kh(s.shape,h);x!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:x}}),y.push(s))}if(r!=null){let x=kh(r.shape,h);x!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:x}}),y.push(r))}if(!((c===1||d===1)&&p>l8)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let x=l[0]*l[1]*(l[2]+1),A={dataId:e.dataId,shape:[1,x,a.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Qd(u.shape,A.shape),()=>`packed reshape ${u.shape} to ${A.shape} isn't free`);let w=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});y.push(w);let I=wh({a:A,b:w,backend:n,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),T=n.texData.get(I.dataId);v.assert(T.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,T.shape=a.outShape,g=tn({inputs:{x:I},backend:n}),g.shape=a.outShape,y.push(I)}else{let x=a.outHeight*a.outWidth,A=pe({inputs:{x:e},backend:n,attrs:{shape:h?[a.batchSize,x,a.inChannels]:[a.batchSize,a.inChannels,x]}}),b=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}}),w=wh({a:h?A:b,b:h?b:A,transposeA:!h,transposeB:f,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=pe({inputs:{x:w},backend:n,attrs:{shape:a.outShape}}),y.push(A),y.push(b),y.push(w)}for(let x of y)n.disposeIntermediateTensorInfo(x);return g}function y8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:c,outHeight:d,dataFormat:h}=a,m=h==="channelsLast",f=l*u*p,g=d*c,y=[a.batchSize,f,g],x=!0,A=!1,b=[];if(s!=null){let G=kh(s.shape,m);G!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:G}}),b.push(s))}if(r!=null){let G=kh(r.shape,m);G!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:G}}),b.push(r))}let w=pe({inputs:{x:t},backend:n,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});b.push(w);let I=new $X(y,a),T=[e.shape,[a.padInfo.top,a.padInfo.left],[a.strideHeight,a.strideWidth],[a.dilationHeight,a.dilationWidth],[a.inChannels],[a.filterWidth*a.inChannels],[a.outWidth]],N=n.runWebGLProgram(I,[e],"float32",T),M=pe({inputs:{x:N},backend:n,attrs:{shape:y}});b.push(N),b.push(M);let P=r!=null,E=s!=null,C=o==="leakyrelu",_=o?ep(o,!0):null,O=new o8(m?M.shape:w.shape,m?w.shape:M.shape,m?[a.batchSize,g,a.outChannels]:[a.batchSize,a.outChannels,g],x,A,P,_,E,C),B=m?[M,w]:[w,M];if(r&&B.push(r),E&&B.push(s),C){let G=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));B.push(G),b.push(G)}let F=n.runWebGLProgram(O,B,"float32"),U=pe({inputs:{x:F},backend:n,attrs:{shape:a.outShape}});b.push(F);for(let G of b)n.disposeIntermediateTensorInfo(G);return U}function FX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=g8({x:r,filter:s,convInfo:d,backend:a});else if(d.strideWidth<=2&&c==="channelsLast"&&W().getBool("WEBGL_EXP_CONV")){let f=new f8(d),g=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=a.runWebGLProgram(f,[r,s],"float32",g)}else if(W().getBool("WEBGL_CONV_IM2COL"))h=y8({x:r,filter:s,convInfo:d,backend:a});else{let f=new m8(d);h=a.runWebGLProgram(f,[r,s],"float32")}let m=pe({inputs:{x:h},backend:a,attrs:{shape:d.outShape}});return a.disposeIntermediateTensorInfo(h),m}var DX={kernelName:Ai,backendName:"webgl",kernelFunc:FX},OX=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` + `}};function kh(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function N8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),p=a.inChannels,c=l[0]*l[1]*l[2],d=a.outChannels,h=a.dataFormat==="channelsLast",m=!1,f=!1,g,y=[];if(s!=null){let x=kh(s.shape,h);x!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:x}}),y.push(s))}if(r!=null){let x=kh(r.shape,h);x!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:x}}),y.push(r))}if(!((c===1||d===1)&&p>b8)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let x=l[0]*l[1]*(l[2]+1),A={dataId:e.dataId,shape:[1,x,a.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Zd(u.shape,A.shape),()=>`packed reshape ${u.shape} to ${A.shape} isn't free`);let w=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});y.push(w);let I=wh({a:A,b:w,backend:n,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),T=n.texData.get(I.dataId);v.assert(T.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,T.shape=a.outShape,g=Qa({inputs:{x:I},backend:n}),g.shape=a.outShape,y.push(I)}else{let x=a.outHeight*a.outWidth,A=pe({inputs:{x:e},backend:n,attrs:{shape:h?[a.batchSize,x,a.inChannels]:[a.batchSize,a.inChannels,x]}}),b=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}}),w=wh({a:h?A:b,b:h?b:A,transposeA:!h,transposeB:f,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=pe({inputs:{x:w},backend:n,attrs:{shape:a.outShape}}),y.push(A),y.push(b),y.push(w)}for(let x of y)n.disposeIntermediateTensorInfo(x);return g}function R8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:c,outHeight:d,dataFormat:h}=a,m=h==="channelsLast",f=l*u*p,g=d*c,y=[a.batchSize,f,g],x=!0,A=!1,b=[];if(s!=null){let G=kh(s.shape,m);G!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:G}}),b.push(s))}if(r!=null){let G=kh(r.shape,m);G!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:G}}),b.push(r))}let w=pe({inputs:{x:t},backend:n,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});b.push(w);let I=new qX(y,a),T=[e.shape,[a.padInfo.top,a.padInfo.left],[a.strideHeight,a.strideWidth],[a.dilationHeight,a.dilationWidth],[a.inChannels],[a.filterWidth*a.inChannels],[a.outWidth]],N=n.runWebGLProgram(I,[e],"float32",T),M=pe({inputs:{x:N},backend:n,attrs:{shape:y}});b.push(N),b.push(M);let P=r!=null,E=s!=null,S=o==="leakyrelu",_=o?Jd(o,!0):null,O=new A8(m?M.shape:w.shape,m?w.shape:M.shape,m?[a.batchSize,g,a.outChannels]:[a.batchSize,a.outChannels,g],x,A,P,_,E,S),W=m?[M,w]:[w,M];if(r&&W.push(r),E&&W.push(s),S){let G=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));W.push(G),b.push(G)}let $=n.runWebGLProgram(O,W,"float32"),U=pe({inputs:{x:$},backend:n,attrs:{shape:a.outShape}});b.push($);for(let G of b)n.disposeIntermediateTensorInfo(G);return U}function XX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=N8({x:r,filter:s,convInfo:d,backend:a});else if(d.strideWidth<=2&&c==="channelsLast"&&B().getBool("WEBGL_EXP_CONV")){let f=new T8(d),g=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=a.runWebGLProgram(f,[r,s],"float32",g)}else if(B().getBool("WEBGL_CONV_IM2COL"))h=R8({x:r,filter:s,convInfo:d,backend:a});else{let f=new C8(d);h=a.runWebGLProgram(f,[r,s],"float32")}let m=pe({inputs:{x:h},backend:a,attrs:{shape:d.outShape}});return a.disposeIntermediateTensorInfo(h),m}var KX={kernelName:bi,backendName:"webgl",kernelFunc:XX},YX=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2694,7 +2694,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(dotProd); } - `}},zX=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=a-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=` + `}},ZX=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=a-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -2747,7 +2747,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(dotProd); } - `}},LX=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` + `}},JX=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2789,7 +2789,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(dotProd); } - `}},WX=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,a=e.filterHeight,n=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=a-1-e.padInfo.top,u=n-1-e.padInfo.left;this.userCode=` + `}},QX=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,a=e.filterHeight,n=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=a-1-e.padInfo.top,u=n-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${l}, ${u}); void main() { @@ -2846,7 +2846,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(dotProd); } - `}};function BX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new OX(d);return a.runWebGLProgram(h,[r,s],"float32")}var VX={kernelName:hp,backendName:"webgl",kernelFunc:BX},UX=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=ya(this.outputShape.length);let t=e.filterHeight,a=e.filterWidth,n=t-1-e.padInfo.top,r=a-1-e.padInfo.left;this.userCode=` + `}};function eK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new YX(d);return a.runWebGLProgram(h,[r,s],"float32")}var tK={kernelName:pp,backendName:"webgl",kernelFunc:eK},aK=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=ga(this.outputShape.length);let t=e.filterHeight,a=e.filterWidth,n=t-1-e.padInfo.top,r=a-1-e.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${n}, ${r}); void main() { @@ -2920,17 +2920,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(result); } - `}};function GX(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c);if(W().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&c==="channelsLast"){let h=[[d.strideHeight,d.strideWidth]],m=new UX(d);return a.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new zX(d);return a.runWebGLProgram(h,[r,s],"float32")}}var HX={kernelName:bi,backendName:"webgl",kernelFunc:GX};function jX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=S.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new PX(u);return a.runWebGLProgram(p,[r,s],"float32")}var qX={kernelName:vi,backendName:"webgl",kernelFunc:jX};function XX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=S.computeConv3DInfo(r.shape,l,i,1,o),p=new LX(u);return a.runWebGLProgram(p,[r,s],"float32")}var KX={kernelName:cu,backendName:"webgl",kernelFunc:XX};function YX(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=S.computeConv3DInfo(l,s.shape,o,1,i),p=new WX(u);return a.runWebGLProgram(p,[r,s],"float32")}var ZX={kernelName:wi,backendName:"webgl",kernelFunc:YX},JX=Zu+` + `}};function nK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c);if(B().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&c==="channelsLast"){let h=[[d.strideHeight,d.strideWidth]],m=new aK(d);return a.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new ZX(d);return a.runWebGLProgram(h,[r,s],"float32")}}var rK={kernelName:vi,backendName:"webgl",kernelFunc:nK};function sK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new jX(u);return a.runWebGLProgram(p,[r,s],"float32")}var iK={kernelName:wi,backendName:"webgl",kernelFunc:sK};function oK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=C.computeConv3DInfo(r.shape,l,i,1,o),p=new JX(u);return a.runWebGLProgram(p,[r,s],"float32")}var lK={kernelName:cu,backendName:"webgl",kernelFunc:oK};function uK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=C.computeConv3DInfo(l,s.shape,o,1,i),p=new QX(u);return a.runWebGLProgram(p,[r,s],"float32")}var dK={kernelName:ki,backendName:"webgl",kernelFunc:uK},pK=Yu+` return cos(x); -`,QX=` +`,cK=` vec4 result = cos(x); bvec4 isNaN = isnan(x); - ${al} + ${nl} return result; -`,eK=tt({opSnippet:JX,packedOpSnippet:QX}),tK={kernelName:ki,backendName:"webgl",kernelFunc:eK},aK=` +`,hK=tt({opSnippet:pK,packedOpSnippet:cK}),mK={kernelName:Ii,backendName:"webgl",kernelFunc:hK},fK=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,nK=tt({opSnippet:aK}),rK={kernelName:Ii,backendName:"webgl",kernelFunc:nK},sK=class{constructor(e,t,a,n,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,c]=a;this.outputShape=[u,p,c,l];let d=n==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,A,b]=c>1?[`${(o-1)/(c-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` +`,gK=tt({opSnippet:fK}),yK={kernelName:Si,backendName:"webgl",kernelFunc:gK},xK=class{constructor(e,t,a,n,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,c]=a;this.outputShape=[u,p,c,l];let d=n==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,A,b]=c>1?[`${(o-1)/(c-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` const float height_ratio = float(${f}); const float width_ratio = float(${x}); void main() { @@ -2991,20 +2991,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam setOutput(newValue); } } - `}},iK=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new sK(r.shape,s.shape,o,l,u);return a.runWebGLProgram(p,[r,s,i],"float32")},oK={kernelName:Ti,backendName:"webgl",kernelFunc:iK},ap;(function(e){e.Prod="*",e.Sum="+"})(ap||(ap={}));var E5=class{constructor(e,t,a,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===ap.Prod?"1.0":"0.0",i=a?s:`getX(${M5(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";a?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=` + `}},AK=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new xK(r.shape,s.shape,o,l,u);return a.runWebGLProgram(p,[r,s,i],"float32")},bK={kernelName:Ni,backendName:"webgl",kernelFunc:AK},ep;(function(e){e.Prod="*",e.Sum="+"})(ep||(ep={}));var D5=class{constructor(e,t,a,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===ep.Prod?"1.0":"0.0",i=a?s:`getX(${O5(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";a?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=` void main() { - ${ft(r)} coords = getOutputCoords(); - int end = ${_5(r,"coords",this.op)}; + ${mt(r)} coords = getOutputCoords(); + int end = ${z5(r,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${l}) { int idx = ${u}; - ${_5(r,"coords",this.op)} = idx; - val ${this.op}= getX(${M5(r,"coords",this.op)}); + ${z5(r,"coords",this.op)} = idx; + val ${this.op}= getX(${O5(r,"coords",this.op)}); } setOutput(val); } - `}};function M5(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function _5(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function x8(e,t,a,n,r,s){let i=t.shape.length,o=S.getAxesPermutation([n],i),l=t;o!=null&&(l=Ta({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=S.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=tn({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new E5(e,l.shape,!1,s),m=[[d]],f=c;c=a.runWebGLProgram(h,[c],c.dtype,m),a.disposeIntermediateTensorInfo(f)}if(r){let d=new E5(e,l.shape,r,s),h=c;c=a.runWebGLProgram(d,[c],c.dtype),a.disposeIntermediateTensorInfo(h)}if(o!=null){let d=S.getUndoAxesPermutation(o),h=Ta({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(l),h}return c}function lK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return x8(ap.Prod,r,a,s,i,o)}var uK={kernelName:Si,backendName:"webgl",kernelFunc:lK};function dK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return x8(ap.Sum,r,a,s,i,o)}var pK={kernelName:Ci,backendName:"webgl",kernelFunc:dK};function cK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n;if(r.shape.length===1){let l=a.readSync(r.dataId),u=a.readSync(s.dataId),p=Zv(l,u,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=a.bufferSync(r),u=a.bufferSync(s),p=mH(l,u,i,o);return a.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var hK={kernelName:hu,backendName:"webgl",kernelFunc:cK},mK=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=a,this.userCode=` + `}};function O5(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function z5(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function E8(e,t,a,n,r,s){let i=t.shape.length,o=C.getAxesPermutation([n],i),l=t;o!=null&&(l=Ca({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=C.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=Qa({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new D5(e,l.shape,!1,s),m=[[d]],f=c;c=a.runWebGLProgram(h,[c],c.dtype,m),a.disposeIntermediateTensorInfo(f)}if(r){let d=new D5(e,l.shape,r,s),h=c;c=a.runWebGLProgram(d,[c],c.dtype),a.disposeIntermediateTensorInfo(h)}if(o!=null){let d=C.getUndoAxesPermutation(o),h=Ca({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(l),h}return c}function vK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return E8(ep.Prod,r,a,s,i,o)}var wK={kernelName:Ci,backendName:"webgl",kernelFunc:vK};function kK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return E8(ep.Sum,r,a,s,i,o)}var IK={kernelName:Ti,backendName:"webgl",kernelFunc:kK};function SK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n;if(r.shape.length===1){let l=a.readSync(r.dataId),u=a.readSync(s.dataId),p=l8(l,u,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=a.bufferSync(r),u=a.bufferSync(s),p=TH(l,u,i,o);return a.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var CK={kernelName:hu,backendName:"webgl",kernelFunc:SK},TK=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=a,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3023,7 +3023,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function fK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),m=i==="NHWC"?[o,c,d,h]:[o,h,c,d],f=new mK(m,s,i);return a.runWebGLProgram(f,[r],r.dtype)}var gK={kernelName:Ni,backendName:"webgl",kernelFunc:fK},A8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";a&&(n?l=`float activation(float a) { + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function NK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),m=i==="NHWC"?[o,c,d,h]:[o,h,c,d],f=new TK(m,s,i);return a.runWebGLProgram(f,[r],r.dtype)}var RK={kernelName:Ri,backendName:"webgl",kernelFunc:NK},M8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ga(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";a&&(n?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${a} }`:r?l=`float activation(float a) { @@ -3076,7 +3076,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam ${u} setOutput(result); } - `}},b8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,c=p,d=` + `}},P8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ga(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,c=p,d=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let c=S.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),d;W().getBool("WEBGL_PACK_DEPTHWISECONV")&&c.strideWidth<=2&&c.outChannels/c.inChannels===1?d=new b8(c):d=new A8(c);let h=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];return a.runWebGLProgram(d,[r,s],"float32",h)}var xK={kernelName:Ri,backendName:"webgl",kernelFunc:yK},AK=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` + `}};function EK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,p=l;p==null&&(p=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let c=C.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),d;B().getBool("WEBGL_PACK_DEPTHWISECONV")&&c.strideWidth<=2&&c.outChannels/c.inChannels===1?d=new P8(c):d=new M8(c);let h=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];return a.runWebGLProgram(d,[r,s],"float32",h)}var MK={kernelName:Ei,backendName:"webgl",kernelFunc:EK},PK=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -3300,7 +3300,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(dotProd); } - `}},bK=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=a-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` + `}},_K=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=a-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${s}, ${i}); void main() { @@ -3345,13 +3345,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(dotProd); } - `}};function vK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=S.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new AK(c);return a.runWebGLProgram(d,[r,s],"float32")}var wK={kernelName:mp,backendName:"webgl",kernelFunc:vK};function kK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=S.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new bK(c);return a.runWebGLProgram(d,[r,s],"float32")}var IK={kernelName:fp,backendName:"webgl",kernelFunc:kK},SK=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function $K(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=C.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new PK(c);return a.runWebGLProgram(d,[r,s],"float32")}var FK={kernelName:cp,backendName:"webgl",kernelFunc:$K};function DK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=C.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new _K(c);return a.runWebGLProgram(d,[r,s],"float32")}var OK={kernelName:hp,backendName:"webgl",kernelFunc:DK},zK=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function CK(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=pe({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new SK(s),l=a.runWebGLProgram(o,[i],i.dtype),u=pe({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(l),u}var TK={kernelName:mu,backendName:"webgl",kernelFunc:CK},NK=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:a,padInfo:n,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:c}=n;this.userCode=` + `}};function LK(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=pe({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new zK(s),l=a.runWebGLProgram(o,[i],i.dtype),u=pe({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(l),u}var WK={kernelName:mu,backendName:"webgl",kernelFunc:LK},BK=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:a,padInfo:n,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:c}=n;this.userCode=` const ivec2 strides = ivec2(${r}, ${s}); const ivec2 pads = ivec2(${p}, ${c}); const float neg_infinity = -3.4e38; @@ -3389,7 +3389,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam float result = curVal; setOutput(result); } - `}};function RK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=S.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,c=new NK(u);p=a.runWebGLProgram(c,[r,s],"float32");let d=pe({inputs:{x:p},backend:a,attrs:{shape:u.outShape}});return a.disposeIntermediateTensorInfo(p),d}var EK={kernelName:Ei,backendName:"webgl",kernelFunc:RK};function MK(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=S.decodeEinsumEquation(r,s.length);S.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=S.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,m=[];for(let f=0;f=0&&(d=s0({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeIntermediateTensorInfo(f);return d}var _K={kernelName:yp,backendName:"webgl",kernelFunc:MK},PK="return (x >= 0.0) ? x : (exp(x) - 1.0);",$K=` + `}};function VK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,c=new BK(u);p=a.runWebGLProgram(c,[r,s],"float32");let d=pe({inputs:{x:p},backend:a,attrs:{shape:u.outShape}});return a.disposeIntermediateTensorInfo(p),d}var UK={kernelName:Mi,backendName:"webgl",kernelFunc:VK};function GK(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(r,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=C.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,m=[];for(let f=0;f=0&&(d=s0({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeIntermediateTensorInfo(f);return d}var HK={kernelName:fp,backendName:"webgl",kernelFunc:GK},jK="return (x >= 0.0) ? x : (exp(x) - 1.0);",qK=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3398,29 +3398,29 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,FK=tt({opSnippet:PK,packedOpSnippet:$K}),DK={kernelName:_i,backendName:"webgl",kernelFunc:FK},OK="return (b >= 0.0) ? a : a * (b + 1.0);",zK=` +`,XK=tt({opSnippet:jK,packedOpSnippet:qK}),KK={kernelName:_i,backendName:"webgl",kernelFunc:XK},YK="return (b >= 0.0) ? a : a * (b + 1.0);",ZK=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,LK=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Yu(zK,n.shape,r.shape):new ti(OK,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],n.dtype)},WK={kernelName:fu,backendName:"webgl",kernelFunc:LK},BK=` +`,JK=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ku(ZK,n.shape,r.shape):new ai(YK,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],n.dtype)},QK={kernelName:fu,backendName:"webgl",kernelFunc:JK},eY=` return vec4(equal(a, b)); -`,VK="return float(a == b);",UK=ma({opSnippet:VK,packedOpSnippet:BK,dtype:"bool",cpuKernelImpl:AH}),GK={kernelName:$i,backendName:"webgl",kernelFunc:UK},HK=` +`,tY="return float(a == b);",aY=ha({opSnippet:tY,packedOpSnippet:eY,dtype:"bool",cpuKernelImpl:PH}),nY={kernelName:Fi,backendName:"webgl",kernelFunc:aY},rY=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. - float p = ${S.ERF_P}; - float a1 = ${S.ERF_A1}; - float a2 = ${S.ERF_A2}; - float a3 = ${S.ERF_A3}; - float a4 = ${S.ERF_A4}; - float a5 = ${S.ERF_A5}; + float p = ${C.ERF_P}; + float a1 = ${C.ERF_A1}; + float a2 = ${C.ERF_A2}; + float a3 = ${C.ERF_A3}; + float a4 = ${C.ERF_A4}; + float a5 = ${C.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,jK=tt({opSnippet:HK}),qK={kernelName:Pi,backendName:"webgl",kernelFunc:jK},XK=Zu+` +`,sY=tt({opSnippet:rY}),iY={kernelName:$i,backendName:"webgl",kernelFunc:sY},oY=Yu+` return exp(x); -`,KK=` +`,lY=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -3429,7 +3429,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam result.a = isNaN.a ? x.a : result.a; return result; -`,v8=tt({opSnippet:XK,packedOpSnippet:KK,cpuKernelImpl:bH,dtype:"float32"}),YK={kernelName:Fi,backendName:"webgl",kernelFunc:v8};function L1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),pe({inputs:{x:s},backend:n,attrs:{shape:o}})}var ZK={kernelName:gu,backendName:"webgl",kernelFunc:L1},P5="return exp(x) - 1.0;",JK=tt({opSnippet:P5,packedOpSnippet:P5,cpuKernelImpl:vH}),QK={kernelName:Di,backendName:"webgl",kernelFunc:JK},$5=class{constructor(e,t,a){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let r=a?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=a?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` +`,_8=tt({opSnippet:oY,packedOpSnippet:lY,cpuKernelImpl:_H,dtype:"float32"}),uY={kernelName:Di,backendName:"webgl",kernelFunc:_8};function W1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),pe({inputs:{x:s},backend:n,attrs:{shape:o}})}var dY={kernelName:gu,backendName:"webgl",kernelFunc:W1},L5="return exp(x) - 1.0;",pY=tt({opSnippet:L5,packedOpSnippet:L5,cpuKernelImpl:$H}),cY={kernelName:Oi,backendName:"webgl",kernelFunc:pY},W5=class{constructor(e,t,a){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let r=a?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=a?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${r}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -3462,12 +3462,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function w8(e,t,a){let n=a.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=pe({inputs:{x:e},backend:a,attrs:{shape:[i,s]}}),l=o.shape,u=new $5("real",l,t),p=new $5("imag",l,t),c=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),m=ps({inputs:{real:d,imag:h},backend:a});a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h);let f=pe({inputs:{x:m},backend:a,attrs:{shape:e.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(m),f}function eY(e){let{inputs:t,backend:a}=e,{input:n}=t;return w8(n,!1,a)}var tY={kernelName:xp,backendName:"webgl",kernelFunc:eY},aY=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function $8(e,t,a){let n=a.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=pe({inputs:{x:e},backend:a,attrs:{shape:[i,s]}}),l=o.shape,u=new W5("real",l,t),p=new W5("imag",l,t),c=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),m=hs({inputs:{real:d,imag:h},backend:a});a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h);let f=pe({inputs:{x:m},backend:a,attrs:{shape:e.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(m),f}function hY(e){let{inputs:t,backend:a}=e,{input:n}=t;return $8(n,!1,a)}var mY={kernelName:gp,backendName:"webgl",kernelFunc:hY},fY=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function Qp(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new aY(n,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var nY={kernelName:yu,backendName:"webgl",kernelFunc:Qp},rY=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}};function Jp(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new fY(n,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var gY={kernelName:yu,backendName:"webgl",kernelFunc:Jp},yY=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -3481,7 +3481,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(outputValue); } - `}},sY={kernelName:Oi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new rY(a.shape);return n.runWebGLProgram(r,[a],a.dtype)}},F5="return floor(x);",iY=tt({opSnippet:F5,packedOpSnippet:F5,cpuKernelImpl:wH}),oY={kernelName:zi,backendName:"webgl",kernelFunc:iY},lY=` + `}},xY={kernelName:zi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new yY(a.shape);return n.runWebGLProgram(r,[a],a.dtype)}},B5="return floor(x);",AY=tt({opSnippet:B5,packedOpSnippet:B5,cpuKernelImpl:FH}),bY={kernelName:Li,backendName:"webgl",kernelFunc:AY},vY=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3491,7 +3491,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } else { return NAN; } -`,uY=` +`,wY=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3512,7 +3512,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,dY=ma({opSnippet:lY,packedOpSnippet:uY,dtype:"int32"}),pY={kernelName:Li,backendName:"webgl",kernelFunc:dY},cY=class{constructor(e){this.variableNames=["A"];let t=Ea(),[a,n]=e;this.outputShape=e,this.userCode=` +`,kY=ha({opSnippet:vY,packedOpSnippet:wY,dtype:"int32"}),IY={kernelName:Wi,backendName:"webgl",kernelFunc:kY},SY=class{constructor(e){this.variableNames=["A"];let t=Ra(),[a,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3534,7 +3534,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam setOutput(floor(value * 255.0 + 0.5)); } - `}},hY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Ea(),[a,n]=e;this.outputShape=e,this.userCode=` + `}},CY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Ra(),[a,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); 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-`,yZ=ma({opSnippet:fZ,packedOpSnippet:gZ,dtype:"bool"}),xZ={kernelName:ao,backendName:"webgl",kernelFunc:yZ},AZ=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` +`,EZ=ha({opSnippet:NZ,packedOpSnippet:RZ,dtype:"bool"}),MZ={kernelName:no,backendName:"webgl",kernelFunc:EZ},PZ=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3638,7 +3638,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam float val = x * ${o}; setOutput(val); } - `}},bZ=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` + `}},_Z=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3700,7 +3700,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam vec4 result = xAtOutputCoords * ${o}; setOutput(result); } - `}},vZ=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=W().getBool("WEBGL_PACK_NORMALIZATION")?new bZ(r.shape,s,i,o,l):new AZ(r.shape,s,i,o,l);return a.runWebGLProgram(u,[r],r.dtype)},wZ={kernelName:no,backendName:"webgl",kernelFunc:vZ},kZ=class{constructor(e,t,a,n,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=a,this.alpha=n,this.beta=r,this.userCode=` + `}},$Z=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=B().getBool("WEBGL_PACK_NORMALIZATION")?new _Z(r.shape,s,i,o,l):new PZ(r.shape,s,i,o,l);return a.runWebGLProgram(u,[r],r.dtype)},FZ={kernelName:ro,backendName:"webgl",kernelFunc:$Z},DZ=class{constructor(e,t,a,n,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=a,this.alpha=n,this.beta=r,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3755,16 +3755,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(result); } - `}},IZ=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new kZ(r.shape,o,l,u,p);return a.runWebGLProgram(c,[r,s,i],r.dtype)},SZ={kernelName:Au,backendName:"webgl",kernelFunc:IZ};function CZ(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=nl(i,e.dtype,"max",n),l=pe({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function I8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=S.getAxesPermutation(u,o),c=p!=null,d=a.shouldExecuteOnCPU([r]),h=r;if(c){if(d){let x=a.texData.get(h.dataId).values,A=new Array(o);for(let I=0;I{let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new DZ(r.shape,o,l,u,p);return a.runWebGLProgram(c,[r,s,i],r.dtype)},zZ={kernelName:Au,backendName:"webgl",kernelFunc:OZ};function LZ(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=rl(i,e.dtype,"max",n),l=pe({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function D8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=C.getAxesPermutation(u,o),c=p!=null,d=a.shouldExecuteOnCPU([r]),h=r;if(c){if(d){let x=a.texData.get(h.dataId).values,A=new Array(o);for(let I=0;I`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=S.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return tn({inputs:{x:r},backend:a});let c=new tp(p,"max",!1);return a.runWebGLProgram(c,[r],r.dtype)}var PZ={kernelName:io,backendName:"webgl",kernelFunc:_Z};function $Z(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=S.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new D3(c,"max",!1);return a.runWebGLProgram(d,[r],r.dtype)}var FZ={kernelName:bu,backendName:"webgl",kernelFunc:$Z},DZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,a=e.strideWidth,n=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` +`,UZ=ha({opSnippet:BZ,packedOpSnippet:VZ,cpuKernelImpl:HH}),GZ={kernelName:io,backendName:"webgl",kernelFunc:UZ};function HZ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;Gu(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return Qa({inputs:{x:r},backend:a});let c=new Qd(p,"max",!1);return a.runWebGLProgram(c,[r],r.dtype)}var jZ={kernelName:oo,backendName:"webgl",kernelFunc:HZ};function qZ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=C.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new L3(c,"max",!1);return a.runWebGLProgram(d,[r],r.dtype)}var XZ={kernelName:bu,backendName:"webgl",kernelFunc:qZ},KZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,a=e.strideWidth,n=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -3810,7 +3810,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(dotProd); } - `}},OZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,c=l-1-e.padInfo.top,d=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` + `}},YZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,c=l-1-e.padInfo.top,d=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${p}, ${c}, ${d}); void main() { @@ -3874,16 +3874,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam } setOutput(dotProd); } - `}};function zZ(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=S.computePool3DInfo(i.shape,o,l,c,u,p),h=new D3(d,"max",!0),m=a.runWebGLProgram(h,[i],i.dtype),f=new OZ(d),g=a.runWebGLProgram(f,[r,m],i.dtype);return a.disposeIntermediateTensorInfo(m),g}var LZ={kernelName:wp,backendName:"webgl",kernelFunc:zZ};function WZ(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;Gu([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=S.computePool2DInfo(o.shape,l,u,1,p,c),h=!0,m=new tp(d,"max",h),f=a.runWebGLProgram(m,[o],o.dtype),g=new DZ(d),y=a.runWebGLProgram(g,[r,f],o.dtype);return a.disposeIntermediateTensorInfo(f),y}var BZ={kernelName:vp,backendName:"webgl",kernelFunc:WZ};function VZ(e,t,a,n){let r=new tp(a,"max",!1),s=n.runWebGLProgram(r,[e],"float32");r=new tp(a,"max",!0,!0,t);let i=n.runWebGLProgram(r,[e],"float32");return[s,i]}var UZ={kernelName:vu,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;v.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];v.assert(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=S.computePool2DInfo(n.shape,r,s,u,i),[c,d]=VZ(n,o,p,l);return[c,d]}};function GZ(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=nl(i,"float32","mean",n),l=pe({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var HZ={kernelName:oo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{keepDims:r,axis:s}=t,i=a,o=n.shape.length,l=v.parseAxisParam(s,n.shape),u=l,p=S.getAxesPermutation(u,o),c=p!=null,d=i.shouldExecuteOnCPU([n]),h=[],m=n;if(c){if(d){let A=i.texData.get(m.dataId).values,b=new Array(o);for(let T=0;T{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;v.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=C.computePool2DInfo(n.shape,r,s,u,i),[c,d]=tJ(n,o,p,l);return[c,d]}};function nJ(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=rl(i,"float32","mean",n),l=pe({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var rJ={kernelName:lo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{keepDims:r,axis:s}=t,i=a,o=n.shape.length,l=v.parseAxisParam(s,n.shape),u=l,p=C.getAxesPermutation(u,o),c=p!=null,d=i.shouldExecuteOnCPU([n]),h=[],m=n;if(c){if(d){let A=i.texData.get(m.dataId).values,b=new Array(o);for(let T=0;Tu[0]+e[p]+u[1]);let n=e.length,r=ft(n),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=a==="reflect"?0:1;if(n===1){this.userCode=` +`,uJ=ha({opSnippet:oJ,packedOpSnippet:lJ,cpuKernelImpl:jH}),dJ={kernelName:po,backendName:"webgl",kernelFunc:uJ},pJ=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=t.map((u,p)=>u[0]+e[p]+u[1]);let n=e.length,r=mt(n),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=a==="reflect"?0:1;if(n===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -3912,7 +3912,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam ${r} coords = outC - start; setOutput(getX(${o})); } - `}},QZ=class{constructor(e,t,a){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let n=e.length,r=ft(n),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=Ia("rc",n),l=Ia("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${l.slice(-2).join()})`,c=a==="reflect"?0:1,d="";if(n===1){let h=` + `}},cJ=class{constructor(e,t,a){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let n=e.length,r=mt(n),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=ka("rc",n),l=ka("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${l.slice(-2).join()})`,c=a==="reflect"?0:1,d="";if(n===1){let h=` ${r} source = rc; if (source < start) { source = start * 2 - source - ${c}; @@ -3968,13 +3968,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam ${d} setOutput(result); } - `}},eJ=({inputs:e,backend:t,attrs:a})=>{let{x:n}=e,{paddings:r,mode:s}=a,i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new QZ(n.shape,r,s):new JZ(n.shape,r,s);return t.runWebGLProgram(i,[n],n.dtype)},tJ={kernelName:po,backendName:"webgl",kernelFunc:eJ},aJ=`if (b == 0.0) return NAN; - return mod(a, b);`,nJ=` + `}},hJ=({inputs:e,backend:t,attrs:a})=>{let{x:n}=e,{paddings:r,mode:s}=a,i=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new cJ(n.shape,r,s):new pJ(n.shape,r,s);return t.runWebGLProgram(i,[n],n.dtype)},mJ={kernelName:co,backendName:"webgl",kernelFunc:hJ},fJ=`if (b == 0.0) return NAN; + return mod(a, b);`,gJ=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+al+` + `+nl+` return result; -`,rJ=ma({opSnippet:aJ,packedOpSnippet:nJ}),sJ={kernelName:co,backendName:"webgl",kernelFunc:rJ},iJ=class{constructor(e,t,a){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,a],this.userCode=` +`,yJ=ha({opSnippet:fJ,packedOpSnippet:gJ}),xJ={kernelName:ho,backendName:"webgl",kernelFunc:yJ},AJ=class{constructor(e,t,a){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,a],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -3994,11 +3994,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,$q=tt({opSnippet:Pq}),Fq={kernelNam // If no other event happened, last event happened. setOutput(float(${t-1})); } - `}},oJ=` + `}},bJ=` if (a == b) { return 1.0; }; -return a / b;`,lJ=` +return a / b;`,vJ=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -4016,9 +4016,9 @@ return a / b;`,lJ=` } return result; -`,S8=ma({opSnippet:oJ,packedOpSnippet:lJ,checkOutOfBounds:!0}),uJ={kernelName:Mi,backendName:"webgl",kernelFunc:S8},D5="return a - b;",C8=ma({opSnippet:D5,packedOpSnippet:D5,supportsComplex:!0,cpuKernelImpl:ej}),dJ={kernelName:jo,backendName:"webgl",kernelFunc:C8};function T8(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=I8({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=S.expandShapeToKeepDim(o.shape,i),u=pe({inputs:{x:o},backend:a,attrs:{shape:l}}),p=C8({inputs:{a:r,b:u},backend:a}),c=v8({inputs:{x:p},backend:a}),d=s0({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=pe({inputs:{x:d},backend:a,attrs:{shape:l}}),m=S8({inputs:{a:c,b:h},backend:a});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),m}var pJ={kernelName:Vo,backendName:"webgl",kernelFunc:T8};function cJ(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:T8({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new iJ(u,p,s),d=[[i]],h=a.runWebGLProgram(c,[l],"int32",d);return o||a.disposeIntermediateTensorInfo(l),h}var hJ={kernelName:ho,backendName:"webgl",kernelFunc:cJ},mJ=Mn+` +`,O8=ha({opSnippet:bJ,packedOpSnippet:vJ,checkOutOfBounds:!0}),wJ={kernelName:Pi,backendName:"webgl",kernelFunc:O8},V5="return a - b;",z8=ha({opSnippet:V5,packedOpSnippet:V5,supportsComplex:!0,cpuKernelImpl:hj}),kJ={kernelName:qo,backendName:"webgl",kernelFunc:z8};function L8(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=D8({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=pe({inputs:{x:o},backend:a,attrs:{shape:l}}),p=z8({inputs:{a:r,b:u},backend:a}),c=_8({inputs:{x:p},backend:a}),d=s0({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=pe({inputs:{x:d},backend:a,attrs:{shape:l}}),m=O8({inputs:{a:c,b:h},backend:a});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),m}var IJ={kernelName:Uo,backendName:"webgl",kernelFunc:L8};function SJ(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:L8({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new AJ(u,p,s),d=[[i]],h=a.runWebGLProgram(c,[l],"int32",d);return o||a.disposeIntermediateTensorInfo(l),h}var CJ={kernelName:mo,backendName:"webgl",kernelFunc:SJ},TJ=En+` return -x; -`,fJ=` +`,NJ=` vec4 result = -x; bvec4 isNaN = isnan(x); @@ -4028,14 +4028,14 @@ return a / b;`,lJ=` result.a = isNaN.a ? x.a : result.a; return result; -`;function gJ(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.texData.get(n.dataId),[i,o]=FH(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Hr(n.shape,fJ):r=new Kn(n.shape,mJ),a.runWebGLProgram(r,[n],n.dtype)}var yJ={kernelName:wu,backendName:"webgl",kernelFunc:gJ},xJ=En.nonMaxSuppressionV3Impl;function AJ(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),{selectedIndices:c}=xJ(u,p,i,o,l);return a.makeTensorInfo([c.length],"int32",new Int32Array(c))}var bJ={kernelName:go,backendName:"webgl",kernelFunc:AJ},vJ=En.nonMaxSuppressionV4Impl;function wJ(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),{selectedIndices:d,validOutputs:h}=vJ(p,c,i,o,l,u);return[a.makeTensorInfo([d.length],"int32",new Int32Array(d)),a.makeTensorInfo([],"int32",new Int32Array([h]))]}var kJ={kernelName:ku,backendName:"webgl",kernelFunc:wJ},IJ=En.nonMaxSuppressionV5Impl;function SJ(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),d=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=IJ(p,c,d,h,m,f);return[a.makeTensorInfo([g.length],"int32",new Int32Array(g)),a.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var CJ={kernelName:yo,backendName:"webgl",kernelFunc:SJ},TJ=class{constructor(e,t,a,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`;function RJ(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.texData.get(n.dataId),[i,o]=XH(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r;return B().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new jr(n.shape,NJ):r=new Kn(n.shape,TJ),a.runWebGLProgram(r,[n],n.dtype)}var EJ={kernelName:wu,backendName:"webgl",kernelFunc:RJ},MJ=Rn.nonMaxSuppressionV3Impl;function PJ(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),{selectedIndices:c}=MJ(u,p,i,o,l);return a.makeTensorInfo([c.length],"int32",new Int32Array(c))}var _J={kernelName:yo,backendName:"webgl",kernelFunc:PJ},$J=Rn.nonMaxSuppressionV4Impl;function FJ(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),{selectedIndices:d,validOutputs:h}=$J(p,c,i,o,l,u);return[a.makeTensorInfo([d.length],"int32",new Int32Array(d)),a.makeTensorInfo([],"int32",new Int32Array([h]))]}var DJ={kernelName:ku,backendName:"webgl",kernelFunc:FJ},OJ=Rn.nonMaxSuppressionV5Impl;function zJ(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),d=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=OJ(p,c,d,h,m,f);return[a.makeTensorInfo([g.length],"int32",new Int32Array(g)),a.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var LJ={kernelName:xo,backendName:"webgl",kernelFunc:zJ},WJ=class{constructor(e,t,a,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${n}), float(${a}), float(index == coords.y))); } - `}},NJ=e=>{let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new TJ(u,i,o,l),c=pe({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=a.runWebGLProgram(p,[c],s);a.disposeIntermediateTensorInfo(c);let h=[...r.shape,i],m=pe({inputs:{x:d},backend:a,attrs:{shape:h}});return a.disposeIntermediateTensorInfo(d),m},RJ={kernelName:xo,backendName:"webgl",kernelFunc:NJ};function Ih(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=Jp({inputs:{input:n},backend:a}),s=Ih({inputs:{x:r},backend:a}),i=i0({inputs:{input:n},backend:a}),o=Ih({inputs:{x:i},backend:a}),l=ps({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return Qp({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var EJ={kernelName:Lu,backendName:"webgl",kernelFunc:Ih};function N8(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=Jp({inputs:{input:n},backend:a}),s=N8({inputs:{x:r},backend:a}),i=i0({inputs:{input:n},backend:a}),o=Ih({inputs:{x:i},backend:a}),l=ps({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return Qp({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var MJ={kernelName:Iu,backendName:"webgl",kernelFunc:N8};function _J(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return L1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=L1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=h8({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeIntermediateTensorInfo(p)),u}var PJ={kernelName:Su,backendName:"webgl",kernelFunc:_J},$J=class{constructor(e,t,a){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,r=ft(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` + `}},BJ=e=>{let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new WJ(u,i,o,l),c=pe({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=a.runWebGLProgram(p,[c],s);a.disposeIntermediateTensorInfo(c);let h=[...r.shape,i],m=pe({inputs:{x:d},backend:a,attrs:{shape:h}});return a.disposeIntermediateTensorInfo(d),m},VJ={kernelName:Ao,backendName:"webgl",kernelFunc:BJ};function Ih(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=Zp({inputs:{input:n},backend:a}),s=Ih({inputs:{x:r},backend:a}),i=i0({inputs:{input:n},backend:a}),o=Ih({inputs:{x:i},backend:a}),l=hs({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return Jp({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var UJ={kernelName:Lu,backendName:"webgl",kernelFunc:Ih};function W8(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=Zp({inputs:{input:n},backend:a}),s=W8({inputs:{x:r},backend:a}),i=i0({inputs:{input:n},backend:a}),o=Ih({inputs:{x:i},backend:a}),l=hs({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return Jp({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var GJ={kernelName:Iu,backendName:"webgl",kernelFunc:W8};function HJ(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return W1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=W1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=S8({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeIntermediateTensorInfo(p)),u}var jJ={kernelName:Su,backendName:"webgl",kernelFunc:HJ},qJ=class{constructor(e,t,a){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,r=mt(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -4060,7 +4060,7 @@ return a / b;`,lJ=` setOutput(getX(${o})); } } - `}},FJ=class{constructor(e,t,a){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let n=e.length,r=ft(n),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=Ia("rc",n),l=Ia("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${l.slice(-2).join()})`,c=[`${r} rc = outputLoc;`,`${o[n-1]} += 1; + `}},XJ=class{constructor(e,t,a){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let n=e.length,r=mt(n),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=ka("rc",n),l=ka("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${l.slice(-2).join()})`,c=[`${r} rc = outputLoc;`,`${o[n-1]} += 1; if(${u}) { `,n===1?"":`} rc = outputLoc; @@ -4084,7 +4084,7 @@ return a / b;`,lJ=` ${h} setOutput(result); } - `}},R8=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Qp({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new FJ(r.shape,s,i):new $J(r.shape,s,i),l=[[i]];return a.runWebGLProgram(o,[r],r.dtype,l)},DJ={kernelName:Ao,backendName:"webgl",kernelFunc:R8},OJ=` + `}},B8=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Jp({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XJ(r.shape,s,i):new qJ(r.shape,s,i),l=[[i]];return a.runWebGLProgram(o,[r],r.dtype,l)},KJ={kernelName:bo,backendName:"webgl",kernelFunc:B8},YJ=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4093,7 +4093,7 @@ return a / b;`,lJ=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,zJ=` +`,ZJ=` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -4109,11 +4109,11 @@ return a / b;`,lJ=` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - `+al+` + `+nl+` return result; -`,LJ=ma({opSnippet:OJ,packedOpSnippet:zJ}),WJ={kernelName:bo,backendName:"webgl",kernelFunc:LJ};function BJ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,c=S.getAxesPermutation(p,o),d=r;c!=null&&(d=Ta({inputs:{x:r},backend:a,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,o),l.push(d)),S.assertAxesAreInnerMostDims("prod",p,o);let h;if(a.shouldExecuteOnCPU([d])){let m=a.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=OH(d.shape,d.dtype,m,p);h=a.makeTensorInfo(g,y,f)}else{let[m,f]=S.computeOutAndReduceShapes(d.shape,p),g=v.sizeFromShape(f),y=pe({inputs:{x:d},backend:a,attrs:{shape:[-1,g]}}),x=Pp(r.dtype),A=nl(y,x,"prod",a);h=pe({inputs:{x:A},backend:a,attrs:{shape:m}}),l.push(y),l.push(A)}if(i){l.push(h);let m=S.expandShapeToKeepDim(h.shape,u);h=pe({inputs:{x:h},backend:a,attrs:{shape:m}})}return l.forEach(m=>a.disposeIntermediateTensorInfo(m)),h}var VJ={kernelName:wo,backendName:"webgl",kernelFunc:BJ};function UJ(e){let{inputs:t,backend:a,attrs:n}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=n,l=r.map(y=>a.readSync(y.dataId)),u=r.map(y=>y.shape),p=a.readSync(s.dataId),c=a.readSync(i.dataId),[d,h,m]=zH(l,u,p,s.shape,s.dtype,c,i.shape,o),f=d.map(y=>a.makeTensorInfo([y.length],"int32",y)),g=a.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var GJ={kernelName:Mh,backendName:"webgl",kernelFunc:UJ};function HJ(e){let{inputs:t,backend:a}=e,{starts:n,limits:r,deltas:s}=t,i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=LH(i,n.shape,n.dtype,o,r.shape,l,s.shape),c=a.makeTensorInfo([u.length],"int32",u),d=a.makeTensorInfo([p.length],n.dtype,p);return[c,d]}var jJ={kernelName:_h,backendName:"webgl",kernelFunc:HJ};function qJ(e){let{inputs:t,backend:a,attrs:n}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),c=a.readSync(i.dataId),d=o.map(g=>a.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=WH(u,r.shape,p,s.shape,s.dtype,c,i.shape,d,h,l);return a.makeTensorInfo(m,s.dtype,f)}var XJ={kernelName:Ph,backendName:"webgl",kernelFunc:qJ},E8=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=BH(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},KJ={kernelName:Cu,backendName:"webgl",kernelFunc:E8},YJ="return 1.0 / x;",ZJ=tt({opSnippet:YJ}),JJ={kernelName:ko,backendName:"webgl",kernelFunc:ZJ},QJ=Mn+` +`,JJ=ha({opSnippet:YJ,packedOpSnippet:ZJ}),QJ={kernelName:vo,backendName:"webgl",kernelFunc:JJ};function eQ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,c=C.getAxesPermutation(p,o),d=r;c!=null&&(d=Ca({inputs:{x:r},backend:a,attrs:{perm:c}}),p=C.getInnerMostAxes(p.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",p,o);let h;if(a.shouldExecuteOnCPU([d])){let m=a.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=YH(d.shape,d.dtype,m,p);h=a.makeTensorInfo(g,y,f)}else{let[m,f]=C.computeOutAndReduceShapes(d.shape,p),g=v.sizeFromShape(f),y=pe({inputs:{x:d},backend:a,attrs:{shape:[-1,g]}}),x=Mp(r.dtype),A=rl(y,x,"prod",a);h=pe({inputs:{x:A},backend:a,attrs:{shape:m}}),l.push(y),l.push(A)}if(i){l.push(h);let m=C.expandShapeToKeepDim(h.shape,u);h=pe({inputs:{x:h},backend:a,attrs:{shape:m}})}return l.forEach(m=>a.disposeIntermediateTensorInfo(m)),h}var tQ={kernelName:ko,backendName:"webgl",kernelFunc:eQ};function aQ(e){let{inputs:t,backend:a,attrs:n}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=n,l=r.map(y=>a.readSync(y.dataId)),u=r.map(y=>y.shape),p=a.readSync(s.dataId),c=a.readSync(i.dataId),[d,h,m]=ZH(l,u,p,s.shape,s.dtype,c,i.shape,o),f=d.map(y=>a.makeTensorInfo([y.length],"int32",y)),g=a.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var nQ={kernelName:Mh,backendName:"webgl",kernelFunc:aQ};function rQ(e){let{inputs:t,backend:a}=e,{starts:n,limits:r,deltas:s}=t,i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=JH(i,n.shape,n.dtype,o,r.shape,l,s.shape),c=a.makeTensorInfo([u.length],"int32",u),d=a.makeTensorInfo([p.length],n.dtype,p);return[c,d]}var sQ={kernelName:Ph,backendName:"webgl",kernelFunc:rQ};function iQ(e){let{inputs:t,backend:a,attrs:n}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),c=a.readSync(i.dataId),d=o.map(g=>a.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=QH(u,r.shape,p,s.shape,s.dtype,c,i.shape,d,h,l);return a.makeTensorInfo(m,s.dtype,f)}var oQ={kernelName:_h,backendName:"webgl",kernelFunc:iQ},V8=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=ej(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},lQ={kernelName:Cu,backendName:"webgl",kernelFunc:V8},uQ="return 1.0 / x;",dQ=tt({opSnippet:uQ}),pQ={kernelName:Io,backendName:"webgl",kernelFunc:dQ},cQ=En+` return (x < 0.0) ? 0.0 : x; -`,eQ=` +`,hQ=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4123,9 +4123,9 @@ return a / b;`,lJ=` result.a = isNaN.a ? x.a : result.a; return result; -`,tQ=tt({opSnippet:QJ,packedOpSnippet:eQ}),aQ={kernelName:Io,backendName:"webgl",kernelFunc:tQ},nQ=Mn+` +`,mQ=tt({opSnippet:cQ,packedOpSnippet:hQ}),fQ={kernelName:So,backendName:"webgl",kernelFunc:mQ},gQ=En+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,rQ=` +`,yQ=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4135,7 +4135,7 @@ return a / b;`,lJ=` result.a = isNaN.a ? x.a : result.a; return result; -`,sQ=tt({opSnippet:nQ,packedOpSnippet:rQ}),iQ={kernelName:To,backendName:"webgl",kernelFunc:sQ},oQ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,xQ=tt({opSnippet:gQ,packedOpSnippet:yQ}),AQ={kernelName:No,backendName:"webgl",kernelFunc:xQ},bQ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4168,7 +4168,7 @@ return a / b;`,lJ=` setOutput(newValue); } - `}},lQ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},vQ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/p[0]}, ${u[1]/p[1]}, @@ -4245,7 +4245,7 @@ return a / b;`,lJ=` setOutput(newValue); } - `}};function uQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new lQ(r.shape,l,u,s,i):new oQ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],"float32")}var dQ={kernelName:Co,backendName:"webgl",kernelFunc:uQ},pQ=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,m=Math.ceil(d)*2+2;this.userCode=` + `}};function wQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=B().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new vQ(r.shape,l,u,s,i):new bQ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],"float32")}var kQ={kernelName:To,backendName:"webgl",kernelFunc:wQ},IQ=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,m=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4326,7 +4326,7 @@ return a / b;`,lJ=` setOutput(accumulator); } - `}};function cQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new pQ(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var hQ={kernelName:Ru,backendName:"webgl",kernelFunc:cQ},mQ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function SQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new IQ(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var CQ={kernelName:Ru,backendName:"webgl",kernelFunc:SQ},TQ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4348,7 +4348,7 @@ return a / b;`,lJ=` setOutput(newValue); } - `}},fQ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"0.5":"0.0",d;r?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},NQ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"0.5":"0.0",d;r?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/p[0]}, ${u[1]/p[1]}, @@ -4389,7 +4389,7 @@ return a / b;`,lJ=` setOutput(newValue); } - `}};function gQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new fQ(r.shape,l,u,s,i):new mQ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],r.dtype)}var yQ={kernelName:So,backendName:"webgl",kernelFunc:gQ},xQ=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,m=Math.ceil(d)*2+2;this.userCode=` + `}};function RQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=B().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new NQ(r.shape,l,u,s,i):new TQ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],r.dtype)}var EQ={kernelName:Co,backendName:"webgl",kernelFunc:RQ},MQ=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,m=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4459,17 +4459,17 @@ return a / b;`,lJ=` setOutput(accumulator); } - `}};function AQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new xQ(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var bQ={kernelName:Nu,backendName:"webgl",kernelFunc:AQ},vQ=class{constructor(e,t){this.variableNames=["x"];let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);if(this.outputShape=e,a===1){this.userCode=` + `}};function PQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new MQ(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var _Q={kernelName:Nu,backendName:"webgl",kernelFunc:PQ},$Q=class{constructor(e,t){this.variableNames=["x"];let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);if(this.outputShape=e,a===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); } - `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>n(o)).join(","),s=ft(a);this.userCode=` + `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>n(o)).join(","),s=mt(a);this.userCode=` void main() { ${s} coords = getOutputCoords(); setOutput(getX(${r})); } - `}},wQ=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);this.outputShape=e;let n=Ia("rc",a),r=`${n[a-1]} + 1 < ${this.outputShape[a-1]}`,s=`${n[a-2]} + 1 < ${this.outputShape[a-2]}`,i=ft(a);a===1?this.userCode=` + `}},FQ=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);this.outputShape=e;let n=ka("rc",a),r=`${n[a-1]} + 1 < ${this.outputShape[a-1]}`,s=`${n[a-2]} + 1 < ${this.outputShape[a-2]}`,i=mt(a);a===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -4497,7 +4497,7 @@ return a / b;`,lJ=` } setOutput(result); } - `;function o(h){return c(h)}function l(h){return h[a-1]="("+h[a-1]+" + 1)",c(h)}function u(h){return h[a-2]="("+h[a-2]+" + 1)",c(h)}function p(h){return h[a-1]="("+h[a-1]+" + 1)",h[a-2]="("+h[a-2]+" + 1)",c(h)}function c(h){let m=e.map((y,x)=>d(x,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function d(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function kQ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return tn({inputs:{x:r},backend:a});let l=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wQ(r.shape,o):new vQ(r.shape,o);return a.runWebGLProgram(l,[r],r.dtype)}var IQ={kernelName:No,backendName:"webgl",kernelFunc:kQ},SQ=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let a=e[1],n=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` + `;function o(h){return c(h)}function l(h){return h[a-1]="("+h[a-1]+" + 1)",c(h)}function u(h){return h[a-2]="("+h[a-2]+" + 1)",c(h)}function p(h){return h[a-1]="("+h[a-1]+" + 1)",h[a-2]="("+h[a-2]+" + 1)",c(h)}function c(h){let m=e.map((y,x)=>d(x,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function d(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function DQ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return Qa({inputs:{x:r},backend:a});let l=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new FQ(r.shape,o):new $Q(r.shape,o);return a.runWebGLProgram(l,[r],r.dtype)}var OQ={kernelName:Ro,backendName:"webgl",kernelFunc:DQ},zQ=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let a=e[1],n=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` vec3 fill = vec3(${t.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -4516,7 +4516,7 @@ return a / b;`,lJ=` } setOutput(outputValue); } - `}},CQ={kernelName:Zo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new SQ(n.shape,s),[u,p]=S.getImageCenter(i,n.shape[1],n.shape[2]),c=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[n],n.dtype,c)}},TQ=` + `}},LQ={kernelName:Jo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new 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+`,BQ=tt({opSnippet:WQ}),VQ={kernelName:Eo,backendName:"webgl",kernelFunc:BQ},UQ="return inversesqrt(x);",GQ=tt({opSnippet:UQ,cpuKernelImpl:tj}),HQ={kernelName:Mo,backendName:"webgl",kernelFunc:GQ},W3=class{constructor(e,t,a,n,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let l=mt(r.length),u=mt(s.length),p="";a===1?p="i":a===2&&(p="i, j");let c=`getIndices(${p})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides";this.userCode=` ${l} strides = ${l}(${r}); void main() { @@ -4551,7 +4551,7 @@ return a / b;`,lJ=` } setOutput(mix(${f}, sum, float(found))); } - `}},PQ=class{constructor(e,t,a,n,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=ft(r.length),u=ft(s.length),p="";a===1?p="i":a===2&&(p="i, j");let c=`getIndices(${p})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides",y=t>1?"strides[j + 1]":"strides";this.userCode=` + `}},jQ=class{constructor(e,t,a,n,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=mt(r.length),u=mt(s.length),p="";a===1?p="i":a===2&&(p="i, j");let c=`getIndices(${p})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides",y=t>1?"strides[j + 1]":"strides";this.userCode=` ${l} strides = ${l}(${r}); void main() { @@ -4588,7 +4588,7 @@ return a / b;`,lJ=` } setOutput(mix(${f}, sum, found)); } - `}};function $Q(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=S.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=pe({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),m=pe({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),f=a.makeTensorInfo([],"float32",new Float32Array([0])),g;W().getBool("WEBGL_PACK")?g=new PQ(l,o,h.shape.length,m.shape.length,p,d):g=new O3(l,o,h.shape.length,m.shape.length,p,d);let y=a.runWebGLProgram(g,[m,h,f],m.dtype),x=pe({inputs:{x:y},backend:a,attrs:{shape:i}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(f),x}var FQ={kernelName:Mo,backendName:"webgl",kernelFunc:$Q},DQ=class{constructor(e,t,a,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,a];let r="while (left < right) {",s=`for (int i = 0; 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XQ={kernelName:Po,backendName:"webgl",kernelFunc:qQ},KQ=class{constructor(e,t,a,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,a];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=B().getNumber("WEBGL_VERSION")===2?r:s,o=n==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; @@ -4613,7 +4613,7 @@ return a / b;`,lJ=` setOutput(float(findBound(batch, value))); } - `}};function OQ(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new DQ(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return a.runWebGLProgram(o,[r,s],"int32",l)}var zQ={kernelName:Po,backendName:"webgl",kernelFunc:OQ},LQ=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.outputShape=t;let n,r;if(a>4)throw Error(`Where for rank ${a} is not yet supported`);if(a===1)r="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u4)throw Error(`Where for rank ${a} is not yet supported`);if(a===1)r="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,UQ=tt({opSnippet:VQ}),GQ={kernelName:$o,backendName:"webgl",kernelFunc:UQ},HQ=Zu+` +`,aee=tt({opSnippet:tee}),nee={kernelName:Fo,backendName:"webgl",kernelFunc:aee},ree=Yu+` return 1.0 / (1.0 + exp(-1.0 * x)); -`,jQ=` +`,see=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -4641,20 +4641,20 @@ return a / b;`,lJ=` result.a = isNaN.a ? x.a : result.a; return result; -`,qQ=tt({opSnippet:HQ,packedOpSnippet:jQ,cpuKernelImpl:GH}),XQ={kernelName:zo,backendName:"webgl",kernelFunc:qQ},KQ=` +`,iee=tt({opSnippet:ree,packedOpSnippet:see,cpuKernelImpl:nj}),oee={kernelName:Lo,backendName:"webgl",kernelFunc:iee},lee=` if (isnan(x)) { return 0.0; } return sign(x); -`,YQ=tt({opSnippet:KQ}),ZQ={kernelName:Oo,backendName:"webgl",kernelFunc:YQ},JQ=Zu+` +`,uee=tt({opSnippet:lee}),dee={kernelName:zo,backendName:"webgl",kernelFunc:uee},pee=Yu+` return sin(x); -`,QQ=` +`,cee=` vec4 result = sin(x); bvec4 isNaN = isnan(x); - ${al} + ${nl} return result; -`,eee=tt({opSnippet:JQ,packedOpSnippet:QQ}),tee={kernelName:Fo,backendName:"webgl",kernelFunc:eee},aee=` +`,hee=tt({opSnippet:pee,packedOpSnippet:cee}),mee={kernelName:Do,backendName:"webgl",kernelFunc:hee},fee=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,nee=tt({opSnippet:aee}),ree={kernelName:Do,backendName:"webgl",kernelFunc:nee},see=` +`,gee=tt({opSnippet:fee}),yee={kernelName:Oo,backendName:"webgl",kernelFunc:gee},xee=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -4674,17 +4674,17 @@ return a / b;`,lJ=` result = log(exp_x + 1.0); } return result; 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cee(e){let{inputs:t,backend:a}=e,{inputIndices:n,inputShape:r,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(a.readSync(r.dataId)),o=a.readSync(n.dataId),l=Array.from(a.readSync(s.dataId)),[u,p,c]=qH(o,n.shape,n.dtype,i,l);return[a.makeTensorInfo(p,n.dtype,u),a.makeTensorInfo([c.length],s.dtype,new Int32Array(c))]}var hee={kernelName:$u,backendName:"webgl",kernelFunc:cee};function mee(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${i.shape}`);let 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i=Array.from(a.readSync(r.dataId)),o=a.readSync(n.dataId),l=Array.from(a.readSync(s.dataId)),[u,p,c]=ij(o,n.shape,n.dtype,i,l);return[a.makeTensorInfo(p,n.dtype,u),a.makeTensorInfo([c.length],s.dtype,new Int32Array(c))]}var Cee={kernelName:$u,backendName:"webgl",kernelFunc:See};function Tee(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${s.shape}`);let i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=Qv(i,n.shape,n.dtype,o,l,!0);return a.makeTensorInfo(p,n.dtype,u)}var fee={kernelName:Fu,backendName:"webgl",kernelFunc:mee};function gee(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 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xee(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=S.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let y=a.bufferSync(r),x=a.bufferSync(s),A=v.decodeString(a.readSync(i.dataId)[0]),b=UH(y,x,o,d,p,u,l,c,A,h);return a.makeTensorInfo(o,b.dtype,b.values)}let m=new O3(u,l,r.shape.length,s.shape.length,c,[d,1],h),f=a.runWebGLProgram(m,[s,r,i],s.dtype),g=pe({inputs:{x:f},backend:a,attrs:{shape:o}});return a.disposeIntermediateTensorInfo(f),g}var Aee={kernelName:Uo,backendName:"webgl",kernelFunc:xee};function bee(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=S.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let m=Ju({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,m})}var vee={kernelName:Pu,backendName:"webgl",kernelFunc:bee},O5="return sqrt(x);",wee=tt({opSnippet:O5,packedOpSnippet:O5,cpuKernelImpl:XH}),kee={kernelName:Wo,backendName:"webgl",kernelFunc:wee},Iee="return x * x;",See=tt({opSnippet:Iee}),Cee={kernelName:Sp,backendName:"webgl",kernelFunc:See},z5="return (a - b) * (a - b);",Tee=ma({opSnippet:z5,packedOpSnippet:z5}),Nee={kernelName:Go,backendName:"webgl",kernelFunc:Tee};function Ree(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;if(r.dtype!=="string")throw new Error("Input must be of datatype string");let s=a.readSync(r.dataId),i=S.fromUint8ToStringArray(s),o=KH(i,"string",n);return a.makeTensorInfo(r.shape,"string",o)}var Eee={kernelName:Cp,backendName:"webgl",kernelFunc:Ree};function Mee({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=Mn+` + ${s.shape}`);let i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=d8(i,n.shape,n.dtype,o,l);return a.makeTensorInfo(p,n.dtype,u)}var Eee={kernelName:Du,backendName:"webgl",kernelFunc:Ree};function Mee(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=C.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let y=a.bufferSync(r),x=a.bufferSync(s),A=v.decodeString(a.readSync(i.dataId)[0]),b=aj(y,x,o,d,p,u,l,c,A,h);return a.makeTensorInfo(o,b.dtype,b.values)}let m=new W3(u,l,r.shape.length,s.shape.length,c,[d,1],h),f=a.runWebGLProgram(m,[s,r,i],s.dtype),g=pe({inputs:{x:f},backend:a,attrs:{shape:o}});return a.disposeIntermediateTensorInfo(f),g}var Pee={kernelName:Go,backendName:"webgl",kernelFunc:Mee};function _ee(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=C.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let m=Zu({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return 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_ee={kernelName:os,backendName:"webgl",kernelFunc:Mee},Pee=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=a;let n=a.length,r=ft(a.length),s=ft(a.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=a.map((l,u)=>(o++,a.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` + `,s=new Kn(n.shape,r);return a.runWebGLProgram(s,[n],n.dtype)}var Hee={kernelName:us,backendName:"webgl",kernelFunc:Gee},jee=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=a;let n=a.length,r=mt(a.length),s=mt(a.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=a.map((l,u)=>(o++,a.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` ${r} begin = ${r}(${e}); ${r} strides = ${r}(${t}); @@ -4692,15 +4692,15 @@ return a / b;`,lJ=` ${s} coords = getOutputCoords(); setOutput(getX(${i})); } - `}};function 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Kee={kernelName:_o,backendName:"webgl",kernelFunc:Xee},Yee=class{constructor(e,t){this.variableNames=["A"];let a=new Array(e.length);for(let s=0;s5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let r=0;r5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=$e(r.shape,r.dtype,l),p=tj(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Yee(r.shape,s);return a.runWebGLProgram(i,[r],r.dtype)}var Jee={kernelName:is,backendName:"webgl",kernelFunc:M8},Qee=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` + `}};function dte(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let r=0;r5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=$e(r.shape,r.dtype,l),p=mj(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new ute(r.shape,s);return a.runWebGLProgram(i,[r],r.dtype)}var pte={kernelName:ls,backendName:"webgl",kernelFunc:U8},cte=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4740,7 +4740,7 @@ return a / b;`,lJ=` setOutput(float(i1)); } } - `}},ete=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` + `}},hte=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); @@ -4774,7 +4774,7 @@ return a / b;`,lJ=` setOutput(x0 >= x1 ? float(i0) : float(i1)); } - `}};function Fs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function L5(e){let t=1;for(;tl){let M=a.readSync(r.dataId),[P,E]=aj(M,u,r.dtype,s,i);return[a.makeTensorInfo(P.shape,P.dtype,P.values),a.makeTensorInfo(E.shape,E.dtype,E.values)]}if(s===0)return u[u.length-1]=0,[a.makeTensorInfo(u,r.dtype,[]),a.makeTensorInfo(u,"int32",[])];if(p===1)return[r,Qp({attrs:{shape:u,dtype:"int32",value:0},backend:a})];let c=a.texData.get(r.dataId),d=c!==null&&c.isPacked,h=d?a.unpackTensor(r):r,m=v.sizeFromShape(u)/p,f=pe({inputs:{x:h},attrs:{shape:[m,p]},backend:a});d&&Fs(a,h);let g=L5(s),y=L5(p),x=null,A=()=>x===null?[f,f]:[f,x],b=(M,P,E)=>{let C=A(),_=new Qee(E),O=[[p],[x===null?1:0],[Number.NEGATIVE_INFINITY],[M],[P]],B=x;x=a.runWebGLProgram(_,C,"int32",O),Fs(a,B)};for(let M=1;M=1;E/=2)b(P,E,[m,y])}for(let M=y;M>g;M/=2){let P=A(),E=new ete([m,M/2]),C=[[p],[x===null?1:0],[g]],_=x;x=a.runWebGLProgram(E,P,"int32",C),Fs(a,_);let O=g/2,B=O*2;for(let F=O;F>=1;F/=2)b(B,F,x.shape)}let w=x;x=Ju({inputs:{x},backend:a,attrs:{begin:0,size:[m,s]}}),Fs(a,w);let I=k8({inputs:{x:f,indices:x},backend:a,attrs:{axis:1,batchDims:1}});Fs(a,f);let T=u.slice(0,-1);T.push(s),w=x,x=pe({inputs:{x},attrs:{shape:T},backend:a}),Fs(a,w);let N=I;return I=pe({inputs:{x:I},attrs:{shape:T},backend:a}),Fs(a,N),[I,x]}var ate={kernelName:Ko,backendName:"webgl",kernelFunc:tte},nte=class{constructor(e,t,a,n,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=a==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` + `}};function Os(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function H5(e){let t=1;for(;tl){let M=a.readSync(r.dataId),[P,E]=fj(M,u,r.dtype,s,i);return[a.makeTensorInfo(P.shape,P.dtype,P.values),a.makeTensorInfo(E.shape,E.dtype,E.values)]}if(s===0)return u[u.length-1]=0,[a.makeTensorInfo(u,r.dtype,[]),a.makeTensorInfo(u,"int32",[])];if(p===1)return[r,Jp({attrs:{shape:u,dtype:"int32",value:0},backend:a})];let c=a.texData.get(r.dataId),d=c!==null&&c.isPacked,h=d?a.unpackTensor(r):r,m=v.sizeFromShape(u)/p,f=pe({inputs:{x:h},attrs:{shape:[m,p]},backend:a});d&&Os(a,h);let g=H5(s),y=H5(p),x=null,A=()=>x===null?[f,f]:[f,x],b=(M,P,E)=>{let S=A(),_=new cte(E),O=[[p],[x===null?1:0],[Number.NEGATIVE_INFINITY],[M],[P]],W=x;x=a.runWebGLProgram(_,S,"int32",O),Os(a,W)};for(let M=1;M=1;E/=2)b(P,E,[m,y])}for(let M=y;M>g;M/=2){let P=A(),E=new hte([m,M/2]),S=[[p],[x===null?1:0],[g]],_=x;x=a.runWebGLProgram(E,P,"int32",S),Os(a,_);let O=g/2,W=O*2;for(let $=O;$>=1;$/=2)b(W,$,x.shape)}let w=x;x=Zu({inputs:{x},backend:a,attrs:{begin:0,size:[m,s]}}),Os(a,w);let I=F8({inputs:{x:f,indices:x},backend:a,attrs:{axis:1,batchDims:1}});Os(a,f);let T=u.slice(0,-1);T.push(s),w=x,x=pe({inputs:{x},attrs:{shape:T},backend:a}),Os(a,w);let N=I;return I=pe({inputs:{x:I},attrs:{shape:T},backend:a}),Os(a,N),[I,x]}var fte={kernelName:Yo,backendName:"webgl",kernelFunc:mte},gte=class{constructor(e,t,a,n,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=a==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${o} == 2) { @@ -4886,7 +4886,7 @@ return a / b;`,lJ=` } setOutput(outputValue); } - `}};function 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dae={kernelName:lu,backendName:"wasm",kernelFunc:uae},U8;function pae(e){U8=e.wasm.cwrap(gi,null,["number","number","boolean","number","number","number"])}function cae(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,weights:s}=a,{size:i}=n,o=s.shape.reduce((c,d)=>c*d,1)!==0,l=r.shape.length===1?[i]:[r.shape[0],i],u=t.makeOutput(l,s.dtype);function p(c){return t.dataIdMap.get(c.dataId).id}return U8(p(r),i,o,p(s),nt[s.dtype],p(u)),u}var hae={kernelName:gi,backendName:"wasm",setupFunc:pae,kernelFunc:cae},mae=!0,fae=Ht(uu,mae);function gae(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.typedArrayFromHeap(n),i=a.typedArrayFromHeap(r),o=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeOutput([o.length],"int32",void 0,new Int32Array(o))}var yae={kernelName:du,backendName:"wasm",kernelFunc:gae};function hs(e){let{inputs:{x:t},attrs:{dtype:a},backend:n}=e,r=n.makeOutput(t.shape,a),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(s),r}var 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ene(e){let{backend:t,inputs:a,attrs:n}=e,{x:r}=a,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),m=i==="NHWC"?[o,c,d,h]:[o,h,c,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(m).buffer),A=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),b=t.dataIdMap.get(f.dataId).id;return tw(g,s,i==="NHWC"?1:0,y,r.shape.length-1,x,A,m.length,b),f}var tne={kernelName:Ni,backendName:"wasm",setupFunc:Qae,kernelFunc:ene},aw;function ane(e){aw=e.wasm.cwrap(Ri,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function nne(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:c}=a,d=u==null?[1,1]:u,h=S.computeConv2DInfo(r.shape,s.shape,l,d,p,c,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,x=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,I=h.strideHeight,T=h.strideWidth,N=h.inChannels,M=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let E=n.makeOutput(h.outShape,"float32"),C=n.dataIdMap.get(E.dataId).id;return aw(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,x,A,P,b,w,I,T,N,M,C),E}var rne={kernelName:Ri,backendName:"wasm",setupFunc:ane,kernelFunc:nne},nw;function sne(e){nw=e.wasm.cwrap("Diag",null,["number","number","number","number"])}function ine(e){let{inputs:t,backend:a}=e,{x:n}=t,r=v.sizeFromShape(n.shape),s=a.makeOutput([...n.shape,...n.shape],n.dtype);return nw(a.dataIdMap.get(n.dataId).id,nt[n.dtype],r,a.dataIdMap.get(s.dataId).id),s}var one={kernelName:mu,backendName:"wasm",setupFunc:sne,kernelFunc:ine},rw;function lne(e){rw=e.wasm.cwrap(Ei,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function une(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n;if(r.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${r.dtype} and ${s.dtype}`);let u=S.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=a.makeOutput(u.outShape,r.dtype);return rw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(p.dataId).id,nt[r.dtype],u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.filterHeight,u.filterWidth,u.padInfo.top,u.padInfo.left),p}var dne={kernelName:Ei,backendName:"wasm",setupFunc:lne,kernelFunc:une},sw;function pne(e){sw=e.wasm.cwrap(Ul,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=S.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=a.makeOutput(s.shape,s.dtype);return sw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,nt[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),c}var hne={kernelName:Ul,backendName:"wasm",setupFunc:pne,kernelFunc:cne},iw;function mne(e){iw=e.wasm.cwrap(Vl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=S.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=a.makeOutput(r.shape,r.dtype);return iw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,nt[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),c}var gne={kernelName:Vl,backendName:"wasm",setupFunc:mne,kernelFunc:fne},yne=Qe(_i),ow;function xne(e){ow=e.wasm.cwrap(fu,null,["number","number","number"])}function Ane(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=a.makeOutput(r.shape,"float32"),i=o=>a.dataIdMap.get(o.dataId).id;return ow(i(r),i(n),i(s)),s}var bne={kernelName:fu,backendName:"wasm",setupFunc:xne,kernelFunc:Ane},vne=!1,wne=Ht($i,vne,"bool"),kne=Qe(Pi),Ine=Qe(Fi,"float32");function B1(e){let{inputs:t,attrs:a,backend:n}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Ba({inputs:{x:r},backend:n,attrs:{shape:o}})}var Sne={kernelName:gu,backendName:"wasm",kernelFunc:B1},Cne=Qe(Di,"float32");function lw(e){let{attrs:{shape:t,value:a,dtype:n},backend:r}=e,s=r.makeOutput(t,n);return r.typedArrayFromHeap(s).fill(a),s}var Tne={kernelName:yu,backendName:"wasm",kernelFunc:lw},uw;function Nne(e){uw=e.wasm.cwrap(Oi,null,["number","number","number","number","number","number"])}function Rne(e){let{inputs:t,backend:a}=e,{image:n}=t,r=a.makeOutput(n.shape,n.dtype),s=a.dataIdMap.get(n.dataId).id,i=a.dataIdMap.get(r.dataId).id,[o,l,u,p]=n.shape;return uw(s,o,l,u,p,i),r}var Ene={kernelName:Oi,backendName:"wasm",kernelFunc:Rne,setupFunc:Nne},Mne=Qe(zi),_ne=!1,Pne=Ht(Li,_ne),dw;function $ne(e){dw=e.wasm.cwrap(Wi,null,["number","number","number","number","number","number","number"])}function Fne(e){let{backend:t,inputs:a,attrs:n}=e,{varianceEpsilon:r}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=a,p=t.dataIdMap.get(s.dataId).id,c=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return dw(p,c,d,h,m,r,g),f}var Dne={kernelName:Wi,backendName:"wasm",setupFunc:$ne,kernelFunc:Fne},pw;function One(e){pw=e.wasm.cwrap(Yr,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function zne(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=S.computeConv2DInfo(r.shape,s.shape,l,p,u,d),g=np[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=n.dataIdMap.get(r.dataId).id,x=n.dataIdMap.get(s.dataId).id,A=f.outChannels,b=0;if(i!=null){let X=n.dataIdMap.get(i.dataId);if(X.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${X.shape.length}.`);if(X.shape[0]!==A)throw new Error(`FusedConv2D bias shape (${X.shape}) does not match the number of output channels (${A})`);b=X.id}let w=f.filterHeight,I=f.filterWidth,T=f.padInfo.top,N=f.padInfo.right,M=f.padInfo.bottom,P=f.padInfo.left,E=f.dilationHeight,C=f.dilationWidth,_=f.strideHeight,O=f.strideWidth,B=f.inChannels,F=f.padInfo.type==="SAME"?1:0,U=f.batchSize,G=f.inHeight,q=f.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${c}'. Please use 'NHWC'.`);let H=n.makeOutput(f.outShape,"float32"),V=n.dataIdMap.get(H.dataId).id,Z=o==null?0:n.dataIdMap.get(o.dataId).id;return pw(y,U,G,q,x,w,I,b,T,N,M,P,F,E,C,_,O,B,A,g,Z,m||0,V),H}var Lne={kernelName:Yr,backendName:"wasm",setupFunc:One,kernelFunc:zne},cw;function Wne(e){cw=e.wasm.cwrap(Zr,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Bne(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=S.computeConv2DInfo(r.shape,s.shape,l,p,u,d,!0),g=np[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(r.dataId).id,x=n.dataIdMap.get(s.dataId).id,A=f.outChannels,b=0;if(i!=null){let X=n.dataIdMap.get(i.dataId);if(X.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${X.shape.length}.`);if(X.shape[0]!==A)throw new Error(`FusedDepthwiseConv2D bias shape (${X.shape}) does not match the number of output channels (${A})`);b=X.id}let w=f.filterHeight,I=f.filterWidth,T=f.padInfo.top,N=f.padInfo.right,M=f.padInfo.bottom,P=f.padInfo.left,E=f.dilationHeight,C=f.dilationWidth,_=f.strideHeight,O=f.strideWidth,B=f.inChannels,F=f.padInfo.type==="SAME"?1:0,U=f.batchSize,G=f.inHeight,q=f.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${c}'. Please use 'NHWC'.`);let H=n.makeOutput(f.outShape,"float32"),V=n.dataIdMap.get(H.dataId).id,Z=o==null?0:n.dataIdMap.get(o.dataId).id;return cw(y,U,G,q,x,w,I,b,T,N,M,P,F,E,C,_,O,B,A,g,Z,m||0,V),H}var Vne={kernelName:Zr,backendName:"wasm",setupFunc:Wne,kernelFunc:Bne},hw;function Une(e){hw=e.wasm.cwrap(Bi,null,["number","number","number","number","number","number","array","number"])}function Gne(e){let{backend:t,inputs:a}=e,{params:n,indices:r}=a,[s,i,o,l]=a3.prepareAndValidate(n,r),u=t.makeOutput(s,n.dtype);if(i===0)return u;let p=r.shape,c=p[p.length-1],d=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return hw(d,nt[n.dtype],h,i,c,o,m,f),u}var Hne={kernelName:Bi,backendName:"wasm",setupFunc:Une,kernelFunc:Gne},mw;function jne(e){mw=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function qne(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,indices:s}=a,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let T=0;T=0,()=>`GatherV2: the index value ${N} is not in [0, ${p-1}]`)}let c=S.segment_util.collectGatherOpShapeInfo(r,s,l,o),d=Ba({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),m=Ba({inputs:{x:s},attrs:{shape:[c.batchSize,h/c.batchSize]},backend:t}),f=[c.batchSize,c.outerSize,h/c.batchSize,c.sliceSize],g=t.makeOutput(f,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let y=d.shape.length-1,x=t.dataIdMap.get(d.dataId).id,A=t.dataIdMap.get(m.dataId).id,b=t.dataIdMap.get(g.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(d.shape)).buffer),I=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return mw(x,nt[r.dtype],w,y,A,c.batchSize,I,b),t.disposeData(d.dataId),t.disposeData(m.dataId),g.shape=c.outputShape,g}var Xne={kernelName:xu,backendName:"wasm",setupFunc:jne,kernelFunc:qne},Kne=!1,Yne=Ht(Vi,Kne,"bool"),Zne=!1,Jne=Ht(Ui,Zne,"bool"),Qne=Qe(Hi,"bool"),ere=Qe(ji,"bool"),tre=Qe(qi,"bool"),fw;function are(e){fw=e.wasm.cwrap(Xi,null,["number","number","number","number"])}function nre(e){let{inputs:{x:t},attrs:{alpha:a},backend:n}=e,r=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;fw(r,nt[t.dtype],a,i)}return s}var rre={kernelName:Xi,backendName:"wasm",setupFunc:are,kernelFunc:nre},sre=!1,ire=Ht(Ki,sre,"bool"),ore=!1,lre=Ht(Yi,ore,"bool"),gw;function ure(e){gw=e.wasm.cwrap(Zi,null,["number","number","number","number"])}function dre(e){let{attrs:t,backend:a}=e,{start:n,stop:r,num:s}=t,i=Math.floor(s),o=a.makeOutput([i],"float32");return gw(a.dataIdMap.get(o.dataId).id,n,r,i),o}var pre={kernelName:Zi,backendName:"wasm",setupFunc:ure,kernelFunc:dre},cre=Qe(Ji),hre=Qe(Qi),mre=!1,fre=Ht(eo,mre,"bool"),gre=Qe(to),yre=!1,xre=Ht(ao,yre,"bool"),Are=!1,bre=Ht(bA,Are,"bool"),yw;function vre(e){yw=e.wasm.cwrap(no,null,["number","number","number","number","number","number","number"])}function wre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=a.makeOutput(r.shape,r.dtype);return yw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var kre={kernelName:no,backendName:"wasm",setupFunc:vre,kernelFunc:wre},xw;function Ire(e){xw=e.wasm.cwrap(Au,null,["number","number","number","number","number","number","number","number","number"])}function Sre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let c=a.makeOutput(r.shape,r.dtype);return xw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,i.shape[3],o,l,u,p),c}var Cre={kernelName:Au,backendName:"wasm",setupFunc:Ire,kernelFunc:Sre},Aw;function Tre(e){Aw=e.wasm.cwrap(ro,null,["number","number","number","number"])}function Nre(e){let{backend:t,inputs:a,attrs:n}=e,{reductionIndices:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:c,inputWasTransposed:d}=cs(i,r,t);if(d){let x=t.dataIdMap.get(u.dataId).id;l=u,o=x}let h=l.shape.length;S.assertAxesAreInnerMostDims("max",p,h);let[m,f]=S.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;Aw(o,nt[i.dtype],g,x)}if(d&&t.disposeData(u.dataId),s){let x=S.expandShapeToKeepDim(y.shape,c);y.shape=x}return y}var Rre={kernelName:ro,backendName:"wasm",setupFunc:Tre,kernelFunc:Nre},Ere=!1,Mre=Ht(so,Ere),bw;function _re(e){bw=e.wasm.cwrap(io,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Pre(e){let{inputs:t,attrs:a,backend:n}=e,r=t.x,s=n.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=S.computePool2DInfo(r.shape,i,o,1,l,u),c=p.filterHeight,d=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.dilationHeight,x=p.dilationWidth,A=p.strideHeight,b=p.strideWidth,w=p.inChannels,I=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let T=n.makeOutput(p.outShape,"float32"),N=n.dataIdMap.get(T.dataId).id;return bw(s,r.shape[0],r.shape[1],r.shape[2],c,d,h,m,f,g,y,x,A,b,w,I,N),T}var $re={kernelName:io,backendName:"wasm",setupFunc:_re,kernelFunc:Pre},vw;function Fre(e){vw=e.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Dre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=S.computePool3DInfo(r.shape,s,i,1,o,l,u),c=a.makeOutput(p.outShape,r.dtype);return vw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),c}var Ore={kernelName:bu,backendName:"wasm",setupFunc:Fre,kernelFunc:Dre},ww;function zre(e){ww=e.wasm.cwrap("MaxPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Lre(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=S.computePool3DInfo(s.shape,i,o,1,l,u),c=a.makeOutput(s.shape,s.dtype);return ww(a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),c}var Wre={kernelName:wp,backendName:"wasm",setupFunc:zre,kernelFunc:Lre},kw;function Bre(e){kw=e.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Vre(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=S.computePool2DInfo(s.shape,i,o,1,l,u),c=a.makeOutput(s.shape,s.dtype);return kw(a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),c}var Ure={kernelName:vp,backendName:"wasm",setupFunc:Bre,kernelFunc:Vre},Iw;function Gre(e){Iw=e.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Hre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,includeBatchInIndex:l}=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(S.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=S.computePool2DInfo(r.shape,s,i,[1,1],o),c=a.makeOutput(p.outShape,r.dtype),d=a.makeOutput(p.outShape,"int32");return Iw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,a.dataIdMap.get(d.dataId).id,nt[r.dtype],l,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),[c,d]}var jre={kernelName:vu,backendName:"wasm",setupFunc:Gre,kernelFunc:Hre},Sw;function qre(e){Sw=e.wasm.cwrap(oo,null,["number, number, number"])}function Xre(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=cs(i,r,t),m=c;if(h){let b=t.dataIdMap.get(p.dataId).id;b!==o&&(u=p,l=b,m=S.getInnerMostAxes(m.length,u.shape.length))}S.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=S.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),x=u;u.dtype!=="float32"&&(x=hs({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(x.dataId).id);let A=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(A.dataId).id;Sw(l,y,b)}if(h&&t.disposeData(p.dataId),s){let b=S.expandShapeToKeepDim(A.shape,d);A.shape=b}return u.dtype!=="float32"&&t.disposeData(x.dataId),A}var Kre={kernelName:oo,backendName:"wasm",setupFunc:qre,kernelFunc:Xre},Cw;function Yre(e){Cw=e.wasm.cwrap(lo,null,["number","number","number","number"])}function Zre(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=cs(i,r,t);if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A)}let m=u.shape.length;S.assertAxesAreInnerMostDims("min",c,m);let[f,g]=S.computeOutAndReduceShapes(u.shape,c),y=v.sizeFromShape(g),x=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;Cw(l,nt[i.dtype],y,A)}if(h&&t.disposeData(p.dataId),s){let A=S.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var Jre={kernelName:lo,backendName:"wasm",setupFunc:Yre,kernelFunc:Zre},Qre=!1,ese=Ht(uo,Qre),V1;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(V1||(V1={}));var Tw;function tse(e){Tw=e.wasm.cwrap(po,null,["number","array","number","number","array","array","number","number"])}function ase(e){let{inputs:{x:t},backend:a,attrs:{paddings:n,mode:r}}=e,s=n.map((m,f)=>m[0]+t.shape[f]+m[1]),i=a.dataIdMap.get(t.dataId).id,o=a.makeOutput(s,t.dtype),l=a.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(m=>m[0]),c=n.map(m=>m[1]),d=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new 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Ew(a.dataIdMap.get(l.dataId).id,u,p,s,i,a.dataIdMap.get(c.dataId).id),o||a.disposeData(l.dataId),c}var lse={kernelName:ho,backendName:"wasm",setupFunc:ise,kernelFunc:ose},use=Ht(co,!0),dse=!0,pse=Ht(mo,dse),cse=Qe(wu);function z3(e,t){let a=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=a[0],r=a[1],s=a[2],i=a[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var Mw;function hse(e){Mw=e.wasm.cwrap(go,"number",["number","number","number","number","number"])}function mse(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=a,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,c=Mw(u,p,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=z3(t,c);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var fse={kernelName:go,backendName:"wasm",setupFunc:hse,kernelFunc:mse},_w;function gse(e){_w=e.wasm.cwrap(ku,"number",["number","number","number","number","number","bool"])}function yse(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=_w(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=z3(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),x=t.makeOutput([],"int32",g);return[y,x]}var xse={kernelName:ku,backendName:"wasm",setupFunc:gse,kernelFunc:yse},Pw;function Ase(e){Pw=e.wasm.cwrap(yo,"number",["number","number","number","number","number","number"])}function bse(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Pw(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=z3(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),x=t.makeOutput([m],"float32",f);return[y,x]}var vse={kernelName:yo,backendName:"wasm",setupFunc:Ase,kernelFunc:bse},wse=!1,kse=Ht(fo,wse,"bool"),$w;function Ise(e){$w=e.wasm.cwrap(xo,null,["number","number","number","number","number"])}function Sse(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=a.makeOutput([...r.shape,i],s),p=a.dataIdMap.get(u.dataId).id,c=a.dataIdMap.get(r.dataId).id;return $w(c,i,o,l,p),u}var Cse={kernelName:xo,backendName:"wasm",setupFunc:Ise,kernelFunc:Sse};function Tse(e){let{inputs:{x:t},backend:a}=e,n=a.makeOutput(t.shape,t.dtype);return a.typedArrayFromHeap(n).fill(1),n}var Nse={kernelName:Iu,backendName:"wasm",kernelFunc:Tse};function Rse(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return B1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching 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Dw={kernelName:Ao,backendName:"wasm",kernelFunc:_se,setupFunc:Mse},Pse=!1,$se=Ht(bo,Pse),Ow;function Fse(e){Ow=e.wasm.cwrap(vo,null,["number","number","number"])}function Dse(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=a.dataIdMap.get(n.dataId).id,i=a.dataIdMap.get(r.dataId).id,o=s,l=n,u=l;l.dtype!=="float32"&&(u=hs({backend:a,inputs:{x:n},attrs:{dtype:"float32"}}),o=a.dataIdMap.get(u.dataId).id);let p=a.makeOutput(n.shape,"float32"),c=a.dataIdMap.get(p.dataId).id;return Ow(o,i,c),l.dtype!=="float32"&&a.disposeData(u.dataId),p}var Ose={kernelName:vo,backendName:"wasm",setupFunc:Fse,kernelFunc:Dse},zw;function zse(e){zw=e.wasm.cwrap(wo,null,["number","number","number","number"])}function Lse(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=cs(i,r,t),m=c;if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A,m=S.getInnerMostAxes(m.length,u.shape.length))}S.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=S.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),x=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;zw(l,y,nt[x.dtype],A)}if(h&&t.disposeData(p.dataId),s){let A=S.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var Wse={kernelName:wo,backendName:"wasm",setupFunc:zse,kernelFunc:Lse},Bse=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=h3(n,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Vse={kernelName:Cu,backendName:"wasm",kernelFunc:Bse},Use=!0,Gse=Ht(Mi,Use),Hse=Qe(ko),jse=Qe(Io),qse=Qe(To),Lw;function Xse(e){Lw=e.wasm.cwrap(Co,null,["number","number","number","number","number","number","number","number","number","number"])}function Kse(e){let{backend:t,inputs:a,attrs:n}=e,{images:r}=a,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[p,c,d,h]=r.shape,m=[p,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=hs({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,x=t.makeOutput(m,"float32");if(v.sizeFromShape(r.shape)===0)return x;let A=t.dataIdMap.get(x.dataId).id;return Lw(y,p,c,d,h,l,u,s?1:0,i?1:0,A),g!=null&&t.disposeData(g.dataId),x}var Yse={kernelName:Co,backendName:"wasm",setupFunc:Xse,kernelFunc:Kse},Ww;function Zse(e){Ww=e.wasm.cwrap(Ru,null,["number","number","number","array","array","boolean"])}function Jse(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=a.makeOutput(r.shape,"float32"),l=a.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=hs({backend:a,inputs:{x:r},attrs:{dtype:"float32"}}),l=a.dataIdMap.get(u.dataId)),Ww(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(o.dataId).id,new 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rie(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=a.makeOutput(r.shape,"float32"),l=a.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=hs({backend:a,inputs:{x:r},attrs:{dtype:"float32"}}),l=a.dataIdMap.get(u.dataId)),Vw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(o.dataId).id,new Uint8Array(new Int32Array(r.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),u!=null&&a.disposeData(u.dataId),o}var sie={kernelName:Nu,backendName:"wasm",setupFunc:nie,kernelFunc:rie},Uw;function iie(e){Uw=e.wasm.cwrap(No,null,["number","array","number","array","number","number"])}function oie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=v.parseAxisParam(s,r.shape);if(r.shape.length===0)return o0({inputs:{x:r},backend:a});let o=a.makeOutput(r.shape,r.dtype),l=a.dataIdMap.get(r.dataId).id,u=a.dataIdMap.get(o.dataId).id,p=new Uint8Array(new Int32Array(i).buffer),c=new Uint8Array(new 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tp;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(tp||(tp={}));var G8;function Tte(e){G8=e.wasm.cwrap(Yr,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Nte(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n,d=a.dataIdMap.get(r.dataId).id,h=a.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let N=a.dataIdMap.get(i.dataId);if(N.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${N.shape.length}.`);m=N.id}let f=o==null?0:a.dataIdMap.get(o.dataId).id,g=tp[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the 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s(i){let{backend:o,inputs:l}=i,{a:u,b:p}=l,c=o.dataIdMap.get(u.dataId).id,d=o.dataIdMap.get(p.dataId).id,h=a!=null?a:u.dtype,m=C.assertAndGetBroadcastShape(u.shape,p.shape),f=o.makeOutput(m,h);if(v.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(p.shape).buffer),x=o.dataIdMap.get(f.dataId).id;return n(c,g,u.shape.length,d,y,p.shape.length,nt[u.dtype],x),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var _te=!0,$te=Gt(is,_te),H8;function Fte(e){H8=e.wasm.cwrap(oi,null,["array","number","number","number"])}function Dte(e){let{inputs:t,backend:a}=e,n=a.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(n.shape)===0)return n;let r=t.map(o=>a.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=a.dataIdMap.get(n.dataId).id;return H8(s,r.length,nt[n.dtype],i),n}var Ote={kernelName:oi,backendName:"wasm",setupFunc:Fte,kernelFunc:Dte};function o0(e){let{inputs:{x:t},backend:a}=e;if(t.dtype==="string")return Ve(a.readSync(t.dataId),t.shape,t.dtype);let n=a.makeOutput(t.shape,t.dtype),r=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(n).set(r),n}var zte={kernelName:Hi,backendName:"wasm",kernelFunc:o0},j8;function Lte(e){j8=e.wasm.cwrap(wr,null,["number","array","number","number","number","array","number"])}function ss(e){let{inputs:t,backend:a,attrs:n}=e,[r,s]=Bte(t.x.shape,n.perm),i=!0;for(let m=0;m=r&&(s===-1||n[s]>n[i])&&(s=i);n[s]=r}return[a,n]}var Vte={kernelName:wr,backendName:"wasm",kernelFunc:ss,setupFunc:Lte};function ms(e,t,a){let n=e.shape,r=e.shape.length,s=v.parseAxisParam(t,n),i=s,o=C.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let p=new Array(r);for(let d=0;d`new shape: ${i}, old shape: ${n.shape}. 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kae={kernelName:lu,backendName:"wasm",kernelFunc:wae},tw;function Iae(e){tw=e.wasm.cwrap(yi,null,["number","number","boolean","number","number","number"])}function Sae(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,weights:s}=a,{size:i}=n,o=s.shape.reduce((c,d)=>c*d,1)!==0,l=r.shape.length===1?[i]:[r.shape[0],i],u=t.makeOutput(l,s.dtype);function p(c){return t.dataIdMap.get(c.dataId).id}return tw(p(r),i,o,p(s),nt[s.dtype],p(u)),u}var Cae={kernelName:yi,backendName:"wasm",setupFunc:Iae,kernelFunc:Sae},Tae=!0,Nae=Gt(uu,Tae);function Rae(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.typedArrayFromHeap(n),i=a.typedArrayFromHeap(r),o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeOutput([o.length],"int32",void 0,new Int32Array(o))}var Eae={kernelName:du,backendName:"wasm",kernelFunc:Rae};function fs(e){let{inputs:{x:t},attrs:{dtype:a},backend:n}=e,r=n.makeOutput(t.shape,a),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(s),r}var Mae={kernelName:xi,backendName:"wasm",kernelFunc:fs},Pae=Qe(Ai),aw;function _ae(e){aw=e.wasm.cwrap(os,null,["number","number","number","number"])}function $ae(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o=a.dataIdMap.get(r.dataId).id,l=a.makeOutput(r.shape,r.dtype),u=a.dataIdMap.get(l.dataId).id;return aw(o,s,i,u),l}var Fae={kernelName:os,backendName:"wasm",setupFunc:_ae,kernelFunc:$ae};function nw(e){let{inputs:t,backend:a}=e,n=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=t.map(h=>h.shape);C.assertParamsConsistent(r,n);let s=C.computeOutShape(t.map(h=>h.shape),n),i=t.filter(h=>v.sizeFromShape(h.shape)>0);if(i.length===1)return o0({inputs:{x:i[0]},backend:a});let o=a.makeOutput(s,t[0].dtype);if(v.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(A=>{let b=[-1,v.sizeFromShape(A.shape.slice(n))];return 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y=C.getUndoAxesPermutation(u);g=ss({inputs:{x:d},attrs:{perm:y},backend:a}),a.disposeData(p.dataId),a.disposeData(d.dataId)}return g}var sne={kernelName:Ci,backendName:"wasm",setupFunc:nne,kernelFunc:rne},pw;function ine(e){pw=e.wasm.cwrap(Ti,null,["number","number","number","number","number","number"])}function one(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],l),p=r;u!==null&&(p=ss({inputs:{x:r},attrs:{perm:u},backend:a}));let c=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumsum",[c],l);let d=a.makeOutput(p.shape,p.dtype),h=p.shape[c],m=a.dataIdMap.get(p.dataId).id,f=a.dataIdMap.get(d.dataId).id;pw(m,i?1:0,o?1:0,h,f,nt[r.dtype]);let g=d;if(u!==null){let y=C.getUndoAxesPermutation(u);g=ss({inputs:{x:d},attrs:{perm:y},backend:a}),a.disposeData(p.dataId),a.disposeData(d.dataId)}return g}var lne={kernelName:Ti,backendName:"wasm",setupFunc:ine,kernelFunc:one},cw;function une(e){cw=e.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function dne(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,weights:s}=a,{size:i,binaryOutput:o}=n,l=s.shape.reduce((d,h)=>d*h,1)!==0,u=r.shape.length===1?[i]:[r.shape[0],i],p=t.makeOutput(u,s.dtype);function c(d){return t.dataIdMap.get(d.dataId).id}return cw(c(r),new Uint8Array(new Int32Array(r.shape).buffer),r.shape.length,i,l,c(s),nt[s.dtype],o,c(p)),p}var pne={kernelName:hu,backendName:"wasm",setupFunc:une,kernelFunc:dne},hw;function cne(e){hw=e.wasm.cwrap(Ri,null,["number","number","number","array","number","array","array","number","number"])}function hne(e){let{backend:t,inputs:a,attrs:n}=e,{x:r}=a,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),m=i==="NHWC"?[o,c,d,h]:[o,h,c,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(m).buffer),A=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),b=t.dataIdMap.get(f.dataId).id;return hw(g,s,i==="NHWC"?1:0,y,r.shape.length-1,x,A,m.length,b),f}var mne={kernelName:Ri,backendName:"wasm",setupFunc:cne,kernelFunc:hne},mw;function fne(e){mw=e.wasm.cwrap(Ei,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function gne(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:c}=a,d=u==null?[1,1]:u,h=C.computeConv2DInfo(r.shape,s.shape,l,d,p,c,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,x=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,I=h.strideHeight,T=h.strideWidth,N=h.inChannels,M=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let E=n.makeOutput(h.outShape,"float32"),S=n.dataIdMap.get(E.dataId).id;return mw(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,x,A,P,b,w,I,T,N,M,S),E}var yne={kernelName:Ei,backendName:"wasm",setupFunc:fne,kernelFunc:gne},fw;function xne(e){fw=e.wasm.cwrap("Diag",null,["number","number","number","number"])}function Ane(e){let{inputs:t,backend:a}=e,{x:n}=t,r=v.sizeFromShape(n.shape),s=a.makeOutput([...n.shape,...n.shape],n.dtype);return fw(a.dataIdMap.get(n.dataId).id,nt[n.dtype],r,a.dataIdMap.get(s.dataId).id),s}var bne={kernelName:mu,backendName:"wasm",setupFunc:xne,kernelFunc:Ane},gw;function vne(e){gw=e.wasm.cwrap(Mi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function wne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n;if(r.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${r.dtype} and ${s.dtype}`);let u=C.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=a.makeOutput(u.outShape,r.dtype);return gw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(p.dataId).id,nt[r.dtype],u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.filterHeight,u.filterWidth,u.padInfo.top,u.padInfo.left),p}var kne={kernelName:Mi,backendName:"wasm",setupFunc:vne,kernelFunc:wne},yw;function Ine(e){yw=e.wasm.cwrap(Hl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Sne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=C.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=a.makeOutput(s.shape,s.dtype);return yw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,nt[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),c}var Cne={kernelName:Hl,backendName:"wasm",setupFunc:Ine,kernelFunc:Sne},xw;function Tne(e){xw=e.wasm.cwrap(Gl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Nne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=C.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=a.makeOutput(r.shape,r.dtype);return xw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,nt[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),c}var Rne={kernelName:Gl,backendName:"wasm",setupFunc:Tne,kernelFunc:Nne},Ene=Qe(_i),Aw;function Mne(e){Aw=e.wasm.cwrap(fu,null,["number","number","number"])}function Pne(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=a.makeOutput(r.shape,"float32"),i=o=>a.dataIdMap.get(o.dataId).id;return Aw(i(r),i(n),i(s)),s}var _ne={kernelName:fu,backendName:"wasm",setupFunc:Mne,kernelFunc:Pne},$ne=!1,Fne=Gt(Fi,$ne,"bool"),Dne=Qe($i),One=Qe(Di,"float32");function V1(e){let{inputs:t,attrs:a,backend:n}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),La({inputs:{x:r},backend:n,attrs:{shape:o}})}var zne={kernelName:gu,backendName:"wasm",kernelFunc:V1},Lne=Qe(Oi,"float32");function bw(e){let{attrs:{shape:t,value:a},backend:n}=e,{attrs:{dtype:r}}=e;r=r||v.inferDtype(a);let s=n.makeOutput(t,r);return n.typedArrayFromHeap(s).fill(a),s}var Wne={kernelName:yu,backendName:"wasm",kernelFunc:bw},vw;function Bne(e){vw=e.wasm.cwrap(zi,null,["number","number","number","number","number","number"])}function Vne(e){let{inputs:t,backend:a}=e,{image:n}=t,r=a.makeOutput(n.shape,n.dtype),s=a.dataIdMap.get(n.dataId).id,i=a.dataIdMap.get(r.dataId).id,[o,l,u,p]=n.shape;return vw(s,o,l,u,p,i),r}var Une={kernelName:zi,backendName:"wasm",kernelFunc:Vne,setupFunc:Bne},Gne=Qe(Li),Hne=!1,jne=Gt(Wi,Hne),ww;function qne(e){ww=e.wasm.cwrap(Bi,null,["number","number","number","number","number","number","number"])}function Xne(e){let{backend:t,inputs:a,attrs:n}=e,{varianceEpsilon:r}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=a,p=t.dataIdMap.get(s.dataId).id,c=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return ww(p,c,d,h,m,r,g),f}var Kne={kernelName:Bi,backendName:"wasm",setupFunc:qne,kernelFunc:Xne},kw;function Yne(e){kw=e.wasm.cwrap(Zr,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Zne(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=C.computeConv2DInfo(r.shape,s.shape,l,p,u,d),g=tp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=n.dataIdMap.get(r.dataId).id,x=n.dataIdMap.get(s.dataId).id,A=f.outChannels,b=0;if(i!=null){let X=n.dataIdMap.get(i.dataId);if(X.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${X.shape.length}.`);if(X.shape[0]!==A)throw new Error(`FusedConv2D bias shape (${X.shape}) does not match the number of output channels (${A})`);b=X.id}let w=f.filterHeight,I=f.filterWidth,T=f.padInfo.top,N=f.padInfo.right,M=f.padInfo.bottom,P=f.padInfo.left,E=f.dilationHeight,S=f.dilationWidth,_=f.strideHeight,O=f.strideWidth,W=f.inChannels,$=f.padInfo.type==="SAME"?1:0,U=f.batchSize,G=f.inHeight,q=f.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${c}'. Please use 'NHWC'.`);let H=n.makeOutput(f.outShape,"float32"),V=n.dataIdMap.get(H.dataId).id,Z=o==null?0:n.dataIdMap.get(o.dataId).id;return kw(y,U,G,q,x,w,I,b,T,N,M,P,$,E,S,_,O,W,A,g,Z,m||0,V),H}var Jne={kernelName:Zr,backendName:"wasm",setupFunc:Yne,kernelFunc:Zne},Iw;function Qne(e){Iw=e.wasm.cwrap(Jr,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ere(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=C.computeConv2DInfo(r.shape,s.shape,l,p,u,d,!0),g=tp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(r.dataId).id,x=n.dataIdMap.get(s.dataId).id,A=f.outChannels,b=0;if(i!=null){let X=n.dataIdMap.get(i.dataId);if(X.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${X.shape.length}.`);if(X.shape[0]!==A)throw new Error(`FusedDepthwiseConv2D bias shape (${X.shape}) does not match the number of output channels (${A})`);b=X.id}let w=f.filterHeight,I=f.filterWidth,T=f.padInfo.top,N=f.padInfo.right,M=f.padInfo.bottom,P=f.padInfo.left,E=f.dilationHeight,S=f.dilationWidth,_=f.strideHeight,O=f.strideWidth,W=f.inChannels,$=f.padInfo.type==="SAME"?1:0,U=f.batchSize,G=f.inHeight,q=f.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${c}'. Please use 'NHWC'.`);let H=n.makeOutput(f.outShape,"float32"),V=n.dataIdMap.get(H.dataId).id,Z=o==null?0:n.dataIdMap.get(o.dataId).id;return Iw(y,U,G,q,x,w,I,b,T,N,M,P,$,E,S,_,O,W,A,g,Z,m||0,V),H}var tre={kernelName:Jr,backendName:"wasm",setupFunc:Qne,kernelFunc:ere},Sw;function are(e){Sw=e.wasm.cwrap(Vi,null,["number","number","number","number","number","number","array","number"])}function nre(e){let{backend:t,inputs:a}=e,{params:n,indices:r}=a,[s,i,o,l]=r3.prepareAndValidate(n,r),u=t.makeOutput(s,n.dtype);if(i===0)return u;let p=r.shape,c=p[p.length-1],d=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return Sw(d,nt[n.dtype],h,i,c,o,m,f),u}var rre={kernelName:Vi,backendName:"wasm",setupFunc:are,kernelFunc:nre},Cw;function sre(e){Cw=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function ire(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,indices:s}=a,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let T=0;T=0,()=>`GatherV2: the index value ${N} is not in [0, ${p-1}]`)}let c=C.segment_util.collectGatherOpShapeInfo(r,s,l,o),d=La({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),m=La({inputs:{x:s},attrs:{shape:[c.batchSize,h/c.batchSize]},backend:t}),f=[c.batchSize,c.outerSize,h/c.batchSize,c.sliceSize],g=t.makeOutput(f,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let y=d.shape.length-1,x=t.dataIdMap.get(d.dataId).id,A=t.dataIdMap.get(m.dataId).id,b=t.dataIdMap.get(g.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(d.shape)).buffer),I=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return Cw(x,nt[r.dtype],w,y,A,c.batchSize,I,b),t.disposeData(d.dataId),t.disposeData(m.dataId),g.shape=c.outputShape,g}var ore={kernelName:xu,backendName:"wasm",setupFunc:sre,kernelFunc:ire},lre=!1,ure=Gt(Ui,lre,"bool"),dre=!1,pre=Gt(Gi,dre,"bool"),cre=Qe(ji,"bool"),hre=Qe(qi,"bool"),mre=Qe(Xi,"bool"),Tw;function fre(e){Tw=e.wasm.cwrap(Ki,null,["number","number","number","number"])}function gre(e){let{inputs:{x:t},attrs:{alpha:a},backend:n}=e,r=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;Tw(r,nt[t.dtype],a,i)}return s}var yre={kernelName:Ki,backendName:"wasm",setupFunc:fre,kernelFunc:gre},xre=!1,Are=Gt(Yi,xre,"bool"),bre=!1,vre=Gt(Zi,bre,"bool"),Nw;function wre(e){Nw=e.wasm.cwrap(Ji,null,["number","number","number","number"])}function kre(e){let{attrs:t,backend:a}=e,{start:n,stop:r,num:s}=t,i=Math.floor(s),o=a.makeOutput([i],"float32");return Nw(a.dataIdMap.get(o.dataId).id,n,r,i),o}var Ire={kernelName:Ji,backendName:"wasm",setupFunc:wre,kernelFunc:kre},Sre=Qe(Qi),Cre=Qe(eo),Tre=!1,Nre=Gt(to,Tre,"bool"),Rre=Qe(ao),Ere=!1,Mre=Gt(no,Ere,"bool"),Pre=!1,_re=Gt(CA,Pre,"bool"),Rw;function $re(e){Rw=e.wasm.cwrap(ro,null,["number","number","number","number","number","number","number"])}function Fre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=a.makeOutput(r.shape,r.dtype);return Rw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var Dre={kernelName:ro,backendName:"wasm",setupFunc:$re,kernelFunc:Fre},Ew;function Ore(e){Ew=e.wasm.cwrap(Au,null,["number","number","number","number","number","number","number","number","number"])}function zre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let c=a.makeOutput(r.shape,r.dtype);return Ew(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,i.shape[3],o,l,u,p),c}var Lre={kernelName:Au,backendName:"wasm",setupFunc:Ore,kernelFunc:zre},Mw;function Wre(e){Mw=e.wasm.cwrap(so,null,["number","number","number","number"])}function Bre(e){let{backend:t,inputs:a,attrs:n}=e,{reductionIndices:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:c,inputWasTransposed:d}=ms(i,r,t);if(d){let x=t.dataIdMap.get(u.dataId).id;l=u,o=x}let h=l.shape.length;C.assertAxesAreInnerMostDims("max",p,h);let[m,f]=C.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;Mw(o,nt[i.dtype],g,x)}if(d&&t.disposeData(u.dataId),s){let x=C.expandShapeToKeepDim(y.shape,c);y.shape=x}return y}var Vre={kernelName:so,backendName:"wasm",setupFunc:Wre,kernelFunc:Bre},Ure=!1,Gre=Gt(io,Ure),Pw;function Hre(e){Pw=e.wasm.cwrap(oo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function jre(e){let{inputs:t,attrs:a,backend:n}=e,r=t.x,s=n.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=C.computePool2DInfo(r.shape,i,o,1,l,u),c=p.filterHeight,d=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.dilationHeight,x=p.dilationWidth,A=p.strideHeight,b=p.strideWidth,w=p.inChannels,I=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let T=n.makeOutput(p.outShape,"float32"),N=n.dataIdMap.get(T.dataId).id;return Pw(s,r.shape[0],r.shape[1],r.shape[2],c,d,h,m,f,g,y,x,A,b,w,I,N),T}var qre={kernelName:oo,backendName:"wasm",setupFunc:Hre,kernelFunc:jre},_w;function Xre(e){_w=e.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Kre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=C.computePool3DInfo(r.shape,s,i,1,o,l,u),c=a.makeOutput(p.outShape,r.dtype);return _w(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),c}var Yre={kernelName:bu,backendName:"wasm",setupFunc:Xre,kernelFunc:Kre},$w;function Zre(e){$w=e.wasm.cwrap("MaxPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Jre(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=C.computePool3DInfo(s.shape,i,o,1,l,u),c=a.makeOutput(s.shape,s.dtype);return $w(a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),c}var Qre={kernelName:bp,backendName:"wasm",setupFunc:Zre,kernelFunc:Jre},Fw;function ese(e){Fw=e.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function tse(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=C.computePool2DInfo(s.shape,i,o,1,l,u),c=a.makeOutput(s.shape,s.dtype);return Fw(a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),c}var ase={kernelName:Ap,backendName:"wasm",setupFunc:ese,kernelFunc:tse},Dw;function nse(e){Dw=e.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function rse(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,includeBatchInIndex:l}=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,[1,1],o),c=a.makeOutput(p.outShape,r.dtype),d=a.makeOutput(p.outShape,"int32");return Dw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,a.dataIdMap.get(d.dataId).id,nt[r.dtype],l,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),[c,d]}var sse={kernelName:vu,backendName:"wasm",setupFunc:nse,kernelFunc:rse},Ow;function ise(e){Ow=e.wasm.cwrap(lo,null,["number, number, number"])}function ose(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=ms(i,r,t),m=c;if(h){let b=t.dataIdMap.get(p.dataId).id;b!==o&&(u=p,l=b,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=C.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),x=u;u.dtype!=="float32"&&(x=fs({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(x.dataId).id);let A=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(A.dataId).id;Ow(l,y,b)}if(h&&t.disposeData(p.dataId),s){let b=C.expandShapeToKeepDim(A.shape,d);A.shape=b}return u.dtype!=="float32"&&t.disposeData(x.dataId),A}var lse={kernelName:lo,backendName:"wasm",setupFunc:ise,kernelFunc:ose},zw;function use(e){zw=e.wasm.cwrap(uo,null,["number","number","number","number"])}function dse(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=ms(i,r,t);if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A)}let m=u.shape.length;C.assertAxesAreInnerMostDims("min",c,m);let[f,g]=C.computeOutAndReduceShapes(u.shape,c),y=v.sizeFromShape(g),x=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;zw(l,nt[i.dtype],y,A)}if(h&&t.disposeData(p.dataId),s){let A=C.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var pse={kernelName:uo,backendName:"wasm",setupFunc:use,kernelFunc:dse},cse=!1,hse=Gt(po,cse),U1;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(U1||(U1={}));var Lw;function mse(e){Lw=e.wasm.cwrap(co,null,["number","array","number","number","array","array","number","number"])}function fse(e){let{inputs:{x:t},backend:a,attrs:{paddings:n,mode:r}}=e,s=n.map((m,f)=>m[0]+t.shape[f]+m[1]),i=a.dataIdMap.get(t.dataId).id,o=a.makeOutput(s,t.dtype),l=a.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(m=>m[0]),c=n.map(m=>m[1]),d=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(c).buffer);return Lw(i,u,t.shape.length,nt[t.dtype],d,h,U1[r],l),o}var gse={kernelName:co,backendName:"wasm",kernelFunc:fse,setupFunc:mse},Ww;function yse(e){Ww=e.wasm.cwrap(Uo,null,["number","number","number","number"])}function Bw(e){let{backend:t,inputs:{logits:a},attrs:{dim:n}}=e,r=t.dataIdMap.get(a.dataId).id,s=t.makeOutput(a.shape,a.dtype),i=t.dataIdMap.get(s.dataId).id,o=a.shape[n],l=v.sizeFromShape(a.shape)/o;return v.sizeFromShape(s.shape)===0||Ww(r,i,o,l),s}var xse={kernelName:Uo,backendName:"wasm",setupFunc:yse,kernelFunc:Bw},Vw;function Ase(e){Vw=e.wasm.cwrap(mo,null,["number","number","number","number","number","number"])}function bse(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n;if(r.dtype!=="float32")throw new Error(`Tensor logits must have dtype float32, got ${r.dtype}`);let l=o?r:Bw({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),[u,p]=l.shape,c=a.makeOutput([u,s],"int32");return Vw(a.dataIdMap.get(l.dataId).id,u,p,s,i,a.dataIdMap.get(c.dataId).id),o||a.disposeData(l.dataId),c}var vse={kernelName:mo,backendName:"wasm",setupFunc:Ase,kernelFunc:bse},wse=Gt(ho,!0),kse=!0,Ise=Gt(fo,kse),Sse=Qe(wu);function B3(e,t){let a=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=a[0],r=a[1],s=a[2],i=a[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var Uw;function Cse(e){Uw=e.wasm.cwrap(yo,"number",["number","number","number","number","number"])}function Tse(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=a,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,c=Uw(u,p,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=B3(t,c);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var Nse={kernelName:yo,backendName:"wasm",setupFunc:Cse,kernelFunc:Tse},Gw;function Rse(e){Gw=e.wasm.cwrap(ku,"number",["number","number","number","number","number","bool"])}function Ese(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Gw(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=B3(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),x=t.makeOutput([],"int32",g);return[y,x]}var Mse={kernelName:ku,backendName:"wasm",setupFunc:Rse,kernelFunc:Ese},Hw;function Pse(e){Hw=e.wasm.cwrap(xo,"number",["number","number","number","number","number","number"])}function _se(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Hw(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=B3(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),x=t.makeOutput([m],"float32",f);return[y,x]}var $se={kernelName:xo,backendName:"wasm",setupFunc:Pse,kernelFunc:_se},Fse=!1,Dse=Gt(go,Fse,"bool"),jw;function Ose(e){jw=e.wasm.cwrap(Ao,null,["number","number","number","number","number"])}function zse(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=a.makeOutput([...r.shape,i],s),p=a.dataIdMap.get(u.dataId).id,c=a.dataIdMap.get(r.dataId).id;return jw(c,i,o,l,p),u}var Lse={kernelName:Ao,backendName:"wasm",setupFunc:Ose,kernelFunc:zse};function Wse(e){let{inputs:{x:t},backend:a}=e,n=a.makeOutput(t.shape,t.dtype);return a.typedArrayFromHeap(n).fill(1),n}var Bse={kernelName:Iu,backendName:"wasm",kernelFunc:Wse};function Vse(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return V1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=V1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=nw({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeData(p.dataId)),u}var Use={kernelName:Su,backendName:"wasm",kernelFunc:Vse},qw;function Gse(e){qw=e.wasm.cwrap(bo,null,["number","array","number","number","array","array","number","number"])}function Hse(e){let{inputs:{x:t},backend:a,attrs:{paddings:n,constantValue:r}}=e,s=n.map((m,f)=>m[0]+t.shape[f]+m[1]);if(v.sizeFromShape(t.shape)===0)return bw({backend:a,attrs:{shape:s,value:r,dtype:t.dtype}});let i=a.dataIdMap.get(t.dataId).id,o=a.makeOutput(s,t.dtype),l=a.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(m=>m[0]),c=n.map(m=>m[1]),d=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(c).buffer);return qw(i,u,t.shape.length,nt[t.dtype],d,h,r,l),o}var Xw={kernelName:bo,backendName:"wasm",kernelFunc:Hse,setupFunc:Gse},jse=!1,qse=Gt(vo,jse),Kw;function Xse(e){Kw=e.wasm.cwrap(wo,null,["number","number","number"])}function Kse(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=a.dataIdMap.get(n.dataId).id,i=a.dataIdMap.get(r.dataId).id,o=s,l=n,u=l;l.dtype!=="float32"&&(u=fs({backend:a,inputs:{x:n},attrs:{dtype:"float32"}}),o=a.dataIdMap.get(u.dataId).id);let p=a.makeOutput(n.shape,"float32"),c=a.dataIdMap.get(p.dataId).id;return Kw(o,i,c),l.dtype!=="float32"&&a.disposeData(u.dataId),p}var Yse={kernelName:wo,backendName:"wasm",setupFunc:Xse,kernelFunc:Kse},Yw;function Zse(e){Yw=e.wasm.cwrap(ko,null,["number","number","number","number"])}function Jse(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=ms(i,r,t),m=c;if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=C.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),x=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;Yw(l,y,nt[x.dtype],A)}if(h&&t.disposeData(p.dataId),s){let A=C.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var Qse={kernelName:ko,backendName:"wasm",setupFunc:Zse,kernelFunc:Jse},eie=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=g3(n,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},tie={kernelName:Cu,backendName:"wasm",kernelFunc:eie},aie=!0,nie=Gt(Pi,aie),rie=Qe(Io),sie=Qe(So),iie=Qe(No),Zw;function oie(e){Zw=e.wasm.cwrap(To,null,["number","number","number","number","number","number","number","number","number","number"])}function lie(e){let{backend:t,inputs:a,attrs:n}=e,{images:r}=a,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[p,c,d,h]=r.shape,m=[p,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=fs({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,x=t.makeOutput(m,"float32");if(v.sizeFromShape(r.shape)===0)return x;let A=t.dataIdMap.get(x.dataId).id;return Zw(y,p,c,d,h,l,u,s?1:0,i?1:0,A),g!=null&&t.disposeData(g.dataId),x}var uie={kernelName:To,backendName:"wasm",setupFunc:oie,kernelFunc:lie},Jw;function die(e){Jw=e.wasm.cwrap(Ru,null,["number","number","number","array","array","boolean"])}function pie(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=a.makeOutput(r.shape,"float32"),l=a.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=fs({backend:a,inputs:{x:r},attrs:{dtype:"float32"}}),l=a.dataIdMap.get(u.dataId)),Jw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(o.dataId).id,new Uint8Array(new Int32Array(r.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),u!=null&&a.disposeData(u.dataId),o}var cie={kernelName:Ru,backendName:"wasm",setupFunc:die,kernelFunc:pie},Qw;function hie(e){Qw=e.wasm.cwrap(Co,null,["number","number","number","number","number","number","number","number","number","number"])}function mie(e){let{backend:t,inputs:a,attrs:n}=e,{images:r}=a,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[p,c,d,h]=r.shape,m=[p,l,u,h],f=t.makeOutput(m,"float32");if(v.sizeFromShape(r.shape)===0)return f;let g=t.dataIdMap.get(r.dataId),y;g.dtype!=="float32"&&(y=fs({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),g=t.dataIdMap.get(y.dataId));let x=g.id,A=t.dataIdMap.get(f.dataId).id;return Qw(x,p,c,d,h,l,u,s?1:0,i?1:0,A),y!=null&&t.disposeData(y.dataId),f}var fie={kernelName:Co,backendName:"wasm",setupFunc:hie,kernelFunc:mie},ek;function gie(e){ek=e.wasm.cwrap(Nu,null,["number","number","number","array","array","boolean"])}function yie(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=a.makeOutput(r.shape,"float32"),l=a.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=fs({backend:a,inputs:{x:r},attrs:{dtype:"float32"}}),l=a.dataIdMap.get(u.dataId)),ek(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(o.dataId).id,new Uint8Array(new Int32Array(r.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),u!=null&&a.disposeData(u.dataId),o}var xie={kernelName:Nu,backendName:"wasm",setupFunc:gie,kernelFunc:yie},tk;function Aie(e){tk=e.wasm.cwrap(Ro,null,["number","array","number","array","number","number"])}function bie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=v.parseAxisParam(s,r.shape);if(r.shape.length===0)return o0({inputs:{x:r},backend:a});let o=a.makeOutput(r.shape,r.dtype),l=a.dataIdMap.get(r.dataId).id,u=a.dataIdMap.get(o.dataId).id,p=new Uint8Array(new Int32Array(i).buffer),c=new Uint8Array(new Int32Array(r.shape).buffer);tk(l,p,i.length,c,r.shape.length,u);let d=La({inputs:{x:o},attrs:{shape:r.shape},backend:a});return a.disposeData(o.dataId),d}var vie={kernelName:Ro,backendName:"wasm",kernelFunc:bie,setupFunc:Aie},ak;function wie(e){ak=e.wasm.cwrap(Jo,null,["number","number","number","number","number","number","number","number","array","number","number"])}function kie(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=n,l=a.makeOutput(r.shape,r.dtype),u=a.dataIdMap.get(r.dataId).id,p=a.dataIdMap.get(l.dataId).id,[c,d,h,m]=r.shape,[f,g]=C.getImageCenter(o,d,h),y=i===0,x=255,A=typeof i=="number"?[i,i,i,y?0:x]:[...i,x],b=new Uint8Array(new Int32Array(A).buffer);return ak(u,c,d,h,m,s,f,g,b,A.length,p),l}var Iie={kernelName:Jo,backendName:"wasm",kernelFunc:kie,setupFunc:wie},Sie=Qe(Eo),Cie=Qe(Mo),nk;function Tie(e){nk=e.wasm.cwrap(Po,null,["number","number","number","number","number","number","array","number","number"])}function 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i=t.dataIdMap.get(n.dataId).id,o=t.dataIdMap.get(r.dataId).id,l=t.dataIdMap.get(s.dataId).id,u=n.shape[0],p=v.sizeFromShape(s.shape),c=t.makeOutput([u,p],n.dtype),d=t.dataIdMap.get(c.dataId).id,h=t.makeOutput([p],s.dtype),m=t.dataIdMap.get(h.dataId).id,f=t.makeOutput([3],"int32"),g=t.dataIdMap.get(f.dataId).id;lk(i,o,l,u,d,m,g);let y=t.readSync(f.dataId),x;switch(y[0]){case 0:{x=C.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(y[1],y[2]);break}case 1:{x=C.getSparseReshapeNegativeOutputDimErrorMessage(y[1],y[2]);break}case 2:x=C.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();break;case 3:{let A=Array.from(t.readSync(r.dataId)),b=Array.from(t.readSync(h.dataId));x=C.getSparseReshapeInputOutputMultipleErrorMessage(A,b);break}case 4:{let A=Array.from(t.readSync(r.dataId)),b=Array.from(t.readSync(h.dataId));x=C.getSparseReshapeInputOutputMismatchErrorMessage(A,b);break}default:x=""}if(t.disposeData(f.dataId),x)throw t.disposeData(c.dataId),t.disposeData(h.dataId),new 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moe(e){mk=e.wasm.cwrap(jo,null,["number","array","number","array","array","array","array","array","number","number"])}function foe(e){let{backend:t,inputs:a,attrs:n}=e,{x:r}=a,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Nt.sliceInfo(r.shape,s,i,o,l,u,p,c,d),w;if(f)w=La({inputs:{x:r},backend:t,attrs:{shape:m}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let I=Nt.computeOutShape(x,A,b),T=ni({inputs:{x:r},backend:t,attrs:{begin:x,size:I}});w=La({inputs:{x:T},backend:t,attrs:{shape:m}}),t.disposeData(T.dataId)}else{let I=t.makeOutput(h,"float32"),T=t.dataIdMap.get(r.dataId).id,N=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),M=new Uint8Array(new Int32Array(x).buffer),P=new Uint8Array(new Int32Array(A).buffer),E=new Uint8Array(new Int32Array(b).buffer),S=new 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Aoe(e){let{backend:t,inputs:a,attrs:n}=e,{input:r,delimiter:s}=a,{skipEmpty:i}=n,o=t.readSync(r.dataId),l=t.readSync(s.dataId),[u,p,c]=A3(o,l[0],i),d=p.length,h=t.makeOutput([d,2],"int32");t.typedArrayFromHeap(h).set(u);let m=t.makeOutput([d],"string"),f=t.dataIdMap.get(m.dataId);f.stringBytes=p;let g=t.makeOutput([2],"int32");return t.typedArrayFromHeap(g).set(c),[h,m,g]}var boe={kernelName:Sp,backendName:"wasm",kernelFunc:Aoe};function voe(e){let{backend:t,inputs:a,attrs:n}=e,{input:r}=a,{numBuckets:s}=n,i=t.readSync(r.dataId),o=b3(i,s),l=t.makeOutput(r.shape,"int32");return t.typedArrayFromHeap(l).set(o),l}var woe={kernelName:Cp,backendName:"wasm",kernelFunc:voe},koe=!0,Ioe=Gt(qo,koe),fk;function Soe(e){fk=e.wasm.cwrap(Vo,null,["number","number","number","number"])}function Coe(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=ms(i,r,t),m=c;if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("sum",m,u.shape.length);let[f,g]=C.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),x=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;fk(l,y,nt[x.dtype],A)}if(h&&t.disposeData(p.dataId),s){let A=C.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var Toe={kernelName:Vo,backendName:"wasm",setupFunc:Soe,kernelFunc:Coe},Noe=Qe(Xo),Roe=Qe(Ko),gk;function Eoe(e){gk=e.wasm.cwrap(_o,null,["number","number","number","number","number","number","array","number","number","number"])}function Moe(e){let{backend:t,inputs:a,attrs:n}=e,{tensor:r,indices:s,updates:i}=a,{}=n,o=t.makeOutput(r.shape,r.dtype);if(v.sizeFromShape(r.shape)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=Hh.calculateShapes(i,s,r.shape),h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(i.dataId).id,f=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(c).buffer),y=t.dataIdMap.get(o.dataId).id;return gk(h,m,nt[i.dtype],l,u,p,g,d,y,f),o}var Poe={kernelName:_o,backendName:"wasm",setupFunc:Eoe,kernelFunc:Moe},yk;function _oe(e){yk=e.wasm.cwrap(ls,null,["number","array","number","array","number","number"])}function $oe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,s=a.dataIdMap.get(r.dataId).id,{reps:i}=n,o=new Array(r.shape.length);for(let d=0;d{let{x:n}=e,{k:r,sorted:s}=a,i=t.dataIdMap.get(n.dataId).id,o=new Uint8Array(new Int32Array(n.shape).buffer),l=n.shape.slice();l[l.length-1]=r;let u=t.makeOutput(l,n.dtype),p=t.dataIdMap.get(u.dataId).id,c=t.makeOutput(l,"int32"),d=t.dataIdMap.get(c.dataId).id;return 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${e?"main(getGlobalIndex());":"main();"}; } - `,a}function Joe(e){return` + `,a}function ple(e){return` @compute @workgroup_size(${e.workgroupSize[0]}, ${e.workgroupSize[1]}, ${e.workgroupSize[2]}) -`}function Qoe(e,t,a){let n=[],r=a.workgroupSize[0]*a.workgroupSize[1]*a.workgroupSize[2];if(a.outputComponent=a.outputComponent?a.outputComponent:1,n.push(` +`}function cle(e,t,a){let n=[],r=a.workgroupSize[0]*a.workgroupSize[1]*a.workgroupSize[2];if(a.outputComponent=a.outputComponent?a.outputComponent:1,n.push(` var localId: vec3; var localIndex: u32; @@ -5036,12 +5036,12 @@ return a / b;`,lJ=` // Only used when the y/z dimension of workgroup size is 1. fn getGlobalIndex() -> i32 { - ${dk(a)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + + ${wk(a)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + workgroupId.y * numWorkgroups.x + workgroupId.x) * ${r}u + localIndex); `} } - `),a.pixelsOpType!=null){let h=a.pixelsOpType===eu.FROM_PIXELS?`@group(0) @binding(0) var result: array<${Us(t.dtype,a.outputComponent)}>;`:`@group(0) @binding(1) var inBuf : array<${Us(e[0].dtype,a.outputComponent)}>;`,m=t.shape.length===3?"vec2":"i32";n.push(` + `),a.pixelsOpType!=null){let h=a.pixelsOpType===eu.FROM_PIXELS?`@group(0) @binding(0) var result: array<${Gs(t.dtype,a.outputComponent)}>;`:`@group(0) @binding(1) var inBuf : array<${Gs(e[0].dtype,a.outputComponent)}>;`,m=t.shape.length===3?"vec2":"i32";n.push(` struct Uniform { outShapeStrides : ${m}, size : i32, @@ -5051,21 +5051,21 @@ return a / b;`,lJ=` ${h} @group(0) @binding(2) var uniforms: Uniform; - `);let f=K5(a);return[X5,n.join(` -`),oh(t.shape),a.getUserCode(),q5(f,a)].join(` -`)}let s,i,o="struct Uniforms { NAN : f32, INFINITY : f32, ";a.variableNames.forEach((h,m)=>{let f=$t(e[m].shape.length);o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${f}, `,s=e[m].shape.length-1,i=$t(s),o+=`${h.charAt(0).toLowerCase()+h.slice(1)}ShapeStrides: ${i}, `});let l=$t(t.shape.length);o+=`outShape : ${l}, `,s=t.shape.length-1,i=$t(s),o+=` - outShapeStrides: ${i}, `,a.size&&(o+="size : i32, "),a.uniforms&&(o+=a.uniforms),o+="};",o=lle(o),n.push(o),a.atomic?n.push(` + `);let f=tA(a);return[eA,n.join(` +`),ih(t.shape),a.getUserCode(),Q5(f,a)].join(` +`)}let s,i,o="struct Uniforms { NAN : f32, INFINITY : f32, ";a.variableNames.forEach((h,m)=>{let f=_t(e[m].shape.length);o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${f}, `,s=e[m].shape.length-1,i=_t(s),o+=`${h.charAt(0).toLowerCase()+h.slice(1)}ShapeStrides: ${i}, `});let l=_t(t.shape.length);o+=`outShape : ${l}, `,s=t.shape.length-1,i=_t(s),o+=` + outShapeStrides: ${i}, `,a.size&&(o+="size : i32, "),a.uniforms&&(o+=a.uniforms),o+="};",o=vle(o),n.push(o),a.atomic?n.push(` @group(0) @binding(0) var result: array>; `):n.push(` - @group(0) @binding(0) var result: array<${Us(t.dtype,a.outputComponent)}>; + @group(0) @binding(0) var result: array<${Gs(t.dtype,a.outputComponent)}>; `),a.variableNames.forEach((h,m)=>{n.push(` - @group(0) @binding(${1+m}) var ${h}: array<${a.variableComponents?Us(e[m].dtype,a.variableComponents[m]):Us(e[m].dtype,a.outputComponent)}>; + @group(0) @binding(${1+m}) var ${h}: array<${a.variableComponents?Gs(e[m].dtype,a.variableComponents[m]):Gs(e[m].dtype,a.outputComponent)}>; `)}),o!==""&&n.push(` @group(0) @binding(${1+a.variableNames.length}) var uniforms: Uniforms; - `);let u=sle(t.shape,a.dispatchLayout),p=[X5,n.join(` -`)+tle,oh(t.shape),u,ile(t.shape.length)];a.atomic||p.push(ole(t.shape,t.dtype,a.outputComponent)),a.variableNames.forEach((h,m)=>{p.push(`${oh(e[m].shape,h)}`)});let c=e.map((h,m)=>rle(h,t.shape,a.variableComponents?a.variableComponents[m]:a.outputComponent,a.dispatchLayout.x.length===t.shape.length)).join(` -`);p.push(c),p.push(a.getUserCode());let d=K5(a);return p.push(q5(d,a)),p.join(` -`)}function ele(e,t,a){let n=e.shaderKey;if(e.pixelsOpType!=null)return n;let r=[],s=[];t.forEach(p=>{r.push(p.shape),s.push(p.dtype)}),r.push(a.shape),s.push(a.dtype);let i=t.map(p=>S.getBroadcastDims(p.shape,a.shape)),o=t.map(p=>v.arraysEqual(p.shape,a.shape)).join("_"),l=i.map(p=>p.join("_")).join(";"),u=dk(e)?"flatDispatch":"";return n+="_"+(e.workgroupSize?e.workgroupSize.join(","):"")+r.map(p=>p.length).join(",")+s.join(",")+e.variableNames.join(",")+l+o+u,n}var X5=` + `);let u=xle(t.shape,a.dispatchLayout),p=[eA,n.join(` +`)+mle,ih(t.shape),u,Ale(t.shape.length)];a.atomic||p.push(ble(t.shape,t.dtype,a.outputComponent)),a.variableNames.forEach((h,m)=>{p.push(`${ih(e[m].shape,h)}`)});let c=e.map((h,m)=>yle(h,t.shape,a.variableComponents?a.variableComponents[m]:a.outputComponent,a.dispatchLayout.x.length===t.shape.length)).join(` +`);p.push(c),p.push(a.getUserCode());let d=tA(a);return p.push(Q5(d,a)),p.join(` +`)}function hle(e,t,a){let n=e.shaderKey;if(e.pixelsOpType!=null)return n;let r=[],s=[];t.forEach(p=>{r.push(p.shape),s.push(p.dtype)}),r.push(a.shape),s.push(a.dtype);let i=t.map(p=>C.getBroadcastDims(p.shape,a.shape)),o=t.map(p=>v.arraysEqual(p.shape,a.shape)).join("_"),l=i.map(p=>p.join("_")).join(";"),u=wk(e)?"flatDispatch":"";return n+="_"+(e.workgroupSize?e.workgroupSize.join(","):"")+r.map(p=>p.length).join(",")+s.join(",")+e.variableNames.join(",")+l+o+u,n}var eA=` struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32}; struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32}; @@ -5115,63 +5115,63 @@ return a / b;`,lJ=` let floatToUint: vec4 = bitcast>(val); return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u); } -`,tle=` +`,mle=` fn isinf(val: f32) -> bool { return abs(val) == uniforms.INFINITY; } -`;function oh(e,t=""){let a=e.length,n=t!==""?`get${t.charAt(0).toUpperCase()+t.slice(1)}CoordsFromIndex`:"getCoordsFromIndex",r=t!==""?`${t.charAt(0).toLowerCase()+t.slice(1)}ShapeStrides`:"outShapeStrides";if(a<=1)return`fn ${n}(index : i32) -> i32 { return index; }`;let s=v.computeStrides(e),i=$t(a),o=[];for(let u=0;u vec2 { +`;function ih(e,t=""){let a=e.length,n=t!==""?`get${t.charAt(0).toUpperCase()+t.slice(1)}CoordsFromIndex`:"getCoordsFromIndex",r=t!==""?`${t.charAt(0).toLowerCase()+t.slice(1)}ShapeStrides`:"outShapeStrides";if(a<=1)return`fn ${n}(index : i32) -> i32 { return index; }`;let s=v.computeStrides(e),i=_t(a),o=[];for(let u=0;u vec2 { let d0 = index / uniforms.${r}; let d1 = index - d0 * uniforms.${r}; return vec2(d0, d1); - }`;let l;return l="var index2 = index;"+s.map((u,p)=>{let c=`let ${o[p]} = index2 / uniforms.${r}.${Sr(p)}`,d=p===s.length-1?`let ${o[p+1]} = index2 - ${o[p]} * uniforms.${r}.${Sr(p)}`:`index2 = index2 - ${o[p]} * uniforms.${r}.${Sr(p)}`;return`${c}; ${d};`}).join(""),` + }`;let l;return l="var index2 = index;"+s.map((u,p)=>{let c=`let ${o[p]} = index2 / uniforms.${r}.${Ir(p)}`,d=p===s.length-1?`let ${o[p+1]} = index2 - ${o[p]} * uniforms.${r}.${Ir(p)}`:`index2 = index2 - ${o[p]} * uniforms.${r}.${Ir(p)}`;return`${c}; ${d};`}).join(""),` fn ${n}(index : i32) -> ${i} { ${l} return ${i}(${o.join(",")}); } - `}function ale(e,t){let a=e.name,n=e.shape.length,r=$t(n),s="get"+a.charAt(0).toUpperCase()+a.slice(1),i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=i.map(p=>`${p} : i32`).join(", ");if(n<1)return` - fn ${s}() -> ${Ke(t)} { - return ${Ke(t)}(${a}[0]); + `}function fle(e,t){let a=e.name,n=e.shape.length,r=_t(n),s="get"+a.charAt(0).toUpperCase()+a.slice(1),i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=i.map(p=>`${p} : i32`).join(", ");if(n<1)return` + fn ${s}() -> ${Xe(t)} { + return ${Xe(t)}(${a}[0]); } `;let l=`uniforms.${a.charAt(0).toLowerCase()+a.slice(1)}Shape`,u=`${n}D`;return n===0&&(u="1D"),` - fn ${s}(${o}) -> ${Ke(t)} { - return ${Ke(t)}(${a}[getIndexFromCoords${u}(${r}(${i.join(",")}), + fn ${s}(${o}) -> ${Xe(t)} { + return ${Xe(t)}(${a}[getIndexFromCoords${u}(${r}(${i.join(",")}), ${l})${t===1?"":` / ${t}`}]); } - `}function nle(e,t,a,n){let r=e.name,s=r.charAt(0).toUpperCase()+r.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=$t(l);if(v.arraysEqual(e.shape,t)&&n)return` - fn ${i}Index(globalIndex : i32) -> ${Ke(a)} { - return ${Ke(a)}(${r}[globalIndex]); + `}function gle(e,t,a,n){let r=e.name,s=r.charAt(0).toUpperCase()+r.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=_t(l);if(v.arraysEqual(e.shape,t)&&n)return` + fn ${i}Index(globalIndex : i32) -> ${Xe(a)} { + return ${Xe(a)}(${r}[globalIndex]); } - fn ${i}Coords(coords : ${u}) -> ${Ke(a)} { - return ${Ke(a)}(${r}[${l>1?"getOutputIndexFromCoords(coords)":"coords"}${a===1?"":` / ${a}`}]); + fn ${i}Coords(coords : ${u}) -> ${Xe(a)} { + return ${Xe(a)}(${r}[${l>1?"getOutputIndexFromCoords(coords)":"coords"}${a===1?"":` / ${a}`}]); } - `;let p=S.getBroadcastDims(e.shape,t),c=l-o,d="";if(o===0)return` - fn ${i}Index(globalIndex : i32) -> ${Ke(a)}{ + `;let p=C.getBroadcastDims(e.shape,t),c=l-o,d="";if(o===0)return` + fn ${i}Index(globalIndex : i32) -> ${Xe(a)}{ return get${s}(); } - fn ${i}Coords(coords : ${u}) -> ${Ke(a)}{ + fn ${i}Coords(coords : ${u}) -> ${Xe(a)}{ return get${s}(); } - `;l<2&&p.length>=1?d="coords = 0;":d=p.map(g=>`coords.${Sr(g+c)} = 0;`).join(` -`);let h="";if(l<2&&o>0)h="coords";else if(l>1){let g=$t(o),y=e.shape.map((x,A)=>`coords.${Sr(A+c)}`).join(", ");h=`${g}(${y})`}else h="coords";let m=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,f=`${o}D`;return` - fn ${i}Index(globalIndex : i32) -> ${Ke(a)} { + `;l<2&&p.length>=1?d="coords = 0;":d=p.map(g=>`coords.${Ir(g+c)} = 0;`).join(` +`);let h="";if(l<2&&o>0)h="coords";else if(l>1){let g=_t(o),y=e.shape.map((x,A)=>`coords.${Ir(A+c)}`).join(", ");h=`${g}(${y})`}else h="coords";let m=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,f=`${o}D`;return` + fn ${i}Index(globalIndex : i32) -> ${Xe(a)} { var coords = getCoordsFromIndex(globalIndex); ${d} - return ${Ke(a)}(${r}[getIndexFromCoords${f}(${h}, ${m})${a===1?"":` / ${a}`}]); + return ${Xe(a)}(${r}[getIndexFromCoords${f}(${h}, ${m})${a===1?"":` / ${a}`}]); } - fn ${i}Coords(coordsIn : ${u}) -> ${Ke(a)} { + fn ${i}Coords(coordsIn : ${u}) -> ${Xe(a)} { var coords = coordsIn; ${d} - return ${Ke(a)}(${r}[getIndexFromCoords${f}(${h}, ${m})${a===1?"":` / ${a}`}]); + return ${Xe(a)}(${r}[getIndexFromCoords${f}(${h}, ${m})${a===1?"":` / ${a}`}]); } -`}function rle(e,t,a,n){let r=ale(e,a);return e.shape.length<=t.length&&(r+=nle(e,t,a,n)),r}function sle(e,t){let{x:a,y:n=[],z:r=[]}=t,s=e.length,i=a.length+n.length+r.length;if(i!==s)return"";if(a.length===s)return`fn getOutputCoords() -> ${$t(s)}{ +`}function yle(e,t,a,n){let r=fle(e,a);return e.shape.length<=t.length&&(r+=gle(e,t,a,n)),r}function xle(e,t){let{x:a,y:n=[],z:r=[]}=t,s=e.length,i=a.length+n.length+r.length;if(i!==s)return"";if(a.length===s)return`fn getOutputCoords() -> ${_t(s)}{ let globalIndex = getGlobalIndex(); return getCoordsFromIndex(globalIndex); } - `;let o="",l=[a,n,r];for(let d=0;d ${p} { + `;let o="",l=[a,n,r];for(let d=0;d ${p} { ${o} -`;return u.length===0?c+=`return ${p}(0); }`:c+=`return ${p}(${u.join(",")}); }`,c}function ile(e){let t="";switch(e){case 0:case 1:t+=` +`;return u.length===0?c+=`return ${p}(0); }`:c+=`return ${p}(${u.join(",")}); }`,c}function Ale(e){let t="";switch(e){case 0:case 1:t+=` fn getOutputIndexFromCoords(coords : i32) -> i32 { return coords; } @@ -5205,56 +5205,56 @@ return a / b;`,lJ=` coords.u * uniforms.outShapeStrides.u + coords.v; } - `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function dk(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function Us(e,t=1){if(e==="float32")return Ke(t,"f32");if(e==="int32"||e==="bool")return Ke(t,"i32");throw new Error(`type ${e} is not supported.`)}function ole(e,t,a){let n=e.length,r=Us(t,a),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Ke(a)}) { + `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function wk(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function Gs(e,t=1){if(e==="float32")return Xe(t,"f32");if(e==="int32"||e==="bool")return Xe(t,"i32");throw new Error(`type ${e} is not supported.`)}function ble(e,t,a){let n=e.length,r=Gs(t,a),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Xe(a)}) { result[flatIndex] = ${r}(value); } - fn setOutputAtIndexI32(flatIndex : i32, value : ${Ke(a,"i32")}) { + fn setOutputAtIndexI32(flatIndex : i32, value : ${Xe(a,"i32")}) { result[flatIndex] = ${r}(value); } - `;if(n>=2){let i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=$t(n);s+=` - fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : ${Ke(a)}) { + `;if(n>=2){let i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=_t(n);s+=` + fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : ${Xe(a)}) { let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")})); setOutputAtIndex(flatIndex${a===1?"":` / ${a}`}, value); } - fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : ${Ke(a,"i32")}) { + fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : ${Xe(a,"i32")}) { let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")})); setOutputAtIndexI32(flatIndex${a===1?"":` / ${a}`}, value); } - `}return s}function lle(e){let t=/(\w+)\s*:\s*vec(5|6)/g;e=e.replace(t,n=>"@align(16) "+n);let a=/vec(5|6)\s*,\s*(\w+)/g;return e=e.replace(a,(n,r,s)=>`vec${r}, @align(16) ${s}`),e}function K5(e){return!(e.dispatchLayout.hasOwnProperty("y")&&e.dispatchLayout.y.length!==0||e.dispatchLayout.hasOwnProperty("z")&&e.dispatchLayout.z.length!==0)}var pk={};Ze(pk,{GPUBytesPerElement:()=>H1,MatMulProgramType:()=>Dn,assertNotComplex:()=>U3,computeDispatch:()=>de,computeWorkPerThreadForConv2d:()=>B3,computeWorkgroupInfoForMatMul:()=>ck,computeWorkgroupSizeForConv2d:()=>W3,flatDispatchLayout:()=>me,isWebGPUSupported:()=>V3,tilesFitEvenlyIntoShape:()=>ule});var js=e=>{let t=1;for(let a=0;aa%e[n]===0)}function de(e,t,a=[1,1,1],n=[1,1,1]){let[r,s,i]=[Math.ceil(js(e.x.map(o=>t[o]))/(a[0]*n[0])),e.y?Math.ceil(js(e.y.map(o=>t[o]))/(a[1]*n[1])):1,e.z?Math.ceil(js(e.z.map(o=>t[o]))/(a[2]*n[2])):1];return[r,s,i]}function ck(e,t,a,n=!1){let r=[8,8,1],s=[4,4,1];return n||(e<=8&&(s[1]=1),t<=16&&a<=16&&(r[0]=4)),{workgroupSize:r,elementsPerThread:s}}function W3(e,t,a=!1){if(a)return[8,8,1];let n=js(e.x.map(s=>t[s])),r=js(e.y.map(s=>t[s]));return n<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function B3(e,t,a=!1){if(a)return[4,4,1];let n=js(e.x.map(s=>t[s])),r=js(e.y.map(s=>t[s]));return n<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function me(e){return{x:e.map((t,a)=>a)}}function H1(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function V3(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function U3(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var Dn;(function(e){e[e.MatMulReduceProgram=0]="MatMulReduceProgram",e[e.MatMulSplitKProgram=1]="MatMulSplitKProgram",e[e.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",e[e.MatMulPackedProgram=3]="MatMulPackedProgram",e[e.MatMulMax=4]="MatMulMax"})(Dn||(Dn={}));var dle=W().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),ple=(e,t)=>{let a=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=a))return r;v.assert(r[0]>a&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y 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t=this.tensorMap.get(e);if(!(!t||!t.resource)){if(t.external){t.resource=null;return}t.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(t.resource):t.resource instanceof GPUTexture&&this.textureManager.releaseTexture(t.resource),t.resource=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,a){if(a==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:a,shape:t,values:e,refCount:1}),n}move(e,t,a,n,r){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:n,shape:a,values:t,refCount:r})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let e;try{e=await Promise.all(Object.values(this.pipelineCache))}catch(t){throw new Error(t.message)}Object.keys(this.pipelineCache).map((t,a)=>{this.pipelineCache[t]=e[a]})}async getBufferData(e){if(W().getBool("WEBGPU_ENGINE_COMPILE_ONLY"))return console.warn("The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false"),null;let t=e.size,a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,a,0,t),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=a.getMappedRange().slice(0);return a.unmap(),a!=null&&this.bufferManager.releaseBuffer(a),W().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let a=this.tensorMap.get(e);return a.values=t,a.values}readSync(e){let t=this.tensorMap.get(e),{values:a,complexTensorInfos:n}=t;if(a!=null||t.dtype==="string")return a;if(t.dtype==="complex64"){let m=this.readSync(n.real.dataId),f=this.readSync(n.imag.dataId),g=v.convertBackendValuesAndArrayBuffer(S.mergeRealAndImagArrays(m,f).buffer,"float32");return this.convertAndCacheOnCPU(e,g),g}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn("The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead."));let r=["opaque","premultiplied"],s=t.resource,i=s.size;v.assert(i%4===0,()=>"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.");let o=i/4,l=new ArrayBuffer(i),u=256,p=256,c=r.map(m=>new OffscreenCanvas(u,p)),d=new OffscreenCanvas(u,p);this.endComputePassEncoder(),c.map((m,f)=>{let g=m.getContext("webgpu");return g.configure({device:this.device,format:"bgra8unorm",usage:GPUTextureUsage.COPY_DST,alphaMode:r[f]}),g.getCurrentTexture()}).map((m,f)=>{let g=u*4,y=(T,N,M)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:s,bytesPerRow:g,offset:M},{texture:m},{width:T,height:N}),this.submitQueue();let P=d.getContext("2d",{willReadFrequently:!0});P.clearRect(0,0,T,N),P.drawImage(c[f],0,0);let E=P.getImageData(0,0,T,N).data,C=r[f],_=new Uint8ClampedArray(l,M,T*N*4);for(let O=0;O<_.length;O+=4)if(C==="premultiplied")_[O+3]=E[O+3];else{let B=E[O];_[O]=E[O+2],_[O+1]=E[O+1],_[O+2]=B}},x=Math.floor(o/(u*p)),A=u,b=p,w=0;for(let T=0;T0&&(y(A,b,w),w+=b*(u*4)),A=I%u,A>0&&y(A,1,w)});let h=v.convertBackendValuesAndArrayBuffer(l,t.dtype);return this.convertAndCacheOnCPU(e,h),h}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:a}=t;if(a!=null)return a;let n;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=r[0],i=r[1];n=S.mergeRealAndImagArrays(s,i)}else{let r=await this.getBufferData(t.resource);n=v.convertBackendValuesAndArrayBuffer(r,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e){let t=e.size,a=e.usage,n=this.bufferManager.acquireBuffer(t,a);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,a){let n=e.buffer;if(a==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let r={id:this.nextDataId()};this.tensorMap.set(r,{dtype:a,shape:t,values:null,refCount:1,external:e.zeroCopy});let s=this.tensorMap.get(r),i=H1(s.dtype)*v.sizeFromShape(s.shape);if(e.buffer.sizev.decodeString(n));return $e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return $e(e.shape,e.dtype,t)}async time(e){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled."),this.hasTimestampQueryWarned=!0);let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,a){return t==="string"&&a!=null&&a.length>0&&v.isString(a[0])&&(a=a.map(n=>v.encodeString(n))),{dataId:this.write(a,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId).resource;return t instanceof GPUBuffer?{buffer:t}:t instanceof GPUTexture?t.createView():t}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resource!=null)return;let a=H1(t.dtype)*v.sizeFromShape(t.shape),n,r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(t.values){if(n=this.bufferManager.acquireBuffer(a,r,!0),n.mapState==="unmapped"){let s=this.bufferManager.acquireBuffer(a,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),i=s.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(i).set(t.values):new Float32Array(i).set(t.values),s.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(s,0,n,0,a),this.stagingPendingDisposal.push(s)}else{let s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap()}t.values=null}else n=this.bufferManager.acquireBuffer(a,r);t.resource=n}makeUniforms(e){let t=0,a=0,n=[],r=1;e.forEach(o=>{o.data.length===0&&(o.data=[1]);let l;switch(o.data.length){case 1:l=4;break;case 2:l=8;break;case 3:l=16;break;case 4:l=16;break;case 5:l=16;break;case 6:l=16;break;default:v.assert(!1,()=>`Unsupported ${o.data.length}D shape`)}(a===5||a===6)&&(l=16),l>r&&(r=l),t=Math.ceil(t/l)*l,a=o.data.length,n.push(t),t+=o.data.length*4}),t=Math.ceil(t/r)*r;let s=new ArrayBuffer(t);e.forEach((o,l)=>{let u=n[l];o.type==="int32"?new Int32Array(s,u,o.data.length).set(o.data):o.type==="uint32"?new Uint32Array(s,u,o.data.length).set(o.data):new Float32Array(s,u,o.data.length).set(o.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(i,0,s,0,t),this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,a,n,r){if(r||(r=this.makeTensorInfo(e.outputShape,a)),v.sizeFromShape(r.shape)===0)return this.tensorMap.get(r.dataId).values=v.getTypedArrayFromDType(r.dtype,0),r;this.uploadToGPU(r.dataId),e.dispatch=ple(this.device,e);let s=t.map((o,l)=>{if(o.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(o.dataId),{dtype:this.tensorMap.get(o.dataId).dtype,shape:o.shape,name:e.variableNames[l]}});e.shaderKey=ele(e,s,r);let i=W().getBool("WEBGPU_ENGINE_COMPILE_ONLY");return e.shaderKey in this.pipelineCache||(this.pipelineCache[e.shaderKey]=Zoe(this.device,e,s,r,i)),e.pipeline=this.pipelineCache[e.shaderKey],i||this.recordAndSubmit(e,r,t,n),r}recordAndSubmit(e,t,a,n){if(e.pipeline instanceof Promise)throw new Error("Please call checkCompileCompletionAsync to ensure parallel compilation is done!");let r=[],s=[],i="int32";if(e.pixelsOpType==null){r.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),s=a.concat(t).map(d=>d.shape);let c="int32";s.map(d=>{r.push({type:c,data:d});let h=v.computeStrides(d);r.push({type:c,data:h})})}else{let c=v.computeStrides(t.shape);r.push({type:i,data:c})}if(e.size){let c=v.sizeFromShape(e.outputShape);r.push({type:i,data:[e.outputComponent?c/e.outputComponent:c]})}n&&(r=[...r,...n]);let o=[this.tensorToBinding(t),...a.map(c=>this.tensorToBinding(c)),this.makeUniforms(r)];a.forEach(c=>{this.commandQueueOwnedIds.add(c.dataId)}),this.commandQueueOwnedIds.add(t.dataId);let l=this.device.createBindGroup({layout:e.pipeline.getBindGroupLayout(0),entries:o.map((c,d)=>({binding:d,resource:c}))}),u=this.activeTimers!=null;this.ensureCommandEncoderReady();let p={};u&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.querySetCount})),p.timestampWrites=[{querySet:this.querySet,queryIndex:0,location:"beginning"},{querySet:this.querySet,queryIndex:1,location:"end"}],this.computePassEncoder=this.commandEncoder.beginComputePass(p)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(p)),this.computePassEncoder.setPipeline(e.pipeline),this.computePassEncoder.setBindGroup(0,l),this.computePassEncoder.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),this.dispatchCountInPass++,(u||W().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchCountInPass||e.pixelsOpType===eu.DRAW)&&(this.endComputePassEncoder(),u?this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let e=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.querySetCount*8),this.submitQueue(),await e.mapAsync(GPUMapMode.READ);let t=new BigUint64Array(e.getMappedRange()),a=Number(t[1]-t[0])/1e6;return e.unmap(),this.bufferManager.releaseBuffer(e),a}shouldExecuteOnCPU(e,t=dle){return W().getBool("WEBGPU_CPU_FORWARD")&&e.every(a=>this.tensorMap.get(a.dataId).resource==null&&v.sizeFromShape(a.shape){let e={powerPreference:W().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),a={},n=[];t.features.has("timestamp-query")&&n.push("timestamp-query"),t.features.has("bgra8unorm-storage")&&n.push(["bgra8unorm-storage"]),a.requiredFeatures=n;let r=t.limits;a.requiredLimits={maxComputeWorkgroupStorageSize:r.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:r.maxStorageBufferBindingSize,maxBufferSize:r.maxBufferSize,maxComputeWorkgroupSizeX:r.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:r.maxComputeInvocationsPerWorkgroup};let s=await t.requestDevice(a),i=await t.requestAdapterInfo();return new u0(s,i)},3);var Pe;(function(e){e[e.ADD=0]="ADD",e[e.ATAN2=1]="ATAN2",e[e.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",e[e.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",e[e.DIV=4]="DIV",e[e.ELU_DER=5]="ELU_DER",e[e.EQUAL=6]="EQUAL",e[e.FLOOR_DIV=7]="FLOOR_DIV",e[e.GREATER=8]="GREATER",e[e.GREATER_EQUAL=9]="GREATER_EQUAL",e[e.LESS=10]="LESS",e[e.LESS_EQUAL=11]="LESS_EQUAL",e[e.LOGICAL_AND=12]="LOGICAL_AND",e[e.LOGICAL_OR=13]="LOGICAL_OR",e[e.MAX=14]="MAX",e[e.MIN=15]="MIN",e[e.MOD=16]="MOD",e[e.MUL=17]="MUL",e[e.NOT_EQUAL=18]="NOT_EQUAL",e[e.POW=19]="POW",e[e.PRELU=20]="PRELU",e[e.SQUARED_DIFFERENCE=21]="SQUARED_DIFFERENCE",e[e.SUB=22]="SUB"})(Pe||(Pe={}));var cle="let resultTemp = a + b;",hle="let resultTemp = atan2(a, b);",mle="let resultTemp = areal * breal - aimag * bimag;",fle="let resultTemp = areal * bimag + aimag * breal;",gle="let resultTemp = a / b;",yle="let resultTemp = select(a * (b + 1.0), a, b >= b - b);",xle=` + `}return s}function vle(e){let t=/(\w+)\s*:\s*vec(5|6)/g;e=e.replace(t,n=>"@align(16) "+n);let a=/vec(5|6)\s*,\s*(\w+)/g;return e=e.replace(a,(n,r,s)=>`vec${r}, @align(16) ${s}`),e}function tA(e){return!(e.dispatchLayout.hasOwnProperty("y")&&e.dispatchLayout.y.length!==0||e.dispatchLayout.hasOwnProperty("z")&&e.dispatchLayout.z.length!==0)}var kk={};Ze(kk,{GPUBytesPerElement:()=>j1,MatMulProgramType:()=>Dn,assertNotComplex:()=>j3,computeDispatch:()=>de,computeWorkPerThreadForConv2d:()=>G3,computeWorkgroupInfoForMatMul:()=>Ik,computeWorkgroupSizeForConv2d:()=>U3,flatDispatchLayout:()=>me,isWebGPUSupported:()=>H3,tilesFitEvenlyIntoShape:()=>wle});var qs=e=>{let t=1;for(let a=0;aa%e[n]===0)}function de(e,t,a=[1,1,1],n=[1,1,1]){let[r,s,i]=[Math.ceil(qs(e.x.map(o=>t[o]))/(a[0]*n[0])),e.y?Math.ceil(qs(e.y.map(o=>t[o]))/(a[1]*n[1])):1,e.z?Math.ceil(qs(e.z.map(o=>t[o]))/(a[2]*n[2])):1];return[r,s,i]}function Ik(e,t,a,n=!1){let r=[8,8,1],s=[4,4,1];return n||(e<=8&&(s[1]=1),t<=16&&a<=16&&(r[0]=4)),{workgroupSize:r,elementsPerThread:s}}function U3(e,t,a=!1){if(a)return[8,8,1];let n=qs(e.x.map(s=>t[s])),r=qs(e.y.map(s=>t[s]));return n<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function G3(e,t,a=!1){if(a)return[4,4,1];let n=qs(e.x.map(s=>t[s])),r=qs(e.y.map(s=>t[s]));return n<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function me(e){return{x:e.map((t,a)=>a)}}function j1(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function H3(){return!!(globalThis&&globalThis.navigator&&globalThis.navigator.gpu)}function j3(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var Dn;(function(e){e[e.MatMulReduceProgram=0]="MatMulReduceProgram",e[e.MatMulSplitKProgram=1]="MatMulSplitKProgram",e[e.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",e[e.MatMulPackedProgram=3]="MatMulPackedProgram",e[e.MatMulMax=4]="MatMulMax"})(Dn||(Dn={}));var kle=B().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Ile=(e,t)=>{let a=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=a))return r;v.assert(r[0]>a&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(r[0]));return s>a?(s=Math.ceil(Math.cbrt(r[0])),v.assert(s<=a,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},q3=class Sk extends au{nextDataId(){return Sk.nextDataId++}constructor(t,a){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchCountInPass=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.queryResolveBuffer=null,this.querySet=null,this.querySetCount=2,this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,this.hasReadSyncWarned=!1,this.hasTimestampQueryWarned=!1,!H3())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=t,this.queue=t.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new ile(a),this.supportTimestampQuery=this.device.features.has("timestamp-query"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new ole(this.device),this.textureManager=new lle(this.device),this.tensorMap=new rp(this,It()),B().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:t,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(t,a=!1){if(!this.tensorMap.has(t))return!0;let n=this.tensorMap.get(t);return a?n.refCount=0:n.refCount--,n.refCount>0?!1:(n.complexTensorInfos!=null&&(this.disposeData(n.complexTensorInfos.real.dataId),this.disposeData(n.complexTensorInfos.imag.dataId)),this.commandQueueOwnedIds.has(t)?(this.tensorDataPendingDisposal.push(t),!0):(this.releaseResource(t),this.tensorMap.delete(t),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(t){let a=this.tensorMap.get(t);if(!(!a||!a.resource)){if(a.external){a.resource=null;return}a.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(a.resource):a.resource instanceof GPUTexture&&this.textureManager.releaseTexture(a.resource),a.resource=null}}refCount(t){return this.tensorMap.has(t)?this.tensorMap.get(t).refCount:0}incRef(t){let a=this.tensorMap.get(t);a.refCount++}decRef(t){if(this.tensorMap.has(t)){let a=this.tensorMap.get(t);a.refCount--}}write(t,a,n){if(n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.tensorMap.set(r,{dtype:n,shape:a,values:t,refCount:1}),r}move(t,a,n,r,s){if(r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(t,{dtype:r,shape:n,values:a,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(t=>{this.releaseResource(t),this.tensorMap.delete(t)}),this.uniformPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t)),this.stagingPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let t;try{t=await Promise.all(Object.values(this.pipelineCache))}catch(a){throw new Error(a.message)}Object.keys(this.pipelineCache).map((a,n)=>{this.pipelineCache[a]=t[n]})}async getBufferData(t){if(B().getBool("WEBGPU_ENGINE_COMPILE_ONLY"))return console.warn("The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false"),null;let a=t.size,n=this.bufferManager.acquireBuffer(a,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,n,0,a),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let r=n.getMappedRange().slice(0);return n.unmap(),n!=null&&this.bufferManager.releaseBuffer(n),B().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),r}convertAndCacheOnCPU(t,a){let n=this.tensorMap.get(t);return n.values=a,n.values}readSync(t){let a=this.tensorMap.get(t),{values:n,complexTensorInfos:r}=a;if(n!=null||a.dtype==="string")return n;if(a.dtype==="complex64"){let f=this.readSync(r.real.dataId),g=this.readSync(r.imag.dataId),y=v.convertBackendValuesAndArrayBuffer(C.mergeRealAndImagArrays(f,g).buffer,"float32");return this.convertAndCacheOnCPU(t,y),y}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn("The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead."));let s=["opaque","premultiplied"],i=a.resource,o=i.size;v.assert(o%4===0,()=>"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.");let l=o/4,u=new ArrayBuffer(o),p=256,c=256,d=s.map(f=>new OffscreenCanvas(p,c)),h=new OffscreenCanvas(p,c);this.endComputePassEncoder(),d.map((f,g)=>{let y=f.getContext("webgpu");return y.configure({device:this.device,format:"bgra8unorm",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),y.getCurrentTexture()}).map((f,g)=>{let y=p*4,x=(N,M,P)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:i,bytesPerRow:y,offset:P},{texture:f},{width:N,height:M}),this.submitQueue();let E=h.getContext("2d",{willReadFrequently:!0});E.clearRect(0,0,N,M),E.drawImage(d[g],0,0);let S=E.getImageData(0,0,N,M).data,_=s[g],O=new Uint8ClampedArray(u,P,N*M*4);for(let W=0;W0&&(x(b,w,I),I+=w*(p*4)),b=T%p,b>0&&x(b,1,I)});let m=v.convertBackendValuesAndArrayBuffer(u,a.dtype);return this.convertAndCacheOnCPU(t,m),m}async read(t){if(!this.tensorMap.has(t))throw new Error(`Tensor ${t} was not registered!`);let a=this.tensorMap.get(t),{values:n}=a;if(n!=null)return n;let r;if(a.dtype==="complex64"){let s=await Promise.all([this.read(a.complexTensorInfos.real.dataId),this.read(a.complexTensorInfos.imag.dataId)]),i=s[0],o=s[1];r=C.mergeRealAndImagArrays(i,o)}else{let s=await this.getBufferData(a.resource);r=v.convertBackendValuesAndArrayBuffer(s,a.dtype)}return this.convertAndCacheOnCPU(t,r),r}copyBuffer(t){let a=t.size,n=t.usage,r=this.bufferManager.acquireBuffer(a,n);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,r,0,a),this.submitQueue(),r}createTensorFromGPUData(t,a,n){let r=t.buffer;if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:n,shape:a,values:null,refCount:1,external:t.zeroCopy});let i=this.tensorMap.get(s),o=j1(i.dtype)*v.sizeFromShape(i.shape);if(t.buffer.sizev.decodeString(r));return $e(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return $e(t.shape,t.dtype,a)}async time(t){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --enable-dawn-features=allow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled."),this.hasTimestampQueryWarned=!0);let a=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=v.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=v.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=a,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},l=await Promise.all(s);return o.kernelMs=v.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}makeTensorInfo(t,a,n){return a==="string"&&n!=null&&n.length>0&&v.isString(n[0])&&(n=n.map(r=>v.encodeString(r))),{dataId:this.write(n,t,a),shape:t,dtype:a}}tensorToBinding(t){if(!t)return null;let a=this.tensorMap.get(t.dataId).resource;return a instanceof GPUBuffer?{buffer:a}:a instanceof GPUTexture?a.createView():a}uploadToGPU(t){let a=this.tensorMap.get(t);if(a.resource!=null)return;let n=j1(a.dtype)*v.sizeFromShape(a.shape),r,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(a.values){if(r=this.bufferManager.acquireBuffer(n,s,!0),r.mapState==="unmapped"){let i=this.bufferManager.acquireBuffer(n,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),o=i.getMappedRange();a.dtype==="int32"||a.dtype==="bool"?new Int32Array(o).set(a.values):new Float32Array(o).set(a.values),i.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(i,0,r,0,n),this.stagingPendingDisposal.push(i)}else{let i=r.getMappedRange();a.dtype==="int32"||a.dtype==="bool"?new Int32Array(i).set(a.values):new Float32Array(i).set(a.values),r.unmap()}a.values=null}else r=this.bufferManager.acquireBuffer(n,s);a.resource=r}makeUniforms(t){let a=0,n=0,r=[],s=1;t.forEach(l=>{l.data.length===0&&(l.data=[1]);let u;switch(l.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:v.assert(!1,()=>`Unsupported ${l.data.length}D shape`)}(n===5||n===6)&&(u=16),u>s&&(s=u),a=Math.ceil(a/u)*u,n=l.data.length,r.push(a),a+=l.data.length*4}),a=Math.ceil(a/s)*s;let i=new ArrayBuffer(a);t.forEach((l,u)=>{let p=r[u];l.type==="int32"?new Int32Array(i,p,l.data.length).set(l.data):l.type==="uint32"?new Uint32Array(i,p,l.data.length).set(l.data):new Float32Array(i,p,l.data.length).set(l.data)});let o=this.bufferManager.acquireBuffer(a,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(o,0,i,0,a),this.uniformPendingDisposal.push(o),{offset:0,size:a,buffer:o}}runWebGPUProgram(t,a,n,r,s){if(s||(s=this.makeTensorInfo(t.outputShape,n)),v.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=v.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),t.dispatch=Ile(this.device,t);let i=a.map((l,u)=>{if(l.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(l.dataId),{dtype:this.tensorMap.get(l.dataId).dtype,shape:l.shape,name:t.variableNames[u]}});t.shaderKey=hle(t,i,s);let o=B().getBool("WEBGPU_ENGINE_COMPILE_ONLY");return t.shaderKey in this.pipelineCache||(this.pipelineCache[t.shaderKey]=dle(this.device,t,i,s,o)),t.pipeline=this.pipelineCache[t.shaderKey],o||this.recordAndSubmit(t,s,a,r),s}recordAndSubmit(t,a,n,r){if(t.pipeline instanceof Promise)throw new Error("Please call checkCompileCompletionAsync to ensure parallel compilation is done!");let s=[],i=[],o="int32";if(t.pixelsOpType==null){s.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),i=n.concat(a).map(h=>h.shape);let d="int32";i.map(h=>{s.push({type:d,data:h});let m=v.computeStrides(h);s.push({type:d,data:m})})}else{let d=v.computeStrides(a.shape);s.push({type:o,data:d})}if(t.size){let d=v.sizeFromShape(t.outputShape);s.push({type:o,data:[t.outputComponent?d/t.outputComponent:d]})}r&&(s=[...s,...r]);let l=[this.tensorToBinding(a),...n.map(d=>this.tensorToBinding(d)),this.makeUniforms(s)];n.forEach(d=>{this.commandQueueOwnedIds.add(d.dataId)}),this.commandQueueOwnedIds.add(a.dataId);let u=this.device.createBindGroup({layout:t.pipeline.getBindGroupLayout(0),entries:l.map((d,h)=>({binding:h,resource:d}))}),p=this.activeTimers!=null;this.ensureCommandEncoderReady();let c={};p&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.querySetCount})),c.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:0,endOfPassWriteIndex:1},this.computePassEncoder=this.commandEncoder.beginComputePass(c)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(c)),this.computePassEncoder.setPipeline(t.pipeline),this.computePassEncoder.setBindGroup(0,u),this.computePassEncoder.dispatchWorkgroups(t.dispatch[0],t.dispatch[1],t.dispatch[2]),this.dispatchCountInPass++,(p||B().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchCountInPass||t.pixelsOpType===eu.DRAW)&&(this.endComputePassEncoder(),p?this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let t=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.querySetCount*8),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let a=new BigUint64Array(t.getMappedRange()),n=Number(a[1]-a[0])/1e6;return t.unmap(),this.bufferManager.releaseBuffer(t),n}shouldExecuteOnCPU(t,a=kle){return B().getBool("WEBGPU_CPU_FORWARD")&&t.every(n=>this.tensorMap.get(n.dataId).resource==null&&v.sizeFromShape(n.shape){let e={powerPreference:B().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),a={},n=[];t.features.has("timestamp-query")&&n.push("timestamp-query"),t.features.has("bgra8unorm-storage")&&n.push(["bgra8unorm-storage"]),a.requiredFeatures=n;let r=t.limits;a.requiredLimits={maxComputeWorkgroupStorageSize:r.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:r.maxStorageBufferBindingSize,maxBufferSize:r.maxBufferSize,maxComputeWorkgroupSizeX:r.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:r.maxComputeInvocationsPerWorkgroup};let s=await t.requestDevice(a),i=await t.requestAdapterInfo();return new q3(s,i)},3);var _e;(function(e){e[e.ADD=0]="ADD",e[e.ATAN2=1]="ATAN2",e[e.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",e[e.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",e[e.DIV=4]="DIV",e[e.ELU_DER=5]="ELU_DER",e[e.EQUAL=6]="EQUAL",e[e.FLOOR_DIV=7]="FLOOR_DIV",e[e.GREATER=8]="GREATER",e[e.GREATER_EQUAL=9]="GREATER_EQUAL",e[e.LESS=10]="LESS",e[e.LESS_EQUAL=11]="LESS_EQUAL",e[e.LOGICAL_AND=12]="LOGICAL_AND",e[e.LOGICAL_OR=13]="LOGICAL_OR",e[e.MAX=14]="MAX",e[e.MIN=15]="MIN",e[e.MOD=16]="MOD",e[e.MUL=17]="MUL",e[e.NOT_EQUAL=18]="NOT_EQUAL",e[e.POW=19]="POW",e[e.PRELU=20]="PRELU",e[e.SQUARED_DIFFERENCE=21]="SQUARED_DIFFERENCE",e[e.SUB=22]="SUB"})(_e||(_e={}));var Sle="let resultTemp = a + b;",Cle="let resultTemp = atan2(a, b);",Tle="let resultTemp = areal * breal - aimag * bimag;",Nle="let resultTemp = areal * bimag + aimag * breal;",Rle="let resultTemp = a / b;",Ele="let resultTemp = select(a * (b + 1.0), a, b >= b - b);",Mle=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a == b); -`,Ale=` +`,Ple=` let remainder = select(a % b, round(a % b), (round(a) == a) & (round(b) == b)); let quotient = (a - remainder) / b; let resultTemp = round(select(quotient, quotient - 1, sign(remainder) == -sign(b))); -`,ble=` +`,_le=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a > b); -`,vle=` +`,$le=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a >= b); -`,wle=` +`,Fle=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a < b); -`,kle=` +`,Dle=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a <= b); -`,Ile="return f32(a >= 1.0 && b >= 1.0);",Sle=`return (vec4(a >= vec4(1.0)) * - vec4(b >= vec4(1.0)));`,Cle="return f32(a >= 1.0 || b >= 1.0);",Tle=`return min(vec4(a >= vec4(1.0)) + - vec4(b >= vec4(1.0)), vec4(1.0));`,Nle="let resultTemp = max(a, b);",Rle="let resultTemp = min(a, b);",Ele=` +`,Ole="return f32(a >= 1.0 && b >= 1.0);",zle=`return (vec4(a >= vec4(1.0)) * + vec4(b >= vec4(1.0)));`,Lle="return f32(a >= 1.0 || b >= 1.0);",Wle=`return min(vec4(a >= vec4(1.0)) + + vec4(b >= vec4(1.0)), vec4(1.0));`,Ble="let resultTemp = max(a, b);",Vle="let resultTemp = min(a, b);",Ule=` let isNaN = b == 0.; var resultTemp = a % b; resultTemp = select((resultTemp + b) % b, resultTemp, (a < 0. && b < 0.) || (a >= 0. && b > 0.)); -`,Mle=` +`,Gle=` let isNaN = !vec4(b); var resultTemp = vec4(a % b); if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) { @@ -5269,20 +5269,20 @@ return a / b;`,lJ=` if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) { resultTemp[3] = (resultTemp[3] + b[3]) % b[3]; } -`,_le="let resultTemp = a * b;",Ple=` +`,Hle="let resultTemp = a * b;",jle=` var resultTemp = f32(a != b); let valueForNaN = 1.0; -`,$le=` +`,qle=` var resultTemp = vec4(a != b); let valueForNaN = 1.0; -`,Fle=` +`,Xle=` let isNaN = a < 0.0 && floor(b) < b; if (b == 0.0) { return 1.0; } var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b), round(abs(b) % 2.0) != 1.0); -`,Dle=` +`,Kle=` let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1); let isModRound1 = vec4(isModRound1Bool); let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); @@ -5303,10 +5303,10 @@ return a / b;`,lJ=` resultTemp.a = 1.0; } let isNaN = (a < vec4(0.0)) & (floor(b) < b); -`,Ole="if (a < 0.0) { return b * a; } return a;",zle=` +`,Yle="if (a < 0.0) { return b * a; } return a;",Zle=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`,Lle="let resultTemp = (a - b) * (a - b);",Wle="let resultTemp = a - b;";function G3(e,t){let a;do{switch(e){case Pe.ATAN2:a=hle;break;case Pe.MAX:a=Nle;break;case Pe.MIN:a=Rle;break;case Pe.MOD:a=t?Mle:Ele;break;case Pe.NOT_EQUAL:a=t?$le:Ple;break;case Pe.POW:a=t?Dle:Fle;break;default:continue}let n,r,s;return t?(n="isnanVec4",r="vec4",s="vec4"):(n="isnan",r="f32",s="bool"),` +`,Jle="let resultTemp = (a - b) * (a - b);",Qle="let resultTemp = a - b;";function X3(e,t){let a;do{switch(e){case _e.ATAN2:a=Cle;break;case _e.MAX:a=Ble;break;case _e.MIN:a=Vle;break;case _e.MOD:a=t?Gle:Ule;break;case _e.NOT_EQUAL:a=t?qle:jle;break;case _e.POW:a=t?Kle:Xle;break;default:continue}let n,r,s;return t?(n="isnanVec4",r="vec4",s="vec4"):(n="isnan",r="f32",s="bool"),` let aIsNaN = ${n}(a); let aPostLegalization = select(a, ${r}(42), aIsNaN); let bIsNaN = ${n}(b); @@ -5321,30 +5321,30 @@ return a / b;`,lJ=` resultTemp, ${r}(valueForNaN), ${s}(isNaN) | aIsNaN | bIsNaN); } - `}while(!1);switch(e){case Pe.ADD:a=cle;break;case Pe.COMPLEX_MULTIPLY_IMAG:a=fle;break;case Pe.COMPLEX_MULTIPLY_REAL:a=mle;break;case Pe.DIV:a=gle;break;case Pe.ELU_DER:a=yle;break;case Pe.EQUAL:a=xle;break;case Pe.FLOOR_DIV:a=Ale;break;case Pe.GREATER:a=ble;break;case Pe.GREATER_EQUAL:a=vle;break;case Pe.LESS:a=wle;break;case Pe.LESS_EQUAL:a=kle;break;case Pe.LOGICAL_AND:return t?Sle:Ile;case Pe.LOGICAL_OR:return t?Tle:Cle;case Pe.MUL:a=_le;break;case Pe.PRELU:return t?zle:Ole;case Pe.SQUARED_DIFFERENCE:a=Lle;break;case Pe.SUB:a=Wle;break;default:}return` + `}while(!1);switch(e){case _e.ADD:a=Sle;break;case _e.COMPLEX_MULTIPLY_IMAG:a=Nle;break;case _e.COMPLEX_MULTIPLY_REAL:a=Tle;break;case _e.DIV:a=Rle;break;case _e.ELU_DER:a=Ele;break;case _e.EQUAL:a=Mle;break;case _e.FLOOR_DIV:a=Ple;break;case _e.GREATER:a=_le;break;case _e.GREATER_EQUAL:a=$le;break;case _e.LESS:a=Fle;break;case _e.LESS_EQUAL:a=Dle;break;case _e.LOGICAL_AND:return t?zle:Ole;case _e.LOGICAL_OR:return t?Wle:Lle;case _e.MUL:a=Hle;break;case _e.PRELU:return t?Zle:Yle;case _e.SQUARED_DIFFERENCE:a=Jle;break;case _e.SUB:a=Qle;break;default:}return` ${a} return resultTemp; - `}var le;(function(e){e[e.ABS=0]="ABS",e[e.ACOS=1]="ACOS",e[e.ACOSH=2]="ACOSH",e[e.ASIN=3]="ASIN",e[e.ASINH=4]="ASINH",e[e.ATAN=5]="ATAN",e[e.ATANH=6]="ATANH",e[e.CEIL=7]="CEIL",e[e.COS=8]="COS",e[e.COSH=9]="COSH",e[e.ELU=10]="ELU",e[e.ERF=11]="ERF",e[e.EXP=12]="EXP",e[e.EXPM1=13]="EXPM1",e[e.FLOOR=14]="FLOOR",e[e.IS_FINITE=15]="IS_FINITE",e[e.IS_INF=16]="IS_INF",e[e.IS_NAN=17]="IS_NAN",e[e.LINEAR=18]="LINEAR",e[e.LOG=19]="LOG",e[e.LOG1P=20]="LOG1P",e[e.LOGICAL_NOT=21]="LOGICAL_NOT",e[e.NEG=22]="NEG",e[e.RELU=23]="RELU",e[e.RELU6=24]="RELU6",e[e.LEAKYRELU=25]="LEAKYRELU",e[e.RECIPROCAL=26]="RECIPROCAL",e[e.ROUND=27]="ROUND",e[e.RSQRT=28]="RSQRT",e[e.SELU=29]="SELU",e[e.SIGMOID=30]="SIGMOID",e[e.SIGN=31]="SIGN",e[e.SIN=32]="SIN",e[e.SINH=33]="SINH",e[e.SOFTPLUS=34]="SOFTPLUS",e[e.SQRT=35]="SQRT",e[e.SQUARE=36]="SQUARE",e[e.STEP=37]="STEP",e[e.TAN=38]="TAN",e[e.TANH=39]="TANH",e[e.TO_INT=40]="TO_INT"})(le||(le={}));var Ble="return abs(a);",Vle=` + `}var le;(function(e){e[e.ABS=0]="ABS",e[e.ACOS=1]="ACOS",e[e.ACOSH=2]="ACOSH",e[e.ASIN=3]="ASIN",e[e.ASINH=4]="ASINH",e[e.ATAN=5]="ATAN",e[e.ATANH=6]="ATANH",e[e.CEIL=7]="CEIL",e[e.COS=8]="COS",e[e.COSH=9]="COSH",e[e.ELU=10]="ELU",e[e.ERF=11]="ERF",e[e.EXP=12]="EXP",e[e.EXPM1=13]="EXPM1",e[e.FLOOR=14]="FLOOR",e[e.IS_FINITE=15]="IS_FINITE",e[e.IS_INF=16]="IS_INF",e[e.IS_NAN=17]="IS_NAN",e[e.LINEAR=18]="LINEAR",e[e.LOG=19]="LOG",e[e.LOG1P=20]="LOG1P",e[e.LOGICAL_NOT=21]="LOGICAL_NOT",e[e.NEG=22]="NEG",e[e.RELU=23]="RELU",e[e.RELU6=24]="RELU6",e[e.LEAKYRELU=25]="LEAKYRELU",e[e.RECIPROCAL=26]="RECIPROCAL",e[e.ROUND=27]="ROUND",e[e.RSQRT=28]="RSQRT",e[e.SELU=29]="SELU",e[e.SIGMOID=30]="SIGMOID",e[e.SIGN=31]="SIGN",e[e.SIN=32]="SIN",e[e.SINH=33]="SINH",e[e.SOFTPLUS=34]="SOFTPLUS",e[e.SQRT=35]="SQRT",e[e.SQUARE=36]="SQUARE",e[e.STEP=37]="STEP",e[e.TAN=38]="TAN",e[e.TANH=39]="TANH",e[e.TO_INT=40]="TO_INT"})(le||(le={}));var eue="return abs(a);",tue=` if (abs(a) > 1.) { return uniforms.NAN; } return acos(a); -`,Ule=` +`,aue=` if (a < 1.) { return uniforms.NAN; } return acosh(a); -`,Gle=` +`,nue=` if (abs(a) > 1.) { return uniforms.NAN; } return asin(a); -`,Hle="return asinh(a);",jle=` +`,rue="return asinh(a);",sue=` if (isnan(a)) { return uniforms.NAN; } return atan(a); -`,qle=` +`,iue=` if (abs(a) > 1.) { return uniforms.NAN; } @@ -5355,10 +5355,10 @@ return a / b;`,lJ=` return -uniforms.INFINITY; } return atanh(a); -`,Xle="return ceil(a);",Kle="return cos(a);",Yle=` +`,oue="return ceil(a);",lue="return cos(a);",uue=` let e2x = exp(-a); return (e2x + 1.0 / e2x) / 2.0; -`,Zle="return exp(a) - 1.0;",Jle="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Qle=` +`,due="return exp(a) - 1.0;",pue="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",cue=` var resFloat = exp(a) - vec4(1.0); if (a.r >= 0.0) { resFloat.r = a.r; @@ -5373,40 +5373,40 @@ return a / b;`,lJ=` resFloat.a = a.a; } return resFloat; -`,eue=` +`,hue=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. - let p = ${S.ERF_P}; - let a1 = ${S.ERF_A1}; - let a2 = ${S.ERF_A2}; - let a3 = ${S.ERF_A3}; - let a4 = ${S.ERF_A4}; - let a5 = ${S.ERF_A5}; + let p = ${C.ERF_P}; + let a1 = ${C.ERF_A1}; + let a2 = ${C.ERF_A2}; + let a3 = ${C.ERF_A3}; + let a4 = ${C.ERF_A4}; + let a5 = ${C.ERF_A5}; let sign = sign(a); let absA = abs(a); let t = 1.0 / (1.0 + p * absA); return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA)); -`,tue="return exp(a);",aue="return floor(a);",nue="return f32(!isnan(a) && !isinf(a));",rue="return f32(isinf(a));",sue="return f32(isnan(a));",iue="return a;",oue=`if (a < 0.0) { return uniforms.NAN; } - return log(a);`,lue=` +`,mue="return exp(a);",fue="return floor(a);",gue="return f32(!isnan(a) && !isinf(a));",yue="return f32(isinf(a));",xue="return f32(isnan(a));",Aue="return a;",bue=`if (a < 0.0) { return uniforms.NAN; } + return log(a);`,vue=` if (isnan(a)) { return a; } return log(1.0 + a); -`,uue="return f32(!(a >= 1.0));",due="return -a;",pue="if (a < 0.0) { return uniforms.alpha * a; } return a;",cue=` +`,wue="return f32(!(a >= 1.0));",kue="return -a;",Iue="if (a < 0.0) { return uniforms.alpha * a; } return a;",Sue=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a); -`,hue="return 1.0 / a;",mue="return select(a, 0.0, a < 0.0);",fue="return clamp(a, 0.0, 6.0);",gue="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",yue=` +`,Cue="return 1.0 / a;",Tue="return select(a, 0.0, a < 0.0);",Nue="return clamp(a, 0.0, 6.0);",Rue="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",Eue=` return select(a, vec4(0.0), a < vec4(0.0)); -`,xue="return round(a);",Aue="return inverseSqrt(a);",bue=` +`,Mue="return round(a);",Pue="return inverseSqrt(a);",_ue=` if (a >= 0.0) { - return ${S.SELU_SCALE} * a; + return ${C.SELU_SCALE} * a; } else { - return ${S.SELU_SCALEALPHA} * (exp(a) - 1.0); + return ${C.SELU_SCALEALPHA} * (exp(a) - 1.0); } -`,vue="return 1.0 / (1.0 + exp(-1.0 * a));",wue="return sign(a);",kue="return sin(a);",Iue=` +`,$ue="return 1.0 / (1.0 + exp(-1.0 * a));",Fue="return sign(a);",Due="return sin(a);",Oue=` let e2x = exp(a); return (e2x - 1.0 / e2x) / 2.0; -`,Sue=` +`,zue=` let epsilon = 1.1920928955078125e-7; let threshold = log(epsilon) + 2.0; @@ -5421,31 +5421,31 @@ return a / b;`,lJ=` } else { return log(exp_a + 1.0); } -`,Cue="return sqrt(a);",Tue="return a * a;",Nue=` +`,Lue="return sqrt(a);",Wue="return a * a;",Bue=` if (isnan(a)) { return a; } return select(uniforms.stepAlpha, 1.0, a > 0.0); -`,Rue="return tan(a);",Eue=` +`,Vue="return tan(a);",Uue=` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); -`,Mue="return f32(i32((a)));";function Os(e,t){switch(e){case le.ABS:return Ble;case le.ACOS:return Vle;case le.ACOSH:return Ule;case le.ASIN:return Gle;case le.ASINH:return Hle;case le.ATAN:return jle;case le.ATANH:return qle;case le.COS:return Kle;case le.COSH:return Yle;case le.CEIL:return Xle;case le.ELU:return t?Qle:Jle;case le.ERF:return eue;case le.EXP:return tue;case le.EXPM1:return Zle;case le.FLOOR:return aue;case le.IS_FINITE:return nue;case le.IS_INF:return rue;case le.IS_NAN:return sue;case le.LINEAR:return iue;case le.LOG:return oue;case le.LOG1P:return lue;case le.LOGICAL_NOT:return uue;case le.NEG:return due;case le.LEAKYRELU:return t?cue:pue;case le.RECIPROCAL:return hue;case le.RELU:return t?yue:mue;case le.RELU6:return t?gue:fue;case le.ROUND:return xue;case le.RSQRT:return Aue;case le.SELU:return bue;case le.SIGMOID:return vue;case le.SIGN:return wue;case le.SIN:return kue;case le.SINH:return Iue;case le.SOFTPLUS:return Sue;case le.SQRT:return Cue;case le.SQUARE:return Tue;case le.STEP:return Nue;case le.TAN:return Rue;case le.TANH:return Eue;case le.TO_INT:return Mue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function _r(e,t=!1,a=!1,n=3){if(e===null)return"";let r="";if(e==="linear")r=Os(le.LINEAR);else if(e==="relu")r=Os(le.RELU,a);else if(e==="elu")r=Os(le.ELU,a);else if(e==="relu6")r=Os(le.RELU6,a);else if(e==="prelu")r=G3(Pe.PRELU,a);else if(e==="sigmoid")r=Os(le.SIGMOID,a);else if(e==="leakyrelu")r=Os(le.LEAKYRELU,a);else throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);let s=Ke(a?4:1),i="";return t?i=` +`,Gue="return f32(i32((a)));";function Ls(e,t){switch(e){case le.ABS:return eue;case le.ACOS:return tue;case le.ACOSH:return aue;case le.ASIN:return nue;case le.ASINH:return rue;case le.ATAN:return sue;case le.ATANH:return iue;case le.COS:return lue;case le.COSH:return uue;case le.CEIL:return oue;case le.ELU:return t?cue:pue;case le.ERF:return hue;case le.EXP:return mue;case le.EXPM1:return due;case le.FLOOR:return fue;case le.IS_FINITE:return gue;case le.IS_INF:return yue;case le.IS_NAN:return xue;case le.LINEAR:return Aue;case le.LOG:return bue;case le.LOG1P:return vue;case le.LOGICAL_NOT:return wue;case le.NEG:return kue;case le.LEAKYRELU:return t?Sue:Iue;case le.RECIPROCAL:return Cue;case le.RELU:return t?Eue:Tue;case le.RELU6:return t?Rue:Nue;case le.ROUND:return Mue;case le.RSQRT:return Pue;case le.SELU:return _ue;case le.SIGMOID:return $ue;case le.SIGN:return Fue;case le.SIN:return Due;case le.SINH:return Oue;case le.SOFTPLUS:return zue;case le.SQRT:return Lue;case le.SQUARE:return Wue;case le.STEP:return Bue;case le.TAN:return Vue;case le.TANH:return Uue;case le.TO_INT:return Gue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Pr(e,t=!1,a=!1,n=3){if(e===null)return"";let r="";if(e==="linear")r=Ls(le.LINEAR);else if(e==="relu")r=Ls(le.RELU,a);else if(e==="elu")r=Ls(le.ELU,a);else if(e==="relu6")r=Ls(le.RELU6,a);else if(e==="prelu")r=X3(_e.PRELU,a);else if(e==="sigmoid")r=Ls(le.SIGMOID,a);else if(e==="leakyrelu")r=Ls(le.LEAKYRELU,a);else throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);let s=Xe(a?4:1),i="";return t?i=` fn activation(a : ${s}, coords : vec${n}) -> ${s} { let b = getPreluActivationWeightsByOutputCoords(coords); ${r} }`:i=` fn activation(a : ${s}, coords : vec${n}) -> ${s} { ${r} - }`,i}function rl(e,t){return` + }`,i}function sl(e,t){return` ${e?"value = value + getBiasByOutputCoords(coords);":""} ${t?"value = activation(value, coords);":""} - `}function hk(e,t,a=!1,n=!1,r=!1,s=1){v.assert(e&&s===1||!e,()=>`transposeA ${e} is not compatible with component size ${s}`);let i=` + `}function Ck(e,t,a=!1,n=!1,r=!1,s=1){v.assert(e&&s===1||!e,()=>`transposeA ${e} is not compatible with component size ${s}`);let i=` ${e?"value = getA(batch, col, row);":"value = getA(batch, row, col);"} `,o=t?"value = getB(batch, col, row);":"value = getB(batch, row, col);";return` - fn mm_readA(batch: i32, row: i32, col: i32) -> ${Ke(s)} { - var value = ${Ke(s)}(0.0); + fn mm_readA(batch: i32, row: i32, col: i32) -> ${Xe(s)} { + var value = ${Xe(s)}(0.0); ${a&&r?i:` ${e?"if(row < uniforms.dimAOuter && col < uniforms.dimInner)":"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"} { @@ -5455,23 +5455,23 @@ return a / b;`,lJ=` return value; } - fn mm_readB(batch: i32, row: i32, col: i32) -> ${Ke(s)} { - var value = ${Ke(s)}(0.0); + fn mm_readB(batch: i32, row: i32, col: i32) -> ${Xe(s)} { + var value = ${Xe(s)}(0.0); ${o} return value; } - `}function H3(e,t,a,n,r=!1,s=!1,i=!1,o=1){return` - ${hk(a,n,r,s,i,o)} - fn mm_write(batch: i32, row: i32, col: i32, valueIn: ${Ke(o)}) { + `}function K3(e,t,a,n,r=!1,s=!1,i=!1,o=1){return` + ${Ck(a,n,r,s,i,o)} + fn mm_write(batch: i32, row: i32, col: i32, valueIn: ${Xe(o)}) { ${r&&s?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} { var value = valueIn; let coords = vec3(batch, row, col); - ${rl(e,t)} + ${sl(e,t)} setOutputAtCoords(coords[0], coords[1], coords[2], value); } } - `}var _ue=(e,t)=>e?` + `}var Hue=(e,t)=>e?` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol * ${t}); @@ -5479,7 +5479,7 @@ return a / b;`,lJ=` mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRow + innerRow, kStart + inputCol * ${t}); - `,Pue=(e,t,a,n)=>{if(e)return` + `,jue=(e,t,a,n)=>{if(e)return` for (var k = 0; k < ${n}; k++) { let BCached0 = mm_Bsub[k][tileCol]; let ACached0 = mm_Asub[k][localRow]; @@ -5493,7 +5493,7 @@ return a / b;`,lJ=` let ACached = mm_Asub[tileRow + i][k]; ${s} } - }`}};function d0(e,t,a=!1,n=32,r=!1,s=32,i=!1){let o=t[1]*e[1],l=t[0]*e[0],u=a?o:n,p=a?n:o,c=u/t[0],d=n/t[1],h=e[1],m=e[0];return v.assert((a&&c===4&&e[1]===4||!a&&(c===3||c===4))&&u%t[0]===0&&n%t[1]===0&&e[0]===4,()=>`If transposeA ${a} is true, innerElementSize ${c} and workPerThread[1] ${e[1]} must be 4. + }`}};function u0(e,t,a=!1,n=32,r=!1,s=32,i=!1){let o=t[1]*e[1],l=t[0]*e[0],u=a?o:n,p=a?n:o,c=u/t[0],d=n/t[1],h=e[1],m=e[0];return v.assert((a&&c===4&&e[1]===4||!a&&(c===3||c===4))&&u%t[0]===0&&n%t[1]===0&&e[0]===4,()=>`If transposeA ${a} is true, innerElementSize ${c} and workPerThread[1] ${e[1]} must be 4. Otherwise, innerElementSize ${c} must be 3 or 4. tileAWidth ${u} must be divisible by workgroupSize[0]${t[0]}. tileInner ${n} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`),` var mm_Asub : array, ${u/c}>, ${p}>; @@ -5523,7 +5523,7 @@ return a / b;`,lJ=` for (var innerRow = 0; innerRow < ${h}; innerRow++) { let inputRow = tileRow + innerRow; let inputCol = tileCol; - ${_ue(a,c)} + ${Hue(a,c)} } // Load one tile of B into local memory. @@ -5536,14 +5536,14 @@ return a / b;`,lJ=` workgroupBarrier(); // Compute acc values for a single thread. - ${Pue(a,c,h,n)} + ${jue(a,c,h,n)} workgroupBarrier(); } for (var innerRow = 0; innerRow < ${h}; innerRow++) { mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); } - }`}var Y5=e=>e?` + }`}var aA=e=>e?` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol); @@ -5551,7 +5551,7 @@ return a / b;`,lJ=` mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRowStart + inputRow, kStart + inputCol); - `,$ue=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function p0(e,t,a=!1,n=32,r=!1,s=32,i=!1,o=!1){let l=e[1]*t[1],u=e[0]*t[0],p=a?l:n,c=a?n:l;v.assert(c%t[1]===0&&p%t[0]===0&&n%t[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}, tileInner ${n} must be divisible by workgroupSize[1]${t[1]}`);let d=c/t[1],h=p/t[0],m=n/t[1],f=e[1],g=e[0],y=i?` + `,que=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function d0(e,t,a=!1,n=32,r=!1,s=32,i=!1,o=!1){let l=e[1]*t[1],u=e[0]*t[0],p=a?l:n,c=a?n:l;v.assert(c%t[1]===0&&p%t[0]===0&&n%t[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}, tileInner ${n} must be divisible by workgroupSize[1]${t[1]}`);let d=c/t[1],h=p/t[0],m=n/t[1],f=e[1],g=e[0],y=i?` let localRow = i32(localId.y); let localCol = i32(localId.x); let globalRowStart = i32(workgroupId.y) * ${l}; @@ -5562,7 +5562,7 @@ return a / b;`,lJ=` // Load one tile of A into local memory. for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${t[1]}) { for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${t[0]}) { - ${Y5(a)} + ${aA(a)} } } // Load one tile of B into local memory. @@ -5617,7 +5617,7 @@ return a / b;`,lJ=` for (var innerCol = 0; innerCol < ${h}; innerCol++) { let inputRow = tileRowA + innerRow; let inputCol = tileColA + innerCol; - ${Y5(a)} + ${aA(a)} } } @@ -5642,7 +5642,7 @@ return a / b;`,lJ=` } for (var innerRow = 0; innerRow < ${f}; innerRow++) { - ${$ue(a)} + ${que(a)} for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = fma(ACached, BCached[innerCol], acc[innerRow][innerCol]); @@ -5680,7 +5680,7 @@ return a / b;`,lJ=` } ${y} } - `}var Fue=e=>e?` + `}var Xue=e=>e?` mm_readA(batchA, colA, globalRow), mm_readA(batchA, colA + 1, globalRow), mm_readA(batchA, colA + 2, globalRow), @@ -5690,7 +5690,7 @@ return a / b;`,lJ=` mm_readA(batchA, globalRow, colA + 1), mm_readA(batchA, globalRow, colA + 2), mm_readA(batchA, globalRow, colA + 3) - `;function Due(e,t=!1){v.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`);let a=e[0]*4;return` + `;function Kue(e,t=!1){v.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`);let a=e[0]*4;return` var mm_Asub : array, ${e[0]}>; ${ue()} { @@ -5709,7 +5709,7 @@ return a / b;`,lJ=` for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. let colA = t * ${a} + tileCol * 4; - mm_Asub[tileCol] = vec4(${Fue(t)}); + mm_Asub[tileCol] = vec4(${Xue(t)}); workgroupBarrier(); // Compute acc values for a single thread. @@ -5729,11 +5729,11 @@ return a / b;`,lJ=` mm_write(batch, globalRow, globalCol, acc); } - `}var Oue=class{constructor(e,t,a=!1,n=!1,r=null,s=null,i=null,o=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=a?e[1]:e[2];if(this.isVec4=(l%4===0&&!a||t[1]%4===0&&a)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!a,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let c=ck(t[1],l,t[2],a);this.workgroupSize=c.workgroupSize,this.elementsPerThread=c.elementsPerThread}this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let u=r!=null,p=i!=null;u&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=o,this.transposeA=a,this.transposeB=n,this.addBias=u,this.activation=s,this.hasPreluActivationWeights=p,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${a}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,a){let n=this.workgroupSize[1]*this.elementsPerThread[1],r=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=r;let s=e%n===0,i=t%r===0,o=a%this.tileInner===0;return[s,i,o]}getUserCode(){return` - ${_r(this.activation,this.hasPreluActivationWeights,this.isVec4)} - ${H3(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} - ${this.isVec4?d0(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?Due(this.workgroupSize,this.transposeA):p0(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)} - `}};function zue(e){return` + `}var Yue=class{constructor(e,t,a=!1,n=!1,r=null,s=null,i=null,o=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=a?e[1]:e[2];if(this.isVec4=(l%4===0&&!a||t[1]%4===0&&a)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!a,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let c=Ik(t[1],l,t[2],a);this.workgroupSize=c.workgroupSize,this.elementsPerThread=c.elementsPerThread}this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let u=r!=null,p=i!=null;u&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=o,this.transposeA=a,this.transposeB=n,this.addBias=u,this.activation=s,this.hasPreluActivationWeights=p,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${a}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,a){let n=this.workgroupSize[1]*this.elementsPerThread[1],r=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=r;let s=e%n===0,i=t%r===0,o=a%this.tileInner===0;return[s,i,o]}getUserCode(){return` + ${Pr(this.activation,this.hasPreluActivationWeights,this.isVec4)} + ${K3(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} + ${this.isVec4?u0(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?Kue(this.workgroupSize,this.transposeA):d0(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)} + `}};function Zue(e){return` var sumValues : array; ${ue()} { let coords = getOutputCoords(); @@ -5766,11 +5766,11 @@ return a / b;`,lJ=` mm_write(batch, row, col, sum); } } - `}var Lue=class{constructor(e,t=!1,a=!1,n=null,r=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=a,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${a}`}getUserCode(){return` - ${_r(this.activation,this.hasPreluActivationWeights)} - ${H3(this.addBias,this.activation,this.transposeA,this.transposeB)} - ${zue(this.workgroupSize[0])} - `}};function Wue(e){let t=e[1],a=e[0],n=t>a?t:a;return` + `}var Jue=class{constructor(e,t=!1,a=!1,n=null,r=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=a,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${a}`}getUserCode(){return` + ${Pr(this.activation,this.hasPreluActivationWeights)} + ${K3(this.addBias,this.activation,this.transposeA,this.transposeB)} + ${Zue(this.workgroupSize[0])} + `}};function Que(e){let t=e[1],a=e[0],n=t>a?t:a;return` var mm_Asub : array, ${t}>; var mm_Bsub : array, ${n}>; @@ -5822,44 +5822,44 @@ return a / b;`,lJ=` mm_write(batch, globalRow, globalCol, acc); } - `}var Bue=class{constructor(e,t,a,n=!1,r=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=a,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(a[2]/this.workgroupSize[0]),Math.ceil(a[1]/this.workgroupSize[1]),a[0]];let l=s!=null;l&&this.variableNames.push("bias");let u=o!=null;u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${r}`}getUserCode(){return` - ${_r(this.activation,this.hasPreluActivationWeights)} - ${H3(this.addBias,this.activation,this.transposeA,this.transposeB)} - ${Wue(this.workgroupSize)} - `}},Vue=class{constructor(e,t,a=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,v.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let r=(a&&this.outputShape[1]%4===0||!a&&t%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=r?4:1,r||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=de(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=a,this.transposeB=n,this.shaderKey=`matMulSplitK_${a}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let e=this.outputComponent;return` - ${hk(!1,this.transposeB,!1,!1,!1,e)} - fn mm_write(batch: i32, row : i32, col : i32, value : ${Ke(e)}) { + `}var ede=class{constructor(e,t,a,n=!1,r=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=a,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(a[2]/this.workgroupSize[0]),Math.ceil(a[1]/this.workgroupSize[1]),a[0]];let l=s!=null;l&&this.variableNames.push("bias");let u=o!=null;u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${r}`}getUserCode(){return` + ${Pr(this.activation,this.hasPreluActivationWeights)} + ${K3(this.addBias,this.activation,this.transposeA,this.transposeB)} + ${Que(this.workgroupSize)} + `}},tde=class{constructor(e,t,a=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,v.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let r=(a&&this.outputShape[1]%4===0||!a&&t%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=r?4:1,r||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=de(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=a,this.transposeB=n,this.shaderKey=`matMulSplitK_${a}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let e=this.outputComponent;return` + ${Ck(!1,this.transposeB,!1,!1,!1,e)} + fn mm_write(batch: i32, row : i32, col : i32, value : ${Xe(e)}) { if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { let coords = vec3(batch, row, col); let flatIndex = getOutputIndexFromCoords(coords); // The problem is that we should initialize output to zero before using. // Otherwise, the original value will be added to the result. for (var i = 0; i < ${e}; i = i + 1) { - ${ms("&result[flatIndex + i]",`${e>1?"value[i]":"value"}`,"float32")} + ${gs("&result[flatIndex + i]",`${e>1?"value[i]":"value"}`,"float32")} } } } - ${e===4?d0(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):p0(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} - `}},Uue=class{constructor(e,t=null,a=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${a}`}getUserCode(){return` - ${_r(this.activation,this.hasPreluActivationWeights)} + ${e===4?u0(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):d0(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} + `}},ade=class{constructor(e,t=null,a=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${a}`}getUserCode(){return` + ${Pr(this.activation,this.hasPreluActivationWeights)} ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); - ${rl(this.addBias,this.activation)} + ${sl(this.addBias,this.activation)} setOutputAtIndex(index, value); } } - `}},Gue=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return` + `}},nde=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.value); } } - `}};function Va(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new Gue(n),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],s,o)}}var Hue={kernelName:yu,backendName:"webgpu",kernelFunc:Va};function ke(e){let{inputs:t,attrs:a}=e,{x:n}=t,{shape:r}=a,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(r,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var jue={kernelName:Tu,backendName:"webgpu",kernelFunc:ke};function c0({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],d=n?t.shape[p-1]:t.shape[p-2],h=a?e.shape[u-1]:e.shape[u-2],m=n?t.shape[p-2]:t.shape[p-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(f),x=v.sizeFromShape(g),A=Qo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,m]);v.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],w=n?[x,m,d]:[x,d,m],I=ke({inputs:{x:e},backend:r,attrs:{shape:b}}),T=ke({inputs:{x:t},backend:r,attrs:{shape:w}}),N=[I,T],M=Math.max(y,x),P=[I,T],E=[{type:"int32",data:[h]},{type:"int32",data:[m]},{type:"int32",data:[c]}],C,_,O=[M,h,m],B=W().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(B<0){let U=W().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),G=U>0?U:r.thresholdToIncreaseWorkgroups,q=M*Math.ceil(h/32)*Math.ceil(m/32);q<=G||h<=8&&q<=G*2?M*h*m<=128?B=Dn.MatMulReduceProgram:M===1&&d>=2e3?B=Dn.MatMulSplitKProgram:B=Dn.MatMulSmallOutputSizeProgram:B=Dn.MatMulPackedProgram}switch(B){case Dn.MatMulReduceProgram:C=new Lue(O,a,n,s,l,i);break;case Dn.MatMulSplitKProgram:{if(_=Va({backend:r,attrs:{shape:O,value:0,dtype:e.dtype}}),C=new Vue(O,d,a,n),s||l){_=r.runWebGPUProgram(C,P,e.dtype,E,_);let G=new Uue(_.shape,s,l,i),q=null,H=[_];s&&H.push(s),i&&H.push(i),l==="leakyrelu"&&(q=[{type:"float32",data:[o]}],G.uniforms+=" alpha : f32,");let V=r.runWebGPUProgram(G,H,_.dtype,q);N.push(_);let Z=ke({inputs:{x:V},backend:r,attrs:{shape:A}});N.push(V);for(let X of N)r.disposeData(X.dataId);return Z}break}case Dn.MatMulSmallOutputSizeProgram:C=new Bue(b,w,O,a,n,s,l,i);break;case Dn.MatMulPackedProgram:let U=r.adapterInfo.isIntel();C=new Oue(b,O,a,n,s,l,i,U);break;default:throw new Error(`Unsupported MatMulProgramType ${B}.`)}s&&P.push(s),i&&P.push(i),l==="leakyrelu"&&(E.push({type:"float32",data:[o]}),C.uniforms+=" alpha : f32,"),_=r.runWebGPUProgram(C,P,e.dtype,E,_);let F=ke({inputs:{x:_},backend:r,attrs:{shape:A}});N.push(_);for(let U of N)r.disposeData(U.dataId);return F}function que(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return c0({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var Xue={kernelName:Kr,backendName:"webgpu",kernelFunc:que},Z5=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,a),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` + `}};function Wa(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new nde(n),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],s,o)}}var rde={kernelName:yu,backendName:"webgpu",kernelFunc:Wa};function ke(e){let{inputs:t,attrs:a}=e,{x:n}=t,{shape:r}=a,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(r,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var sde={kernelName:Tu,backendName:"webgpu",kernelFunc:ke};function p0({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],d=n?t.shape[p-1]:t.shape[p-2],h=a?e.shape[u-1]:e.shape[u-2],m=n?t.shape[p-2]:t.shape[p-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(f),x=v.sizeFromShape(g),A=el.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,m]);v.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],w=n?[x,m,d]:[x,d,m],I=ke({inputs:{x:e},backend:r,attrs:{shape:b}}),T=ke({inputs:{x:t},backend:r,attrs:{shape:w}}),N=[I,T],M=Math.max(y,x),P=[I,T],E=[{type:"int32",data:[h]},{type:"int32",data:[m]},{type:"int32",data:[c]}],S,_,O=[M,h,m],W=B().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(W<0){let U=B().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),G=U>0?U:r.thresholdToIncreaseWorkgroups,q=M*Math.ceil(h/32)*Math.ceil(m/32);q<=G||h<=8&&q<=G*2?M*h*m<=128?W=Dn.MatMulReduceProgram:M===1&&d>=2e3?W=Dn.MatMulSplitKProgram:W=Dn.MatMulSmallOutputSizeProgram:W=Dn.MatMulPackedProgram}switch(W){case Dn.MatMulReduceProgram:S=new Jue(O,a,n,s,l,i);break;case Dn.MatMulSplitKProgram:{if(_=Wa({backend:r,attrs:{shape:O,value:0,dtype:e.dtype}}),S=new tde(O,d,a,n),s||l){_=r.runWebGPUProgram(S,P,e.dtype,E,_);let G=new ade(_.shape,s,l,i),q=null,H=[_];s&&H.push(s),i&&H.push(i),l==="leakyrelu"&&(q=[{type:"float32",data:[o]}],G.uniforms+=" alpha : f32,");let V=r.runWebGPUProgram(G,H,_.dtype,q);N.push(_);let Z=ke({inputs:{x:V},backend:r,attrs:{shape:A}});N.push(V);for(let X of N)r.disposeData(X.dataId);return Z}break}case Dn.MatMulSmallOutputSizeProgram:S=new ede(b,w,O,a,n,s,l,i);break;case Dn.MatMulPackedProgram:let U=r.adapterInfo.isIntel();S=new Yue(b,O,a,n,s,l,i,U);break;default:throw new Error(`Unsupported MatMulProgramType ${W}.`)}s&&P.push(s),i&&P.push(i),l==="leakyrelu"&&(E.push({type:"float32",data:[o]}),S.uniforms+=" alpha : f32,"),_=r.runWebGPUProgram(S,P,e.dtype,E,_);let $=ke({inputs:{x:_},backend:r,attrs:{shape:A}});N.push(_);for(let U of N)r.disposeData(U.dataId);return $}function ide(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return p0({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var ode={kernelName:Yr,backendName:"webgpu",kernelFunc:ide},nA=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,a),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { - ${G3(this.op,!1)} + ${X3(this.op,!1)} } ${ue("index")} { @@ -5871,9 +5871,9 @@ return a / b;`,lJ=` setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag)); } } - `}},Ch=class{constructor(e,t,a){if(this.size=!0,this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,a),this.dispatchLayout=me(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&a.length>1&&t[0]<128,this.useSharedMemoryWithB=a.length<=1&&t.length>1&&a[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?a[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type="shared",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,r=a.length>0&&a[a.length-1]%4===0;n&&r?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(v.isScalarShape(a)||a[a.length-1]===1)||r&&(v.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type="nonshared",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?"vec4":"f32",a=` + `}},Ch=class{constructor(e,t,a){if(this.size=!0,this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,a),this.dispatchLayout=me(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&a.length>1&&t[0]<128,this.useSharedMemoryWithB=a.length<=1&&t.length>1&&a[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?a[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type="shared",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,r=a.length>0&&a[a.length-1]%4===0;n&&r?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(v.isScalarShape(a)||a[a.length-1]===1)||r&&(v.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type="nonshared",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?"vec4":"f32",a=` fn binaryOperation(a : ${t}, b : ${t}) -> ${t} { - ${G3(this.op,this.outputComponent===4)} + ${X3(this.op,this.outputComponent===4)} }; `;if(this.type==="shared"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",r=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index); let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}]; @@ -5904,9 +5904,9 @@ return a / b;`,lJ=` setOutputAtIndex(index, binaryOperation(a, b)); } } - `;return e}};function an(e){let{inputs:t}=e,{x:a}=t;return e.backend.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var Kue={kernelName:Gi,backendName:"webgpu",kernelFunc:an};function sl(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.tensorMap.get(s.dataId),o=an({inputs:{x:n},backend:a}),l=an({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var Yue={kernelName:pp,backendName:"webgpu",kernelFunc:sl},Qu=class{constructor(e,t,a=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,a!==""&&(this.uniforms=a),this.shaderKey=`unary_${t}`}getUserCode(){return` + `;return e}};function en(e){let{inputs:t}=e,{x:a}=t;return e.backend.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var lde={kernelName:Hi,backendName:"webgpu",kernelFunc:en};function il(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.tensorMap.get(s.dataId),o=en({inputs:{x:n},backend:a}),l=en({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var ude={kernelName:up,backendName:"webgpu",kernelFunc:il},Ju=class{constructor(e,t,a=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,a!==""&&(this.uniforms=a),this.shaderKey=`unary_${t}`}getUserCode(){return` fn unaryOperation(a : f32) -> f32 { - ${Os(this.op,!1)} + ${Ls(this.op,!1)} } ${ue("index")} { if (index < uniforms.size) { @@ -5914,7 +5914,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, unaryOperation(a)); } } - `}};function at({opType:e,cpuKernelImpl:t,dtype:a}){return({inputs:n,backend:r})=>{let{x:s}=n,i=r,o=a||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),p=t(u.values,o);return i.makeTensorInfo(s.shape,o,p)}let l=new Qu(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function ta({opType:e,cpuKernelImpl:t,supportsComplex:a=!1,dtype:n}){return({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(a&&i.dtype==="complex64"){let c=l.tensorMap.get(i.dataId),d=l.tensorMap.get(o.dataId),h,m;if(e!==Pe.MUL)[h,m]=[[c.complexTensorInfos.real,d.complexTensorInfos.real],[c.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:x.dataId,dtype:x.dtype,shape:o.shape},w=new Ch(e,i.shape,o.shape);return l.runWebGPUProgram(w,[A,b],pa(y.dtype,x.dtype))});else{let g=new Z5(Pe.COMPLEX_MULTIPLY_REAL,i.shape,o.shape),y=new Z5(Pe.COMPLEX_MULTIPLY_IMAG,i.shape,o.shape),x=[{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:i.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=l.runWebGPUProgram(g,x,"float32"),m=l.runWebGPUProgram(y,x,"float32")}let f=sl({inputs:{real:h,imag:m},backend:l});return l.disposeData(h.dataId),l.disposeData(m.dataId),f}let u=n||pa(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let c=l.tensorMap.get(i.dataId).values,d=l.tensorMap.get(o.dataId).values,h=i.dtype==="string"?S.fromUint8ToStringArray(c):c,m=i.dtype==="string"?S.fromUint8ToStringArray(d):d,[f,g]=t(i.shape,o.shape,h,m,u);return l.makeTensorInfo(g,u,f)}let p=new Ch(e,i.shape,o.shape);return l.runWebGPUProgram(p,[i,o],u)}}var{addImpl:Zue,castImpl:Jue,ceilImpl:Que,concatImpl:ede,equalImpl:tde,expImpl:ade,expm1Impl:nde,floorImpl:rde,floorDivImpl:sde,gatherNdImpl:ide,gatherV2Impl:ode,greaterEqualImpl:lde,greaterImpl:ude,lessEqualImpl:dde,lessImpl:pde,logImpl:cde,maxImpl:hde,maximumImpl:mde,minimumImpl:fde,multiplyImpl:gde,negImpl:yde,notEqualImpl:xde,prodImpl:Ade,rangeImpl:bde,rsqrtImpl:vde,scatterImpl:wde,simpleAbsImpl:kde,sliceImpl:Ide,stridedSliceImpl:Sde,stringNGramsImpl:Cde,subImpl:Tde,tileImpl:Nde,topKImpl:Rde,transposeImpl:Ede,uniqueImpl:q3e}=e0,Mde=at({opType:le.ABS,cpuKernelImpl:kde}),_de={kernelName:ru,backendName:"webgpu",kernelFunc:Mde},Pde=at({opType:le.ACOS}),$de={kernelName:ri,backendName:"webgpu",kernelFunc:Pde},Fde=at({opType:le.ACOSH}),Dde={kernelName:si,backendName:"webgpu",kernelFunc:Fde},Ode=ta({opType:Pe.ADD,cpuKernelImpl:Zue,supportsComplex:!0}),zde={kernelName:rs,backendName:"webgpu",kernelFunc:Ode},Lde=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(a=>{e.push(`let v${a} = get${a}ByOutputCoords(coords);`)});let t=this.variableNames.map(a=>`v${a}`).join(" + ");return` + `}};function at({opType:e,cpuKernelImpl:t,dtype:a}){return({inputs:n,backend:r})=>{let{x:s}=n,i=r,o=a||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),p=t(u.values,o);return i.makeTensorInfo(s.shape,o,p)}let l=new Ju(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function ea({opType:e,cpuKernelImpl:t,supportsComplex:a=!1,dtype:n}){return({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(a&&i.dtype==="complex64"){let c=l.tensorMap.get(i.dataId),d=l.tensorMap.get(o.dataId),h,m;if(e!==_e.MUL)[h,m]=[[c.complexTensorInfos.real,d.complexTensorInfos.real],[c.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:x.dataId,dtype:x.dtype,shape:o.shape},w=new Ch(e,i.shape,o.shape);return l.runWebGPUProgram(w,[A,b],da(y.dtype,x.dtype))});else{let g=new nA(_e.COMPLEX_MULTIPLY_REAL,i.shape,o.shape),y=new nA(_e.COMPLEX_MULTIPLY_IMAG,i.shape,o.shape),x=[{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:i.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=l.runWebGPUProgram(g,x,"float32"),m=l.runWebGPUProgram(y,x,"float32")}let f=il({inputs:{real:h,imag:m},backend:l});return l.disposeData(h.dataId),l.disposeData(m.dataId),f}let u=n||da(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let c=l.tensorMap.get(i.dataId).values,d=l.tensorMap.get(o.dataId).values,h=i.dtype==="string"?C.fromUint8ToStringArray(c):c,m=i.dtype==="string"?C.fromUint8ToStringArray(d):d,[f,g]=t(i.shape,o.shape,h,m,u);return l.makeTensorInfo(g,u,f)}let p=new Ch(e,i.shape,o.shape);return l.runWebGPUProgram(p,[i,o],u)}}var{addImpl:dde,castImpl:pde,ceilImpl:cde,concatImpl:hde,equalImpl:mde,expImpl:fde,expm1Impl:gde,floorImpl:yde,floorDivImpl:xde,gatherNdImpl:Ade,gatherV2Impl:bde,greaterEqualImpl:vde,greaterImpl:wde,lessEqualImpl:kde,lessImpl:Ide,logImpl:Sde,maxImpl:Cde,maximumImpl:Tde,minimumImpl:Nde,multiplyImpl:Rde,negImpl:Ede,notEqualImpl:Mde,prodImpl:Pde,rangeImpl:_de,rsqrtImpl:$de,scatterImpl:Fde,simpleAbsImpl:Dde,sliceImpl:Ode,stridedSliceImpl:zde,stringNGramsImpl:Lde,subImpl:Wde,tileImpl:Bde,topKImpl:Vde,transposeImpl:Ude,uniqueImpl:iye}=e0,Gde=at({opType:le.ABS,cpuKernelImpl:Dde}),Hde={kernelName:ru,backendName:"webgpu",kernelFunc:Gde},jde=at({opType:le.ACOS}),qde={kernelName:si,backendName:"webgpu",kernelFunc:jde},Xde=at({opType:le.ACOSH}),Kde={kernelName:ii,backendName:"webgpu",kernelFunc:Xde},Yde=ea({opType:_e.ADD,cpuKernelImpl:dde,supportsComplex:!0}),Zde={kernelName:is,backendName:"webgpu",kernelFunc:Yde},Jde=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(a=>{e.push(`let v${a} = get${a}ByOutputCoords(coords);`)});let t=this.variableNames.map(a=>`v${a}`).join(" + ");return` ${ue("index")} { for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; @@ -5926,7 +5926,7 @@ return a / b;`,lJ=` } } } - `}};function Wde(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return an({inputs:{x:n[0]},backend:a});let r=n.map(o=>o.dtype).reduce((o,l)=>pa(o,l)),s=n.map(o=>o.shape),i=new Lde(s);return a.runWebGPUProgram(i,n,r)}var Bde={kernelName:ii,backendName:"webgpu",kernelFunc:Wde},Vde=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[16,16,1];let a=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return` + `}};function Qde(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return en({inputs:{x:n[0]},backend:a});let r=n.map(o=>o.dtype).reduce((o,l)=>da(o,l)),s=n.map(o=>o.shape),i=new Jde(s);return a.runWebGPUProgram(i,n,r)}var epe={kernelName:oi,backendName:"webgpu",kernelFunc:Qde},tpe=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[16,16,1];let a=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return` var tile : array, ${this.workgroupSize[0]}>; ${ue()} { var x = i32(workgroupId.x) * ${e} + i32(localId.x); @@ -5945,7 +5945,7 @@ return a / b;`,lJ=` [localId.y]); } } - `}},Ude=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=new Array(t);for(let n=0;n=32768&&a>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0",a=this.workgroupSize[0];this.reduceType==="min"||this.reduceType==="max"?(e=` + `}};function Tk(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=new Array(t);for(let n=0;n=32768&&a>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0",a=this.workgroupSize[0];this.reduceType==="min"||this.reduceType==="max"?(e=` if (isnan(candidate)) { bestValue = uniforms.NAN; } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue) @@ -6004,7 +6004,7 @@ return a / b;`,lJ=` ${n} } } - `}},jde={mean:"float32",all:"bool",any:"bool"};function il(e,t,a,n,r){let s=e.shape.length,i=[],o=v.parseAxisParam(t,e.shape),l=o,u=S.getAxesPermutation(l,s),p=e;u!=null&&(p=ar({inputs:{x:e},attrs:{perm:u},backend:r}),l=S.getInnerMostAxes(l.length,s),i.push(p)),S.assertAxesAreInnerMostDims(n,l,s);let[c,d]=S.computeOutAndReduceShapes(p.shape,l),h=c;a&&(h=S.expandShapeToKeepDim(c,o));let m;if((n==="max"||n==="prod")&&r.shouldExecuteOnCPU([p])){let f=r.tensorMap.get(p.dataId).values;switch(n){case"max":let g=hde(f,v.sizeFromShape(d),h,e.dtype);m=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:x,outDtype:A}=Ade(p.shape,p.dtype,f,l);m=r.makeTensorInfo(x,A,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let f=v.sizeFromShape(d),g=v.sizeFromShape(p.shape)/f,y={windowSize:f,inSize:f,batchSize:g,outSize:1},x=jde[n]||Pp(e.dtype),A=[{type:"int32",data:[f]}],b=new Hde(y,n,r.device.limits.maxComputeWorkgroupSizeX),w=r.runWebGPUProgram(b,[p],x,A);i.push(w),m=ke({inputs:{x:w},attrs:{shape:h},backend:r})}return i.forEach(f=>r.disposeData(f.dataId)),m}function qde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return il(r,i,s,"all",a)}var Xde={kernelName:oi,backendName:"webgpu",kernelFunc:qde};function Kde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return il(r,i,s,"any",a)}var Yde={kernelName:li,backendName:"webgpu",kernelFunc:Kde},fk=class{constructor(e,t,a){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=a==="min"?"<":">";let[r,s]=S.computeOutAndReduceShapes(e,n);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=me(this.outputShape),v.sizeFromShape(s)<32?(this.type="plain",this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=de(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Sr(this.inputShape.length-1)}`,a=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let r=0;rr.disposeData(f.dataId)),m}function ipe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return ol(r,i,s,"all",a)}var ope={kernelName:li,backendName:"webgpu",kernelFunc:ipe};function lpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return ol(r,i,s,"any",a)}var upe={kernelName:ui,backendName:"webgpu",kernelFunc:lpe},Nk=class{constructor(e,t,a){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=a==="min"?"<":">";let[r,s]=C.computeOutAndReduceShapes(e,n);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=me(this.outputShape),v.sizeFromShape(s)<32?(this.type="plain",this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=de(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Ir(this.inputShape.length-1)}`,a=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let r=0;r u32 { return ((a - 1u) / b + 1u); } @@ -6070,7 +6070,7 @@ return a / b;`,lJ=` setOutputAtIndexI32(index, bestIndex); } } - `}};function Zde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=S.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=ar({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=S.getInnerMostAxes(i.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=new fk(l.shape,i[0],"max"),c=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var Jde={kernelName:su,backendName:"webgpu",kernelFunc:Zde};function Qde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=S.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=ar({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=S.getInnerMostAxes(i.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=new fk(l.shape,i[0],"min"),c=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var epe={kernelName:iu,backendName:"webgpu",kernelFunc:Qde},tpe=at({opType:le.ASIN}),ape={kernelName:ui,backendName:"webgpu",kernelFunc:tpe},npe=at({opType:le.ASINH}),rpe={kernelName:di,backendName:"webgpu",kernelFunc:npe},spe=at({opType:le.ATAN}),ipe={kernelName:pi,backendName:"webgpu",kernelFunc:spe},ope=ta({opType:Pe.ATAN2}),lpe={kernelName:hi,backendName:"webgpu",kernelFunc:ope},upe=at({opType:le.ATANH}),dpe={kernelName:ci,backendName:"webgpu",kernelFunc:upe},ppe=class{constructor(e){this.variableNames=["x"],this.uniforms="strides : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` + `}};function dpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=ar({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=new Nk(l.shape,i[0],"max"),c=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var ppe={kernelName:su,backendName:"webgpu",kernelFunc:dpe};function cpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=ar({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=new Nk(l.shape,i[0],"min"),c=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var hpe={kernelName:iu,backendName:"webgpu",kernelFunc:cpe},mpe=at({opType:le.ASIN}),fpe={kernelName:di,backendName:"webgpu",kernelFunc:mpe},gpe=at({opType:le.ASINH}),ype={kernelName:pi,backendName:"webgpu",kernelFunc:gpe},xpe=at({opType:le.ATAN}),Ape={kernelName:ci,backendName:"webgpu",kernelFunc:xpe},bpe=ea({opType:_e.ATAN2}),vpe={kernelName:mi,backendName:"webgpu",kernelFunc:bpe},wpe=at({opType:le.ATANH}),kpe={kernelName:hi,backendName:"webgpu",kernelFunc:wpe},Ipe=class{constructor(e){this.variableNames=["x"],this.uniforms="strides : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -6085,7 +6085,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, value); } } - `}},rp=class{constructor(e,t,a=!1,n=!1,r=!1){if(this.variableNames=["x"],this.uniforms="strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=a,this.flattenPositions=n,this.includeBatchIndex=r,this.shaderKey=`pool2D_${t}_${a}_${n}_${r}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue = resultValue + value; count = count + 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); + `}},ap=class{constructor(e,t,a=!1,n=!1,r=!1){if(this.variableNames=["x"],this.uniforms="strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=a,this.flattenPositions=n,this.includeBatchIndex=r,this.shaderKey=`pool2D_${t}_${a}_${n}_${r}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue = resultValue + value; count = count + 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); if (value >= currMaxValue) { maxValue = value; maxValueFound = 1.0; @@ -6126,7 +6126,7 @@ return a / b;`,lJ=` ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${t});`} } } - `}},j3=class{constructor(e,t,a=!1,n=!1,r=!1){if(this.variableNames=["x"],this.uniforms="strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=a,this.flattenPositions=n,this.includeBatchIndex=r,this.shaderKey=`pool3D_${t}_${a}_${n}_${r}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue += value; count += 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); + `}},Y3=class{constructor(e,t,a=!1,n=!1,r=!1){if(this.variableNames=["x"],this.uniforms="strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=a,this.flattenPositions=n,this.includeBatchIndex=r,this.shaderKey=`pool3D_${t}_${a}_${n}_${r}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue += value; count += 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); if (value >= currMaxValue) { maxValue = value; maxValueFound = 1.0; @@ -6175,7 +6175,7 @@ return a / b;`,lJ=` ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${t});`} } } - `}};function gk(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n;return il(r,s,i,"max",a)}var cpe={kernelName:ro,backendName:"webgpu",kernelFunc:gk};function yk(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return il(r,i,s,"mean",a)}var hpe={kernelName:oo,backendName:"webgpu",kernelFunc:yk};function xk(e,t,a,n){if(t.filterWidth===1&&t.filterHeight===1&&v.arraysEqual(t.inShape,t.outShape))return an({inputs:{x:e},backend:n});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let i=e.shape.length,o=ke({inputs:{x:e},backend:n,attrs:{shape:[e.shape[i-3]*e.shape[i-2],e.shape[i-1]]}}),l;a==="avg"?l=yk({inputs:{x:o},backend:n,attrs:{axis:0,keepDims:!1}}):(v.assert(a==="max",()=>`Invalid pool type ${a}`),l=gk({inputs:{x:o},backend:n,attrs:{reductionIndices:0,keepDims:!1}}));let u=ke({inputs:{x:l},backend:n,attrs:{shape:t.outShape}});return n.disposeData(o.dataId),n.disposeData(l.dataId),u}let r,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?r=new ppe(t):(a==="avg"?r=new rp(t,"avg"):(v.assert(a==="max",()=>`Invalid pool type ${a}`),r=new rp(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),n.runWebGPUProgram(r,[e],e.dtype,s)}function mpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=S.computePool2DInfo(r.shape,s,i,u,o,l);return xk(r,p,"avg",a)}var fpe={kernelName:mi,backendName:"webgpu",kernelFunc:mpe};function gpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=S.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new j3(c,"avg"),h=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.front,c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.inDepth,c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]}];return a.runWebGPUProgram(d,[r],r.dtype,h)}var ype={kernelName:ou,backendName:"webgpu",kernelFunc:gpe},xpe=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, + `}};function Rk(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n;return ol(r,s,i,"max",a)}var Spe={kernelName:so,backendName:"webgpu",kernelFunc:Rk};function Ek(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return ol(r,i,s,"mean",a)}var Cpe={kernelName:lo,backendName:"webgpu",kernelFunc:Ek};function Mk(e,t,a,n){if(t.filterWidth===1&&t.filterHeight===1&&v.arraysEqual(t.inShape,t.outShape))return en({inputs:{x:e},backend:n});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let i=e.shape.length,o=ke({inputs:{x:e},backend:n,attrs:{shape:[e.shape[i-3]*e.shape[i-2],e.shape[i-1]]}}),l;a==="avg"?l=Ek({inputs:{x:o},backend:n,attrs:{axis:0,keepDims:!1}}):(v.assert(a==="max",()=>`Invalid pool type ${a}`),l=Rk({inputs:{x:o},backend:n,attrs:{reductionIndices:0,keepDims:!1}}));let u=ke({inputs:{x:l},backend:n,attrs:{shape:t.outShape}});return n.disposeData(o.dataId),n.disposeData(l.dataId),u}let r,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?r=new Ipe(t):(a==="avg"?r=new ap(t,"avg"):(v.assert(a==="max",()=>`Invalid pool type ${a}`),r=new ap(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),n.runWebGPUProgram(r,[e],e.dtype,s)}function Tpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=C.computePool2DInfo(r.shape,s,i,u,o,l);return Mk(r,p,"avg",a)}var Npe={kernelName:fi,backendName:"webgpu",kernelFunc:Tpe};function Rpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=C.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new Y3(c,"avg"),h=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.front,c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.inDepth,c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]}];return a.runWebGPUProgram(d,[r],r.dtype,h)}var Epe={kernelName:ou,backendName:"webgpu",kernelFunc:Rpe},Mpe=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool2DBackprop"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -6214,7 +6214,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, dotProd); } } - `}},Ape=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, + `}},Ppe=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool3DBackprop"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -6263,7 +6263,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, dotProd); } } - `}};function bpe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=S.computePool3DInfo(i.shape,o,l,1,u,p),d=new Ape(c),h=1/(c.filterDepth*c.filterHeight*c.filterWidth),m=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.effectiveFilterDepth-1-c.padInfo.front,c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:"int32",data:[c.outDepth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"float32",data:[h]}];return a.runWebGPUProgram(d,[r],i.dtype,m)}var vpe={kernelName:dp,backendName:"webgpu",kernelFunc:bpe};function wpe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;U3([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=S.computePool2DInfo(i.shape,o,l,1,u),c=new xpe(p),d=1/(p.filterHeight*p.filterWidth),h=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.effectiveFilterHeight-1-p.padInfo.top,p.effectiveFilterWidth-1-p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]},{type:"int32",data:[p.outHeight]},{type:"int32",data:[p.outWidth]},{type:"float32",data:[d]}];return a.runWebGPUProgram(c,[r],i.dtype,h)}var kpe={kernelName:up,backendName:"webgpu",kernelFunc:wpe};function Ipe(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return c0({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var Spe={kernelName:fi,backendName:"webgpu",kernelFunc:Ipe},Cpe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${$t(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=$t(this.rank),t=Tpe(this.rank),a;return this.start.length===1?a=this.outputShape.map((n,r)=>"sourceLoc = uniforms.start + coords;"):a=this.outputShape.map((n,r)=>`sourceLoc.${j1[r]} = uniforms.start.${Sr(r)} + coords.${j1[r]};`),` + `}};function _pe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=C.computePool3DInfo(i.shape,o,l,1,u,p),d=new Ppe(c),h=1/(c.filterDepth*c.filterHeight*c.filterWidth),m=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.effectiveFilterDepth-1-c.padInfo.front,c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:"int32",data:[c.outDepth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"float32",data:[h]}];return a.runWebGPUProgram(d,[r],i.dtype,m)}var $pe={kernelName:lp,backendName:"webgpu",kernelFunc:_pe};function Fpe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;j3([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=C.computePool2DInfo(i.shape,o,l,1,u),c=new Mpe(p),d=1/(p.filterHeight*p.filterWidth),h=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.effectiveFilterHeight-1-p.padInfo.top,p.effectiveFilterWidth-1-p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]},{type:"int32",data:[p.outHeight]},{type:"int32",data:[p.outWidth]},{type:"float32",data:[d]}];return a.runWebGPUProgram(c,[r],i.dtype,h)}var Dpe={kernelName:op,backendName:"webgpu",kernelFunc:Fpe};function Ope(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return p0({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var zpe={kernelName:gi,backendName:"webgpu",kernelFunc:Ope},Lpe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${_t(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=_t(this.rank),t=Wpe(this.rank),a;return this.start.length===1?a=this.outputShape.map((n,r)=>"sourceLoc = uniforms.start + coords;"):a=this.outputShape.map((n,r)=>`sourceLoc.${q1[r]} = uniforms.start.${Ir(r)} + coords.${q1[r]};`),` ${ue("index")} { if (index < uniforms.size) { var sourceLoc : ${e}; @@ -6273,16 +6273,16 @@ return a / b;`,lJ=` setOutputAtIndex(index, getSource(${t})); } } - `}},j1=["x","y","z","w","u","v"];function Tpe(e){if(e===1)return"sourceLoc";if(e<=6)return j1.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function ed(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=Nt.parseSliceParams(r,s,i);if(Nt.assertParamsValid(r,o,l),a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.tensorMap.get(r.dataId),d=Ide(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}if(v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);let u=new Cpe(o,l),p=[{type:"int32",data:o}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var Npe={kernelName:Mu,backendName:"webgpu",kernelFunc:ed},Rpe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=S.getReshaped(r.shape,s,o),u=S.getPermuted(l.length,s.length),p=S.getReshapedPermuted(r.shape,s,o),c=S.getSliceBeginCoords(i,s.length),d=S.getSliceSize(p,i,s.length),h=[],m=ke({inputs:{x:r},backend:a,attrs:{shape:l}}),f=ar({inputs:{x:m},backend:a,attrs:{perm:u}}),g=ke({inputs:{x:f},backend:a,attrs:{shape:p}}),y=ed({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(x=>a.disposeData(x.dataId)),y},Epe={kernelName:lu,backendName:"webgpu",kernelFunc:Rpe},Mpe=` + `}},q1=["x","y","z","w","u","v"];function Wpe(e){if(e===1)return"sourceLoc";if(e<=6)return q1.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Qu(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=Nt.parseSliceParams(r,s,i);if(Nt.assertParamsValid(r,o,l),a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.tensorMap.get(r.dataId),d=Ode(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}if(v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);let u=new Lpe(o,l),p=[{type:"int32",data:o}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var Bpe={kernelName:Mu,backendName:"webgpu",kernelFunc:Qu},Vpe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=C.getReshaped(r.shape,s,o),u=C.getPermuted(l.length,s.length),p=C.getReshapedPermuted(r.shape,s,o),c=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(p,i,s.length),h=[],m=ke({inputs:{x:r},backend:a,attrs:{shape:l}}),f=ar({inputs:{x:m},backend:a,attrs:{perm:u}}),g=ke({inputs:{x:f},backend:a,attrs:{shape:p}}),y=Qu({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(x=>a.disposeData(x.dataId)),y},Upe={kernelName:lu,backendName:"webgpu",kernelFunc:Vpe},Gpe=` fn bincount_write(index: i32, value: f32) { - ${ms("&result[index]","value","float32")} + ${gs("&result[index]","value","float32")} } -`,_pe=` +`,Hpe=` fn bincount_write(index: i32, value: f32) { atomicStore(&result[index], bitcast(value)); } -`,Ak=class{constructor(e,t,a=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=a,a&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` - ${this.binaryOutput?_pe:Mpe} +`,Pk=class{constructor(e,t,a=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=a,a&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` + ${this.binaryOutput?Hpe:Gpe} ${ue("index")} { ${this.rank===1?`if (index < uniforms.xShape) { let indexVal = i32(getX(index)); @@ -6299,7 +6299,7 @@ return a / b;`,lJ=` } }`} } - `}};function Ppe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=v.sizeFromShape(r.shape),l=v.sizeFromShape(s.shape)>0,u=[i],p=s.dtype,c=Va({backend:a,attrs:{shape:u,value:0,dtype:p}}),d=new Ak([o],l),h=[{type:"int32",data:[i]}],m=l?[r,s]:[r];return a.runWebGPUProgram(d,m,p,h,c)}var $pe={kernelName:gi,backendName:"webgpu",kernelFunc:Ppe},Fpe=class{constructor(e){this.outputShape=[],this.variableNames=["s0","s1"],this.uniforms="s0Size : i32, s1Size : i32, ",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="broadcastArgs"}getUserCode(){return` + `}};function jpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=v.sizeFromShape(r.shape),l=v.sizeFromShape(s.shape)>0,u=[i],p=s.dtype,c=Wa({backend:a,attrs:{shape:u,value:0,dtype:p}}),d=new Pk([o],l),h=[{type:"int32",data:[i]}],m=l?[r,s]:[r];return a.runWebGPUProgram(d,m,p,h,c)}var qpe={kernelName:yi,backendName:"webgpu",kernelFunc:jpe},Xpe=class{constructor(e){this.outputShape=[],this.variableNames=["s0","s1"],this.uniforms="s0Size : i32, s1Size : i32, ",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="broadcastArgs"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { var s0 = 1.0; @@ -6324,7 +6324,7 @@ return a / b;`,lJ=` } } } - `}};function Dpe(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t;if(a.shouldExecuteOnCPU([n,r])){let p=a.tensorMap.get(n.dataId),c=a.tensorMap.get(r.dataId),d=p.values,h=c.values,m=S.assertAndGetBroadcastShape(Array.from(d),Array.from(h));return a.makeTensorInfo([m.length],"int32",Int32Array.from(m))}let s=v.sizeFromShape(n.shape),i=v.sizeFromShape(r.shape),o=Math.max(s,i),l=new Fpe(o),u=[{type:"int32",data:[s]},{type:"int32",data:[i]}];return a.runWebGPUProgram(l,[n,r],"int32",u)}var Ope={kernelName:du,backendName:"webgpu",kernelFunc:Dpe},bk=ta({opType:Pe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:xde}),zpe={kernelName:fo,backendName:"webgpu",kernelFunc:bk};function ec(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return an({inputs:{x:r.complexTensorInfos.real},backend:a})}var Lpe={kernelName:kp,backendName:"webgpu",kernelFunc:ec};function Wpe(e,t){let a=new Qu(e.shape,le.TO_INT),n=t.runWebGPUProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function q1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return an({inputs:{x:r},backend:a});let i=yn(r.shape),o=q1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=sl({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeData(o.dataId),l}if(r.dtype==="complex64"){let i=ec({inputs:{input:r},backend:a}),o=q1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeData(i.dataId),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=an({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.tensorMap.get(r.dataId).values,[o,l,u]=Jue(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return Wpe(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=bk({inputs:{a:r,b:i},backend:a});return a.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var Bpe={kernelName:yi,backendName:"webgpu",kernelFunc:q1},Vpe=at({opType:le.CEIL,cpuKernelImpl:Que}),Upe={kernelName:xi,backendName:"webgpu",kernelFunc:Vpe},Gpe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` + `}};function Kpe(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t;if(a.shouldExecuteOnCPU([n,r])){let p=a.tensorMap.get(n.dataId),c=a.tensorMap.get(r.dataId),d=p.values,h=c.values,m=C.assertAndGetBroadcastShape(Array.from(d),Array.from(h));return a.makeTensorInfo([m.length],"int32",Int32Array.from(m))}let s=v.sizeFromShape(n.shape),i=v.sizeFromShape(r.shape),o=Math.max(s,i),l=new Xpe(o),u=[{type:"int32",data:[s]},{type:"int32",data:[i]}];return a.runWebGPUProgram(l,[n,r],"int32",u)}var Ype={kernelName:du,backendName:"webgpu",kernelFunc:Kpe},_k=ea({opType:_e.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Mde}),Zpe={kernelName:go,backendName:"webgpu",kernelFunc:_k};function Qp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return en({inputs:{x:r.complexTensorInfos.real},backend:a})}var Jpe={kernelName:vp,backendName:"webgpu",kernelFunc:Qp};function Qpe(e,t){let a=new Ju(e.shape,le.TO_INT),n=t.runWebGPUProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function X1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return en({inputs:{x:r},backend:a});let i=gn(r.shape),o=X1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=il({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeData(o.dataId),l}if(r.dtype==="complex64"){let i=Qp({inputs:{input:r},backend:a}),o=X1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeData(i.dataId),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=en({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.tensorMap.get(r.dataId).values,[o,l,u]=pde(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return Qpe(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=_k({inputs:{a:r,b:i},backend:a});return a.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var ece={kernelName:xi,backendName:"webgpu",kernelFunc:X1},tce=at({opType:le.CEIL,cpuKernelImpl:cde}),ace={kernelName:Ai,backendName:"webgpu",kernelFunc:tce},nce=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` ${ue("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); @@ -6334,7 +6334,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, clampedValue); } } - `}},Hpe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return` + `}},rce=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return` ${ue("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); @@ -6345,7 +6345,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal)); } } - `}};function jpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return v.sizeFromShape(r.shape)%4===0?o=new Gpe(r.shape):o=new Hpe(r.shape),a.runWebGPUProgram(o,[r],r.dtype,l)}var qpe={kernelName:ss,backendName:"webgpu",kernelFunc:jpe},Xpe=class{constructor(e){this.outputShape=[],this.variableNames=["real","imag"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="complexAbs"}getUserCode(){return` + `}};function sce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return v.sizeFromShape(r.shape)%4===0?o=new nce(r.shape):o=new rce(r.shape),a.runWebGPUProgram(o,[r],r.dtype,l)}var ice={kernelName:os,backendName:"webgpu",kernelFunc:sce},oce=class{constructor(e){this.outputShape=[],this.variableNames=["real","imag"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="complexAbs"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let re = abs(getRealByOutputIndex(index)); @@ -6357,7 +6357,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0)); } } - `}};function J5(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Kpe(e){let{inputs:t,backend:a}=e,{x:n}=t,r=a.tensorMap.get(n.dataId),s=new Xpe(n.shape),i=[J5(n,r.complexTensorInfos.real),J5(n,r.complexTensorInfos.imag)];return a.runWebGPUProgram(s,i,i[0].dtype)}var Ype={kernelName:cp,backendName:"webgpu",kernelFunc:Kpe},Zpe=class{constructor(e){this.uniforms="",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;n`T${a}`),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;nec({inputs:{input:A},backend:a})),f=e.map(A=>h0({inputs:{input:A},backend:a})),g=_d(m,t,a),y=_d(f,t,a),x=sl({inputs:{real:g,imag:y},backend:a});return m.forEach(A=>a.disposeData(A.dataId)),f.forEach(A=>a.disposeData(A.dataId)),a.disposeData(g.dataId),a.disposeData(y.dataId),x}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let m=e.map(w=>{let I=[-1,v.sizeFromShape(w.shape.slice(t))];return ke({inputs:{x:w},backend:a,attrs:{shape:I}})}),f=m.map(w=>({vals:a.readSync(w.dataId),shape:w.shape})),g=S.computeOutShape(m.map(w=>w.shape),1),y=m[0].shape[0]===1,x=ede(f,g,n,y),A=S.computeOutShape(e.map(w=>w.shape),t),b=a.makeTensorInfo(A,n,x);return m.forEach(w=>a.disposeData(w.dataId)),b}let s=a.device.limits.maxStorageBuffersPerShaderStage-1;if(e.length>s){let m=[];for(let g=0;gm.shape),u=new Zpe(l),p=[],c=new Array(l.length-1);if(c.length>0){c[0]=l[0][1],p.push({type:"int32",data:[c[0]]});for(let m=1;ma.disposeData(m.dataId));let h=ke({inputs:{x:d},backend:a,attrs:{shape:o}});return a.disposeData(d.dataId),h}function Qpe(e,t,a){let n=S.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ke({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape.slice(0,t)),v.sizeFromShape(r.shape.slice(t))]}})),outShape:n}}function vk(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);S.assertParamsConsistent(i,s);let o=S.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?an({inputs:{x:l[0]},backend:a}):_d(l,s,a)}var ece={kernelName:pu,backendName:"webgpu",kernelFunc:vk};function tce(e,t,a,n,r=!1,s=null,i=!1,o=4,l=4,u=4){let p=N=>{switch(N){case 1:return"resData = f32(x[xIndex]);";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = vec4(x[xIndex / 4]);";default:throw new Error(`innerElementSize ${N} is not supported.`)}},c=N=>{switch(N){case 1:return"return f32(W[row * uniforms.wShape[3] + col]);";case 4:return"return vec4(W[(row * uniforms.wShape[3] + col) / 4]);";default:throw new Error(`innerElementSize ${N} is not supported.`)}},d=e?` + `}};function c0(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return en({inputs:{x:r.complexTensorInfos.imag},backend:a})}var pce={kernelName:xp,backendName:"webgpu",kernelFunc:c0};function Md(e,t,a){let n=e[0].dtype;if(n==="complex64"){let m=e.map(A=>Qp({inputs:{input:A},backend:a})),f=e.map(A=>c0({inputs:{input:A},backend:a})),g=Md(m,t,a),y=Md(f,t,a),x=il({inputs:{real:g,imag:y},backend:a});return m.forEach(A=>a.disposeData(A.dataId)),f.forEach(A=>a.disposeData(A.dataId)),a.disposeData(g.dataId),a.disposeData(y.dataId),x}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let m=e.map(w=>{let I=[-1,v.sizeFromShape(w.shape.slice(t))];return ke({inputs:{x:w},backend:a,attrs:{shape:I}})}),f=m.map(w=>({vals:a.readSync(w.dataId),shape:w.shape})),g=C.computeOutShape(m.map(w=>w.shape),1),y=m[0].shape[0]===1,x=hde(f,g,n,y),A=C.computeOutShape(e.map(w=>w.shape),t),b=a.makeTensorInfo(A,n,x);return m.forEach(w=>a.disposeData(w.dataId)),b}let s=a.device.limits.maxStorageBuffersPerShaderStage-1;if(e.length>s){let m=[];for(let g=0;gm.shape),u=new dce(l),p=[],c=new Array(l.length-1);if(c.length>0){c[0]=l[0][1],p.push({type:"int32",data:[c[0]]});for(let m=1;ma.disposeData(m.dataId));let h=ke({inputs:{x:d},backend:a,attrs:{shape:o}});return a.disposeData(d.dataId),h}function cce(e,t,a){let n=C.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ke({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape.slice(0,t)),v.sizeFromShape(r.shape.slice(t))]}})),outShape:n}}function $k(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);C.assertParamsConsistent(i,s);let o=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?en({inputs:{x:l[0]},backend:a}):Md(l,s,a)}var hce={kernelName:pu,backendName:"webgpu",kernelFunc:$k};function mce(e,t,a,n,r=!1,s=null,i=!1,o=4,l=4,u=4){let p=N=>{switch(N){case 1:return"resData = f32(x[xIndex]);";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = vec4(x[xIndex / 4]);";default:throw new Error(`innerElementSize ${N} is not supported.`)}},c=N=>{switch(N){case 1:return"return f32(W[row * uniforms.wShape[3] + col]);";case 4:return"return vec4(W[(row * uniforms.wShape[3] + col) / 4]);";default:throw new Error(`innerElementSize ${N} is not supported.`)}},d=e?` let coord = vec4(batch, xRow, xCol, xCh); `:` let coord = vec4(batch, xCh, xRow, xCol); @@ -6398,7 +6398,7 @@ return a / b;`,lJ=` let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * WRow - uniforms.pads[0]; let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * WCol - uniforms.pads[1]; let xCh = ${y} % inChannels; - var resData = ${Ke(o)}(0.0); + var resData = ${Xe(o)}(0.0); // The bounds checking is always needed since we use it to pad zero for // the 'same' padding type. if (xRow >= 0 && xRow < ${m} && xCol >= 0 && xCol < ${f}) { @@ -6411,13 +6411,13 @@ return a / b;`,lJ=` if (row < uniforms.dimAOuter && col < uniforms.dimInner) { ${x} } - return ${Ke(o)}(0.0);`:n&&a?` + return ${Xe(o)}(0.0);`:n&&a?` ${x}`:` if (row < uniforms.dimInner && col < uniforms.dimBOuter) { ${x} } - return ${Ke(o)}(0.0);`,b=`${c(l)}`,w=Ke(u),I=Ke(e?o:l),T=Ke(e?l:o);return` - ${_r(s,i,u===4,4)} + return ${Xe(o)}(0.0);`,b=`${c(l)}`,w=Xe(u),I=Xe(e?o:l),T=Xe(e?l:o);return` + ${Pr(s,i,u===4,4)} fn mm_readA(batch: i32, row : i32, col : i32) -> ${I} { ${e?A:b} } @@ -6432,14 +6432,14 @@ return a / b;`,lJ=` var value = valueIn; let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"}; ${h} - ${rl(r,s)} + ${sl(r,s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } - }`}var ace=class{constructor(e,t,a,n,r=!1,s=null,i=!1,o=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=W3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=B3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),r&&(this.variableNames.push("bias"),this.variableComponents.push(4)),i&&(this.variableNames.push("preluActivationWeights"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],r&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=o,this.addBias=r,this.activation=s,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=a%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?d0(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):p0(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` - ${tce(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])} + }`}var fce=class{constructor(e,t,a,n,r=!1,s=null,i=!1,o=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=U3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=G3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),r&&(this.variableNames.push("bias"),this.variableComponents.push(4)),i&&(this.variableNames.push("preluActivationWeights"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],r&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=o,this.addBias=r,this.activation=s,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=a%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?u0(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):d0(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` + ${mce(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])} ${e} - `}},nce=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=a,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` - ${_r(this.activation,this.hasPreluActivationWeights,!1,4)} + `}},gce=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=a,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` + ${Pr(this.activation,this.hasPreluActivationWeights,!1,4)} fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{ let coords = vec4(batch, row, col, chan); if (coordsInBounds4D(coords, uniforms.xShape)) { @@ -6460,7 +6460,7 @@ return a / b;`,lJ=` let coords = ${this.isChannelsLast?"vec4(batch, row, col, chan);":"vec4(batch, chan, row, col);"} if (coordsInBounds4D(coords, uniforms.outShape)) { var value = valueIn; - ${rl(this.addBias,this.activation)} + ${sl(this.addBias,this.activation)} setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value); } } @@ -6484,7 +6484,7 @@ return a / b;`,lJ=` } writeResult(batch, outRow, outCol, outChannel, acc); } - `}},rce=class{constructor(e,t){this.variableNames=["x"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32, + `}},yce=class{constructor(e,t){this.variableNames=["x"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32, inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?"coords[1]":"coords[2]",n=this.isChannelsLast?"coords[2]":"coords[1]",r=this.isChannelsLast?"getX(batch, xRow, xCol, ch)":"getX(batch, ch, xRow, xCol)";return` ${ue("index")} { let coords = getCoordsFromIndex(index); @@ -6508,7 +6508,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, value); } } - `}};function Th(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function sce({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a.dataFormat==="channelsLast",u=!l,p=!1,c=l&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=[],h,m;if(c){let y=a.inHeight*a.inWidth*a.inChannels;h=ke({inputs:{x:e},backend:n,attrs:{shape:[1,a.batchSize,y]}}),m=ke({inputs:{x:t},backend:n,attrs:{shape:[1,y,a.outChannels]}})}else h=ke({inputs:{x:e},backend:n,attrs:{shape:l?[a.batchSize,a.inHeight*a.inWidth,a.inChannels]:[a.batchSize,a.inChannels,a.inHeight*a.inWidth]}}),m=ke({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});if(d.push(h),d.push(m),s!=null){let y=Th(s.shape,l);y!=null&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:y}}),d.push(s))}if(r!=null){let y=Th(r.shape,l);y!=null&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:y}}),d.push(r))}let f=c0({a:l?h:m,b:l?m:h,transposeA:u,transposeB:p,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=ke({inputs:{x:f},backend:n,attrs:{shape:a.outShape}});d.push(f);for(let y of d)n.disposeData(y.dataId);return g}function ice({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,strideWidth:c,strideHeight:d,padInfo:h,outWidth:m,outHeight:f,dilationWidth:g,dilationHeight:y,dataFormat:x}=a,A=x==="channelsLast",b=l*u*p,w=f*m,I=A?[a.batchSize,w,b]:[a.batchSize,b,w],T=new rce(I,A),N=[{type:"int32",data:[h.top,h.left]},{type:"int32",data:[d,c]},{type:"int32",data:[y,g]},{type:"int32",data:[m]},{type:"int32",data:[p*l]},{type:"int32",data:[p]}],M=n.runWebGPUProgram(T,[e],e.dtype,N),P=[];P.push(M);let E=ke({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});if(P.push(E),s!=null){let O=Th(s.shape,A);O!=null&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:O}}),P.push(s))}if(r!=null){let O=Th(r.shape,A);O!=null&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:O}}),P.push(r))}let C=c0({a:A?M:E,b:A?E:M,transposeA:!A,transposeB:!1,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),_=ke({inputs:{x:C},backend:n,attrs:{shape:a.outShape}});P.push(C);for(let O of P)n.disposeData(O.dataId);return _}function wk({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r!=null,u=s!=null,p=a.dataFormat==="channelsLast",c=p&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=W().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!d&&(c||a.filterHeight===1&&a.filterWidth===1&&a.dilationHeight===1&&a.dilationWidth===1&&a.strideHeight===1&&a.strideWidth===1&&(a.padInfo.type==="SAME"||a.padInfo.type==="VALID")))return sce({x:e,filter:t,convInfo:a,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let h=W().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),m=h>-1?h:n.thresholdToIncreaseWorkgroups,f=a.batchSize*Math.ceil(a.outHeight*a.outWidth/32)*Math.ceil(a.outChannels/32);if(W().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||f<=m)return ice({x:e,filter:t,convInfo:a,backend:n,bias:r,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let g,y=[a.padInfo.top,a.padInfo.left],x=[{type:"int32",data:[a.filterHeight,a.filterWidth]},{type:"int32",data:[...y]},{type:"int32",data:[a.strideHeight,a.strideWidth]},{type:"int32",data:[a.dilationHeight,a.dilationWidth]}];if(d)g=new nce(a,l,o,u);else{let I=p?a.outHeight*a.outWidth:a.outChannels,T=p?a.outChannels:a.outHeight*a.outWidth,N=a.filterHeight*a.filterWidth*a.inChannels;x.push({type:"int32",data:[I]},{type:"int32",data:[T]},{type:"int32",data:[N]});let M=n.adapterInfo.isIntel();g=new ace(a,I,T,N,l,o,u,M)}let A=[],b=[e,t];l&&(!p&&r.shape.length===1&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:[r.shape[0],1,1]}}),A.push(r)),b.push(r)),u&&(!p&&s.shape.length===1&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}}),A.push(s)),b.push(s)),o==="leakyrelu"&&(x.push({type:"float32",data:[i]}),g.uniforms+=" alpha : f32,");let w=n.runWebGPUProgram(g,b,e.dtype,x);for(let I of A)n.disposeData(I.dataId);return w}function oce(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c);return wk({x:r,filter:s,convInfo:d,backend:n})}var lce={kernelName:Ai,backendName:"webgpu",kernelFunc:oce},uce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=e.inShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?3:1,n=` + `}};function Th(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function xce({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a.dataFormat==="channelsLast",u=!l,p=!1,c=l&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=[],h,m;if(c){let y=a.inHeight*a.inWidth*a.inChannels;h=ke({inputs:{x:e},backend:n,attrs:{shape:[1,a.batchSize,y]}}),m=ke({inputs:{x:t},backend:n,attrs:{shape:[1,y,a.outChannels]}})}else h=ke({inputs:{x:e},backend:n,attrs:{shape:l?[a.batchSize,a.inHeight*a.inWidth,a.inChannels]:[a.batchSize,a.inChannels,a.inHeight*a.inWidth]}}),m=ke({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});if(d.push(h),d.push(m),s!=null){let y=Th(s.shape,l);y!=null&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:y}}),d.push(s))}if(r!=null){let y=Th(r.shape,l);y!=null&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:y}}),d.push(r))}let f=p0({a:l?h:m,b:l?m:h,transposeA:u,transposeB:p,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=ke({inputs:{x:f},backend:n,attrs:{shape:a.outShape}});d.push(f);for(let y of d)n.disposeData(y.dataId);return g}function Ace({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,strideWidth:c,strideHeight:d,padInfo:h,outWidth:m,outHeight:f,dilationWidth:g,dilationHeight:y,dataFormat:x}=a,A=x==="channelsLast",b=l*u*p,w=f*m,I=A?[a.batchSize,w,b]:[a.batchSize,b,w],T=new yce(I,A),N=[{type:"int32",data:[h.top,h.left]},{type:"int32",data:[d,c]},{type:"int32",data:[y,g]},{type:"int32",data:[m]},{type:"int32",data:[p*l]},{type:"int32",data:[p]}],M=n.runWebGPUProgram(T,[e],e.dtype,N),P=[];P.push(M);let E=ke({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});if(P.push(E),s!=null){let O=Th(s.shape,A);O!=null&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:O}}),P.push(s))}if(r!=null){let O=Th(r.shape,A);O!=null&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:O}}),P.push(r))}let S=p0({a:A?M:E,b:A?E:M,transposeA:!A,transposeB:!1,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),_=ke({inputs:{x:S},backend:n,attrs:{shape:a.outShape}});P.push(S);for(let O of P)n.disposeData(O.dataId);return _}function Fk({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r!=null,u=s!=null,p=a.dataFormat==="channelsLast",c=p&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=B().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!d&&(c||a.filterHeight===1&&a.filterWidth===1&&a.dilationHeight===1&&a.dilationWidth===1&&a.strideHeight===1&&a.strideWidth===1&&(a.padInfo.type==="SAME"||a.padInfo.type==="VALID")))return xce({x:e,filter:t,convInfo:a,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let h=B().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),m=h>-1?h:n.thresholdToIncreaseWorkgroups,f=a.batchSize*Math.ceil(a.outHeight*a.outWidth/32)*Math.ceil(a.outChannels/32);if(B().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||f<=m)return Ace({x:e,filter:t,convInfo:a,backend:n,bias:r,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let g,y=[a.padInfo.top,a.padInfo.left],x=[{type:"int32",data:[a.filterHeight,a.filterWidth]},{type:"int32",data:[...y]},{type:"int32",data:[a.strideHeight,a.strideWidth]},{type:"int32",data:[a.dilationHeight,a.dilationWidth]}];if(d)g=new gce(a,l,o,u);else{let I=p?a.outHeight*a.outWidth:a.outChannels,T=p?a.outChannels:a.outHeight*a.outWidth,N=a.filterHeight*a.filterWidth*a.inChannels;x.push({type:"int32",data:[I]},{type:"int32",data:[T]},{type:"int32",data:[N]});let M=n.adapterInfo.isIntel();g=new fce(a,I,T,N,l,o,u,M)}let A=[],b=[e,t];l&&(!p&&r.shape.length===1&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:[r.shape[0],1,1]}}),A.push(r)),b.push(r)),u&&(!p&&s.shape.length===1&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}}),A.push(s)),b.push(s)),o==="leakyrelu"&&(x.push({type:"float32",data:[i]}),g.uniforms+=" alpha : f32,");let w=n.runWebGPUProgram(g,b,e.dtype,x);for(let I of A)n.disposeData(I.dataId);return w}function bce(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c);return Fk({x:r,filter:s,convInfo:d,backend:n})}var vce={kernelName:bi,backendName:"webgpu",kernelFunc:bce},wce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=e.inShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?3:1,n=` ${ue()} { let batch = i32(globalId.z) / uniforms.outShape[1]; let r = i32(globalId.z) % uniforms.outShape[1]; @@ -6651,7 +6651,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, dotProd); } } - `}},dce=class{constructor(e){this.variableNames=["x","dy"],this.uniforms="pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return` + `}},kce=class{constructor(e){this.variableNames=["x","dy"],this.uniforms="pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return` ${ue("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -6692,7 +6692,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, dotProd); } } - `}},pce=class{constructor(e){this.variableNames=["x","dy"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32, + `}},Ice=class{constructor(e){this.variableNames=["x","dy"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32, outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3DDerFilter"}getUserCode(){return` ${ue("index")} { if(index < uniforms.size) { @@ -6733,7 +6733,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, dotProd); } } - `}},cce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3, + `}},Sce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3, outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3DDerInput"}getUserCode(){return` ${ue("index")} { if(index < uniforms.size) { @@ -6787,7 +6787,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, dotProd); } } - `}};function hce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new dce(d),m=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]},{type:"int32",data:[d.inHeight]},{type:"int32",data:[d.inWidth]}];return a.runWebGPUProgram(h,[r,s],r.dtype,m)}var mce={kernelName:hp,backendName:"webgpu",kernelFunc:hce};function fce(e=4){let t=n=>{switch(n){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` + `}};function Cce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new kce(d),m=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]},{type:"int32",data:[d.inHeight]},{type:"int32",data:[d.inWidth]}];return a.runWebGPUProgram(h,[r,s],r.dtype,m)}var Tce={kernelName:pp,backendName:"webgpu",kernelFunc:Cce};function Nce(e=4){let t=n=>{switch(n){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` let coord1 = vec4(coordX, coordY, col + 1, rowInner); let coord2 = vec4(coordX, coordY, col + 2, rowInner); let coord3 = vec4(coordX, coordY, col + 3, rowInner); @@ -6806,10 +6806,10 @@ return a / b;`,lJ=` let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.strides[0]); let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.strides[1]); if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) { - return ${Ke(e)}(0.0); + return ${Xe(e)}(0.0); } if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) { - return ${Ke(e)}(0.0); + return ${Xe(e)}(0.0); } let coord = vec4( batch, @@ -6818,12 +6818,12 @@ return a / b;`,lJ=` col % uniforms.outBackprop[3]); return x[getIndexFromCoords4D(coord, uniforms.xShape)/${e}];`} } - return ${Ke(e)}(0.0);`;return` - fn mm_readA(batch: i32, row : i32, col : i32) -> ${Ke(e)} { + return ${Xe(e)}(0.0);`;return` + fn mm_readA(batch: i32, row : i32, col : i32) -> ${Xe(e)} { ${a} } - fn mm_readB(batch: i32, row : i32, col : i32) -> ${Ke(e)} { + fn mm_readB(batch: i32, row : i32, col : i32) -> ${Xe(e)} { let coordX = uniforms.filterDims.x - 1 - row / (uniforms.filterDims[1] * uniforms.outBackprop[3]); let coordY = uniforms.filterDims.y - 1 - @@ -6834,10 +6834,10 @@ return a / b;`,lJ=` let coord = vec4(coordX, coordY, col, rowInner); ${t(e)} } - return ${Ke(e)}(0.0); + return ${Xe(e)}(0.0); } - fn mm_write(batch: i32, row : i32, col : i32, valueInput : ${Ke(e)}) { + fn mm_write(batch: i32, row : i32, col : i32, valueInput : ${Xe(e)}) { if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { var value = valueInput; let outCoord = vec4( @@ -6847,10 +6847,10 @@ return a / b;`,lJ=` col); result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${e}] = value; } - }`}var gce=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=W3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=B3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?d0(this.elementsPerThread,this.workgroupSize):p0(this.elementsPerThread,this.workgroupSize);return` - ${fce(this.isVec4?4:1)} + }`}var Rce=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=U3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=G3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?u0(this.elementsPerThread,this.workgroupSize):d0(this.elementsPerThread,this.workgroupSize);return` + ${Nce(this.isVec4?4:1)} ${e} - `}};function yce(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],m;if(W().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||d.dataFormat!=="channelsLast")m=new uce(d);else{m=new gce(d);let f=d.inHeight*d.inWidth,g=d.inChannels,y=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[f]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return a.runWebGPUProgram(m,[r,s],"float32",h)}var xce={kernelName:bi,backendName:"webgpu",kernelFunc:yce},Ace=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3dnaive"}getUserCode(){return` + `}};function Ece(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],m;if(B().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||d.dataFormat!=="channelsLast")m=new wce(d);else{m=new Rce(d);let f=d.inHeight*d.inWidth,g=d.inChannels,y=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[f]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return a.runWebGPUProgram(m,[r,s],"float32",h)}var Mce={kernelName:vi,backendName:"webgpu",kernelFunc:Ece},Pce=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3dnaive"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); @@ -6932,7 +6932,7 @@ return a / b;`,lJ=` } setOutputAtIndex(index, dotProd); } - }`}};function bce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=S.computeConv3DInfo(r.shape,s.shape,i,l,o),p=[u.padInfo.front,u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],d=new Ace(u),h=pa(r.dtype,s.dtype);return a.runWebGPUProgram(d,[r,s],h,c)}var vce={kernelName:vi,backendName:"webgpu",kernelFunc:bce};function wce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=S.computeConv3DInfo(r.shape,l,i,1,o),p=new pce(u),c=[{type:"int32",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.batchSize]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.inDepth]},{type:"int32",data:[u.inHeight]},{type:"int32",data:[u.inWidth]}];return a.runWebGPUProgram(p,[r,s],s.dtype,c)}var kce={kernelName:cu,backendName:"webgpu",kernelFunc:wce};function Ice(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,pad:o,inputShape:l}=n,u=S.computeConv3DInfo(l,s.shape,i,1,o),p=new cce(u),c=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.outChannels]}];return a.runWebGPUProgram(p,[r,s],r.dtype,c)}var Sce={kernelName:wi,backendName:"webgpu",kernelFunc:Ice},Cce=at({opType:le.COS}),Tce={kernelName:ki,backendName:"webgpu",kernelFunc:Cce},Nce=at({opType:le.COSH}),Rce={kernelName:Ii,backendName:"webgpu",kernelFunc:Nce},Ece=class{constructor(e,t,a,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,a[0],a[1],e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[a,n,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` + }`}};function _ce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeConv3DInfo(r.shape,s.shape,i,l,o),p=[u.padInfo.front,u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],d=new Pce(u),h=da(r.dtype,s.dtype);return a.runWebGPUProgram(d,[r,s],h,c)}var $ce={kernelName:wi,backendName:"webgpu",kernelFunc:_ce};function Fce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=C.computeConv3DInfo(r.shape,l,i,1,o),p=new Ice(u),c=[{type:"int32",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.batchSize]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.inDepth]},{type:"int32",data:[u.inHeight]},{type:"int32",data:[u.inWidth]}];return a.runWebGPUProgram(p,[r,s],s.dtype,c)}var Dce={kernelName:cu,backendName:"webgpu",kernelFunc:Fce};function Oce(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,pad:o,inputShape:l}=n,u=C.computeConv3DInfo(l,s.shape,i,1,o),p=new Sce(u),c=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.outChannels]}];return a.runWebGPUProgram(p,[r,s],r.dtype,c)}var zce={kernelName:ki,backendName:"webgpu",kernelFunc:Oce},Lce=at({opType:le.COS}),Wce={kernelName:Ii,backendName:"webgpu",kernelFunc:Lce},Bce=at({opType:le.COSH}),Vce={kernelName:Si,backendName:"webgpu",kernelFunc:Bce},Uce=class{constructor(e,t,a,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,a[0],a[1],e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[a,n,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -6988,23 +6988,23 @@ return a / b;`,lJ=` } } } - `}},Mce=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Ece(r.shape[3],s.shape,o,l),c=[{type:"float32",data:[u]}];return a.runWebGPUProgram(p,[r,s,i],"float32",c)},_ce={kernelName:Ti,backendName:"webgpu",kernelFunc:Mce},sp;(function(e){e.Prod="*",e.Sum="+"})(sp||(sp={}));var Q5=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=a,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===sp.Prod?"1.0":"0.0",a=this.exclusive?t:`getX(${eA(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],r="",s="";return this.exclusive?(r=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),` + `}},Gce=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Uce(r.shape[3],s.shape,o,l),c=[{type:"float32",data:[u]}];return a.runWebGPUProgram(p,[r,s,i],"float32",c)},Hce={kernelName:Ni,backendName:"webgpu",kernelFunc:Gce},np;(function(e){e.Prod="*",e.Sum="+"})(np||(np={}));var sA=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=a,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===np.Prod?"1.0":"0.0",a=this.exclusive?t:`getX(${iA(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],r="",s="";return this.exclusive?(r=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),` ${ue("index")} { if (index < uniforms.size) { var coords = getCoordsFromIndex(index); - let end = ${tA(e,"coords",this.op)}; + let end = ${oA(e,"coords",this.op)}; var val = ${a}; let pow2 = i32(pow(2.0, uniforms.index)); if (${r}) { let idx = ${s}; - ${tA(e,"coords",this.op)} = idx; - val ${this.op}= getX(${eA(e,"coords",this.op)}); + ${oA(e,"coords",this.op)} = idx; + val ${this.op}= getX(${iA(e,"coords",this.op)}); } setOutputAtIndex(index, val); } } - `}};function eA(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function tA(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function kk(e,t,a,n,r,s){let i=t.shape.length,o=S.getAxesPermutation([n],i),l=t;o!=null&&(l=ar({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=S.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=an({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new Q5(e,l.shape,!1,s),m=c,f=[{type:"float32",data:[d]}];c=a.runWebGPUProgram(h,[c],c.dtype,f),a.disposeData(m.dataId)}if(r){let d=new Q5(e,l.shape,r,s),h=c,m=[{type:"float32",data:[0]}];c=a.runWebGPUProgram(d,[c],c.dtype,m),a.disposeData(h.dataId)}if(o!=null){let d=S.getUndoAxesPermutation(o),h=ar({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeData(c.dataId),a.disposeData(l.dataId),h}return c}function Pce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return kk(sp.Prod,r,a,s,i,o)}var $ce={kernelName:Si,backendName:"webgpu",kernelFunc:Pce};function Fce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return kk(sp.Sum,r,a,s,i,o)}var Dce={kernelName:Ci,backendName:"webgpu",kernelFunc:Fce};function Oce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n,l=r.shape.length===1,u=v.sizeFromShape(s.shape)>0,p=s.dtype,c=l?[r.shape[0]]:[r.shape[0],r.shape[1]],d=l?[i]:[r.shape[0],i],h=Va({backend:a,attrs:{shape:d,value:0,dtype:p}}),m=new Ak(c,u,o),f=[{type:"int32",data:[i]}],g=u?[r,s]:[r];return a.runWebGPUProgram(m,g,p,f,h)}var zce={kernelName:hu,backendName:"webgpu",kernelFunc:Oce},Lce=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` + `}};function iA(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function oA(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Dk(e,t,a,n,r,s){let i=t.shape.length,o=C.getAxesPermutation([n],i),l=t;o!=null&&(l=ar({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=C.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=en({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new sA(e,l.shape,!1,s),m=c,f=[{type:"float32",data:[d]}];c=a.runWebGPUProgram(h,[c],c.dtype,f),a.disposeData(m.dataId)}if(r){let d=new sA(e,l.shape,r,s),h=c,m=[{type:"float32",data:[0]}];c=a.runWebGPUProgram(d,[c],c.dtype,m),a.disposeData(h.dataId)}if(o!=null){let d=C.getUndoAxesPermutation(o),h=ar({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeData(c.dataId),a.disposeData(l.dataId),h}return c}function jce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return Dk(np.Prod,r,a,s,i,o)}var qce={kernelName:Ci,backendName:"webgpu",kernelFunc:jce};function Xce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return Dk(np.Sum,r,a,s,i,o)}var Kce={kernelName:Ti,backendName:"webgpu",kernelFunc:Xce};function Yce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n,l=r.shape.length===1,u=v.sizeFromShape(s.shape)>0,p=s.dtype,c=l?[r.shape[0]]:[r.shape[0],r.shape[1]],d=l?[i]:[r.shape[0],i],h=Wa({backend:a,attrs:{shape:d,value:0,dtype:p}}),m=new Pk(c,u,o),f=[{type:"int32",data:[i]}],g=u?[r,s]:[r];return a.runWebGPUProgram(m,g,p,f,h)}var Zce={kernelName:hu,backendName:"webgpu",kernelFunc:Yce},Jce=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7024,8 +7024,8 @@ return a / b;`,lJ=` let rlt = ${this.getInputSamplingString()}; setOutputAtIndex(index, rlt); } - }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Wce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),m=i==="NHWC"?[o,c,d,h]:[o,h,c,d],f=[{type:"int32",data:[s]}],g=new Lce(m,i);return a.runWebGPUProgram(g,[r],r.dtype,f)}var Bce={kernelName:Ni,backendName:"webgpu",kernelFunc:Wce},Vce=class{constructor(e,t,a,n=!1,r=null,s=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=r,this.hasPreluActivation=s,this.filterHeight=t,this.filterWidth=a,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],a=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` - ${_r(this.activation,this.hasPreluActivation,!1,4)} + }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Qce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),m=i==="NHWC"?[o,c,d,h]:[o,h,c,d],f=[{type:"int32",data:[s]}],g=new Jce(m,i);return a.runWebGPUProgram(g,[r],r.dtype,f)}var ehe={kernelName:Ri,backendName:"webgpu",kernelFunc:Qce},the=class{constructor(e,t,a,n=!1,r=null,s=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=r,this.hasPreluActivation=s,this.filterHeight=t,this.filterWidth=a,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],a=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` + ${Pr(this.activation,this.hasPreluActivation,!1,4)} var mm_Asub : array, ${a}>; var mm_Bsub : array, ${this.filterHeight}>; @@ -7081,13 +7081,13 @@ return a / b;`,lJ=` value = fma(xVal, wVal, value); } } - ${rl(this.addBias,this.activation)} + ${sl(this.addBias,this.activation)} if (coordsInBounds4D(coords, uniforms.outShape)) { setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } - `}},Ik=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2, virtualWidth : i32,",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let r=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=me(r),this.dispatch=de(this.dispatchLayout,r,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${a}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,a=this.convInfo.strideWidth;return` - ${_r(this.activation,this.hasPreluActivation,!0,4)} + `}},Ok=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2, virtualWidth : i32,",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let r=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=me(r),this.dispatch=de(this.dispatchLayout,r,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${a}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,a=this.convInfo.strideWidth;return` + ${Pr(this.activation,this.hasPreluActivation,!0,4)} fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 { var value = vec4(0.0); if (col >=0 && col < uniforms.inDims[1]) { @@ -7136,14 +7136,14 @@ return a / b;`,lJ=` let coords = vec4(batch, r, c + i, d1); if (coordsInBounds4D(coords, uniforms.outShape)) { var value = dotProd[i]; - ${rl(this.addBias,this.activation)} + ${sl(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } } - `}},Sk=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32, + `}},zk=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32, filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` - ${_r(this.activation,this.hasPreluActivation,!1,4)} + ${Pr(this.activation,this.hasPreluActivation,!1,4)} ${ue("index")} { if (index < uniforms.size) { @@ -7204,11 +7204,11 @@ return a / b;`,lJ=` } } } - ${rl(this.addBias,this.activation)} + ${sl(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } - `}};function Uce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=S.convertConv2DDataFormat(l),d=u;d==null&&(d=[1,1]);let h=S.computeConv2DInfo(r.shape,s.shape,i,d,o,p,!0,c),m=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],f=h.dataFormat==="channelsLast",g;return!f&&h.inHeight>16&&h.inWidth>16&&h.strideHeight===1&&h.strideWidth===1&&h.dilationWidth===1&&h.dilationHeight===1&&h.inChannels===h.outChannels?g=new Vce(h.outShape,h.filterHeight,h.filterWidth):f&&h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(g=new Ik(h),m.push({type:"int32",data:[g.virtualWidth]})):(g=new Sk(h),m.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),a.runWebGPUProgram(g,[r,s],r.dtype,m)}var Gce={kernelName:Ri,backendName:"webgpu",kernelFunc:Uce},Hce=class{constructor(e){this.variableNames=["x","dy"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, + `}};function ahe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=C.convertConv2DDataFormat(l),d=u;d==null&&(d=[1,1]);let h=C.computeConv2DInfo(r.shape,s.shape,i,d,o,p,!0,c),m=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],f=h.dataFormat==="channelsLast",g;return!f&&h.inHeight>16&&h.inWidth>16&&h.strideHeight===1&&h.strideWidth===1&&h.dilationWidth===1&&h.dilationHeight===1&&h.inChannels===h.outChannels?g=new the(h.outShape,h.filterHeight,h.filterWidth):f&&h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(g=new Ok(h),m.push({type:"int32",data:[g.virtualWidth]})):(g=new zk(h),m.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),a.runWebGPUProgram(g,[r,s],r.dtype,m)}var nhe={kernelName:Ei,backendName:"webgpu",kernelFunc:ahe},rhe=class{constructor(e){this.variableNames=["x","dy"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_filter"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -7244,7 +7244,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, dotProd); } } - `}},jce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, + `}},she=class{constructor(e){this.variableNames=["dy","W"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_input"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -7287,7 +7287,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, dotProd); } } - `}};function qce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=S.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new Hce(c),h=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"int32",data:[c.inHeight]},{type:"int32",data:[c.inWidth]},{type:"int32",data:[c.batchSize]},{type:"int32",data:[c.outChannels/c.inChannels]}];return a.runWebGPUProgram(d,[r,s],"float32",h)}var Xce={kernelName:mp,backendName:"webgpu",kernelFunc:qce};function Kce(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=S.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new jce(c),h=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.filterHeight-1-c.padInfo.top,c.filterWidth-1-c.padInfo.left]},{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"int32",data:[c.outChannels/c.inChannels]}];return a.runWebGPUProgram(d,[r,s],r.dtype,h)}var Yce={kernelName:fp,backendName:"webgpu",kernelFunc:Kce},Zce=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return` + `}};function ihe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=C.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new rhe(c),h=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"int32",data:[c.inHeight]},{type:"int32",data:[c.inWidth]},{type:"int32",data:[c.batchSize]},{type:"int32",data:[c.outChannels/c.inChannels]}];return a.runWebGPUProgram(d,[r,s],"float32",h)}var ohe={kernelName:cp,backendName:"webgpu",kernelFunc:ihe};function lhe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=C.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new she(c),h=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.filterHeight-1-c.padInfo.top,c.filterWidth-1-c.padInfo.left]},{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"int32",data:[c.outChannels/c.inChannels]}];return a.runWebGPUProgram(d,[r,s],r.dtype,h)}var uhe={kernelName:hp,backendName:"webgpu",kernelFunc:lhe},dhe=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); @@ -7295,7 +7295,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, value); } } - `}};function Jce(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=ke({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new Zce(s),l=a.runWebGPUProgram(o,[i],i.dtype),u=ke({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var Qce={kernelName:mu,backendName:"webgpu",kernelFunc:Jce},ehe=class{constructor(e){this.variableNames=["x","w"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return` + `}};function phe(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=ke({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new dhe(s),l=a.runWebGPUProgram(o,[i],i.dtype),u=ke({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var che={kernelName:mu,backendName:"webgpu",kernelFunc:phe},hhe=class{constructor(e){this.variableNames=["x","w"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let neg_infinity = -3.4e38; @@ -7327,7 +7327,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, curVal); } } - `}};function the(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=S.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=[u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],d=new ehe(u);return a.runWebGPUProgram(d,[r,s],r.dtype,c)}var ahe={kernelName:Ei,backendName:"webgpu",kernelFunc:the},nhe=class{constructor(e,t){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.inShape,this.dispatchLayout=me(e.outShape),this.dispatch=de(this.dispatchLayout,e.outShape,this.workgroupSize),t!=="float32"&&t!=="int32")throw new Error(`Dilation2DBackpropInput only supports float32 and int32 + `}};function mhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=[u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],d=new hhe(u);return a.runWebGPUProgram(d,[r,s],r.dtype,c)}var fhe={kernelName:Mi,backendName:"webgpu",kernelFunc:mhe},ghe=class{constructor(e,t){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.inShape,this.dispatchLayout=me(e.outShape),this.dispatch=de(this.dispatchLayout,e.outShape,this.workgroupSize),t!=="float32"&&t!=="int32")throw new Error(`Dilation2DBackpropInput only supports float32 and int32 types, does not support ${t} type.`);this.type=t,this.shaderKey="dilation2DBackpropInput"}getUserCode(){return` ${ue("index")} { if (index < uniforms.dySize) { @@ -7368,10 +7368,10 @@ return a / b;`,lJ=` let flatIndexIn = d + uniforms.xShape[3] * (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b)); let value = getDy(b, r, c, d); - ${ms("&result[flatIndexIn]","value",this.type)} + ${gs("&result[flatIndexIn]","value",this.type)} } } - `}},rhe=class{constructor(e,t,a){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.filterShape,this.dispatchLayout=me(e.outShape),this.dispatch=de(this.dispatchLayout,e.outShape,this.workgroupSize),a!=="float32"&&a!=="int32")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32 + `}},yhe=class{constructor(e,t,a){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.filterShape,this.dispatchLayout=me(e.outShape),this.dispatch=de(this.dispatchLayout,e.outShape,this.workgroupSize),a!=="float32"&&a!=="int32")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32 types, does not support ${a} type.`);this.type=a,this.shaderKey="dilation2DBackpropFilter"}getUserCode(){return` ${ue("index")} { if (index < uniforms.dySize) { @@ -7411,10 +7411,10 @@ return a / b;`,lJ=` let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]); let value = getDy(b, r, c, d); - ${ms("&result[flatIndexIn]","value",this.type)} + ${gs("&result[flatIndexIn]","value",this.type)} } } - `}};function she(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n,p=S.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=s.dtype,d=new rhe(p,s.shape,c),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[v.sizeFromShape(p.outShape)]}],m=Va({backend:a,attrs:{shape:s.shape,value:0,dtype:c}});return a.runWebGPUProgram(d,[r,s,i],c,h,m)}var ihe={kernelName:Ul,backendName:"webgpu",kernelFunc:she};function ohe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n,p=S.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=r.dtype,d=new nhe(p,c),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[v.sizeFromShape(p.outShape)]}],m=Va({backend:a,attrs:{shape:p.inShape,value:0,dtype:c}});return a.runWebGPUProgram(d,[r,s,i],c,h,m)}var lhe={kernelName:Vl,backendName:"webgpu",kernelFunc:ohe},uhe=class{constructor(e,t,a){this.variableNames=["Image"],this.uniforms="alpha: f32,",this.workgroupSize=[64,1,1],this.pixelsOpType=eu.DRAW,this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=t,this.textureFormat=a,this.shaderKey=`draw_${t}_${a}`}getUserCode(){let e,t=this.type==="float32"?"value":"value / 255.0";return e=` + `}};function xhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n,p=C.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=s.dtype,d=new yhe(p,s.shape,c),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[v.sizeFromShape(p.outShape)]}],m=Wa({backend:a,attrs:{shape:s.shape,value:0,dtype:c}});return a.runWebGPUProgram(d,[r,s,i],c,h,m)}var Ahe={kernelName:Hl,backendName:"webgpu",kernelFunc:xhe};function bhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n,p=C.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=r.dtype,d=new ghe(p,c),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[v.sizeFromShape(p.outShape)]}],m=Wa({backend:a,attrs:{shape:p.inShape,value:0,dtype:c}});return a.runWebGPUProgram(d,[r,s,i],c,h,m)}var vhe={kernelName:Gl,backendName:"webgpu",kernelFunc:bhe},whe=class{constructor(e,t,a){this.variableNames=["Image"],this.uniforms="alpha: f32,",this.workgroupSize=[64,1,1],this.pixelsOpType=eu.DRAW,this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=t,this.textureFormat=a,this.shaderKey=`draw_${t}_${a}`}getUserCode(){let e,t=this.type==="float32"?"value":"value / 255.0";return e=` if (uniforms.numChannels == 1) { rgba[0] = ${t}; rgba[1] = ${t}; @@ -7437,7 +7437,7 @@ return a / b;`,lJ=` textureStore(outImage, vec2(coords.yx), rgba); } } - `}};function dhe(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{canvas:s,options:i}=n,[o,l]=r.shape.slice(0,2),{imageOptions:u}=i||{},p=(u==null?void 0:u.alpha)||1,c=a.device.features.has("bgra8unorm-storage")?"bgra8unorm":"rgba8unorm",d=[o,l],h=new uhe(d,r.dtype,c);s.width=l,s.height=o;let m="webgpu",f=s.getContext(m),g;f||(g=new OffscreenCanvas(l,o),f=g.getContext(m));let y=r.shape.length===3?r.shape[2]:1;f.configure({device:a.device,format:c,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:"premultiplied"});let x="int32",A=a.makeTensorInfo(d,x),b=a.tensorMap.get(A.dataId);b.resource=f.getCurrentTexture(),b.external=!0;let w=[{type:"uint32",data:[y]},{type:"float32",data:[p]}];if(a.runWebGPUProgram(h,[r],x,w,A),g){let I=s.getContext("2d");if(!I)throw new Error("Please make sure this canvas has only been used for 2d or webgpu context!");I.drawImage(g,0,0)}return a.disposeData(A.dataId),r}var phe={kernelName:gp,backendName:"webgpu",kernelFunc:dhe},Ck=ta({opType:Pe.MUL,cpuKernelImpl:gde,supportsComplex:!0}),che={kernelName:mo,backendName:"webgpu",kernelFunc:Ck};function Tk(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return il(r,s,i,"sum",a)}var hhe={kernelName:Bo,backendName:"webgpu",kernelFunc:Tk};function mhe(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=S.decodeEinsumEquation(r,s.length);S.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=S.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,m=[];for(let f=0;f=0&&(d=Tk({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeData(f.dataId);return d}var fhe={kernelName:yp,backendName:"webgpu",kernelFunc:mhe},ghe=at({opType:le.ELU}),yhe={kernelName:_i,backendName:"webgpu",kernelFunc:ghe},xhe=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=new Ch(Pe.ELU_DER,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],n.dtype)},Ahe={kernelName:fu,backendName:"webgpu",kernelFunc:xhe},bhe=ta({opType:Pe.EQUAL,dtype:"bool",cpuKernelImpl:tde}),vhe={kernelName:$i,backendName:"webgpu",kernelFunc:bhe},whe=at({opType:le.ERF}),khe={kernelName:Pi,backendName:"webgpu",kernelFunc:whe},Ihe=at({opType:le.EXP,cpuKernelImpl:ade,dtype:"float32"}),She={kernelName:Fi,backendName:"webgpu",kernelFunc:Ihe};function X1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ke({inputs:{x:s},backend:n,attrs:{shape:o}})}var Che={kernelName:gu,backendName:"webgpu",kernelFunc:X1},The=at({opType:le.EXPM1,cpuKernelImpl:nde}),Nhe={kernelName:Di,backendName:"webgpu",kernelFunc:The},aA=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` + `}};function khe(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{canvas:s,options:i}=n,[o,l]=r.shape.slice(0,2),{imageOptions:u}=i||{},p=(u==null?void 0:u.alpha)||1,c=a.device.features.has("bgra8unorm-storage")?"bgra8unorm":"rgba8unorm",d=[o,l],h=new whe(d,r.dtype,c);s.width=l,s.height=o;let m="webgpu",f=s.getContext(m),g;f||(g=new OffscreenCanvas(l,o),f=g.getContext(m));let y=r.shape.length===3?r.shape[2]:1;f.configure({device:a.device,format:c,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:"premultiplied"});let x="int32",A=a.makeTensorInfo(d,x),b=a.tensorMap.get(A.dataId);b.resource=f.getCurrentTexture(),b.external=!0;let w=[{type:"uint32",data:[y]},{type:"float32",data:[p]}];if(a.runWebGPUProgram(h,[r],x,w,A),g){let I=s.getContext("2d");if(!I)throw new Error("Please make sure this canvas has only been used for 2d or webgpu context!");I.drawImage(g,0,0)}return a.disposeData(A.dataId),r}var Ihe={kernelName:mp,backendName:"webgpu",kernelFunc:khe},Lk=ea({opType:_e.MUL,cpuKernelImpl:Rde,supportsComplex:!0}),She={kernelName:fo,backendName:"webgpu",kernelFunc:Lk};function Wk(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return ol(r,s,i,"sum",a)}var Che={kernelName:Vo,backendName:"webgpu",kernelFunc:Wk};function The(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(r,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=C.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,m=[];for(let f=0;f=0&&(d=Wk({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeData(f.dataId);return d}var Nhe={kernelName:fp,backendName:"webgpu",kernelFunc:The},Rhe=at({opType:le.ELU}),Ehe={kernelName:_i,backendName:"webgpu",kernelFunc:Rhe},Mhe=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=new Ch(_e.ELU_DER,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],n.dtype)},Phe={kernelName:fu,backendName:"webgpu",kernelFunc:Mhe},_he=ea({opType:_e.EQUAL,dtype:"bool",cpuKernelImpl:mde}),$he={kernelName:Fi,backendName:"webgpu",kernelFunc:_he},Fhe=at({opType:le.ERF}),Dhe={kernelName:$i,backendName:"webgpu",kernelFunc:Fhe},Ohe=at({opType:le.EXP,cpuKernelImpl:fde,dtype:"float32"}),zhe={kernelName:Di,backendName:"webgpu",kernelFunc:Ohe};function K1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ke({inputs:{x:s},backend:n,attrs:{shape:o}})}var Lhe={kernelName:gu,backendName:"webgpu",kernelFunc:K1},Whe=at({opType:le.EXPM1,cpuKernelImpl:gde}),Bhe={kernelName:Oi,backendName:"webgpu",kernelFunc:Whe},lA=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 { ${this.component==="real"?"return real * expR - imag * expI;":"return real * expI + imag * expR;"} } @@ -7470,7 +7470,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, mulMatDFT(coords[0], coords[1])); } } - `}};function Nk(e,t,a){let n=a.tensorMap.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=[],l=ke({inputs:{x:e},backend:a,attrs:{shape:[i,s]}});o.push(l);let u=l.shape,p=new aA("real",u),c=new aA("imag",u),d=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],h=t?2*Math.PI:-2*Math.PI,m=t?u[1]:1,f=[{type:"float32",data:[h]},{type:"float32",data:[m]}],g=a.runWebGPUProgram(p,d,"float32",f);o.push(g);let y=a.runWebGPUProgram(c,d,"float32",f);o.push(y);let x=sl({inputs:{real:g,imag:y},backend:a});o.push(x);let A=ke({inputs:{x},backend:a,attrs:{shape:e.shape}});return o.forEach(b=>a.disposeData(b.dataId)),A}function Rhe(e){let{inputs:t,backend:a}=e,{input:n}=t;return Nk(n,!1,a)}var Ehe={kernelName:xp,backendName:"webgpu",kernelFunc:Rhe},Mhe=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` + `}};function Bk(e,t,a){let n=a.tensorMap.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=[],l=ke({inputs:{x:e},backend:a,attrs:{shape:[i,s]}});o.push(l);let u=l.shape,p=new lA("real",u),c=new lA("imag",u),d=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],h=t?2*Math.PI:-2*Math.PI,m=t?u[1]:1,f=[{type:"float32",data:[h]},{type:"float32",data:[m]}],g=a.runWebGPUProgram(p,d,"float32",f);o.push(g);let y=a.runWebGPUProgram(c,d,"float32",f);o.push(y);let x=il({inputs:{real:g,imag:y},backend:a});o.push(x);let A=ke({inputs:{x},backend:a,attrs:{shape:e.shape}});return o.forEach(b=>a.disposeData(b.dataId)),A}function Vhe(e){let{inputs:t,backend:a}=e,{input:n}=t;return Bk(n,!1,a)}var Uhe={kernelName:gp,backendName:"webgpu",kernelFunc:Vhe},Ghe=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7479,7 +7479,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, outputValue); } } - `}},_he={kernelName:Oi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new Mhe(a.shape);return n.runWebGPUProgram(r,[a],a.dtype)}},Phe=at({opType:le.FLOOR,cpuKernelImpl:rde}),$he={kernelName:zi,backendName:"webgpu",kernelFunc:Phe},Fhe=ta({opType:Pe.FLOOR_DIV,cpuKernelImpl:sde,dtype:"int32"}),Dhe={kernelName:Li,backendName:"webgpu",kernelFunc:Fhe},Ohe=class{constructor(e,t,a=!1){this.pixelsOpType=eu.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=a,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` + `}},Hhe={kernelName:zi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new Ghe(a.shape);return n.runWebGPUProgram(r,[a],a.dtype)}},jhe=at({opType:le.FLOOR,cpuKernelImpl:yde}),qhe={kernelName:Li,backendName:"webgpu",kernelFunc:jhe},Xhe=ea({opType:_e.FLOOR_DIV,cpuKernelImpl:xde,dtype:"int32"}),Khe={kernelName:Wi,backendName:"webgpu",kernelFunc:Xhe},Yhe=class{constructor(e,t,a=!1){this.pixelsOpType=eu.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=a,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` @binding(1) @group(0) var src: ${this.importVideo?"texture_external":"texture_2d"}; ${ue("index")} { let flatIndex = index * uniforms.numChannels; @@ -7491,7 +7491,7 @@ return a / b;`,lJ=` } } } - `}},zhe={kernelName:zd,backendName:"webgpu",kernelFunc:Lhe},_l,e1=W().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function Lhe(e){let{inputs:t,backend:a,attrs:n}=e,{pixels:r}=t,{numChannels:s}=n;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[p,c]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[c,p,s],h=!1,m=i||o;if(u||l||m){let x;if(h)x=a.device.importExternalTexture({source:r});else{if(m){let _=W().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(_l==null||_!==e1)&&(e1=_,_l=document.createElement("canvas").getContext("2d",{willReadFrequently:e1})),_l.canvas.width=p,_l.canvas.height=c,_l.drawImage(r,0,0,p,c),r=_l.canvas}let P=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,E="rgba8unorm",C=a.textureManager.acquireTexture(d[1],d[0],E,P);a.queue.copyExternalImageToTexture({source:r},{texture:C},[d[1],d[0]]),x=C}let A=v.sizeFromShape(d),b=v.computeStrides(d),w=new Ohe(d,s,h),I=[{type:"uint32",data:[A]},{type:"uint32",data:[s]},{type:"uint32",data:[...b]}],T=a.makeTensorInfo([c,p],"int32"),N=a.tensorMap.get(T.dataId);N.resource=x;let M=a.runWebGPUProgram(w,[T],"int32",I);return a.disposeData(T.dataId),M}let f=r.data,g=f;if(s!=null&&s!==4){g=new Uint8Array(r.width*r.height*s);let x=f.length,A=0;for(let b=0;b(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } } - `}},Bhe={kernelName:Wi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n,scale:r,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=a,p=[n,i,o],c=null;s!=null&&(c=s.shape,p.push(s));let d=null;r!=null&&(d=r.shape,p.push(r));let h=new Whe(n.shape,i.shape,o.shape,c,d),m=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,p,n.dtype,m)}};function Vhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=S.convertConv2DDataFormat(p),g=S.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!1,f);return wk({x:r,filter:s,convInfo:g,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:m,activation:h})}var Uhe={kernelName:Yr,backendName:"webgpu",kernelFunc:Vhe};function Ghe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:c,activation:d,leakyreluAlpha:h}=n,m=p;m==null&&(m=[1,1]),v.assert(S.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let f=S.computeConv2DInfo(r.shape,s.shape,l,m,u,c,!0),g=[r,s],y=i!=null,x=o!=null;y&&g.push(i),x&&g.push(o);let A=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.inHeight,f.inWidth]}],b;return f.outHeight>4&&f.outWidth>4&&f.strideWidth<=2&&f.inChannels===f.outChannels&&f.dilationHeight===1&&f.dilationWidth===1&&f.inChannels%4===0?(b=new Ik(f,y,d,x),A.push({type:"int32",data:[b.virtualWidth]})):(b=new Sk(f,y,d,x),A.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]})),d==="leakyrelu"&&(A.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),a.runWebGPUProgram(b,g,"float32",A)}var Hhe={kernelName:Zr,backendName:"webgpu",kernelFunc:Ghe},jhe=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${$t(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` + `}},e0e={kernelName:Bi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n,scale:r,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=a,p=[n,i,o],c=null;s!=null&&(c=s.shape,p.push(s));let d=null;r!=null&&(d=r.shape,p.push(r));let h=new Qhe(n.shape,i.shape,o.shape,c,d),m=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,p,n.dtype,m)}};function t0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=C.convertConv2DDataFormat(p),g=C.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!1,f);return Fk({x:r,filter:s,convInfo:g,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:m,activation:h})}var a0e={kernelName:Zr,backendName:"webgpu",kernelFunc:t0e};function n0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:c,activation:d,leakyreluAlpha:h}=n,m=p;m==null&&(m=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let f=C.computeConv2DInfo(r.shape,s.shape,l,m,u,c,!0),g=[r,s],y=i!=null,x=o!=null;y&&g.push(i),x&&g.push(o);let A=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.inHeight,f.inWidth]}],b;return f.outHeight>4&&f.outWidth>4&&f.strideWidth<=2&&f.inChannels===f.outChannels&&f.dilationHeight===1&&f.dilationWidth===1&&f.inChannels%4===0?(b=new Ok(f,y,d,x),A.push({type:"int32",data:[b.virtualWidth]})):(b=new zk(f,y,d,x),A.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]})),d==="leakyrelu"&&(A.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),a.runWebGPUProgram(b,g,"float32",A)}var r0e={kernelName:Jr,backendName:"webgpu",kernelFunc:n0e},s0e=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${_t(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7518,7 +7518,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, getA(flattenIndex, coords[1])); } } - `}};function qhe(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=r.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,p,c]=S.prepareAndValidate(n,r),d=ke({inputs:{x:r},backend:a,attrs:{shape:[u,i]}}),h=ke({inputs:{x:n},backend:a,attrs:{shape:[v.sizeFromShape(n.shape)/p,p]}});if(a.shouldExecuteOnCPU([n,r])||n.dtype==="string"){let x=a.readSync(r.dataId),A=a.bufferSync(n),b=ide(x,A,n.dtype,u,i,p,c,n.shape,o);return a.makeTensorInfo(l,n.dtype,b.values)}let m=new jhe(i,[u,p]),f=[{type:"int32",data:[i]},{type:"int32",data:c}],g=a.runWebGPUProgram(m,[h,d],h.dtype,f),y=ke({inputs:{x:g},backend:a,attrs:{shape:l}});return a.disposeData(d.dataId),a.disposeData(h.dataId),a.disposeData(g.dataId),y}var Xhe={kernelName:Bi,backendName:"webgpu",kernelFunc:qhe},Khe=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=Yhe(this.aShape);return` + `}};function i0e(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=r.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,p,c]=C.prepareAndValidate(n,r),d=ke({inputs:{x:r},backend:a,attrs:{shape:[u,i]}}),h=ke({inputs:{x:n},backend:a,attrs:{shape:[v.sizeFromShape(n.shape)/p,p]}});if(a.shouldExecuteOnCPU([n,r])||n.dtype==="string"){let x=a.readSync(r.dataId),A=a.bufferSync(n),b=Ade(x,A,n.dtype,u,i,p,c,n.shape,o);return a.makeTensorInfo(l,n.dtype,b.values)}let m=new s0e(i,[u,p]),f=[{type:"int32",data:[i]},{type:"int32",data:c}],g=a.runWebGPUProgram(m,[h,d],h.dtype,f),y=ke({inputs:{x:g},backend:a,attrs:{shape:l}});return a.disposeData(d.dataId),a.disposeData(h.dataId),a.disposeData(g.dataId),y}var o0e={kernelName:Vi,backendName:"webgpu",kernelFunc:i0e},l0e=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=u0e(this.aShape);return` ${ue("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); @@ -7527,13 +7527,13 @@ return a / b;`,lJ=` setOutputAtIndex(index, inBounds * getA(${e})); } } - `}};function Yhe(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;na.disposeData(T.dataId)),a.makeTensorInfo(u.outputShape,I.dtype,I.values)}let f=new Khe(d.shape,m),g=a.runWebGPUProgram(f,[d,h],d.dtype);c.push(g);let y=ke({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(x=>a.disposeData(x.dataId)),y}var Zhe={kernelName:xu,backendName:"webgpu",kernelFunc:Rk},Jhe=ta({opType:Pe.GREATER,cpuKernelImpl:ude,dtype:"bool"}),Qhe={kernelName:Vi,backendName:"webgpu",kernelFunc:Jhe},e0e=ta({opType:Pe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:lde}),t0e={kernelName:Ui,backendName:"webgpu",kernelFunc:e0e};function a0e(e){let{inputs:t,backend:a}=e,{input:n}=t;return Nk(n,!0,a)}var n0e={kernelName:Ap,backendName:"webgpu",kernelFunc:a0e},r0e=at({opType:le.IS_FINITE,dtype:"bool"}),s0e={kernelName:Hi,backendName:"webgpu",kernelFunc:r0e},i0e=at({opType:le.IS_INF,dtype:"bool"}),o0e={kernelName:ji,backendName:"webgpu",kernelFunc:i0e},l0e=at({opType:le.IS_NAN,dtype:"bool"}),u0e={kernelName:qi,backendName:"webgpu",kernelFunc:l0e};function d0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new Qu(r.shape,le.LEAKYRELU,"alpha : f32,");return a.runWebGPUProgram(o,[r],"float32",i)}var p0e={kernelName:Xi,backendName:"webgpu",kernelFunc:d0e},c0e=ta({opType:Pe.LESS,dtype:"bool",cpuKernelImpl:pde}),h0e={kernelName:Ki,backendName:"webgpu",kernelFunc:c0e},m0e=ta({opType:Pe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:dde}),f0e={kernelName:Yi,backendName:"webgpu",kernelFunc:m0e},g0e=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return` + `}};function u0e(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;na.disposeData(T.dataId)),a.makeTensorInfo(u.outputShape,I.dtype,I.values)}let f=new l0e(d.shape,m),g=a.runWebGPUProgram(f,[d,h],d.dtype);c.push(g);let y=ke({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(x=>a.disposeData(x.dataId)),y}var d0e={kernelName:xu,backendName:"webgpu",kernelFunc:Vk},p0e=ea({opType:_e.GREATER,cpuKernelImpl:wde,dtype:"bool"}),c0e={kernelName:Ui,backendName:"webgpu",kernelFunc:p0e},h0e=ea({opType:_e.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:vde}),m0e={kernelName:Gi,backendName:"webgpu",kernelFunc:h0e};function f0e(e){let{inputs:t,backend:a}=e,{input:n}=t;return Bk(n,!0,a)}var g0e={kernelName:yp,backendName:"webgpu",kernelFunc:f0e},y0e=at({opType:le.IS_FINITE,dtype:"bool"}),x0e={kernelName:ji,backendName:"webgpu",kernelFunc:y0e},A0e=at({opType:le.IS_INF,dtype:"bool"}),b0e={kernelName:qi,backendName:"webgpu",kernelFunc:A0e},v0e=at({opType:le.IS_NAN,dtype:"bool"}),w0e={kernelName:Xi,backendName:"webgpu",kernelFunc:v0e};function k0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new Ju(r.shape,le.LEAKYRELU,"alpha : f32,");return a.runWebGPUProgram(o,[r],"float32",i)}var I0e={kernelName:Ki,backendName:"webgpu",kernelFunc:k0e},S0e=ea({opType:_e.LESS,dtype:"bool",cpuKernelImpl:Ide}),C0e={kernelName:Yi,backendName:"webgpu",kernelFunc:S0e},T0e=ea({opType:_e.LESS_EQUAL,dtype:"bool",cpuKernelImpl:kde}),N0e={kernelName:Zi,backendName:"webgpu",kernelFunc:T0e},R0e=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step); } } - `}};function y0e(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=(r-n)/(s-1),o=new g0e(s),l=[{type:"float32",data:[n]},{type:"float32",data:[i]}];return t.runWebGPUProgram(o,[],"float32",l)}var x0e={kernelName:Zi,backendName:"webgpu",kernelFunc:y0e},A0e=at({opType:le.LOG,cpuKernelImpl:cde}),b0e={kernelName:Ji,backendName:"webgpu",kernelFunc:A0e},v0e=at({opType:le.LOG1P}),w0e={kernelName:Qi,backendName:"webgpu",kernelFunc:v0e},k0e=ta({opType:Pe.LOGICAL_AND,dtype:"bool"}),I0e={kernelName:eo,backendName:"webgpu",kernelFunc:k0e},S0e=at({opType:le.LOGICAL_NOT}),C0e={kernelName:to,backendName:"webgpu",kernelFunc:S0e},T0e=ta({opType:Pe.LOGICAL_OR}),N0e={kernelName:ao,backendName:"webgpu",kernelFunc:T0e},Ek=` + `}};function E0e(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=(r-n)/(s-1),o=new R0e(s),l=[{type:"float32",data:[n]},{type:"float32",data:[i]}];return t.runWebGPUProgram(o,[],"float32",l)}var M0e={kernelName:Ji,backendName:"webgpu",kernelFunc:E0e},P0e=at({opType:le.LOG,cpuKernelImpl:Sde}),_0e={kernelName:Qi,backendName:"webgpu",kernelFunc:P0e},$0e=at({opType:le.LOG1P}),F0e={kernelName:eo,backendName:"webgpu",kernelFunc:$0e},D0e=ea({opType:_e.LOGICAL_AND,dtype:"bool"}),O0e={kernelName:to,backendName:"webgpu",kernelFunc:D0e},z0e=at({opType:le.LOGICAL_NOT}),L0e={kernelName:ao,backendName:"webgpu",kernelFunc:z0e},W0e=ea({opType:_e.LOGICAL_OR}),B0e={kernelName:no,backendName:"webgpu",kernelFunc:W0e},Uk=` var powValue = 0.0; let basis = uniforms.bias + uniforms.alpha * sum; if (uniforms.beta == 0.5) { @@ -7543,7 +7543,7 @@ return a / b;`,lJ=` } else { powValue = exp(log(basis) * (-uniforms.beta)); } -`,R0e=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn"}getUserCode(){return` +`,V0e=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); @@ -7561,12 +7561,12 @@ return a / b;`,lJ=` sum = sum + z * z; } } - ${Ek} + ${Uk} setOutputAtIndex(index, x * powValue); } } - `}},E0e=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,v.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=de(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey="lrn_shared"}getUserCode(){return` + `}},U0e=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,v.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=de(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey="lrn_shared"}getUserCode(){return` var lrnSub: array; const elementsPerWorkgroup = ${this.elementsPerWorkgroup}; const maxAllowRadius = ${this.maxAllowRadius}; @@ -7594,11 +7594,11 @@ return a / b;`,lJ=` let z = lrnSub[index + i]; sum = sum + z * z; } - ${Ek} + ${Uk} setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue); } - } `}};function M0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u;s>16?u=new R0e(r.shape):u=new E0e(r.shape,s);let p=[{type:"int32",data:[s]},{type:"float32",data:[i]},{type:"float32",data:[o]},{type:"float32",data:[l]}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var _0e={kernelName:no,backendName:"webgpu",kernelFunc:M0e},P0e=class{constructor(e){this.outputShape=[],this.variableNames=["inputImage","outputImage","dy"],this.uniforms="depthRadius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn_grad"}getUserCode(){return` + } `}};function G0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u;s>16?u=new V0e(r.shape):u=new U0e(r.shape,s);let p=[{type:"int32",data:[s]},{type:"float32",data:[i]},{type:"float32",data:[o]},{type:"float32",data:[l]}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var H0e={kernelName:ro,backendName:"webgpu",kernelFunc:G0e},j0e=class{constructor(e){this.outputShape=[],this.variableNames=["inputImage","outputImage","dy"],this.uniforms="depthRadius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn_grad"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); @@ -7648,7 +7648,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, result); } } - `}};function $0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new P0e(r.shape),d=[{type:"int32",data:[o]},{type:"float32",data:[l]},{type:"float32",data:[u]},{type:"float32",data:[p]}];return a.runWebGPUProgram(c,[r,s,i],r.dtype,d)}var F0e={kernelName:Au,backendName:"webgpu",kernelFunc:$0e},D0e=ta({opType:Pe.MAX,cpuKernelImpl:mde}),O0e={kernelName:so,backendName:"webgpu",kernelFunc:D0e};function z0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=S.computePool2DInfo(r.shape,s,i,u,o,l);return xk(r,p,"max",a)}var L0e={kernelName:io,backendName:"webgpu",kernelFunc:z0e};function W0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=S.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new j3(c,"max"),h=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.front,c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.inDepth,c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]}];return a.runWebGPUProgram(d,[r],r.dtype,h)}var B0e={kernelName:bu,backendName:"webgpu",kernelFunc:W0e},V0e=class{constructor(e){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, + `}};function q0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new j0e(r.shape),d=[{type:"int32",data:[o]},{type:"float32",data:[l]},{type:"float32",data:[u]},{type:"float32",data:[p]}];return a.runWebGPUProgram(c,[r,s,i],r.dtype,d)}var X0e={kernelName:Au,backendName:"webgpu",kernelFunc:q0e},K0e=ea({opType:_e.MAX,cpuKernelImpl:Tde}),Y0e={kernelName:io,backendName:"webgpu",kernelFunc:K0e};function Z0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=C.computePool2DInfo(r.shape,s,i,u,o,l);return Mk(r,p,"max",a)}var J0e={kernelName:oo,backendName:"webgpu",kernelFunc:Z0e};function Q0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=C.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new Y3(c,"max"),h=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.front,c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.inDepth,c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]}];return a.runWebGPUProgram(d,[r],r.dtype,h)}var eme={kernelName:bu,backendName:"webgpu",kernelFunc:Q0e},tme=class{constructor(e){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool2DBackprop"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -7693,7 +7693,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, dotProd); } } - `}},U0e=class{constructor(e){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, + `}},ame=class{constructor(e){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool3DBackprop"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -7751,7 +7751,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, dotProd); } } - `}};function G0e(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=S.computePool3DInfo(i.shape,o,l,c,u,p),h=new j3(d,"max",!0),m=[{type:"int32",data:[d.strideDepth,d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.front,d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.inDepth,d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth]}],f=a.runWebGPUProgram(h,[i],"int32",m),g=new U0e(d);m=[{type:"int32",data:[d.strideDepth,d.strideHeight,d.strideWidth]},{type:"int32",data:[d.effectiveFilterDepth-1-d.padInfo.front,d.effectiveFilterHeight-1-d.padInfo.top,d.effectiveFilterWidth-1-d.padInfo.left]},{type:"int32",data:[d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth]},{type:"int32",data:[d.outDepth]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]}];let y=a.runWebGPUProgram(g,[r,f],i.dtype,m);return a.disposeData(f.dataId),y}var H0e={kernelName:wp,backendName:"webgpu",kernelFunc:G0e};function j0e(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;U3([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=S.computePool2DInfo(o.shape,l,u,1,p,c),h=new rp(d,"max",!0),m=[{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]}],f=a.runWebGPUProgram(h,[o],"int32",m),g=new V0e(d);m=[{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.effectiveFilterHeight-1-d.padInfo.top,d.effectiveFilterWidth-1-d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]}];let y=a.runWebGPUProgram(g,[r,f],o.dtype,m);return a.disposeData(f.dataId),y}var q0e={kernelName:vp,backendName:"webgpu",kernelFunc:j0e};function X0e(e){let{inputs:t,backend:a,attrs:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=n,{x:l}=t;v.assert(l.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${l.shape.length}.`);let u=[1,1];v.assert(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=S.computePool2DInfo(l.shape,r,s,u,i),c=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]}],d=new rp(p,"max",!1),h=a.runWebGPUProgram(d,[l],l.dtype,c);d=new rp(p,"max",!0,!0,o);let m=a.runWebGPUProgram(d,[l],"int32",c);return[h,m]}var K0e={kernelName:vu,backendName:"webgpu",kernelFunc:X0e};function Y0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return il(r,s,i,"min",a)}var Z0e={kernelName:lo,backendName:"webgpu",kernelFunc:Y0e},J0e=ta({opType:Pe.MIN,cpuKernelImpl:fde}),Q0e={kernelName:uo,backendName:"webgpu",kernelFunc:J0e},eme=class{constructor(e,t,a){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,r)=>n[0]+e[r]+n[1]),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,r)=>{this.uniforms+=` pad${r} : vec2,`}),this.offset=a==="reflect"?0:1,this.shaderKey=`mirrorPad_${a}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),a=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",r=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=$t(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` + `}};function nme(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,c,u,p),h=new Y3(d,"max",!0),m=[{type:"int32",data:[d.strideDepth,d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.front,d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.inDepth,d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth]}],f=a.runWebGPUProgram(h,[i],"int32",m),g=new ame(d);m=[{type:"int32",data:[d.strideDepth,d.strideHeight,d.strideWidth]},{type:"int32",data:[d.effectiveFilterDepth-1-d.padInfo.front,d.effectiveFilterHeight-1-d.padInfo.top,d.effectiveFilterWidth-1-d.padInfo.left]},{type:"int32",data:[d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth]},{type:"int32",data:[d.outDepth]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]}];let y=a.runWebGPUProgram(g,[r,f],i.dtype,m);return a.disposeData(f.dataId),y}var rme={kernelName:bp,backendName:"webgpu",kernelFunc:nme};function sme(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;j3([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=C.computePool2DInfo(o.shape,l,u,1,p,c),h=new ap(d,"max",!0),m=[{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]}],f=a.runWebGPUProgram(h,[o],"int32",m),g=new tme(d);m=[{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.effectiveFilterHeight-1-d.padInfo.top,d.effectiveFilterWidth-1-d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]}];let y=a.runWebGPUProgram(g,[r,f],o.dtype,m);return a.disposeData(f.dataId),y}var ime={kernelName:Ap,backendName:"webgpu",kernelFunc:sme};function ome(e){let{inputs:t,backend:a,attrs:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=n,{x:l}=t;v.assert(l.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${l.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=C.computePool2DInfo(l.shape,r,s,u,i),c=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]}],d=new ap(p,"max",!1),h=a.runWebGPUProgram(d,[l],l.dtype,c);d=new ap(p,"max",!0,!0,o);let m=a.runWebGPUProgram(d,[l],"int32",c);return[h,m]}var lme={kernelName:vu,backendName:"webgpu",kernelFunc:ome};function ume(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return ol(r,s,i,"min",a)}var dme={kernelName:uo,backendName:"webgpu",kernelFunc:ume},pme=ea({opType:_e.MIN,cpuKernelImpl:Nde}),cme={kernelName:po,backendName:"webgpu",kernelFunc:pme},hme=class{constructor(e,t,a){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,r)=>n[0]+e[r]+n[1]),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,r)=>{this.uniforms+=` pad${r} : vec2,`}),this.offset=a==="reflect"?0:1,this.shaderKey=`mirrorPad_${a}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),a=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",r=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=_t(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` ${ue("index")} { if (index < uniforms.size) { let start = ${i}(${t}); @@ -7768,7 +7768,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, getX(${o})); } } - `}},tme={kernelName:po,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{paddings:r,mode:s}=t,i=a,o=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new eme(n.shape,r,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}},ame=ta({opType:Pe.MOD}),nme={kernelName:co,backendName:"webgpu",kernelFunc:ame},rme=class{constructor(e,t){this.variableNames=["probs"],this.outputShape=[],this.uniforms="seed : f32, numOutcomes: i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="multinomial"}getUserCode(){return` + `}},mme={kernelName:co,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{paddings:r,mode:s}=t,i=a,o=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new hme(n.shape,r,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}},fme=ea({opType:_e.MOD}),gme={kernelName:ho,backendName:"webgpu",kernelFunc:fme},yme=class{constructor(e,t){this.variableNames=["probs"],this.outputShape=[],this.uniforms="seed : f32, numOutcomes: i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="multinomial"}getUserCode(){return` //Based on the work of Dave Hoskins //https://www.shadertoy.com/view/4djSRW fn random (seed : f32, resultUV : vec2) -> f32 { @@ -7801,7 +7801,7 @@ return a / b;`,lJ=` setOutputAtIndexI32(index, uniforms.numOutcomes - 1); } } - `}},sme=class{constructor(e){this.variableNames=["logits"],this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey="softmax"}getUserCode(){return` + `}},xme=class{constructor(e){this.variableNames=["logits"],this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey="softmax"}getUserCode(){return` var buf : array; var rowMaxShared : f32; var rowSumShared : f32; @@ -7860,7 +7860,7 @@ return a / b;`,lJ=` setOutputAtCoords(row, col, value); } } - `}};function Mk(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=ke({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape)/r.shape[s],r.shape[s]]}}),o=new sme(i.shape),l=a.runWebGPUProgram(o,[i],r.dtype),u=ke({inputs:{x:l},backend:a,attrs:{shape:r.shape}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var ime={kernelName:Vo,backendName:"webgpu",kernelFunc:Mk};function ome(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:Mk({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new rme(u,s),d=[{type:"float32",data:[i]},{type:"int32",data:[p]}],h=a.runWebGPUProgram(c,[l],"int32",d);return o||a.disposeData(l.dataId),h}var lme={kernelName:ho,backendName:"webgpu",kernelFunc:ome};function ume(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.tensorMap.get(n.dataId),[i,o]=yde(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r=new Qu(n.shape,le.NEG);return a.runWebGPUProgram(r,[n],n.dtype)}var dme={kernelName:wu,backendName:"webgpu",kernelFunc:ume};function pme(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),{selectedIndices:c}=En.nonMaxSuppressionV3Impl(u,p,i,o,l);return a.makeTensorInfo([c.length],"int32",new Int32Array(c))}var cme={kernelName:go,backendName:"webgpu",kernelFunc:pme};function hme(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),d=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=En.nonMaxSuppressionV5Impl(p,c,d,h,m,f);return[a.makeTensorInfo([g.length],"int32",new Int32Array(g)),a.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var mme={kernelName:yo,backendName:"webgpu",kernelFunc:hme},fme=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` + `}};function Gk(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=ke({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape)/r.shape[s],r.shape[s]]}}),o=new xme(i.shape),l=a.runWebGPUProgram(o,[i],r.dtype),u=ke({inputs:{x:l},backend:a,attrs:{shape:r.shape}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var Ame={kernelName:Uo,backendName:"webgpu",kernelFunc:Gk};function bme(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:Gk({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new yme(u,s),d=[{type:"float32",data:[i]},{type:"int32",data:[p]}],h=a.runWebGPUProgram(c,[l],"int32",d);return o||a.disposeData(l.dataId),h}var vme={kernelName:mo,backendName:"webgpu",kernelFunc:bme};function wme(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.tensorMap.get(n.dataId),[i,o]=Ede(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r=new Ju(n.shape,le.NEG);return a.runWebGPUProgram(r,[n],n.dtype)}var kme={kernelName:wu,backendName:"webgpu",kernelFunc:wme};function Ime(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),{selectedIndices:c}=Rn.nonMaxSuppressionV3Impl(u,p,i,o,l);return a.makeTensorInfo([c.length],"int32",new Int32Array(c))}var Sme={kernelName:yo,backendName:"webgpu",kernelFunc:Ime};function Cme(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),d=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=Rn.nonMaxSuppressionV5Impl(p,c,d,h,m,f);return[a.makeTensorInfo([g.length],"int32",new Int32Array(g)),a.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Tme={kernelName:xo,backendName:"webgpu",kernelFunc:Cme},Nme=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` ${ue("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7868,7 +7868,7 @@ return a / b;`,lJ=` f32(i32(round(getX(coords.x))) == coords.y))); } } - `}};function gme(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new fme(u,i),c=ke({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=[{type:"float32",data:[o]},{type:"float32",data:[l]}],h=a.runWebGPUProgram(p,[c],s,d);a.disposeData(c.dataId);let m=[...r.shape,i],f=ke({inputs:{x:h},backend:a,attrs:{shape:m}});return a.disposeData(h.dataId),f}var yme={kernelName:xo,backendName:"webgpu",kernelFunc:gme};function Nh(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=ec({inputs:{input:n},backend:a}),s=Nh({inputs:{x:r},backend:a}),i=h0({inputs:{input:n},backend:a}),o=Nh({inputs:{x:i},backend:a}),l=sl({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Va({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var xme={kernelName:Lu,backendName:"webgpu",kernelFunc:Nh};function _k(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=ec({inputs:{input:n},backend:a}),s=_k({inputs:{x:r},backend:a}),i=h0({inputs:{input:n},backend:a}),o=Nh({inputs:{x:i},backend:a}),l=sl({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Va({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var Ame={kernelName:Iu,backendName:"webgpu",kernelFunc:_k};function bme(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return X1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=X1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=vk({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeData(p.dataId)),u}var vme={kernelName:Su,backendName:"webgpu",kernelFunc:bme};function Pk(e,t=!1){let a=e.length,n=$t(a),r=e.map((c,d)=>`uniforms.pad${d}[0]`).join(","),s=e.map((c,d)=>`uniforms.pad${d}[0] + uniforms.xShape${a>1?`[${d}]`:""}`).join(","),i=a>1?`${n}(${r})`:`${r}`,o=a>1?`${n}(${s})`:`${s}`,l=a>1?"any(paddedCoords < start)":"paddedCoords < start",u=a>1?"any(paddedCoords >= end)":"paddedCoords >= end",p=a>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a):"coords";return` + `}};function Rme(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new Nme(u,i),c=ke({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=[{type:"float32",data:[o]},{type:"float32",data:[l]}],h=a.runWebGPUProgram(p,[c],s,d);a.disposeData(c.dataId);let m=[...r.shape,i],f=ke({inputs:{x:h},backend:a,attrs:{shape:m}});return a.disposeData(h.dataId),f}var Eme={kernelName:Ao,backendName:"webgpu",kernelFunc:Rme};function Nh(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=Qp({inputs:{input:n},backend:a}),s=Nh({inputs:{x:r},backend:a}),i=c0({inputs:{input:n},backend:a}),o=Nh({inputs:{x:i},backend:a}),l=il({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Wa({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var Mme={kernelName:Lu,backendName:"webgpu",kernelFunc:Nh};function Hk(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=Qp({inputs:{input:n},backend:a}),s=Hk({inputs:{x:r},backend:a}),i=c0({inputs:{input:n},backend:a}),o=Nh({inputs:{x:i},backend:a}),l=il({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Wa({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var Pme={kernelName:Iu,backendName:"webgpu",kernelFunc:Hk};function _me(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return K1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=K1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=$k({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeData(p.dataId)),u}var $me={kernelName:Su,backendName:"webgpu",kernelFunc:_me};function jk(e,t=!1){let a=e.length,n=_t(a),r=e.map((c,d)=>`uniforms.pad${d}[0]`).join(","),s=e.map((c,d)=>`uniforms.pad${d}[0] + uniforms.xShape${a>1?`[${d}]`:""}`).join(","),i=a>1?`${n}(${r})`:`${r}`,o=a>1?`${n}(${s})`:`${s}`,l=a>1?"any(paddedCoords < start)":"paddedCoords < start",u=a>1?"any(paddedCoords >= end)":"paddedCoords >= end",p=a>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a):"coords";return` let start = ${i}; let end = ${o}; if (${l} || ${u}) { @@ -7877,14 +7877,14 @@ return a / b;`,lJ=` let coords = paddedCoords - start; setOutputAtIndex(index, getX(${p})); } - `}var wme=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((a,n)=>a[0]+e[n]+a[1]),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((a,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){return` + `}var Fme=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((a,n)=>a[0]+e[n]+a[1]),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((a,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let paddedCoords = getCoordsFromIndex(index); - ${Pk(this.xShape)} + ${jk(this.xShape)} } } - `}},kme=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return an({inputs:{x:r},backend:a});if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Va({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new wme(r.shape,s);return a.runWebGPUProgram(l,[r],r.dtype,o)},Ime={kernelName:Ao,backendName:"webgpu",kernelFunc:kme},Sme=ta({opType:Pe.POW}),Cme={kernelName:bo,backendName:"webgpu",kernelFunc:Sme};function Tme(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=new Ch(Pe.PRELU,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],"float32")}var Nme={kernelName:vo,backendName:"webgpu",kernelFunc:Tme};function Rme(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return il(r,s,i,"prod",a)}var Eme={kernelName:wo,backendName:"webgpu",kernelFunc:Rme},Mme=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=bde(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},_me={kernelName:Cu,backendName:"webgpu",kernelFunc:Mme},Pme=ta({opType:Pe.DIV}),$me={kernelName:Mi,backendName:"webgpu",kernelFunc:Pme},Fme=at({opType:le.RECIPROCAL}),Dme={kernelName:ko,backendName:"webgpu",kernelFunc:Fme},Ome=at({opType:le.RELU}),zme={kernelName:Io,backendName:"webgpu",kernelFunc:Ome},Lme=at({opType:le.RELU6}),Wme={kernelName:To,backendName:"webgpu",kernelFunc:Lme},Bme=class{constructor(e,t,a){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` + `}},Dme=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return en({inputs:{x:r},backend:a});if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Wa({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new Fme(r.shape,s);return a.runWebGPUProgram(l,[r],r.dtype,o)},Ome={kernelName:bo,backendName:"webgpu",kernelFunc:Dme},zme=ea({opType:_e.POW}),Lme={kernelName:vo,backendName:"webgpu",kernelFunc:zme};function Wme(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=new Ch(_e.PRELU,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],"float32")}var Bme={kernelName:wo,backendName:"webgpu",kernelFunc:Wme};function Vme(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return ol(r,s,i,"prod",a)}var Ume={kernelName:ko,backendName:"webgpu",kernelFunc:Vme},Gme=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=_de(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},Hme={kernelName:Cu,backendName:"webgpu",kernelFunc:Gme},jme=ea({opType:_e.DIV}),qme={kernelName:Pi,backendName:"webgpu",kernelFunc:jme},Xme=at({opType:le.RECIPROCAL}),Kme={kernelName:Io,backendName:"webgpu",kernelFunc:Xme},Yme=at({opType:le.RELU}),Zme={kernelName:So,backendName:"webgpu",kernelFunc:Yme},Jme=at({opType:le.RELU6}),Qme={kernelName:No,backendName:"webgpu",kernelFunc:Jme},efe=class{constructor(e,t,a){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7927,7 +7927,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, newValue); } } - `}};function Vme(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[o?.5:0]}],h=new Bme(r.shape,l,u);return a.runWebGPUProgram(h,[r],"float32",d)}var Ume={kernelName:Co,backendName:"webgpu",kernelFunc:Vme},Gme=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32, + `}};function tfe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[o?.5:0]}],h=new efe(r.shape,l,u);return a.runWebGPUProgram(h,[r],"float32",d)}var afe={kernelName:To,backendName:"webgpu",kernelFunc:tfe},nfe=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32, invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeBilinearBackprop_${t}`}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -8002,7 +8002,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, accumulator); } } - `}};function Hme(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,[,o,l]=r.shape,[,u,p]=s.shape,c=[i&&u>1?o-1:o,i&&p>1?l-1:l],d=[i&&u>1?u-1:u,i&&p>1?p-1:p],h=c[0]/d[0],m=c[1]/d[1],f=1/h,g=1/m,y=Math.ceil(f)*2+2,x=Math.ceil(g)*2+2,A=new Gme(r.shape,i),b=[{type:"int32",data:c},{type:"int32",data:d},{type:"float32",data:[h]},{type:"float32",data:[m]},{type:"float32",data:[f]},{type:"float32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[x]}];return a.runWebGPUProgram(A,[s],s.dtype,b)}var jme={kernelName:Ru,backendName:"webgpu",kernelFunc:Hme},qme=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="vec2(rc) * effectiveInputOverOutputRatioRC",` + `}};function rfe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,[,o,l]=r.shape,[,u,p]=s.shape,c=[i&&u>1?o-1:o,i&&p>1?l-1:l],d=[i&&u>1?u-1:u,i&&p>1?p-1:p],h=c[0]/d[0],m=c[1]/d[1],f=1/h,g=1/m,y=Math.ceil(f)*2+2,x=Math.ceil(g)*2+2,A=new nfe(r.shape,i),b=[{type:"int32",data:c},{type:"int32",data:d},{type:"float32",data:[h]},{type:"float32",data:[m]},{type:"float32",data:[f]},{type:"float32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[x]}];return a.runWebGPUProgram(A,[s],s.dtype,b)}var sfe={kernelName:Ru,backendName:"webgpu",kernelFunc:rfe},ife=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="vec2(rc) * effectiveInputOverOutputRatioRC",` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -8033,7 +8033,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, newValue); } } - `}};function Xme(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[s?.5:0]}],h=new qme(r.shape,l,u,i);return a.runWebGPUProgram(h,[r],r.dtype,d)}var Kme={kernelName:So,backendName:"webgpu",kernelFunc:Xme},Yme=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, + `}};function ofe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[s?.5:0]}],h=new ife(r.shape,l,u,i);return a.runWebGPUProgram(h,[r],r.dtype,d)}var lfe={kernelName:Co,backendName:"webgpu",kernelFunc:ofe},ufe=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeNearestNeigborBackprop_${t}`}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -8093,7 +8093,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, accumulator); } } - `}};function Zme(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,[,o,l]=r.shape,[,u,p]=s.shape,c=[i&&u>1?o-1:o,i&&p>1?l-1:l],d=[i&&u>1?u-1:u,i&&p>1?p-1:p],h=c[0]/d[0],m=c[1]/d[1],f=1/h,g=1/m,y=Math.ceil(f)*2+2,x=Math.ceil(g)*2+2,A=new Yme(r.shape,i),b=[{type:"int32",data:c},{type:"int32",data:d},{type:"float32",data:[f]},{type:"float32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[x]}];return a.runWebGPUProgram(A,[s],s.dtype,b)}var Jme={kernelName:Nu,backendName:"webgpu",kernelFunc:Zme},Qme=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` + `}};function dfe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,[,o,l]=r.shape,[,u,p]=s.shape,c=[i&&u>1?o-1:o,i&&p>1?l-1:l],d=[i&&u>1?u-1:u,i&&p>1?p-1:p],h=c[0]/d[0],m=c[1]/d[1],f=1/h,g=1/m,y=Math.ceil(f)*2+2,x=Math.ceil(g)*2+2,A=new ufe(r.shape,i),b=[{type:"int32",data:c},{type:"int32",data:d},{type:"float32",data:[f]},{type:"float32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[x]}];return a.runWebGPUProgram(A,[s],s.dtype,b)}var pfe={kernelName:Nu,backendName:"webgpu",kernelFunc:dfe},cfe=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` // Using uniform variables as judging conditions, so the function has // coherent execution within all threads. @@ -8123,7 +8123,7 @@ return a / b;`,lJ=` reverseCoords[1], reverseCoords[2], reverseCoords[3])); } } - `}};function efe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length;if(i===0)return an({inputs:{x:r},backend:a});let o=r.shape,l=[1,1,1,1];o.forEach((g,y)=>{let x=y+4-i;l[x]=g});let u=v.parseAxisParam(s,r.shape),p=[0,0,0,0];u.forEach(g=>{let y=g+4-i;p[y]=1});let c=[{type:"int32",data:p}],d=ke({inputs:{x:r},backend:a,attrs:{shape:l}}),h=new Qme(l),m=a.runWebGPUProgram(h,[d],d.dtype,c);a.disposeData(d.dataId);let f=ke({inputs:{x:m},backend:a,attrs:{shape:o}});return a.disposeData(m.dataId),f}var tfe={kernelName:No,backendName:"webgpu",kernelFunc:efe},afe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, + `}};function hfe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length;if(i===0)return en({inputs:{x:r},backend:a});let o=r.shape,l=[1,1,1,1];o.forEach((g,y)=>{let x=y+4-i;l[x]=g});let u=v.parseAxisParam(s,r.shape),p=[0,0,0,0];u.forEach(g=>{let y=g+4-i;p[y]=1});let c=[{type:"int32",data:p}],d=ke({inputs:{x:r},backend:a,attrs:{shape:l}}),h=new cfe(l),m=a.runWebGPUProgram(h,[d],d.dtype,c);a.disposeData(d.dataId);let f=ke({inputs:{x:m},backend:a,attrs:{shape:o}});return a.disposeData(m.dataId),f}var mfe={kernelName:Ro,backendName:"webgpu",kernelFunc:hfe},ffe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -8144,7 +8144,7 @@ return a / b;`,lJ=` setOutputAtIndex(index, outputValue); } } - `}},nfe={kernelName:Zo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new afe(n.shape,s),[u,p]=S.getImageCenter(i,n.shape[1],n.shape[2]),c=[{type:"float32",data:[u]},{type:"float32",data:[p]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof s=="number"?c.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):c.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,c)}},rfe=at({opType:le.ROUND}),sfe={kernelName:Ro,backendName:"webgpu",kernelFunc:rfe},ife=at({opType:le.RSQRT,cpuKernelImpl:vde}),ofe={kernelName:Eo,backendName:"webgpu",kernelFunc:ife},Dd=class{constructor(e,t,a,n,r,s,i,o=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.sumDupeIndices=o,this.dispatchLayout=me(e),this.dispatch=de(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${a}_${n}_${this.sliceDimGreaterThanOne}_${i}_${o}`;let l=$t(r.length);this.uniforms=`sliceDim : i32, strides: ${l}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=a}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,a=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",r="";this.dispatchLayout.x.length===1?(n="flattenedIndex",r=` + `}},gfe={kernelName:Jo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new ffe(n.shape,s),[u,p]=C.getImageCenter(i,n.shape[1],n.shape[2]),c=[{type:"float32",data:[u]},{type:"float32",data:[p]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof s=="number"?c.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):c.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,c)}},yfe=at({opType:le.ROUND}),xfe={kernelName:Eo,backendName:"webgpu",kernelFunc:yfe},Afe=at({opType:le.RSQRT,cpuKernelImpl:$de}),bfe={kernelName:Mo,backendName:"webgpu",kernelFunc:Afe},Fd=class{constructor(e,t,a,n,r,s,i,o=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.sumDupeIndices=o,this.dispatchLayout=me(e),this.dispatch=de(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${a}_${n}_${this.sliceDimGreaterThanOne}_${i}_${o}_${r.length}`;let l=_t(r.length);this.uniforms=`sliceDim : i32, strides: ${l}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=a}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,a=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",r="";this.dispatchLayout.x.length===1?(n="flattenedIndex",r=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 { return index; } @@ -8170,12 +8170,12 @@ return a / b;`,lJ=` flattenedIndex = flattenedIndex + indexInside * ${a}; } let updateValue = - ${Us(this.type)}(${s}); + ${Gs(this.type)}(${s}); let flatIndex = getOutputIndexFromCoords(${n}); - ${this.sumDupeIndices?ms("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} + ${this.sumDupeIndices?gs("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} } - }`}};function lfe(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=S.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=ke({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),m=ke({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),f=m.dtype,g=Va({backend:a,attrs:{shape:d,value:0,dtype:f}}),y=v.sizeFromShape(m.shape),x=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[y]}],A=new Dd(m.shape,o,h.shape.length,m.shape.length,p,d,f),b=a.runWebGPUProgram(A,[m,h],f,x,g),w=ke({inputs:{x:b},backend:a,attrs:{shape:i}});return a.disposeData(h.dataId),a.disposeData(m.dataId),a.disposeData(b.dataId),w}var ufe={kernelName:Mo,backendName:"webgpu",kernelFunc:lfe},dfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return` + }`}};function vfe(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=C.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=ke({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),m=ke({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),f=m.dtype,g=Wa({backend:a,attrs:{shape:d,value:0,dtype:f}}),y=v.sizeFromShape(m.shape),x=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[y]}],A=new Fd(m.shape,o,h.shape.length,m.shape.length,p,d,f),b=a.runWebGPUProgram(A,[m,h],f,x,g),w=ke({inputs:{x:b},backend:a,attrs:{shape:i}});return a.disposeData(h.dataId),a.disposeData(m.dataId),a.disposeData(b.dataId),w}var wfe={kernelName:Po,backendName:"webgpu",kernelFunc:vfe},kfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return` fn findBound(batch: i32, value: f32) -> i32 { var left = i32(0); var right = uniforms.numInputs; @@ -8197,7 +8197,7 @@ return a / b;`,lJ=` setOutputAtIndexI32(index, findBound(coords[0], value)); } } - `}};function pfe(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new dfe([s.shape[0],s.shape[1]],i),l=[{type:"int32",data:[r.shape[1]]}];return a.runWebGPUProgram(o,[r,s],"int32",l)}var cfe={kernelName:Po,backendName:"webgpu",kernelFunc:pfe},hfe=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=a,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],r=[];for(let s=0;s4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],r=[];for(let s=0;s{this.uniforms+=` pad${l} : vec2,`}),this.shaderKey=`spaceToBatchND_${r}`}getUserCode(){let e=$t(this.outputShape.length),t=mk(this.newDim);return` - ${oh(this.paddedXShape,"PaddedX")} + `}};function Tfe(e){let{inputs:t,backend:a}=e,{condition:n,t:r,e:s}=t,i=new Cfe(n.shape.length,r.shape,r.shape.length);return a.runWebGPUProgram(i,[n,r,s],da(r.dtype,s.dtype))}var Nfe={kernelName:Eu,backendName:"webgpu",kernelFunc:Tfe},Rfe=at({opType:le.SELU}),Efe={kernelName:Fo,backendName:"webgpu",kernelFunc:Rfe},Mfe=at({opType:le.SIGMOID}),Pfe={kernelName:Lo,backendName:"webgpu",kernelFunc:Mfe},_fe=at({opType:le.SIGN}),$fe={kernelName:zo,backendName:"webgpu",kernelFunc:_fe},Ffe=at({opType:le.SIN}),Dfe={kernelName:Do,backendName:"webgpu",kernelFunc:Ffe},Ofe=at({opType:le.SINH}),zfe={kernelName:Oo,backendName:"webgpu",kernelFunc:Ofe},Lfe=at({opType:le.SOFTPLUS}),Wfe={kernelName:Wo,backendName:"webgpu",kernelFunc:Lfe},Bfe=class{constructor(e,t,a,n,r,s){this.variableNames=["x"],this.outputShape=[],this.uniforms="",this.workgroupSize=[64,1,1],this.size=!0;let i=new Array(n.length);for(let o=0;o{this.uniforms+=` pad${l} : vec2,`}),this.shaderKey=`spaceToBatchND_${r}`}getUserCode(){let e=_t(this.outputShape.length),t=Tk(this.newDim);return` + ${ih(this.paddedXShape,"PaddedX")} ${ue("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${e}(${t}), uniforms.reshapedPaddedXShape); let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex); - ${Pk(this.xShape,!0)} + ${jk(this.xShape,!0)} } } - `}},Rfe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=[[0,0]];l.push(...i);for(let x=1+s.length;xx[0]+r.shape[A]+x[1]),p=S.getReshaped(u,s,o,!1),c=S.getPermuted(p.length,s.length,!1),d=S.getReshapedPermuted(u,s,o,!1),h=v.computeStrides(u),m=new Nfe(r.shape,u,l,p,c,h.length),f=[{type:"int32",data:p},{type:"int32",data:h}];l.map(x=>f.push({type:"int32",data:[x[0],x[1]]}));let g=a.runWebGPUProgram(m,[r],r.dtype,f),y=ke({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeData(g.dataId),y},Efe={kernelName:_u,backendName:"webgpu",kernelFunc:Rfe},Mfe=class{constructor(e,t,a){this.variableNames=["input","indices","segmentIds"],this.outputShape=[],this.uniforms="segmentSize : i32, sparseSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e,this.type=a,this.dispatchLayout=me([t]),this.dispatch=de(this.dispatchLayout,[t],this.workgroupSize),this.shaderKey="sparseSegmentSum"}getUserCode(){return` + `}},Vfe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=[[0,0]];l.push(...i);for(let x=1+s.length;xx[0]+r.shape[A]+x[1]),p=C.getReshaped(u,s,o,!1),c=C.getPermuted(p.length,s.length,!1),d=C.getReshapedPermuted(u,s,o,!1),h=v.computeStrides(u),m=new Bfe(r.shape,u,l,p,c,h.length),f=[{type:"int32",data:p},{type:"int32",data:h}];l.map(x=>f.push({type:"int32",data:[x[0],x[1]]}));let g=a.runWebGPUProgram(m,[r],r.dtype,f),y=ke({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeData(g.dataId),y},Ufe={kernelName:Pu,backendName:"webgpu",kernelFunc:Vfe},Gfe=class{constructor(e,t,a){this.variableNames=["input","indices","segmentIds"],this.outputShape=[],this.uniforms="segmentSize : i32, sparseSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e,this.type=a,this.dispatchLayout=me([t]),this.dispatch=de(this.dispatchLayout,[t],this.workgroupSize),this.shaderKey="sparseSegmentSum"}getUserCode(){return` ${ue("index")} { if (index < uniforms.sparseSize) { let indexInSegmentIds = index / uniforms.segmentSize; @@ -8229,17 +8229,17 @@ return a / b;`,lJ=` let value = input[indexInInput * uniforms.segmentSize + indexInSegment]; let outIndex = segmentId * uniforms.segmentSize + indexInSegment; - ${ms("&result[outIndex]","value",this.type)} + ${gs("&result[outIndex]","value",this.type)} } } - `}},_fe=class{constructor(e,t){this.variableNames=["segmentIds"],this.outputShape=[],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=[e],this.dispatchLayout=me(t),this.dispatch=de(this.dispatchLayout,t,this.workgroupSize),this.shaderKey="sparseSegmentIdCountProgram"}getUserCode(){return` + `}},Hfe=class{constructor(e,t){this.variableNames=["segmentIds"],this.outputShape=[],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=[e],this.dispatchLayout=me(t),this.dispatch=de(this.dispatchLayout,t,this.workgroupSize),this.shaderKey="sparseSegmentIdCountProgram"}getUserCode(){return` ${ue("index")} { if (index < uniforms.segmentIdsShape) { let segmentId = segmentIds[index]; - ${ms("&result[segmentId]","1","int32")} + ${gs("&result[segmentId]","1","int32")} } } - `}},Pfe=class{constructor(e,t){this.variableNames=["segmentSum","sameSegmentIdCount"],this.outputShape=[],this.uniforms="segmentSize : i32",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.type=t,this.dispatchLayout=me(e),this.dispatch=de(this.dispatchLayout,e,this.workgroupSize),this.shaderKey="sparseSegmentMean"}getUserCode(){return` + `}},jfe=class{constructor(e,t){this.variableNames=["segmentSum","sameSegmentIdCount"],this.outputShape=[],this.uniforms="segmentSize : i32",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.type=t,this.dispatchLayout=me(e),this.dispatch=de(this.dispatchLayout,e,this.workgroupSize),this.shaderKey="sparseSegmentMean"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let segmentId = index / uniforms.segmentSize; @@ -8249,21 +8249,21 @@ return a / b;`,lJ=` } } } - `}};function $k(e,t,a,n=!1,r){let s=v.sizeFromShape(e.shape)/e.shape[0],i=e.dtype,o=v.sizeFromShape(t.shape),l=r.readSync(a.dataId),u=o>0?l[o-1]+1:0,p,c=e.shape.slice();c[0]=u;let d=o*s,h=Va({backend:r,attrs:{shape:c,value:0,dtype:i}});p=new Mfe(c,d,i);let m=[{type:"int32",data:[s]},{type:"int32",data:[d]}],f=r.runWebGPUProgram(p,[e,t,a],i,m,h);if(n)return f;let g=Va({backend:r,attrs:{shape:[u],value:0,dtype:"int32"}});p=new _fe(u,a.shape);let y=r.runWebGPUProgram(p,[a],"int32",null,g),x=Va({backend:r,attrs:{shape:c,value:0,dtype:i}});p=new Pfe(c,i),m=[{type:"int32",data:[s]}];let A=r.runWebGPUProgram(p,[f,y],i,m,x);return r.disposeData(f.dataId),r.disposeData(y.dataId),A}function $fe(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;return $k(n,r,s,!1,a)}var Ffe={kernelName:Fu,backendName:"webgpu",kernelFunc:$fe};function Dfe(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;return $k(n,r,s,!0,a)}var Ofe={kernelName:Du,backendName:"webgpu",kernelFunc:Dfe},zfe=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n0?l[o-1]+1:0,p,c=e.shape.slice();c[0]=u;let d=o*s,h=Wa({backend:r,attrs:{shape:c,value:0,dtype:i}});p=new Gfe(c,d,i);let m=[{type:"int32",data:[s]},{type:"int32",data:[d]}],f=r.runWebGPUProgram(p,[e,t,a],i,m,h);if(n)return f;let g=Wa({backend:r,attrs:{shape:[u],value:0,dtype:"int32"}});p=new Hfe(u,a.shape);let y=r.runWebGPUProgram(p,[a],"int32",null,g),x=Wa({backend:r,attrs:{shape:c,value:0,dtype:i}});p=new jfe(c,i),m=[{type:"int32",data:[s]}];let A=r.runWebGPUProgram(p,[f,y],i,m,x);return r.disposeData(f.dataId),r.disposeData(y.dataId),A}function qfe(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;return qk(n,r,s,!1,a)}var Xfe={kernelName:Fu,backendName:"webgpu",kernelFunc:qfe};function Kfe(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;return qk(n,r,s,!0,a)}var Yfe={kernelName:Du,backendName:"webgpu",kernelFunc:Kfe},Zfe=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let 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t2e={kernelName:Go,backendName:"webgpu",kernelFunc:e2e};function a2e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=C.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let m=Qu({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,m})}var n2e={kernelName:_u,backendName:"webgpu",kernelFunc:a2e},r2e=at({opType:le.SQRT}),s2e={kernelName:Bo,backendName:"webgpu",kernelFunc:r2e},i2e={kernelName:kp,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:a}=e,n=t,r=new Ju(a.shape,le.SQUARE);return n.runWebGPUProgram(r,[a],a.dtype)}},o2e=ea({opType:_e.SQUARED_DIFFERENCE}),l2e={kernelName:Ho,backendName:"webgpu",kernelFunc:o2e};function u2e({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=new Ju(n.shape,le.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[t.alpha]}];return a.runWebGPUProgram(r,[n],n.dtype,s)}var d2e={kernelName:us,backendName:"webgpu",kernelFunc:u2e},p2e=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=_t(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let a=0;t=this.outputShape.map((n,r)=>(a++,this.outputShape.length===1?`coords * uniforms.strides[${r}] + uniforms.begin[${r}]`:`coords[${a-1}] * uniforms.strides[${r}] + uniforms.begin[${r}]`)).join(",")}return` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, getX(${t})); } } - `}};function 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f=ke({inputs:{x:i},backend:a,attrs:{shape:[l,u]}});h.push(f);let g=ke({inputs:{x:r},backend:a,attrs:{shape:d}});h.push(g);let y=q3({inputs:{x:g},backend:a,attrs:{reps:Array(d.length).fill(1)}}),x=new Dd([l,u],o,m.shape.length,f.shape.length,p,d,r.dtype,!1),A=v.sizeFromShape([l,u]),b=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[A]}],w=a.runWebGPUProgram(x,[f,m],g.dtype,b,y);h.push(w);let I=ke({inputs:{x:w},backend:a,attrs:{shape:r.shape}});return h.forEach(T=>a.disposeData(T.dataId)),I}var d2e={kernelName:_o,backendName:"webgpu",kernelFunc:u2e},p2e=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, + `}};function c2e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Nt.sliceInfo(r.shape,s,i,o,l,u,p,c,d),w;if(f)w=ke({inputs:{x:r},backend:a,attrs:{shape:m}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let I=Nt.computeOutShape(x,A,b),T=Qu({inputs:{x:r},backend:a,attrs:{begin:x,size:I}});w=ke({inputs:{x:T},backend:a,attrs:{shape:m}}),a.disposeData(T.dataId)}else if(a.shouldExecuteOnCPU([r])){let I=a.readSync(r.dataId),T=$e(r.shape,r.dtype,I),N=zde(h,T,b,x);w=a.makeTensorInfo(m,r.dtype,N.values)}else{let I=new p2e(h),T=[{type:"int32",data:x},{type:"int32",data:b}],N=a.runWebGPUProgram(I,[r],r.dtype,T);w=ke({inputs:{x:N},backend:a,attrs:{shape:m}}),a.disposeData(N.dataId)}return w}var h2e={kernelName:jo,backendName:"webgpu",kernelFunc:c2e};function 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f=ke({inputs:{x:i},backend:a,attrs:{shape:[l,u]}});h.push(f);let g=ke({inputs:{x:r},backend:a,attrs:{shape:d}});h.push(g);let y=Z3({inputs:{x:g},backend:a,attrs:{reps:Array(d.length).fill(1)}}),x=new Fd([l,u],o,m.shape.length,f.shape.length,p,d,r.dtype,!1),A=v.sizeFromShape([l,u]),b=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[A]}],w=a.runWebGPUProgram(x,[f,m],g.dtype,b,y);h.push(w);let I=ke({inputs:{x:w},backend:a,attrs:{shape:r.shape}});return h.forEach(T=>a.disposeData(T.dataId)),I}var k2e={kernelName:_o,backendName:"webgpu",kernelFunc:w2e},I2e=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -8331,7 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F2e={kernelName:Np,backendName:"webgpu",kernelFunc:$2e},D2e=[ode,Hde,qde,Kde,Zde,epe,ope,upe,ppe,hpe,fpe,ype,Ape,vpe,kpe,Npe,Epe,$pe,Dpe,zpe,Upe,qpe,Ype,ece,ace,ice,ude,uce,hce,vce,Tce,Mce,$ce,Dce,zce,Wce,Vce,Hce,qce,Kce,Zce,ehe,ohe,uhe,nhe,che,fhe,Ahe,vhe,Ihe,Nhe,Ehe,Phe,$he,Dhe,zhe,Lhe,Bhe,Uhe,rde,Hhe,Zhe,qhe,Khe,e0e,a0e,r0e,o0e,d0e,c0e,m0e,lde,g0e,pce,x0e,b0e,w0e,I0e,C0e,N0e,M0e,F0e,_0e,O0e,L0e,B0e,H0e,X0e,Spe,Y0e,J0e,ime,eme,rme,lme,Cpe,dme,cme,mme,gme,vme,She,kme,Sme,Tme,Zpe,Eme,Pme,$me,Ome,Lme,Bme,Ume,Hme,Jpe,qme,Kme,Zme,Qme,sde,afe,sfe,lfe,pfe,mfe,gfe,xfe,bfe,wfe,Sfe,Nfe,Efe,Pfe,$fe,Dfe,zfe,Bpe,d2e,h2e,f2e,Ame,Wfe,Ufe,Xfe,Yfe,t2e,n2e,s2e,i2e,l2e,y2e,Che,A2e,v2e,k2e,Qfe,T2e,E2e,npe,P2e,F2e,Mme];for(let e of D2e)yn(e);var dA="4.14.0",O2e="4.14.0",z2e="4.14.0",L2e="4.14.0",W2e="4.14.0",B2e="4.14.0",ec={tfjs:dA,"tfjs-core":dA,"tfjs-converter":O2e,"tfjs-backend-cpu":z2e,"tfjs-backend-webgl":L2e,"tfjs-backend-wasm":W2e,"tfjs-backend-webgpu":B2e},J3=void 0;function K(...e){let t=new 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Kk=` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -8533,7 +8533,7 @@ return a / b;`,lJ=` vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`;var Ok=` +`;var Yk=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -8545,7 +8545,7 @@ return a / b;`,lJ=` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`,zk=` +`,Zk=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -8557,7 +8557,7 @@ return a / b;`,lJ=` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`,Lk=` +`,Jk=` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -8570,7 +8570,7 @@ return a / b;`,lJ=` vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`,Wk=` +`,Qk=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -8593,7 +8593,7 @@ return a / 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a=rt.faceLabels.slice();if(a=ut(a,"[id]",e.id.toFixed(0)),e.score&&(a=ut(a,"[score]",100*e.score)),e.gender&&(a=ut(a,"[gender]",e.gender)),e.genderScore&&(a=ut(a,"[genderScore]",100*e.genderScore)),e.age&&(a=ut(a,"[age]",e.age)),e.distance&&(a=ut(a,"[distance]",100*e.distance)),e.real&&(a=ut(a,"[real]",100*e.real)),e.live&&(a=ut(a,"[live]",100*e.live)),e.emotion&&e.emotion.length>0){let d=e.emotion.map(h=>`${Math.trunc(100*h.score)}% ${h.emotion}`);d.length>3&&(d.length=3),a=ut(a,"[emotions]",d.join(" "))}(s=(r=e.rotation)==null?void 0:r.angle)!=null&&s.roll&&(a=ut(a,"[roll]",ul(e.rotation.angle.roll))),(o=(i=e.rotation)==null?void 0:i.angle)!=null&&o.yaw&&(a=ut(a,"[yaw]",ul(e.rotation.angle.yaw))),(u=(l=e.rotation)==null?void 0:l.angle)!=null&&u.pitch&&(a=ut(a,"[pitch]",ul(e.rotation.angle.pitch))),(c=(p=e.rotation)==null?void 0:p.gaze)!=null&&c.bearing&&(a=ut(a,"[gaze]",ul(e.rotation.gaze.bearing))),vn(t,a,e.box[0],e.box[1],rt)}function Cge(e,t){var a,n,r,s;if((a=e.annotations)!=null&&a.leftEyeIris&&((n=e.annotations)!=null&&n.leftEyeIris[0])){t.strokeStyle=rt.useDepth?"rgba(255, 200, 255, 0.3)":rt.color,t.beginPath();let i=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,o=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],i,o,0,0,2*Math.PI),t.stroke(),rt.fillPolygons&&(t.fillStyle=rt.useDepth?"rgba(255, 255, 200, 0.3)":rt.color,t.fill())}if((r=e.annotations)!=null&&r.rightEyeIris&&((s=e.annotations)!=null&&s.rightEyeIris[0])){t.strokeStyle=rt.useDepth?"rgba(255, 200, 255, 0.3)":rt.color,t.beginPath();let i=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,o=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],i,o,0,0,2*Math.PI),t.stroke(),rt.fillPolygons&&(t.fillStyle=rt.useDepth?"rgba(255, 255, 200, 0.3)":rt.color,t.fill())}}function Tge(e,t){var a;if(rt.drawGaze&&((a=e.rotation)!=null&&a.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*ul(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*ul(e.rotation.angle.pitch)/90,s=new Path2D(` M ${e.box[0]+e.box[2]/2} ${e.box[1]} C ${n} ${e.box[1]}, @@ -8628,7 +8628,7 @@ return a / b;`,lJ=` ${e.box[0]} ${r}, ${e.box[0]+e.box[2]} ${r}, ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} - `);t.stroke(i),t.stroke(s)}}function fge(e,t){var a;if(rt.drawGaze&&((a=e.rotation)!=null&&a.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let n=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];ty(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[n[0],n[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];ty(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function gge(e,t){if(rt.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let a=0;ae.mesh[r]);ey(t,n,rt)}hge(e,t)}}function yge(e,t){if(rt.drawPoints)if((e==null?void 0:e.mesh.length)>=468)for(let a=0;a0&&(yge(r,n),gge(r,n),mge(r,n),fge(r,n))}}function w0(e,t,a){var s,i;let n=Et(Dt,a);if(!t||!e)return;let r=vn(e);if(r){r.lineJoin="round";for(let o=0;o0)){let l=n.bodyLabels.slice();l=ut(l,"[id]",t[o].id.toFixed(0)),l=ut(l,"[score]",100*t[o].score),wn(r,l,t[o].box[0],t[o].box[1],n)}if(n.drawPoints&&t[o].keypoints)for(let l=0;l0&&t[o].keypoints){r.font=n.font;for(let l of t[o].keypoints){if(!l.score||l.score===0)continue;let u=n.bodyPartLabels.slice();u=ut(u,"[label]",l.part),u=ut(u,"[score]",100*l.score),wn(r,u,l.position[0],l.position[1],n)}}if(n.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let l of Object.values(t[o].annotations))for(let u of l)Yk(r,u,n)}}}function k0(e,t,a){var s,i;let n=Et(Dt,a);if(!t||!e)return;let r=vn(e);if(r){r.lineJoin="round",r.font=n.font;for(let o of t){if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,dr(r,o.box[0],o.box[1],o.box[2],o.box[3],n),n.drawLabels&&((s=n.handLabels)==null?void 0:s.length)>0){let l=n.handLabels.slice();l=ut(l,"[id]",o.id.toFixed(0)),l=ut(l,"[label]",o.label),l=ut(l,"[score]",100*o.score),wn(r,l,o.box[0],o.box[1],n)}r.stroke()}if(n.drawPoints&&o.keypoints&&o.keypoints.length>0)for(let l of o.keypoints)r.fillStyle=ul(l[2],n),ur(r,l[0],l[1],0,n);if(n.drawLabels&&o.annotations&&((i=n.fingerLabels)==null?void 0:i.length)>0)for(let[l,u]of Object.entries(o.annotations)){let p=n.fingerLabels.slice();p=ut(p,"[label]",l),wn(r,p,u[u.length-1][0],u[u.length-1][1],n)}if(n.drawPolygons&&o.annotations){let l=u=>{if(!(!u||u.length===0||!u[0]))for(let p=0;p0?p-1:0][0],u[p>0?p-1:0][1]),r.lineTo(u[p][0],u[p][1]),r.stroke()}};r.lineWidth=n.lineWidth,l(o.annotations.index),l(o.annotations.middle),l(o.annotations.ring),l(o.annotations.pinky),l(o.annotations.thumb)}}}}function I0(e,t,a){var s;let n=Et(Dt,a);if(!t||!e)return;let r=vn(e);if(r){r.lineJoin="round",r.font=n.font;for(let i of t)if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,dr(r,i.box[0],i.box[1],i.box[2],i.box[3],n),n.drawLabels&&((s=n.objectLabels)==null?void 0:s.length)>0){let o=n.objectLabels.slice();o=ut(o,"[id]",i.id.toFixed(0)),o=ut(o,"[label]",i.label),o=ut(o,"[score]",100*i.score),wn(r,o,i.box[0],i.box[1],n)}r.stroke()}}}function S0(e,t,a){var r;let n=Et(Dt,a);if(!(!t||!e)&&n.drawGestures&&((r=n.gestureLabels)==null?void 0:r.length)>0){let s=vn(e);if(!s)return;s.font=n.font,s.fillStyle=n.color;let i=1;for(let o=0;o1&&u[1].length>0){let p=l[1]>0?`#${l[1]}`:"",c=n.gestureLabels.slice();c=ut(c,"[where]",l[0]),c=ut(c,"[who]",p),c=ut(c,"[what]",u[1]),wn(s,c,8,2+i*n.lineHeight,n),i+=1}}}}var ys={face:`face + `);t.stroke(i),t.stroke(s)}}function Nge(e,t){var a;if(rt.drawGaze&&((a=e.rotation)!=null&&a.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let n=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];iy(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[n[0],n[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];iy(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function Rge(e,t){if(rt.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let a=0;ae.mesh[r]);sy(t,n,rt)}Cge(e,t)}}function Ege(e,t){if(rt.drawPoints)if((e==null?void 0:e.mesh.length)>=468)for(let a=0;a0&&(Ege(r,n),Rge(r,n),Tge(r,n),Nge(r,n))}}function v0(e,t,a){var s,i;let n=Et(Ft,a);if(!t||!e)return;let r=bn(e);if(r){r.lineJoin="round";for(let o=0;o0)){let l=n.bodyLabels.slice();l=ut(l,"[id]",t[o].id.toFixed(0)),l=ut(l,"[score]",100*t[o].score),vn(r,l,t[o].box[0],t[o].box[1],n)}if(n.drawPoints&&t[o].keypoints)for(let l=0;l0&&t[o].keypoints){r.font=n.font;for(let l of t[o].keypoints){if(!l.score||l.score===0)continue;let u=n.bodyPartLabels.slice();u=ut(u,"[label]",l.part),u=ut(u,"[score]",100*l.score),vn(r,u,l.position[0],l.position[1],n)}}if(n.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let l of Object.values(t[o].annotations))for(let u of l)u9(r,u,n)}}}function w0(e,t,a){var s,i;let n=Et(Ft,a);if(!t||!e)return;let r=bn(e);if(r){r.lineJoin="round",r.font=n.font;for(let o of t){if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,ur(r,o.box[0],o.box[1],o.box[2],o.box[3],n),n.drawLabels&&((s=n.handLabels)==null?void 0:s.length)>0){let l=n.handLabels.slice();l=ut(l,"[id]",o.id.toFixed(0)),l=ut(l,"[label]",o.label),l=ut(l,"[score]",100*o.score),vn(r,l,o.box[0],o.box[1],n)}r.stroke()}if(n.drawPoints&&o.keypoints&&o.keypoints.length>0)for(let l of o.keypoints)r.fillStyle=dl(l[2],n),lr(r,l[0],l[1],0,n);if(n.drawLabels&&o.annotations&&((i=n.fingerLabels)==null?void 0:i.length)>0)for(let[l,u]of Object.entries(o.annotations)){let p=n.fingerLabels.slice();p=ut(p,"[label]",l),vn(r,p,u[u.length-1][0],u[u.length-1][1],n)}if(n.drawPolygons&&o.annotations){let l=u=>{if(!(!u||u.length===0||!u[0]))for(let p=0;p0?p-1:0][0],u[p>0?p-1:0][1]),r.lineTo(u[p][0],u[p][1]),r.stroke()}};r.lineWidth=n.lineWidth,l(o.annotations.index),l(o.annotations.middle),l(o.annotations.ring),l(o.annotations.pinky),l(o.annotations.thumb)}}}}function k0(e,t,a){var s;let n=Et(Ft,a);if(!t||!e)return;let r=bn(e);if(r){r.lineJoin="round",r.font=n.font;for(let i of t)if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,ur(r,i.box[0],i.box[1],i.box[2],i.box[3],n),n.drawLabels&&((s=n.objectLabels)==null?void 0:s.length)>0){let o=n.objectLabels.slice();o=ut(o,"[id]",i.id.toFixed(0)),o=ut(o,"[label]",i.label),o=ut(o,"[score]",100*i.score),vn(r,o,i.box[0],i.box[1],n)}r.stroke()}}}function I0(e,t,a){var r;let n=Et(Ft,a);if(!(!t||!e)&&n.drawGestures&&((r=n.gestureLabels)==null?void 0:r.length)>0){let s=bn(e);if(!s)return;s.font=n.font,s.fillStyle=n.color;let i=1;for(let o=0;o1&&u[1].length>0){let p=l[1]>0?`#${l[1]}`:"",c=n.gestureLabels.slice();c=ut(c,"[where]",l[0]),c=ut(c,"[who]",p),c=ut(c,"[what]",u[1]),vn(s,c,8,2+i*n.lineHeight,n),i+=1}}}}var As={face:`face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -8637,7 +8637,7 @@ return a / b;`,lJ=` live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var sy=0;function Age(e,t,a){let n=Et(Dt,a);if(!t||!e)return;let r=vn(e);if(r){r.lineJoin="round",r.font=n.font;for(let s=0;sly,kpt:()=>oy});var oy=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],ly={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var kn,ml=224,Qk,wge=5,N0=[8,16,32,32,32];function kge(){let e=[],t=0;for(;ta.x)),y:Vt(e.map(a=>a.y))}}async function e9(e){if(ne.initial&&(kn=null),!kn&&e.body.detector&&e.body.detector.modelPath){kn=await _e(e.body.detector.modelPath);let t=kn!=null&&kn.executor?Object.values(kn.modelSignature.inputs):void 0;ml=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&kn&&K("cached model:",kn.modelUrl);return kge(),kn}var Jk=[5,5];function Ige(e,t){return De(()=>{let a=Ca(e,12,1),n=Oe(a[0]),r=Oe(a[1]),s=Oe(a[2]),i=Oe(a[3]);n=we(ve(n,ml),t.x),r=we(ve(r,ml),t.y),s=te(ve(s,ml),Jk[0]),i=te(ve(i,ml),Jk[1]);let o=xe(n,ve(s,2)),l=xe(r,ve(i,2)),u=we(o,s),p=we(l,i);return ca([o,l,u,p],1)})}async function Sge(e,t,a,n){var u,p;let r=[],s={};s.boxes=Ige(e,Qk),s.scores=Wa(t),s.nms=await fe.nonMaxSuppressionAsync(s.boxes,s.scores,1,((u=a.body.detector)==null?void 0:u.minConfidence)||.1,((p=a.body.detector)==null?void 0:p.iouThreshold)||.1);let i=await s.nms.data(),o=await s.scores.data(),l=await s.boxes.array();for(let c of Array.from(i)){let d=o[c],h=l[c],m=[Math.round(h[0]*n[0]),Math.round(h[1]*n[1]),Math.round(h[2]*n[0]),Math.round(h[3]*n[1])],f={score:d,boxRaw:h,box:m};r.push(f)}return Object.keys(s).forEach(c=>J(s[c])),r}async function t9(e,t,a){let n={};n.res=kn==null?void 0:kn.execute(e,["Identity"]),n.logitsRaw=Fe(n.res,[0,0,0],[1,-1,1]),n.boxesRaw=Fe(n.res,[0,0,1],[1,-1,-1]),n.logits=Oe(n.logitsRaw),n.boxes=Oe(n.boxesRaw);let r=await Sge(n.boxes,n.logits,t,a);return Object.keys(n).forEach(s=>J(n[s])),r}function xs(e,t=[1,1]){let a=[e.map(o=>o[0]),e.map(o=>o[1])],n=[Math.min(...a[0]),Math.min(...a[1])],r=[Math.max(...a[0]),Math.max(...a[1])],s=[n[0],n[1],r[0]-n[0],r[1]-n[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function a9(e,t=[1,1]){let a=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...a[0]),Math.min(...a[1])],r=[Math.max(...a[0]),Math.max(...a[1])],s=[(n[0]+r[0])/2,(n[1]+r[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+r[0],-s[1]+r[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function R0(e,t){let a=[e[2]*t,e[3]*t];return[e[0]-(a[0]-e[2])/2,e[1]-(a[1]-e[3])/2,a[0],a[1]]}var Ha,dy=256,uy=Number.MAX_SAFE_INTEGER,Cge={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},M0=[],As=[[0,0],[0,0],[0,0],[0,0]],n9=0,r9=e=>1-1/(1+Math.exp(e)),i9=e=>e9(e);async function o9(e){if(ne.initial&&(Ha=null),Ha)e.debug&&K("cached model:",Ha.modelUrl);else{Ha=await _e(e.body.modelPath);let t=Ha!=null&&Ha.executor?Object.values(Ha.modelSignature.inputs):void 0;dy=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Ha}function s9(e,t,a){var s,i;let n={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((i=e==null?void 0:e.shape)!=null&&i[2]))return e;let r;if(a&&(n.cropped=fe.cropAndResize(e,[a],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let o=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],l=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];As=[[0,0],o,l,[0,0]],n.pad=or(n.cropped||e,As),n.resize=fe.resizeBilinear(n.pad,[t,t]),r=ve(n.resize,ze.tf255)}else e.shape[1]!==t?(n.resize=fe.resizeBilinear(n.cropped||e,[t,t]),r=ve(n.resize,ze.tf255)):r=ve(n.cropped||e,ze.tf255);return Object.keys(n).forEach(o=>J(n[o])),r}function Tge(e,t,a){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+As[2][0]+As[2][1])/t[0]-As[2][0]),Math.trunc(n.position[1]*(t[1]+As[1][0]+As[1][1])/t[1]-As[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],2*n.position[2]/(t[0]+t[1])];if(a){let n=a[2]-a[0],r=a[3]-a[1];for(let s of e)s.positionRaw=[s.positionRaw[0]/r+a[1],s.positionRaw[1]/n+a[0],s.positionRaw[2]],s.position=[Math.trunc(s.positionRaw[0]*t[0]),Math.trunc(s.positionRaw[1]*t[1]),s.positionRaw[2]]}return e}function Nge(e){let t=e.find(o=>o.part==="leftPalm"),a=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((a.position[2]||0)+(n.position[2]||0))/2;let r=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");r.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function Rge(e,t,a){if(!(Ha!=null&&Ha.executor))return null;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=Ha==null?void 0:Ha.execute(e,Cge.landmarks);let r=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(m=>J(n[m]));let o=[],l=5;for(let m=0;mm.position),c=xs(p,[a[0],a[1]]),d={};for(let[m,f]of Object.entries(ly)){let g=[];for(let y=0;yb.part===f[y]),A=u.find(b=>b.part===f[y+1]);x&&A&&g.push([x.position,A.position])}d[m]=g}return{id:0,score:Math.trunc(100*r)/100,box:c.box,boxRaw:c.boxRaw,keypoints:u,annotations:d}}async function py(e,t){var s,i,o;let a=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>ae()-n9,r=uy<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&M0!==null)uy++;else{let l=[];if((i=(s=t.body)==null?void 0:s.detector)!=null&&i.enabled){let u=s9(e,224);l=await t9(u,t,a),J(u)}else l=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let u=0;uJ(n[u])),r}async function my(e,t){if(!(ja!=null&&ja.executor))return[];let a=(t.object.skipTime||0)>ae()-u9,n=hy<(t.object.skipFrames||0);return t.skipAllowed&&a&&n&&cy.length>0?(hy++,cy):(hy=0,new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],i=fe.resizeBilinear(e,[fl,fl]),o=t.object.enabled?ja==null?void 0:ja.execute(i,["tower_0/detections"]):null;u9=ae(),J(i);let l=await Ege(o,s,t);cy=l,r(l)}))}var _0={};xr(_0,{connected:()=>gy,kpt:()=>fy});var fy=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],gy={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Mt,c9=0,_a={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},yy=Number.MAX_SAFE_INTEGER;async function h9(e){return ne.initial&&(Mt=null),Mt?e.debug&&K("cached model:",Mt.modelUrl):Mt=await _e(e.body.modelPath),Mt}async function Mge(e,t){let[a,n]=e.shape,r=Q(e,[n*a]),s=ga(r,0),i=(await s.data())[0];if(i>t){let o=sr(r,0),l=Bu(o,a),u=(await l.data())[0],p=ve(o,a),c=(await p.data())[0];return J([r,s,o,l,p]),[u,c,i]}return J([r,s]),[0,0,i]}async function xy(e,t){if(!(Mt!=null&&Mt.executor)||!(Mt!=null&&Mt.inputs[0].shape))return[];let a=(t.body.skipTime||0)>ae()-c9,n=yy<(t.body.skipFrames||0);return t.skipAllowed&&a&&n&&Object.keys(_a.keypoints).length>0?(yy++,[_a]):(yy=0,new Promise(async r=>{let s=De(()=>{var m,f;let c=fe.resizeBilinear(e,[((m=Mt==null?void 0:Mt.inputs[0].shape)==null?void 0:m[2])||0,((f=Mt==null?void 0:Mt.inputs[0].shape)==null?void 0:f[1])||0],!1),d=te(c,ze.tf2);return xe(d,ze.tf1)}),i;if(t.body.enabled&&(i=Mt==null?void 0:Mt.execute(s)),c9=ae(),J(s),i){_a.keypoints.length=0;let c=Oe(i);J(i);let d=Ra(c,2);J(c);for(let h=0;h(t.body.minConfidence||0)&&_a.keypoints.push({score:Math.round(100*g)/100,part:fy[h],positionRaw:[m/Mt.inputs[0].shape[2],f/Mt.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Mt.inputs[0].shape[2]),Math.round(e.shape[1]*f/Mt.inputs[0].shape[1])]})}d.forEach(h=>J(h))}_a.score=_a.keypoints.reduce((c,d)=>d.score>c?d.score:c,0);let o=_a.keypoints.map(c=>c.position[0]),l=_a.keypoints.map(c=>c.position[1]);_a.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=_a.keypoints.map(c=>c.positionRaw[0]),p=_a.keypoints.map(c=>c.positionRaw[1]);_a.boxRaw=[Math.min(...u),Math.min(...p),Math.max(...u)-Math.min(...u),Math.max(...p)-Math.min(...p)];for(let[c,d]of Object.entries(gy)){let h=[];for(let m=0;my.part===d[m]),g=_a.keypoints.find(y=>y.part===d[m+1]);f&&g&&f.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([f.position,g.position])}_a.annotations[c]=h}r([_a])}))}var nd=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],P0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],$0=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],F0=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],y9=(e,t,a)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],s=e.landmarks.map(i=>[(i[0]+a[0])*t[0],(i[1]+a[1])*t[1]]);return{startPoint:n,endPoint:r,landmarks:s,confidence:e.confidence}},Ay=(e,t,a)=>{let n=t.shape[1],r=t.shape[2],s=[e.startPoint[1]/n,e.startPoint[0]/r,e.endPoint[1]/n,e.endPoint[0]/r],i=fe.cropAndResize(t,[s],[0],a),o=ve(i,ze.tf255);return J(i),o},D0=(e,t)=>{let a=P0(e),n=nd(e),r=[t*n[0]/2,t*n[1]/2];return{startPoint:[a[0]-r[0],a[1]-r[1]],endPoint:[a[0]+r[0],a[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence,size:n}},O0=e=>{let t=P0(e),a=nd(e),n=Math.max(...a)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence,size:[Math.round(a[0]),Math.round(a[1])]}},x9=e=>{let t=e.map(n=>n[0]),a=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...a)],endPoint:[Math.max(...t),Math.max(...a)],landmarks:e}},by=[[1,0,0],[0,1,0],[0,0,1]],_ge=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),Pge=(e,t)=>_ge(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var f9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],gl=(e,t)=>{let a=0;for(let n=0;n{let a=[];for(let n=0;n{let 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u=P0(t),p=[u[0]/a.shape[2],u[1]/a.shape[1]],c=fe.rotateWithOffset(a,s,0,[p[0],p[1]]);i=A9(-s,u),o=Ay(t,c,[n,n]),J(c)}else o=Ay(t,a,[n,n]);else o=Ay(t,a,[n,n]);return[s,i,o]}var Oge=e=>{let t=e.map(n=>n[0]),a=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...a)+(Math.max(...a)-Math.min(...a))/2]},k9=(e,t)=>{let a=Oge(e),n=nd(t);return{startPoint:[a[0]-n[0]/2,a[1]-n[1]/2],endPoint:[a[0]+n[0]/2,a[1]+n[1]/2]}};var I9=6,zge=1.4,Bn,z0=null,bs=0,rd=null,S9=()=>bs;async function C9(e){var t;return ne.initial&&(Bn=null),Bn?e.debug&&K("cached model:",Bn.modelUrl):Bn=await _e((t=e.face.detector)==null?void 0:t.modelPath),bs=Bn.executor&&Bn.inputs[0].shape?Bn.inputs[0].shape[2]:256,rd=Ge(bs,"int32"),z0=Yn(b9(bs)),Bn}function Lge(e){if(!z0||!rd)return yn([0,0]);let t={};t.boxStarts=Fe(e,[0,1],[-1,2]),t.centers=we(t.boxStarts,z0),t.boxSizes=Fe(e,[0,3],[-1,2]),t.boxSizesNormalized=ve(t.boxSizes,rd),t.centersNormalized=ve(t.centers,rd),t.halfBoxSize=ve(t.boxSizesNormalized,ze.tf2),t.starts=xe(t.centersNormalized,t.halfBoxSize),t.ends=we(t.centersNormalized,t.halfBoxSize),t.startNormalized=te(t.starts,rd),t.endNormalized=te(t.ends,rd);let a=Wu([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>J(t[n])),a}async function T9(e,t){var o,l,u,p,c,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let a={};a.resized=fe.resizeBilinear(e,[bs,bs]),a.div=ve(a.resized,ze.tf127),a.normalized=xe(a.div,ze.tf05);let n=Bn==null?void 0:Bn.execute(a.normalized);if(Array.isArray(n)&&n.length>2){let h=n.sort((m,f)=>m.size-f.size);a.concat384=lt([h[0],h[2]],2),a.concat512=lt([h[1],h[3]],2),a.concat=lt([a.concat512,a.concat384],1),a.batch=Oe(a.concat,[0])}else Array.isArray(n)?a.batch=Oe(n[0]):a.batch=Oe(n);J(n),a.boxes=Lge(a.batch),a.logits=Fe(a.batch,[0,0],[-1,1]),a.sigmoid=Wa(a.logits),a.scores=Oe(a.sigmoid),a.nms=await fe.nonMaxSuppressionAsync(a.boxes,a.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let r=await a.nms.array(),s=[],i=await a.scores.data();for(let h=0;h(((p=t.face.detector)==null?void 0:p.minConfidence)||0)){let f={};f.bbox=Fe(a.boxes,[r[h],0],[1,-1]),f.slice=Fe(a.batch,[r[h],I9-1],[1,-1]),f.squeeze=Oe(f.slice),f.landmarks=Q(f.squeeze,[I9,-1]);let g=await f.bbox.data(),y={startPoint:[g[0],g[1]],endPoint:[g[2],g[3]],landmarks:await f.landmarks.array(),confidence:m};f.anchor=Fe(z0,[r[h],0],[1,2]);let x=await f.anchor.data(),A=y9(y,[(e.shape[2]||0)/bs,(e.shape[1]||0)/bs],x),b=D0(A,t.face.scale||zge),w=O0(b);w.size[0]>(((c=t.face.detector)==null?void 0:c.minSize)||0)&&w.size[1]>(((d=t.face.detector)==null?void 0:d.minSize)||0)&&s.push(w),Object.keys(f).forEach(I=>J(f[I]))}}return Object.keys(a).forEach(h=>J(a[h])),s}var rn,vs=0,Wge=2.3,wy=Pn.leftEyeLower0,ky=Pn.rightEyeLower0,sd={leftBounds:[wy[0],wy[wy.length-1]],rightBounds:[ky[0],ky[ky.length-1]]},id={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function _9(e){var t,a;return ne.initial&&(rn=null),rn?e.debug&&K("cached model:",rn.modelUrl):rn=await _e((t=e.face.iris)==null?void 0:t.modelPath),vs=rn!=null&&rn.executor&&((a=rn.inputs)!=null&&a[0].shape)?rn.inputs[0].shape[2]:0,vs===-1&&(vs=64),rn}function L0(e,t,a,n){for(let r=0;r{let t=e[sd.leftBounds[0]][2],a=e[sd.rightBounds[0]][2];return t-a},R9=(e,t,a,n,r,s=!1)=>{let i=O0(D0(x9([e[a],e[n]]),Wge)),o=nd(i),l=fe.cropAndResize(t,[[i.startPoint[1]/r,i.startPoint[0]/r,i.endPoint[1]/r,i.endPoint[0]/r]],[0],[vs,vs]);if(s&&ne.kernels.includes("flipleftright")){let u=fe.flipLeftRight(l);J(l),l=u}return{box:i,boxSize:o,crop:l}},E9=(e,t,a,n=!1)=>{let r=[];for(let s=0;s{let n=e[Pn[`${a}EyeUpper0`][id.upperCenter]][2],r=e[Pn[`${a}EyeLower0`][id.lowerCenter]][2],s=(n+r)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=r),[i[0],i[1],l]})};async function P9(e,t,a){if(!(rn!=null&&rn.executor))return e;let{box:n,boxSize:r,crop:s}=R9(e,t,sd.leftBounds[0],sd.leftBounds[1],a,!0),{box:i,boxSize:o,crop:l}=R9(e,t,sd.rightBounds[0],sd.rightBounds[1],a,!0),u=lt([s,l]);J(s),J(l);let p=rn.execute(u);J(u);let c=await p.data();J(p);let d=c.slice(0,id.numCoordinates*3),{rawCoords:h,iris:m}=E9(d,n,r,!0),f=c.slice(id.numCoordinates*3),{rawCoords:g,iris:y}=E9(f,i,o,!1),x=Bge(e);Math.abs(x)<30?(L0(e,h,"left",null),L0(e,g,"right",null)):x<1?L0(e,h,"left",["EyeUpper0","EyeLower0"]):L0(e,g,"right",["EyeUpper0","EyeLower0"]);let A=M9(e,m,"left"),b=M9(e,y,"right");return e.concat(A).concat(b)}async function F9(e,t){var s,i,o,l,u,p,c,d,h,m;let a={lips:await((i=(s=t.filter(f=>f.size===160))==null?void 0:s[0])==null?void 0:i.data()),irisL:await((l=(o=t.filter(f=>f.size===10))==null?void 0:o[0])==null?void 0:l.data()),eyeL:await((p=(u=t.filter(f=>f.size===142))==null?void 0:u[0])==null?void 0:p.data()),irisR:await((d=(c=t.filter(f=>f.size===10))==null?void 0:c[1])==null?void 0:d.data()),eyeR:await((m=(h=t.filter(f=>f.size===142))==null?void 0:h[1])==null?void 0:m.data())};for(let f of Object.values(a))if(!f)return e;let n=cl.reduce((f,g)=>f+=e[g][2],0)/cl.length;for(let f=0;ff+=e[g][2],0)/hl.length;for(let f=0;fae()-pr.timestamp,n=pr.skipped<(((u=t.face.detector)==null?void 0:u.skipFrames)||0);!t.skipAllowed||!a||!n||pr.boxes.length===0?(pr.boxes=await T9(e,t),pr.timestamp=ae(),pr.skipped=0):pr.skipped++;let r=[],s=[],i=0,o=uc;for(let x=0;x[T[0]/(e.shape[2]||0),T[1]/(e.shape[1]||0),(T[2]||0)/o]);for(let T of Object.keys(dl))I.annotations[T]=[I.mesh[dl[T]]]}else if(!Ct)t.debug&&K("face mesh detection requested, but model is not loaded");else{if((h=t.face.attention)!=null&&h.enabled&&!ne.kernels.includes("atan2"))return t.face.attention.enabled=!1,J(I.tensor),r;let T=Ct.execute(I.tensor),M=await T.find(P=>P.shape[P.shape.length-1]===1).data();if(I.faceScore=Math.round(100*M[0])/100,I.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(A.confidence=I.faceScore,t.face.mesh.keepInvalid){I.box=$0(A,e),I.boxRaw=F0(A,e),I.size=A.size,I.score=I.boxScore,I.mesh=A.landmarks,I.meshRaw=I.mesh.map(P=>[P[0]/(e.shape[2]||1),P[1]/(e.shape[1]||1),(P[2]||0)/o]);for(let P of Object.keys(dl))I.annotations[P]=[I.mesh[dl[P]]]}}else{let P=T.find(O=>O.shape[O.shape.length-1]===1404),E=Q(P,[-1,3]),C=await E.array();J(E),(f=t.face.attention)!=null&&f.enabled?C=await F9(C,T):(g=t.face.iris)!=null&&g.enabled&&(C=await P9(C,I.tensor,uc)),I.mesh=v9(C,A,b,w,uc),I.meshRaw=I.mesh.map(O=>[O[0]/(e.shape[2]||0),O[1]/(e.shape[1]||0),(O[2]||0)/o]);for(let O of Object.keys(Pn))I.annotations[O]=Pn[O].map(B=>I.mesh[B]);I.score=I.faceScore;let _={...k9(I.mesh,A),confidence:A.confidence,landmarks:A.landmarks,size:A.size};I.box=$0(_,e),I.boxRaw=F0(_,e),I.size=_.size,s.push(_)}J(T)}I.score>(((y=t.face.detector)==null?void 0:y.minConfidence)||1)?r.push(I):J(I.tensor)}return pr.boxes=s,r}async function O9(e){var t,a,n,r,s,i;return ne.initial&&(Ct=null),(t=e.face.attention)!=null&&t.enabled&&(Ct!=null&&Ct.signature)&&Object.keys(((a=Ct==null?void 0:Ct.signature)==null?void 0:a.outputs)||{}).length<6&&(Ct=null),Ct?e.debug&&K("cached model:",Ct.modelUrl):(n=e.face.attention)!=null&&n.enabled?Ct=await _e(e.face.attention.modelPath):Ct=await _e((r=e.face.mesh)==null?void 0:r.modelPath),uc=Ct.executor&&((s=Ct==null?void 0:Ct.inputs)!=null&&s[0].shape)?(i=Ct==null?void 0:Ct.inputs)==null?void 0:i[0].shape[2]:256,Ct}var z9=pl,L9=oc;var Cy=[],ra,W0=[],W9=0,B9=0,Sy=Number.MAX_SAFE_INTEGER,Ty=!1;async function V9(e){var t,a,n;return ne.initial&&(ra=null),ra?e.debug&&K("cached model:",ra.modelUrl):(ra=await _e((t=e.face.emotion)==null?void 0:t.modelPath),Ty=((n=(a=ra==null?void 0:ra.inputs)==null?void 0:a[0].shape)==null?void 0:n[3])===3,Ty?Cy=["angry","disgust","fear","happy","neutral","sad","surprise"]:Cy=["angry","disgust","fear","happy","sad","surprise","neutral"]),ra}async function Ny(e,t,a,n){var i,o;if(!ra)return[];let r=Sy<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>ae()-B9;return t.skipAllowed&&s&&r&&W9===n&&W0[a]&&W0[a].length>0?(Sy++,W0[a]):(Sy=0,new Promise(async l=>{var p,c,d;let u=[];if((p=t.face.emotion)!=null&&p.enabled){let h={},m=ra!=null&&ra.inputs[0].shape?ra.inputs[0].shape[2]:0;if(((c=t.face.emotion)==null?void 0:c.crop)>0){let g=(d=t.face.emotion)==null?void 0:d.crop,y=[[g,g,1-g,1-g]];h.resize=fe.cropAndResize(e,y,[0],[m,m])}else h.resize=fe.resizeBilinear(e,[m,m],!1);Ty?(h.mul=te(h.resize,255),h.normalize=xe(h.mul,[103.939,116.779,123.68]),h.emotion=ra==null?void 0:ra.execute(h.normalize)):(h.channels=te(h.resize,ze.rgb),h.grayscale=ot(h.channels,3,!0),h.grayscaleSub=xe(h.grayscale,ze.tf05),h.grayscaleMul=te(h.grayscaleSub,ze.tf2),h.emotion=ra==null?void 0:ra.execute(h.grayscaleMul)),B9=ae();let f=await h.emotion.data();for(let g=0;g(t.face.emotion.minConfidence||0)&&u.push({score:Math.min(.99,Math.trunc(100*f[g])/100),emotion:Cy[g]});u.sort((g,y)=>y.score-g.score),Object.keys(h).forEach(g=>J(h[g]))}W0[a]=u,W9=n,l(u)}))}var sa,ws=[],G9=0,H9=0,Ry=Number.MAX_SAFE_INTEGER;async function j9(e){var t;return ne.initial&&(sa=null),sa?e.debug&&K("cached model:",sa.modelUrl):sa=await _e((t=e.face.description)==null?void 0:t.modelPath),sa}function Uge(e,t){var s,i;let a=e.image||e.tensor||e;if(!(sa!=null&&sa.inputs[0].shape))return a;let n;if(((s=t.face.description)==null?void 0:s.crop)>0){let o=(i=t.face.description)==null?void 0:i.crop,l=[[o,o,1-o,1-o]];n=fe.cropAndResize(a,l,[0],[sa.inputs[0].shape[2],sa.inputs[0].shape[1]])}else n=fe.resizeBilinear(a,[sa.inputs[0].shape[2],sa.inputs[0].shape[1]],!1);let r=te(n,ze.tf255);return J(n),r}async function Ey(e,t,a,n){var o,l,u,p;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(sa!=null&&sa.executor))return r;let s=Ry<(((o=t.face.description)==null?void 0:o.skipFrames)||0),i=(((l=t.face.description)==null?void 0:l.skipTime)||0)>ae()-G9;return t.skipAllowed&&s&&i&&H9===n&&((u=ws==null?void 0:ws[a])==null?void 0:u.age)>0&&((p=ws==null?void 0:ws[a])==null?void 0:p.genderScore)>0?(Ry++,ws[a]):(Ry=0,new Promise(async c=>{var d;if((d=t.face.description)!=null&&d.enabled){let h=Uge(e,t),m=sa==null?void 0:sa.execute(h);G9=ae(),J(h);let g=await m.find(N=>N.shape[1]===1).data(),y=Math.trunc(200*Math.abs(g[0]-.5))/100;y>(t.face.description.minConfidence||0)&&(r.gender=g[0]<=.5?"female":"male",r.genderScore=Math.min(.99,y));let x=sr(m.find(N=>N.shape[1]===100),1),A=(await x.data())[0];J(x);let w=await m.find(N=>N.shape[1]===100).data();r.age=Math.round(w[A-1]>w[A+1]?10*A-100*w[A-1]:10*A+100*w[A+1])/10,(Number.isNaN(g[0])||Number.isNaN(w[0]))&&K("faceres error:",{model:sa,result:m});let I=m.find(N=>N.shape[1]===1024),T=I?await I.data():[];r.descriptor=Array.from(T),m.forEach(N=>J(N))}ws[a]=r,H9=n,c(r)}))}var od=.1,My=.5;function Gge(e,t,a){let n=!1,r=a.length-1;for(let s=0;st!=a[r].y>t&&e<(a[r].x-a[s].x)*(t-a[s].y)/(a[r].y-a[s].y)+a[s].x&&(n=!n);return n}async function X9(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,a=e.tensor.shape[1]||0,n=await e.tensor.buffer(),r=[];for(let i of Pn.silhouette)r.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});od&&od>0&&(r=r.map(i=>({x:i.x>.5?i.x+od:i.x-od,y:i.y>.5?i.y+od:i.y-od})));for(let i=0;iae()-Y9,s=_y<(((o=t.face.antispoof)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&r&&s&&K9===n&&B0[a]?(_y++,B0[a]):(_y=0,new Promise(async l=>{let u=fe.resizeBilinear(e,[ia!=null&&ia.inputs[0].shape?ia.inputs[0].shape[2]:0,ia!=null&&ia.inputs[0].shape?ia.inputs[0].shape[1]:0],!1),p=ia==null?void 0:ia.execute(u),c=(await p.data())[0];B0[a]=Math.round(100*c)/100,K9=n,Y9=ae(),J([u,p]),l(B0[a])}))}var oa,V0=[],$y=Number.MAX_SAFE_INTEGER,Q9=0,eI=0;async function tI(e){var t;return ne.initial&&(oa=null),oa?e.debug&&K("cached model:",oa.modelUrl):oa=await _e((t=e.face.liveness)==null?void 0:t.modelPath),oa}async function Fy(e,t,a,n){var i,o;if(!(oa!=null&&oa.executor))return 0;let r=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>ae()-eI,s=$y<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&r&&s&&Q9===n&&V0[a]?($y++,V0[a]):($y=0,new Promise(async l=>{let u=fe.resizeBilinear(e,[oa!=null&&oa.inputs[0].shape?oa.inputs[0].shape[2]:0,oa!=null&&oa.inputs[0].shape?oa.inputs[0].shape[1]:0],!1),p=oa==null?void 0:oa.execute(u),c=(await p.data())[0];V0[a]=Math.round(100*c)/100,Q9=n,eI=ae(),J([u,p]),l(V0[a])}))}var $n,Dy=[],jge=["white","black","asian","indian","other"],qge=[15,23,28,35.5,45.5,55.5,65],nI=0,rI=0,Oy=Number.MAX_SAFE_INTEGER;async function sI(e){var t;return ne.initial&&($n=null),$n?e.debug&&K("cached model:",$n.modelUrl):$n=await _e((t=e.face.gear)==null?void 0:t.modelPath),$n}async function zy(e,t,a,n){var i,o;if(!$n)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=Oy<(((i=t.face.gear)==null?void 0:i.skipFrames)||0),s=(((o=t.face.gear)==null?void 0:o.skipTime)||0)>ae()-rI;return t.skipAllowed&&s&&r&&nI===n&&Dy[a]?(Oy++,Dy[a]):(Oy=0,new Promise(async l=>{var y,x,A,b;if(!($n!=null&&$n.inputs[0].shape))return;let u={},p=[[0,.1,.9,.9]];if(((y=t.face.gear)==null?void 0:y.crop)>0){let w=(x=t.face.gear)==null?void 0:x.crop;p=[[w,w,1-w,1-w]]}u.resize=fe.cropAndResize(e,p,[0],[$n.inputs[0].shape[2],$n.inputs[0].shape[1]]);let c={age:0,gender:"unknown",genderScore:0,race:[]};(A=t.face.gear)!=null&&A.enabled&&([u.age,u.gender,u.race]=$n.execute(u.resize,["age_output","gender_output","race_output"]));let d=await u.gender.data();c.gender=d[0]>d[1]?"male":"female",c.genderScore=Math.round(100*(d[0]>d[1]?d[0]:d[1]))/100;let h=await u.race.data();for(let w=0;w(((b=t.face.gear)==null?void 0:b.minConfidence)||.2)&&c.race.push({score:Math.round(100*h[w])/100,race:jge[w]});c.race.sort((w,I)=>I.score-w.score);let f=Array.from(await u.age.data()).map((w,I)=>[qge[I],w]).sort((w,I)=>I[1]-w[1]),g=f[0][0];for(let w=1;wJ(u[w])),Dy[a]=c,nI=n,rI=ae(),l(c)}))}var Pa,U0=[],oI=0,lI=0,Ly=Number.MAX_SAFE_INTEGER;async function uI(e){return ne.initial&&(Pa=null),Pa?e.debug&&K("cached model:",Pa.modelUrl):Pa=await _e(e.face.ssrnet.modelPathAge),Pa}async function Wy(e,t,a,n){var i,o,l,u;if(!Pa)return{age:0};let r=Ly<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>ae()-lI;return t.skipAllowed&&r&&s&&oI===n&&((l=U0[a])!=null&&l.age)&&((u=U0[a])==null?void 0:u.age)>0?(Ly++,U0[a]):(Ly=0,new Promise(async p=>{var h,m,f;if(!(Pa!=null&&Pa.inputs)||!Pa.inputs[0]||!Pa.inputs[0].shape)return;let c={};if(((h=t.face.ssrnet)==null?void 0:h.crop)>0){let g=(m=t.face.ssrnet)==null?void 0:m.crop,y=[[g,g,1-g,1-g]];c.resize=fe.cropAndResize(e,y,[0],[Pa.inputs[0].shape[2],Pa.inputs[0].shape[1]])}else c.resize=fe.resizeBilinear(e,[Pa.inputs[0].shape[2],Pa.inputs[0].shape[1]],!1);c.enhance=te(c.resize,ze.tf255);let d={age:0};if((f=t.face.ssrnet)!=null&&f.enabled&&(c.age=Pa.execute(c.enhance)),c.age){let g=await c.age.data();d.age=Math.trunc(10*g[0])/10}Object.keys(c).forEach(g=>J(c[g])),U0[a]=d,oI=n,lI=ae(),p(d)}))}var Aa,G0=[],pI=0,cI=0,By=Number.MAX_SAFE_INTEGER,Vy=[.2989,.587,.114];async function hI(e){var t;return ne.initial&&(Aa=null),Aa?e.debug&&K("cached model:",Aa.modelUrl):Aa=await _e((t=e.face.ssrnet)==null?void 0:t.modelPathGender),Aa}async function Uy(e,t,a,n){var i,o,l,u;if(!Aa)return{gender:"unknown",genderScore:0};let r=By<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>ae()-cI;return t.skipAllowed&&r&&s&&pI===n&&((l=G0[a])!=null&&l.gender)&&((u=G0[a])==null?void 0:u.genderScore)>0?(By++,G0[a]):(By=0,new Promise(async p=>{var m,f,g;if(!(Aa!=null&&Aa.inputs[0].shape))return;let c={};if(((m=t.face.ssrnet)==null?void 0:m.crop)>0){let y=(f=t.face.ssrnet)==null?void 0:f.crop,x=[[y,y,1-y,1-y]];c.resize=fe.cropAndResize(e,x,[0],[Aa.inputs[0].shape[2],Aa.inputs[0].shape[1]])}else c.resize=fe.resizeBilinear(e,[Aa.inputs[0].shape[2],Aa.inputs[0].shape[1]],!1);c.enhance=De(()=>{var x,A;let y;if(((A=(x=Aa==null?void 0:Aa.inputs)==null?void 0:x[0].shape)==null?void 0:A[3])===1){let[b,w,I]=Ca(c.resize,3,3),T=te(b,Vy[0]),N=te(w,Vy[1]),M=te(I,Vy[2]),P=Fh([T,N,M]);y=te(xe(P,ze.tf05),2)}else y=te(xe(c.resize,ze.tf05),2);return y});let d={gender:"unknown",genderScore:0};(g=t.face.ssrnet)!=null&&g.enabled&&(c.gender=Aa.execute(c.enhance));let h=await c.gender.data();d.gender=h[0]>h[1]?"female":"male",d.genderScore=h[0]>h[1]?Math.trunc(100*h[0])/100:Math.trunc(100*h[1])/100,Object.keys(c).forEach(y=>J(c[y])),G0[a]=d,pI=n,cI=ae(),p(d)}))}var sn,Gy=[],fI=0,gI=0,yI=Number.MAX_SAFE_INTEGER;async function xI(e){var t;return ne.initial&&(sn=null),sn?e.debug&&K("cached model:",sn.modelUrl):sn=await _e((t=e.face.mobilefacenet)==null?void 0:t.modelPath),sn}async function Hy(e,t,a,n){var i,o;if(!(sn!=null&&sn.executor))return[];let r=yI<(((i=t.face.mobilefacenet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.mobilefacenet)==null?void 0:o.skipTime)||0)>ae()-gI;return t.skipAllowed&&s&&r&&fI===n&&Gy[a]?(yI++,Gy[a]):new Promise(async l=>{var p;let u=[];if((p=t.face.mobilefacenet)!=null&&p.enabled&&(sn!=null&&sn.inputs[0].shape)){let c={};c.crop=fe.resizeBilinear(e,[sn.inputs[0].shape[2],sn.inputs[0].shape[1]],!1),c.data=sn.execute(c.crop);let d=await c.data.data();u=Array.from(d),Object.keys(c).forEach(h=>J(c[h]))}Gy[a]=u,fI=n,gI=ae(),l(u)})}var on,jy=[],bI=0,vI=0,wI=Number.MAX_SAFE_INTEGER;async function kI(e){return ne.initial&&(on=null),on?e.debug&&K("cached model:",on.modelUrl):on=await _e(e.face.insightface.modelPath),on}async function qy(e,t,a,n){var i,o;if(!(on!=null&&on.executor))return[];let r=wI<(((i=t.face.insightface)==null?void 0:i.skipFrames)||0),s=(((o=t.face.insightface)==null?void 0:o.skipTime)||0)>ae()-vI;return t.skipAllowed&&s&&r&&bI===n&&jy[a]?(wI++,jy[a]):new Promise(async l=>{var p;let u=[];if((p=t.face.insightface)!=null&&p.enabled&&(on!=null&&on.inputs[0].shape)){let c={};c.crop=fe.resizeBilinear(e,[on.inputs[0].shape[2],on.inputs[0].shape[1]],!1),c.data=on.execute(c.crop);let d=await 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y=Math.abs(e[i].mesh[145][1]-e[i].annotations.rightEyeIris[0][1])/e[i].box[3],x=Math.abs(e[i].mesh[374][1]-e[i].annotations.leftEyeIris[0][1])/e[i].box[3];(x<.01||y<.01||x>.022||y>.022)&&(h=!1),(x<.01||y<.01)&&t.push({iris:i,gesture:"looking down"}),(x>.022||y>.022)&&t.push({iris:i,gesture:"looking up"}),h&&t.push({iris:i,gesture:"looking center"})}return t},zI=e=>{if(!e)return[];let t=[];for(let a=0;a0){let r=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:a,gesture:`${r.name} forward`});let s=n.reduce((i,o)=>i.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:a,endPoint:n,palmLandmarks:r,confidence:e.confidence}}function q0(e,t=1.5){let a=dc(e),n=j0(e),r=[t*n[0]/2,t*n[1]/2],s=[a[0]-r[0],a[1]-r[1]],i=[a[0]+r[0],a[1]+r[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function X0(e){let t=dc(e),a=j0(e),r=Math.max(...a)/2,s=[t[0]-r,t[1]-r],i=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function r3e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function UI(e,t){let a=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return r3e(a)}var LI=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ns(e,t){let a=0;for(let n=0;n[i.x,i.y]),this.anchorsTensor=Yn(this.anchors),this.inputSize=((s=(r=(n=(a=this==null?void 0:this.model)==null?void 0:a.inputs)==null?void 0:n[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=Vt([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Vt([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let a={};a.boxOffsets=Fe(t,[0,0],[-1,2]),a.boxSizes=Fe(t,[0,2],[-1,2]),a.div=ve(a.boxOffsets,this.inputSizeTensor),a.boxCenterPoints=we(a.div,this.anchorsTensor),a.halfBoxSizes=ve(a.boxSizes,this.doubleInputSizeTensor),a.sub=xe(a.boxCenterPoints,a.halfBoxSizes),a.startPoints=te(a.sub,this.inputSizeTensor),a.add=we(a.boxCenterPoints,a.halfBoxSizes),a.endPoints=te(a.add,this.inputSizeTensor);let n=Wu([a.startPoints,a.endPoints],1);return Object.keys(a).forEach(r=>J(a[r])),n}normalizeLandmarks(t,a){let n={};n.reshape=Q(t,[-1,7,2]),n.div=ve(n.reshape,this.inputSizeTensor),n.landmarks=we(n.div,this.anchors[a]?this.anchors[a]:0);let r=te(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>J(n[s])),r}async predict(t,a){var o;let n={};n.resize=fe.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=ve(n.resize,ze.tf127),n.image=xe(n.div,ze.tf1),n.batched=this.model.execute(n.image),n.predictions=Oe(n.batched),n.slice=Fe(n.predictions,[0,0],[-1,1]),n.sigmoid=Wa(n.slice),n.scores=Oe(n.sigmoid);let r=await n.scores.data();n.boxes=Fe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await fe.nonMaxSuppressionAsync(n.norm,n.scores,3*(((o=a.hand)==null?void 0:o.maxDetected)||1),a.hand.iouThreshold,a.hand.minConfidence);let s=await n.nms.array(),i=[];for(let l of s){let u={};u.box=Fe(n.norm,[l,0],[1,-1]),u.slice=Fe(n.predictions,[l,5],[1,14]),u.norm=this.normalizeLandmarks(u.slice,l),u.palmLandmarks=Q(u.norm,[-1,2]);let p=await u.box.data(),c=p.slice(0,2),d=p.slice(2,4),h=await u.palmLandmarks.array(),m={startPoint:c,endPoint:d,palmLandmarks:h,confidence:r[l]},f=VI(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);i.push(f),Object.keys(u).forEach(g=>J(u[g]))}return Object.keys(n).forEach(l=>J(n[l])),i}};var l3e=5,qI=1.65,XI=[0,5,9,13,17,1,2],u3e=0,d3e=2,KI=0,Y0=class{constructor(t,a){he(this,"handDetector");he(this,"handPoseModel");he(this,"inputSize");he(this,"storedBoxes");he(this,"skipped");he(this,"detectedHands");var n,r,s;this.handDetector=t,this.handPoseModel=a,this.inputSize=((s=(r=(n=this.handPoseModel)==null?void 0:n.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let a=t.map(i=>i[0]),n=t.map(i=>i[1]),r=[Math.min(...a),Math.min(...n)],s=[Math.max(...a),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,a){let n=t.map(s=>Jy([...s,1],a)),r=this.calculateLandmarksBoundingBox(n);return q0(X0(r),l3e)}getBoxForHandLandmarks(t){let a=this.calculateLandmarksBoundingBox(t),n=q0(X0(a),qI);n.palmLandmarks=[];for(let r=0;r[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=Zy(n,[0,0]),u=o.map(h=>[...Jy(h,l),h[2]]),p=GI(r),c=[...dc(a),1],d=[Ns(c,p[0]),Ns(c,p[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,a){let n=!1,r,s=(a.hand.skipTime||0)>ae()-KI,i=this.skipped<(a.hand.skipFrames||0);a.skipAllowed&&s&&i?this.skipped++:(r=await this.handDetector.predict(t,a),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==a.hand.maxDetected||!a.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l=a.hand.minConfidence/4){let w=Q(A,[-1,3]),I=await w.array();J(A),J(w);let T=this.transformRawCoords(I,f,p,m),N=this.getBoxForHandLandmarks(T);this.storedBoxes[l]={...N,confidence:b};let M={landmarks:T,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:N.startPoint,bottomRight:N.endPoint}};o.push(M)}else this.storedBoxes[l]=null;J(A)}else{let p=q0(X0(u),qI),c={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:p.startPoint,bottomRight:p.endPoint},landmarks:[]};o.push(c)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>a.hand.maxDetected&&(o.length=a.hand.maxDetected),o}};var YI={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},bl,vl,Qy;function c3e(){let e=bl?new K0(bl):void 0;e&&vl&&(Qy=new Y0(e,vl))}async function ex(e,t){Qy||c3e();let a=await Qy.estimateHands(e,t);if(!a)return[];let n=[];for(let r=0;ra[r].landmarks[c]);let i=a[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let p of i)p[0]o[2]&&(o[2]=p[0]),p[1]>o[3]&&(o[3]=p[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=a[r].box?[Math.trunc(Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.max(0,a[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,a[r].box.bottomRight[0])-Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,a[r].box.bottomRight[1])-Math.max(0,a[r].box.topLeft[1]))]:[0,0,0,0],l=[a[r].box.topLeft[0]/(e.shape[2]||0),a[r].box.topLeft[1]/(e.shape[1]||0),(a[r].box.bottomRight[0]-a[r].box.topLeft[0])/(e.shape[2]||0),(a[r].box.bottomRight[1]-a[r].box.topLeft[1])/(e.shape[1]||0)];let u=H0(i);n.push({id:r,score:Math.round(100*a[r].confidence)/100,boxScore:Math.round(100*a[r].boxConfidence)/100,fingerScore:Math.round(100*a[r].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function ZI(e){var t;return ne.initial&&(bl=null),bl?e.debug&&K("cached model:",bl.modelUrl):bl=await _e((t=e.hand.detector)==null?void 0:t.modelPath),bl}async function JI(e){var t;return ne.initial&&(vl=null),vl?e.debug&&K("cached model:",vl.modelUrl):vl=await _e((t=e.hand.skeleton)==null?void 0:t.modelPath),vl}var zt=[null,null],h3e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Rs=[[0,0],[0,0]],m3e=["hand","fist","pinch","point","face","tip","pinchtip"],eS=4,tS=1.6,f3e=512,g3e=1.4,Z0=Number.MAX_SAFE_INTEGER,tx=0,Fr=[0,0],Ot={boxes:[],hands:[]},aS={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function nS(e){var t;if(ne.initial&&(zt[0]=null),zt[0])e.debug&&K("cached model:",zt[0].modelUrl);else{b0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),zt[0]=await _e((t=e.hand.detector)==null?void 0:t.modelPath);let a=zt[0].executor?Object.values(zt[0].modelSignature.inputs):void 0;Rs[0][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Rs[0][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return zt[0]}async function rS(e){var t;if(ne.initial&&(zt[1]=null),zt[1])e.debug&&K("cached model:",zt[1].modelUrl);else{zt[1]=await _e((t=e.hand.skeleton)==null?void 0:t.modelPath);let a=zt[1].executor?Object.values(zt[1].modelSignature.inputs):void 0;Rs[1][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Rs[1][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return zt[1]}async function y3e(e,t){let a=[];if(!e||!zt[0])return a;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,f3e),i=Math.round(s*r/8)*8;n.resize=fe.resizeBilinear(e,[s,i]),n.cast=Ue(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await zt[0].executeAsync(n.cast,h3e),n.boxes=Oe(n.rawBoxes,[0,2]),n.scores=Oe(n.rawScores,[0]);let o=Ra(n.scores,1);J(o[eS]),o.splice(eS,1),n.filtered=ca(o,1),J(o),n.max=ga(n.filtered,1),n.argmax=sr(n.filtered,1);let l=0;n.nms=await fe.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),p=await n.max.data(),c=await n.argmax.data();for(let d of Array.from(u)){let h=Fe(n.boxes,d,1),m=await h.data();J(h);let f=[m[1],m[0],m[3]-m[1],m[2]-m[0]],g=R0(f,g3e),y=[Math.trunc(f[0]*Fr[0]),Math.trunc(f[1]*Fr[1]),Math.trunc(f[2]*Fr[0]),Math.trunc(f[3]*Fr[1])],x=p[d],A=m3e[c[d]],b={id:l++,score:x,box:y,boxRaw:g,label:A};a.push(b)}return Object.keys(n).forEach(d=>J(n[d])),a.sort((d,h)=>h.score-d.score),a.length>(t.hand.maxDetected||1)&&(a.length=t.hand.maxDetected||1),a}async function ax(e,t,a){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&zt[1]&&a.hand.landmarks&&t.score>(a.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=fe.cropAndResize(e,[s],[0],[Rs[1][0],Rs[1][1]],"bilinear"),r.div=ve(r.crop,ze.tf255),[r.score,r.keypoints]=zt[1].execute(r.div,["Identity_1","Identity"]);let i=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(a.hand.minConfidence||0)){n.fingerScore=o,r.reshaped=Q(r.keypoints,[-1,3]);let p=(await r.reshaped.array()).map(c=>[c[0]/Rs[1][1],c[1]/Rs[1][0],c[2]||0]).map(c=>[c[0]*t.boxRaw[2],c[1]*t.boxRaw[3],c[2]||0]);n.keypoints=p.map(c=>[Fr[0]*(c[0]+t.boxRaw[0]),Fr[1]*(c[1]+t.boxRaw[1]),c[2]||0]),n.landmarks=H0(n.keypoints);for(let c of Object.keys(aS))n.annotations[c]=aS[c].map(d=>n.landmarks&&n.keypoints[d]?n.keypoints[d]:null)}Object.keys(r).forEach(l=>J(r[l]))}return n}async function nx(e,t){var r,s;if(!((r=zt[0])!=null&&r.executor)||!((s=zt[1])!=null&&s.executor)||!zt[0].inputs[0].shape||!zt[1].inputs[0].shape)return[];Fr=[e.shape[2]||0,e.shape[1]||0],Z0++;let a=(t.hand.skipTime||0)>ae()-tx,n=Z0<(t.hand.skipFrames||0);return t.skipAllowed&&a&&n?Ot.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>ae()-tx,l=Z0<3*(t.hand.skipFrames||0);t.skipAllowed&&Ot.hands.length===t.hand.maxDetected?Ot.hands=await Promise.all(Ot.boxes.map(p=>ax(e,p,t))):t.skipAllowed&&o&&l&&Ot.hands.length>0?Ot.hands=await Promise.all(Ot.boxes.map(p=>ax(e,p,t))):(Ot.boxes=await y3e(e,t),tx=ae(),Ot.hands=await Promise.all(Ot.boxes.map(p=>ax(e,p,t))),Z0=0);let u=[...Ot.boxes];if(Ot.boxes.length=0,t.cacheSensitivity>0)for(let p=0;p.05&&c.box[3]/(e.shape[1]||1)>.05&&Ot.hands[p].fingerScore&&Ot.hands[p].fingerScore>(t.hand.minConfidence||0)){let d=R0(c.box,tS),h=R0(c.boxRaw,tS);Ot.boxes.push({...u[p],box:d,boxRaw:h})}}for(let p=0;p({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var pc={};xr(pc,{connected:()=>Q0,horizontal:()=>rx,kpt:()=>J0,relative:()=>ix,vertical:()=>sx});var J0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],rx=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],sx=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],ix=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Q0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Ae=cr(),ox=0;function iS(e,t){var i,o,l,u,p,c,d,h,m,f,g,y,x,A,b,w,I,T,N,M,P,E,C,_,O,B;let a=ae();if(!e)return cr();let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ae.canvas=e.canvas),e.error&&(Ae.error=e.error),!Ae.body||e.body.length!==Ae.body.length)Ae.body=JSON.parse(JSON.stringify(e.body));else for(let F=0;F((r-1)*Ae.body[F].box[X]+Z)/r),G=e.body[F].boxRaw.map((Z,X)=>((r-1)*Ae.body[F].boxRaw[X]+Z)/r),q=e.body[F].keypoints.map((Z,X)=>{var re,ee,ge,ie,be,Ce,Re,Le,Xe;return{score:Z.score,part:Z.part,position:[Ae.body[F].keypoints[X]?((r-1)*(Ae.body[F].keypoints[X].position[0]||0)+(Z.position[0]||0))/r:Z.position[0],Ae.body[F].keypoints[X]?((r-1)*(Ae.body[F].keypoints[X].position[1]||0)+(Z.position[1]||0))/r:Z.position[1],Ae.body[F].keypoints[X]?((r-1)*(Ae.body[F].keypoints[X].position[2]||0)+(Z.position[2]||0))/r:Z.position[2]],positionRaw:[Ae.body[F].keypoints[X]?((r-1)*(Ae.body[F].keypoints[X].positionRaw[0]||0)+(Z.positionRaw[0]||0))/r:Z.positionRaw[0],Ae.body[F].keypoints[X]?((r-1)*(Ae.body[F].keypoints[X].positionRaw[1]||0)+(Z.positionRaw[1]||0))/r:Z.positionRaw[1],Ae.body[F].keypoints[X]?((r-1)*(Ae.body[F].keypoints[X].positionRaw[2]||0)+(Z.positionRaw[2]||0))/r:Z.positionRaw[2]],distance:[Ae.body[F].keypoints[X]?((r-1)*(((re=Ae.body[F].keypoints[X].distance)==null?void 0:re[0])||0)+(((ee=Z.distance)==null?void 0:ee[0])||0))/r:(ge=Z.distance)==null?void 0:ge[0],Ae.body[F].keypoints[X]?((r-1)*(((ie=Ae.body[F].keypoints[X].distance)==null?void 0:ie[1])||0)+(((be=Z.distance)==null?void 0:be[1])||0))/r:(Ce=Z.distance)==null?void 0:Ce[1],Ae.body[F].keypoints[X]?((r-1)*(((Re=Ae.body[F].keypoints[X].distance)==null?void 0:Re[2])||0)+(((Le=Z.distance)==null?void 0:Le[2])||0))/r:(Xe=Z.distance)==null?void 0:Xe[2]]}}),H={},V={connected:{}};(i=t.body.modelPath)!=null&&i.includes("efficientpose")?V=_0:(o=t.body.modelPath)!=null&&o.includes("blazepose")?V=T0:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(V=pc);for(let[Z,X]of Object.entries(V.connected)){let re=[];for(let ee=0;eebe.part===X[ee]),ie=q.find(be=>be.part===X[ee+1]);ge&&ie&&re.push([ge.position,ie.position])}H[Z]=re}Ae.body[F]={...e.body[F],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!Ae.hand||e.hand.length!==Ae.hand.length)Ae.hand=JSON.parse(JSON.stringify(e.hand));else for(let F=0;F((r-1)*Ae.hand[F].box[Z]+V)/r),G=e.hand[F].boxRaw.map((V,Z)=>((r-1)*Ae.hand[F].boxRaw[Z]+V)/r);Ae.hand[F].keypoints.length!==e.hand[F].keypoints.length&&(Ae.hand[F].keypoints=e.hand[F].keypoints);let q=e.hand[F].keypoints&&e.hand[F].keypoints.length>0?e.hand[F].keypoints.map((V,Z)=>V.map((X,re)=>((r-1)*(Ae.hand[F].keypoints[Z][re]||1)+(X||0))/r)):[],H={};if(Object.keys(Ae.hand[F].annotations).length!==Object.keys(e.hand[F].annotations).length)Ae.hand[F].annotations=e.hand[F].annotations,H=Ae.hand[F].annotations;else if(e.hand[F].annotations)for(let V of Object.keys(e.hand[F].annotations))H[V]=(c=(p=(u=e.hand[F])==null?void 0:u.annotations)==null?void 0:p[V])!=null&&c[0]?e.hand[F].annotations[V].map((Z,X)=>Z.map((re,ee)=>((r-1)*Ae.hand[F].annotations[V][X][ee]+re)/r)):null;Ae.hand[F]={...e.hand[F],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!Ae.face||e.face.length!==Ae.face.length)Ae.face=JSON.parse(JSON.stringify(e.face));else for(let F=0;F((r-1)*Ae.face[F].box[V]+H)/r),G=e.face[F].boxRaw.map((H,V)=>((r-1)*Ae.face[F].boxRaw[V]+H)/r),q=e.face[F].annotations;if(Object.keys(Ae.face[F].annotations).length!==Object.keys(e.face[F].annotations).length)Ae.face[F].annotations=e.face[F].annotations,q=Ae.face[F].annotations;else if(e.face[F].annotations)for(let H of Object.keys(e.face[F].annotations))q[H]=(m=(h=(d=e.face[F])==null?void 0:d.annotations)==null?void 0:h[H])!=null&&m[0]?e.face[F].annotations[H].map((V,Z)=>V.map((X,re)=>((r-1)*Ae.face[F].annotations[H][Z][re]+X)/r)):null;if(e.face[F].rotation){let H={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};H.matrix=(f=e.face[F].rotation)==null?void 0:f.matrix,H.angle={roll:((r-1)*(((y=(g=Ae.face[F].rotation)==null?void 0:g.angle)==null?void 0:y.roll)||0)+(((A=(x=e.face[F].rotation)==null?void 0:x.angle)==null?void 0:A.roll)||0))/r,yaw:((r-1)*(((w=(b=Ae.face[F].rotation)==null?void 0:b.angle)==null?void 0:w.yaw)||0)+(((T=(I=e.face[F].rotation)==null?void 0:I.angle)==null?void 0:T.yaw)||0))/r,pitch:((r-1)*(((M=(N=Ae.face[F].rotation)==null?void 0:N.angle)==null?void 0:M.pitch)||0)+(((E=(P=e.face[F].rotation)==null?void 0:P.angle)==null?void 0:E.pitch)||0))/r},H.gaze={bearing:((r-1)*(((C=Ae.face[F].rotation)==null?void 0:C.gaze.bearing)||0)+(((_=e.face[F].rotation)==null?void 0:_.gaze.bearing)||0))/r,strength:((r-1)*(((O=Ae.face[F].rotation)==null?void 0:O.gaze.strength)||0)+(((B=e.face[F].rotation)==null?void 0:B.gaze.strength)||0))/r},Ae.face[F]={...e.face[F],rotation:H,box:U,boxRaw:G,annotations:q}}else Ae.face[F]={...e.face[F],box:U,boxRaw:G,annotations:q}}if(!Ae.object||e.object.length!==Ae.object.length)Ae.object=JSON.parse(JSON.stringify(e.object));else for(let F=0;F((r-1)*Ae.object[F].box[H]+q)/r),G=e.object[F].boxRaw.map((q,H)=>((r-1)*Ae.object[F].boxRaw[H]+q)/r);Ae.object[F]={...e.object[F],box:U,boxRaw:G}}if(e.persons){let F=e.persons;if(!Ae.persons||F.length!==Ae.persons.length)Ae.persons=JSON.parse(JSON.stringify(F));else for(let U=0;U((r-1)*Ae.persons[U].box[q]+G)/r)}e.gesture&&(Ae.gesture=e.gesture),Ae.width=e.width,Ae.height=e.height;let s=ae();return ox=ne.perfadd?ox+Math.round(s-a):Math.round(s-a),e.performance&&(Ae.performance={...e.performance,interpolate:ox}),Ae}var ba;async function lx(e){return!ba||ne.initial?ba=await _e(e.segmentation.modelPath):e.debug&&K("cached model:",ba.modelUrl),ba}async function oS(e,t){var r;if(ba||(ba=await lx(t)),!(ba!=null&&ba.executor)||!((r=ba==null?void 0:ba.inputs)!=null&&r[0].shape))return null;let a={};a.resize=fe.resizeBilinear(e,[ba.inputs[0].shape?ba.inputs[0].shape[1]:0,ba.inputs[0].shape?ba.inputs[0].shape[2]:0],!1),a.norm=ve(a.resize,ze.tf255),a.res=ba.execute(a.norm),a.squeeze=Oe(a.res,[0]),[a.bgRaw,a.fgRaw]=Ra(a.squeeze,2),a.fg=Vh(a.fgRaw),a.mul=te(a.fg,ze.tf255),a.expand=Bt(a.mul,2),a.output=fe.resizeBilinear(a.expand,[e.shape[1]||0,e.shape[2]||0]);let n;switch(t.segmentation.mode||"default"){case"default":a.input=Oe(e),a.concat=lt([a.input,a.output],-1),n=Ue(a.concat,"int32");break;case"alpha":n=Ue(a.output,"int32");break;default:n=Ve(0)}return Object.keys(a).forEach(s=>J(a[s])),n}var em={};xr(em,{distance:()=>ux,find:()=>b3e,similarity:()=>A3e});function ux(e,t,a={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let n=0;for(let r=0;r{if(e===0)return 1;let s=(1-(t===2?Math.sqrt(e):e**(1/t))/100-a)/(n-a);return Math.max(Math.min(s,1),0)};function A3e(e,t,a={order:2,multiplier:25,min:.2,max:.8}){let n=ux(e,t,a);return uS(n,a.order||2,a.min||0,a.max||1)}function b3e(e,t,a={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,r=-1;for(let i=0;imc,validateModel:()=>om});var dS=.005,ln={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function dx(e){for(let t of rx){let a=e.keypoints.findIndex(r=>r.part===t[0]),n=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[a]&&e.keypoints[n]&&e.keypoints[a].position[0]r&&r.part===t[0]),n=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[a]&&e.keypoints[n]&&e.keypoints[a].position[1]u&&u.part===t[0]),r=e.keypoints.findIndex(u=>u&&u.part===t[1]),s=e.keypoints.findIndex(u=>u&&u.part===a[0]),i=e.keypoints.findIndex(u=>u&&u.part===a[1]);if(!e.keypoints[s]||!e.keypoints[i])continue;let o=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[i].position[0]-e.keypoints[n].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[i].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0])]:[0,0];if(o[0]>o[1]||l[0]>l[1]){let u=e.keypoints[n];e.keypoints[n]=e.keypoints[r],e.keypoints[r]=u}}}function pS(e){for(let t=0;te.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],a.pad=or(e,ln.padding),a.resize=fe.resizeBilinear(a.pad,[t,t]);let n=Ue(a.resize,"int32");return Object.keys(a).forEach(i=>J(a[i])),n}function hS(e,t){e.keypoints=e.keypoints.filter(n=>n==null?void 0:n.position);for(let n of e.keypoints)n.position=[n.position[0]*(t[0]+ln.padding[2][0]+ln.padding[2][1])/t[0]-ln.padding[2][0],n.position[1]*(t[1]+ln.padding[1][0]+ln.padding[1][1])/t[1]-ln.padding[1][0]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1]];let a=xs(e.keypoints.map(n=>n.position),t);return e.box=a.box,e.boxRaw=a.boxRaw,e}var qt,tm=0,px=Number.MAX_SAFE_INTEGER,wl={boxes:[],bodies:[],last:0};async function mS(e){var t;return ne.initial&&(qt=null),qt?e.debug&&K("cached model:",qt.modelUrl):(b0(["size"],e),qt=await _e(e.body.modelPath)),tm=qt!=null&&qt.executor&&((t=qt==null?void 0:qt.inputs)!=null&&t[0].shape)?qt.inputs[0].shape[2]:0,tm<64&&(tm=256),W().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS&&W().set("WEBGL_USE_SHAPES_UNIFORMS",!1),qt}function w3e(e,t,a){let n=e[0][0],r=[],s=0;for(let p=0;pt.body.minConfidence){let c=[n[p][1],n[p][0]];r.push({score:Math.round(100*s)/100,part:J0[p],positionRaw:c,position:[Math.round((a.shape[2]||0)*c[0]),Math.round((a.shape[1]||0)*c[1])]})}s=r.reduce((p,c)=>c.score>p?c.score:p,0);let i=[],o=xs(r.map(p=>p.position),[a.shape[2],a.shape[1]]),l={};for(let[p,c]of Object.entries(Q0)){let d=[];for(let h=0;hg.part===c[h]),f=r.find(g=>g.part===c[h+1]);m&&f&&m.score>(t.body.minConfidence||0)&&f.score>(t.body.minConfidence||0)&&d.push([m.position,f.position])}l[p]=d}let u={id:0,score:s,box:o.box,boxRaw:o.boxRaw,keypoints:r,annotations:l};return dx(u),i.push(u),i}function k3e(e,t,a){let n=[];for(let r=0;rt.body.minConfidence){let o=[];for(let d=0;d<17;d++){let h=s[3*d+2];if(h>t.body.minConfidence){let m=[s[3*d+1],s[3*d+0]];o.push({part:J0[d],score:Math.round(100*h)/100,positionRaw:m,position:[Math.round((a.shape[2]||0)*m[0]),Math.round((a.shape[1]||0)*m[1])]})}}let l=[s[51+1],s[51+0],s[51+3]-s[51+1],s[51+2]-s[51+0]],u=[Math.trunc(l[0]*(a.shape[2]||0)),Math.trunc(l[1]*(a.shape[1]||0)),Math.trunc(l[2]*(a.shape[2]||0)),Math.trunc(l[3]*(a.shape[1]||0))],p={};for(let[d,h]of Object.entries(Q0)){let m=[];for(let f=0;fx.part===h[f]),y=o.find(x=>x.part===h[f+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&m.push([g.position,y.position])}p[d]=m}let c={id:r,score:i,box:u,boxRaw:l,keypoints:[...o],annotations:p};dx(c),n.push(c)}}return n.sort((r,s)=>s.score-r.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function cx(e,t){var r;if(!(qt!=null&&qt.executor)||!((r=qt==null?void 0:qt.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(wl.boxes.length=0),px++;let a=(t.body.skipTime||0)>ae()-wl.last,n=px<(t.body.skipFrames||0);return t.skipAllowed&&a&&n?wl.bodies:new Promise(async s=>{let i={};px=0,i.input=cS(e,tm),i.res=qt==null?void 0:qt.execute(i.input),wl.last=ae();let o=await 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O=[_[0]*t[0],_[1]*t[1],_[2]*t[0],_[3]*t[1]],B={id:n++,score:Math.round(100*w)/100,class:b+1,label:ad[b].label,box:O.map(F=>Math.trunc(F)),boxRaw:_};r.push(B)}}J([h,f,g,y])}let i=r.map(c=>[c.boxRaw[1],c.boxRaw[0],c.boxRaw[3],c.boxRaw[2]]),o=r.map(c=>c.score),l=[];if(i&&i.length>0){let c=await fe.nonMaxSuppressionAsync(i,o,a.object.maxDetected||0,a.object.iouThreshold,a.object.minConfidence);l=Array.from(await c.data()),J(c)}return r=r.filter((c,d)=>l.includes(d)).sort((c,d)=>d.score-c.score),r}async function mx(e,t){if(!(Fn!=null&&Fn.executor))return[];let a=(t.object.skipTime||0)>ae()-gS,n=hx<(t.object.skipFrames||0);return t.skipAllowed&&a&&n&&am.length>0?(hx++,am):(hx=0,!ne.kernels.includes("mod")||!ne.kernels.includes("sparsetodense")?am:new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],i=fe.resizeBilinear(e,[rm,rm],!1),o=ve(i,ze.tf255),l=Ys(o,[0,3,1,2]),u;t.object.enabled&&(u=Fn.execute(l)),gS=ae();let p=await I3e(u,s,t);am=p,J([i,o,l,...u]),r(p)}))}var hc=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],S3e=hc.length,cc=hc.reduce((e,t,a)=>(e[t]=a,e),{}),C3e=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],F6e=C3e.map(([e,t])=>[cc[e],cc[t]]),AS=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function bS(e){let t=e.reduce(({maxX:a,maxY:n,minX:r,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(a,i),maxY:Math.max(n,o),minX:Math.min(r,i),minY:Math.min(s,o)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function vS(e,[t,a],[n,r]){let s=t/n,i=a/r,o=(u,p)=>({id:p,score:u.score,boxRaw:[u.box[0]/r,u.box[1]/n,u.box[2]/r,u.box[3]/n],box:[Math.trunc(u.box[0]*i),Math.trunc(u.box[1]*s),Math.trunc(u.box[2]*i),Math.trunc(u.box[3]*s)],keypoints:u.keypoints.map(({score:c,part:d,position:h})=>({score:c,part:d,position:[Math.trunc(h.x*i),Math.trunc(h.y*s)],positionRaw:[h.x/n,h.y/n]})),annotations:{}});return e.map((u,p)=>o(u,p))}var sm=class{constructor(t,a){he(this,"priorityQueue");he(this,"numberOfElements");he(this,"getElementValue");this.priorityQueue=new 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n=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"],r=["biasadd","fusedbatchnormv3","matmul","switch","shape","merge","split","broadcastto"],s=[],i=[],o=t.modelUrl,l=t.executor;if((p=l==null?void 0:l.graph)!=null&&p.nodes)for(let c of Object.values(l.graph.nodes)){let d=c.op.toLowerCase();s.includes(d)||s.push(d)}else!l&&e.config.debug&&K("model not loaded",a);for(let c of s)!n.includes(c)&&!r.includes(c)&&!e.env.kernels.includes(c)&&!e.env.kernels.includes(c.replace("_",""))&&!e.env.kernels.includes(c.replace("native",""))&&!e.env.kernels.includes(c.replace("v2",""))&&i.push(c);return e.config.debug&&i.length>0&&K("model validation failed:",a,i),i.length>0?{name:a,missing:i,ops:s,url:o}:null}var mc=class{constructor(t){he(this,"instance");he(this,"models",{});this.models={},this.instance=t}stats(){let t=0,a=0,n=0;for(let s of Object.values(xa))t+=s.sizeFromManifest,a+=s.sizeLoadedWeights,n+=s.sizeDesired;let 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t)h.box[0]>b.box[0]&&h.box[0]b.box[1]&&h.box[1]+h.box[3]m.body.box[0]&&b.box[0]+b.box[2]m.body.box[1]&&b.box[1]+b.box[3]m.body.box[0]&&b.box[1]+b.box[3]>m.body.box[1]&&b.box[1]+b.box[3]{b&&b.length===4&&(f.push(b[0],b[0]+b[2]),g.push(b[1],b[1]+b[3]))};y(m.face.box),y((p=m.body)==null?void 0:p.box),y((c=m.hands.left)==null?void 0:c.box),y((d=m.hands.right)==null?void 0:d.box);let x=Math.min(...f),A=Math.min(...g);m.box=[x,A,Math.max(...f)-x,Math.max(...g)-A],r!=null&&r[1]&&(r!=null&&r[2])&&(m.boxRaw=[m.box[0]/r[2],m.box[1]/r[1],m.box[2]/r[2],m.box[3]/r[1]]),i.push(m)}return i}var lm=` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var dy=0;function Pge(e,t,a){let n=Et(Ft,a);if(!t||!e)return;let r=bn(e);if(r){r.lineJoin="round",r.font=n.font;for(let s=0;shy,kpt:()=>cy});var 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wn,fl=224,c9,Fge=5,T0=[8,16,32,32,32];function Dge(){let e=[],t=0;for(;ta.x)),y:Bt(e.map(a=>a.y))}}async function h9(e){if(ne.initial&&(wn=null),!wn&&e.body.detector&&e.body.detector.modelPath){wn=await Pe(e.body.detector.modelPath);let t=wn!=null&&wn.executor?Object.values(wn.modelSignature.inputs):void 0;fl=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&wn&&K("cached model:",wn.modelUrl);return Dge(),wn}var p9=[5,5];function Oge(e,t){return De(()=>{let a=Sa(e,12,1),n=Oe(a[0]),r=Oe(a[1]),s=Oe(a[2]),i=Oe(a[3]);n=we(ve(n,fl),t.x),r=we(ve(r,fl),t.y),s=te(ve(s,fl),p9[0]),i=te(ve(i,fl),p9[1]);let o=xe(n,ve(s,2)),l=xe(r,ve(i,2)),u=we(o,s),p=we(l,i);return pa([o,l,u,p],1)})}async function zge(e,t,a,n){var u,p;let r=[],s={};s.boxes=Oge(e,c9),s.scores=za(t),s.nms=await fe.nonMaxSuppressionAsync(s.boxes,s.scores,1,((u=a.body.detector)==null?void 0:u.minConfidence)||.1,((p=a.body.detector)==null?void 0:p.iouThreshold)||.1);let i=await s.nms.data(),o=await 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a=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...a[0]),Math.min(...a[1])],r=[Math.max(...a[0]),Math.max(...a[1])],s=[(n[0]+r[0])/2,(n[1]+r[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+r[0],-s[1]+r[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function N0(e,t){let a=[e[2]*t,e[3]*t];return[e[0]-(a[0]-e[2])/2,e[1]-(a[1]-e[3])/2,a[0],a[1]]}var Ua,fy=256,my=Number.MAX_SAFE_INTEGER,Lge={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},E0=[],vs=[[0,0],[0,0],[0,0],[0,0]],g9=0,y9=e=>1-1/(1+Math.exp(e)),A9=e=>h9(e);async function b9(e){if(ne.initial&&(Ua=null),Ua)e.debug&&K("cached model:",Ua.modelUrl);else{Ua=await Pe(e.body.modelPath);let t=Ua!=null&&Ua.executor?Object.values(Ua.modelSignature.inputs):void 0;fy=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Ua}function x9(e,t,a){var s,i;let n={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((i=e==null?void 0:e.shape)!=null&&i[2]))return e;let r;if(a&&(n.cropped=fe.cropAndResize(e,[a],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let o=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],l=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];vs=[[0,0],o,l,[0,0]],n.pad=ir(n.cropped||e,vs),n.resize=fe.resizeBilinear(n.pad,[t,t]),r=ve(n.resize,ze.tf255)}else e.shape[1]!==t?(n.resize=fe.resizeBilinear(n.cropped||e,[t,t]),r=ve(n.resize,ze.tf255)):r=ve(n.cropped||e,ze.tf255);return Object.keys(n).forEach(o=>J(n[o])),r}function Wge(e,t,a){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+vs[2][0]+vs[2][1])/t[0]-vs[2][0]),Math.trunc(n.position[1]*(t[1]+vs[1][0]+vs[1][1])/t[1]-vs[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],2*n.position[2]/(t[0]+t[1])];if(a){let 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y=0;yb.part===f[y]),A=u.find(b=>b.part===f[y+1]);x&&A&&g.push([x.position,A.position])}d[m]=g}return{id:0,score:Math.trunc(100*r)/100,box:c.box,boxRaw:c.boxRaw,keypoints:u,annotations:d}}async function gy(e,t){var s,i,o;let a=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>ae()-g9,r=my<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&E0!==null)my++;else{let l=[];if((i=(s=t.body)==null?void 0:s.detector)!=null&&i.enabled){let u=x9(e,224);l=await m9(u,t,a),J(u)}else l=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let u=0;uJ(n[u])),r}async function Ay(e,t){if(!(Ga!=null&&Ga.executor))return[];let a=(t.object.skipTime||0)>ae()-w9,n=xy<(t.object.skipFrames||0);return t.skipAllowed&&a&&n&&yy.length>0?(xy++,yy):(xy=0,new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],i=fe.resizeBilinear(e,[gl,gl]),o=t.object.enabled?Ga==null?void 0:Ga.execute(i,["tower_0/detections"]):null;w9=ae(),J(i);let l=await Uge(o,s,t);yy=l,r(l)}))}var M0={};yr(M0,{connected:()=>vy,kpt:()=>by});var by=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],vy={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Mt,S9=0,Ma={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},wy=Number.MAX_SAFE_INTEGER;async function C9(e){return ne.initial&&(Mt=null),Mt?e.debug&&K("cached model:",Mt.modelUrl):Mt=await Pe(e.body.modelPath),Mt}async function Gge(e,t){let[a,n]=e.shape,r=Q(e,[n*a]),s=fa(r,0),i=(await s.data())[0];if(i>t){let o=rr(r,0),l=Bu(o,a),u=(await l.data())[0],p=ve(o,a),c=(await p.data())[0];return J([r,s,o,l,p]),[u,c,i]}return J([r,s]),[0,0,i]}async function ky(e,t){if(!(Mt!=null&&Mt.executor)||!(Mt!=null&&Mt.inputs[0].shape))return[];let a=(t.body.skipTime||0)>ae()-S9,n=wy<(t.body.skipFrames||0);return t.skipAllowed&&a&&n&&Object.keys(Ma.keypoints).length>0?(wy++,[Ma]):(wy=0,new Promise(async r=>{let s=De(()=>{var m,f;let c=fe.resizeBilinear(e,[((m=Mt==null?void 0:Mt.inputs[0].shape)==null?void 0:m[2])||0,((f=Mt==null?void 0:Mt.inputs[0].shape)==null?void 0:f[1])||0],!1),d=te(c,ze.tf2);return xe(d,ze.tf1)}),i;if(t.body.enabled&&(i=Mt==null?void 0:Mt.execute(s)),S9=ae(),J(s),i){Ma.keypoints.length=0;let c=Oe(i);J(i);let d=Na(c,2);J(c);for(let h=0;h(t.body.minConfidence||0)&&Ma.keypoints.push({score:Math.round(100*g)/100,part:by[h],positionRaw:[m/Mt.inputs[0].shape[2],f/Mt.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Mt.inputs[0].shape[2]),Math.round(e.shape[1]*f/Mt.inputs[0].shape[1])]})}d.forEach(h=>J(h))}Ma.score=Ma.keypoints.reduce((c,d)=>d.score>c?d.score:c,0);let o=Ma.keypoints.map(c=>c.position[0]),l=Ma.keypoints.map(c=>c.position[1]);Ma.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=Ma.keypoints.map(c=>c.positionRaw[0]),p=Ma.keypoints.map(c=>c.positionRaw[1]);Ma.boxRaw=[Math.min(...u),Math.min(...p),Math.max(...u)-Math.min(...u),Math.max(...p)-Math.min(...p)];for(let[c,d]of Object.entries(vy)){let h=[];for(let m=0;my.part===d[m]),g=Ma.keypoints.find(y=>y.part===d[m+1]);f&&g&&f.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([f.position,g.position])}Ma.annotations[c]=h}r([Ma])}))}var ad=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],P0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],_0=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],$0=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],E9=(e,t,a)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],s=e.landmarks.map(i=>[(i[0]+a[0])*t[0],(i[1]+a[1])*t[1]]);return{startPoint:n,endPoint:r,landmarks:s,confidence:e.confidence}},Iy=(e,t,a)=>{let n=t.shape[1],r=t.shape[2],s=[e.startPoint[1]/n,e.startPoint[0]/r,e.endPoint[1]/n,e.endPoint[0]/r],i=fe.cropAndResize(t,[s],[0],a),o=ve(i,ze.tf255);return J(i),o},F0=(e,t)=>{let a=P0(e),n=ad(e),r=[t*n[0]/2,t*n[1]/2];return{startPoint:[a[0]-r[0],a[1]-r[1]],endPoint:[a[0]+r[0],a[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence,size:n}},D0=e=>{let t=P0(e),a=ad(e),n=Math.max(...a)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence,size:[Math.round(a[0]),Math.round(a[1])]}},M9=e=>{let t=e.map(n=>n[0]),a=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...a)],endPoint:[Math.max(...t),Math.max(...a)],landmarks:e}},Sy=[[1,0,0],[0,1,0],[0,0,1]],Hge=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),jge=(e,t)=>Hge(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var N9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],yl=(e,t)=>{let a=0;for(let n=0;n{let a=[];for(let n=0;n{let 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u=P0(t),p=[u[0]/a.shape[2],u[1]/a.shape[1]],c=fe.rotateWithOffset(a,s,0,[p[0],p[1]]);i=P9(-s,u),o=Iy(t,c,[n,n]),J(c)}else o=Iy(t,a,[n,n]);else o=Iy(t,a,[n,n]);return[s,i,o]}var Yge=e=>{let t=e.map(n=>n[0]),a=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...a)+(Math.max(...a)-Math.min(...a))/2]},D9=(e,t)=>{let a=Yge(e),n=ad(t);return{startPoint:[a[0]-n[0]/2,a[1]-n[1]/2],endPoint:[a[0]+n[0]/2,a[1]+n[1]/2]}};var O9=6,Zge=1.4,Bn,O0=null,ws=0,nd=null,z9=()=>ws;async function L9(e){var t;return ne.initial&&(Bn=null),Bn?e.debug&&K("cached model:",Bn.modelUrl):Bn=await Pe((t=e.face.detector)==null?void 0:t.modelPath),ws=Bn.executor&&Bn.inputs[0].shape?Bn.inputs[0].shape[2]:256,nd=Ge(ws,"int32"),O0=Yn(_9(ws)),Bn}function Jge(e){if(!O0||!nd)return gn([0,0]);let t={};t.boxStarts=Fe(e,[0,1],[-1,2]),t.centers=we(t.boxStarts,O0),t.boxSizes=Fe(e,[0,3],[-1,2]),t.boxSizesNormalized=ve(t.boxSizes,nd),t.centersNormalized=ve(t.centers,nd),t.halfBoxSize=ve(t.boxSizesNormalized,ze.tf2),t.starts=xe(t.centersNormalized,t.halfBoxSize),t.ends=we(t.centersNormalized,t.halfBoxSize),t.startNormalized=te(t.starts,nd),t.endNormalized=te(t.ends,nd);let a=Wu([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>J(t[n])),a}async function W9(e,t){var o,l,u,p,c,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let a={};a.resized=fe.resizeBilinear(e,[ws,ws]),a.div=ve(a.resized,ze.tf127),a.normalized=xe(a.div,ze.tf05);let n=Bn==null?void 0:Bn.execute(a.normalized);if(Array.isArray(n)&&n.length>2){let h=n.sort((m,f)=>m.size-f.size);a.concat384=lt([h[0],h[2]],2),a.concat512=lt([h[1],h[3]],2),a.concat=lt([a.concat512,a.concat384],1),a.batch=Oe(a.concat,[0])}else Array.isArray(n)?a.batch=Oe(n[0]):a.batch=Oe(n);J(n),a.boxes=Jge(a.batch),a.logits=Fe(a.batch,[0,0],[-1,1]),a.sigmoid=za(a.logits),a.scores=Oe(a.sigmoid),a.nms=await fe.nonMaxSuppressionAsync(a.boxes,a.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let r=await a.nms.array(),s=[],i=await a.scores.data();for(let h=0;h(((p=t.face.detector)==null?void 0:p.minConfidence)||0)){let f={};f.bbox=Fe(a.boxes,[r[h],0],[1,-1]),f.slice=Fe(a.batch,[r[h],O9-1],[1,-1]),f.squeeze=Oe(f.slice),f.landmarks=Q(f.squeeze,[O9,-1]);let g=await f.bbox.data(),y={startPoint:[g[0],g[1]],endPoint:[g[2],g[3]],landmarks:await f.landmarks.array(),confidence:m};f.anchor=Fe(O0,[r[h],0],[1,2]);let x=await f.anchor.data(),A=E9(y,[(e.shape[2]||0)/ws,(e.shape[1]||0)/ws],x),b=F0(A,t.face.scale||Zge),w=D0(b);w.size[0]>(((c=t.face.detector)==null?void 0:c.minSize)||0)&&w.size[1]>(((d=t.face.detector)==null?void 0:d.minSize)||0)&&s.push(w),Object.keys(f).forEach(I=>J(f[I]))}}return Object.keys(a).forEach(h=>J(a[h])),s}var an,ks=0,Qge=2.3,Ty=Pn.leftEyeLower0,Ny=Pn.rightEyeLower0,rd={leftBounds:[Ty[0],Ty[Ty.length-1]],rightBounds:[Ny[0],Ny[Ny.length-1]]},sd={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function H9(e){var t,a;return ne.initial&&(an=null),an?e.debug&&K("cached model:",an.modelUrl):an=await Pe((t=e.face.iris)==null?void 0:t.modelPath),ks=an!=null&&an.executor&&((a=an.inputs)!=null&&a[0].shape)?an.inputs[0].shape[2]:0,ks===-1&&(ks=64),an}function z0(e,t,a,n){for(let r=0;r{let t=e[rd.leftBounds[0]][2],a=e[rd.rightBounds[0]][2];return t-a},V9=(e,t,a,n,r,s=!1)=>{let i=D0(F0(M9([e[a],e[n]]),Qge)),o=ad(i),l=fe.cropAndResize(t,[[i.startPoint[1]/r,i.startPoint[0]/r,i.endPoint[1]/r,i.endPoint[0]/r]],[0],[ks,ks]);if(s&&ne.kernels.includes("flipleftright")){let u=fe.flipLeftRight(l);J(l),l=u}return{box:i,boxSize:o,crop:l}},U9=(e,t,a,n=!1)=>{let r=[];for(let s=0;s{let n=e[Pn[`${a}EyeUpper0`][sd.upperCenter]][2],r=e[Pn[`${a}EyeLower0`][sd.lowerCenter]][2],s=(n+r)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=r),[i[0],i[1],l]})};async function j9(e,t,a){if(!(an!=null&&an.executor))return e;let{box:n,boxSize:r,crop:s}=V9(e,t,rd.leftBounds[0],rd.leftBounds[1],a,!0),{box:i,boxSize:o,crop:l}=V9(e,t,rd.rightBounds[0],rd.rightBounds[1],a,!0),u=lt([s,l]);J(s),J(l);let p=an.execute(u);J(u);let c=await p.data();J(p);let d=c.slice(0,sd.numCoordinates*3),{rawCoords:h,iris:m}=U9(d,n,r,!0),f=c.slice(sd.numCoordinates*3),{rawCoords:g,iris:y}=U9(f,i,o,!1),x=e3e(e);Math.abs(x)<30?(z0(e,h,"left",null),z0(e,g,"right",null)):x<1?z0(e,h,"left",["EyeUpper0","EyeLower0"]):z0(e,g,"right",["EyeUpper0","EyeLower0"]);let A=G9(e,m,"left"),b=G9(e,y,"right");return e.concat(A).concat(b)}async function X9(e,t){var s,i,o,l,u,p,c,d,h,m;let a={lips:await((i=(s=t.filter(f=>f.size===160))==null?void 0:s[0])==null?void 0:i.data()),irisL:await((l=(o=t.filter(f=>f.size===10))==null?void 0:o[0])==null?void 0:l.data()),eyeL:await((p=(u=t.filter(f=>f.size===142))==null?void 0:u[0])==null?void 0:p.data()),irisR:await((d=(c=t.filter(f=>f.size===10))==null?void 0:c[1])==null?void 0:d.data()),eyeR:await((m=(h=t.filter(f=>f.size===142))==null?void 0:h[1])==null?void 0:m.data())};for(let f of Object.values(a))if(!f)return e;let n=hl.reduce((f,g)=>f+=e[g][2],0)/hl.length;for(let f=0;ff+=e[g][2],0)/ml.length;for(let f=0;fae()-dr.timestamp,n=dr.skipped<(((u=t.face.detector)==null?void 0:u.skipFrames)||0);!t.skipAllowed||!a||!n||dr.boxes.length===0?(dr.boxes=await W9(e,t),dr.timestamp=ae(),dr.skipped=0):dr.skipped++;let r=[],s=[],i=0,o=lc;for(let x=0;x[T[0]/(e.shape[2]||0),T[1]/(e.shape[1]||0),(T[2]||0)/o]);for(let T of Object.keys(pl))I.annotations[T]=[I.mesh[pl[T]]]}else if(!Ct)t.debug&&K("face mesh detection requested, but model is not loaded");else{if((h=t.face.attention)!=null&&h.enabled&&!ne.kernels.includes("atan2"))return t.face.attention.enabled=!1,J(I.tensor),r;let T=Ct.execute(I.tensor),M=await T.find(P=>P.shape[P.shape.length-1]===1).data();if(I.faceScore=Math.round(100*M[0])/100,I.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(A.confidence=I.faceScore,t.face.mesh.keepInvalid){I.box=_0(A,e),I.boxRaw=$0(A,e),I.size=A.size,I.score=I.boxScore,I.mesh=A.landmarks,I.meshRaw=I.mesh.map(P=>[P[0]/(e.shape[2]||1),P[1]/(e.shape[1]||1),(P[2]||0)/o]);for(let P of Object.keys(pl))I.annotations[P]=[I.mesh[pl[P]]]}}else{let P=T.find(O=>O.shape[O.shape.length-1]===1404),E=Q(P,[-1,3]),S=await E.array();J(E),(f=t.face.attention)!=null&&f.enabled?S=await X9(S,T):(g=t.face.iris)!=null&&g.enabled&&(S=await j9(S,I.tensor,lc)),I.mesh=$9(S,A,b,w,lc),I.meshRaw=I.mesh.map(O=>[O[0]/(e.shape[2]||0),O[1]/(e.shape[1]||0),(O[2]||0)/o]);for(let O of Object.keys(Pn))I.annotations[O]=Pn[O].map(W=>I.mesh[W]);I.score=I.faceScore;let _={...D9(I.mesh,A),confidence:A.confidence,landmarks:A.landmarks,size:A.size};I.box=_0(_,e),I.boxRaw=$0(_,e),I.size=_.size,s.push(_)}J(T)}I.score>(((y=t.face.detector)==null?void 0:y.minConfidence)||1)?r.push(I):J(I.tensor)}return dr.boxes=s,r}async function Y9(e){var t,a,n,r,s,i;return ne.initial&&(Ct=null),(t=e.face.attention)!=null&&t.enabled&&(Ct!=null&&Ct.signature)&&Object.keys(((a=Ct==null?void 0:Ct.signature)==null?void 0:a.outputs)||{}).length<6&&(Ct=null),Ct?e.debug&&K("cached model:",Ct.modelUrl):(n=e.face.attention)!=null&&n.enabled?Ct=await Pe(e.face.attention.modelPath):Ct=await Pe((r=e.face.mesh)==null?void 0:r.modelPath),lc=Ct.executor&&((s=Ct==null?void 0:Ct.inputs)!=null&&s[0].shape)?(i=Ct==null?void 0:Ct.inputs)==null?void 0:i[0].shape[2]:256,Ct}var Z9=cl,J9=ic;var My=[],na,L0=[],Q9=0,eI=0,Ey=Number.MAX_SAFE_INTEGER,Py=!1;async function tI(e){var t,a,n;return ne.initial&&(na=null),na?e.debug&&K("cached model:",na.modelUrl):(na=await Pe((t=e.face.emotion)==null?void 0:t.modelPath),Py=((n=(a=na==null?void 0:na.inputs)==null?void 0:a[0].shape)==null?void 0:n[3])===3,Py?My=["angry","disgust","fear","happy","neutral","sad","surprise"]:My=["angry","disgust","fear","happy","sad","surprise","neutral"]),na}async function _y(e,t,a,n){var i,o;if(!na)return[];let r=Ey<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>ae()-eI;return t.skipAllowed&&s&&r&&Q9===n&&L0[a]&&L0[a].length>0?(Ey++,L0[a]):(Ey=0,new Promise(async l=>{var p,c,d;let u=[];if((p=t.face.emotion)!=null&&p.enabled){let h={},m=na!=null&&na.inputs[0].shape?na.inputs[0].shape[2]:0;if(((c=t.face.emotion)==null?void 0:c.crop)>0){let g=(d=t.face.emotion)==null?void 0:d.crop,y=[[g,g,1-g,1-g]];h.resize=fe.cropAndResize(e,y,[0],[m,m])}else 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t.skipAllowed&&r&&s&&cI===n&&B0[a]?(Ly++,B0[a]):(Ly=0,new Promise(async l=>{let u=fe.resizeBilinear(e,[ia!=null&&ia.inputs[0].shape?ia.inputs[0].shape[2]:0,ia!=null&&ia.inputs[0].shape?ia.inputs[0].shape[1]:0],!1),p=ia==null?void 0:ia.execute(u),c=(await p.data())[0];B0[a]=Math.round(100*c)/100,cI=n,hI=ae(),J([u,p]),l(B0[a])}))}var _n,By=[],s3e=["white","black","asian","indian","other"],i3e=[15,23,28,35.5,45.5,55.5,65],gI=0,yI=0,Vy=Number.MAX_SAFE_INTEGER;async function xI(e){var t;return ne.initial&&(_n=null),_n?e.debug&&K("cached model:",_n.modelUrl):_n=await Pe((t=e.face.gear)==null?void 0:t.modelPath),_n}async function Uy(e,t,a,n){var i,o;if(!_n)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=Vy<(((i=t.face.gear)==null?void 0:i.skipFrames)||0),s=(((o=t.face.gear)==null?void 0:o.skipTime)||0)>ae()-yI;return t.skipAllowed&&s&&r&&gI===n&&By[a]?(Vy++,By[a]):(Vy=0,new Promise(async l=>{var y,x,A,b;if(!(_n!=null&&_n.inputs[0].shape))return;let u={},p=[[0,.1,.9,.9]];if(((y=t.face.gear)==null?void 0:y.crop)>0){let w=(x=t.face.gear)==null?void 0:x.crop;p=[[w,w,1-w,1-w]]}u.resize=fe.cropAndResize(e,p,[0],[_n.inputs[0].shape[2],_n.inputs[0].shape[1]]);let c={age:0,gender:"unknown",genderScore:0,race:[]};(A=t.face.gear)!=null&&A.enabled&&([u.age,u.gender,u.race]=_n.execute(u.resize,["age_output","gender_output","race_output"]));let d=await u.gender.data();c.gender=d[0]>d[1]?"male":"female",c.genderScore=Math.round(100*(d[0]>d[1]?d[0]:d[1]))/100;let h=await u.race.data();for(let w=0;w(((b=t.face.gear)==null?void 0:b.minConfidence)||.2)&&c.race.push({score:Math.round(100*h[w])/100,race:s3e[w]});c.race.sort((w,I)=>I.score-w.score);let f=Array.from(await u.age.data()).map((w,I)=>[i3e[I],w]).sort((w,I)=>I[1]-w[1]),g=f[0][0];for(let w=1;wJ(u[w])),By[a]=c,gI=n,yI=ae(),l(c)}))}var Pa,V0=[],bI=0,vI=0,Gy=Number.MAX_SAFE_INTEGER;async function wI(e){return ne.initial&&(Pa=null),Pa?e.debug&&K("cached model:",Pa.modelUrl):Pa=await 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xa,U0=[],II=0,SI=0,jy=Number.MAX_SAFE_INTEGER,qy=[.2989,.587,.114];async function CI(e){var t;return ne.initial&&(xa=null),xa?e.debug&&K("cached model:",xa.modelUrl):xa=await Pe((t=e.face.ssrnet)==null?void 0:t.modelPathGender),xa}async function Xy(e,t,a,n){var i,o,l,u;if(!xa)return{gender:"unknown",genderScore:0};let r=jy<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>ae()-SI;return t.skipAllowed&&r&&s&&II===n&&((l=U0[a])!=null&&l.gender)&&((u=U0[a])==null?void 0:u.genderScore)>0?(jy++,U0[a]):(jy=0,new Promise(async p=>{var m,f,g;if(!(xa!=null&&xa.inputs[0].shape))return;let c={};if(((m=t.face.ssrnet)==null?void 0:m.crop)>0){let y=(f=t.face.ssrnet)==null?void 0:f.crop,x=[[y,y,1-y,1-y]];c.resize=fe.cropAndResize(e,x,[0],[xa.inputs[0].shape[2],xa.inputs[0].shape[1]])}else c.resize=fe.resizeBilinear(e,[xa.inputs[0].shape[2],xa.inputs[0].shape[1]],!1);c.enhance=De(()=>{var x,A;let y;if(((A=(x=xa==null?void 0:xa.inputs)==null?void 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Object.keys(a).forEach(r=>J(a[r])),n}normalizeLandmarks(t,a){let n={};n.reshape=Q(t,[-1,7,2]),n.div=ve(n.reshape,this.inputSizeTensor),n.landmarks=we(n.div,this.anchors[a]?this.anchors[a]:0);let r=te(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>J(n[s])),r}async predict(t,a){var o;let n={};n.resize=fe.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=ve(n.resize,ze.tf127),n.image=xe(n.div,ze.tf1),n.batched=this.model.execute(n.image),n.predictions=Oe(n.batched),n.slice=Fe(n.predictions,[0,0],[-1,1]),n.sigmoid=za(n.slice),n.scores=Oe(n.sigmoid);let r=await n.scores.data();n.boxes=Fe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await fe.nonMaxSuppressionAsync(n.norm,n.scores,3*(((o=a.hand)==null?void 0:o.maxDetected)||1),a.hand.iouThreshold,a.hand.minConfidence);let s=await n.nms.array(),i=[];for(let l of s){let u={};u.box=Fe(n.norm,[l,0],[1,-1]),u.slice=Fe(n.predictions,[l,5],[1,14]),u.norm=this.normalizeLandmarks(u.slice,l),u.palmLandmarks=Q(u.norm,[-1,2]);let p=await u.box.data(),c=p.slice(0,2),d=p.slice(2,4),h=await u.palmLandmarks.array(),m={startPoint:c,endPoint:d,palmLandmarks:h,confidence:r[l]},f=tS(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);i.push(f),Object.keys(u).forEach(g=>J(u[g]))}return Object.keys(n).forEach(l=>J(n[l])),i}};var v3e=5,iS=1.65,oS=[0,5,9,13,17,1,2],w3e=0,k3e=2,lS=0,K0=class{constructor(t,a){he(this,"handDetector");he(this,"handPoseModel");he(this,"inputSize");he(this,"storedBoxes");he(this,"skipped");he(this,"detectedHands");var n,r,s;this.handDetector=t,this.handPoseModel=a,this.inputSize=((s=(r=(n=this.handPoseModel)==null?void 0:n.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let a=t.map(i=>i[0]),n=t.map(i=>i[1]),r=[Math.min(...a),Math.min(...n)],s=[Math.max(...a),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,a){let n=t.map(s=>nx([...s,1],a)),r=this.calculateLandmarksBoundingBox(n);return j0(q0(r),v3e)}getBoxForHandLandmarks(t){let a=this.calculateLandmarksBoundingBox(t),n=j0(q0(a),iS);n.palmLandmarks=[];for(let r=0;r[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=ax(n,[0,0]),u=o.map(h=>[...nx(h,l),h[2]]),p=nS(r),c=[...uc(a),1],d=[Es(c,p[0]),Es(c,p[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,a){let n=!1,r,s=(a.hand.skipTime||0)>ae()-lS,i=this.skipped<(a.hand.skipFrames||0);a.skipAllowed&&s&&i?this.skipped++:(r=await this.handDetector.predict(t,a),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==a.hand.maxDetected||!a.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l=a.hand.minConfidence/4){let w=Q(A,[-1,3]),I=await w.array();J(A),J(w);let T=this.transformRawCoords(I,f,p,m),N=this.getBoxForHandLandmarks(T);this.storedBoxes[l]={...N,confidence:b};let M={landmarks:T,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:N.startPoint,bottomRight:N.endPoint}};o.push(M)}else this.storedBoxes[l]=null;J(A)}else{let p=j0(q0(u),iS),c={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:p.startPoint,bottomRight:p.endPoint},landmarks:[]};o.push(c)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>a.hand.maxDetected&&(o.length=a.hand.maxDetected),o}};var uS={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},vl,wl,rx;function S3e(){let e=vl?new X0(vl):void 0;e&&wl&&(rx=new K0(e,wl))}async function sx(e,t){rx||S3e();let a=await rx.estimateHands(e,t);if(!a)return[];let n=[];for(let r=0;ra[r].landmarks[c]);let i=a[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let p of i)p[0]o[2]&&(o[2]=p[0]),p[1]>o[3]&&(o[3]=p[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=a[r].box?[Math.trunc(Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.max(0,a[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,a[r].box.bottomRight[0])-Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,a[r].box.bottomRight[1])-Math.max(0,a[r].box.topLeft[1]))]:[0,0,0,0],l=[a[r].box.topLeft[0]/(e.shape[2]||0),a[r].box.topLeft[1]/(e.shape[1]||0),(a[r].box.bottomRight[0]-a[r].box.topLeft[0])/(e.shape[2]||0),(a[r].box.bottomRight[1]-a[r].box.topLeft[1])/(e.shape[1]||0)];let u=G0(i);n.push({id:r,score:Math.round(100*a[r].confidence)/100,boxScore:Math.round(100*a[r].boxConfidence)/100,fingerScore:Math.round(100*a[r].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function dS(e){var t;return ne.initial&&(vl=null),vl?e.debug&&K("cached model:",vl.modelUrl):vl=await Pe((t=e.hand.detector)==null?void 0:t.modelPath),vl}async function pS(e){var t;return ne.initial&&(wl=null),wl?e.debug&&K("cached model:",wl.modelUrl):wl=await Pe((t=e.hand.skeleton)==null?void 0:t.modelPath),wl}var Ot=[null,null],C3e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ms=[[0,0],[0,0]],T3e=["hand","fist","pinch","point","face","tip","pinchtip"],hS=4,mS=1.6,N3e=512,R3e=1.4,Y0=Number.MAX_SAFE_INTEGER,ix=0,Fr=[0,0],Dt={boxes:[],hands:[]},fS={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function gS(e){var t;if(ne.initial&&(Ot[0]=null),Ot[0])e.debug&&K("cached model:",Ot[0].modelUrl);else{A0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Ot[0]=await Pe((t=e.hand.detector)==null?void 0:t.modelPath);let a=Ot[0].executor?Object.values(Ot[0].modelSignature.inputs):void 0;Ms[0][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Ms[0][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Ot[0]}async function yS(e){var t;if(ne.initial&&(Ot[1]=null),Ot[1])e.debug&&K("cached model:",Ot[1].modelUrl);else{Ot[1]=await Pe((t=e.hand.skeleton)==null?void 0:t.modelPath);let a=Ot[1].executor?Object.values(Ot[1].modelSignature.inputs):void 0;Ms[1][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Ms[1][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Ot[1]}async function E3e(e,t){let a=[];if(!e||!Ot[0])return a;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,N3e),i=Math.round(s*r/8)*8;n.resize=fe.resizeBilinear(e,[s,i]),n.cast=Ue(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await Ot[0].executeAsync(n.cast,C3e),n.boxes=Oe(n.rawBoxes,[0,2]),n.scores=Oe(n.rawScores,[0]);let o=Na(n.scores,1);J(o[hS]),o.splice(hS,1),n.filtered=pa(o,1),J(o),n.max=fa(n.filtered,1),n.argmax=rr(n.filtered,1);let l=0;n.nms=await fe.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),p=await n.max.data(),c=await n.argmax.data();for(let d of Array.from(u)){let h=Fe(n.boxes,d,1),m=await h.data();J(h);let f=[m[1],m[0],m[3]-m[1],m[2]-m[0]],g=N0(f,R3e),y=[Math.trunc(f[0]*Fr[0]),Math.trunc(f[1]*Fr[1]),Math.trunc(f[2]*Fr[0]),Math.trunc(f[3]*Fr[1])],x=p[d],A=T3e[c[d]],b={id:l++,score:x,box:y,boxRaw:g,label:A};a.push(b)}return Object.keys(n).forEach(d=>J(n[d])),a.sort((d,h)=>h.score-d.score),a.length>(t.hand.maxDetected||1)&&(a.length=t.hand.maxDetected||1),a}async function ox(e,t,a){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Ot[1]&&a.hand.landmarks&&t.score>(a.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=fe.cropAndResize(e,[s],[0],[Ms[1][0],Ms[1][1]],"bilinear"),r.div=ve(r.crop,ze.tf255),[r.score,r.keypoints]=Ot[1].execute(r.div,["Identity_1","Identity"]);let i=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(a.hand.minConfidence||0)){n.fingerScore=o,r.reshaped=Q(r.keypoints,[-1,3]);let p=(await r.reshaped.array()).map(c=>[c[0]/Ms[1][1],c[1]/Ms[1][0],c[2]||0]).map(c=>[c[0]*t.boxRaw[2],c[1]*t.boxRaw[3],c[2]||0]);n.keypoints=p.map(c=>[Fr[0]*(c[0]+t.boxRaw[0]),Fr[1]*(c[1]+t.boxRaw[1]),c[2]||0]),n.landmarks=G0(n.keypoints);for(let c of Object.keys(fS))n.annotations[c]=fS[c].map(d=>n.landmarks&&n.keypoints[d]?n.keypoints[d]:null)}Object.keys(r).forEach(l=>J(r[l]))}return n}async function lx(e,t){var r,s;if(!((r=Ot[0])!=null&&r.executor)||!((s=Ot[1])!=null&&s.executor)||!Ot[0].inputs[0].shape||!Ot[1].inputs[0].shape)return[];Fr=[e.shape[2]||0,e.shape[1]||0],Y0++;let a=(t.hand.skipTime||0)>ae()-ix,n=Y0<(t.hand.skipFrames||0);return t.skipAllowed&&a&&n?Dt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>ae()-ix,l=Y0<3*(t.hand.skipFrames||0);t.skipAllowed&&Dt.hands.length===t.hand.maxDetected?Dt.hands=await Promise.all(Dt.boxes.map(p=>ox(e,p,t))):t.skipAllowed&&o&&l&&Dt.hands.length>0?Dt.hands=await Promise.all(Dt.boxes.map(p=>ox(e,p,t))):(Dt.boxes=await E3e(e,t),ix=ae(),Dt.hands=await Promise.all(Dt.boxes.map(p=>ox(e,p,t))),Y0=0);let u=[...Dt.boxes];if(Dt.boxes.length=0,t.cacheSensitivity>0)for(let p=0;p.05&&c.box[3]/(e.shape[1]||1)>.05&&Dt.hands[p].fingerScore&&Dt.hands[p].fingerScore>(t.hand.minConfidence||0)){let d=N0(c.box,mS),h=N0(c.boxRaw,mS);Dt.boxes.push({...u[p],box:d,boxRaw:h})}}for(let p=0;p({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var dc={};yr(dc,{connected:()=>J0,horizontal:()=>ux,kpt:()=>Z0,relative:()=>px,vertical:()=>dx});var Z0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],ux=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],dx=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],px=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],J0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Ae=pr(),cx=0;function AS(e,t){var i,o,l,u,p,c,d,h,m,f,g,y,x,A,b,w,I,T,N,M,P,E,S,_,O,W;let a=ae();if(!e)return pr();let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ae.canvas=e.canvas),e.error&&(Ae.error=e.error),!Ae.body||e.body.length!==Ae.body.length)Ae.body=JSON.parse(JSON.stringify(e.body));else for(let $=0;$((r-1)*Ae.body[$].box[X]+Z)/r),G=e.body[$].boxRaw.map((Z,X)=>((r-1)*Ae.body[$].boxRaw[X]+Z)/r),q=e.body[$].keypoints.map((Z,X)=>{var re,ee,ge,ie,be,Ce,Re,Le,qe;return{score:Z.score,part:Z.part,position:[Ae.body[$].keypoints[X]?((r-1)*(Ae.body[$].keypoints[X].position[0]||0)+(Z.position[0]||0))/r:Z.position[0],Ae.body[$].keypoints[X]?((r-1)*(Ae.body[$].keypoints[X].position[1]||0)+(Z.position[1]||0))/r:Z.position[1],Ae.body[$].keypoints[X]?((r-1)*(Ae.body[$].keypoints[X].position[2]||0)+(Z.position[2]||0))/r:Z.position[2]],positionRaw:[Ae.body[$].keypoints[X]?((r-1)*(Ae.body[$].keypoints[X].positionRaw[0]||0)+(Z.positionRaw[0]||0))/r:Z.positionRaw[0],Ae.body[$].keypoints[X]?((r-1)*(Ae.body[$].keypoints[X].positionRaw[1]||0)+(Z.positionRaw[1]||0))/r:Z.positionRaw[1],Ae.body[$].keypoints[X]?((r-1)*(Ae.body[$].keypoints[X].positionRaw[2]||0)+(Z.positionRaw[2]||0))/r:Z.positionRaw[2]],distance:[Ae.body[$].keypoints[X]?((r-1)*(((re=Ae.body[$].keypoints[X].distance)==null?void 0:re[0])||0)+(((ee=Z.distance)==null?void 0:ee[0])||0))/r:(ge=Z.distance)==null?void 0:ge[0],Ae.body[$].keypoints[X]?((r-1)*(((ie=Ae.body[$].keypoints[X].distance)==null?void 0:ie[1])||0)+(((be=Z.distance)==null?void 0:be[1])||0))/r:(Ce=Z.distance)==null?void 0:Ce[1],Ae.body[$].keypoints[X]?((r-1)*(((Re=Ae.body[$].keypoints[X].distance)==null?void 0:Re[2])||0)+(((Le=Z.distance)==null?void 0:Le[2])||0))/r:(qe=Z.distance)==null?void 0:qe[2]]}}),H={},V={connected:{}};(i=t.body.modelPath)!=null&&i.includes("efficientpose")?V=M0:(o=t.body.modelPath)!=null&&o.includes("blazepose")?V=C0:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(V=dc);for(let[Z,X]of Object.entries(V.connected)){let re=[];for(let ee=0;eebe.part===X[ee]),ie=q.find(be=>be.part===X[ee+1]);ge&&ie&&re.push([ge.position,ie.position])}H[Z]=re}Ae.body[$]={...e.body[$],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!Ae.hand||e.hand.length!==Ae.hand.length)Ae.hand=JSON.parse(JSON.stringify(e.hand));else for(let $=0;$((r-1)*Ae.hand[$].box[Z]+V)/r),G=e.hand[$].boxRaw.map((V,Z)=>((r-1)*Ae.hand[$].boxRaw[Z]+V)/r);Ae.hand[$].keypoints.length!==e.hand[$].keypoints.length&&(Ae.hand[$].keypoints=e.hand[$].keypoints);let q=e.hand[$].keypoints&&e.hand[$].keypoints.length>0?e.hand[$].keypoints.map((V,Z)=>V.map((X,re)=>((r-1)*(Ae.hand[$].keypoints[Z][re]||1)+(X||0))/r)):[],H={};if(Object.keys(Ae.hand[$].annotations).length!==Object.keys(e.hand[$].annotations).length)Ae.hand[$].annotations=e.hand[$].annotations,H=Ae.hand[$].annotations;else if(e.hand[$].annotations)for(let V of Object.keys(e.hand[$].annotations))H[V]=(c=(p=(u=e.hand[$])==null?void 0:u.annotations)==null?void 0:p[V])!=null&&c[0]?e.hand[$].annotations[V].map((Z,X)=>Z.map((re,ee)=>((r-1)*Ae.hand[$].annotations[V][X][ee]+re)/r)):null;Ae.hand[$]={...e.hand[$],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!Ae.face||e.face.length!==Ae.face.length)Ae.face=JSON.parse(JSON.stringify(e.face));else for(let $=0;$((r-1)*Ae.face[$].box[V]+H)/r),G=e.face[$].boxRaw.map((H,V)=>((r-1)*Ae.face[$].boxRaw[V]+H)/r),q=e.face[$].annotations;if(Object.keys(Ae.face[$].annotations).length!==Object.keys(e.face[$].annotations).length)Ae.face[$].annotations=e.face[$].annotations,q=Ae.face[$].annotations;else if(e.face[$].annotations)for(let H of Object.keys(e.face[$].annotations))q[H]=(m=(h=(d=e.face[$])==null?void 0:d.annotations)==null?void 0:h[H])!=null&&m[0]?e.face[$].annotations[H].map((V,Z)=>V.map((X,re)=>((r-1)*Ae.face[$].annotations[H][Z][re]+X)/r)):null;if(e.face[$].rotation){let H={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};H.matrix=(f=e.face[$].rotation)==null?void 0:f.matrix,H.angle={roll:((r-1)*(((y=(g=Ae.face[$].rotation)==null?void 0:g.angle)==null?void 0:y.roll)||0)+(((A=(x=e.face[$].rotation)==null?void 0:x.angle)==null?void 0:A.roll)||0))/r,yaw:((r-1)*(((w=(b=Ae.face[$].rotation)==null?void 0:b.angle)==null?void 0:w.yaw)||0)+(((T=(I=e.face[$].rotation)==null?void 0:I.angle)==null?void 0:T.yaw)||0))/r,pitch:((r-1)*(((M=(N=Ae.face[$].rotation)==null?void 0:N.angle)==null?void 0:M.pitch)||0)+(((E=(P=e.face[$].rotation)==null?void 0:P.angle)==null?void 0:E.pitch)||0))/r},H.gaze={bearing:((r-1)*(((S=Ae.face[$].rotation)==null?void 0:S.gaze.bearing)||0)+(((_=e.face[$].rotation)==null?void 0:_.gaze.bearing)||0))/r,strength:((r-1)*(((O=Ae.face[$].rotation)==null?void 0:O.gaze.strength)||0)+(((W=e.face[$].rotation)==null?void 0:W.gaze.strength)||0))/r},Ae.face[$]={...e.face[$],rotation:H,box:U,boxRaw:G,annotations:q}}else Ae.face[$]={...e.face[$],box:U,boxRaw:G,annotations:q}}if(!Ae.object||e.object.length!==Ae.object.length)Ae.object=JSON.parse(JSON.stringify(e.object));else for(let $=0;$((r-1)*Ae.object[$].box[H]+q)/r),G=e.object[$].boxRaw.map((q,H)=>((r-1)*Ae.object[$].boxRaw[H]+q)/r);Ae.object[$]={...e.object[$],box:U,boxRaw:G}}if(e.persons){let $=e.persons;if(!Ae.persons||$.length!==Ae.persons.length)Ae.persons=JSON.parse(JSON.stringify($));else for(let U=0;U<$.length;U++)Ae.persons[U].box=$[U].box.map((G,q)=>((r-1)*Ae.persons[U].box[q]+G)/r)}e.gesture&&(Ae.gesture=e.gesture),Ae.width=e.width,Ae.height=e.height;let s=ae();return cx=ne.perfadd?cx+Math.round(s-a):Math.round(s-a),e.performance&&(Ae.performance={...e.performance,interpolate:cx}),Ae}var Aa;async function hx(e){return!Aa||ne.initial?Aa=await Pe(e.segmentation.modelPath):e.debug&&K("cached model:",Aa.modelUrl),Aa}async function bS(e,t){var r;if(Aa||(Aa=await hx(t)),!(Aa!=null&&Aa.executor)||!((r=Aa==null?void 0:Aa.inputs)!=null&&r[0].shape))return null;let 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r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await RS(n.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("meet")&&(r=await oS(n.tensor,this.config)),(o=this.config.segmentation.modelPath)!=null&&o.includes("selfie")&&(r=await MS(n.tensor,this.config)),J(n.tensor),r}compare(t,a){return Gk(this.config,t,a)}async init(){await ic(this,!0),await this.tf.ready(),Z3()}async load(t){this.state="load";let a=ae(),n=Object.values(this.models.models).filter(i=>i).length;t&&(this.config=Et(this.config,t)),this.env.initial&&(await ic(this,!1)||K("error: backend check failed"),await Op(),this.env.browser&&(this.config.debug&&K("configuration:",this.config),this.config.debug&&K("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&K("tf engine 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xy(o.tensor,d):[]:(T=this.config.body.modelPath)!=null&&T.includes("movenet")&&(u=this.config.body.enabled?await cx(o.tensor,d):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Et(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((M=(N=this.config.hand.detector)==null?void 0:N.modelPath)!=null&&M.includes("handdetect")?p=this.config.hand.enabled?ex(o.tensor,h):[]:(E=(P=this.config.hand.detector)==null?void 0:P.modelPath)!=null&&E.includes("handtrack")&&(p=this.config.hand.enabled?nx(o.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ae(),(_=(C=this.config.hand.detector)==null?void 0:C.modelPath)!=null&&_.includes("handdetect")?p=this.config.hand.enabled?await ex(o.tensor,h):[]:(B=(O=this.config.hand.detector)==null?void 0:O.modelPath)!=null&&B.includes("handtrack")&&(p=this.config.hand.enabled?await nx(o.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((F=this.config.object.modelPath)!=null&&F.includes("nanodet")?c=this.config.object.enabled?mx(o.tensor,this.config):[]:(U=this.config.object.modelPath)!=null&&U.includes("centernet")&&(c=this.config.object.enabled?my(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ae(),(G=this.config.object.modelPath)!=null&&G.includes("nanodet")?c=this.config.object.enabled?await mx(o.tensor,this.config):[]:(q=this.config.object.modelPath)!=null&&q.includes("centernet")&&(c=this.config.object.enabled?await my(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,p,c]=await Promise.all([l,u,p,c])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=ae(),m=[...DI(l),...FI(u),...zI(p),...OI(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ae()-i):Math.trunc(ae()-i);let f=((H=this.process.tensor)==null?void 0:H.shape)||[0,0,0,0];this.result={face:l,body:u,hand:p,gesture:m,object:c,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return PS(l,u,p,m,f)}},J(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}async sleep(t){return new Promise(a=>{setTimeout(a,t)})}async video(t,a=!0,n=0){a?(Ka(this,Es)[t.id]||(this.config.debug&&K("video start",t.id),Ka(this,Es)[t.id]=!0),!t.paused&&Ka(this,Es)[t.id]&&t.readyState>=2&&await this.detect(t),n>0&&await this.sleep(n),Ka(this,Es)[t.id]&&requestAnimationFrame(()=>this.video(t,a,n))):(this.config.debug&&K("video stop",t.id),Ka(this,Es)[t.id]=!1)}};cd=new WeakMap,fc=new WeakMap,gc=new WeakMap,dm=new WeakMap,Es=new WeakMap;return AC(H3e);})(); +2Q==`;async function J3e(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(i=>i.blob()),a,n;switch(e.config.warmup){case"face":a=await t(om);break;case"body":case"full":a=await t(lm);break;default:a=null}if(a){let r=await createImageBitmap(a);n=await e.detect(r,e.config),r.close()}return n}async function Q3e(e){return new Promise(t=>{let a;switch(e.config.warmup){case"face":a="data:image/jpeg;base64,"+om;break;case"full":case"body":a="data:image/jpeg;base64,"+lm;break;default:a=""}let n;if(typeof Image!="undefined")n=new Image;else if(ne.Image)n=new ne.Image;else{t(void 0);return}n.onload=async()=>{let r=Mn(n.naturalWidth,n.naturalHeight);if(!r)K("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(n,0,0);let i=await e.image(r,!0),o=i.tensor?await e.detect(i.tensor,e.config):void 0;t(o)}},a?n.src=a:t(void 0)})}async function eye(e){let t=r=>Buffer.from(r,"base64"),a;e.config.warmup==="face"?a=t(om):a=t(lm);let n;if("node"in Ke&&ca()==="tensorflow"){let r=J3.decodeJpeg(a),s=Wt(r,0);e.tf.dispose(r),n=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&K("Warmup tfjs-node not loaded");return n}async function tye(e){let t;return typeof createImageBitmap=="function"?t=await J3e(e):typeof Image!="undefined"||ne.Canvas!==void 0?t=await Q3e(e):t=await eye(e),t}async function aye(e){var o,l,u,p;if(!B().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=ca(),a=nr();if(t!=="webgl"&&t!=="humangl"||!(a!=null&&a.checkCompileCompletion))return;B().set("ENGINE_COMPILE_ONLY",!0);let n=It().state.numTensors,r=[];for(let[c,d]of Object.entries(e.models.models)){if(!d)continue;let h=d!=null&&d.modelSignature&&((l=(o=d==null?void 0:d.inputs)==null?void 0:o[0])!=null&&l.shape)?[...d.inputs[0].shape]:[1,64,64,3],m=d!=null&&d.modelSignature&&((p=(u=d==null?void 0:d.inputs)==null?void 0:u[0])!=null&&p.dtype)?d.inputs[0].dtype:"float32";for(let g=0;gJ(y)):J(g)}catch(g){e.config.debug&&K("compile fail model:",c)}J(f)}let s=await a.checkCompileCompletionAsync();a.getUniformLocations(),e.config.debug&&K("compile pass:",{models:r,kernels:s.length}),B().set("ENGINE_COMPILE_ONLY",!1);let i=It().state.numTensors;i-n>0&&K("tensor leak:",i-n)}async function qS(e,t){await sc(e,!1);let a=ae();return e.state="warmup",t&&(e.config=Et(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?pr():new Promise(async n=>{await e.models.load(),await aye(e);let r=await tye(e),s=ae();e.config.debug&&K("warmup",e.config.warmup,Math.round(s-a),"ms"),e.emit("warmup"),n(r)})}var pd,mc,fc,um,Ps,Ex=class{constructor(t){he(this,"version");he(this,"config");he(this,"result");he(this,"state");he(this,"process");he(this,"tf");he(this,"env",ne);he(this,"draw",S0);he(this,"match",Q0);he(this,"models");he(this,"events");he(this,"faceTriangulation");he(this,"faceUVMap");he(this,"performance");jn(this,pd,void 0);jn(this,mc,void 0);jn(this,fc,void 0);he(this,"analyze",(...t)=>{if(!qa(this,mc))return;let a=this.tf.engine().state.numTensors,n=qa(this,pd);xr(this,pd,a);let r=a-n;r!==0&&K(...t,r)});jn(this,um,t=>{if(!qa(this,fc))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof gt))return"input must be a tensor";try{this.tf.getBackend()}catch(a){return"backend not loaded"}return null});he(this,"webcam",new x0);he(this,"emit",t=>{var a;(a=this.events)!=null&&a.dispatchEvent&&this.events.dispatchEvent(new Event(t))});jn(this,Ps,{});let a=(ec.tfjs||s3).replace(/-(.*)/,"");ll.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${a}/dist/`,ll.modelBasePath=ne.browser?"../models/":"file://models/",this.version=ry,Object.defineProperty(this,"version",{value:ry}),this.config=JSON.parse(JSON.stringify(ll)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Et(this.config,t)),r9(this.config),this.tf=Ke,this.state="idle",xr(this,pd,0),xr(this,mc,!1),xr(this,fc,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new hc(this),py(),this.result=pr(),this.process={tensor:null,canvas:null},this.faceTriangulation=Z9,this.faceUVMap=J9,im(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&K(`version: ${this.version}`),this.config.debug&&K(`tfjs version: ${this.tf.version["tfjs-core"]}`);let n=JSON.parse(JSON.stringify(this.env));delete n.kernels,delete n.initial,delete n.perfadd,this.config.debug&&K("environment:",n)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(ll)),this.config.backend=t,ay(),ne.initial=!0}validate(t){let a=Q3(ll,t||this.config);return a.length===0&&(this.config=Et(this.config,t)),a}now(){return ae()}image(t,a=!1){return g0(t,this.config,a)}async segmentation(t,a){var s,i,o;if(a&&(this.config=Et(this.config,a)),!this.config.segmentation.enabled)return null;let n=await g0(t,this.config);if(!n.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await VS(n.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("meet")&&(r=await bS(n.tensor,this.config)),(o=this.config.segmentation.modelPath)!=null&&o.includes("selfie")&&(r=await GS(n.tensor,this.config)),J(n.tensor),r}compare(t,a){return n9(this.config,t,a)}async init(){await sc(this,!0),await this.tf.ready(),ay()}async load(t){this.state="load";let a=ae(),n=Object.values(this.models.models).filter(i=>i).length;t&&(this.config=Et(this.config,t)),this.env.initial&&(await sc(this,!1)||K("error: backend check failed"),await Fp(),this.env.browser&&(this.config.debug&&K("configuration:",this.config),this.config.debug&&K("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&K("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(i=>i).length!==n&&(this.models.validate(),this.emit("load"));let s=Math.trunc(ae()-a);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return AS(t,this.config)}async warmup(t){let a=ae(),n=await qS(this,t),r=ae();return this.performance.warmup=Math.trunc(r-a),n}async profile(t,a){let n=await this.tf.profile(()=>this.detect(t,a)),r={},s=0;for(let o of n.kernels){let l=Number(o.kernelTimeMs)||0;r[o.name]?r[o.name]+=l:r[o.name]=l,s+=l}let i=[];Object.entries(r).forEach(o=>i.push({kernel:o[0],time:o[1],perc:0}));for(let o of i)o.perc=Math.round(1e3*o.time/s)/1e3,o.time=Math.round(1e3*o.time)/1e3;return i.sort((o,l)=>l.time-o.time),i.length=20,i}async detect(t,a){return this.state="detect",new Promise(async n=>{var g,y,x,A,b,w,I,T,N,M,P,E,S,_,O,W,$,U,G,q,H;this.state="config";let r;this.config=Et(this.config,a),this.state="check";let s=qa(this,um).call(this,t);s&&(K(s,t),this.emit("error"),n(pr(s)));let i=ae();await this.load(),r=ae(),this.state="image";let o=await g0(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(ae()-r):Math.trunc(ae()-r),this.analyze("Get Image:"),!o.tensor){this.config.debug&&K("could not convert input to tensor"),this.emit("error"),n(pr("could not convert input to tensor"));return}this.emit("image"),r=ae(),this.config.skipAllowed=await a9(this.config,o.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(ae()-r):Math.trunc(ae()-r),this.analyze("Check Changed:");let l=[],u=[],p=[],c=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?Qy(this,o.tensor):[],this.performance.face&&delete this.performance.face):(r=ae(),l=this.config.face.enabled?await Qy(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let d=this.config.body.maxDetected===-1?Et(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?Ix(o.tensor,d):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?gy(o.tensor,d):[]:(x=this.config.body.modelPath)!=null&&x.includes("efficientpose")?u=this.config.body.enabled?ky(o.tensor,d):[]:(A=this.config.body.modelPath)!=null&&A.includes("movenet")&&(u=this.config.body.enabled?yx(o.tensor,d):[]),this.performance.body&&delete this.performance.body):(r=ae(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await Ix(o.tensor,d):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?u=this.config.body.enabled?await gy(o.tensor,d):[]:(I=this.config.body.modelPath)!=null&&I.includes("efficientpose")?u=this.config.body.enabled?await ky(o.tensor,d):[]:(T=this.config.body.modelPath)!=null&&T.includes("movenet")&&(u=this.config.body.enabled?await yx(o.tensor,d):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Et(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((M=(N=this.config.hand.detector)==null?void 0:N.modelPath)!=null&&M.includes("handdetect")?p=this.config.hand.enabled?sx(o.tensor,h):[]:(E=(P=this.config.hand.detector)==null?void 0:P.modelPath)!=null&&E.includes("handtrack")&&(p=this.config.hand.enabled?lx(o.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ae(),(_=(S=this.config.hand.detector)==null?void 0:S.modelPath)!=null&&_.includes("handdetect")?p=this.config.hand.enabled?await sx(o.tensor,h):[]:(W=(O=this.config.hand.detector)==null?void 0:O.modelPath)!=null&&W.includes("handtrack")&&(p=this.config.hand.enabled?await lx(o.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(($=this.config.object.modelPath)!=null&&$.includes("nanodet")?c=this.config.object.enabled?Ax(o.tensor,this.config):[]:(U=this.config.object.modelPath)!=null&&U.includes("centernet")&&(c=this.config.object.enabled?Ay(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ae(),(G=this.config.object.modelPath)!=null&&G.includes("nanodet")?c=this.config.object.enabled?await Ax(o.tensor,this.config):[]:(q=this.config.object.modelPath)!=null&&q.includes("centernet")&&(c=this.config.object.enabled?await Ay(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,p,c]=await Promise.all([l,u,p,c])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=ae(),m=[...KI(l),...XI(u),...ZI(p),...YI(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ae()-i):Math.trunc(ae()-i);let f=((H=this.process.tensor)==null?void 0:H.shape)||[0,0,0,0];this.result={face:l,body:u,hand:p,gesture:m,object:c,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return jS(l,u,p,m,f)}},J(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}async sleep(t){return new Promise(a=>{setTimeout(a,t)})}async video(t,a=!0,n=0){a?(qa(this,Ps)[t.id]||(this.config.debug&&K("video 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G1:null),R.filter=!!e0,e0!=null&&e0.add?(e0.reset(),t.filter.brightness!==0&&e0.add("brightness",t.filter.brightness),t.filter.contrast!==0&&e0.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&e0.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&e0.add("blur",t.filter.blur),t.filter.saturation!==0&&e0.add("saturation",t.filter.saturation),t.filter.hue!==0&&e0.add("hue",t.filter.hue),t.filter.negative&&e0.add("negative"),t.filter.sepia&&e0.add("sepia"),t.filter.vintage&&e0.add("brownie"),t.filter.sepia&&e0.add("sepia"),t.filter.kodachrome&&e0.add("kodachrome"),t.filter.technicolor&&e0.add("technicolor"),t.filter.polaroid&&e0.add("polaroid"),t.filter.pixelate!==0&&e0.add("pixelate",t.filter.pixelate),((m=e0.get())==null?void 0:m.length)>1?M0=e0.apply(R0):M0=e0.draw(R0)):(t.debug&&h("input process error: cannot initialize filters"),R.webgl.supported=!1,t.filter.enabled=!1,q2(R0,M0))):(q2(R0,M0),e0&&(e0=null),R.filter=!!e0),!n)return{tensor:null,canvas:M0};if(!M0)throw new Error("canvas error: cannot create output");let l,c=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(R.browser&&I.browser)l=I.browser?I.browser.fromPixels(e):null;else{c=e.data.length/e.height/e.width;let y=new Uint8Array(e.data.buffer);l=I.tensor(y,[e.height,e.width,c],"int32")}else if((!y2||M0.width!==y2.width||M0.height!==y2.height)&&(y2=Ae(M0.width,M0.height)),I.browser&&R.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=I.browser.fromPixels(M0):(y2=q2(M0),l=I.browser.fromPixels(y2));else{let T=q2(M0).getContext("2d").getImageData(0,0,s,A);c=T.data.length/s/A;let g=new Uint8Array(T.data.buffer);l=I.tensor(g,[s,A,c])}if(c===4){let y=I.slice3d(l,[0,0,0],[-1,-1,3]);I.dispose(l),l=y}if(!l)throw new Error("input error: cannot create tensor");let x=I.cast(l,"float32"),i=t.filter.equalization?await Z2(x):I.expandDims(x,0);if(I.dispose([l,x]),t.filter.autoBrightness){let y=I.max(i),u=await 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n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&n&&(this.webgl.version=n.getParameter(n.VERSION),this.webgl.vendor=n.getParameter(n.VENDOR),this.webgl.renderer=n.getParameter(n.RENDERER),this.webgl.shader=n.getParameter(n.SHADING_LANGUAGE_VERSION)),this.webgpu.supported=this.browser&&typeof navigator!="undefined"&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{if(this.webgpu.supported){let o=await navigator.gpu.requestAdapter();this.webgpu.adapter=await(o==null?void 0:o.requestAdapterInfo())}}catch(o){this.webgpu.supported=!1}try{this.kernels=W0.getKernelsForBackend(W0.getBackend()).map(o=>o.kernelName.toLowerCase())}catch(o){}}updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}};z2=new WeakMap,S2=new WeakMap,j2=new WeakMap;var R=new E2;var K2=class{constructor(){w(this,"config");w(this,"element");w(this,"stream");w(this,"devices",[]);w(this,"enumerate",async()=>{try{let t=await navigator.mediaDevices.enumerateDevices();this.devices=t.filter(n=>n.kind==="videoinput")}catch(t){this.devices=[]}return this.devices});w(this,"start",async t=>{var r,s;if(t!=null&&t.debug&&(this.config.debug=t==null?void 0:t.debug),t!=null&&t.crop&&(this.config.crop=t==null?void 0:t.crop),t!=null&&t.mode&&(this.config.mode=t==null?void 0:t.mode),t!=null&&t.width&&(this.config.width=t==null?void 0:t.width),t!=null&&t.height&&(this.config.height=t==null?void 0:t.height),t!=null&&t.id&&(this.config.id=t==null?void 0:t.id),t!=null&&t.element)if(typeof t.element=="string"){let A=document.getElementById(t.element);if(A&&A instanceof HTMLVideoElement)this.element=A;else return this.config.debug&&h("webcam","cannot get dom element",t.element),`webcam error: cannot get dom element: ${t.element}`}else if(t.element instanceof 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error: no devices";try{this.stream=await navigator.mediaDevices.getUserMedia(n)}catch(A){return h("webcam",A),`webcam error: ${A}`}return this.stream?(this.element.srcObject=this.stream,await new Promise(A=>{this.element?this.element.onloadeddata=()=>A(!0):A(!1)}),await this.element.play(),this.config.debug&&h("webcam",{width:this.width,height:this.height,label:this.label,stream:this.stream,track:this.track,settings:this.settings,constraints:this.constraints,capabilities:this.capabilities}),`webcam: ${this.label}`):(this.config.debug&&h("webcam error","no stream"),"webcam error no stream")});w(this,"pause",()=>{this.element&&this.element.pause()});w(this,"play",async()=>{this.element&&await this.element.play()});w(this,"stop",()=>{this.config.debug&&h("webcam","stop"),this.track&&this.track.stop()});this.config={element:void 0,debug:!0,mode:"front",crop:!1,width:0,height:0}}get track(){if(this.stream)return this.stream.getVideoTracks()[0]}get capabilities(){if(this.track)return 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D0={cacheModels:!0,cacheSupported:!0,verbose:!0,debug:!1,modelBasePath:""},I0={};async function xs(e,t){return D0.debug&&h("load model fetch:",e,t),fetch(e,t)}function X1(e){D0.cacheModels=e.cacheModels,D0.verbose=e.debug,D0.modelBasePath=e.modelBasePath}async function L(e){var c,x,i,f;let t=O1(D0.modelBasePath,e||"");t.toLowerCase().endsWith(".json")||(t+=".json");let n=t.includes("/")?t.split("/"):t.split("\\"),o=n[n.length-1].replace(".json",""),r="indexeddb://"+o;I0[o]={name:o,sizeFromManifest:0,sizeLoadedWeights:0,sizeDesired:e5[o],inCache:!1,url:""},D0.cacheSupported=typeof indexedDB!="undefined";let s={};try{s=D0.cacheSupported&&D0.cacheModels?await f2.io.listModels():{}}catch(d){D0.cacheSupported=!1}I0[o].inCache=D0.cacheSupported&&D0.cacheModels&&Object.keys(s).includes(r),I0[o].url=I0[o].inCache?r:t;let A=typeof fetch=="undefined"?{}:{fetchFunc:(d,m)=>xs(d,m)},a=new f2.GraphModel(I0[o].url,A),l=!1;try{a.findIOHandler(),D0.debug&&h("model load 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G1:null),R.filter=!!e0,e0!=null&&e0.add?(e0.reset(),t.filter.brightness!==0&&e0.add("brightness",t.filter.brightness),t.filter.contrast!==0&&e0.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&e0.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&e0.add("blur",t.filter.blur),t.filter.saturation!==0&&e0.add("saturation",t.filter.saturation),t.filter.hue!==0&&e0.add("hue",t.filter.hue),t.filter.negative&&e0.add("negative"),t.filter.sepia&&e0.add("sepia"),t.filter.vintage&&e0.add("brownie"),t.filter.sepia&&e0.add("sepia"),t.filter.kodachrome&&e0.add("kodachrome"),t.filter.technicolor&&e0.add("technicolor"),t.filter.polaroid&&e0.add("polaroid"),t.filter.pixelate!==0&&e0.add("pixelate",t.filter.pixelate),((m=e0.get())==null?void 0:m.length)>1?M0=e0.apply(R0):M0=e0.draw(R0)):(t.debug&&h("input process error: cannot initialize filters"),R.webgl.supported=!1,t.filter.enabled=!1,q2(R0,M0))):(q2(R0,M0),e0&&(e0=null),R.filter=!!e0),!n)return{tensor:null,canvas:M0};if(!M0)throw new Error("canvas error: cannot create output");let l,c=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(R.browser&&I.browser)l=I.browser?I.browser.fromPixels(e):null;else{c=e.data.length/e.height/e.width;let y=new Uint8Array(e.data.buffer);l=I.tensor(y,[e.height,e.width,c],"int32")}else if((!y2||M0.width!==y2.width||M0.height!==y2.height)&&(y2=Ae(M0.width,M0.height)),I.browser&&R.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=I.browser.fromPixels(M0):(y2=q2(M0),l=I.browser.fromPixels(y2));else{let T=q2(M0).getContext("2d").getImageData(0,0,s,A);c=T.data.length/s/A;let g=new Uint8Array(T.data.buffer);l=I.tensor(g,[s,A,c])}if(c===4){let y=I.slice3d(l,[0,0,0],[-1,-1,3]);I.dispose(l),l=y}if(!l)throw new Error("input error: cannot create tensor");let x=I.cast(l,"float32"),i=t.filter.equalization?await Z2(x):I.expandDims(x,0);if(I.dispose([l,x]),t.filter.autoBrightness){let y=I.max(i),u=await 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K2=class{constructor(){w(this,"config");w(this,"element");w(this,"stream");w(this,"devices",[]);w(this,"enumerate",async()=>{try{let t=await navigator.mediaDevices.enumerateDevices();this.devices=t.filter(n=>n.kind==="videoinput")}catch(t){this.devices=[]}return this.devices});w(this,"start",async t=>{var r,s;if(t!=null&&t.debug&&(this.config.debug=t==null?void 0:t.debug),t!=null&&t.crop&&(this.config.crop=t==null?void 0:t.crop),t!=null&&t.mode&&(this.config.mode=t==null?void 0:t.mode),t!=null&&t.width&&(this.config.width=t==null?void 0:t.width),t!=null&&t.height&&(this.config.height=t==null?void 0:t.height),t!=null&&t.id&&(this.config.id=t==null?void 0:t.id),t!=null&&t.element)if(typeof t.element=="string"){let A=document.getElementById(t.element);if(A&&A instanceof HTMLVideoElement)this.element=A;else return this.config.debug&&h("webcam","cannot get dom element",t.element),`webcam error: cannot get dom element: ${t.element}`}else if(t.element instanceof 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error: no devices";try{this.stream=await navigator.mediaDevices.getUserMedia(n)}catch(A){return h("webcam",A),`webcam error: ${A}`}return this.stream?(this.element.srcObject=this.stream,await new Promise(A=>{this.element?this.element.onloadeddata=()=>A(!0):A(!1)}),await this.element.play(),this.config.debug&&h("webcam",{width:this.width,height:this.height,label:this.label,stream:this.stream,track:this.track,settings:this.settings,constraints:this.constraints,capabilities:this.capabilities}),`webcam: ${this.label}`):(this.config.debug&&h("webcam error","no stream"),"webcam error no stream")});w(this,"pause",()=>{this.element&&this.element.pause()});w(this,"play",async()=>{this.element&&await this.element.play()});w(this,"stop",()=>{this.config.debug&&h("webcam","stop"),this.track&&this.track.stop()});this.config={element:void 0,debug:!0,mode:"front",crop:!1,width:0,height:0}}get track(){if(this.stream)return this.stream.getVideoTracks()[0]}get capabilities(){if(this.track)return 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D0={cacheModels:!0,cacheSupported:!0,verbose:!0,debug:!1,modelBasePath:""},I0={};async function xs(e,t){return D0.debug&&h("load model fetch:",e,t),fetch(e,t)}function X1(e){D0.cacheModels=e.cacheModels,D0.verbose=e.debug,D0.modelBasePath=e.modelBasePath}async function L(e){var c,x,i,f;let t=O1(D0.modelBasePath,e||"");t.toLowerCase().endsWith(".json")||(t+=".json");let n=t.includes("/")?t.split("/"):t.split("\\"),o=n[n.length-1].replace(".json",""),r="indexeddb://"+o;I0[o]={name:o,sizeFromManifest:0,sizeLoadedWeights:0,sizeDesired:e5[o],inCache:!1,url:""},D0.cacheSupported=typeof indexedDB!="undefined";let s={};try{s=D0.cacheSupported&&D0.cacheModels?await f2.io.listModels():{}}catch(d){D0.cacheSupported=!1}I0[o].inCache=D0.cacheSupported&&D0.cacheModels&&Object.keys(s).includes(r),I0[o].url=I0[o].inCache?r:t;let A=typeof fetch=="undefined"?{}:{fetchFunc:(d,m)=>xs(d,m)},a=new f2.GraphModel(I0[o].url,A),l=!1;try{a.findIOHandler(),D0.debug&&h("model load 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n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function st(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var Z0,x5=256,d5=Number.MAX_SAFE_INTEGER,tA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},at=[],Oe=[[0,0],[0,0],[0,0],[0,0]],o3=0,r3=e=>1-1/(1+Math.exp(e)),A3=e=>e3(e);async function a3(e){if(R.initial&&(Z0=null),Z0)e.debug&&h("cached model:",Z0.modelUrl);else{Z0=await L(e.body.modelPath);let t=Z0!=null&&Z0.executor?Object.values(Z0.modelSignature.inputs):void 0;x5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Z0}function s3(e,t,n){var s,A;let o={};if(!((s=e==null?void 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t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,ft),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,u2),t.centersNormalized=O.div(t.centers,u2),t.halfBoxSize=O.div(t.boxSizesNormalized,C.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,u2),t.endNormalized=O.mul(t.ends,u2);let n=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>O.dispose(t[o])),n}async function w3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=O.image.resizeBilinear(e,[Ce,Ce]),n.div=O.div(n.resized,C.tf127),n.normalized=O.sub(n.div,C.tf05);let o=xe==null?void 0:xe.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((m,y)=>m.size-y.size);n.concat384=O.concat([d[0],d[2]],2),n.concat512=O.concat([d[1],d[3]],2),n.concat=O.concat([n.concat512,n.concat384],1),n.batch=O.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=O.squeeze(o[0]):n.batch=O.squeeze(o);O.dispose(o),n.boxes=fA(n.batch),n.logits=O.slice(n.batch,[0,0],[-1,1]),n.sigmoid=O.sigmoid(n.logits),n.scores=O.squeeze(n.sigmoid),n.nms=await O.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=O.slice(n.boxes,[r[d],0],[1,-1]),y.slice=O.slice(n.batch,[r[d],M3-1],[1,-1]),y.squeeze=O.squeeze(y.slice),y.landmarks=O.reshape(y.squeeze,[M3,-1]);let u=await y.bbox.data(),T={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await y.landmarks.array(),confidence:m};y.anchor=O.slice(ft,[r[d],0],[1,2]);let g=await y.anchor.data(),p=u3(T,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce],g),b=xt(p,t.face.scale||yA),k=yt(b);k.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&k.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(k),Object.keys(y).forEach(P=>O.dispose(y[P]))}}return Object.keys(n).forEach(d=>O.dispose(n[d])),s}var ye=G(H());var K0,We=0,mA=2.3,M5=ae.leftEyeLower0,P5=ae.rightEyeLower0,h2={leftBounds:[M5[0],M5[M5.length-1]],rightBounds:[P5[0],P5[P5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function I3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&h("cached model:",K0.modelUrl):K0=await L((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function mt(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},z3=(e,t,n,o,r,s=!1)=>{let A=yt(xt(h3([e[n],e[o]]),mA)),a=p2(A),l=ye.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=ye.image.flipLeftRight(l);ye.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},S3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function N3(e,t,n){if(!(K0!=null&&K0.executor))return e;let{box:o,boxSize:r,crop:s}=z3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=z3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=ye.concat([s,l]);ye.dispose(s),ye.dispose(l);let x=K0.execute(c);ye.dispose(c);let i=await x.data();ye.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:m}=S3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:u,iris:T}=S3(y,A,a,!1),g=pA(e);Math.abs(g)<30?(mt(e,d,"left",null),mt(e,u,"right",null)):g<1?mt(e,d,"left",["EyeUpper0","EyeLower0"]):mt(e,u,"right",["EyeUpper0","EyeLower0"]);let p=j3(e,m,"left"),b=j3(e,T,"right");return e.concat(p).concat(b)}async function O3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,u)=>y+=e[u][2],0)/e2.length;for(let y=0;yy+=e[u][2],0)/t2.length;for(let y=0;yv()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await w3(e,t),ge.timestamp=v(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=O2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&h("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),_=await N.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*_[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=ct(p,e),P.boxRaw=dt(p,e),P.size=p.size,P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=N.find(n0=>n0.shape[n0.shape.length-1]===1404),$=De.reshape(Z,[-1,3]),A0=await $.array();De.dispose($),(y=t.face.attention)!=null&&y.enabled?A0=await O3(A0,N):(u=t.face.iris)!=null&&u.enabled&&(A0=await N3(A0,P.tensor,O2)),P.mesh=T3(A0,p,b,k,O2),P.meshRaw=P.mesh.map(n0=>[n0[0]/(e.shape[2]||0),n0[1]/(e.shape[1]||0),(n0[2]||0)/a]);for(let n0 of Object.keys(ae))P.annotations[n0]=ae[n0].map(j0=>P.mesh[j0]);P.score=P.faceScore;let t0={...R3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=ct(t0,e),P.boxRaw=dt(t0,e),P.size=t0.size,s.push(t0)}De.dispose(N)}P.score>(((T=t.face.detector)==null?void 0:T.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function W3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&h("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await L(e.face.attention.modelPath):r0=await L((r=e.face.mesh)==null?void 0:r.modelPath),O2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var D3=$e,F3=N2;var J0=G(H());var E5=[],P0,pt=[],B3=0,H3=0,w5=Number.MAX_SAFE_INTEGER,z5=!1;async function G3(e){var t,n,o;return R.initial&&(P0=null),P0?e.debug&&h("cached model:",P0.modelUrl):(P0=await L((t=e.face.emotion)==null?void 0:t.modelPath),z5=((o=(n=P0==null?void 0:P0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,z5?E5=["angry","disgust","fear","happy","neutral","sad","surprise"]:E5=["angry","disgust","fear","happy","sad","surprise","neutral"]),P0}async function S5(e,t,n,o){var A,a;if(!P0)return[];let r=w5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-H3;return t.skipAllowed&&s&&r&&B3===o&&pt[n]&&pt[n].length>0?(w5++,pt[n]):(w5=0,new Promise(async l=>{var x,i,f;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let d={},m=P0!=null&&P0.inputs[0].shape?P0.inputs[0].shape[2]:0;if(((i=t.face.emotion)==null?void 0:i.crop)>0){let u=(f=t.face.emotion)==null?void 0:f.crop,T=[[u,u,1-u,1-u]];d.resize=J0.image.cropAndResize(e,T,[0],[m,m])}else d.resize=J0.image.resizeBilinear(e,[m,m],!1);z5?(d.mul=J0.mul(d.resize,255),d.normalize=J0.sub(d.mul,[103.939,116.779,123.68]),d.emotion=P0==null?void 0:P0.execute(d.normalize)):(d.channels=J0.mul(d.resize,C.rgb),d.grayscale=J0.sum(d.channels,3,!0),d.grayscaleSub=J0.sub(d.grayscale,C.tf05),d.grayscaleMul=J0.mul(d.grayscaleSub,C.tf2),d.emotion=P0==null?void 0:P0.execute(d.grayscaleMul)),H3=v();let y=await d.emotion.data();for(let u=0;u(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*y[u])/100),emotion:E5[u]});c.sort((u,T)=>T.score-u.score),Object.keys(d).forEach(u=>J0.dispose(d[u]))}pt[n]=c,B3=o,l(c)}))}var ie=G(H());var k0,Fe=[],Z3=0,X3=0,j5=Number.MAX_SAFE_INTEGER;async function q3(e){var t;return R.initial&&(k0=null),k0?e.debug&&h("cached model:",k0.modelUrl):k0=await L((t=e.face.description)==null?void 0:t.modelPath),k0}function hA(e,t){var s,A;let n=e.image||e.tensor||e;if(!(k0!=null&&k0.inputs[0].shape))return n;let o;if(((s=t.face.description)==null?void 0:s.crop)>0){let a=(A=t.face.description)==null?void 0:A.crop,l=[[a,a,1-a,1-a]];o=ie.image.cropAndResize(n,l,[0],[k0.inputs[0].shape[2],k0.inputs[0].shape[1]])}else o=ie.image.resizeBilinear(n,[k0.inputs[0].shape[2],k0.inputs[0].shape[1]],!1);let r=ie.mul(o,C.tf255);return ie.dispose(o),r}async function I5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(k0!=null&&k0.executor))return r;let s=j5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>v()-Z3;return t.skipAllowed&&s&&A&&X3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((x=Fe==null?void 0:Fe[n])==null?void 0:x.genderScore)>0?(j5++,Fe[n]):(j5=0,new Promise(async i=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=hA(e,t),m=k0==null?void 0:k0.execute(d);Z3=v(),ie.dispose(d);let u=await m.find(V=>V.shape[1]===1).data(),T=Math.trunc(200*Math.abs(u[0]-.5))/100;T>(t.face.description.minConfidence||0)&&(r.gender=u[0]<=.5?"female":"male",r.genderScore=Math.min(.99,T));let g=ie.argMax(m.find(V=>V.shape[1]===100),1),p=(await g.data())[0];ie.dispose(g);let k=await m.find(V=>V.shape[1]===100).data();r.age=Math.round(k[p-1]>k[p+1]?10*p-100*k[p-1]:10*p+100*k[p+1])/10,(Number.isNaN(u[0])||Number.isNaN(k[0]))&&h("faceres error:",{model:k0,result:m});let P=m.find(V=>V.shape[1]===1024),N=P?await P.data():[];r.descriptor=Array.from(N),m.forEach(V=>ie.dispose(V))}Fe[n]=r,X3=o,i(r)}))}var g2=.1,N5=.5;function bA(e,t,n){let o=!1,r=n.length-1;for(let s=0;st!=n[r].y>t&&e<(n[r].x-n[s].x)*(t-n[s].y)/(n[r].y-n[s].y)+n[s].x&&(o=!o);return o}async function Y3(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,o=await e.tensor.buffer(),r=[];for(let A of ae.silhouette)r.push({x:(e.mesh[A][0]-e.box[0])/e.box[2],y:(e.mesh[A][1]-e.box[1])/e.box[3]});g2&&g2>0&&(r=r.map(A=>({x:A.x>.5?A.x+g2:A.x-g2,y:A.y>.5?A.y+g2:A.y-g2})));for(let A=0;Av()-J3,s=L5<(((a=t.face.antispoof)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&K3===o&&ut[n]?(L5++,ut[n]):(L5=0,new Promise(async l=>{let c=ht.image.resizeBilinear(e,[w0!=null&&w0.inputs[0].shape?w0.inputs[0].shape[2]:0,w0!=null&&w0.inputs[0].shape?w0.inputs[0].shape[1]:0],!1),x=w0==null?void 0:w0.execute(c),i=(await x.data())[0];ut[n]=Math.round(100*i)/100,K3=o,J3=v(),ht.dispose([c,x]),l(ut[n])}))}var gt=G(H());var E0,bt=[],C5=Number.MAX_SAFE_INTEGER,$3=0,en=0;async function tn(e){var t;return R.initial&&(E0=null),E0?e.debug&&h("cached model:",E0.modelUrl):E0=await L((t=e.face.liveness)==null?void 0:t.modelPath),E0}async function W5(e,t,n,o){var A,a;if(!(E0!=null&&E0.executor))return 0;let r=(((A=t.face.liveness)==null?void 0:A.skipTime)||0)>v()-en,s=C5<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&$3===o&&bt[n]?(C5++,bt[n]):(C5=0,new Promise(async l=>{let c=gt.image.resizeBilinear(e,[E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[2]:0,E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[1]:0],!1),x=E0==null?void 0:E0.execute(c),i=(await x.data())[0];bt[n]=Math.round(100*i)/100,$3=o,en=v(),gt.dispose([c,x]),l(bt[n])}))}var Tt=G(H());var le,D5=[],TA=["white","black","asian","indian","other"],vA=[15,23,28,35.5,45.5,55.5,65],on=0,rn=0,F5=Number.MAX_SAFE_INTEGER;async function sn(e){var t;return R.initial&&(le=null),le?e.debug&&h("cached model:",le.modelUrl):le=await L((t=e.face.gear)==null?void 0:t.modelPath),le}async function B5(e,t,n,o){var A,a;if(!le)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=F5<(((A=t.face.gear)==null?void 0:A.skipFrames)||0),s=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>v()-rn;return t.skipAllowed&&s&&r&&on===o&&D5[n]?(F5++,D5[n]):(F5=0,new Promise(async l=>{var T,g,p,b;if(!(le!=null&&le.inputs[0].shape))return;let c={},x=[[0,.1,.9,.9]];if(((T=t.face.gear)==null?void 0:T.crop)>0){let k=(g=t.face.gear)==null?void 0:g.crop;x=[[k,k,1-k,1-k]]}c.resize=Tt.image.cropAndResize(e,x,[0],[le.inputs[0].shape[2],le.inputs[0].shape[1]]);let i={age:0,gender:"unknown",genderScore:0,race:[]};(p=t.face.gear)!=null&&p.enabled&&([c.age,c.gender,c.race]=le.execute(c.resize,["age_output","gender_output","race_output"]));let f=await 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G0.all)n[G0.getName(o)]={curl:He.getName(t.curls[o]),direction:c0.getName(t.directions[o])};return n}function On(e){let t=[];if(!e||e.length===0)return t;let n=Ln(e);for(let o of En){let r=o.matchAgainst(n.curls,n.directions);r>=EA&&t.push({name:o.name,confidence:r})}return t}var Cn=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&o&&r&&o.position[1]l.part==="leftShoulder"),a=e[n].keypoints.find(l=>l.part==="rightShoulder");A&&a&&Math.abs(A.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${A.position[1]>a.position[1]?"left":"right"}`})}return t},Wn=e=>{if(!e)return[];let t=[];for(let n=0;n450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},Dn=e=>{var n,o,r,s;if(!e)return[];let t=[];for(let A=0;A.06||u>.06)&&(d=!1),y>u?u>.04&&t.push({iris:A,gesture:"looking right"}):y>.04&&t.push({iris:A,gesture:"looking left"});let T=Math.abs(e[A].mesh[145][1]-e[A].annotations.rightEyeIris[0][1])/e[A].box[3],g=Math.abs(e[A].mesh[374][1]-e[A].annotations.leftEyeIris[0][1])/e[A].box[3];(g<.01||T<.01||g>.022||T>.022)&&(d=!1),(g<.01||T<.01)&&t.push({iris:A,gesture:"looking down"}),(g>.022||T>.022)&&t.push({iris:A,gesture:"looking up"}),d&&t.push({iris:A,gesture:"looking center"})}return t},Fn=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=o.reduce((A,a)=>(A.position[2]||0)<(a.position[2]||0)?A:a);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((A,a)=>A.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function Et(e,t=1.5){let n=C2(e),o=wt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function zt(e){let t=C2(e),n=wt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function NA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Xn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return NA(n)}var Bn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Xe(e,t){let n=0;for(let o=0;o[A.x,A.y]),this.anchorsTensor=W.tensor2d(this.anchors),this.inputSize=((s=(r=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=W.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=W.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=W.slice(t,[0,0],[-1,2]),n.boxSizes=W.slice(t,[0,2],[-1,2]),n.div=W.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=W.add(n.div,this.anchorsTensor),n.halfBoxSizes=W.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=W.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=W.mul(n.sub,this.inputSizeTensor),n.add=W.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=W.mul(n.add,this.inputSizeTensor);let o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),f=x.slice(2,4),d=await c.palmLandmarks.array(),m={startPoint:i,endPoint:f,palmLandmarks:d,confidence:r[l]},y=Zn(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(y),Object.keys(c).forEach(u=>W.dispose(c[u]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var $0=G(H());var WA=5,Kn=1.65,Jn=[0,5,9,13,17,1,2],DA=0,FA=2,Qn=0,jt=class{constructor(t,n){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>e1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return Et(zt(r),WA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Et(zt(n),Kn);o.palmLandmarks=[];for(let r=0;r[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=$5(o,[0,0]),c=a.map(d=>[...e1(d,l),d[2]]),x=qn(r),i=[...C2(n),1],f=[Xe(i,x[0]),Xe(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>v()-Qn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A?this.skipped++:(r=await this.handDetector.predict(t,n),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...V,confidence:b};let _={landmarks:N,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(_)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let x=Et(zt(c),Kn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var _n={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,t1;function HA(){let e=l2?new St(l2):void 0;e&&c2&&(t1=new jt(e,c2))}async function n1(e,t){t1||HA();let n=await t1.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=kt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function $n(e){var t;return R.initial&&(l2=null),l2?e.debug&&h("cached model:",l2.modelUrl):l2=await L((t=e.hand.detector)==null?void 0:t.modelPath),l2}async function eo(e){var t;return R.initial&&(c2=null),c2?e.debug&&h("cached 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0;qe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function Ao(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&h("cached model:",p0[1].modelUrl);else{p0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;qe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function qA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,ZA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,GA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[no]),a.splice(no,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=Q.slice(o.boxes,f,1),m=await d.data();Q.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],u=st(y,XA),T=[Math.trunc(y[0]*we[0]),Math.trunc(y[1]*we[1]),Math.trunc(y[2]*we[0]),Math.trunc(y[3]*we[1])],g=x[f],p=VA[i[f]],b={id:l++,score:g,box:T,boxRaw:u,label:p};n.push(b)}return Object.keys(o).forEach(f=>Q.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function r1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[qe[1][0],qe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/qe[1][1],i[1]/qe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[we[0]*(i[0]+t.boxRaw[0]),we[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=kt(o.keypoints);for(let i of Object.keys(ro))o.annotations[i]=ro[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function s1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];we=[e.shape[2]||0,e.shape[1]||0],It++;let n=(t.hand.skipTime||0)>v()-o1,o=It<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-o1,l=It<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):(m0.boxes=await qA(e,t),o1=v(),m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))),It=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=st(i.box,oo),d=st(i.boxRaw,oo);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var W2={};ze(W2,{connected:()=>Lt,horizontal:()=>A1,kpt:()=>Nt,relative:()=>i1,vertical:()=>a1});var Nt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],A1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],a1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],i1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Lt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=Te(),l1=0;function io(e,t){var 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M=0;M((r-1)*z.face[M].box[E]+K)/r),C0=e.face[M].boxRaw.map((K,E)=>((r-1)*z.face[M].boxRaw[E]+K)/r),x0=e.face[M].annotations;if(Object.keys(z.face[M].annotations).length!==Object.keys(e.face[M].annotations).length)z.face[M].annotations=e.face[M].annotations,x0=z.face[M].annotations;else if(e.face[M].annotations)for(let K of Object.keys(e.face[M].annotations))x0[K]=(m=(d=(f=e.face[M])==null?void 0:f.annotations)==null?void 0:d[K])!=null&&m[0]?e.face[M].annotations[K].map((E,B)=>E.map((X,U0)=>((r-1)*z.face[M].annotations[K][B][U0]+X)/r)):null;if(e.face[M].rotation){let K={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};K.matrix=(y=e.face[M].rotation)==null?void 0:y.matrix,K.angle={roll:((r-1)*(((T=(u=z.face[M].rotation)==null?void 0:u.angle)==null?void 0:T.roll)||0)+(((p=(g=e.face[M].rotation)==null?void 0:g.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((k=(b=z.face[M].rotation)==null?void 0:b.angle)==null?void 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M=e.persons;if(!z.persons||M.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(M));else for(let T0=0;T0((r-1)*z.persons[T0].box[x0]+C0)/r)}e.gesture&&(z.gesture=e.gesture),z.width=e.width,z.height=e.height;let s=v();return l1=R.perfadd?l1+Math.round(s-n):Math.round(s-n),e.performance&&(z.performance={...e.performance,interpolate:l1}),z}var s0=G(H());var L0;async function c1(e){return!L0||R.initial?L0=await L(e.segmentation.modelPath):e.debug&&h("cached model:",L0.modelUrl),L0}async function lo(e,t){var r;if(L0||(L0=await c1(t)),!(L0!=null&&L0.executor)||!((r=L0==null?void 0:L0.inputs)!=null&&r[0].shape))return null;let n={};n.resize=s0.image.resizeBilinear(e,[L0.inputs[0].shape?L0.inputs[0].shape[1]:0,L0.inputs[0].shape?L0.inputs[0].shape[2]:0],!1),n.norm=s0.div(n.resize,C.tf255),n.res=L0.execute(n.norm),n.squeeze=s0.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=s0.unstack(n.squeeze,2),n.fg=s0.softmax(n.fgRaw),n.mul=s0.mul(n.fg,C.tf255),n.expand=s0.expandDims(n.mul,2),n.output=s0.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=s0.squeeze(e),n.concat=s0.concat([n.input,n.output],-1),o=s0.cast(n.concat,"int32");break;case"alpha":o=s0.cast(n.output,"int32");break;default:o=s0.tensor(0)}return Object.keys(n).forEach(s=>s0.dispose(n[s])),o}var Ot={};ze(Ot,{distance:()=>d1,find:()=>KA,similarity:()=>YA});function d1(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let r=0;r{if(e===0)return 1;let s=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return Math.max(Math.min(s,1),0)};function 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e.keypoints)o.position=[o.position[0]*(t[0]+ee.padding[2][0]+ee.padding[2][1])/t[0]-ee.padding[2][0],o.position[1]*(t[1]+ee.padding[1][0]+ee.padding[1][1])/t[1]-ee.padding[1][0]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1]];let n=Le(e.keypoints.map(o=>o.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var b0,Ct=0,y1=Number.MAX_SAFE_INTEGER,d2={boxes:[],bodies:[],last:0};async function uo(e){var t;return R.initial&&(b0=null),b0?e.debug&&h("cached model:",b0.modelUrl):(J2(["size"],e),b0=await L(e.body.modelPath)),Ct=b0!=null&&b0.executor&&((t=b0==null?void 0:b0.inputs)!=null&&t[0].shape)?b0.inputs[0].shape[2]:0,Ct<64&&(Ct=256),D2.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS&&D2.env().set("WEBGL_USE_SHAPES_UNIFORMS",!1),b0}function QA(e,t,n){let o=e[0][0],r=[],s=0;for(let x=0;xt.body.minConfidence){let 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l=[s[51+1],s[51+0],s[51+3]-s[51+1],s[51+2]-s[51+0]],c=[Math.trunc(l[0]*(n.shape[2]||0)),Math.trunc(l[1]*(n.shape[1]||0)),Math.trunc(l[2]*(n.shape[2]||0)),Math.trunc(l[3]*(n.shape[1]||0))],x={};for(let[f,d]of Object.entries(Lt)){let m=[];for(let y=0;yg.part===d[y]),T=a.find(g=>g.part===d[y+1]);u&&T&&u.score>(t.body.minConfidence||0)&&T.score>(t.body.minConfidence||0)&&m.push([u.position,T.position])}x[f]=m}let i={id:r,score:A,box:c,boxRaw:l,keypoints:[...a],annotations:x};x1(i),o.push(i)}}return o.sort((r,s)=>s.score-r.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function f1(e,t){var r;if(!(b0!=null&&b0.executor)||!((r=b0==null?void 0:b0.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(d2.boxes.length=0),y1++;let n=(t.body.skipTime||0)>v()-d2.last,o=y1<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?d2.bodies:new Promise(async s=>{let A={};y1=0,A.input=mo(e,Ct),A.res=b0==null?void 0:b0.execute(A.input),d2.last=v();let a=await A.res.array();d2.bodies=A.res.shape[2]===17?QA(a,t,e):_A(a,t,e);for(let l of d2.bodies)po(l,[e.shape[2]||1,e.shape[1]||1]),fo(l.keypoints);Object.keys(A).forEach(l=>D2.dispose(A[l])),s(d2.bodies)})}var S0=G(H());var ce,Wt=[],bo=0,m1=Number.MAX_SAFE_INTEGER,Ft=0,Dt=2.5;async function go(e){if(!ce||R.initial){ce=await L(e.object.modelPath);let t=ce!=null&&ce.executor?Object.values(ce.modelSignature.inputs):void 0;Ft=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&h("cached model:",ce.modelUrl);return ce}async function $A(e,t,n){var c,x;let o=0,r=[],s=Ft;for(let i of[1,2,4]){let f=i*13,d=S0.squeeze(e.find(p=>p.shape[1]===f**2&&(p.shape[2]||0)===m2.length)),m=await d.array(),y=S0.squeeze(e.find(p=>p.shape[1]===f**2&&(p.shape[2]||0)(n.object.minConfidence||0)&&b!==61){let P=(.5+Math.trunc(p%f))/f,N=(.5+Math.trunc(p/f))/f,V=g[p].map(M=>M*(f/i/s)),[_,Z]=[P-Dt/i*V[0],N-Dt/i*V[1]],[$,A0]=[P+Dt/i*V[2]-_,N+Dt/i*V[3]-Z],t0=[_,Z,$,A0];t0=t0.map(M=>Math.max(0,Math.min(M,1)));let n0=[t0[0]*t[0],t0[1]*t[1],t0[2]*t[0],t0[3]*t[1]],j0={id:o++,score:Math.round(100*k)/100,class:b+1,label:m2[b].label,box:n0.map(M=>Math.trunc(M)),boxRaw:t0};r.push(j0)}}S0.dispose([d,y,u,T])}let A=r.map(i=>[i.boxRaw[1],i.boxRaw[0],i.boxRaw[3],i.boxRaw[2]]),a=r.map(i=>i.score),l=[];if(A&&A.length>0){let i=await S0.image.nonMaxSuppressionAsync(A,a,n.object.maxDetected||0,n.object.iouThreshold,n.object.minConfidence);l=Array.from(await i.data()),S0.dispose(i)}return r=r.filter((i,f)=>l.includes(f)).sort((i,f)=>f.score-i.score),r}async function p1(e,t){if(!(ce!=null&&ce.executor))return[];let n=(t.object.skipTime||0)>v()-bo,o=m1<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&Wt.length>0?(m1++,Wt):(m1=0,!R.kernels.includes("mod")||!R.kernels.includes("sparsetodense")?Wt:new Promise(async 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B2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],ea=B2.length,F2=B2.reduce((e,t,n)=>(e[t]=n,e),{}),ta=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],X7=ta.map(([e,t])=>[F2[e],F2[t]]),vo=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?k3(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)!=null&&o.enabled)&&!this.models.antispoof?Q3(this.instance.config):null,n.liveness=this.instance.config.face.enabled&&((r=this.instance.config.face.liveness)!=null&&r.enabled)&&!this.models.liveness?tn(this.instance.config):null,n.faceres=this.instance.config.face.enabled&&((s=this.instance.config.face.description)!=null&&s.enabled)&&!this.models.faceres?q3(this.instance.config):null,n.emotion=this.instance.config.face.enabled&&((A=this.instance.config.face.emotion)!=null&&A.enabled)&&!this.models.emotion?G3(this.instance.config):null,n.iris=this.instance.config.face.enabled&&((a=this.instance.config.face.iris)!=null&&a.enabled)&&!((l=this.instance.config.face.attention)!=null&&l.enabled)&&!this.models.iris?I3(this.instance.config):null,n.facemesh=this.instance.config.face.enabled&&((c=this.instance.config.face.mesh)!=null&&c.enabled)&&!this.models.facemesh?W3(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((x=this.instance.config.face.gear)!=null&&x.enabled)&&!this.models.gear?sn(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((i=this.instance.config.face.ssrnet)!=null&&i.enabled)&&!this.models.ssrnetage?cn(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((f=this.instance.config.face.ssrnet)!=null&&f.enabled)&&!this.models.ssrnetgender?fn(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((d=this.instance.config.face.mobilefacenet)!=null&&d.enabled)&&!this.models.mobilefacenet?bn(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((m=this.instance.config.face.insightface)!=null&&m.enabled)&&!this.models.insightface?Mn(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((y=this.instance.config.body.modelPath)!=null&&y.includes("blazepose"))?a3(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?A3(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((u=this.instance.config.body.modelPath)!=null&&u.includes("efficientpose"))?y3(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((T=this.instance.config.body.modelPath)!=null&&T.includes("movenet"))?uo(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((g=this.instance.config.body.modelPath)!=null&&g.includes("posenet"))?Eo(this.instance.config):null,n.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((b=(p=this.instance.config.hand.detector)==null?void 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t)d.box[0]>b.box[0]&&d.box[0]b.box[1]&&d.box[1]+d.box[3]m.body.box[0]&&b.box[0]+b.box[2]m.body.box[1]&&b.box[1]+b.box[3]m.body.box[0]&&b.box[1]+b.box[3]>m.body.box[1]&&b.box[1]+b.box[3]{b&&b.length===4&&(y.push(b[0],b[0]+b[2]),u.push(b[1],b[1]+b[3]))};T(m.face.box),T((x=m.body)==null?void 0:x.box),T((i=m.hands.left)==null?void 0:i.box),T((f=m.hands.right)==null?void 0:f.box);let g=Math.min(...y),p=Math.min(...u);m.box=[g,p,Math.max(...y)-g,Math.max(...u)-p],r!=null&&r[1]&&(r!=null&&r[2])&&(m.boxRaw=[m.box[0]/r[2],m.box[1]/r[1],m.box[2]/r[2],m.box[3]/r[1]]),A.push(m)}return A}var d0=G(H());var Vt=` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var a5=0;function Ys(e,t,n){let o=a0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round",r.font=o.font;for(let s=0;sc5,kpt:()=>l5});var l5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],c5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var D=G(H());var se,n2=224,$1,Qs=5,rt=[8,16,32,32,32];function _s(){let e=[],t=0;for(;tn.x)),y:D.tensor1d(e.map(n=>n.y))}}async function e3(e){if(R.initial&&(se=null),!se&&e.body.detector&&e.body.detector.modelPath){se=await L(e.body.detector.modelPath);let t=se!=null&&se.executor?Object.values(se.modelSignature.inputs):void 0;n2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&se&&h("cached model:",se.modelUrl);return _s(),se}var _1=[5,5];function $s(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,n2),t.x),r=D.add(D.div(r,n2),t.y),s=D.mul(D.div(s,n2),_1[0]),A=D.mul(D.div(A,n2),_1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),x=D.add(l,A);return D.stack([a,l,c,x],1)})}async function eA(e,t,n,o){var c,x;let r=[],s={};s.boxes=$s(e,$1),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 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n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function st(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var Z0,x5=256,d5=Number.MAX_SAFE_INTEGER,tA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},at=[],Oe=[[0,0],[0,0],[0,0],[0,0]],o3=0,r3=e=>1-1/(1+Math.exp(e)),A3=e=>e3(e);async function a3(e){if(R.initial&&(Z0=null),Z0)e.debug&&h("cached model:",Z0.modelUrl);else{Z0=await L(e.body.modelPath);let t=Z0!=null&&Z0.executor?Object.values(Z0.modelSignature.inputs):void 0;x5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Z0}function s3(e,t,n){var s,A;let o={};if(!((s=e==null?void 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t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,ft),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,u2),t.centersNormalized=O.div(t.centers,u2),t.halfBoxSize=O.div(t.boxSizesNormalized,C.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,u2),t.endNormalized=O.mul(t.ends,u2);let n=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>O.dispose(t[o])),n}async function w3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=O.image.resizeBilinear(e,[Ce,Ce]),n.div=O.div(n.resized,C.tf127),n.normalized=O.sub(n.div,C.tf05);let o=xe==null?void 0:xe.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((m,y)=>m.size-y.size);n.concat384=O.concat([d[0],d[2]],2),n.concat512=O.concat([d[1],d[3]],2),n.concat=O.concat([n.concat512,n.concat384],1),n.batch=O.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=O.squeeze(o[0]):n.batch=O.squeeze(o);O.dispose(o),n.boxes=fA(n.batch),n.logits=O.slice(n.batch,[0,0],[-1,1]),n.sigmoid=O.sigmoid(n.logits),n.scores=O.squeeze(n.sigmoid),n.nms=await O.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=O.slice(n.boxes,[r[d],0],[1,-1]),y.slice=O.slice(n.batch,[r[d],M3-1],[1,-1]),y.squeeze=O.squeeze(y.slice),y.landmarks=O.reshape(y.squeeze,[M3,-1]);let u=await y.bbox.data(),T={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await y.landmarks.array(),confidence:m};y.anchor=O.slice(ft,[r[d],0],[1,2]);let g=await y.anchor.data(),p=u3(T,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce],g),b=xt(p,t.face.scale||yA),k=yt(b);k.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&k.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(k),Object.keys(y).forEach(P=>O.dispose(y[P]))}}return Object.keys(n).forEach(d=>O.dispose(n[d])),s}var ye=G(H());var K0,We=0,mA=2.3,M5=ae.leftEyeLower0,P5=ae.rightEyeLower0,h2={leftBounds:[M5[0],M5[M5.length-1]],rightBounds:[P5[0],P5[P5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function I3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&h("cached model:",K0.modelUrl):K0=await L((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function mt(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},z3=(e,t,n,o,r,s=!1)=>{let A=yt(xt(h3([e[n],e[o]]),mA)),a=p2(A),l=ye.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=ye.image.flipLeftRight(l);ye.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},S3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function N3(e,t,n){if(!(K0!=null&&K0.executor))return e;let{box:o,boxSize:r,crop:s}=z3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=z3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=ye.concat([s,l]);ye.dispose(s),ye.dispose(l);let x=K0.execute(c);ye.dispose(c);let i=await x.data();ye.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:m}=S3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:u,iris:T}=S3(y,A,a,!1),g=pA(e);Math.abs(g)<30?(mt(e,d,"left",null),mt(e,u,"right",null)):g<1?mt(e,d,"left",["EyeUpper0","EyeLower0"]):mt(e,u,"right",["EyeUpper0","EyeLower0"]);let p=j3(e,m,"left"),b=j3(e,T,"right");return e.concat(p).concat(b)}async function O3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,u)=>y+=e[u][2],0)/e2.length;for(let y=0;yy+=e[u][2],0)/t2.length;for(let y=0;yv()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await w3(e,t),ge.timestamp=v(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=O2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&h("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),_=await N.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*_[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=ct(p,e),P.boxRaw=dt(p,e),P.size=p.size,P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=N.find(n0=>n0.shape[n0.shape.length-1]===1404),$=De.reshape(Z,[-1,3]),A0=await $.array();De.dispose($),(y=t.face.attention)!=null&&y.enabled?A0=await O3(A0,N):(u=t.face.iris)!=null&&u.enabled&&(A0=await N3(A0,P.tensor,O2)),P.mesh=T3(A0,p,b,k,O2),P.meshRaw=P.mesh.map(n0=>[n0[0]/(e.shape[2]||0),n0[1]/(e.shape[1]||0),(n0[2]||0)/a]);for(let n0 of Object.keys(ae))P.annotations[n0]=ae[n0].map(j0=>P.mesh[j0]);P.score=P.faceScore;let t0={...R3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=ct(t0,e),P.boxRaw=dt(t0,e),P.size=t0.size,s.push(t0)}De.dispose(N)}P.score>(((T=t.face.detector)==null?void 0:T.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function W3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&h("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await L(e.face.attention.modelPath):r0=await L((r=e.face.mesh)==null?void 0:r.modelPath),O2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var D3=$e,F3=N2;var J0=G(H());var E5=[],P0,pt=[],B3=0,H3=0,w5=Number.MAX_SAFE_INTEGER,z5=!1;async function G3(e){var t,n,o;return R.initial&&(P0=null),P0?e.debug&&h("cached model:",P0.modelUrl):(P0=await L((t=e.face.emotion)==null?void 0:t.modelPath),z5=((o=(n=P0==null?void 0:P0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,z5?E5=["angry","disgust","fear","happy","neutral","sad","surprise"]:E5=["angry","disgust","fear","happy","sad","surprise","neutral"]),P0}async function S5(e,t,n,o){var A,a;if(!P0)return[];let r=w5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-H3;return t.skipAllowed&&s&&r&&B3===o&&pt[n]&&pt[n].length>0?(w5++,pt[n]):(w5=0,new Promise(async l=>{var x,i,f;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let d={},m=P0!=null&&P0.inputs[0].shape?P0.inputs[0].shape[2]:0;if(((i=t.face.emotion)==null?void 0:i.crop)>0){let u=(f=t.face.emotion)==null?void 0:f.crop,T=[[u,u,1-u,1-u]];d.resize=J0.image.cropAndResize(e,T,[0],[m,m])}else d.resize=J0.image.resizeBilinear(e,[m,m],!1);z5?(d.mul=J0.mul(d.resize,255),d.normalize=J0.sub(d.mul,[103.939,116.779,123.68]),d.emotion=P0==null?void 0:P0.execute(d.normalize)):(d.channels=J0.mul(d.resize,C.rgb),d.grayscale=J0.sum(d.channels,3,!0),d.grayscaleSub=J0.sub(d.grayscale,C.tf05),d.grayscaleMul=J0.mul(d.grayscaleSub,C.tf2),d.emotion=P0==null?void 0:P0.execute(d.grayscaleMul)),H3=v();let y=await d.emotion.data();for(let u=0;u(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*y[u])/100),emotion:E5[u]});c.sort((u,T)=>T.score-u.score),Object.keys(d).forEach(u=>J0.dispose(d[u]))}pt[n]=c,B3=o,l(c)}))}var ie=G(H());var k0,Fe=[],Z3=0,X3=0,j5=Number.MAX_SAFE_INTEGER;async function q3(e){var t;return R.initial&&(k0=null),k0?e.debug&&h("cached model:",k0.modelUrl):k0=await L((t=e.face.description)==null?void 0:t.modelPath),k0}function hA(e,t){var s,A;let n=e.image||e.tensor||e;if(!(k0!=null&&k0.inputs[0].shape))return n;let o;if(((s=t.face.description)==null?void 0:s.crop)>0){let a=(A=t.face.description)==null?void 0:A.crop,l=[[a,a,1-a,1-a]];o=ie.image.cropAndResize(n,l,[0],[k0.inputs[0].shape[2],k0.inputs[0].shape[1]])}else o=ie.image.resizeBilinear(n,[k0.inputs[0].shape[2],k0.inputs[0].shape[1]],!1);let r=ie.mul(o,C.tf255);return ie.dispose(o),r}async function I5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(k0!=null&&k0.executor))return r;let s=j5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>v()-Z3;return t.skipAllowed&&s&&A&&X3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((x=Fe==null?void 0:Fe[n])==null?void 0:x.genderScore)>0?(j5++,Fe[n]):(j5=0,new Promise(async i=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=hA(e,t),m=k0==null?void 0:k0.execute(d);Z3=v(),ie.dispose(d);let u=await m.find(V=>V.shape[1]===1).data(),T=Math.trunc(200*Math.abs(u[0]-.5))/100;T>(t.face.description.minConfidence||0)&&(r.gender=u[0]<=.5?"female":"male",r.genderScore=Math.min(.99,T));let g=ie.argMax(m.find(V=>V.shape[1]===100),1),p=(await g.data())[0];ie.dispose(g);let k=await m.find(V=>V.shape[1]===100).data();r.age=Math.round(k[p-1]>k[p+1]?10*p-100*k[p-1]:10*p+100*k[p+1])/10,(Number.isNaN(u[0])||Number.isNaN(k[0]))&&h("faceres error:",{model:k0,result:m});let P=m.find(V=>V.shape[1]===1024),N=P?await P.data():[];r.descriptor=Array.from(N),m.forEach(V=>ie.dispose(V))}Fe[n]=r,X3=o,i(r)}))}var g2=.1,N5=.5;function bA(e,t,n){let o=!1,r=n.length-1;for(let s=0;st!=n[r].y>t&&e<(n[r].x-n[s].x)*(t-n[s].y)/(n[r].y-n[s].y)+n[s].x&&(o=!o);return o}async function Y3(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,o=await e.tensor.buffer(),r=[];for(let A of ae.silhouette)r.push({x:(e.mesh[A][0]-e.box[0])/e.box[2],y:(e.mesh[A][1]-e.box[1])/e.box[3]});g2&&g2>0&&(r=r.map(A=>({x:A.x>.5?A.x+g2:A.x-g2,y:A.y>.5?A.y+g2:A.y-g2})));for(let A=0;Av()-J3,s=L5<(((a=t.face.antispoof)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&K3===o&&ut[n]?(L5++,ut[n]):(L5=0,new Promise(async l=>{let c=ht.image.resizeBilinear(e,[w0!=null&&w0.inputs[0].shape?w0.inputs[0].shape[2]:0,w0!=null&&w0.inputs[0].shape?w0.inputs[0].shape[1]:0],!1),x=w0==null?void 0:w0.execute(c),i=(await x.data())[0];ut[n]=Math.round(100*i)/100,K3=o,J3=v(),ht.dispose([c,x]),l(ut[n])}))}var gt=G(H());var E0,bt=[],C5=Number.MAX_SAFE_INTEGER,$3=0,en=0;async function tn(e){var t;return R.initial&&(E0=null),E0?e.debug&&h("cached model:",E0.modelUrl):E0=await L((t=e.face.liveness)==null?void 0:t.modelPath),E0}async function W5(e,t,n,o){var A,a;if(!(E0!=null&&E0.executor))return 0;let r=(((A=t.face.liveness)==null?void 0:A.skipTime)||0)>v()-en,s=C5<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&$3===o&&bt[n]?(C5++,bt[n]):(C5=0,new Promise(async l=>{let c=gt.image.resizeBilinear(e,[E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[2]:0,E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[1]:0],!1),x=E0==null?void 0:E0.execute(c),i=(await x.data())[0];bt[n]=Math.round(100*i)/100,$3=o,en=v(),gt.dispose([c,x]),l(bt[n])}))}var Tt=G(H());var le,D5=[],TA=["white","black","asian","indian","other"],vA=[15,23,28,35.5,45.5,55.5,65],on=0,rn=0,F5=Number.MAX_SAFE_INTEGER;async function sn(e){var t;return R.initial&&(le=null),le?e.debug&&h("cached model:",le.modelUrl):le=await L((t=e.face.gear)==null?void 0:t.modelPath),le}async function B5(e,t,n,o){var A,a;if(!le)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=F5<(((A=t.face.gear)==null?void 0:A.skipFrames)||0),s=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>v()-rn;return t.skipAllowed&&s&&r&&on===o&&D5[n]?(F5++,D5[n]):(F5=0,new Promise(async l=>{var T,g,p,b;if(!(le!=null&&le.inputs[0].shape))return;let c={},x=[[0,.1,.9,.9]];if(((T=t.face.gear)==null?void 0:T.crop)>0){let k=(g=t.face.gear)==null?void 0:g.crop;x=[[k,k,1-k,1-k]]}c.resize=Tt.image.cropAndResize(e,x,[0],[le.inputs[0].shape[2],le.inputs[0].shape[1]]);let i={age:0,gender:"unknown",genderScore:0,race:[]};(p=t.face.gear)!=null&&p.enabled&&([c.age,c.gender,c.race]=le.execute(c.resize,["age_output","gender_output","race_output"]));let f=await 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G0.all)n[G0.getName(o)]={curl:He.getName(t.curls[o]),direction:c0.getName(t.directions[o])};return n}function On(e){let t=[];if(!e||e.length===0)return t;let n=Ln(e);for(let o of En){let r=o.matchAgainst(n.curls,n.directions);r>=EA&&t.push({name:o.name,confidence:r})}return t}var Cn=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&o&&r&&o.position[1]l.part==="leftShoulder"),a=e[n].keypoints.find(l=>l.part==="rightShoulder");A&&a&&Math.abs(A.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${A.position[1]>a.position[1]?"left":"right"}`})}return t},Wn=e=>{if(!e)return[];let t=[];for(let n=0;n450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},Dn=e=>{var n,o,r,s;if(!e)return[];let t=[];for(let A=0;A.06||u>.06)&&(d=!1),y>u?u>.04&&t.push({iris:A,gesture:"looking right"}):y>.04&&t.push({iris:A,gesture:"looking left"});let T=Math.abs(e[A].mesh[145][1]-e[A].annotations.rightEyeIris[0][1])/e[A].box[3],g=Math.abs(e[A].mesh[374][1]-e[A].annotations.leftEyeIris[0][1])/e[A].box[3];(g<.01||T<.01||g>.022||T>.022)&&(d=!1),(g<.01||T<.01)&&t.push({iris:A,gesture:"looking down"}),(g>.022||T>.022)&&t.push({iris:A,gesture:"looking up"}),d&&t.push({iris:A,gesture:"looking center"})}return t},Fn=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=o.reduce((A,a)=>(A.position[2]||0)<(a.position[2]||0)?A:a);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((A,a)=>A.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function Et(e,t=1.5){let n=C2(e),o=wt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function zt(e){let t=C2(e),n=wt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function NA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Xn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return NA(n)}var Bn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Xe(e,t){let n=0;for(let o=0;o[A.x,A.y]),this.anchorsTensor=W.tensor2d(this.anchors),this.inputSize=((s=(r=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=W.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=W.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=W.slice(t,[0,0],[-1,2]),n.boxSizes=W.slice(t,[0,2],[-1,2]),n.div=W.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=W.add(n.div,this.anchorsTensor),n.halfBoxSizes=W.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=W.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=W.mul(n.sub,this.inputSizeTensor),n.add=W.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=W.mul(n.add,this.inputSizeTensor);let o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),f=x.slice(2,4),d=await c.palmLandmarks.array(),m={startPoint:i,endPoint:f,palmLandmarks:d,confidence:r[l]},y=Zn(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(y),Object.keys(c).forEach(u=>W.dispose(c[u]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var $0=G(H());var WA=5,Kn=1.65,Jn=[0,5,9,13,17,1,2],DA=0,FA=2,Qn=0,jt=class{constructor(t,n){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>e1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return Et(zt(r),WA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Et(zt(n),Kn);o.palmLandmarks=[];for(let r=0;r[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=$5(o,[0,0]),c=a.map(d=>[...e1(d,l),d[2]]),x=qn(r),i=[...C2(n),1],f=[Xe(i,x[0]),Xe(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>v()-Qn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A?this.skipped++:(r=await this.handDetector.predict(t,n),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...V,confidence:b};let _={landmarks:N,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(_)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let x=Et(zt(c),Kn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var _n={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,t1;function HA(){let e=l2?new St(l2):void 0;e&&c2&&(t1=new jt(e,c2))}async function n1(e,t){t1||HA();let n=await t1.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=kt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function $n(e){var t;return R.initial&&(l2=null),l2?e.debug&&h("cached model:",l2.modelUrl):l2=await L((t=e.hand.detector)==null?void 0:t.modelPath),l2}async function eo(e){var t;return R.initial&&(c2=null),c2?e.debug&&h("cached 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0;qe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function Ao(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&h("cached model:",p0[1].modelUrl);else{p0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;qe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function qA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,ZA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,GA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[no]),a.splice(no,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=Q.slice(o.boxes,f,1),m=await d.data();Q.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],u=st(y,XA),T=[Math.trunc(y[0]*we[0]),Math.trunc(y[1]*we[1]),Math.trunc(y[2]*we[0]),Math.trunc(y[3]*we[1])],g=x[f],p=VA[i[f]],b={id:l++,score:g,box:T,boxRaw:u,label:p};n.push(b)}return Object.keys(o).forEach(f=>Q.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function r1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[qe[1][0],qe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/qe[1][1],i[1]/qe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[we[0]*(i[0]+t.boxRaw[0]),we[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=kt(o.keypoints);for(let i of Object.keys(ro))o.annotations[i]=ro[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function s1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];we=[e.shape[2]||0,e.shape[1]||0],It++;let n=(t.hand.skipTime||0)>v()-o1,o=It<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-o1,l=It<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):(m0.boxes=await qA(e,t),o1=v(),m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))),It=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=st(i.box,oo),d=st(i.boxRaw,oo);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var W2={};ze(W2,{connected:()=>Lt,horizontal:()=>A1,kpt:()=>Nt,relative:()=>i1,vertical:()=>a1});var Nt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],A1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],a1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],i1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Lt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=Te(),l1=0;function io(e,t){var 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M=0;M((r-1)*z.face[M].box[E]+K)/r),C0=e.face[M].boxRaw.map((K,E)=>((r-1)*z.face[M].boxRaw[E]+K)/r),x0=e.face[M].annotations;if(Object.keys(z.face[M].annotations).length!==Object.keys(e.face[M].annotations).length)z.face[M].annotations=e.face[M].annotations,x0=z.face[M].annotations;else if(e.face[M].annotations)for(let K of Object.keys(e.face[M].annotations))x0[K]=(m=(d=(f=e.face[M])==null?void 0:f.annotations)==null?void 0:d[K])!=null&&m[0]?e.face[M].annotations[K].map((E,B)=>E.map((X,U0)=>((r-1)*z.face[M].annotations[K][B][U0]+X)/r)):null;if(e.face[M].rotation){let K={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};K.matrix=(y=e.face[M].rotation)==null?void 0:y.matrix,K.angle={roll:((r-1)*(((T=(u=z.face[M].rotation)==null?void 0:u.angle)==null?void 0:T.roll)||0)+(((p=(g=e.face[M].rotation)==null?void 0:g.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((k=(b=z.face[M].rotation)==null?void 0:b.angle)==null?void 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M=e.persons;if(!z.persons||M.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(M));else for(let T0=0;T0((r-1)*z.persons[T0].box[x0]+C0)/r)}e.gesture&&(z.gesture=e.gesture),z.width=e.width,z.height=e.height;let s=v();return l1=R.perfadd?l1+Math.round(s-n):Math.round(s-n),e.performance&&(z.performance={...e.performance,interpolate:l1}),z}var s0=G(H());var L0;async function c1(e){return!L0||R.initial?L0=await L(e.segmentation.modelPath):e.debug&&h("cached model:",L0.modelUrl),L0}async function lo(e,t){var r;if(L0||(L0=await c1(t)),!(L0!=null&&L0.executor)||!((r=L0==null?void 0:L0.inputs)!=null&&r[0].shape))return null;let n={};n.resize=s0.image.resizeBilinear(e,[L0.inputs[0].shape?L0.inputs[0].shape[1]:0,L0.inputs[0].shape?L0.inputs[0].shape[2]:0],!1),n.norm=s0.div(n.resize,C.tf255),n.res=L0.execute(n.norm),n.squeeze=s0.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=s0.unstack(n.squeeze,2),n.fg=s0.softmax(n.fgRaw),n.mul=s0.mul(n.fg,C.tf255),n.expand=s0.expandDims(n.mul,2),n.output=s0.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=s0.squeeze(e),n.concat=s0.concat([n.input,n.output],-1),o=s0.cast(n.concat,"int32");break;case"alpha":o=s0.cast(n.output,"int32");break;default:o=s0.tensor(0)}return Object.keys(n).forEach(s=>s0.dispose(n[s])),o}var Ot={};ze(Ot,{distance:()=>d1,find:()=>KA,similarity:()=>YA});function d1(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let r=0;r{if(e===0)return 1;let s=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return Math.max(Math.min(s,1),0)};function 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e.keypoints)o.position=[o.position[0]*(t[0]+ee.padding[2][0]+ee.padding[2][1])/t[0]-ee.padding[2][0],o.position[1]*(t[1]+ee.padding[1][0]+ee.padding[1][1])/t[1]-ee.padding[1][0]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1]];let n=Le(e.keypoints.map(o=>o.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var b0,Ct=0,y1=Number.MAX_SAFE_INTEGER,d2={boxes:[],bodies:[],last:0};async function uo(e){var t;return R.initial&&(b0=null),b0?e.debug&&h("cached model:",b0.modelUrl):(J2(["size"],e),b0=await L(e.body.modelPath)),Ct=b0!=null&&b0.executor&&((t=b0==null?void 0:b0.inputs)!=null&&t[0].shape)?b0.inputs[0].shape[2]:0,Ct<64&&(Ct=256),D2.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS&&D2.env().set("WEBGL_USE_SHAPES_UNIFORMS",!1),b0}function QA(e,t,n){let o=e[0][0],r=[],s=0;for(let x=0;xt.body.minConfidence){let 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l=[s[52],s[51],s[54]-s[52],s[53]-s[51]],c=[Math.trunc(l[0]*(n.shape[2]||0)),Math.trunc(l[1]*(n.shape[1]||0)),Math.trunc(l[2]*(n.shape[2]||0)),Math.trunc(l[3]*(n.shape[1]||0))],x={};for(let[f,d]of Object.entries(Lt)){let m=[];for(let y=0;yg.part===d[y]),T=a.find(g=>g.part===d[y+1]);u&&T&&u.score>(t.body.minConfidence||0)&&T.score>(t.body.minConfidence||0)&&m.push([u.position,T.position])}x[f]=m}let i={id:r,score:A,box:c,boxRaw:l,keypoints:[...a],annotations:x};x1(i),o.push(i)}}return o.sort((r,s)=>s.score-r.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function f1(e,t){var r;if(!(b0!=null&&b0.executor)||!((r=b0==null?void 0:b0.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(d2.boxes.length=0),y1++;let n=(t.body.skipTime||0)>v()-d2.last,o=y1<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?d2.bodies:new Promise(async s=>{let A={};y1=0,A.input=mo(e,Ct),A.res=b0==null?void 0:b0.execute(A.input),d2.last=v();let a=await A.res.array();d2.bodies=A.res.shape[2]===17?QA(a,t,e):_A(a,t,e);for(let l of d2.bodies)po(l,[e.shape[2]||1,e.shape[1]||1]),fo(l.keypoints);Object.keys(A).forEach(l=>D2.dispose(A[l])),s(d2.bodies)})}var S0=G(H());var ce,Wt=[],bo=0,m1=Number.MAX_SAFE_INTEGER,Ft=0,Dt=2.5;async function go(e){if(!ce||R.initial){ce=await L(e.object.modelPath);let t=ce!=null&&ce.executor?Object.values(ce.modelSignature.inputs):void 0;Ft=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&h("cached model:",ce.modelUrl);return ce}async function $A(e,t,n){var c,x;let o=0,r=[],s=Ft;for(let i of[1,2,4]){let f=i*13,d=S0.squeeze(e.find(p=>p.shape[1]===f**2&&(p.shape[2]||0)===m2.length)),m=await d.array(),y=S0.squeeze(e.find(p=>p.shape[1]===f**2&&(p.shape[2]||0)(n.object.minConfidence||0)&&b!==61){let 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B2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],ea=B2.length,F2=B2.reduce((e,t,n)=>(e[t]=n,e),{}),ta=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],X7=ta.map(([e,t])=>[F2[e],F2[t]]),vo=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?k3(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)!=null&&o.enabled)&&!this.models.antispoof?Q3(this.instance.config):null,n.liveness=this.instance.config.face.enabled&&((r=this.instance.config.face.liveness)!=null&&r.enabled)&&!this.models.liveness?tn(this.instance.config):null,n.faceres=this.instance.config.face.enabled&&((s=this.instance.config.face.description)!=null&&s.enabled)&&!this.models.faceres?q3(this.instance.config):null,n.emotion=this.instance.config.face.enabled&&((A=this.instance.config.face.emotion)!=null&&A.enabled)&&!this.models.emotion?G3(this.instance.config):null,n.iris=this.instance.config.face.enabled&&((a=this.instance.config.face.iris)!=null&&a.enabled)&&!((l=this.instance.config.face.attention)!=null&&l.enabled)&&!this.models.iris?I3(this.instance.config):null,n.facemesh=this.instance.config.face.enabled&&((c=this.instance.config.face.mesh)!=null&&c.enabled)&&!this.models.facemesh?W3(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((x=this.instance.config.face.gear)!=null&&x.enabled)&&!this.models.gear?sn(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((i=this.instance.config.face.ssrnet)!=null&&i.enabled)&&!this.models.ssrnetage?cn(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((f=this.instance.config.face.ssrnet)!=null&&f.enabled)&&!this.models.ssrnetgender?fn(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((d=this.instance.config.face.mobilefacenet)!=null&&d.enabled)&&!this.models.mobilefacenet?bn(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((m=this.instance.config.face.insightface)!=null&&m.enabled)&&!this.models.insightface?Mn(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((y=this.instance.config.body.modelPath)!=null&&y.includes("blazepose"))?a3(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?A3(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((u=this.instance.config.body.modelPath)!=null&&u.includes("efficientpose"))?y3(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((T=this.instance.config.body.modelPath)!=null&&T.includes("movenet"))?uo(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((g=this.instance.config.body.modelPath)!=null&&g.includes("posenet"))?Eo(this.instance.config):null,n.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((b=(p=this.instance.config.hand.detector)==null?void 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p=Math.cos(g),b=Math.sin(g),k=.213,P=.715,N=.072;T.colorMatrix([k+p*(1-k)+b*-k,P+p*-P+b*-P,N+p*-N+b*(1-N),0,0,k+p*-k+b*.143,P+p*(1-P)+b*.14,N+p*-N+b*-.283,0,0,k+p*-k+b*-(1-k),P+p*-P+b*P,N+p*(1-N)+b*N,0,0,0,0,0,1,0])},desaturateLuminance:()=>{T.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{T.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{T.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{T.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{T.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{T.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{T.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{T.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:g=>{let p=new Float32Array(g),b=1/l.width,k=1/l.height,P=u(G1);P&&(i.uniform1fv(P.uniform.m,p),i.uniform2f(P.uniform.px,b,k),y())},detectEdges:()=>{T.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{T.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{T.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:g=>{let p=g||1;T.convolution.call(this,[0,-1*p,0,-1*p,1+4*p,-1*p,0,-1*p,0])},emboss:g=>{let p=g||1;T.convolution.call(this,[-2*p,-1*p,0,-1*p,1,1*p,0,1*p,2*p])},blur:g=>{let p=g/7/l.width,b=g/7/l.height,k=u(H1);k&&(i.uniform2f(k.uniform.px,0,b),y(x.INTERMEDIATE),i.uniform2f(k.uniform.px,p,0),y())},pixelate:g=>{let p=g/l.width,b=g/l.height,k=u(B1);k&&(i.uniform2f(k.uniform.size,p,b),y())}};this.add=function(g){let p=Array.prototype.slice.call(arguments,1),b=T[g];s.push({func:b,args:p})},this.reset=function(){s=[]},this.get=function(){return s},this.apply=function(g){f(g.width,g.height),e=0,t||(t=i.createTexture()),i.bindTexture(i.TEXTURE_2D,t),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.NEAREST),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,g);for(let p=0;px.data())),A=Math.max(s[0][0],s[1][0],s[2][0]),l=(A>1?255:1)/A,c;if(l>1){let x=[U.sub(n[0],o[0]),U.sub(n[1],o[1]),U.sub(n[2],o[2])],i=[U.sub(r[0],o[0]),U.sub(r[1],o[1]),U.sub(r[2],o[2])],f=[U.mul(x[0],l),U.mul(x[1],l),U.mul(x[2],l)],d=U.stack([f[0],f[1],f[2]],2);c=U.reshape(d,[1,t.shape[0]||0,t.shape[1]||0,3]),U.dispose([...x,...i,...f,d])}else c=U.expandDims(t,0);return U.dispose([...n,...o,...r,n,t,e]),c}var U2=3840,R0=null,M0=null,y2=null,e0,ne={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function e5(){ne.inputSum=0,ne.cacheDiff=1,ne.sumMethod=0,ne.inputTensor=void 0}function Ae(e,t){let n;if(R.browser)if(R.worker){if(typeof OffscreenCanvas=="undefined")throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");n=new OffscreenCanvas(e,t)}else if(typeof document!="undefined")n=document.createElement("canvas"),n.width=e,n.height=t;else if(typeof navigator!="undefined"&&navigator.product==="ReactNative")if(typeof R.Canvas!="undefined")n=new R.Canvas(e,t);else if(typeof globalThis.Canvas!="undefined")n=new globalThis.Canvas(e,t);else throw new Error("canvas error: attempted to use canvas in react-native without canvas support installed");else throw new Error("canvas error: attempted to run in browser but DOM is not defined");else typeof R.Canvas!="undefined"?n=new R.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t));return n}function Y2(e,t){let n=t||Ae(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}async function K2(e,t,n=!0){var f,d,m;if(!e)return t.debug&&h("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof I.Tensor)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input error: type not recognized");if(e instanceof I.Tensor){let y=null;if(e.isDisposedInternal)throw new Error("input error: attempted to use tensor but it is disposed");if(!e.shape)throw new Error("input error: attempted to use tensor without a shape");if(e.shape.length===3){if(e.shape[2]===3)y=I.expandDims(e,0);else if(e.shape[2]===4){let u=I.slice3d(e,[0,0,0],[-1,-1,3]);y=I.expandDims(u,0),I.dispose(u)}}else e.shape.length===4&&(e.shape[3]===3?y=I.clone(e):e.shape[3]===4&&(y=I.slice4d(e,[0,0,0,0],[-1,-1,-1,3])));if(y==null||y.shape.length!==4||y.shape[0]!==1||y.shape[3]!==3)throw new Error(`input error: attempted to use tensor with unrecognized shape: ${e.shape.toString()}`);if(y.dtype==="int32"){let u=I.cast(y,"float32");I.dispose(y),y=u}return{tensor:y,canvas:t.filter.return?M0:null}}if(typeof e.readyState!="undefined"&&e.readyState<=2)return t.debug&&h("input stream is not ready"),{tensor:null,canvas:R0};let o=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,r=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!o||!r)return t.debug&&h("cannot determine input dimensions"),{tensor:null,canvas:R0};let s=o,A=r;if(s>U2&&(s=U2,A=Math.trunc(s*r/o)),A>U2&&(A=U2,s=Math.trunc(A*o/r)),(((f=t.filter)==null?void 0:f.width)||0)>0?s=t.filter.width:(((d=t.filter)==null?void 0:d.height)||0)>0&&(s=o*((t.filter.height||0)/r)),(t.filter.height||0)>0?A=t.filter.height:(t.filter.width||0)>0&&(A=r*((t.filter.width||0)/o)),!s||!A)throw new Error("input error: cannot determine dimension");(!R0||R0.width!==s||R0.height!==A)&&(R0=Ae(s,A));let a=R0.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?a.putImageData(e,0,0):t.filter.flip&&typeof a.translate!="undefined"?(a.translate(o,0),a.scale(-1,1),a.drawImage(e,0,0,o,r,0,0,R0.width,R0.height),a.setTransform(1,0,0,1,0,0)):a.drawImage(e,0,0,o,r,0,0,R0.width,R0.height),(!M0||R0.width!==M0.width||R0.height!==M0.height)&&(M0=Ae(R0.width,R0.height)),t.filter.enabled&&R.webgl.supported?(e0||(e0=R.browser?new V1:null),R.filter=!!e0,e0!=null&&e0.add?(e0.reset(),t.filter.brightness!==0&&e0.add("brightness",t.filter.brightness),t.filter.contrast!==0&&e0.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&e0.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&e0.add("blur",t.filter.blur),t.filter.saturation!==0&&e0.add("saturation",t.filter.saturation),t.filter.hue!==0&&e0.add("hue",t.filter.hue),t.filter.negative&&e0.add("negative"),t.filter.sepia&&e0.add("sepia"),t.filter.vintage&&e0.add("brownie"),t.filter.sepia&&e0.add("sepia"),t.filter.kodachrome&&e0.add("kodachrome"),t.filter.technicolor&&e0.add("technicolor"),t.filter.polaroid&&e0.add("polaroid"),t.filter.pixelate!==0&&e0.add("pixelate",t.filter.pixelate),((m=e0.get())==null?void 0:m.length)>1?M0=e0.apply(R0):M0=e0.draw(R0)):(t.debug&&h("input process error: cannot initialize filters"),R.webgl.supported=!1,t.filter.enabled=!1,Y2(R0,M0))):(Y2(R0,M0),e0&&(e0=null),R.filter=!!e0),!n)return{tensor:null,canvas:M0};if(!M0)throw new Error("canvas error: cannot create output");let l,c=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(R.browser&&I.browser)l=I.browser?I.browser.fromPixels(e):null;else{c=e.data.length/e.height/e.width;let y=new Uint8Array(e.data.buffer);l=I.tensor(y,[e.height,e.width,c],"int32")}else if((!y2||M0.width!==y2.width||M0.height!==y2.height)&&(y2=Ae(M0.width,M0.height)),I.browser&&R.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=I.browser.fromPixels(M0):(y2=Y2(M0),l=I.browser.fromPixels(y2));else{let T=Y2(M0).getContext("2d").getImageData(0,0,s,A);c=T.data.length/s/A;let g=new Uint8Array(T.data.buffer);l=I.tensor(g,[s,A,c])}if(c===4){let y=I.slice3d(l,[0,0,0],[-1,-1,3]);I.dispose(l),l=y}if(!l)throw new Error("input error: cannot create tensor");let x=I.cast(l,"float32"),i=t.filter.equalization?await X2(x):I.expandDims(x,0);if(I.dispose([l,x]),t.filter.autoBrightness){let y=I.max(i),u=await y.data();t.filter.brightness=u[0]>1?1-u[0]/255:1-u[0],I.dispose(y)}return{tensor:i,canvas:t.filter.return?M0:null}}async function Z1(e,t){let n=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>3840||t.shape[2]>2160)return n;if(!ne.inputTensor)ne.inputTensor=I.clone(t);else if(ne.inputTensor.shape[1]!==t.shape[1]||ne.inputTensor.shape[2]!==t.shape[2])I.dispose(ne.inputTensor),ne.inputTensor=I.clone(t);else{let o={};o.diff=I.sub(t,ne.inputTensor),o.squared=I.mul(o.diff,o.diff),o.sum=I.sum(o.squared);let s=(await o.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;I.dispose([ne.inputTensor,o.diff,o.squared,o.sum]),ne.inputTensor=I.clone(t),n=s<=(e.cacheSensitivity||0)}return n}async function q1(e,t,n){let o={};if(!t||!n||t.shape.length!==4||t.shape.length!==n.shape.length)return e.debug||h("invalid input tensor or tensor shapes do not match:",t.shape,n.shape),0;if(t.shape[0]!==1||n.shape[0]!==1||t.shape[3]!==3||n.shape[3]!==3)return e.debug||h("input tensors must 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0,flags:[]});w(this,"kernels",[]);ue(this,j2,void 0);ue(this,I2,void 0);ue(this,N2,void 0);if(this.browser=typeof navigator!="undefined",this.node=typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined",this.tfjs={version:W0.version["tfjs-core"]},this.offscreen=typeof OffscreenCanvas!="undefined",this.initial=!0,this.worker=this.browser&&this.offscreen?typeof WorkerGlobalScope!="undefined":void 0,typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined"){let t=navigator.userAgent||"",n=t.match(/\(([^()]+)\)/g);if(n!=null&&n[0]){let o=n[0].match(/\(([^()]+)\)/g);this.platform=o!=null&&o[0]?o[0].replace(/\(|\)/g,""):"",this.agent=t.replace(n[0],""),this.platform[1]&&(this.agent=this.agent.replace(n[1],"")),this.agent=this.agent.replace(/ /g," ")}}else typeof process!="undefined"&&(this.platform=`${process.platform} ${process.arch}`,this.agent=`NodeJS ${process.version}`)}get Canvas(){return Y0(this,j2)}set Canvas(t){Re(this,j2,t),globalThis.Canvas=t}get Image(){return Y0(this,I2)}set Image(t){Re(this,I2,t),globalThis.Image=t}get ImageData(){return Y0(this,N2)}set ImageData(t){Re(this,N2,t),globalThis.ImageData=t}async updateBackend(){this.backends=Object.keys(W0.engine().registryFactory);try{this.tensorflow={version:W0.backend().binding?W0.backend().binding.TF_Version:void 0,gpu:W0.backend().binding?W0.backend().binding.isUsingGpuDevice():void 0}}catch(o){}this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&(this.wasm.simd=await W0.env().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await W0.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Ae(100,100),n=t?t.getContext("webgl2"):void 0;this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&n&&(this.webgl.version=n.getParameter(n.VERSION),this.webgl.vendor=n.getParameter(n.VENDOR),this.webgl.renderer=n.getParameter(n.RENDERER),this.webgl.shader=n.getParameter(n.SHADING_LANGUAGE_VERSION)),this.webgpu.supported=this.browser&&typeof navigator!="undefined"&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{if(this.webgpu.supported){let o=await navigator.gpu.requestAdapter();this.webgpu.adapter=await(o==null?void 0:o.requestAdapterInfo())}}catch(o){this.webgpu.supported=!1}try{this.kernels=W0.getKernelsForBackend(W0.getBackend()).map(o=>o.kernelName.toLowerCase())}catch(o){}}updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}};j2=new WeakMap,I2=new WeakMap,N2=new WeakMap;var R=new S2;var Q2=class{constructor(){w(this,"config");w(this,"element");w(this,"stream");w(this,"devices",[]);w(this,"enumerate",async()=>{try{let t=await navigator.mediaDevices.enumerateDevices();this.devices=t.filter(n=>n.kind==="videoinput")}catch(t){this.devices=[]}return this.devices});w(this,"start",async t=>{var r,s;if(t!=null&&t.debug&&(this.config.debug=t==null?void 0:t.debug),t!=null&&t.crop&&(this.config.crop=t==null?void 0:t.crop),t!=null&&t.mode&&(this.config.mode=t==null?void 0:t.mode),t!=null&&t.width&&(this.config.width=t==null?void 0:t.width),t!=null&&t.height&&(this.config.height=t==null?void 0:t.height),t!=null&&t.id&&(this.config.id=t==null?void 0:t.id),t!=null&&t.element)if(typeof t.element=="string"){let A=document.getElementById(t.element);if(A&&A instanceof HTMLVideoElement)this.element=A;else return this.config.debug&&h("webcam","cannot get dom element",t.element),`webcam error: cannot get dom element: ${t.element}`}else if(t.element instanceof HTMLVideoElement)this.element=t.element;else return this.config.debug&&h("webcam","unknown dom element",t.element),`webcam error: unknown dom element: ${t.element}`;else this.element=document.createElement("video");let n={audio:!1,video:{facingMode:this.config.mode==="front"?"user":"environment",resizeMode:this.config.crop?"crop-and-scale":"none"}};if(((r=this.config)==null?void 0:r.width)>0&&(n.video.width={ideal:this.config.width}),((s=this.config)==null?void 0:s.height)>0&&(n.video.height={ideal:this.config.height}),this.config.id&&(n.video.deviceId=this.config.id),this.element.addEventListener("play",()=>{this.config.debug&&h("webcam","play")}),this.element.addEventListener("pause",()=>{this.config.debug&&h("webcam","pause")}),this.element.addEventListener("click",async()=>{!this.element||!this.stream||(this.element.paused?await this.element.play():this.element.pause())}),!(navigator!=null&&navigator.mediaDevices))return this.config.debug&&h("webcam error","no devices"),"webcam 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p=Math.cos(g),b=Math.sin(g),k=.213,P=.715,N=.072;T.colorMatrix([k+p*(1-k)+b*-k,P+p*-P+b*-P,N+p*-N+b*(1-N),0,0,k+p*-k+b*.143,P+p*(1-P)+b*.14,N+p*-N+b*-.283,0,0,k+p*-k+b*-(1-k),P+p*-P+b*P,N+p*(1-N)+b*N,0,0,0,0,0,1,0])},desaturateLuminance:()=>{T.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{T.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{T.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{T.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{T.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{T.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{T.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{T.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:g=>{let p=new Float32Array(g),b=1/l.width,k=1/l.height,P=u(G1);P&&(i.uniform1fv(P.uniform.m,p),i.uniform2f(P.uniform.px,b,k),y())},detectEdges:()=>{T.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{T.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{T.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:g=>{let p=g||1;T.convolution.call(this,[0,-1*p,0,-1*p,1+4*p,-1*p,0,-1*p,0])},emboss:g=>{let p=g||1;T.convolution.call(this,[-2*p,-1*p,0,-1*p,1,1*p,0,1*p,2*p])},blur:g=>{let p=g/7/l.width,b=g/7/l.height,k=u(H1);k&&(i.uniform2f(k.uniform.px,0,b),y(x.INTERMEDIATE),i.uniform2f(k.uniform.px,p,0),y())},pixelate:g=>{let p=g/l.width,b=g/l.height,k=u(B1);k&&(i.uniform2f(k.uniform.size,p,b),y())}};this.add=function(g){let p=Array.prototype.slice.call(arguments,1),b=T[g];s.push({func:b,args:p})},this.reset=function(){s=[]},this.get=function(){return s},this.apply=function(g){f(g.width,g.height),e=0,t||(t=i.createTexture()),i.bindTexture(i.TEXTURE_2D,t),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.NEAREST),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,g);for(let p=0;px.data())),A=Math.max(s[0][0],s[1][0],s[2][0]),l=(A>1?255:1)/A,c;if(l>1){let x=[U.sub(n[0],o[0]),U.sub(n[1],o[1]),U.sub(n[2],o[2])],i=[U.sub(r[0],o[0]),U.sub(r[1],o[1]),U.sub(r[2],o[2])],f=[U.mul(x[0],l),U.mul(x[1],l),U.mul(x[2],l)],d=U.stack([f[0],f[1],f[2]],2);c=U.reshape(d,[1,t.shape[0]||0,t.shape[1]||0,3]),U.dispose([...x,...i,...f,d])}else c=U.expandDims(t,0);return U.dispose([...n,...o,...r,n,t,e]),c}var U2=3840,R0=null,M0=null,y2=null,e0,ne={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function e5(){ne.inputSum=0,ne.cacheDiff=1,ne.sumMethod=0,ne.inputTensor=void 0}function Ae(e,t){let n;if(R.browser)if(R.worker){if(typeof OffscreenCanvas=="undefined")throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");n=new OffscreenCanvas(e,t)}else if(typeof document!="undefined")n=document.createElement("canvas"),n.width=e,n.height=t;else if(typeof navigator!="undefined"&&navigator.product==="ReactNative")if(typeof R.Canvas!="undefined")n=new R.Canvas(e,t);else if(typeof globalThis.Canvas!="undefined")n=new globalThis.Canvas(e,t);else throw new Error("canvas error: attempted to use canvas in react-native without canvas support installed");else throw new Error("canvas error: attempted to run in browser but DOM is not defined");else typeof R.Canvas!="undefined"?n=new R.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t));return n}function Y2(e,t){let n=t||Ae(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}async function K2(e,t,n=!0){var f,d,m;if(!e)return t.debug&&h("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof I.Tensor)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input error: type not recognized");if(e instanceof I.Tensor){let y=null;if(e.isDisposedInternal)throw new Error("input error: attempted to use tensor but it is disposed");if(!e.shape)throw new 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s=o,A=r;if(s>U2&&(s=U2,A=Math.trunc(s*r/o)),A>U2&&(A=U2,s=Math.trunc(A*o/r)),(((f=t.filter)==null?void 0:f.width)||0)>0?s=t.filter.width:(((d=t.filter)==null?void 0:d.height)||0)>0&&(s=o*((t.filter.height||0)/r)),(t.filter.height||0)>0?A=t.filter.height:(t.filter.width||0)>0&&(A=r*((t.filter.width||0)/o)),!s||!A)throw new Error("input error: cannot determine dimension");(!R0||R0.width!==s||R0.height!==A)&&(R0=Ae(s,A));let a=R0.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?a.putImageData(e,0,0):t.filter.flip&&typeof a.translate!="undefined"?(a.translate(o,0),a.scale(-1,1),a.drawImage(e,0,0,o,r,0,0,R0.width,R0.height),a.setTransform(1,0,0,1,0,0)):a.drawImage(e,0,0,o,r,0,0,R0.width,R0.height),(!M0||R0.width!==M0.width||R0.height!==M0.height)&&(M0=Ae(R0.width,R0.height)),t.filter.enabled&&R.webgl.supported?(e0||(e0=R.browser?new V1:null),R.filter=!!e0,e0!=null&&e0.add?(e0.reset(),t.filter.brightness!==0&&e0.add("brightness",t.filter.brightness),t.filter.contrast!==0&&e0.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&e0.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&e0.add("blur",t.filter.blur),t.filter.saturation!==0&&e0.add("saturation",t.filter.saturation),t.filter.hue!==0&&e0.add("hue",t.filter.hue),t.filter.negative&&e0.add("negative"),t.filter.sepia&&e0.add("sepia"),t.filter.vintage&&e0.add("brownie"),t.filter.sepia&&e0.add("sepia"),t.filter.kodachrome&&e0.add("kodachrome"),t.filter.technicolor&&e0.add("technicolor"),t.filter.polaroid&&e0.add("polaroid"),t.filter.pixelate!==0&&e0.add("pixelate",t.filter.pixelate),((m=e0.get())==null?void 0:m.length)>1?M0=e0.apply(R0):M0=e0.draw(R0)):(t.debug&&h("input process error: cannot initialize filters"),R.webgl.supported=!1,t.filter.enabled=!1,Y2(R0,M0))):(Y2(R0,M0),e0&&(e0=null),R.filter=!!e0),!n)return{tensor:null,canvas:M0};if(!M0)throw new 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y.data();t.filter.brightness=u[0]>1?1-u[0]/255:1-u[0],I.dispose(y)}return{tensor:i,canvas:t.filter.return?M0:null}}async function Z1(e,t){let n=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>3840||t.shape[2]>2160)return n;if(!ne.inputTensor)ne.inputTensor=I.clone(t);else if(ne.inputTensor.shape[1]!==t.shape[1]||ne.inputTensor.shape[2]!==t.shape[2])I.dispose(ne.inputTensor),ne.inputTensor=I.clone(t);else{let o={};o.diff=I.sub(t,ne.inputTensor),o.squared=I.mul(o.diff,o.diff),o.sum=I.sum(o.squared);let s=(await o.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;I.dispose([ne.inputTensor,o.diff,o.squared,o.sum]),ne.inputTensor=I.clone(t),n=s<=(e.cacheSensitivity||0)}return n}async function q1(e,t,n){let o={};if(!t||!n||t.shape.length!==4||t.shape.length!==n.shape.length)return e.debug||h("invalid input tensor or tensor shapes do not match:",t.shape,n.shape),0;if(t.shape[0]!==1||n.shape[0]!==1||t.shape[3]!==3||n.shape[3]!==3)return e.debug||h("input tensors must 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Canvas(){return Y0(this,j2)}set Canvas(t){Re(this,j2,t),globalThis.Canvas=t}get Image(){return Y0(this,I2)}set Image(t){Re(this,I2,t),globalThis.Image=t}get ImageData(){return Y0(this,N2)}set ImageData(t){Re(this,N2,t),globalThis.ImageData=t}async updateBackend(){this.backends=Object.keys(W0.engine().registryFactory);try{this.tensorflow={version:W0.backend().binding?W0.backend().binding.TF_Version:void 0,gpu:W0.backend().binding?W0.backend().binding.isUsingGpuDevice():void 0}}catch(o){}this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&(this.wasm.simd=await W0.env().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await W0.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Ae(100,100),n=t?t.getContext("webgl2"):void 0;this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&n&&(this.webgl.version=n.getParameter(n.VERSION),this.webgl.vendor=n.getParameter(n.VENDOR),this.webgl.renderer=n.getParameter(n.RENDERER),this.webgl.shader=n.getParameter(n.SHADING_LANGUAGE_VERSION)),this.webgpu.supported=this.browser&&typeof navigator!="undefined"&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{if(this.webgpu.supported){let o=await navigator.gpu.requestAdapter();this.webgpu.adapter=await(o==null?void 0:o.requestAdapterInfo())}}catch(o){this.webgpu.supported=!1}try{this.kernels=W0.getKernelsForBackend(W0.getBackend()).map(o=>o.kernelName.toLowerCase())}catch(o){}}updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}};j2=new WeakMap,I2=new WeakMap,N2=new WeakMap;var R=new S2;var Q2=class{constructor(){w(this,"config");w(this,"element");w(this,"stream");w(this,"devices",[]);w(this,"enumerate",async()=>{try{let t=await navigator.mediaDevices.enumerateDevices();this.devices=t.filter(n=>n.kind==="videoinput")}catch(t){this.devices=[]}return this.devices});w(this,"start",async t=>{var r,s;if(t!=null&&t.debug&&(this.config.debug=t==null?void 0:t.debug),t!=null&&t.crop&&(this.config.crop=t==null?void 0:t.crop),t!=null&&t.mode&&(this.config.mode=t==null?void 0:t.mode),t!=null&&t.width&&(this.config.width=t==null?void 0:t.width),t!=null&&t.height&&(this.config.height=t==null?void 0:t.height),t!=null&&t.id&&(this.config.id=t==null?void 0:t.id),t!=null&&t.element)if(typeof t.element=="string"){let A=document.getElementById(t.element);if(A&&A instanceof HTMLVideoElement)this.element=A;else return this.config.debug&&h("webcam","cannot get dom element",t.element),`webcam error: cannot get dom element: ${t.element}`}else if(t.element instanceof 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o=t.shape[1],r=t.shape[2],s=[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r],A=Me.image.cropAndResize(t,[s],[0],n),a=Me.div(A,C.tf255);return Me.dispose(A),a},ft=(e,t)=>{let n=dt(e),o=p2(e),r=[t*o[0]/2,t*o[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence,size:o}},mt=e=>{let t=dt(e),n=p2(e),o=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-o),Math.round(t[1]-o)],endPoint:[Math.round(t[0]+o),Math.round(t[1]+o)],landmarks:e.landmarks,confidence:e.confidence,size:[Math.round(n[0]),Math.round(n[1])]}},b3=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},R5=[[1,0,0],[0,1,0],[0,0,1]],aA=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),iA=(e,t)=>aA(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var p3=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],r2=(e,t)=>{let n=0;for(let o=0;o{let n=[];for(let o=0;o{let n=[],o=e.length;for(let r=0;r{let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],s=p3(t[0],t[1]),A=u3(s,r),a=p3(-t[0],-t[1]);return u3(A,a)},cA=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-r2(t[0],n),-r2(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]},dA=(e,t)=>[r2(e,t[0]),r2(e,t[1])];function T3(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o[s[0]/r*(d[0]-r/2),s[1]/r*(d[1]-r/2),d[2]||0]),a=n&&n!==0&&Math.abs(n)>.2,l=a?g3(n,[0,0]):R5,c=a?A.map(d=>[...dA(d,l),d[2]]):A,x=a?cA(o):R5,i=dt(t),f=[r2(i,x[0]),r2(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2]||0)])}function R3(e,t,n,o){let r=t.landmarks.length>=s5.count?s5.symmetryLine:_e.symmetryLine,s=0,A=R5,a;if(e&&R.kernels.includes("rotatewithoffset"))if(s=iA(t.landmarks[r[0]],t.landmarks[r[1]]),s&&s!==0&&Math.abs(s)>.2){let c=dt(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=Me.image.rotateWithOffset(n,s,0,[x[0],x[1]]);A=g3(-s,c),a=v5(t,i,[o,o]),Me.dispose(i)}else a=v5(t,n,[o,o]);else a=v5(t,n,[o,o]);return[s,A,a]}var xA=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},M3=(e,t)=>{let n=xA(e),o=p2(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var P3=6,yA=1.4,xe,pt=null,Ce=0,u2=null,k3=()=>Ce;async function w3(e){var t;return R.initial&&(xe=null),xe?e.debug&&h("cached model:",xe.modelUrl):xe=await O((t=e.face.detector)==null?void 0:t.modelPath),Ce=xe.executor&&xe.inputs[0].shape?xe.inputs[0].shape[2]:256,u2=L.scalar(Ce,"int32"),pt=L.tensor2d(T3(Ce)),xe}function fA(e){if(!pt||!u2)return L.zeros([0,0]);let t={};t.boxStarts=L.slice(e,[0,1],[-1,2]),t.centers=L.add(t.boxStarts,pt),t.boxSizes=L.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=L.div(t.boxSizes,u2),t.centersNormalized=L.div(t.centers,u2),t.halfBoxSize=L.div(t.boxSizesNormalized,C.tf2),t.starts=L.sub(t.centersNormalized,t.halfBoxSize),t.ends=L.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=L.mul(t.starts,u2),t.endNormalized=L.mul(t.ends,u2);let n=L.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>L.dispose(t[o])),n}async function E3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=L.image.resizeBilinear(e,[Ce,Ce]),n.div=L.div(n.resized,C.tf127),n.normalized=L.sub(n.div,C.tf05);let o=xe==null?void 0:xe.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((m,y)=>m.size-y.size);n.concat384=L.concat([d[0],d[2]],2),n.concat512=L.concat([d[1],d[3]],2),n.concat=L.concat([n.concat512,n.concat384],1),n.batch=L.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=L.squeeze(o[0]):n.batch=L.squeeze(o);L.dispose(o),n.boxes=fA(n.batch),n.logits=L.slice(n.batch,[0,0],[-1,1]),n.sigmoid=L.sigmoid(n.logits),n.scores=L.squeeze(n.sigmoid),n.nms=await L.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=L.slice(n.boxes,[r[d],0],[1,-1]),y.slice=L.slice(n.batch,[r[d],P3-1],[1,-1]),y.squeeze=L.squeeze(y.slice),y.landmarks=L.reshape(y.squeeze,[P3,-1]);let u=await y.bbox.data(),T={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await y.landmarks.array(),confidence:m};y.anchor=L.slice(pt,[r[d],0],[1,2]);let g=await y.anchor.data(),p=h3(T,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce],g),b=ft(p,t.face.scale||yA),k=mt(b);k.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&k.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(k),Object.keys(y).forEach(P=>L.dispose(y[P]))}}return Object.keys(n).forEach(d=>L.dispose(n[d])),s}var ye=G(H());var K0,We=0,mA=2.3,P5=ae.leftEyeLower0,k5=ae.rightEyeLower0,h2={leftBounds:[P5[0],P5[P5.length-1]],rightBounds:[k5[0],k5[k5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function N3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&h("cached model:",K0.modelUrl):K0=await O((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function ut(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},S3=(e,t,n,o,r,s=!1)=>{let A=mt(ft(b3([e[n],e[o]]),mA)),a=p2(A),l=ye.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=ye.image.flipLeftRight(l);ye.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},j3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function O3(e,t,n){if(!(K0!=null&&K0.executor))return e;let{box:o,boxSize:r,crop:s}=S3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=S3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=ye.concat([s,l]);ye.dispose(s),ye.dispose(l);let x=K0.execute(c);ye.dispose(c);let i=await x.data();ye.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:m}=j3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:u,iris:T}=j3(y,A,a,!1),g=pA(e);Math.abs(g)<30?(ut(e,d,"left",null),ut(e,u,"right",null)):g<1?ut(e,d,"left",["EyeUpper0","EyeLower0"]):ut(e,u,"right",["EyeUpper0","EyeLower0"]);let p=I3(e,m,"left"),b=I3(e,T,"right");return e.concat(p).concat(b)}async function C3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,u)=>y+=e[u][2],0)/e2.length;for(let y=0;yy+=e[u][2],0)/t2.length;for(let y=0;yv()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await E3(e,t),ge.timestamp=v(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=W2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&h("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),_=await N.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*_[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=xt(p,e),P.boxRaw=yt(p,e),P.size=p.size,P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=N.find(n0=>n0.shape[n0.shape.length-1]===1404),$=De.reshape(Z,[-1,3]),A0=await $.array();De.dispose($),(y=t.face.attention)!=null&&y.enabled?A0=await C3(A0,N):(u=t.face.iris)!=null&&u.enabled&&(A0=await O3(A0,P.tensor,W2)),P.mesh=v3(A0,p,b,k,W2),P.meshRaw=P.mesh.map(n0=>[n0[0]/(e.shape[2]||0),n0[1]/(e.shape[1]||0),(n0[2]||0)/a]);for(let n0 of Object.keys(ae))P.annotations[n0]=ae[n0].map(j0=>P.mesh[j0]);P.score=P.faceScore;let t0={...M3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=xt(t0,e),P.boxRaw=yt(t0,e),P.size=t0.size,s.push(t0)}De.dispose(N)}P.score>(((T=t.face.detector)==null?void 0:T.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function D3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&h("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await O(e.face.attention.modelPath):r0=await O((r=e.face.mesh)==null?void 0:r.modelPath),W2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var F3=$e,B3=L2;var J0=G(H());var z5=[],P0,ht=[],H3=0,G3=0,E5=Number.MAX_SAFE_INTEGER,S5=!1;async function V3(e){var t,n,o;return R.initial&&(P0=null),P0?e.debug&&h("cached model:",P0.modelUrl):(P0=await O((t=e.face.emotion)==null?void 0:t.modelPath),S5=((o=(n=P0==null?void 0:P0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,S5?z5=["angry","disgust","fear","happy","neutral","sad","surprise"]:z5=["angry","disgust","fear","happy","sad","surprise","neutral"]),P0}async function j5(e,t,n,o){var A,a;if(!P0)return[];let r=E5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-G3;return t.skipAllowed&&s&&r&&H3===o&&ht[n]&&ht[n].length>0?(E5++,ht[n]):(E5=0,new Promise(async l=>{var x,i,f;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let d={},m=P0!=null&&P0.inputs[0].shape?P0.inputs[0].shape[2]:0;if(((i=t.face.emotion)==null?void 0:i.crop)>0){let u=(f=t.face.emotion)==null?void 0:f.crop,T=[[u,u,1-u,1-u]];d.resize=J0.image.cropAndResize(e,T,[0],[m,m])}else d.resize=J0.image.resizeBilinear(e,[m,m],!1);S5?(d.mul=J0.mul(d.resize,255),d.normalize=J0.sub(d.mul,[103.939,116.779,123.68]),d.emotion=P0==null?void 0:P0.execute(d.normalize)):(d.channels=J0.mul(d.resize,C.rgb),d.grayscale=J0.sum(d.channels,3,!0),d.grayscaleSub=J0.sub(d.grayscale,C.tf05),d.grayscaleMul=J0.mul(d.grayscaleSub,C.tf2),d.emotion=P0==null?void 0:P0.execute(d.grayscaleMul)),G3=v();let y=await d.emotion.data();for(let u=0;u(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*y[u])/100),emotion:z5[u]});c.sort((u,T)=>T.score-u.score),Object.keys(d).forEach(u=>J0.dispose(d[u]))}ht[n]=c,H3=o,l(c)}))}var ie=G(H());var k0,Fe=[],q3=0,X3=0,I5=Number.MAX_SAFE_INTEGER;async function U3(e){var t;return R.initial&&(k0=null),k0?e.debug&&h("cached model:",k0.modelUrl):k0=await O((t=e.face.description)==null?void 0:t.modelPath),k0}function hA(e,t){var s,A;let n=e.image||e.tensor||e;if(!(k0!=null&&k0.inputs[0].shape))return n;let o;if(((s=t.face.description)==null?void 0:s.crop)>0){let a=(A=t.face.description)==null?void 0:A.crop,l=[[a,a,1-a,1-a]];o=ie.image.cropAndResize(n,l,[0],[k0.inputs[0].shape[2],k0.inputs[0].shape[1]])}else o=ie.image.resizeBilinear(n,[k0.inputs[0].shape[2],k0.inputs[0].shape[1]],!1);let r=ie.mul(o,C.tf255);return ie.dispose(o),r}async function N5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(k0!=null&&k0.executor))return r;let s=I5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>v()-q3;return t.skipAllowed&&s&&A&&X3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((x=Fe==null?void 0:Fe[n])==null?void 0:x.genderScore)>0?(I5++,Fe[n]):(I5=0,new Promise(async i=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=hA(e,t),m=k0==null?void 0:k0.execute(d);q3=v(),ie.dispose(d);let u=await m.find(V=>V.shape[1]===1).data(),T=Math.trunc(200*Math.abs(u[0]-.5))/100;T>(t.face.description.minConfidence||0)&&(r.gender=u[0]<=.5?"female":"male",r.genderScore=Math.min(.99,T));let g=ie.argMax(m.find(V=>V.shape[1]===100),1),p=(await g.data())[0];ie.dispose(g);let k=await m.find(V=>V.shape[1]===100).data();r.age=Math.round(k[p-1]>k[p+1]?10*p-100*k[p-1]:10*p+100*k[p+1])/10,(Number.isNaN(u[0])||Number.isNaN(k[0]))&&h("faceres error:",{model:k0,result:m});let P=m.find(V=>V.shape[1]===1024),N=P?await 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x.data())[0];bt[n]=Math.round(100*i)/100,J3=o,Q3=v(),gt.dispose([c,x]),l(bt[n])}))}var vt=G(H());var E0,Tt=[],W5=Number.MAX_SAFE_INTEGER,en=0,tn=0;async function nn(e){var t;return R.initial&&(E0=null),E0?e.debug&&h("cached model:",E0.modelUrl):E0=await O((t=e.face.liveness)==null?void 0:t.modelPath),E0}async function D5(e,t,n,o){var A,a;if(!(E0!=null&&E0.executor))return 0;let r=(((A=t.face.liveness)==null?void 0:A.skipTime)||0)>v()-tn,s=W5<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&en===o&&Tt[n]?(W5++,Tt[n]):(W5=0,new Promise(async l=>{let c=vt.image.resizeBilinear(e,[E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[2]:0,E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[1]:0],!1),x=E0==null?void 0:E0.execute(c),i=(await x.data())[0];Tt[n]=Math.round(100*i)/100,en=o,tn=v(),vt.dispose([c,x]),l(Tt[n])}))}var Rt=G(H());var le,F5=[],TA=["white","black","asian","indian","other"],vA=[15,23,28,35.5,45.5,55.5,65],rn=0,sn=0,B5=Number.MAX_SAFE_INTEGER;async function An(e){var t;return R.initial&&(le=null),le?e.debug&&h("cached model:",le.modelUrl):le=await O((t=e.face.gear)==null?void 0:t.modelPath),le}async function H5(e,t,n,o){var A,a;if(!le)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=B5<(((A=t.face.gear)==null?void 0:A.skipFrames)||0),s=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>v()-sn;return t.skipAllowed&&s&&r&&rn===o&&F5[n]?(B5++,F5[n]):(B5=0,new Promise(async l=>{var T,g,p,b;if(!(le!=null&&le.inputs[0].shape))return;let c={},x=[[0,.1,.9,.9]];if(((T=t.face.gear)==null?void 0:T.crop)>0){let k=(g=t.face.gear)==null?void 0:g.crop;x=[[k,k,1-k,1-k]]}c.resize=Rt.image.cropAndResize(e,x,[0],[le.inputs[0].shape[2],le.inputs[0].shape[1]]);let i={age:0,gender:"unknown",genderScore:0,race:[]};(p=t.face.gear)!=null&&p.enabled&&([c.age,c.gender,c.race]=le.execute(c.resize,["age_output","gender_output","race_output"]));let f=await c.gender.data();i.gender=f[0]>f[1]?"male":"female",i.genderScore=Math.round(100*(f[0]>f[1]?f[0]:f[1]))/100;let d=await c.race.data();for(let k=0;k(((b=t.face.gear)==null?void 0:b.minConfidence)||.2)&&i.race.push({score:Math.round(100*d[k])/100,race:TA[k]});i.race.sort((k,P)=>P.score-k.score);let y=Array.from(await c.age.data()).map((k,P)=>[vA[P],k]).sort((k,P)=>P[1]-k[1]),u=y[0][0];for(let k=1;kRt.dispose(c[k])),F5[n]=i,rn=o,sn=v(),l(i)}))}var s2=G(H());var H0,Mt=[],ln=0,cn=0,G5=Number.MAX_SAFE_INTEGER;async function dn(e){return R.initial&&(H0=null),H0?e.debug&&h("cached model:",H0.modelUrl):H0=await O(e.face.ssrnet.modelPathAge),H0}async function V5(e,t,n,o){var A,a,l,c;if(!H0)return{age:0};let r=G5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>v()-cn;return t.skipAllowed&&r&&s&&ln===o&&((l=Mt[n])!=null&&l.age)&&((c=Mt[n])==null?void 0:c.age)>0?(G5++,Mt[n]):(G5=0,new Promise(async x=>{var d,m,y;if(!(H0!=null&&H0.inputs)||!H0.inputs[0]||!H0.inputs[0].shape)return;let i={};if(((d=t.face.ssrnet)==null?void 0:d.crop)>0){let u=(m=t.face.ssrnet)==null?void 0:m.crop,T=[[u,u,1-u,1-u]];i.resize=s2.image.cropAndResize(e,T,[0],[H0.inputs[0].shape[2],H0.inputs[0].shape[1]])}else i.resize=s2.image.resizeBilinear(e,[H0.inputs[0].shape[2],H0.inputs[0].shape[1]],!1);i.enhance=s2.mul(i.resize,C.tf255);let f={age:0};if((y=t.face.ssrnet)!=null&&y.enabled&&(i.age=H0.execute(i.enhance)),i.age){let u=await i.age.data();f.age=Math.trunc(10*u[0])/10}Object.keys(i).forEach(u=>s2.dispose(i[u])),Mt[n]=f,ln=o,cn=v(),x(f)}))}var v0=G(H());var N0,Pt=[],yn=0,fn=0,Z5=Number.MAX_SAFE_INTEGER,q5=[.2989,.587,.114];async function mn(e){var t;return R.initial&&(N0=null),N0?e.debug&&h("cached model:",N0.modelUrl):N0=await O((t=e.face.ssrnet)==null?void 0:t.modelPathGender),N0}async function X5(e,t,n,o){var A,a,l,c;if(!N0)return{gender:"unknown",genderScore:0};let r=Z5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>v()-fn;return t.skipAllowed&&r&&s&&yn===o&&((l=Pt[n])!=null&&l.gender)&&((c=Pt[n])==null?void 0:c.genderScore)>0?(Z5++,Pt[n]):(Z5=0,new Promise(async x=>{var m,y,u;if(!(N0!=null&&N0.inputs[0].shape))return;let i={};if(((m=t.face.ssrnet)==null?void 0:m.crop)>0){let T=(y=t.face.ssrnet)==null?void 0:y.crop,g=[[T,T,1-T,1-T]];i.resize=v0.image.cropAndResize(e,g,[0],[N0.inputs[0].shape[2],N0.inputs[0].shape[1]])}else i.resize=v0.image.resizeBilinear(e,[N0.inputs[0].shape[2],N0.inputs[0].shape[1]],!1);i.enhance=v0.tidy(()=>{var g,p;let T;if(((p=(g=N0==null?void 0:N0.inputs)==null?void 0:g[0].shape)==null?void 0:p[3])===1){let[b,k,P]=v0.split(i.resize,3,3),N=v0.mul(b,q5[0]),V=v0.mul(k,q5[1]),_=v0.mul(P,q5[2]),Z=v0.addN([N,V,_]);T=v0.mul(v0.sub(Z,C.tf05),2)}else T=v0.mul(v0.sub(i.resize,C.tf05),2);return T});let f={gender:"unknown",genderScore:0};(u=t.face.ssrnet)!=null&&u.enabled&&(i.gender=N0.execute(i.enhance));let d=await i.gender.data();f.gender=d[0]>d[1]?"female":"male",f.genderScore=d[0]>d[1]?Math.trunc(100*d[0])/100:Math.trunc(100*d[1])/100,Object.keys(i).forEach(T=>v0.dispose(i[T])),Pt[n]=f,yn=o,fn=v(),x(f)}))}var kt=G(H());var Q0,U5=[],un=0,hn=0,bn=Number.MAX_SAFE_INTEGER;async function gn(e){var t;return R.initial&&(Q0=null),Q0?e.debug&&h("cached model:",Q0.modelUrl):Q0=await O((t=e.face.mobilefacenet)==null?void 0:t.modelPath),Q0}async function Y5(e,t,n,o){var A,a;if(!(Q0!=null&&Q0.executor))return[];let r=bn<(((A=t.face.mobilefacenet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.mobilefacenet)==null?void 0:a.skipTime)||0)>v()-hn;return t.skipAllowed&&s&&r&&un===o&&U5[n]?(bn++,U5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.mobilefacenet)!=null&&x.enabled&&(Q0!=null&&Q0.inputs[0].shape)){let i={};i.crop=kt.image.resizeBilinear(e,[Q0.inputs[0].shape[2],Q0.inputs[0].shape[1]],!1),i.data=Q0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>kt.dispose(i[d]))}U5[n]=c,un=o,hn=v(),l(c)})}var wt=G(H());var _0,K5=[],vn=0,Rn=0,Mn=Number.MAX_SAFE_INTEGER;async function Pn(e){return R.initial&&(_0=null),_0?e.debug&&h("cached model:",_0.modelUrl):_0=await O(e.face.insightface.modelPath),_0}async function J5(e,t,n,o){var A,a;if(!(_0!=null&&_0.executor))return[];let r=Mn<(((A=t.face.insightface)==null?void 0:A.skipFrames)||0),s=(((a=t.face.insightface)==null?void 0:a.skipTime)||0)>v()-Rn;return t.skipAllowed&&s&&r&&vn===o&&K5[n]?(Mn++,K5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.insightface)!=null&&x.enabled&&(_0!=null&&_0.inputs[0].shape)){let i={};i.crop=wt.image.resizeBilinear(e,[_0.inputs[0].shape[2],_0.inputs[0].shape[1]],!1),i.data=_0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>wt.dispose(i[d]))}K5[n]=c,vn=o,Rn=v(),l(c)})}var RA=e=>{let t=(i,f)=>Math.atan2(i[1]-f[1],i[0]-f[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],o=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=r?e.mesh[473]:e.mesh[468],A=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],a=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(A[0]-s[0])/a[0]-n[0],o*(s[1]-A[1])/a[1]-n[1]],c=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},wn=(e,t)=>{let n=y=>{let u=Math.sqrt(y[0]*y[0]+y[1]*y[1]+y[2]*y[2]);return y[0]/=u,y[1]/=u,y[2]/=u,y},o=(y,u)=>{let T=y[0]-u[0],g=y[1]-u[1],p=y[2]-u[2];return[T,g,p]},r=(y,u)=>{let T=y[1]*u[2]-y[2]*u[1],g=y[2]*u[0]-y[0]*u[2],p=y[0]*u[1]-y[1]*u[0];return[T,g,p]},s=y=>{let[u,T,g,p,b,k,P,N,V]=y,_,Z,$;return 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this.handDetector.predict(t,n),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...V,confidence:b};let _={landmarks:N,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(_)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let x=St(jt(c),Jn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var $n={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,n1;function HA(){let e=l2?new It(l2):void 0;e&&c2&&(n1=new Nt(e,c2))}async function o1(e,t){n1||HA();let n=await n1.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Et(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function eo(e){var t;return R.initial&&(l2=null),l2?e.debug&&h("cached model:",l2.modelUrl):l2=await O((t=e.hand.detector)==null?void 0:t.modelPath),l2}async function to(e){var t;return R.initial&&(c2=null),c2?e.debug&&h("cached model:",c2.modelUrl):c2=await O((t=e.hand.skeleton)==null?void 0:t.modelPath),c2}var Q=G(H());var p0=[null,null],GA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Xe=[[0,0],[0,0]],VA=["hand","fist","pinch","point","face","tip","pinchtip"],oo=4,ro=1.6,ZA=512,qA=1.4,Ot=Number.MAX_SAFE_INTEGER,r1=0,we=[0,0],m0={boxes:[],hands:[]},so={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function Ao(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&h("cached model:",p0[0].modelUrl);else{_2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await O((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;Xe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function ao(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&h("cached model:",p0[1].modelUrl);else{p0[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;Xe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function XA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,ZA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,GA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[oo]),a.splice(oo,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=Q.slice(o.boxes,f,1),m=await d.data();Q.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],u=at(y,qA),T=[Math.trunc(y[0]*we[0]),Math.trunc(y[1]*we[1]),Math.trunc(y[2]*we[0]),Math.trunc(y[3]*we[1])],g=x[f],p=VA[i[f]],b={id:l++,score:g,box:T,boxRaw:u,label:p};n.push(b)}return Object.keys(o).forEach(f=>Q.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function s1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[we[0]*(i[0]+t.boxRaw[0]),we[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Et(o.keypoints);for(let i of Object.keys(so))o.annotations[i]=so[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function A1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];we=[e.shape[2]||0,e.shape[1]||0],Ot++;let n=(t.hand.skipTime||0)>v()-r1,o=Ot<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-r1,l=Ot<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>s1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>s1(e,x,t))):(m0.boxes=await XA(e,t),r1=v(),m0.hands=await Promise.all(m0.boxes.map(x=>s1(e,x,t))),Ot=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=at(i.box,ro),d=at(i.boxRaw,ro);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var F2={};ze(F2,{connected:()=>Ct,horizontal:()=>a1,kpt:()=>Lt,relative:()=>l1,vertical:()=>i1});var Lt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],a1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],i1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],l1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Ct={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=Te(),c1=0;function lo(e,t){var A,a,l,c,x,i,f,d,m,y,u,T,g,p,b,k,P,N,V,_,Z,$,A0,t0,n0,j0;let n=v();if(!e)return Te();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let M=0;M((r-1)*z.body[M].box[q]+B)/r),C0=e.body[M].boxRaw.map((B,q)=>((r-1)*z.body[M].boxRaw[q]+B)/r),x0=e.body[M].keypoints.map((B,q)=>{var 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M=0;M((r-1)*z.face[M].box[E]+K)/r),C0=e.face[M].boxRaw.map((K,E)=>((r-1)*z.face[M].boxRaw[E]+K)/r),x0=e.face[M].annotations;if(Object.keys(z.face[M].annotations).length!==Object.keys(e.face[M].annotations).length)z.face[M].annotations=e.face[M].annotations,x0=z.face[M].annotations;else if(e.face[M].annotations)for(let K of Object.keys(e.face[M].annotations))x0[K]=(m=(d=(f=e.face[M])==null?void 0:f.annotations)==null?void 0:d[K])!=null&&m[0]?e.face[M].annotations[K].map((E,B)=>E.map((q,U0)=>((r-1)*z.face[M].annotations[K][B][U0]+q)/r)):null;if(e.face[M].rotation){let K={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};K.matrix=(y=e.face[M].rotation)==null?void 0:y.matrix,K.angle={roll:((r-1)*(((T=(u=z.face[M].rotation)==null?void 0:u.angle)==null?void 0:T.roll)||0)+(((p=(g=e.face[M].rotation)==null?void 0:g.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((k=(b=z.face[M].rotation)==null?void 0:b.angle)==null?void 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M=e.persons;if(!z.persons||M.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(M));else for(let T0=0;T0((r-1)*z.persons[T0].box[x0]+C0)/r)}e.gesture&&(z.gesture=e.gesture),z.width=e.width,z.height=e.height;let s=v();return c1=R.perfadd?c1+Math.round(s-n):Math.round(s-n),e.performance&&(z.performance={...e.performance,interpolate:c1}),z}var s0=G(H());var O0;async function d1(e){return!O0||R.initial?O0=await O(e.segmentation.modelPath):e.debug&&h("cached model:",O0.modelUrl),O0}async function co(e,t){var r;if(O0||(O0=await d1(t)),!(O0!=null&&O0.executor)||!((r=O0==null?void 0:O0.inputs)!=null&&r[0].shape))return null;let n={};n.resize=s0.image.resizeBilinear(e,[O0.inputs[0].shape?O0.inputs[0].shape[1]:0,O0.inputs[0].shape?O0.inputs[0].shape[2]:0],!1),n.norm=s0.div(n.resize,C.tf255),n.res=O0.execute(n.norm),n.squeeze=s0.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=s0.unstack(n.squeeze,2),n.fg=s0.softmax(n.fgRaw),n.mul=s0.mul(n.fg,C.tf255),n.expand=s0.expandDims(n.mul,2),n.output=s0.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=s0.squeeze(e),n.concat=s0.concat([n.input,n.output],-1),o=s0.cast(n.concat,"int32");break;case"alpha":o=s0.cast(n.output,"int32");break;default:o=s0.tensor(0)}return Object.keys(n).forEach(s=>s0.dispose(n[s])),o}var Wt={};ze(Wt,{distance:()=>x1,find:()=>KA,similarity:()=>YA});function x1(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let r=0;r{if(e===0)return 1;let s=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return Math.max(Math.min(s,1),0)};function 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l=[s[51+1],s[51+0],s[51+3]-s[51+1],s[51+2]-s[51+0]],c=[Math.trunc(l[0]*(n.shape[2]||0)),Math.trunc(l[1]*(n.shape[1]||0)),Math.trunc(l[2]*(n.shape[2]||0)),Math.trunc(l[3]*(n.shape[1]||0))],x={};for(let[f,d]of Object.entries(Ct)){let m=[];for(let y=0;yg.part===d[y]),T=a.find(g=>g.part===d[y+1]);u&&T&&u.score>(t.body.minConfidence||0)&&T.score>(t.body.minConfidence||0)&&m.push([u.position,T.position])}x[f]=m}let i={id:r,score:A,box:c,boxRaw:l,keypoints:[...a],annotations:x};y1(i),o.push(i)}}return o.sort((r,s)=>s.score-r.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function m1(e,t){var r;if(!(b0!=null&&b0.executor)||!((r=b0==null?void 0:b0.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(d2.boxes.length=0),f1++;let n=(t.body.skipTime||0)>v()-d2.last,o=f1<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?d2.bodies:new Promise(async s=>{let A={};f1=0,A.input=po(e,Dt),A.res=b0==null?void 0:b0.execute(A.input),d2.last=v();let a=await 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G2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],ea=G2.length,H2=G2.reduce((e,t,n)=>(e[t]=n,e),{}),ta=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],q7=ta.map(([e,t])=>[H2[e],H2[t]]),Ro=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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a=T=>({y:s.get(T.y,T.x,e),x:s.get(T.y,T.x,s.shape[2]/2+e)}),l=(T,g,p)=>({y:g1(Math.round(T.y/M2),0,g-1),x:g1(Math.round(T.x/M2),0,p-1)}),[c,x]=o.shape,i=l(t.position,c,x),f=a(i),m=T1(t.position,f);for(let T=0;T[H2[f],H2[d]]),A=s.map(([,f])=>f),a=s.map(([f])=>f),l=t.shape[2],c=A.length,x=new Array(l),i=b1(e.part,M2,n);x[e.part.id]={score:e.score,part:G2[e.part.id],position:i};for(let f=c-1;f>=0;--f){let d=A[f],m=a[f];x[d]&&!x[m]&&(x[m]=wo(f,x[d],m,t,n,r))}for(let f=0;ft){a=!1;break}if(!a)break}return a}function aa(e,t){let[n,o,r]=t.shape,s=new Gt(n*o*r,({score:A})=>A);for(let A=0;A{var A;let s=(A=r[o])==null?void 0:A.position;return s?ko(n,t,s.y,s.x)<=ra:!1})}function ia(e,t){return t.reduce((o,{position:r,score:s},A)=>(Eo(e,r,A)||(o+=s),o),0)/t.length}function la(e,t,n,o,r,s){let A=[],a=aa(s,t);for(;A.lengthd.score>s);let i=ia(A,x),f=Mo(x);i>s&&A.push({keypoints:x,box:f,score:Math.round(100*i)/100})}return A}async function v1(e,t){if(!(te!=null&&te.executor))return[];let 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n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?w3(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)!=null&&o.enabled)&&!this.models.antispoof?_3(this.instance.config):null,n.liveness=this.instance.config.face.enabled&&((r=this.instance.config.face.liveness)!=null&&r.enabled)&&!this.models.liveness?nn(this.instance.config):null,n.faceres=this.instance.config.face.enabled&&((s=this.instance.config.face.description)!=null&&s.enabled)&&!this.models.faceres?U3(this.instance.config):null,n.emotion=this.instance.config.face.enabled&&((A=this.instance.config.face.emotion)!=null&&A.enabled)&&!this.models.emotion?V3(this.instance.config):null,n.iris=this.instance.config.face.enabled&&((a=this.instance.config.face.iris)!=null&&a.enabled)&&!((l=this.instance.config.face.attention)!=null&&l.enabled)&&!this.models.iris?N3(this.instance.config):null,n.facemesh=this.instance.config.face.enabled&&((c=this.instance.config.face.mesh)!=null&&c.enabled)&&!this.models.facemesh?D3(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((x=this.instance.config.face.gear)!=null&&x.enabled)&&!this.models.gear?An(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((i=this.instance.config.face.ssrnet)!=null&&i.enabled)&&!this.models.ssrnetage?dn(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((f=this.instance.config.face.ssrnet)!=null&&f.enabled)&&!this.models.ssrnetgender?mn(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((d=this.instance.config.face.mobilefacenet)!=null&&d.enabled)&&!this.models.mobilefacenet?gn(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((m=this.instance.config.face.insightface)!=null&&m.enabled)&&!this.models.insightface?Pn(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((y=this.instance.config.body.modelPath)!=null&&y.includes("blazepose"))?i3(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?a3(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((u=this.instance.config.body.modelPath)!=null&&u.includes("efficientpose"))?f3(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((T=this.instance.config.body.modelPath)!=null&&T.includes("movenet"))?ho(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((g=this.instance.config.body.modelPath)!=null&&g.includes("posenet"))?zo(this.instance.config):null,n.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((b=(p=this.instance.config.hand.detector)==null?void 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Object.entries(n))j0!=null&&j0.then&&j0.then(M=>this.models[n0]=M);await Promise.all(Object.values(n))}list(){let t=Object.keys(this.models).map(n=>{var o;return{name:n,loaded:this.models[n]!==null,size:0,url:this.models[n]?(o=this.models[n])==null?void 0:o.modelUrl:null}});for(let n of t){let o=Object.keys(I0).find(r=>r.startsWith(n.name));o&&(n.size=I0[o].sizeLoadedWeights,n.url=I0[o].url)}return t}loaded(){return this.list().filter(o=>o.loaded).map(o=>o.name)}validate(){let t=[];for(let n of Object.keys(this.models)){let o=this.models[n];if(!o)continue;let r=Zt(this.instance,o,n);r&&t.push(r)}return t}};function Wo(e,t,n,o,r){var a,l,c,x,i,f;let s=0,A=[];for(let d of e){let m={id:s++,face:d,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let b of t)d.box[0]>b.box[0]&&d.box[0]b.box[1]&&d.box[1]+d.box[3]m.body.box[0]&&b.box[0]+b.box[2]m.body.box[1]&&b.box[1]+b.box[3]m.body.box[0]&&b.box[1]+b.box[3]>m.body.box[1]&&b.box[1]+b.box[3]{b&&b.length===4&&(y.push(b[0],b[0]+b[2]),u.push(b[1],b[1]+b[3]))};T(m.face.box),T((x=m.body)==null?void 0:x.box),T((i=m.hands.left)==null?void 0:i.box),T((f=m.hands.right)==null?void 0:f.box);let g=Math.min(...y),p=Math.min(...u);m.box=[g,p,Math.max(...y)-g,Math.max(...u)-p],r!=null&&r[1]&&(r!=null&&r[2])&&(m.boxRaw=[m.box[0]/r[2],m.box[1]/r[1],m.box[2]/r[2],m.box[3]/r[1]]),A.push(m)}return A}var d0=G(H());var qt=` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var i5=0;function Ys(e,t,n){let o=a0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round",r.font=o.font;for(let s=0;sd5,kpt:()=>c5});var c5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],d5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var D=G(H());var se,n2=224,e3,Qs=5,At=[8,16,32,32,32];function _s(){let e=[],t=0;for(;tn.x)),y:D.tensor1d(e.map(n=>n.y))}}async function t3(e){if(R.initial&&(se=null),!se&&e.body.detector&&e.body.detector.modelPath){se=await O(e.body.detector.modelPath);let t=se!=null&&se.executor?Object.values(se.modelSignature.inputs):void 0;n2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&se&&h("cached model:",se.modelUrl);return _s(),se}var $1=[5,5];function $s(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,n2),t.x),r=D.add(D.div(r,n2),t.y),s=D.mul(D.div(s,n2),$1[0]),A=D.mul(D.div(A,n2),$1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),x=D.add(l,A);return D.stack([a,l,c,x],1)})}async function eA(e,t,n,o){var c,x;let r=[],s={};s.boxes=$s(e,e3),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 0:c.minConfidence)||.1,((x=n.body.detector)==null?void 0:x.iouThreshold)||.1);let A=await s.nms.data(),a=await s.scores.data(),l=await s.boxes.array();for(let i of Array.from(A)){let f=a[i],d=l[i],m=[Math.round(d[0]*o[0]),Math.round(d[1]*o[1]),Math.round(d[2]*o[0]),Math.round(d[3]*o[1])],y={score:f,boxRaw:d,box:m};r.push(y)}return Object.keys(s).forEach(i=>D.dispose(s[i])),r}async function n3(e,t,n){let o={};o.res=se==null?void 0:se.execute(e,["Identity"]),o.logitsRaw=D.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=D.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=D.squeeze(o.logitsRaw),o.boxes=D.squeeze(o.boxesRaw);let r=await eA(o.boxes,o.logits,t,n);return Object.keys(o).forEach(s=>D.dispose(o[s])),r}function Oe(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[o[0],o[1],r[0]-o[0],r[1]-o[1]],A=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:A}}function o3(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function at(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var Z0,y5=256,x5=Number.MAX_SAFE_INTEGER,tA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},lt=[],Le=[[0,0],[0,0],[0,0],[0,0]],r3=0,s3=e=>1-1/(1+Math.exp(e)),a3=e=>t3(e);async function i3(e){if(R.initial&&(Z0=null),Z0)e.debug&&h("cached model:",Z0.modelUrl);else{Z0=await O(e.body.modelPath);let t=Z0!=null&&Z0.executor?Object.values(Z0.modelSignature.inputs):void 0;y5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Z0}function A3(e,t,n){var s,A;let o={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;let r;if(n&&(o.cropped=q0.image.cropAndResize(e,[n],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],l=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Le=[[0,0],a,l,[0,0]],o.pad=q0.pad(o.cropped||e,Le),o.resize=q0.image.resizeBilinear(o.pad,[t,t]),r=q0.div(o.resize,C.tf255)}else e.shape[1]!==t?(o.resize=q0.image.resizeBilinear(o.cropped||e,[t,t]),r=q0.div(o.resize,C.tf255)):r=q0.div(o.cropped||e,C.tf255);return Object.keys(o).forEach(a=>q0.dispose(o[a])),r}function nA(e,t,n){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+Le[2][0]+Le[2][1])/t[0]-Le[2][0]),Math.trunc(o.position[1]*(t[1]+Le[1][0]+Le[1][1])/t[1]-Le[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(n){let o=n[2]-n[0],r=n[3]-n[1];for(let s of e)s.positionRaw=[s.positionRaw[0]/r+n[1],s.positionRaw[1]/o+n[0],s.positionRaw[2]],s.position=[Math.trunc(s.positionRaw[0]*t[0]),Math.trunc(s.positionRaw[1]*t[1]),s.positionRaw[2]]}return e}function oA(e){let t=e.find(a=>a.part==="leftPalm"),n=e.find(a=>a.part==="leftWrist"),o=e.find(a=>a.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),s=e.find(a=>a.part==="rightWrist"),A=e.find(a=>a.part==="rightIndex");r.position[2]=((s.position[2]||0)+(A.position[2]||0))/2}async function rA(e,t,n){if(!(Z0!=null&&Z0.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=Z0==null?void 0:Z0.execute(e,tA.landmarks);let r=(await o.poseflag.data())[0],s=await o.ld.data(),A=await o.world.data();Object.keys(o).forEach(m=>q0.dispose(o[m]));let a=[],l=5;for(let m=0;mm.position),i=Oe(x,[n[0],n[1]]),f={};for(let[m,y]of Object.entries(d5)){let u=[];for(let T=0;Tb.part===y[T]),p=c.find(b=>b.part===y[T+1]);g&&p&&u.push([g.position,p.position])}f[m]=u}return{id:0,score:Math.trunc(100*r)/100,box:i.box,boxRaw:i.boxRaw,keypoints:c,annotations:f}}async function f5(e,t){var s,A,a;let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>v()-r3,r=x5<(t.body.skipFrames||0);if(t.skipAllowed&&o&&r&<!==null)x5++;else{let l=[];if((A=(s=t.body)==null?void 0:s.detector)!=null&&A.enabled){let c=A3(e,224);l=await n3(c,t,n),q0.dispose(c)}else l=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let c=0;cF0.dispose(o[c])),r}async function u5(e,t){if(!(X0!=null&&X0.executor))return[];let n=(t.object.skipTime||0)>v()-c3,o=p5<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&m5.length>0?(p5++,m5):(p5=0,new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],A=F0.image.resizeBilinear(e,[o2,o2]),a=t.object.enabled?X0==null?void 0:X0.execute(A,["tower_0/detections"]):null;c3=v(),F0.dispose(A);let l=await sA(a,s,t);m5=l,r(l)}))}var J=G(H());var ct={};ze(ct,{connected:()=>b5,kpt:()=>h5});var h5=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],b5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var i0,y3=0,B0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},g5=Number.MAX_SAFE_INTEGER;async function f3(e){return R.initial&&(i0=null),i0?e.debug&&h("cached model:",i0.modelUrl):i0=await O(e.body.modelPath),i0}async function AA(e,t){let[n,o]=e.shape,r=J.reshape(e,[o*n]),s=J.max(r,0),A=(await s.data())[0];if(A>t){let a=J.argMax(r,0),l=J.mod(a,n),c=(await l.data())[0],x=J.div(a,n),i=(await x.data())[0];return J.dispose([r,s,a,l,x]),[c,i,A]}return J.dispose([r,s]),[0,0,A]}async function T5(e,t){if(!(i0!=null&&i0.executor)||!(i0!=null&&i0.inputs[0].shape))return[];let n=(t.body.skipTime||0)>v()-y3,o=g5<(t.body.skipFrames||0);return t.skipAllowed&&n&&o&&Object.keys(B0.keypoints).length>0?(g5++,[B0]):(g5=0,new Promise(async r=>{let s=J.tidy(()=>{var m,y;let i=J.image.resizeBilinear(e,[((m=i0==null?void 0:i0.inputs[0].shape)==null?void 0:m[2])||0,((y=i0==null?void 0:i0.inputs[0].shape)==null?void 0:y[1])||0],!1),f=J.mul(i,C.tf2);return J.sub(f,C.tf1)}),A;if(t.body.enabled&&(A=i0==null?void 0:i0.execute(s)),y3=v(),J.dispose(s),A){B0.keypoints.length=0;let i=J.squeeze(A);J.dispose(A);let f=J.unstack(i,2);J.dispose(i);for(let d=0;d(t.body.minConfidence||0)&&B0.keypoints.push({score:Math.round(100*u)/100,part:h5[d],positionRaw:[m/i0.inputs[0].shape[2],y/i0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/i0.inputs[0].shape[2]),Math.round(e.shape[1]*y/i0.inputs[0].shape[1])]})}f.forEach(d=>J.dispose(d))}B0.score=B0.keypoints.reduce((i,f)=>f.score>i?f.score:i,0);let a=B0.keypoints.map(i=>i.position[0]),l=B0.keypoints.map(i=>i.position[1]);B0.box=[Math.min(...a),Math.min(...l),Math.max(...a)-Math.min(...a),Math.max(...l)-Math.min(...l)];let c=B0.keypoints.map(i=>i.positionRaw[0]),x=B0.keypoints.map(i=>i.positionRaw[1]);B0.boxRaw=[Math.min(...c),Math.min(...x),Math.max(...c)-Math.min(...c),Math.max(...x)-Math.min(...x)];for(let[i,f]of Object.entries(b5)){let d=[];for(let m=0;mT.part===f[m]),u=B0.keypoints.find(T=>T.part===f[m+1]);y&&u&&y.score>(t.body.minConfidence||0)&&u.score>(t.body.minConfidence||0)&&d.push([y.position,u.position])}B0.annotations[i]=d}r([B0])}))}var l0=G(H());var De=G(H());var 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o=t.shape[1],r=t.shape[2],s=[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r],A=Me.image.cropAndResize(t,[s],[0],n),a=Me.div(A,C.tf255);return Me.dispose(A),a},ft=(e,t)=>{let n=dt(e),o=p2(e),r=[t*o[0]/2,t*o[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence,size:o}},mt=e=>{let t=dt(e),n=p2(e),o=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-o),Math.round(t[1]-o)],endPoint:[Math.round(t[0]+o),Math.round(t[1]+o)],landmarks:e.landmarks,confidence:e.confidence,size:[Math.round(n[0]),Math.round(n[1])]}},b3=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},R5=[[1,0,0],[0,1,0],[0,0,1]],aA=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),iA=(e,t)=>aA(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var p3=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],r2=(e,t)=>{let n=0;for(let o=0;o{let n=[];for(let o=0;o{let n=[],o=e.length;for(let r=0;r{let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],s=p3(t[0],t[1]),A=u3(s,r),a=p3(-t[0],-t[1]);return u3(A,a)},cA=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-r2(t[0],n),-r2(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]},dA=(e,t)=>[r2(e,t[0]),r2(e,t[1])];function T3(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o[s[0]/r*(d[0]-r/2),s[1]/r*(d[1]-r/2),d[2]||0]),a=n&&n!==0&&Math.abs(n)>.2,l=a?g3(n,[0,0]):R5,c=a?A.map(d=>[...dA(d,l),d[2]]):A,x=a?cA(o):R5,i=dt(t),f=[r2(i,x[0]),r2(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2]||0)])}function R3(e,t,n,o){let r=t.landmarks.length>=s5.count?s5.symmetryLine:_e.symmetryLine,s=0,A=R5,a;if(e&&R.kernels.includes("rotatewithoffset"))if(s=iA(t.landmarks[r[0]],t.landmarks[r[1]]),s&&s!==0&&Math.abs(s)>.2){let c=dt(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=Me.image.rotateWithOffset(n,s,0,[x[0],x[1]]);A=g3(-s,c),a=v5(t,i,[o,o]),Me.dispose(i)}else a=v5(t,n,[o,o]);else a=v5(t,n,[o,o]);return[s,A,a]}var xA=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},M3=(e,t)=>{let n=xA(e),o=p2(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var P3=6,yA=1.4,xe,pt=null,Ce=0,u2=null,k3=()=>Ce;async function w3(e){var t;return R.initial&&(xe=null),xe?e.debug&&h("cached model:",xe.modelUrl):xe=await O((t=e.face.detector)==null?void 0:t.modelPath),Ce=xe.executor&&xe.inputs[0].shape?xe.inputs[0].shape[2]:256,u2=L.scalar(Ce,"int32"),pt=L.tensor2d(T3(Ce)),xe}function fA(e){if(!pt||!u2)return L.zeros([0,0]);let t={};t.boxStarts=L.slice(e,[0,1],[-1,2]),t.centers=L.add(t.boxStarts,pt),t.boxSizes=L.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=L.div(t.boxSizes,u2),t.centersNormalized=L.div(t.centers,u2),t.halfBoxSize=L.div(t.boxSizesNormalized,C.tf2),t.starts=L.sub(t.centersNormalized,t.halfBoxSize),t.ends=L.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=L.mul(t.starts,u2),t.endNormalized=L.mul(t.ends,u2);let n=L.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>L.dispose(t[o])),n}async function E3(e,t){var a,l,c,x,i,f;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=L.image.resizeBilinear(e,[Ce,Ce]),n.div=L.div(n.resized,C.tf127),n.normalized=L.sub(n.div,C.tf05);let o=xe==null?void 0:xe.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let d=o.sort((m,y)=>m.size-y.size);n.concat384=L.concat([d[0],d[2]],2),n.concat512=L.concat([d[1],d[3]],2),n.concat=L.concat([n.concat512,n.concat384],1),n.batch=L.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=L.squeeze(o[0]):n.batch=L.squeeze(o);L.dispose(o),n.boxes=fA(n.batch),n.logits=L.slice(n.batch,[0,0],[-1,1]),n.sigmoid=L.sigmoid(n.logits),n.scores=L.squeeze(n.sigmoid),n.nms=await L.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),s=[],A=await n.scores.data();for(let d=0;d(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let y={};y.bbox=L.slice(n.boxes,[r[d],0],[1,-1]),y.slice=L.slice(n.batch,[r[d],P3-1],[1,-1]),y.squeeze=L.squeeze(y.slice),y.landmarks=L.reshape(y.squeeze,[P3,-1]);let u=await y.bbox.data(),T={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await y.landmarks.array(),confidence:m};y.anchor=L.slice(pt,[r[d],0],[1,2]);let g=await y.anchor.data(),p=h3(T,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce],g),b=ft(p,t.face.scale||yA),k=mt(b);k.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&k.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(k),Object.keys(y).forEach(P=>L.dispose(y[P]))}}return Object.keys(n).forEach(d=>L.dispose(n[d])),s}var ye=G(H());var K0,We=0,mA=2.3,P5=ae.leftEyeLower0,k5=ae.rightEyeLower0,h2={leftBounds:[P5[0],P5[P5.length-1]],rightBounds:[k5[0],k5[k5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function N3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&h("cached model:",K0.modelUrl):K0=await O((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function ut(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},S3=(e,t,n,o,r,s=!1)=>{let A=mt(ft(b3([e[n],e[o]]),mA)),a=p2(A),l=ye.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=ye.image.flipLeftRight(l);ye.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},j3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function O3(e,t,n){if(!(K0!=null&&K0.executor))return e;let{box:o,boxSize:r,crop:s}=S3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=S3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=ye.concat([s,l]);ye.dispose(s),ye.dispose(l);let x=K0.execute(c);ye.dispose(c);let i=await x.data();ye.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:m}=j3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:u,iris:T}=j3(y,A,a,!1),g=pA(e);Math.abs(g)<30?(ut(e,d,"left",null),ut(e,u,"right",null)):g<1?ut(e,d,"left",["EyeUpper0","EyeLower0"]):ut(e,u,"right",["EyeUpper0","EyeLower0"]);let p=I3(e,m,"left"),b=I3(e,T,"right");return e.concat(p).concat(b)}async function C3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,u)=>y+=e[u][2],0)/e2.length;for(let y=0;yy+=e[u][2],0)/t2.length;for(let y=0;yv()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await E3(e,t),ge.timestamp=v(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=W2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&h("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),_=await N.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*_[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=xt(p,e),P.boxRaw=yt(p,e),P.size=p.size,P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=N.find(n0=>n0.shape[n0.shape.length-1]===1404),$=De.reshape(Z,[-1,3]),A0=await $.array();De.dispose($),(y=t.face.attention)!=null&&y.enabled?A0=await C3(A0,N):(u=t.face.iris)!=null&&u.enabled&&(A0=await O3(A0,P.tensor,W2)),P.mesh=v3(A0,p,b,k,W2),P.meshRaw=P.mesh.map(n0=>[n0[0]/(e.shape[2]||0),n0[1]/(e.shape[1]||0),(n0[2]||0)/a]);for(let n0 of Object.keys(ae))P.annotations[n0]=ae[n0].map(j0=>P.mesh[j0]);P.score=P.faceScore;let t0={...M3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=xt(t0,e),P.boxRaw=yt(t0,e),P.size=t0.size,s.push(t0)}De.dispose(N)}P.score>(((T=t.face.detector)==null?void 0:T.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function D3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&h("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await O(e.face.attention.modelPath):r0=await O((r=e.face.mesh)==null?void 0:r.modelPath),W2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var F3=$e,B3=L2;var J0=G(H());var z5=[],P0,ht=[],H3=0,G3=0,E5=Number.MAX_SAFE_INTEGER,S5=!1;async function V3(e){var t,n,o;return R.initial&&(P0=null),P0?e.debug&&h("cached model:",P0.modelUrl):(P0=await O((t=e.face.emotion)==null?void 0:t.modelPath),S5=((o=(n=P0==null?void 0:P0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,S5?z5=["angry","disgust","fear","happy","neutral","sad","surprise"]:z5=["angry","disgust","fear","happy","sad","surprise","neutral"]),P0}async function j5(e,t,n,o){var A,a;if(!P0)return[];let r=E5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-G3;return t.skipAllowed&&s&&r&&H3===o&&ht[n]&&ht[n].length>0?(E5++,ht[n]):(E5=0,new Promise(async l=>{var x,i,f;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let d={},m=P0!=null&&P0.inputs[0].shape?P0.inputs[0].shape[2]:0;if(((i=t.face.emotion)==null?void 0:i.crop)>0){let u=(f=t.face.emotion)==null?void 0:f.crop,T=[[u,u,1-u,1-u]];d.resize=J0.image.cropAndResize(e,T,[0],[m,m])}else d.resize=J0.image.resizeBilinear(e,[m,m],!1);S5?(d.mul=J0.mul(d.resize,255),d.normalize=J0.sub(d.mul,[103.939,116.779,123.68]),d.emotion=P0==null?void 0:P0.execute(d.normalize)):(d.channels=J0.mul(d.resize,C.rgb),d.grayscale=J0.sum(d.channels,3,!0),d.grayscaleSub=J0.sub(d.grayscale,C.tf05),d.grayscaleMul=J0.mul(d.grayscaleSub,C.tf2),d.emotion=P0==null?void 0:P0.execute(d.grayscaleMul)),G3=v();let y=await d.emotion.data();for(let u=0;u(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*y[u])/100),emotion:z5[u]});c.sort((u,T)=>T.score-u.score),Object.keys(d).forEach(u=>J0.dispose(d[u]))}ht[n]=c,H3=o,l(c)}))}var ie=G(H());var k0,Fe=[],q3=0,X3=0,I5=Number.MAX_SAFE_INTEGER;async function U3(e){var t;return R.initial&&(k0=null),k0?e.debug&&h("cached model:",k0.modelUrl):k0=await O((t=e.face.description)==null?void 0:t.modelPath),k0}function hA(e,t){var s,A;let n=e.image||e.tensor||e;if(!(k0!=null&&k0.inputs[0].shape))return n;let o;if(((s=t.face.description)==null?void 0:s.crop)>0){let a=(A=t.face.description)==null?void 0:A.crop,l=[[a,a,1-a,1-a]];o=ie.image.cropAndResize(n,l,[0],[k0.inputs[0].shape[2],k0.inputs[0].shape[1]])}else o=ie.image.resizeBilinear(n,[k0.inputs[0].shape[2],k0.inputs[0].shape[1]],!1);let r=ie.mul(o,C.tf255);return ie.dispose(o),r}async function N5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(k0!=null&&k0.executor))return r;let s=I5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>v()-q3;return t.skipAllowed&&s&&A&&X3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((x=Fe==null?void 0:Fe[n])==null?void 0:x.genderScore)>0?(I5++,Fe[n]):(I5=0,new Promise(async i=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=hA(e,t),m=k0==null?void 0:k0.execute(d);q3=v(),ie.dispose(d);let u=await m.find(V=>V.shape[1]===1).data(),T=Math.trunc(200*Math.abs(u[0]-.5))/100;T>(t.face.description.minConfidence||0)&&(r.gender=u[0]<=.5?"female":"male",r.genderScore=Math.min(.99,T));let g=ie.argMax(m.find(V=>V.shape[1]===100),1),p=(await g.data())[0];ie.dispose(g);let k=await m.find(V=>V.shape[1]===100).data();r.age=Math.round(k[p-1]>k[p+1]?10*p-100*k[p-1]:10*p+100*k[p+1])/10,(Number.isNaN(u[0])||Number.isNaN(k[0]))&&h("faceres error:",{model:k0,result:m});let P=m.find(V=>V.shape[1]===1024),N=P?await 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x.data())[0];bt[n]=Math.round(100*i)/100,J3=o,Q3=v(),gt.dispose([c,x]),l(bt[n])}))}var vt=G(H());var E0,Tt=[],W5=Number.MAX_SAFE_INTEGER,en=0,tn=0;async function nn(e){var t;return R.initial&&(E0=null),E0?e.debug&&h("cached model:",E0.modelUrl):E0=await O((t=e.face.liveness)==null?void 0:t.modelPath),E0}async function D5(e,t,n,o){var A,a;if(!(E0!=null&&E0.executor))return 0;let r=(((A=t.face.liveness)==null?void 0:A.skipTime)||0)>v()-tn,s=W5<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&en===o&&Tt[n]?(W5++,Tt[n]):(W5=0,new Promise(async l=>{let c=vt.image.resizeBilinear(e,[E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[2]:0,E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[1]:0],!1),x=E0==null?void 0:E0.execute(c),i=(await x.data())[0];Tt[n]=Math.round(100*i)/100,en=o,tn=v(),vt.dispose([c,x]),l(Tt[n])}))}var Rt=G(H());var le,F5=[],TA=["white","black","asian","indian","other"],vA=[15,23,28,35.5,45.5,55.5,65],rn=0,sn=0,B5=Number.MAX_SAFE_INTEGER;async function An(e){var t;return R.initial&&(le=null),le?e.debug&&h("cached model:",le.modelUrl):le=await O((t=e.face.gear)==null?void 0:t.modelPath),le}async function H5(e,t,n,o){var A,a;if(!le)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=B5<(((A=t.face.gear)==null?void 0:A.skipFrames)||0),s=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>v()-sn;return t.skipAllowed&&s&&r&&rn===o&&F5[n]?(B5++,F5[n]):(B5=0,new Promise(async l=>{var T,g,p,b;if(!(le!=null&&le.inputs[0].shape))return;let c={},x=[[0,.1,.9,.9]];if(((T=t.face.gear)==null?void 0:T.crop)>0){let k=(g=t.face.gear)==null?void 0:g.crop;x=[[k,k,1-k,1-k]]}c.resize=Rt.image.cropAndResize(e,x,[0],[le.inputs[0].shape[2],le.inputs[0].shape[1]]);let i={age:0,gender:"unknown",genderScore:0,race:[]};(p=t.face.gear)!=null&&p.enabled&&([c.age,c.gender,c.race]=le.execute(c.resize,["age_output","gender_output","race_output"]));let f=await c.gender.data();i.gender=f[0]>f[1]?"male":"female",i.genderScore=Math.round(100*(f[0]>f[1]?f[0]:f[1]))/100;let d=await c.race.data();for(let k=0;k(((b=t.face.gear)==null?void 0:b.minConfidence)||.2)&&i.race.push({score:Math.round(100*d[k])/100,race:TA[k]});i.race.sort((k,P)=>P.score-k.score);let y=Array.from(await c.age.data()).map((k,P)=>[vA[P],k]).sort((k,P)=>P[1]-k[1]),u=y[0][0];for(let k=1;kRt.dispose(c[k])),F5[n]=i,rn=o,sn=v(),l(i)}))}var s2=G(H());var H0,Mt=[],ln=0,cn=0,G5=Number.MAX_SAFE_INTEGER;async function dn(e){return R.initial&&(H0=null),H0?e.debug&&h("cached model:",H0.modelUrl):H0=await O(e.face.ssrnet.modelPathAge),H0}async function V5(e,t,n,o){var A,a,l,c;if(!H0)return{age:0};let r=G5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>v()-cn;return t.skipAllowed&&r&&s&&ln===o&&((l=Mt[n])!=null&&l.age)&&((c=Mt[n])==null?void 0:c.age)>0?(G5++,Mt[n]):(G5=0,new Promise(async x=>{var d,m,y;if(!(H0!=null&&H0.inputs)||!H0.inputs[0]||!H0.inputs[0].shape)return;let i={};if(((d=t.face.ssrnet)==null?void 0:d.crop)>0){let u=(m=t.face.ssrnet)==null?void 0:m.crop,T=[[u,u,1-u,1-u]];i.resize=s2.image.cropAndResize(e,T,[0],[H0.inputs[0].shape[2],H0.inputs[0].shape[1]])}else i.resize=s2.image.resizeBilinear(e,[H0.inputs[0].shape[2],H0.inputs[0].shape[1]],!1);i.enhance=s2.mul(i.resize,C.tf255);let f={age:0};if((y=t.face.ssrnet)!=null&&y.enabled&&(i.age=H0.execute(i.enhance)),i.age){let u=await i.age.data();f.age=Math.trunc(10*u[0])/10}Object.keys(i).forEach(u=>s2.dispose(i[u])),Mt[n]=f,ln=o,cn=v(),x(f)}))}var v0=G(H());var N0,Pt=[],yn=0,fn=0,Z5=Number.MAX_SAFE_INTEGER,q5=[.2989,.587,.114];async function mn(e){var t;return R.initial&&(N0=null),N0?e.debug&&h("cached model:",N0.modelUrl):N0=await O((t=e.face.ssrnet)==null?void 0:t.modelPathGender),N0}async function X5(e,t,n,o){var A,a,l,c;if(!N0)return{gender:"unknown",genderScore:0};let r=Z5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>v()-fn;return t.skipAllowed&&r&&s&&yn===o&&((l=Pt[n])!=null&&l.gender)&&((c=Pt[n])==null?void 0:c.genderScore)>0?(Z5++,Pt[n]):(Z5=0,new Promise(async x=>{var m,y,u;if(!(N0!=null&&N0.inputs[0].shape))return;let i={};if(((m=t.face.ssrnet)==null?void 0:m.crop)>0){let T=(y=t.face.ssrnet)==null?void 0:y.crop,g=[[T,T,1-T,1-T]];i.resize=v0.image.cropAndResize(e,g,[0],[N0.inputs[0].shape[2],N0.inputs[0].shape[1]])}else i.resize=v0.image.resizeBilinear(e,[N0.inputs[0].shape[2],N0.inputs[0].shape[1]],!1);i.enhance=v0.tidy(()=>{var g,p;let T;if(((p=(g=N0==null?void 0:N0.inputs)==null?void 0:g[0].shape)==null?void 0:p[3])===1){let[b,k,P]=v0.split(i.resize,3,3),N=v0.mul(b,q5[0]),V=v0.mul(k,q5[1]),_=v0.mul(P,q5[2]),Z=v0.addN([N,V,_]);T=v0.mul(v0.sub(Z,C.tf05),2)}else T=v0.mul(v0.sub(i.resize,C.tf05),2);return T});let f={gender:"unknown",genderScore:0};(u=t.face.ssrnet)!=null&&u.enabled&&(i.gender=N0.execute(i.enhance));let d=await i.gender.data();f.gender=d[0]>d[1]?"female":"male",f.genderScore=d[0]>d[1]?Math.trunc(100*d[0])/100:Math.trunc(100*d[1])/100,Object.keys(i).forEach(T=>v0.dispose(i[T])),Pt[n]=f,yn=o,fn=v(),x(f)}))}var kt=G(H());var Q0,U5=[],un=0,hn=0,bn=Number.MAX_SAFE_INTEGER;async function gn(e){var t;return R.initial&&(Q0=null),Q0?e.debug&&h("cached model:",Q0.modelUrl):Q0=await O((t=e.face.mobilefacenet)==null?void 0:t.modelPath),Q0}async function Y5(e,t,n,o){var A,a;if(!(Q0!=null&&Q0.executor))return[];let r=bn<(((A=t.face.mobilefacenet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.mobilefacenet)==null?void 0:a.skipTime)||0)>v()-hn;return t.skipAllowed&&s&&r&&un===o&&U5[n]?(bn++,U5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.mobilefacenet)!=null&&x.enabled&&(Q0!=null&&Q0.inputs[0].shape)){let i={};i.crop=kt.image.resizeBilinear(e,[Q0.inputs[0].shape[2],Q0.inputs[0].shape[1]],!1),i.data=Q0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>kt.dispose(i[d]))}U5[n]=c,un=o,hn=v(),l(c)})}var wt=G(H());var _0,K5=[],vn=0,Rn=0,Mn=Number.MAX_SAFE_INTEGER;async function Pn(e){return R.initial&&(_0=null),_0?e.debug&&h("cached model:",_0.modelUrl):_0=await O(e.face.insightface.modelPath),_0}async function J5(e,t,n,o){var A,a;if(!(_0!=null&&_0.executor))return[];let r=Mn<(((A=t.face.insightface)==null?void 0:A.skipFrames)||0),s=(((a=t.face.insightface)==null?void 0:a.skipTime)||0)>v()-Rn;return t.skipAllowed&&s&&r&&vn===o&&K5[n]?(Mn++,K5[n]):new Promise(async l=>{var x;let c=[];if((x=t.face.insightface)!=null&&x.enabled&&(_0!=null&&_0.inputs[0].shape)){let i={};i.crop=wt.image.resizeBilinear(e,[_0.inputs[0].shape[2],_0.inputs[0].shape[1]],!1),i.data=_0.execute(i.crop);let f=await i.data.data();c=Array.from(f),Object.keys(i).forEach(d=>wt.dispose(i[d]))}K5[n]=c,vn=o,Rn=v(),l(c)})}var RA=e=>{let t=(i,f)=>Math.atan2(i[1]-f[1],i[0]-f[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],o=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=r?e.mesh[473]:e.mesh[468],A=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],a=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(A[0]-s[0])/a[0]-n[0],o*(s[1]-A[1])/a[1]-n[1]],c=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},wn=(e,t)=>{let n=y=>{let u=Math.sqrt(y[0]*y[0]+y[1]*y[1]+y[2]*y[2]);return y[0]/=u,y[1]/=u,y[2]/=u,y},o=(y,u)=>{let T=y[0]-u[0],g=y[1]-u[1],p=y[2]-u[2];return[T,g,p]},r=(y,u)=>{let T=y[1]*u[2]-y[2]*u[1],g=y[2]*u[0]-y[0]*u[2],p=y[0]*u[1]-y[1]*u[0];return[T,g,p]},s=y=>{let[u,T,g,p,b,k,P,N,V]=y,_,Z,$;return 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this.handDetector.predict(t,n),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...V,confidence:b};let _={landmarks:N,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(_)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let x=St(jt(c),Jn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var $n={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,n1;function HA(){let e=l2?new It(l2):void 0;e&&c2&&(n1=new Nt(e,c2))}async function o1(e,t){n1||HA();let n=await n1.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Et(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function eo(e){var t;return R.initial&&(l2=null),l2?e.debug&&h("cached model:",l2.modelUrl):l2=await O((t=e.hand.detector)==null?void 0:t.modelPath),l2}async function to(e){var t;return R.initial&&(c2=null),c2?e.debug&&h("cached model:",c2.modelUrl):c2=await O((t=e.hand.skeleton)==null?void 0:t.modelPath),c2}var Q=G(H());var p0=[null,null],GA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Xe=[[0,0],[0,0]],VA=["hand","fist","pinch","point","face","tip","pinchtip"],oo=4,ro=1.6,ZA=512,qA=1.4,Ot=Number.MAX_SAFE_INTEGER,r1=0,we=[0,0],m0={boxes:[],hands:[]},so={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function Ao(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&h("cached model:",p0[0].modelUrl);else{_2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await O((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;Xe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function ao(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&h("cached model:",p0[1].modelUrl);else{p0[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;Xe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function XA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,ZA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,GA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[oo]),a.splice(oo,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=Q.slice(o.boxes,f,1),m=await d.data();Q.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],u=at(y,qA),T=[Math.trunc(y[0]*we[0]),Math.trunc(y[1]*we[1]),Math.trunc(y[2]*we[0]),Math.trunc(y[3]*we[1])],g=x[f],p=VA[i[f]],b={id:l++,score:g,box:T,boxRaw:u,label:p};n.push(b)}return Object.keys(o).forEach(f=>Q.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function s1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[we[0]*(i[0]+t.boxRaw[0]),we[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Et(o.keypoints);for(let i of Object.keys(so))o.annotations[i]=so[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function A1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];we=[e.shape[2]||0,e.shape[1]||0],Ot++;let n=(t.hand.skipTime||0)>v()-r1,o=Ot<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-r1,l=Ot<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>s1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>s1(e,x,t))):(m0.boxes=await XA(e,t),r1=v(),m0.hands=await Promise.all(m0.boxes.map(x=>s1(e,x,t))),Ot=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=at(i.box,ro),d=at(i.boxRaw,ro);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var F2={};ze(F2,{connected:()=>Ct,horizontal:()=>a1,kpt:()=>Lt,relative:()=>l1,vertical:()=>i1});var Lt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],a1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],i1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],l1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Ct={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=Te(),c1=0;function lo(e,t){var A,a,l,c,x,i,f,d,m,y,u,T,g,p,b,k,P,N,V,_,Z,$,A0,t0,n0,j0;let n=v();if(!e)return Te();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let M=0;M((r-1)*z.body[M].box[q]+B)/r),C0=e.body[M].boxRaw.map((B,q)=>((r-1)*z.body[M].boxRaw[q]+B)/r),x0=e.body[M].keypoints.map((B,q)=>{var 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M=0;M((r-1)*z.face[M].box[E]+K)/r),C0=e.face[M].boxRaw.map((K,E)=>((r-1)*z.face[M].boxRaw[E]+K)/r),x0=e.face[M].annotations;if(Object.keys(z.face[M].annotations).length!==Object.keys(e.face[M].annotations).length)z.face[M].annotations=e.face[M].annotations,x0=z.face[M].annotations;else if(e.face[M].annotations)for(let K of Object.keys(e.face[M].annotations))x0[K]=(m=(d=(f=e.face[M])==null?void 0:f.annotations)==null?void 0:d[K])!=null&&m[0]?e.face[M].annotations[K].map((E,B)=>E.map((q,U0)=>((r-1)*z.face[M].annotations[K][B][U0]+q)/r)):null;if(e.face[M].rotation){let K={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};K.matrix=(y=e.face[M].rotation)==null?void 0:y.matrix,K.angle={roll:((r-1)*(((T=(u=z.face[M].rotation)==null?void 0:u.angle)==null?void 0:T.roll)||0)+(((p=(g=e.face[M].rotation)==null?void 0:g.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((k=(b=z.face[M].rotation)==null?void 0:b.angle)==null?void 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M=e.persons;if(!z.persons||M.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(M));else for(let T0=0;T0((r-1)*z.persons[T0].box[x0]+C0)/r)}e.gesture&&(z.gesture=e.gesture),z.width=e.width,z.height=e.height;let s=v();return c1=R.perfadd?c1+Math.round(s-n):Math.round(s-n),e.performance&&(z.performance={...e.performance,interpolate:c1}),z}var s0=G(H());var O0;async function d1(e){return!O0||R.initial?O0=await O(e.segmentation.modelPath):e.debug&&h("cached model:",O0.modelUrl),O0}async function co(e,t){var r;if(O0||(O0=await d1(t)),!(O0!=null&&O0.executor)||!((r=O0==null?void 0:O0.inputs)!=null&&r[0].shape))return null;let n={};n.resize=s0.image.resizeBilinear(e,[O0.inputs[0].shape?O0.inputs[0].shape[1]:0,O0.inputs[0].shape?O0.inputs[0].shape[2]:0],!1),n.norm=s0.div(n.resize,C.tf255),n.res=O0.execute(n.norm),n.squeeze=s0.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=s0.unstack(n.squeeze,2),n.fg=s0.softmax(n.fgRaw),n.mul=s0.mul(n.fg,C.tf255),n.expand=s0.expandDims(n.mul,2),n.output=s0.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=s0.squeeze(e),n.concat=s0.concat([n.input,n.output],-1),o=s0.cast(n.concat,"int32");break;case"alpha":o=s0.cast(n.output,"int32");break;default:o=s0.tensor(0)}return Object.keys(n).forEach(s=>s0.dispose(n[s])),o}var Wt={};ze(Wt,{distance:()=>x1,find:()=>KA,similarity:()=>YA});function x1(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let r=0;r{if(e===0)return 1;let s=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return Math.max(Math.min(s,1),0)};function 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l=[s[52],s[51],s[54]-s[52],s[53]-s[51]],c=[Math.trunc(l[0]*(n.shape[2]||0)),Math.trunc(l[1]*(n.shape[1]||0)),Math.trunc(l[2]*(n.shape[2]||0)),Math.trunc(l[3]*(n.shape[1]||0))],x={};for(let[f,d]of Object.entries(Ct)){let m=[];for(let y=0;yg.part===d[y]),T=a.find(g=>g.part===d[y+1]);u&&T&&u.score>(t.body.minConfidence||0)&&T.score>(t.body.minConfidence||0)&&m.push([u.position,T.position])}x[f]=m}let i={id:r,score:A,box:c,boxRaw:l,keypoints:[...a],annotations:x};y1(i),o.push(i)}}return o.sort((r,s)=>s.score-r.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function m1(e,t){var r;if(!(b0!=null&&b0.executor)||!((r=b0==null?void 0:b0.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(d2.boxes.length=0),f1++;let n=(t.body.skipTime||0)>v()-d2.last,o=f1<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?d2.bodies:new Promise(async s=>{let A={};f1=0,A.input=po(e,Dt),A.res=b0==null?void 0:b0.execute(A.input),d2.last=v();let a=await 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G2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],ea=G2.length,H2=G2.reduce((e,t,n)=>(e[t]=n,e),{}),ta=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],q7=ta.map(([e,t])=>[H2[e],H2[t]]),Ro=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function Mo(e){let t=e.reduce(({maxX:n,maxY:o,minX:r,minY:s},{position:{x:A,y:a}})=>({maxX:Math.max(n,A),maxY:Math.max(o,a),minX:Math.min(r,A),minY:Math.min(s,a)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function Po(e,[t,n],[o,r]){let s=t/o,A=n/r,a=(c,x)=>({id:x,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/o,c.box[2]/r,c.box[3]/o],box:[Math.trunc(c.box[0]*A),Math.trunc(c.box[1]*s),Math.trunc(c.box[2]*A),Math.trunc(c.box[3]*s)],keypoints:c.keypoints.map(({score:i,part:f,position:d})=>({score:i,part:f,position:[Math.trunc(d.x*A),Math.trunc(d.y*s)],positionRaw:[d.x/o,d.y/o]})),annotations:{}});return e.map((c,x)=>a(c,x))}var Gt=class{constructor(t,n){w(this,"priorityQueue");w(this,"numberOfElements");w(this,"getElementValue");this.priorityQueue=new 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a=T=>({y:s.get(T.y,T.x,e),x:s.get(T.y,T.x,s.shape[2]/2+e)}),l=(T,g,p)=>({y:g1(Math.round(T.y/M2),0,g-1),x:g1(Math.round(T.x/M2),0,p-1)}),[c,x]=o.shape,i=l(t.position,c,x),f=a(i),m=T1(t.position,f);for(let T=0;T[H2[f],H2[d]]),A=s.map(([,f])=>f),a=s.map(([f])=>f),l=t.shape[2],c=A.length,x=new Array(l),i=b1(e.part,M2,n);x[e.part.id]={score:e.score,part:G2[e.part.id],position:i};for(let f=c-1;f>=0;--f){let d=A[f],m=a[f];x[d]&&!x[m]&&(x[m]=wo(f,x[d],m,t,n,r))}for(let f=0;ft){a=!1;break}if(!a)break}return a}function aa(e,t){let[n,o,r]=t.shape,s=new Gt(n*o*r,({score:A})=>A);for(let A=0;A{var A;let s=(A=r[o])==null?void 0:A.position;return s?ko(n,t,s.y,s.x)<=ra:!1})}function ia(e,t){return t.reduce((o,{position:r,score:s},A)=>(Eo(e,r,A)||(o+=s),o),0)/t.length}function la(e,t,n,o,r,s){let A=[],a=aa(s,t);for(;A.lengthd.score>s);let i=ia(A,x),f=Mo(x);i>s&&A.push({keypoints:x,box:f,score:Math.round(100*i)/100})}return A}async function v1(e,t){if(!(te!=null&&te.executor))return[];let n=V0.tidy(()=>{if(!te.inputs[0].shape)return[];let A=V0.image.resizeBilinear(e,[te.inputs[0].shape[2],te.inputs[0].shape[1]]),a=V0.sub(V0.div(V0.cast(A,"float32"),127.5),1),c=te.execute(a,oa).map(x=>V0.squeeze(x,[0]));return c[1]=V0.sigmoid(c[1]),c}),o=await Promise.all(n.map(A=>A.buffer()));for(let A of n)V0.dispose(A);let r=la(o[0],o[1],o[2],o[3],t.body.maxDetected,t.body.minConfidence);return te.inputs[0].shape?Po(r,[e.shape[1],e.shape[2]],[te.inputs[0].shape[2],te.inputs[0].shape[1]]):[]}async function zo(e){return!te||R.initial?te=await O(e.body.modelPath):e.debug&&h("cached model:",te.modelUrl),te}var F=G(H());var ve,ca=["fgr","pha","r1o","r2o","r3o","r4o"],g0={},M1=0;function Io(e){F.dispose([g0.r1i,g0.r2i,g0.r3i,g0.r4i,g0.downsample_ratio]),g0.r1i=F.tensor(0),g0.r2i=F.tensor(0),g0.r3i=F.tensor(0),g0.r4i=F.tensor(0),M1=e.segmentation.ratio||.5,g0.downsample_ratio=F.tensor(M1)}async function P1(e){return!ve||R.initial?ve=await O(e.segmentation.modelPath):e.debug&&h("cached 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t-n},z3=(e,t,n,o,r,s=!1)=>{let A=yt(xt(h3([e[n],e[o]]),mA)),a=p2(A),l=ye.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=ye.image.flipLeftRight(l);ye.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},S3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function N3(e,t,n){if(!(K0!=null&&K0.executor))return e;let{box:o,boxSize:r,crop:s}=z3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=z3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=ye.concat([s,l]);ye.dispose(s),ye.dispose(l);let x=K0.execute(c);ye.dispose(c);let i=await x.data();ye.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:m}=S3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:u,iris:T}=S3(y,A,a,!1),g=pA(e);Math.abs(g)<30?(mt(e,d,"left",null),mt(e,u,"right",null)):g<1?mt(e,d,"left",["EyeUpper0","EyeLower0"]):mt(e,u,"right",["EyeUpper0","EyeLower0"]);let p=j3(e,m,"left"),b=j3(e,T,"right");return e.concat(p).concat(b)}async function O3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,u)=>y+=e[u][2],0)/e2.length;for(let y=0;yy+=e[u][2],0)/t2.length;for(let y=0;yv()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await w3(e,t),ge.timestamp=v(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=O2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&h("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),_=await N.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*_[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=ct(p,e),P.boxRaw=dt(p,e),P.size=p.size,P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=N.find(n0=>n0.shape[n0.shape.length-1]===1404),$=De.reshape(Z,[-1,3]),A0=await $.array();De.dispose($),(y=t.face.attention)!=null&&y.enabled?A0=await O3(A0,N):(u=t.face.iris)!=null&&u.enabled&&(A0=await N3(A0,P.tensor,O2)),P.mesh=T3(A0,p,b,k,O2),P.meshRaw=P.mesh.map(n0=>[n0[0]/(e.shape[2]||0),n0[1]/(e.shape[1]||0),(n0[2]||0)/a]);for(let n0 of Object.keys(ae))P.annotations[n0]=ae[n0].map(j0=>P.mesh[j0]);P.score=P.faceScore;let t0={...R3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=ct(t0,e),P.boxRaw=dt(t0,e),P.size=t0.size,s.push(t0)}De.dispose(N)}P.score>(((T=t.face.detector)==null?void 0:T.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function W3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&h("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await L(e.face.attention.modelPath):r0=await L((r=e.face.mesh)==null?void 0:r.modelPath),O2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var D3=$e,F3=N2;var J0=G(H());var E5=[],P0,pt=[],B3=0,H3=0,w5=Number.MAX_SAFE_INTEGER,z5=!1;async function G3(e){var t,n,o;return R.initial&&(P0=null),P0?e.debug&&h("cached model:",P0.modelUrl):(P0=await L((t=e.face.emotion)==null?void 0:t.modelPath),z5=((o=(n=P0==null?void 0:P0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,z5?E5=["angry","disgust","fear","happy","neutral","sad","surprise"]:E5=["angry","disgust","fear","happy","sad","surprise","neutral"]),P0}async function S5(e,t,n,o){var A,a;if(!P0)return[];let r=w5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-H3;return t.skipAllowed&&s&&r&&B3===o&&pt[n]&&pt[n].length>0?(w5++,pt[n]):(w5=0,new Promise(async l=>{var x,i,f;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let d={},m=P0!=null&&P0.inputs[0].shape?P0.inputs[0].shape[2]:0;if(((i=t.face.emotion)==null?void 0:i.crop)>0){let u=(f=t.face.emotion)==null?void 0:f.crop,T=[[u,u,1-u,1-u]];d.resize=J0.image.cropAndResize(e,T,[0],[m,m])}else d.resize=J0.image.resizeBilinear(e,[m,m],!1);z5?(d.mul=J0.mul(d.resize,255),d.normalize=J0.sub(d.mul,[103.939,116.779,123.68]),d.emotion=P0==null?void 0:P0.execute(d.normalize)):(d.channels=J0.mul(d.resize,C.rgb),d.grayscale=J0.sum(d.channels,3,!0),d.grayscaleSub=J0.sub(d.grayscale,C.tf05),d.grayscaleMul=J0.mul(d.grayscaleSub,C.tf2),d.emotion=P0==null?void 0:P0.execute(d.grayscaleMul)),H3=v();let y=await d.emotion.data();for(let u=0;u(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*y[u])/100),emotion:E5[u]});c.sort((u,T)=>T.score-u.score),Object.keys(d).forEach(u=>J0.dispose(d[u]))}pt[n]=c,B3=o,l(c)}))}var ie=G(H());var k0,Fe=[],Z3=0,X3=0,j5=Number.MAX_SAFE_INTEGER;async function q3(e){var t;return R.initial&&(k0=null),k0?e.debug&&h("cached model:",k0.modelUrl):k0=await L((t=e.face.description)==null?void 0:t.modelPath),k0}function hA(e,t){var s,A;let n=e.image||e.tensor||e;if(!(k0!=null&&k0.inputs[0].shape))return n;let o;if(((s=t.face.description)==null?void 0:s.crop)>0){let a=(A=t.face.description)==null?void 0:A.crop,l=[[a,a,1-a,1-a]];o=ie.image.cropAndResize(n,l,[0],[k0.inputs[0].shape[2],k0.inputs[0].shape[1]])}else o=ie.image.resizeBilinear(n,[k0.inputs[0].shape[2],k0.inputs[0].shape[1]],!1);let r=ie.mul(o,C.tf255);return ie.dispose(o),r}async function I5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(k0!=null&&k0.executor))return r;let s=j5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>v()-Z3;return t.skipAllowed&&s&&A&&X3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((x=Fe==null?void 0:Fe[n])==null?void 0:x.genderScore)>0?(j5++,Fe[n]):(j5=0,new Promise(async i=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=hA(e,t),m=k0==null?void 0:k0.execute(d);Z3=v(),ie.dispose(d);let u=await m.find(V=>V.shape[1]===1).data(),T=Math.trunc(200*Math.abs(u[0]-.5))/100;T>(t.face.description.minConfidence||0)&&(r.gender=u[0]<=.5?"female":"male",r.genderScore=Math.min(.99,T));let g=ie.argMax(m.find(V=>V.shape[1]===100),1),p=(await g.data())[0];ie.dispose(g);let k=await m.find(V=>V.shape[1]===100).data();r.age=Math.round(k[p-1]>k[p+1]?10*p-100*k[p-1]:10*p+100*k[p+1])/10,(Number.isNaN(u[0])||Number.isNaN(k[0]))&&h("faceres error:",{model:k0,result:m});let P=m.find(V=>V.shape[1]===1024),N=P?await P.data():[];r.descriptor=Array.from(N),m.forEach(V=>ie.dispose(V))}Fe[n]=r,X3=o,i(r)}))}var g2=.1,N5=.5;function bA(e,t,n){let o=!1,r=n.length-1;for(let s=0;st!=n[r].y>t&&e<(n[r].x-n[s].x)*(t-n[s].y)/(n[r].y-n[s].y)+n[s].x&&(o=!o);return o}async function Y3(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,o=await e.tensor.buffer(),r=[];for(let A of ae.silhouette)r.push({x:(e.mesh[A][0]-e.box[0])/e.box[2],y:(e.mesh[A][1]-e.box[1])/e.box[3]});g2&&g2>0&&(r=r.map(A=>({x:A.x>.5?A.x+g2:A.x-g2,y:A.y>.5?A.y+g2:A.y-g2})));for(let A=0;Av()-J3,s=L5<(((a=t.face.antispoof)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&K3===o&&ut[n]?(L5++,ut[n]):(L5=0,new Promise(async l=>{let c=ht.image.resizeBilinear(e,[w0!=null&&w0.inputs[0].shape?w0.inputs[0].shape[2]:0,w0!=null&&w0.inputs[0].shape?w0.inputs[0].shape[1]:0],!1),x=w0==null?void 0:w0.execute(c),i=(await x.data())[0];ut[n]=Math.round(100*i)/100,K3=o,J3=v(),ht.dispose([c,x]),l(ut[n])}))}var gt=G(H());var E0,bt=[],C5=Number.MAX_SAFE_INTEGER,$3=0,en=0;async function tn(e){var t;return R.initial&&(E0=null),E0?e.debug&&h("cached model:",E0.modelUrl):E0=await L((t=e.face.liveness)==null?void 0:t.modelPath),E0}async function W5(e,t,n,o){var A,a;if(!(E0!=null&&E0.executor))return 0;let r=(((A=t.face.liveness)==null?void 0:A.skipTime)||0)>v()-en,s=C5<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&$3===o&&bt[n]?(C5++,bt[n]):(C5=0,new Promise(async l=>{let c=gt.image.resizeBilinear(e,[E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[2]:0,E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[1]:0],!1),x=E0==null?void 0:E0.execute(c),i=(await x.data())[0];bt[n]=Math.round(100*i)/100,$3=o,en=v(),gt.dispose([c,x]),l(bt[n])}))}var Tt=G(H());var le,D5=[],TA=["white","black","asian","indian","other"],vA=[15,23,28,35.5,45.5,55.5,65],on=0,rn=0,F5=Number.MAX_SAFE_INTEGER;async function sn(e){var t;return R.initial&&(le=null),le?e.debug&&h("cached model:",le.modelUrl):le=await L((t=e.face.gear)==null?void 0:t.modelPath),le}async function B5(e,t,n,o){var A,a;if(!le)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=F5<(((A=t.face.gear)==null?void 0:A.skipFrames)||0),s=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>v()-rn;return t.skipAllowed&&s&&r&&on===o&&D5[n]?(F5++,D5[n]):(F5=0,new Promise(async l=>{var T,g,p,b;if(!(le!=null&&le.inputs[0].shape))return;let c={},x=[[0,.1,.9,.9]];if(((T=t.face.gear)==null?void 0:T.crop)>0){let k=(g=t.face.gear)==null?void 0:g.crop;x=[[k,k,1-k,1-k]]}c.resize=Tt.image.cropAndResize(e,x,[0],[le.inputs[0].shape[2],le.inputs[0].shape[1]]);let i={age:0,gender:"unknown",genderScore:0,race:[]};(p=t.face.gear)!=null&&p.enabled&&([c.age,c.gender,c.race]=le.execute(c.resize,["age_output","gender_output","race_output"]));let f=await c.gender.data();i.gender=f[0]>f[1]?"male":"female",i.genderScore=Math.round(100*(f[0]>f[1]?f[0]:f[1]))/100;let d=await c.race.data();for(let k=0;k(((b=t.face.gear)==null?void 0:b.minConfidence)||.2)&&i.race.push({score:Math.round(100*d[k])/100,race:TA[k]});i.race.sort((k,P)=>P.score-k.score);let y=Array.from(await c.age.data()).map((k,P)=>[vA[P],k]).sort((k,P)=>P[1]-k[1]),u=y[0][0];for(let k=1;kTt.dispose(c[k])),D5[n]=i,on=o,rn=v(),l(i)}))}var s2=G(H());var H0,vt=[],an=0,ln=0,H5=Number.MAX_SAFE_INTEGER;async function cn(e){return R.initial&&(H0=null),H0?e.debug&&h("cached model:",H0.modelUrl):H0=await L(e.face.ssrnet.modelPathAge),H0}async function G5(e,t,n,o){var A,a,l,c;if(!H0)return{age:0};let r=H5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>v()-ln;return t.skipAllowed&&r&&s&&an===o&&((l=vt[n])!=null&&l.age)&&((c=vt[n])==null?void 0:c.age)>0?(H5++,vt[n]):(H5=0,new Promise(async x=>{var d,m,y;if(!(H0!=null&&H0.inputs)||!H0.inputs[0]||!H0.inputs[0].shape)return;let i={};if(((d=t.face.ssrnet)==null?void 0:d.crop)>0){let u=(m=t.face.ssrnet)==null?void 0:m.crop,T=[[u,u,1-u,1-u]];i.resize=s2.image.cropAndResize(e,T,[0],[H0.inputs[0].shape[2],H0.inputs[0].shape[1]])}else i.resize=s2.image.resizeBilinear(e,[H0.inputs[0].shape[2],H0.inputs[0].shape[1]],!1);i.enhance=s2.mul(i.resize,C.tf255);let f={age:0};if((y=t.face.ssrnet)!=null&&y.enabled&&(i.age=H0.execute(i.enhance)),i.age){let u=await i.age.data();f.age=Math.trunc(10*u[0])/10}Object.keys(i).forEach(u=>s2.dispose(i[u])),vt[n]=f,an=o,ln=v(),x(f)}))}var v0=G(H());var N0,Rt=[],xn=0,yn=0,V5=Number.MAX_SAFE_INTEGER,Z5=[.2989,.587,.114];async function fn(e){var t;return R.initial&&(N0=null),N0?e.debug&&h("cached model:",N0.modelUrl):N0=await L((t=e.face.ssrnet)==null?void 0:t.modelPathGender),N0}async function X5(e,t,n,o){var A,a,l,c;if(!N0)return{gender:"unknown",genderScore:0};let r=V5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>v()-yn;return t.skipAllowed&&r&&s&&xn===o&&((l=Rt[n])!=null&&l.gender)&&((c=Rt[n])==null?void 0:c.genderScore)>0?(V5++,Rt[n]):(V5=0,new Promise(async x=>{var m,y,u;if(!(N0!=null&&N0.inputs[0].shape))return;let i={};if(((m=t.face.ssrnet)==null?void 0:m.crop)>0){let T=(y=t.face.ssrnet)==null?void 0:y.crop,g=[[T,T,1-T,1-T]];i.resize=v0.image.cropAndResize(e,g,[0],[N0.inputs[0].shape[2],N0.inputs[0].shape[1]])}else i.resize=v0.image.resizeBilinear(e,[N0.inputs[0].shape[2],N0.inputs[0].shape[1]],!1);i.enhance=v0.tidy(()=>{var g,p;let T;if(((p=(g=N0==null?void 0:N0.inputs)==null?void 0:g[0].shape)==null?void 0:p[3])===1){let[b,k,P]=v0.split(i.resize,3,3),N=v0.mul(b,Z5[0]),V=v0.mul(k,Z5[1]),_=v0.mul(P,Z5[2]),Z=v0.addN([N,V,_]);T=v0.mul(v0.sub(Z,C.tf05),2)}else T=v0.mul(v0.sub(i.resize,C.tf05),2);return T});let f={gender:"unknown",genderScore:0};(u=t.face.ssrnet)!=null&&u.enabled&&(i.gender=N0.execute(i.enhance));let 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G0.all)n[G0.getName(o)]={curl:He.getName(t.curls[o]),direction:c0.getName(t.directions[o])};return n}function On(e){let t=[];if(!e||e.length===0)return t;let n=Ln(e);for(let o of En){let r=o.matchAgainst(n.curls,n.directions);r>=EA&&t.push({name:o.name,confidence:r})}return t}var Cn=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&o&&r&&o.position[1]l.part==="leftShoulder"),a=e[n].keypoints.find(l=>l.part==="rightShoulder");A&&a&&Math.abs(A.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${A.position[1]>a.position[1]?"left":"right"}`})}return t},Wn=e=>{if(!e)return[];let t=[];for(let n=0;n450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing 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T=Math.abs(e[A].mesh[145][1]-e[A].annotations.rightEyeIris[0][1])/e[A].box[3],g=Math.abs(e[A].mesh[374][1]-e[A].annotations.leftEyeIris[0][1])/e[A].box[3];(g<.01||T<.01||g>.022||T>.022)&&(d=!1),(g<.01||T<.01)&&t.push({iris:A,gesture:"looking down"}),(g>.022||T>.022)&&t.push({iris:A,gesture:"looking up"}),d&&t.push({iris:A,gesture:"looking center"})}return t},Fn=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=o.reduce((A,a)=>(A.position[2]||0)<(a.position[2]||0)?A:a);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((A,a)=>A.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function Et(e,t=1.5){let n=C2(e),o=wt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function zt(e){let t=C2(e),n=wt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function NA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Xn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return NA(n)}var Bn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Xe(e,t){let n=0;for(let o=0;o[A.x,A.y]),this.anchorsTensor=W.tensor2d(this.anchors),this.inputSize=((s=(r=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=W.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=W.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=W.slice(t,[0,0],[-1,2]),n.boxSizes=W.slice(t,[0,2],[-1,2]),n.div=W.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=W.add(n.div,this.anchorsTensor),n.halfBoxSizes=W.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=W.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=W.mul(n.sub,this.inputSizeTensor),n.add=W.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=W.mul(n.add,this.inputSizeTensor);let o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),f=x.slice(2,4),d=await c.palmLandmarks.array(),m={startPoint:i,endPoint:f,palmLandmarks:d,confidence:r[l]},y=Zn(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(y),Object.keys(c).forEach(u=>W.dispose(c[u]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var $0=G(H());var WA=5,Kn=1.65,Jn=[0,5,9,13,17,1,2],DA=0,FA=2,Qn=0,jt=class{constructor(t,n){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>e1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return Et(zt(r),WA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Et(zt(n),Kn);o.palmLandmarks=[];for(let r=0;r[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=$5(o,[0,0]),c=a.map(d=>[...e1(d,l),d[2]]),x=qn(r),i=[...C2(n),1],f=[Xe(i,x[0]),Xe(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>v()-Qn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A?this.skipped++:(r=await this.handDetector.predict(t,n),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...V,confidence:b};let _={landmarks:N,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(_)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let x=Et(zt(c),Kn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var _n={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,t1;function HA(){let e=l2?new St(l2):void 0;e&&c2&&(t1=new jt(e,c2))}async function n1(e,t){t1||HA();let n=await t1.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=kt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function $n(e){var t;return R.initial&&(l2=null),l2?e.debug&&h("cached model:",l2.modelUrl):l2=await L((t=e.hand.detector)==null?void 0:t.modelPath),l2}async function eo(e){var t;return R.initial&&(c2=null),c2?e.debug&&h("cached model:",c2.modelUrl):c2=await L((t=e.hand.skeleton)==null?void 0:t.modelPath),c2}var Q=G(H());var p0=[null,null],GA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],qe=[[0,0],[0,0]],VA=["hand","fist","pinch","point","face","tip","pinchtip"],no=4,oo=1.6,ZA=512,XA=1.4,It=Number.MAX_SAFE_INTEGER,o1=0,we=[0,0],m0={boxes:[],hands:[]},ro={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function so(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&h("cached model:",p0[0].modelUrl);else{J2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;qe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function Ao(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&h("cached model:",p0[1].modelUrl);else{p0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;qe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function qA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,ZA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,GA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[no]),a.splice(no,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=Q.slice(o.boxes,f,1),m=await d.data();Q.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],u=st(y,XA),T=[Math.trunc(y[0]*we[0]),Math.trunc(y[1]*we[1]),Math.trunc(y[2]*we[0]),Math.trunc(y[3]*we[1])],g=x[f],p=VA[i[f]],b={id:l++,score:g,box:T,boxRaw:u,label:p};n.push(b)}return Object.keys(o).forEach(f=>Q.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function r1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[qe[1][0],qe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/qe[1][1],i[1]/qe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[we[0]*(i[0]+t.boxRaw[0]),we[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=kt(o.keypoints);for(let i of Object.keys(ro))o.annotations[i]=ro[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function s1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];we=[e.shape[2]||0,e.shape[1]||0],It++;let n=(t.hand.skipTime||0)>v()-o1,o=It<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-o1,l=It<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):(m0.boxes=await qA(e,t),o1=v(),m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))),It=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=st(i.box,oo),d=st(i.boxRaw,oo);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var W2={};ze(W2,{connected:()=>Lt,horizontal:()=>A1,kpt:()=>Nt,relative:()=>i1,vertical:()=>a1});var Nt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],A1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],a1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],i1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Lt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=Te(),l1=0;function io(e,t){var 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D=G(H());var se,n2=224,$1,Qs=5,rt=[8,16,32,32,32];function _s(){let e=[],t=0;for(;tn.x)),y:D.tensor1d(e.map(n=>n.y))}}async function e3(e){if(R.initial&&(se=null),!se&&e.body.detector&&e.body.detector.modelPath){se=await L(e.body.detector.modelPath);let t=se!=null&&se.executor?Object.values(se.modelSignature.inputs):void 0;n2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&se&&h("cached model:",se.modelUrl);return _s(),se}var _1=[5,5];function $s(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,n2),t.x),r=D.add(D.div(r,n2),t.y),s=D.mul(D.div(s,n2),_1[0]),A=D.mul(D.div(A,n2),_1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),x=D.add(l,A);return D.stack([a,l,c,x],1)})}async function eA(e,t,n,o){var c,x;let r=[],s={};s.boxes=$s(e,$1),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 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y.landmarks.array(),confidence:m};y.anchor=O.slice(ft,[r[d],0],[1,2]);let g=await y.anchor.data(),p=u3(T,[(e.shape[2]||0)/Ce,(e.shape[1]||0)/Ce],g),b=xt(p,t.face.scale||yA),k=yt(b);k.size[0]>(((i=t.face.detector)==null?void 0:i.minSize)||0)&&k.size[1]>(((f=t.face.detector)==null?void 0:f.minSize)||0)&&s.push(k),Object.keys(y).forEach(P=>O.dispose(y[P]))}}return Object.keys(n).forEach(d=>O.dispose(n[d])),s}var ye=G(H());var K0,We=0,mA=2.3,M5=ae.leftEyeLower0,P5=ae.rightEyeLower0,h2={leftBounds:[M5[0],M5[M5.length-1]],rightBounds:[P5[0],P5[P5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function I3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&h("cached model:",K0.modelUrl):K0=await L((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function mt(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},z3=(e,t,n,o,r,s=!1)=>{let A=yt(xt(h3([e[n],e[o]]),mA)),a=p2(A),l=ye.image.cropAndResize(t,[[A.startPoint[1]/r,A.startPoint[0]/r,A.endPoint[1]/r,A.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let c=ye.image.flipLeftRight(l);ye.dispose(l),l=c}return{box:A,boxSize:a,crop:l}},S3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function N3(e,t,n){if(!(K0!=null&&K0.executor))return e;let{box:o,boxSize:r,crop:s}=z3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0),{box:A,boxSize:a,crop:l}=z3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0),c=ye.concat([s,l]);ye.dispose(s),ye.dispose(l);let x=K0.execute(c);ye.dispose(c);let i=await x.data();ye.dispose(x);let f=i.slice(0,b2.numCoordinates*3),{rawCoords:d,iris:m}=S3(f,o,r,!0),y=i.slice(b2.numCoordinates*3),{rawCoords:u,iris:T}=S3(y,A,a,!1),g=pA(e);Math.abs(g)<30?(mt(e,d,"left",null),mt(e,u,"right",null)):g<1?mt(e,d,"left",["EyeUpper0","EyeLower0"]):mt(e,u,"right",["EyeUpper0","EyeLower0"]);let p=j3(e,m,"left"),b=j3(e,T,"right");return e.concat(p).concat(b)}async function O3(e,t){var s,A,a,l,c,x,i,f,d,m;let n={lips:await((A=(s=t.filter(y=>y.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(y=>y.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(y=>y.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((f=(i=t.filter(y=>y.size===10))==null?void 0:i[1])==null?void 0:f.data()),eyeR:await((m=(d=t.filter(y=>y.size===142))==null?void 0:d[1])==null?void 0:m.data())};for(let y of Object.values(n))if(!y)return e;let o=e2.reduce((y,u)=>y+=e[u][2],0)/e2.length;for(let y=0;yy+=e[u][2],0)/t2.length;for(let y=0;yv()-ge.timestamp,o=ge.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||ge.boxes.length===0?(ge.boxes=await w3(e,t),ge.timestamp=v(),ge.skipped=0):ge.skipped++;let r=[],s=[],A=0,a=O2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!r0)t.debug&&h("face mesh detection requested, but model is not loaded");else{if((d=t.face.attention)!=null&&d.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=r0.execute(P.tensor),_=await N.find(Z=>Z.shape[Z.shape.length-1]===1).data();if(P.faceScore=Math.round(100*_[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=ct(p,e),P.boxRaw=dt(p,e),P.size=p.size,P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(Z=>[Z[0]/(e.shape[2]||1),Z[1]/(e.shape[1]||1),(Z[2]||0)/a]);for(let Z of Object.keys(_e))P.annotations[Z]=[P.mesh[_e[Z]]]}}else{let Z=N.find(n0=>n0.shape[n0.shape.length-1]===1404),$=De.reshape(Z,[-1,3]),A0=await $.array();De.dispose($),(y=t.face.attention)!=null&&y.enabled?A0=await O3(A0,N):(u=t.face.iris)!=null&&u.enabled&&(A0=await N3(A0,P.tensor,O2)),P.mesh=T3(A0,p,b,k,O2),P.meshRaw=P.mesh.map(n0=>[n0[0]/(e.shape[2]||0),n0[1]/(e.shape[1]||0),(n0[2]||0)/a]);for(let n0 of Object.keys(ae))P.annotations[n0]=ae[n0].map(j0=>P.mesh[j0]);P.score=P.faceScore;let t0={...R3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=ct(t0,e),P.boxRaw=dt(t0,e),P.size=t0.size,s.push(t0)}De.dispose(N)}P.score>(((T=t.face.detector)==null?void 0:T.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return ge.boxes=s,r}async function W3(e){var t,n,o,r,s,A;return R.initial&&(r0=null),(t=e.face.attention)!=null&&t.enabled&&(r0!=null&&r0.signature)&&Object.keys(((n=r0==null?void 0:r0.signature)==null?void 0:n.outputs)||{}).length<6&&(r0=null),r0?e.debug&&h("cached model:",r0.modelUrl):(o=e.face.attention)!=null&&o.enabled?r0=await L(e.face.attention.modelPath):r0=await L((r=e.face.mesh)==null?void 0:r.modelPath),O2=r0.executor&&((s=r0==null?void 0:r0.inputs)!=null&&s[0].shape)?(A=r0==null?void 0:r0.inputs)==null?void 0:A[0].shape[2]:256,r0}var D3=$e,F3=N2;var J0=G(H());var E5=[],P0,pt=[],B3=0,H3=0,w5=Number.MAX_SAFE_INTEGER,z5=!1;async function G3(e){var t,n,o;return R.initial&&(P0=null),P0?e.debug&&h("cached model:",P0.modelUrl):(P0=await L((t=e.face.emotion)==null?void 0:t.modelPath),z5=((o=(n=P0==null?void 0:P0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,z5?E5=["angry","disgust","fear","happy","neutral","sad","surprise"]:E5=["angry","disgust","fear","happy","sad","surprise","neutral"]),P0}async function S5(e,t,n,o){var A,a;if(!P0)return[];let r=w5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-H3;return t.skipAllowed&&s&&r&&B3===o&&pt[n]&&pt[n].length>0?(w5++,pt[n]):(w5=0,new Promise(async l=>{var x,i,f;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let d={},m=P0!=null&&P0.inputs[0].shape?P0.inputs[0].shape[2]:0;if(((i=t.face.emotion)==null?void 0:i.crop)>0){let u=(f=t.face.emotion)==null?void 0:f.crop,T=[[u,u,1-u,1-u]];d.resize=J0.image.cropAndResize(e,T,[0],[m,m])}else d.resize=J0.image.resizeBilinear(e,[m,m],!1);z5?(d.mul=J0.mul(d.resize,255),d.normalize=J0.sub(d.mul,[103.939,116.779,123.68]),d.emotion=P0==null?void 0:P0.execute(d.normalize)):(d.channels=J0.mul(d.resize,C.rgb),d.grayscale=J0.sum(d.channels,3,!0),d.grayscaleSub=J0.sub(d.grayscale,C.tf05),d.grayscaleMul=J0.mul(d.grayscaleSub,C.tf2),d.emotion=P0==null?void 0:P0.execute(d.grayscaleMul)),H3=v();let y=await d.emotion.data();for(let u=0;u(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*y[u])/100),emotion:E5[u]});c.sort((u,T)=>T.score-u.score),Object.keys(d).forEach(u=>J0.dispose(d[u]))}pt[n]=c,B3=o,l(c)}))}var ie=G(H());var k0,Fe=[],Z3=0,X3=0,j5=Number.MAX_SAFE_INTEGER;async function q3(e){var t;return R.initial&&(k0=null),k0?e.debug&&h("cached model:",k0.modelUrl):k0=await L((t=e.face.description)==null?void 0:t.modelPath),k0}function hA(e,t){var s,A;let n=e.image||e.tensor||e;if(!(k0!=null&&k0.inputs[0].shape))return n;let o;if(((s=t.face.description)==null?void 0:s.crop)>0){let a=(A=t.face.description)==null?void 0:A.crop,l=[[a,a,1-a,1-a]];o=ie.image.cropAndResize(n,l,[0],[k0.inputs[0].shape[2],k0.inputs[0].shape[1]])}else o=ie.image.resizeBilinear(n,[k0.inputs[0].shape[2],k0.inputs[0].shape[1]],!1);let r=ie.mul(o,C.tf255);return ie.dispose(o),r}async function I5(e,t,n,o){var a,l,c,x;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(k0!=null&&k0.executor))return r;let s=j5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>v()-Z3;return t.skipAllowed&&s&&A&&X3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((x=Fe==null?void 0:Fe[n])==null?void 0:x.genderScore)>0?(j5++,Fe[n]):(j5=0,new Promise(async i=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=hA(e,t),m=k0==null?void 0:k0.execute(d);Z3=v(),ie.dispose(d);let u=await m.find(V=>V.shape[1]===1).data(),T=Math.trunc(200*Math.abs(u[0]-.5))/100;T>(t.face.description.minConfidence||0)&&(r.gender=u[0]<=.5?"female":"male",r.genderScore=Math.min(.99,T));let g=ie.argMax(m.find(V=>V.shape[1]===100),1),p=(await g.data())[0];ie.dispose(g);let k=await m.find(V=>V.shape[1]===100).data();r.age=Math.round(k[p-1]>k[p+1]?10*p-100*k[p-1]:10*p+100*k[p+1])/10,(Number.isNaN(u[0])||Number.isNaN(k[0]))&&h("faceres error:",{model:k0,result:m});let P=m.find(V=>V.shape[1]===1024),N=P?await P.data():[];r.descriptor=Array.from(N),m.forEach(V=>ie.dispose(V))}Fe[n]=r,X3=o,i(r)}))}var g2=.1,N5=.5;function bA(e,t,n){let o=!1,r=n.length-1;for(let s=0;st!=n[r].y>t&&e<(n[r].x-n[s].x)*(t-n[s].y)/(n[r].y-n[s].y)+n[s].x&&(o=!o);return o}async function Y3(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,o=await e.tensor.buffer(),r=[];for(let A of ae.silhouette)r.push({x:(e.mesh[A][0]-e.box[0])/e.box[2],y:(e.mesh[A][1]-e.box[1])/e.box[3]});g2&&g2>0&&(r=r.map(A=>({x:A.x>.5?A.x+g2:A.x-g2,y:A.y>.5?A.y+g2:A.y-g2})));for(let A=0;Av()-J3,s=L5<(((a=t.face.antispoof)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&K3===o&&ut[n]?(L5++,ut[n]):(L5=0,new Promise(async l=>{let c=ht.image.resizeBilinear(e,[w0!=null&&w0.inputs[0].shape?w0.inputs[0].shape[2]:0,w0!=null&&w0.inputs[0].shape?w0.inputs[0].shape[1]:0],!1),x=w0==null?void 0:w0.execute(c),i=(await x.data())[0];ut[n]=Math.round(100*i)/100,K3=o,J3=v(),ht.dispose([c,x]),l(ut[n])}))}var gt=G(H());var E0,bt=[],C5=Number.MAX_SAFE_INTEGER,$3=0,en=0;async function tn(e){var t;return R.initial&&(E0=null),E0?e.debug&&h("cached model:",E0.modelUrl):E0=await L((t=e.face.liveness)==null?void 0:t.modelPath),E0}async function W5(e,t,n,o){var A,a;if(!(E0!=null&&E0.executor))return 0;let r=(((A=t.face.liveness)==null?void 0:A.skipTime)||0)>v()-en,s=C5<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&$3===o&&bt[n]?(C5++,bt[n]):(C5=0,new Promise(async l=>{let c=gt.image.resizeBilinear(e,[E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[2]:0,E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[1]:0],!1),x=E0==null?void 0:E0.execute(c),i=(await x.data())[0];bt[n]=Math.round(100*i)/100,$3=o,en=v(),gt.dispose([c,x]),l(bt[n])}))}var Tt=G(H());var le,D5=[],TA=["white","black","asian","indian","other"],vA=[15,23,28,35.5,45.5,55.5,65],on=0,rn=0,F5=Number.MAX_SAFE_INTEGER;async function sn(e){var t;return R.initial&&(le=null),le?e.debug&&h("cached model:",le.modelUrl):le=await L((t=e.face.gear)==null?void 0:t.modelPath),le}async function B5(e,t,n,o){var A,a;if(!le)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=F5<(((A=t.face.gear)==null?void 0:A.skipFrames)||0),s=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>v()-rn;return t.skipAllowed&&s&&r&&on===o&&D5[n]?(F5++,D5[n]):(F5=0,new Promise(async l=>{var T,g,p,b;if(!(le!=null&&le.inputs[0].shape))return;let c={},x=[[0,.1,.9,.9]];if(((T=t.face.gear)==null?void 0:T.crop)>0){let k=(g=t.face.gear)==null?void 0:g.crop;x=[[k,k,1-k,1-k]]}c.resize=Tt.image.cropAndResize(e,x,[0],[le.inputs[0].shape[2],le.inputs[0].shape[1]]);let i={age:0,gender:"unknown",genderScore:0,race:[]};(p=t.face.gear)!=null&&p.enabled&&([c.age,c.gender,c.race]=le.execute(c.resize,["age_output","gender_output","race_output"]));let f=await c.gender.data();i.gender=f[0]>f[1]?"male":"female",i.genderScore=Math.round(100*(f[0]>f[1]?f[0]:f[1]))/100;let d=await c.race.data();for(let k=0;k(((b=t.face.gear)==null?void 0:b.minConfidence)||.2)&&i.race.push({score:Math.round(100*d[k])/100,race:TA[k]});i.race.sort((k,P)=>P.score-k.score);let y=Array.from(await c.age.data()).map((k,P)=>[vA[P],k]).sort((k,P)=>P[1]-k[1]),u=y[0][0];for(let k=1;kTt.dispose(c[k])),D5[n]=i,on=o,rn=v(),l(i)}))}var s2=G(H());var H0,vt=[],an=0,ln=0,H5=Number.MAX_SAFE_INTEGER;async function cn(e){return R.initial&&(H0=null),H0?e.debug&&h("cached model:",H0.modelUrl):H0=await L(e.face.ssrnet.modelPathAge),H0}async function G5(e,t,n,o){var A,a,l,c;if(!H0)return{age:0};let r=H5<(((A=t.face.ssrnet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>v()-ln;return t.skipAllowed&&r&&s&&an===o&&((l=vt[n])!=null&&l.age)&&((c=vt[n])==null?void 0:c.age)>0?(H5++,vt[n]):(H5=0,new Promise(async x=>{var d,m,y;if(!(H0!=null&&H0.inputs)||!H0.inputs[0]||!H0.inputs[0].shape)return;let i={};if(((d=t.face.ssrnet)==null?void 0:d.crop)>0){let u=(m=t.face.ssrnet)==null?void 0:m.crop,T=[[u,u,1-u,1-u]];i.resize=s2.image.cropAndResize(e,T,[0],[H0.inputs[0].shape[2],H0.inputs[0].shape[1]])}else i.resize=s2.image.resizeBilinear(e,[H0.inputs[0].shape[2],H0.inputs[0].shape[1]],!1);i.enhance=s2.mul(i.resize,C.tf255);let f={age:0};if((y=t.face.ssrnet)!=null&&y.enabled&&(i.age=H0.execute(i.enhance)),i.age){let u=await i.age.data();f.age=Math.trunc(10*u[0])/10}Object.keys(i).forEach(u=>s2.dispose(i[u])),vt[n]=f,an=o,ln=v(),x(f)}))}var v0=G(H());var N0,Rt=[],xn=0,yn=0,V5=Number.MAX_SAFE_INTEGER,Z5=[.2989,.587,.114];async function fn(e){var t;return R.initial&&(N0=null),N0?e.debug&&h("cached model:",N0.modelUrl):N0=await L((t=e.face.ssrnet)==null?void 0:t.modelPathGender),N0}async function X5(e,t,n,o){var A,a,l,c;if(!N0)return{gender:"unknown",genderScore:0};let r=V5<(((A=t.face.ssrnet)==null?void 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G0.all)n[G0.getName(o)]={curl:He.getName(t.curls[o]),direction:c0.getName(t.directions[o])};return n}function On(e){let t=[];if(!e||e.length===0)return t;let n=Ln(e);for(let o of En){let r=o.matchAgainst(n.curls,n.directions);r>=EA&&t.push({name:o.name,confidence:r})}return t}var Cn=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&o&&r&&o.position[1]l.part==="leftShoulder"),a=e[n].keypoints.find(l=>l.part==="rightShoulder");A&&a&&Math.abs(A.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${A.position[1]>a.position[1]?"left":"right"}`})}return t},Wn=e=>{if(!e)return[];let t=[];for(let n=0;n450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},Dn=e=>{var n,o,r,s;if(!e)return[];let t=[];for(let A=0;A.06||u>.06)&&(d=!1),y>u?u>.04&&t.push({iris:A,gesture:"looking right"}):y>.04&&t.push({iris:A,gesture:"looking left"});let T=Math.abs(e[A].mesh[145][1]-e[A].annotations.rightEyeIris[0][1])/e[A].box[3],g=Math.abs(e[A].mesh[374][1]-e[A].annotations.leftEyeIris[0][1])/e[A].box[3];(g<.01||T<.01||g>.022||T>.022)&&(d=!1),(g<.01||T<.01)&&t.push({iris:A,gesture:"looking down"}),(g>.022||T>.022)&&t.push({iris:A,gesture:"looking up"}),d&&t.push({iris:A,gesture:"looking center"})}return t},Fn=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=o.reduce((A,a)=>(A.position[2]||0)<(a.position[2]||0)?A:a);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((A,a)=>A.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function Et(e,t=1.5){let n=C2(e),o=wt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function zt(e){let t=C2(e),n=wt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function NA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Xn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return NA(n)}var Bn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Xe(e,t){let n=0;for(let o=0;o[A.x,A.y]),this.anchorsTensor=W.tensor2d(this.anchors),this.inputSize=((s=(r=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=W.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=W.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=W.slice(t,[0,0],[-1,2]),n.boxSizes=W.slice(t,[0,2],[-1,2]),n.div=W.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=W.add(n.div,this.anchorsTensor),n.halfBoxSizes=W.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=W.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=W.mul(n.sub,this.inputSizeTensor),n.add=W.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=W.mul(n.add,this.inputSizeTensor);let o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),f=x.slice(2,4),d=await c.palmLandmarks.array(),m={startPoint:i,endPoint:f,palmLandmarks:d,confidence:r[l]},y=Zn(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(y),Object.keys(c).forEach(u=>W.dispose(c[u]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var $0=G(H());var WA=5,Kn=1.65,Jn=[0,5,9,13,17,1,2],DA=0,FA=2,Qn=0,jt=class{constructor(t,n){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>e1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return Et(zt(r),WA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Et(zt(n),Kn);o.palmLandmarks=[];for(let r=0;r[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=$5(o,[0,0]),c=a.map(d=>[...e1(d,l),d[2]]),x=qn(r),i=[...C2(n),1],f=[Xe(i,x[0]),Xe(i,x[1])];return c.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>v()-Qn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A?this.skipped++:(r=await this.handDetector.predict(t,n),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,y,x,m),V=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...V,confidence:b};let _={landmarks:N,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:V.startPoint,bottomRight:V.endPoint}};a.push(_)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let x=Et(zt(c),Kn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var _n={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,t1;function HA(){let e=l2?new St(l2):void 0;e&&c2&&(t1=new jt(e,c2))}async function n1(e,t){t1||HA();let n=await t1.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=kt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function $n(e){var t;return R.initial&&(l2=null),l2?e.debug&&h("cached model:",l2.modelUrl):l2=await L((t=e.hand.detector)==null?void 0:t.modelPath),l2}async function eo(e){var t;return R.initial&&(c2=null),c2?e.debug&&h("cached model:",c2.modelUrl):c2=await L((t=e.hand.skeleton)==null?void 0:t.modelPath),c2}var Q=G(H());var p0=[null,null],GA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],qe=[[0,0],[0,0]],VA=["hand","fist","pinch","point","face","tip","pinchtip"],no=4,oo=1.6,ZA=512,XA=1.4,It=Number.MAX_SAFE_INTEGER,o1=0,we=[0,0],m0={boxes:[],hands:[]},ro={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function so(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&h("cached model:",p0[0].modelUrl);else{J2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;qe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function Ao(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&h("cached model:",p0[1].modelUrl);else{p0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;qe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function qA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,ZA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,GA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[no]),a.splice(no,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let f of Array.from(c)){let d=Q.slice(o.boxes,f,1),m=await d.data();Q.dispose(d);let y=[m[1],m[0],m[3]-m[1],m[2]-m[0]],u=st(y,XA),T=[Math.trunc(y[0]*we[0]),Math.trunc(y[1]*we[1]),Math.trunc(y[2]*we[0]),Math.trunc(y[3]*we[1])],g=x[f],p=VA[i[f]],b={id:l++,score:g,box:T,boxRaw:u,label:p};n.push(b)}return Object.keys(o).forEach(f=>Q.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function r1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[qe[1][0],qe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/qe[1][1],i[1]/qe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[we[0]*(i[0]+t.boxRaw[0]),we[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=kt(o.keypoints);for(let i of Object.keys(ro))o.annotations[i]=ro[i].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function s1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];we=[e.shape[2]||0,e.shape[1]||0],It++;let n=(t.hand.skipTime||0)>v()-o1,o=It<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-o1,l=It<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))):(m0.boxes=await qA(e,t),o1=v(),m0.hands=await Promise.all(m0.boxes.map(x=>r1(e,x,t))),It=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[x].fingerScore&&m0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=st(i.box,oo),d=st(i.boxRaw,oo);m0.boxes.push({...c[x],box:f,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var W2={};ze(W2,{connected:()=>Lt,horizontal:()=>A1,kpt:()=>Nt,relative:()=>i1,vertical:()=>a1});var Nt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],A1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],a1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],i1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Lt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=Te(),l1=0;function io(e,t){var 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this.comp(t)<0};de.lt=de.lessThan;de.lessThanOrEqual=function(t){return this.comp(t)<=0};de.lte=de.lessThanOrEqual;de.le=de.lessThanOrEqual;de.greaterThan=function(t){return this.comp(t)>0};de.gt=de.greaterThan;de.greaterThanOrEqual=function(t){return this.comp(t)>=0};de.gte=de.greaterThanOrEqual;de.ge=de.greaterThanOrEqual;de.compare=function(t){if(Wr(t)||(t=Fs(t)),this.eq(t))return 0;var e=this.isNegative(),o=t.isNegative();return e&&!o?-1:!e&&o?1:this.unsigned?t.high>>>0>this.high>>>0||t.high===this.high&&t.low>>>0>this.low>>>0?-1:1:this.sub(t).isNegative()?-1:1};de.comp=de.compare;de.negate=function(){return!this.unsigned&&this.eq(Vr)?Vr:this.not().add(rc)};de.neg=de.negate;de.add=function(t){Wr(t)||(t=Fs(t));var e=this.high>>>16,o=this.high&65535,n=this.low>>>16,s=this.low&65535,a=t.high>>>16,i=t.high&65535,p=t.low>>>16,u=t.low&65535,c=0,l=0,m=0,d=0;return 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ids) after running '${t}'`)}runKernelFunc(t){let e,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=ww(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(ww(t)){let{kernelName:f,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=tc(f,this.backendName);$(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let S=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,S);o=this.saveTensorsForBackwardMode(k)}return S}}else{let{forwardFunc:f}=t,h=g=>{n&&(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=t,m=ww(t)?null:t.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?e=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),e=d.outputs)}),n&&this.addTapeNode(u,c,e,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:e.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?e:e[0]}saveTensorsForBackwardMode(t){return 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T.tidy(()=>{$(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let e=r.shape[0],o=r.shape[1],n=wd(e),s=Ur(r),a=fu([[1]],[1,1]),i=Ur(a),p=e>=o?o:e;for(let u=0;u{let d=Xe(s,[u,u],[e-u,1]),f=Ku(d),h=Xe(s,[u,u],[1,1]),g=lo(qu(h,0),fu([[-1]]),fu([[1]])),x=Te(h,se(g,f)),b=je(d,x);b.shape[0]===1?i=Ur(a):i=bt([a,Xe(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let C=cr(je(Ze(g,x),f)),S=Xe(s,[u,0],[e-u,o]),k=se(C,i),_=yc(i);if(u===0)s=Te(S,Ze(k,Ze(_,S)));else{let D=Te(S,Ze(k,Ze(_,S)));s=bt([Xe(s,[0,0],[u,o]),D],0)}let E=yc(k),R=Xe(n,[0,u],[e,n.shape[1]-u]);if(u===0)n=Te(R,Ze(Ze(R,i),E));else{let D=Te(R,Ze(Ze(R,i),E));n=bt([Xe(n,[0,0],[e,u]),D],1)}return[i,s,n]}),Mt([c,l,m])}return!t&&e>o&&(n=Xe(n,[0,0],[e,o]),s=Xe(s,[0,0],[o,o])),[n,s]})}var kN=N({qr_:vj});var Rt;(function(r){r[r.NONE=0]="NONE",r[r.MEAN=1]="MEAN",r[r.SUM=2]="SUM",r[r.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Rt||(Rt={}));function kj(r,t,e=Rt.SUM_BY_NONZERO_WEIGHTS){let o=v(r,"losses","computeWeightedLoss"),n=null;t!=null&&(n=v(t,"weights","computeWeightedLoss"));let s=n==null?o:se(o,n);if(e===Rt.NONE)return s;if(e===Rt.SUM)return ot(s);if(e===Rt.MEAN){if(n==null)return Xu(s);{let a=o.size/n.size,i=je(ot(s),ot(n));return a>1?je(i,ke(a)):i}}if(e===Rt.SUM_BY_NONZERO_WEIGHTS){if(n==null)return je(ot(s),ke(o.size));{let a=se(n,Da(o.shape)),i=We(ot(Pd(a,ke(0))),"float32");return je(ot(s),i)}}throw Error(`Unknown reduction: ${e}`)}var lr=N({computeWeightedLoss_:kj});function Nj(r,t,e,o=Rt.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","absoluteDifference"),s=v(t,"predictions","absoluteDifference"),a=null;e!=null&&(a=v(e,"weights","absoluteDifference")),yt(n.shape,s.shape,"Error in absoluteDifference: ");let i=Jt(Te(n,s));return lr(i,a,o)}var NN=N({absoluteDifference_:Nj});function Tj(r,t,e,o,n=Rt.SUM_BY_NONZERO_WEIGHTS){let 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T.tidy(()=>{$(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let e=r.shape[0],o=r.shape[1],n=bd(e),s=Wr(r),a=cu([[1]],[1,1]),i=Wr(a),p=e>=o?o:e;for(let u=0;u{let d=Xe(s,[u,u],[e-u,1]),f=Bu(d),h=Xe(s,[u,u],[1,1]),g=co(zu(h,0),cu([[-1]]),cu([[1]])),x=Te(h,se(g,f)),b=je(d,x);b.shape[0]===1?i=Wr(a):i=yt([a,Xe(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let C=pr(je(Ze(g,x),f)),S=Xe(s,[u,0],[e-u,o]),k=se(C,i),_=cc(i);if(u===0)s=Te(S,Ze(k,Ze(_,S)));else{let D=Te(S,Ze(k,Ze(_,S)));s=yt([Xe(s,[0,0],[u,o]),D],0)}let E=cc(k),R=Xe(n,[0,u],[e,n.shape[1]-u]);if(u===0)n=Te(R,Ze(Ze(R,i),E));else{let D=Te(R,Ze(Ze(R,i),E));n=yt([Xe(n,[0,0],[e,u]),D],1)}return[i,s,n]}),Ot([c,l,m])}return!t&&e>o&&(n=Xe(n,[0,0],[e,o]),s=Xe(s,[0,0],[o,o])),[n,s]})}var NN=N({qr_:vj});var Et;(function(r){r[r.NONE=0]="NONE",r[r.MEAN=1]="MEAN",r[r.SUM=2]="SUM",r[r.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Et||(Et={}));function kj(r,t,e=Et.SUM_BY_NONZERO_WEIGHTS){let 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Map,Dl=class{getClassName(){return this.constructor.className}static fromConfig(t,e){return new t(e)}},Fa=class{constructor(){this.classNameMap={}}static getMap(){return Fa.instance==null&&(Fa.instance=new Fa),Fa.instance}static register(t){Fa.getMap().classNameMap[t.className]=[t,t.fromConfig]}};function tS(r,t,e){$(r.className!=null,()=>"Class being registered does not have the static className property defined."),$(typeof r.className=="string",()=>"className is required to be a string, but got type "+typeof r.className),$(r.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),typeof t=="undefined"&&(t="Custom"),typeof e=="undefined"&&(e=r.className);let o=e,n=t+">"+o;return Fa.register(r),Qj.set(n,r),eS.set(r,n),r}function Zj(r){return eS.has(r)?eS.get(r):r.className}var Nr=class extends Dl{minimize(t,e=!1,o){let{value:n,grads:s}=this.computeGradients(t,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Mt(s),e?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(t,e){return zw(t,e)}dispose(){this.iterations_!=null&&Mt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ke(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(t){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(t){return this.iterations_=(await t[0].tensor.data())[0],t.slice(1)}};Object.defineProperty(Nr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var np=class extends Nr{static get className(){return"Adadelta"}constructor(t,e,o=null){super(),this.learningRate=t,this.rho=e,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:De(()=>Ht(s).variable(a))});let i=Array.isArray(t)?t[n].tensor:t[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;De(()=>{let c=Ce(se(p,this.rho),se(er(i),1-this.rho)),l=se(je(Dr(Ce(u,this.epsilon)),Dr(Ce(p,this.epsilon))),i),m=Ce(se(u,this.rho),se(er(l),1-this.rho));p.assign(c),u.assign(m);let d=Ce(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Mt(this.accumulatedGrads.map(t=>t.variable)),Mt(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,o=!1;this.accumulatedGrads=t.slice(0,e).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=t.slice(e,e*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var sp=class extends Nr{static get className(){return"Adagrad"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>Ea(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(t)?t[n].tensor:t[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=Ce(i,er(a));i.assign(p);let u=Ce(se(je(a,Dr(Ce(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Mt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(o=>({originalName:o.name,variable:o.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};var ap=class extends Nr{static get className(){return"Adam"}constructor(t,e,o,n=null){super(),this.learningRate=t,this.beta1=e,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(e).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(o=>o.name):Object.keys(t);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);e.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Ht(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Ht(i).variable(p))});let u=Array.isArray(t)?t[a].tensor:t[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=Ce(se(l,this.beta2),se(er(u),1-this.beta2)),f=je(m,o),h=je(d,n);c.assign(m),l.assign(d);let g=Ce(se(je(f,Ce(Dr(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(se(this.accBeta1,this.beta1)),this.accBeta2.assign(se(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Mt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Mt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),De(()=>{this.accBeta1.assign(ui(this.beta1,this.iterations_+1)),this.accBeta2.assign(ui(this.beta2,this.iterations_+1))});let e=t.length/2,o=!1;this.accumulatedFirstMoment=t.slice(0,e).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}};var ip=class extends Nr{static get className(){return"Adamax"}constructor(t,e,o,n=null,s=0){super(),this.learningRate=t,this.beta1=e,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(e).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(o=>o.name):Object.keys(t);De(()=>{let o=Te(1,this.accBeta1),n=je(-this.learningRate,Ce(se(this.iteration,this.decay),1));e.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ht(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ht(i).variable(p)});let u=Array.isArray(t)?t[a].tensor:t[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=se(l,this.beta2),f=Jt(u),h=Fd(d,f);c.assign(m),l.assign(h);let g=Ce(se(je(n,o),je(m,Ce(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Ce(this.iteration,1)),this.accBeta1.assign(se(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Mt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Mt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};var mi=class extends Nr{static get className(){return"SGD"}constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=Array.isArray(t)?t[n].tensor:t[o];if(s==null)return;let a=T.registeredVariables[o];De(()=>{let i=Ce(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=Rr(ke(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(t,e){return new t(e.learningRate)}};var up=class extends mi{static get className(){return"Momentum"}constructor(t,e,o=!1){super(t),this.learningRate=t,this.momentum=e,this.useNesterov=o,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:De(()=>Ht(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(t)?t[n].tensor:t[o];i!=null&&De(()=>{let p,u=Ce(se(this.m,a),i);this.useNesterov?p=Ce(se(this.c,Ce(i,se(u,this.m))),s):p=Ce(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Mt(this.accumulations.map(t=>t.variable))}setMomentum(t){this.momentum=t}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(o=>({originalName:o.name,variable:o.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var pp=class extends Nr{static get className(){return"RMSProp"}constructor(t,e=.9,o=0,n=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Ht(s).variable(a))});let i=Array.isArray(t)?t[n].tensor:t[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;De(()=>{let c=Ce(se(p,this.decay),se(er(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=Ce(se(l,this.decay),se(i,1-this.decay)),d=je(se(i,this.learningRate),Dr(Te(c,Ce(er(m),this.epsilon)))),f=Ce(se(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Te(s,f);s.assign(h)}else{let l=Ce(se(p,this.decay),se(er(i),1-this.decay)),m=Ce(se(u,this.momentum),je(se(i,this.learningRate),Dr(Ce(l,this.epsilon))));p.assign(l),u.assign(m);let d=Te(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Mt(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Mt(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Mt(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=this.centered?t.length/3:t.length/2,o=!1;this.accumulatedMeanSquares=t.slice(0,e).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=t.slice(e,e*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}};var Jj=[np,sp,ap,ip,up,pp,mi];function UN(){for(let r of Jj)tS(r)}var fi={};qe(fi,{CompositeArrayBuffer:()=>jt,browserFiles:()=>HN,browserHTTPRequest:()=>jN,concatenateArrayBuffers:()=>ik,copyModel:()=>wk,decodeWeights:()=>ad,encodeWeights:()=>nk,fromMemory:()=>XN,fromMemorySync:()=>iS,getLoadHandlers:()=>lk,getModelArtifactsForJSON:()=>ic,getModelArtifactsForJSONSync:()=>_w,getModelArtifactsInfoForJSON:()=>va,getSaveHandlers:()=>ck,getWeightSpecs:()=>ud,http:()=>nf,isHTTPScheme:()=>of,listModels:()=>bk,loadWeights:()=>KN,moveModel:()=>Sk,registerLoadRouter:()=>pk,registerSaveRouter:()=>uk,removeModel:()=>Ck,weightsLoaderFactory:()=>sS,withSaveHandler:()=>YN,withSaveHandlerSync:()=>QN});var eX="model",tX=".json",rX=".weights.bin";function GN(r){return new Promise(t=>setTimeout(t)).then(r)}var di=class{constructor(t){if(!A().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a 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UN={};qe(UN,{Serializable:()=>$l,SerializationMap:()=>tf,getRegisteredName:()=>Zj,registerClass:()=>tS});var Qj=new Map,eS=new Map,$l=class{getClassName(){return this.constructor.className}static fromConfig(t,e){return new t(e)}},tf=class r{constructor(){this.classNameMap={}}static getMap(){return r.instance==null&&(r.instance=new r),r.instance}static register(t){r.getMap().classNameMap[t.className]=[t,t.fromConfig]}};function tS(r,t,e){$(r.className!=null,()=>"Class being registered does not have the static className property defined."),$(typeof r.className=="string",()=>"className is required to be a string, but got type "+typeof r.className),$(r.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),typeof t=="undefined"&&(t="Custom"),typeof e=="undefined"&&(e=r.className);let o=e,n=t+">"+o;return tf.register(r),Qj.set(n,r),eS.set(r,n),r}function Zj(r){return eS.has(r)?eS.get(r):r.className}var kr=class extends 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c=Ce(se(p,this.rho),se(Zt(i),1-this.rho)),l=se(je(Rr(Ce(u,this.epsilon)),Rr(Ce(p,this.epsilon))),i),m=Ce(se(u,this.rho),se(Zt(l),1-this.rho));p.assign(c),u.assign(m);let d=Ce(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(t=>t.variable)),Ot(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,o=!1;this.accumulatedGrads=t.slice(0,e).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=t.slice(e,e*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var Zu=class extends kr{static get className(){return"Adagrad"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>Ea(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(t)?t[n].tensor:t[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=Ce(i,Zt(a));i.assign(p);let u=Ce(se(je(a,Rr(Ce(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(o=>({originalName:o.name,variable:o.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};var Ju=class extends kr{static get className(){return"Adam"}constructor(t,e,o,n=null){super(),this.learningRate=t,this.beta1=e,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(e).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(o=>o.name):Object.keys(t);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);e.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Gt(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Gt(i).variable(p))});let u=Array.isArray(t)?t[a].tensor:t[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=Ce(se(l,this.beta2),se(Zt(u),1-this.beta2)),f=je(m,o),h=je(d,n);c.assign(m),l.assign(d);let g=Ce(se(je(f,Ce(Rr(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(se(this.accBeta1,this.beta1)),this.accBeta2.assign(se(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Ot(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),De(()=>{this.accBeta1.assign(ii(this.beta1,this.iterations_+1)),this.accBeta2.assign(ii(this.beta2,this.iterations_+1))});let e=t.length/2,o=!1;this.accumulatedFirstMoment=t.slice(0,e).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}};var ep=class extends kr{static get className(){return"Adamax"}constructor(t,e,o,n=null,s=0){super(),this.learningRate=t,this.beta1=e,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(e).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(o=>o.name):Object.keys(t);De(()=>{let o=Te(1,this.accBeta1),n=je(-this.learningRate,Ce(se(this.iteration,this.decay),1));e.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Gt(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Gt(i).variable(p)});let u=Array.isArray(t)?t[a].tensor:t[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=se(l,this.beta2),f=Qt(u),h=Dd(d,f);c.assign(m),l.assign(h);let g=Ce(se(je(n,o),je(m,Ce(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Ce(this.iteration,1)),this.accBeta1.assign(se(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};var li=class extends kr{static get className(){return"SGD"}constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=Array.isArray(t)?t[n].tensor:t[o];if(s==null)return;let a=T.registeredVariables[o];De(()=>{let i=Ce(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=Er(ke(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD 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performance."),console.log(p);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ke(this.size(),"int32")}async import(t,e){this.checkKeyAndValueTensor(t,e);let o=await t.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),De(()=>{let n=fo(e),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case"HashTable":case"HashTableV2":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I("keyDType",r,t,e),a=I("valueDType",r,t,e),i=new vf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let n=I("tableHandle",r,t,e,o),s=I("keys",r,t,e),a=I("values",r,t,e);return[await o.getHashTableById(n.id).import(s,a)]}case"LookupTableFind":case"LookupTableFindV2":{let 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OT=(r,t,e,o=Je)=>{switch(r.op){case"Equal":return[o.equal(I("a",r,t,e),I("b",r,t,e))];case"NotEqual":return[o.notEqual(I("a",r,t,e),I("b",r,t,e))];case"Greater":return[o.greater(I("a",r,t,e),I("b",r,t,e))];case"GreaterEqual":return[o.greaterEqual(I("a",r,t,e),I("b",r,t,e))];case"Less":return[o.less(I("a",r,t,e),I("b",r,t,e))];case"LessEqual":return[o.lessEqual(I("a",r,t,e),I("b",r,t,e))];case"LogicalAnd":return[o.logicalAnd(I("a",r,t,e),I("b",r,t,e))];case"LogicalNot":return[o.logicalNot(I("a",r,t,e))];case"LogicalOr":return[o.logicalOr(I("a",r,t,e),I("b",r,t,e))];case"Select":case"SelectV2":return[o.where(I("condition",r,t,e),I("a",r,t,e),I("b",r,t,e))];case"BitwiseAnd":return[o.bitwiseAnd(I("a",r,t,e),I("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MT=(r,t,e,o=Je)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[o.matMul(I("a",r,t,e),I("b",r,t,e),I("transposeA",r,t,e),I("transposeB",r,t,e))];case"Einsum":return[o.einsum(I("equation",r,t,e),...I("tensors",r,t,e))];case"Transpose":return[o.transpose(I("x",r,t,e),I("perm",r,t,e))];case"_FusedMatMul":let[n,s]=I("fusedOps",r,t,e),a=n==="biasadd",i=s==="prelu",p=I("numArgs",r,t,e),u=I("leakyreluAlpha",r,t,e);if(a){if(i&&p!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&p!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,l]=I("args",r,t,e);return[o.fused.matMul({a:I("a",r,t,e),b:I("b",r,t,e),transposeA:I("transposeA",r,t,e),transposeB:I("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];case"MatrixBandPart":return[o.linalg.bandPart(I("a",r,t,e),I("numLower",r,t,e),I("numUpper",r,t,e))];default:throw TypeError(`Node type 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WT=(r,t,e,o=Je)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I("indices",r,t,e),I("values",r,t,e),I("denseShape",r,t,e),I("defaultValue",r,t,e));return[n,s,a,i]}case"SparseReshape":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I("inputIndices",r,t,e),I("inputShape",r,t,e),I("newShape",r,t,e));return[n,s]}case"SparseSegmentMean":return[o.sparse.sparseSegmentMean(I("data",r,t,e),I("indices",r,t,e),I("segmentIds",r,t,e))];case"SparseSegmentSum":return[o.sparse.sparseSegmentSum(I("data",r,t,e),I("indices",r,t,e),I("segmentIds",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UT=(r,t,e,o=Je)=>{switch(r.op){case"FFT":return[o.fft(I("x",r,t,e))];case"IFFT":return[o.ifft(I("x",r,t,e))];case"RFFT":return[o.rfft(I("x",r,t,e))];case"IRFFT":return[o.irfft(I("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GT=(r,t,e,o=Je)=>{switch(r.op){case"StaticRegexReplace":return[o.string.staticRegexReplace(I("input",r,t,e),I("pattern",r,t,e),I("rewrite",r,t,e),I("replaceGlobal",r,t,e))];case"StringNGrams":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I("data",r,t,e),I("dataSplits",r,t,e),I("separator",r,t,e),I("nGramWidths",r,t,e),I("leftPad",r,t,e),I("rightPad",r,t,e),I("padWidth",r,t,e),I("preserveShortSequences",r,t,e));return[n,s]}case"StringSplit":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I("input",r,t,e),I("delimiter",r,t,e),I("skipEmpty",r,t,e));return[n,s,a]}case"StringToHashBucketFast":return[o.string.stringToHashBucketFast(I("input",r,t,e),I("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HT=(r,t,e,o=Je)=>{switch(r.op){case"Cast":return[o.cast(I("x",r,t,e),I("dtype",r,t,e))];case"ExpandDims":{let n=I("axis",r,t,e);return[o.expandDims(I("x",r,t,e),n)]}case"Squeeze":{let n=I("axis",r,t,e);return[o.squeeze(I("x",r,t,e),n)]}case"Reshape":return[o.reshape(I("x",r,t,e),I("shape",r,t,e))];case"EnsureShape":return[o.ensureShape(I("x",r,t,e),I("shape",r,t,e))];case"MirrorPad":return[o.mirrorPad(I("x",r,t,e),I("padding",r,t,e),I("mode",r,t,e))];case"PadV2":case"Pad":return[o.pad(I("x",r,t,e),I("padding",r,t,e),I("constantValue",r,t,e))];case"SpaceToBatchND":{let n=I("blockShape",r,t,e),s=I("paddings",r,t,e);return[o.spaceToBatchND(I("x",r,t,e),n,s)]}case"BatchToSpaceND":{let n=I("blockShape",r,t,e),s=I("crops",r,t,e);return[o.batchToSpaceND(I("x",r,t,e),n,s)]}case"DepthToSpace":{let n=I("blockSize",r,t,e),s=I("dataFormat",r,t,e).toUpperCase();return[o.depthToSpace(I("x",r,t,e),n,s)]}case"BroadcastTo":return[o.broadcastTo(I("x",r,t,e),I("shape",r,t,e))];case"BroadcastArgs":return[o.broadcastArgs(I("s0",r,t,e),I("s1",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function OS(r,t,e,o,n=De){let s=((a,i,p)=>{switch(a.category){case"arithmetic":return n(()=>CT(a,i,p));case"basic_math":return n(()=>wT(a,i,p));case"control":return TT(a,i,p);case"convolution":return n(()=>$T(a,i,p));case"creation":return n(()=>ET(a,i,p));case"dynamic":return RT(a,i,p);case"evaluation":return n(()=>DT(a,i,p));case"image":return n(()=>PT(a,i,p));case"graph":return n(()=>AT(a,i,p));case"logical":return n(()=>OT(a,i,p));case"matrices":return n(()=>MT(a,i,p));case"normalization":return n(()=>LT(a,i,p));case"ragged":return n(()=>BT(a,i,p));case"reduction":return n(()=>zT(a,i,p));case"slice_join":return n(()=>VT(a,i,p));case"sparse":return n(()=>WT(a,i,p));case"spectral":return n(()=>UT(a,i,p));case"string":return n(()=>GT(a,i,p));case"transformation":return n(()=>HT(a,i,p));case"hash_table":return FT(a,i,p,o);case"custom":let u=pf(a.op);if(u&&u.customExecutor)return u.customExecutor(new wf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Ll=class{constructor(t={},e={},o={},n={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=o,this.functionMap=n,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function MS(r,t,e,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=new Set(Object.keys(r).map(m=>Tr(m)[0]));o=o||[];let c=new Set(o.map(m=>Tr(m.name)[0])),l=[...t];for(;l.length>0;){let m=l.pop();if((gu(m)||k8(m)||N8(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),e[m.name]==null&&!u.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:t,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function KT(r,t){let{usedNodes:e,inputs:o}=t,n=Object.keys(o).map(g=>Tr(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>e.has(typeof g=="string"?g:g.name);function i(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let p=i([...n,...r.weights,...s]).filter(a),u=i([...p,...Object.values(r.nodes)]).filter(a),c=new Map(u.map(g=>[g.name,g])),l={};for(let g of u){l[g.name]=l[g.name]||0;for(let x of g.children)a(x)||(l[x.name]=Number.POSITIVE_INFINITY),l[x.name]=(l[x.name]||0)+1}let m=Object.entries(l).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(a))--l[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>c.get(g)),h=C8(f,p);return w8(h,p),h}function C8(r,t){let e=new Map(r.map(a=>[a.name,a])),o=t.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=e.get(a);for(let p of i.children)!e.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var Cc=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function w8(r,t){let e=new Map(r.map((i,p)=>[i.name,p])),o=new Set(t.map(i=>i.name)),n=i=>o.has(typeof i=="string"?i:i.name),s=new Set(r.map(i=>i.name)),a=i=>s.has(typeof i=="string"?i:i.name);for(let i of r){for(let p of i.children.filter(a)){if(!e.has(p.name))throw new Cc(`Child ${p.name} of node ${i.name} is unreachable.`);if(e.get(i.name)>e.get(p.name))throw new Cc(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!e.has(p.name))throw new Cc(`Input ${p.name} of node ${i.name} is unreachable.`);if(e.get(p.name)>e.get(i.name))throw new Cc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function qT(r){let t=new Map(r.map((i,p)=>[i.name,p])),e=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>gu(i)?e:p),n=i=>{let p=o[t.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;it[o].map(n=>n.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(o=>{this._functionExecutorMap[o]=new lp(t.functions[o],this)})}getCompilationKey(t,e){let o=t.map(s=>s.name).sort(),n=e.map(s=>s.name).sort();return o.join(this.SEPARATOR)+"--"+n.join(this.SEPARATOR)}compile(t,e){let o=MS(t,e,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let u=e.map(l=>l.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`)}let i=KT(this.graph,o),p=qT(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return Rr(e),e}cloneTensorList(t){return t?t.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,o])=>[e,this.cloneTensorList(o)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let o=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let n=o.map(m=>this.graph.nodes[Tr(m)[0]]),s=e.map(m=>Tr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(t,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=A().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Ll(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,C]=Tr(x,m),S=[];S[C]=t[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=OS(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);d[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,d,m,f,a,g.get(x.name))}return this.parent==null&&m.dispose(f),e.map(x=>zt(x,d,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(o=>t[o]).map(o=>o.map(n=>n.id)));return new Set(e)}checkTensorForDisposal(t,e,o,n,s,a,i){if(!(gu(e)||a.has(t))){for(let p of o[t])p!=null&&(i[p.id]=(i[p.id]||0)+e.children.length);for(let p of e.inputs){if(gu(p))continue;let u=fS(p.name,o,n);if(u!=null)for(let c of u){if(!c||c.kept||s.has(c.id))continue;let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,o,n,s,a){function i(p){return gu(p)||s.has(p.name)}if(!(gu(t)||a==null))for(let p of a){if(i(p))continue;let u=fS(p.name,e,o);for(let c of u)!c||c.kept||n.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=A().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Ll(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(t,a,e,o),p=e.map(m=>zt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(t,e,o){let n=t.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,e,o)}async executeWithControlFlow(t,e,o,n){let s=Object.keys(t),a=s.map(S=>this.graph.nodes[Tr(S)[0]]),i=o.map(S=>Tr(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:c,missingInputs:l,dynamicNode:m,syncInputs:d}=MS(t,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(S=>{let[k,_]=Tr(S),E=[];E[_]=t[S],h[k]=E});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,e,h,b,x,p,g,c);await Promise.all(S)}m==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let C=u.filter(S=>!gu(S)&&!zt(S.name,h,e)).map(S=>S.name);if(C.length>0){let S="";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`)}return h}processStack(t,e,o,n,s,a,i,p,u){let c=[];for(;e.length>0;){let l=e.pop();o.currentContext=l.contexts;let m="";if(l.node.op==="Enter"&&I("isConstant",l.node,n,o)&&([m]=Ls(l.node.name,o)),n[l.node.name]==null){let d=OS(l.node,n,o,this._resourceManager);m||([m]=Ls(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,e,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,e,o,n,s,u))}else this.processChildNodes(l.node,e,o,n,s,u)}return c}processChildNodes(t,e,o,n,s,a){t.children.forEach(i=>{let[p]=Ls(i.name,o);s[p]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!zt(u,n,o))&&(s[p]=!0,e.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!zt(u,n,o))&&(s[p]=!0,e.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let o=t[e],[n]=Tr(e),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(t){var e,o;let n={};for(let s in t){let a=(o=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=t[s]:n[s]=t[s]}return n}checkInputs(t){let e=Object.keys(t).filter(o=>{let[n]=Tr(o);return this.graph.nodes[n]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[o]=Tr(e);if(!this.graph.nodes[o])throw new Error(`The output '${e}' is not found in the graph`)})}};var kf=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var T8="?tfjs-format=file",_8="model.json",Bl=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},o=fi){this.modelUrl=t,this.loadOptions=e,this.version="n/a",this.io=o,e==null&&(this.loadOptions={}),this.resourceManager=new kf}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=this.handler.load();return y.isPromise(t)?t.then(e=>this.loadSync(e)):this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new lp(Ml.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Ml.Instance.transformGraph(t.modelInitializer);this.initializer=new lp(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let o=this.io.getSaveHandlers(t);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${t}'`);t=o[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof ut?[t]:t,o={};return e.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return t}predict(t,e){let o=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(t,e){let o=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(t){var e;if(!(t instanceof ut)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(t[a]=this.resourceIdToCapturedInput[i.resourceId])}return t}t=Array.isArray(t)?t:[t];let o=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+o!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-o} non-resource placeholders, while there are ${t.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((s,a)=>{var i,p,u;let c=(u=(p=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||p===void 0?void 0:p[a])===null||u===void 0?void 0:u.resourceId;return c!=null?s[a]=this.resourceIdToCapturedInput[c]:s[a]=t[n++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(t){if(this.resourceIdToCapturedInput={},this.initializerSignature){let e=this.initializerSignature.outputs,o=Object.keys(e);for(let n=0;n1?o:o[0]}async executeAsync(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let o=await this.executor.executeAsync(t,e);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,o)=>(e[o]=[t[o]],e),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Mt(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function $8(r,t={},e=fi){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof r=="string"&&(r=R8(r));let o=new Bl(r,t,e);return await o.load(),o}function E8(r){if(r==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(r instanceof Array){let[o,n]=r;if(!o)throw new Error("modelJSON must be the first element of the array");if(!n||!(n instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in o))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in o))throw new Error("Model JSON is missing 'weightsManifest'");let s=fi.getWeightSpecs(o.weightsManifest),a=fi.getModelArtifactsForJSONSync(o,s,n);t=fi.fromMemorySync(a)}else if("load"in r)t=r;else if("modelTopology"in r&&"weightSpecs"in r&&"weightData"in r)t=fi.fromMemorySync(r);else throw new Error("Unknown model format");let e=new Bl(t);return e.load(),e}function R8(r){return r.endsWith("/")||(r=r+"/"),`${r}${_8}${T8}`}var D8="4.11.0";function Q(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var A8=Wt.whereImpl,xu=class extends ao{nextDataId(){return xu.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new zo(this,pr())}write(t,e,o){this.firstUse&&(this.firstUse=!1,A().get("IS_NODE")&&w.warn(` + ${o}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),a=wf(s,e),i=o===0?0:r.size/o,p=De(()=>{let c=[];r=W(r,[1,o,i]);for(let l=0;l{switch(r.op){case"If":case"StatelessIf":{let o=I("thenBranch",r,t,e),n=I("elseBranch",r,t,e),s=I("cond",r,t,e),a=I("args",r,t,e);return(await s.data())[0]?e.functionMap[o].executeFunctionAsync(a,e.tensorArrayMap,e.tensorListMap):e.functionMap[n].executeFunctionAsync(a,e.tensorArrayMap,e.tensorListMap)}case"While":case"StatelessWhile":{let o=I("body",r,t,e),n=I("cond",r,t,e),s=I("args",r,t,e),a=await e.functionMap[n].executeFunctionAsync(s,e.tensorArrayMap,e.tensorListMap),i=s.map(c=>c.id),p=await a[0].data();a.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=s;for(;p[0];){let c=u;u=await e.functionMap[o].executeFunctionAsync(u,e.tensorArrayMap,e.tensorListMap);let l=u.map(d=>d.id);c.forEach(d=>{!d.kept&&i.indexOf(d.id)===-1&&l.indexOf(d.id)===-1&&d.dispose()});let m=await e.functionMap[n].executeFunctionAsync(u,e.tensorArrayMap,e.tensorListMap);p=await m[0].data(),m.forEach(d=>{!d.kept&&i.indexOf(d.id)===-1&&l.indexOf(d.id)===-1&&d.dispose()})}return u}case"LoopCond":{let o=I("pred",r,t,e);return[Bs(o)]}case"Switch":{let o=I("pred",r,t,e),n=I("data",r,t,e);return n.kept||(n=Bs(n)),(await o.data())[0]?[void 0,n]:[n,void 0]}case"Merge":{let o=r.inputNames.find(n=>Bt(n,t,e)!==void 0);if(o){let n=Bt(o,t,e);return[Bs(n)]}return}case"Enter":{let o=I("frameName",r,t,e),n=I("tensor",r,t,e);return e.enterFrame(o),[Bs(n)]}case"Exit":{let o=I("tensor",r,t,e);return e.exitFrame(),[Bs(o)]}case"NextIteration":{let o=I("tensor",r,t,e);return e.nextIteration(),[Bs(o)]}case"TensorArrayV3":{let o=I("size",r,t,e),n=I("dtype",r,t,e),s=I("elementShape",r,t,e),a=I("dynamicSize",r,t,e),i=I("clearAfterRead",r,t,e),p=I("identicalElementShapes",r,t,e),u=I("name",r,t,e),c=new Sf(u,n,o,s,p,a,i);return e.addTensorArray(c),[c.idTensor,ke(1)]}case"TensorArrayWriteV3":{let o=I("tensorArrayId",r,t,e),n=I("index",r,t,e),s=I("tensor",r,t,e),a=e.getTensorArray(o.id);return a.write(n,s),[a.idTensor]}case"TensorArrayReadV3":{let o=I("tensorArrayId",r,t,e),n=I("index",r,t,e);return[e.getTensorArray(o.id).read(n)]}case"TensorArrayGatherV3":{let o=I("tensorArrayId",r,t,e),n=I("indices",r,t,e),s=I("dtype",r,t,e);return[e.getTensorArray(o.id).gather(n,s)]}case"TensorArrayScatterV3":{let o=I("tensorArrayId",r,t,e),n=I("indices",r,t,e),s=I("tensor",r,t,e),a=e.getTensorArray(o.id);return a.scatter(n,s),[a.idTensor]}case"TensorArrayConcatV3":{let o=I("tensorArrayId",r,t,e),n=e.getTensorArray(o.id),s=I("dtype",r,t,e);return[n.concat(s)]}case"TensorArraySplitV3":{let o=I("tensorArrayId",r,t,e),n=I("tensor",r,t,e),s=I("lengths",r,t,e),a=e.getTensorArray(o.id);return a.split(s,n),[a.idTensor]}case"TensorArraySizeV3":{let o=I("tensorArrayId",r,t,e),n=e.getTensorArray(o.id);return[ke(n.size(),"int32")]}case"TensorArrayCloseV3":{let o=I("tensorArrayId",r,t,e),n=e.getTensorArray(o.id);return n.clearAndClose(),[n.idTensor]}case"TensorListSetItem":{let o=I("tensorListId",r,t,e),n=I("index",r,t,e),s=I("tensor",r,t,e),a=e.getTensorList(o.id);return a.setItem(n,s),[a.idTensor]}case"TensorListGetItem":{let o=I("tensorListId",r,t,e),n=I("index",r,t,e),s=I("elementShape",r,t,e),a=I("elementDType",r,t,e);return[e.getTensorList(o.id).getItem(n,s,a)]}case"TensorListScatterV2":case"TensorListScatter":{let o=I("indices",r,t,e),n=I("tensor",r,t,e),s=I("elementShape",r,t,e),a=I("numElements",r,t,e),i=NT(n,o,s,a);return e.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let o=I("elementShape",r,t,e),n=I("elementDType",r,t,e),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let a=I(s,r,t,e),i=r.op==="TensorListReserve"?-1:a,p=kT(o,n,a,i);return e.addTensorList(p),[p.idTensor]}case"TensorListGather":{let o=I("tensorListId",r,t,e),n=I("indices",r,t,e),s=I("elementShape",r,t,e),a=I("elementDType",r,t,e);return[e.getTensorList(o.id).gather(n,a,s)]}case"TensorListStack":{let o=I("tensorListId",r,t,e),n=I("elementShape",r,t,e),s=I("elementDType",r,t,e),a=I("numElements",r,t,e);return[e.getTensorList(o.id).stack(n,s,a)]}case"TensorListFromTensor":{let o=I("tensor",r,t,e),n=I("elementShape",r,t,e),s=I("elementDType",r,t,e),a=vT(o,n,s);return e.addTensorList(a),[a.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let o=I("tensorListId",r,t,e),n=e.getTensorList(o.id),s=I("dtype",r,t,e),a=I("elementShape",r,t,e);return[n.concat(s,a)]}case"TensorListPushBack":{let o=I("tensorListId",r,t,e),n=I("tensor",r,t,e),s=e.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case"TensorListPopBack":{let o=I("tensorListId",r,t,e),n=I("elementShape",r,t,e),s=I("elementDType",r,t,e);return[e.getTensorList(o.id).popBack(n,s)]}case"TensorListSplit":{let o=I("tensor",r,t,e),n=I("elementShape",r,t,e),s=I("lengths",r,t,e),a=TT(o,s,n);return e.addTensorList(a),[a.idTensor]}case"TensorListLength":{let o=I("tensorListId",r,t,e),n=e.getTensorList(o.id);return[ke(n.size(),"int32")]}case"TensorListResize":{let o=I("tensorListId",r,t,e),n=I("size",r,t,e),a=e.getTensorList(o.id).resize(n);return e.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function $T(r,t,e){let[o,n]=I("fusedOps",r,t,e),s=o==="biasadd",a=!s,i=n==="prelu",p=o==="fusedbatchnorm",u=I("numArgs",r,t,e);if(s){if(i&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(p)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=I("strides",r,t,e),l=Al(r,t,e),m=I("dataFormat",r,t,e).toUpperCase(),d=I("dilations",r,t,e),[f,h]=I("args",r,t,e);a&&(h=f,f=void 0);let g=I("leakyreluAlpha",r,t,e);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var ET=(r,t,e,o=Je)=>{switch(r.op){case"Conv1D":{let n=I("stride",r,t,e),s=I("pad",r,t,e),a=I("dataFormat",r,t,e).toUpperCase(),i=I("dilation",r,t,e);return[o.conv1d(I("x",r,t,e),I("filter",r,t,e),n,s,a,i)]}case"Conv2D":{let n=I("strides",r,t,e),s=Al(r,t,e),a=I("dataFormat",r,t,e).toUpperCase(),i=I("dilations",r,t,e);return[o.conv2d(I("x",r,t,e),I("filter",r,t,e),[n[1],n[2]],s,a,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=$T(r,t,e);return[o.fused.conv2d({x:I("x",r,t,e),filter:I("filter",r,t,e),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case"FusedDepthwiseConv2dNative":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=$T(r,t,e);return[o.fused.depthwiseConv2d({x:I("x",r,t,e),filter:I("filter",r,t,e),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let n=I("outputShape",r,t,e),s=I("strides",r,t,e),a=Al(r,t,e);return[o.conv2dTranspose(I("x",r,t,e),I("filter",r,t,e),n,[s[1],s[2]],a)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let n=I("strides",r,t,e),s=Al(r,t,e),a=I("dilations",r,t,e),i=I("dataFormat",r,t,e).toUpperCase();return[o.depthwiseConv2d(I("input",r,t,e),I("filter",r,t,e),[n[1],n[2]],s,i,[a[1],a[2]])]}case"Conv3D":{let n=I("strides",r,t,e),s=I("pad",r,t,e),a=I("dataFormat",r,t,e).toUpperCase(),i=I("dilations",r,t,e);return[o.conv3d(I("x",r,t,e),I("filter",r,t,e),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case"AvgPool":{let n=I("strides",r,t,e),s=I("pad",r,t,e),a=I("kernelSize",r,t,e);return[o.avgPool(I("x",r,t,e),[a[1],a[2]],[n[1],n[2]],s)]}case"MaxPool":{let n=I("strides",r,t,e),s=I("pad",r,t,e),a=I("kernelSize",r,t,e);return[o.maxPool(I("x",r,t,e),[a[1],a[2]],[n[1],n[2]],s)]}case"MaxPoolWithArgmax":{let n=I("strides",r,t,e),s=I("pad",r,t,e),a=I("kernelSize",r,t,e),i=I("includeBatchInIndex",r,t,e),{result:p,indexes:u}=o.maxPoolWithArgmax(I("x",r,t,e),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case"AvgPool3D":{let n=I("strides",r,t,e),s=I("pad",r,t,e),a=I("kernelSize",r,t,e);return[o.avgPool3d(I("x",r,t,e),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case"MaxPool3D":{let n=I("strides",r,t,e),s=I("pad",r,t,e),a=I("kernelSize",r,t,e);return[o.maxPool3d(I("x",r,t,e),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case"Dilation2D":{let n=I("strides",r,t,e),s=I("pad",r,t,e),a=I("dilations",r,t,e),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I("x",r,t,e),I("filter",r,t,e),[i,p],s,[u,c],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var RT=(r,t,e,o=Je)=>{switch(r.op){case"Fill":{let n=I("shape",r,t,e),s=I("dtype",r,t,e),a=I("value",r,t,e);return[o.fill(n,a,s)]}case"LinSpace":{let n=I("start",r,t,e),s=I("stop",r,t,e),a=I("num",r,t,e);return[o.linspace(n,s,a)]}case"Multinomial":{let n=I("logits",r,t,e),s=I("numSamples",r,t,e),a=I("seed",r,t,e);return[o.multinomial(n,s,a)]}case"OneHot":{let n=I("indices",r,t,e),s=I("depth",r,t,e),a=I("onValue",r,t,e),i=I("offValue",r,t,e),p=I("dtype",r,t,e);return[o.oneHot(n,s,a,i,p)]}case"Ones":return[o.ones(I("shape",r,t,e),I("dtype",r,t,e))];case"OnesLike":return[o.onesLike(I("x",r,t,e))];case"RandomStandardNormal":return[o.randomStandardNormal(I("shape",r,t,e),I("dtype",r,t,e),I("seed",r,t,e))];case"RandomUniform":return[o.randomUniform(I("shape",r,t,e),I("minval",r,t,e),I("maxval",r,t,e),I("dtype",r,t,e))];case"RandomUniformInt":return[o.randomUniformInt(I("shape",r,t,e),I("minval",r,t,e),I("maxval",r,t,e),I("seed",r,t,e))];case"Range":{let n=I("start",r,t,e),s=I("stop",r,t,e),a=I("step",r,t,e);return[o.range(n,s,a,I("dtype",r,t,e))]}case"TruncatedNormal":{let n=I("shape",r,t,e),s=I("mean",r,t,e),a=I("stdDev",r,t,e),i=I("seed",r,t,e);return[o.truncatedNormal(n,s,a,I("dtype",r,t,e),i)]}case"Zeros":return[o.zeros(I("shape",r,t,e),I("dtype",r,t,e))];case"ZerosLike":return[o.zerosLike(I("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function PS(r,t,e){let o=I("boxes",r,t,e),n=I("scores",r,t,e),s=I("maxOutputSize",r,t,e),a=I("iouThreshold",r,t,e),i=I("scoreThreshold",r,t,e),p=I("softNmsSigma",r,t,e);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var DT=async(r,t,e,o,n=Je)=>{switch(r.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=PS(r,t,e),l=await n.image.nonMaxSuppressionWithScoreAsync(s,a,i,p,u,c);return[l.selectedIndices,l.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=PS(r,t,e),c=I("padToMaxOutputSize",r,t,e),l=await n.image.nonMaxSuppressionPaddedAsync(s,a,i,p,u,c);return[l.selectedIndices,l.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=PS(r,t,e);return[await n.image.nonMaxSuppressionAsync(s,a,i,p,u)]}case"Where":{let s=n.cast(I("condition",r,t,e),"bool"),a=[await n.whereAsync(s)];return s.dispose(),a}case"ListDiff":return n.setdiff1dAsync(I("x",r,t,e),I("y",r,t,e));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var AT=(r,t,e,o=Je)=>{switch(r.op){case"LowerBound":{let n=I("sortedSequence",r,t,e),s=I("values",r,t,e);return[o.lowerBound(n,s)]}case"TopKV2":{let n=I("x",r,t,e),s=I("k",r,t,e),a=I("sorted",r,t,e),i=o.topk(n,s,a);return[i.values,i.indices]}case"UpperBound":{let n=I("sortedSequence",r,t,e),s=I("values",r,t,e);return[o.upperBound(n,s)]}case"Unique":{let n=I("x",r,t,e),s=o.unique(n);return[s.values,s.indices]}case"UniqueV2":{let n=I("x",r,t,e),s=I("axis",r,t,e),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var FT=(r,t,e,o=Je)=>{switch(r.op){case"Const":return t[r.name];case"PlaceholderWithDefault":let n=I("default",r,t,e);return[Bt(r.name,t,e)||n];case"Placeholder":return[Bt(r.name,t,e)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=I("x",r,t,e);return[Bs(c)]}case"IdentityN":return I("x",r,t,e).map(c=>Bs(c));case"Snapshot":let s=I("x",r,t,e);return[Bs(s)];case"Shape":return[o.tensor1d(I("x",r,t,e).shape,"int32")];case"ShapeN":return I("x",r,t,e).map(c=>o.tensor1d(c.shape));case"Size":return[o.scalar(I("x",r,t,e).size,"int32")];case"Rank":return[o.scalar(I("x",r,t,e).rank,"int32")];case"NoOp":return[o.scalar(1)];case"Print":let a=I("x",r,t,e),i=I("data",r,t,e),p=I("message",r,t,e),u=I("summarize",r,t,e);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(p);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ke(this.size(),"int32")}async import(t,e){this.checkKeyAndValueTensor(t,e);let o=await t.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),De(()=>{let n=mo(e),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case"HashTable":case"HashTableV2":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I("keyDType",r,t,e),a=I("valueDType",r,t,e),i=new If(s,a);return o.addHashTable(r.name,i),[i.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let n=I("tableHandle",r,t,e,o),s=I("keys",r,t,e),a=I("values",r,t,e);return[await o.getHashTableById(n.id).import(s,a)]}case"LookupTableFind":case"LookupTableFindV2":{let n=I("tableHandle",r,t,e,o),s=I("keys",r,t,e),a=I("defaultValue",r,t,e);return[await o.getHashTableById(n.id).find(s,a)]}case"LookupTableSize":case"LookupTableSizeV2":{let n=I("tableHandle",r,t,e,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var OT=(r,t,e,o=Je)=>{switch(r.op){case"ResizeBilinear":{let n=I("images",r,t,e),s=I("size",r,t,e),a=I("alignCorners",r,t,e),i=I("halfPixelCenters",r,t,e);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case"ResizeNearestNeighbor":{let n=I("images",r,t,e),s=I("size",r,t,e),a=I("alignCorners",r,t,e),i=I("halfPixelCenters",r,t,e);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case"CropAndResize":{let n=I("image",r,t,e),s=I("boxes",r,t,e),a=I("boxInd",r,t,e),i=I("cropSize",r,t,e),p=I("method",r,t,e),u=I("extrapolationValue",r,t,e);return[o.image.cropAndResize(n,s,a,i,p,u)]}case"ImageProjectiveTransformV3":{let n=I("images",r,t,e),s=I("transforms",r,t,e),a=I("outputShape",r,t,e),i=I("fillValue",r,t,e),p=I("interpolation",r,t,e),u=I("fillMode",r,t,e);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MT=(r,t,e,o=Je)=>{switch(r.op){case"Equal":return[o.equal(I("a",r,t,e),I("b",r,t,e))];case"NotEqual":return[o.notEqual(I("a",r,t,e),I("b",r,t,e))];case"Greater":return[o.greater(I("a",r,t,e),I("b",r,t,e))];case"GreaterEqual":return[o.greaterEqual(I("a",r,t,e),I("b",r,t,e))];case"Less":return[o.less(I("a",r,t,e),I("b",r,t,e))];case"LessEqual":return[o.lessEqual(I("a",r,t,e),I("b",r,t,e))];case"LogicalAnd":return[o.logicalAnd(I("a",r,t,e),I("b",r,t,e))];case"LogicalNot":return[o.logicalNot(I("a",r,t,e))];case"LogicalOr":return[o.logicalOr(I("a",r,t,e),I("b",r,t,e))];case"Select":case"SelectV2":return[o.where(I("condition",r,t,e),I("a",r,t,e),I("b",r,t,e))];case"BitwiseAnd":return[o.bitwiseAnd(I("a",r,t,e),I("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LT=(r,t,e,o=Je)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[o.matMul(I("a",r,t,e),I("b",r,t,e),I("transposeA",r,t,e),I("transposeB",r,t,e))];case"Einsum":return[o.einsum(I("equation",r,t,e),...I("tensors",r,t,e))];case"Transpose":return[o.transpose(I("x",r,t,e),I("perm",r,t,e))];case"_FusedMatMul":let[n,s]=I("fusedOps",r,t,e),a=n==="biasadd",i=s==="prelu",p=I("numArgs",r,t,e),u=I("leakyreluAlpha",r,t,e);if(a){if(i&&p!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&p!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,l]=I("args",r,t,e);return[o.fused.matMul({a:I("a",r,t,e),b:I("b",r,t,e),transposeA:I("transposeA",r,t,e),transposeB:I("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];case"MatrixBandPart":return[o.linalg.bandPart(I("a",r,t,e),I("numLower",r,t,e),I("numUpper",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var BT=(r,t,e,o=Je)=>{switch(r.op){case"EuclideanNorm":return[o.euclideanNorm(I("x",r,t,e),I("axis",r,t,e),I("keepDims",r,t,e))];case"FusedBatchNorm":case"FusedBatchNormV2":return[o.batchNorm(I("x",r,t,e),I("mean",r,t,e),I("variance",r,t,e),I("offset",r,t,e),I("scale",r,t,e),I("epsilon",r,t,e))];case"FusedBatchNormV3":return[o.batchNorm(I("x",r,t,e),I("mean",r,t,e),I("variance",r,t,e),I("offset",r,t,e),I("scale",r,t,e),I("epsilon",r,t,e))];case"LRN":return[o.localResponseNormalization(I("x",r,t,e),I("radius",r,t,e),I("bias",r,t,e),I("alpha",r,t,e),I("beta",r,t,e))];case"Softmax":return[o.softmax(I("x",r,t,e))];case"LogSoftmax":return[o.logSoftmax(I("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var zT=(r,t,e,o=Je)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I("paramsNestedSplits",r,t,e),I("paramsDenseValues",r,t,e),I("indices",r,t,e),I("outputRaggedRank",r,t,e));return n.concat(s)}case"RaggedRange":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I("starts",r,t,e),I("limits",r,t,e),I("splits",r,t,e));return[n,s]}case"RaggedTensorToTensor":return[o.raggedTensorToTensor(I("shape",r,t,e),I("values",r,t,e),I("defaultValue",r,t,e),I("rowPartitionTensors",r,t,e),I("rowPartitionTypes",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var VT=(r,t,e,o=Je)=>{switch(r.op){case"Max":{let i=I("axis",r,t,e),p=I("keepDims",r,t,e);return[o.max(I("x",r,t,e),i,p)]}case"Mean":{let i=I("axis",r,t,e),p=I("keepDims",r,t,e);return[o.mean(I("x",r,t,e),i,p)]}case"Min":{let i=I("axis",r,t,e),p=I("keepDims",r,t,e);return[o.min(I("x",r,t,e),i,p)]}case"Sum":{let i=I("axis",r,t,e),p=I("keepDims",r,t,e);return[o.sum(I("x",r,t,e),i,p)]}case"All":{let i=I("axis",r,t,e),p=I("keepDims",r,t,e);return[o.all(I("x",r,t,e),i,p)]}case"Any":{let i=I("axis",r,t,e),p=I("keepDims",r,t,e);return[o.any(I("x",r,t,e),i,p)]}case"ArgMax":{let i=I("axis",r,t,e);return[o.argMax(I("x",r,t,e),i)]}case"ArgMin":{let i=I("axis",r,t,e);return[o.argMin(I("x",r,t,e),i)]}case"Prod":{let i=I("axis",r,t,e),p=I("keepDims",r,t,e);return[o.prod(I("x",r,t,e),i,p)]}case"Cumprod":{let i=I("axis",r,t,e),p=I("exclusive",r,t,e),u=I("reverse",r,t,e);return[o.cumprod(I("x",r,t,e),i,p,u)]}case"Cumsum":{let i=I("axis",r,t,e),p=I("exclusive",r,t,e),u=I("reverse",r,t,e);return[o.cumsum(I("x",r,t,e),i,p,u)]}case"Bincount":let n=I("x",r,t,e),s=I("weights",r,t,e),a=I("size",r,t,e);return[o.bincount(n,s,a)];case"DenseBincount":{let i=I("x",r,t,e),p=I("weights",r,t,e),u=I("size",r,t,e),c=I("binaryOutput",r,t,e);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var WT=(r,t,e,o=Je)=>{switch(r.op){case"ConcatV2":case"Concat":{let n=I("n",r,t,e),s=I("axis",r,t,e),a=I("tensors",r,t,e);return a=a.slice(0,n),[o.concat(a,s)]}case"Gather":{let n=I("x",r,t,e),s=I("indices",r,t,e);return[o.gather(n,o.cast(s,"int32"),0)]}case"GatherV2":{let n=I("axis",r,t,e),s=I("batchDims",r,t,e),a=I("x",r,t,e),i=I("indices",r,t,e);return[o.gather(a,o.cast(i,"int32"),n,s)]}case"Reverse":{let n=I("dims",r,t,e),s=[];for(let i=0;i{let n=I("axis",r,t,e),s=I("tensors",r,t,e),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error("the input tensors shape does not match");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case"Unpack":{let n=I("axis",r,t,e),s=I("tensor",r,t,e);return o.unstack(s,n)}case"Tile":{let n=I("reps",r,t,e);return[o.tile(I("x",r,t,e),n)]}case"Split":case"SplitV":{let n=I("axis",r,t,e),s=I("numOrSizeSplits",r,t,e),a=I("x",r,t,e);return o.split(a,s,n)}case"ScatterNd":{let n=I("indices",r,t,e),s=I("values",r,t,e),a=I("shape",r,t,e);return[o.scatterND(n,s,a)]}case"GatherNd":{let n=I("x",r,t,e),s=I("indices",r,t,e);return[o.gatherND(n,s)]}case"SparseToDense":{let n=I("sparseIndices",r,t,e),s=I("outputShape",r,t,e),a=I("sparseValues",r,t,e),i=I("defaultValue",r,t,e);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}case"TensorScatterUpdate":{let n=I("indices",r,t,e),s=I("values",r,t,e),a=I("tensor",r,t,e);return[o.tensorScatterUpdate(a,n,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UT=(r,t,e,o=Je)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I("indices",r,t,e),I("values",r,t,e),I("denseShape",r,t,e),I("defaultValue",r,t,e));return[n,s,a,i]}case"SparseReshape":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I("inputIndices",r,t,e),I("inputShape",r,t,e),I("newShape",r,t,e));return[n,s]}case"SparseSegmentMean":return[o.sparse.sparseSegmentMean(I("data",r,t,e),I("indices",r,t,e),I("segmentIds",r,t,e))];case"SparseSegmentSum":return[o.sparse.sparseSegmentSum(I("data",r,t,e),I("indices",r,t,e),I("segmentIds",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GT=(r,t,e,o=Je)=>{switch(r.op){case"FFT":return[o.fft(I("x",r,t,e))];case"IFFT":return[o.ifft(I("x",r,t,e))];case"RFFT":return[o.rfft(I("x",r,t,e))];case"IRFFT":return[o.irfft(I("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HT=(r,t,e,o=Je)=>{switch(r.op){case"StaticRegexReplace":return[o.string.staticRegexReplace(I("input",r,t,e),I("pattern",r,t,e),I("rewrite",r,t,e),I("replaceGlobal",r,t,e))];case"StringNGrams":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I("data",r,t,e),I("dataSplits",r,t,e),I("separator",r,t,e),I("nGramWidths",r,t,e),I("leftPad",r,t,e),I("rightPad",r,t,e),I("padWidth",r,t,e),I("preserveShortSequences",r,t,e));return[n,s]}case"StringSplit":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I("input",r,t,e),I("delimiter",r,t,e),I("skipEmpty",r,t,e));return[n,s,a]}case"StringToHashBucketFast":return[o.string.stringToHashBucketFast(I("input",r,t,e),I("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KT=(r,t,e,o=Je)=>{switch(r.op){case"Cast":return[o.cast(I("x",r,t,e),I("dtype",r,t,e))];case"ExpandDims":{let n=I("axis",r,t,e);return[o.expandDims(I("x",r,t,e),n)]}case"Squeeze":{let n=I("axis",r,t,e);return[o.squeeze(I("x",r,t,e),n)]}case"Reshape":return[o.reshape(I("x",r,t,e),I("shape",r,t,e))];case"EnsureShape":return[o.ensureShape(I("x",r,t,e),I("shape",r,t,e))];case"MirrorPad":return[o.mirrorPad(I("x",r,t,e),I("padding",r,t,e),I("mode",r,t,e))];case"PadV2":case"Pad":return[o.pad(I("x",r,t,e),I("padding",r,t,e),I("constantValue",r,t,e))];case"SpaceToBatchND":{let n=I("blockShape",r,t,e),s=I("paddings",r,t,e);return[o.spaceToBatchND(I("x",r,t,e),n,s)]}case"BatchToSpaceND":{let n=I("blockShape",r,t,e),s=I("crops",r,t,e);return[o.batchToSpaceND(I("x",r,t,e),n,s)]}case"DepthToSpace":{let n=I("blockSize",r,t,e),s=I("dataFormat",r,t,e).toUpperCase();return[o.depthToSpace(I("x",r,t,e),n,s)]}case"BroadcastTo":return[o.broadcastTo(I("x",r,t,e),I("shape",r,t,e))];case"BroadcastArgs":return[o.broadcastArgs(I("s0",r,t,e),I("s1",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function OS(r,t,e,o,n=De){let s=((a,i,p)=>{switch(a.category){case"arithmetic":return n(()=>wT(a,i,p));case"basic_math":return n(()=>ST(a,i,p));case"control":return _T(a,i,p);case"convolution":return n(()=>ET(a,i,p));case"creation":return n(()=>RT(a,i,p));case"dynamic":return DT(a,i,p);case"evaluation":return n(()=>AT(a,i,p));case"image":return n(()=>OT(a,i,p));case"graph":return n(()=>FT(a,i,p));case"logical":return n(()=>MT(a,i,p));case"matrices":return n(()=>LT(a,i,p));case"normalization":return n(()=>BT(a,i,p));case"ragged":return n(()=>zT(a,i,p));case"reduction":return n(()=>VT(a,i,p));case"slice_join":return n(()=>WT(a,i,p));case"sparse":return n(()=>UT(a,i,p));case"spectral":return n(()=>GT(a,i,p));case"string":return n(()=>HT(a,i,p));case"transformation":return n(()=>KT(a,i,p));case"hash_table":return PT(a,i,p,o);case"custom":let u=uf(a.op);if(u&&u.customExecutor)return u.customExecutor(new Cf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Pl=class{constructor(t={},e={},o={},n={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=o,this.functionMap=n,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function MS(r,t,e,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=new Set(Object.keys(r).map(m=>Nr(m)[0]));o=o||[];let c=new Set(o.map(m=>Nr(m.name)[0])),l=[...t];for(;l.length>0;){let m=l.pop();if((mu(m)||k8(m)||N8(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),e[m.name]==null&&!u.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:t,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function qT(r,t){let{usedNodes:e,inputs:o}=t,n=Object.keys(o).map(g=>Nr(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>e.has(typeof g=="string"?g:g.name);function i(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let p=i([...n,...r.weights,...s]).filter(a),u=i([...p,...Object.values(r.nodes)]).filter(a),c=new Map(u.map(g=>[g.name,g])),l={};for(let g of u){l[g.name]=l[g.name]||0;for(let x of g.children)a(x)||(l[x.name]=Number.POSITIVE_INFINITY),l[x.name]=(l[x.name]||0)+1}let m=Object.entries(l).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(a))--l[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>c.get(g)),h=C8(f,p);return w8(h,p),h}function C8(r,t){let e=new Map(r.map(a=>[a.name,a])),o=t.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=e.get(a);for(let p of i.children)!e.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var fc=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function w8(r,t){let e=new Map(r.map((i,p)=>[i.name,p])),o=new Set(t.map(i=>i.name)),n=i=>o.has(typeof i=="string"?i:i.name),s=new Set(r.map(i=>i.name)),a=i=>s.has(typeof i=="string"?i:i.name);for(let i of r){for(let p of i.children.filter(a)){if(!e.has(p.name))throw new fc(`Child ${p.name} of node ${i.name} is unreachable.`);if(e.get(i.name)>e.get(p.name))throw new fc(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!e.has(p.name))throw new fc(`Input ${p.name} of node ${i.name} is unreachable.`);if(e.get(p.name)>e.get(i.name))throw new fc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function jT(r){let t=new Map(r.map((i,p)=>[i.name,p])),e=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>mu(i)?e:p),n=i=>{let p=o[t.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;it[o].map(n=>n.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(o=>{this._functionExecutorMap[o]=new r(t.functions[o],this)})}getCompilationKey(t,e){let o=t.map(s=>s.name).sort(),n=e.map(s=>s.name).sort();return o.join(this.SEPARATOR)+"--"+n.join(this.SEPARATOR)}compile(t,e){let o=MS(t,e,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let u=e.map(l=>l.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`)}let i=qT(this.graph,o),p=jT(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return Er(e),e}cloneTensorList(t){return t?t.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,o])=>[e,this.cloneTensorList(o)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let o=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let n=o.map(m=>this.graph.nodes[Nr(m)[0]]),s=e.map(m=>Nr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(t,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=A().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Pl(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,C]=Nr(x,m),S=[];S[C]=t[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=OS(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);d[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,d,m,f,a,g.get(x.name))}return this.parent==null&&m.dispose(f),e.map(x=>Bt(x,d,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(o=>t[o]).map(o=>o.map(n=>n.id)));return new Set(e)}checkTensorForDisposal(t,e,o,n,s,a,i){if(!(mu(e)||a.has(t))){for(let p of o[t])p!=null&&(i[p.id]=(i[p.id]||0)+e.children.length);for(let p of e.inputs){if(mu(p))continue;let u=fS(p.name,o,n);if(u!=null)for(let c of u){if(!c||c.kept||s.has(c.id))continue;let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,o,n,s,a){function i(p){return mu(p)||s.has(p.name)}if(!(mu(t)||a==null))for(let p of a){if(i(p))continue;let u=fS(p.name,e,o);for(let c of u)!c||c.kept||n.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=A().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Pl(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(t,a,e,o),p=e.map(m=>Bt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(t,e,o){let n=t.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,e,o)}async executeWithControlFlow(t,e,o,n){let s=Object.keys(t),a=s.map(S=>this.graph.nodes[Nr(S)[0]]),i=o.map(S=>Nr(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:c,missingInputs:l,dynamicNode:m,syncInputs:d}=MS(t,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(S=>{let[k,_]=Nr(S),E=[];E[_]=t[S],h[k]=E});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,e,h,b,x,p,g,c);await Promise.all(S)}m==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let C=u.filter(S=>!mu(S)&&!Bt(S.name,h,e)).map(S=>S.name);if(C.length>0){let S="";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`)}return h}processStack(t,e,o,n,s,a,i,p,u){let c=[];for(;e.length>0;){let l=e.pop();o.currentContext=l.contexts;let m="";if(l.node.op==="Enter"&&I("isConstant",l.node,n,o)&&([m]=Ls(l.node.name,o)),n[l.node.name]==null){let d=OS(l.node,n,o,this._resourceManager);m||([m]=Ls(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,e,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,e,o,n,s,u))}else this.processChildNodes(l.node,e,o,n,s,u)}return c}processChildNodes(t,e,o,n,s,a){t.children.forEach(i=>{let[p]=Ls(i.name,o);s[p]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!Bt(u,n,o))&&(s[p]=!0,e.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Bt(u,n,o))&&(s[p]=!0,e.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let o=t[e],[n]=Nr(e),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(t){var e,o;let n={};for(let s in t){let a=(o=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=t[s]:n[s]=t[s]}return n}checkInputs(t){let e=Object.keys(t).filter(o=>{let[n]=Nr(o);return this.graph.nodes[n]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[o]=Nr(e);if(!this.graph.nodes[o])throw new Error(`The output '${e}' is not found in the graph`)})}};var vf=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var T8="?tfjs-format=file",_8="model.json",Ml=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},o=mi){this.modelUrl=t,this.loadOptions=e,this.version="n/a",this.io=o,e==null&&(this.loadOptions={}),this.resourceManager=new vf}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=this.handler.load();return y.isPromise(t)?t.then(e=>this.loadSync(e)):this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Ol(Fl.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Fl.Instance.transformGraph(t.modelInitializer);this.initializer=new Ol(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let o=this.io.getSaveHandlers(t);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${t}'`);t=o[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof mt?[t]:t,o={};return e.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return t}predict(t,e){let o=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(t,e){let o=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(t){var e;if(!(t instanceof mt)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(t[a]=this.resourceIdToCapturedInput[i.resourceId])}return t}t=Array.isArray(t)?t:[t];let o=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+o!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-o} non-resource placeholders, while there are ${t.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((s,a)=>{var i,p,u;let c=(u=(p=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||p===void 0?void 0:p[a])===null||u===void 0?void 0:u.resourceId;return c!=null?s[a]=this.resourceIdToCapturedInput[c]:s[a]=t[n++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(t){if(this.resourceIdToCapturedInput={},this.initializerSignature){let e=this.initializerSignature.outputs,o=Object.keys(e);for(let n=0;n1?o:o[0]}async executeAsync(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let o=await this.executor.executeAsync(t,e);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,o)=>(e[o]=[t[o]],e),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Ot(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function $8(r,t={},e=mi){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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$Y(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;Q([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=c.strideHeight,m=c.strideWidth,d=c.filterHeight,f=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,C=b-1-c.padInfo.left,S=x-1-c.padInfo.top,k=me(a.shape,"float32"),_=1/(d*f),E=e.data.get(n.dataId).values,R=me(n.shape,"float32",E);for(let D=0;D=c.outHeight||Math.floor(j)!==j))for(let q=0;q=c.outWidth||Math.floor(Y)!==Y)continue;let J=R.get(D,j,Y,P);z+=J}}k.set(z*_,D,O,M,P)}return e.makeTensorInfo(k.shape,k.dtype,k.values)}var o$={kernelName:Di,backendName:"cpu",kernelFunc:$Y};function EY(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,scale:s,offset:a,mean:i,variance:p}=t;y.assert(i.shape.length===p.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient 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i=s.reduce((x,b)=>x*b),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=Ve({inputs:{x:n},backend:e,attrs:{shape:p}}),f=It({inputs:{x:d},backend:e,attrs:{perm:u}}),h=Ve({inputs:{x:f},backend:e,attrs:{shape:c}}),g=Fo({inputs:{x:h},backend:e,attrs:{begin:l,size:m}});return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var s$={kernelName:Js,backendName:"cpu",kernelFunc:RY};function DY(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=e.data.get(n.dataId).values,p=e.data.get(s.dataId).values,u=Ic(i,p,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,u)}var a$={kernelName:en,backendName:"cpu",kernelFunc:DY};function AY(r){let{inputs:t,backend:e}=r,{s0:o,s1:n}=t,s=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values,i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return e.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var i$={kernelName:ea,backendName:"cpu",kernelFunc:AY};var FY=Ie(Co,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,o=new Float32Array(y.sizeFromShape(t.shape)),n=e.data.get(t.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=e.data.get(s.dataId).values,p=e.data.get(a.dataId).values;for(let u=0;uh.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let p=t.filter(h=>y.sizeFromShape(h.shape)>0);if(p.length===1)return mr({inputs:{x:p[0]},backend:e});if(p[0].dtype==="complex64"){let h=p.map(S=>Ro({inputs:{input:S},backend:e})),g=p.map(S=>Ma({inputs:{input:S},backend:e})),x=yu({inputs:h,backend:e,attrs:{axis:s}}),b=yu({inputs:g,backend:e,attrs:{axis:s}}),C=Kt({inputs:{real:x,imag:b},backend:e});return h.forEach(S=>e.disposeIntermediateTensorInfo(S)),g.forEach(S=>e.disposeIntermediateTensorInfo(S)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),C}let u=p.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Ve({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=u.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));i=w.computeOutShape(u.map(h=>h.shape),1);let l=u[0].shape[0]===1,m=mp(c,i,t[0].dtype,l),d=w.computeOutShape(p.map(h=>h.shape),s),f=e.makeTensorInfo(d,t[0].dtype,m);return u.forEach(h=>e.disposeIntermediateTensorInfo(h)),f}var l$={kernelName:ta,backendName:"cpu",kernelFunc:yu};function yI(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o;Q([n,s],"conv2d");let l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat==="channelsLast",S=new tt(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),E=k[0],R=C?k[1]:k[2],D=C?k[2]:1,P=C?1:k[1],O=S.strides[0],M=C?S.strides[1]:S.strides[2],L=C?S.strides[2]:1,B=C?1:S.strides[1],z=e.data.get(n.dataId).values,U=e.data.get(s.dataId).values,j=S.values;for(let q=0;q=m.inHeight)continue;let le=oe*_[0],be=Y+ie*R;for(let _e=0;_e=m.inWidth)continue;let lt=le+Pe*_[1],Ge=be+st*D,mt=lt;for(let it=0;it=u.inDepth)continue;let q=U*D[0],Y=O+j*R[1];for(let J=0;J=u.inHeight)continue;let ie=q+ee*D[1],le=Y+oe*R[2];for(let be=0;be=u.inWidth)continue;let st=ie+Fe*D[2],lt=le+Pe*u.inChannels,Ge=st;for(let mt=0;mtMath.cos(r)),y$={kernelName:an,backendName:"cpu",kernelFunc:VY};var WY=Ie(un,r=>Math.cosh(r)),b$={kernelName:un,backendName:"cpu",kernelFunc:WY};function UY(r){let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],"float32"),b=e.data.get(s.dataId).values,C=e.data.get(a.dataId).values,S=e.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let E=0;E=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-P)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let q=0;q1?P*(m-1)+re*z:.5*(P+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?P*(m-1)+q*z:.5*(P+M)*(m-1);if(Y<0||Y>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=e.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,C=b.left,S=b.top,k=d.outChannels/d.inChannels,_=new tt(d.outShape,n.dtype),E=e.data.get(n.dataId).values,R=e.data.get(s.dataId).values,D=_.values;for(let P=0;P=d.inHeight)continue;let q=U*l[0],Y=O+j*c[1];for(let J=0;J=d.inWidth)continue;let ie=q+ee*l[1],le=Y+oe*d.inChannels,be=re,_e=ie;for(let ve=0;ve{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=e,p=t,u=p.data.get(o.dataId).values,c=o.shape.length,l=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:C,strideHeight:S,strideWidth:k,filterHeight:_,filterWidth:E,dilationHeight:R,dilationWidth:D,outShape:P}=w.computeDilation2DInfo(o.shape,n.shape,s,a,"NHWC",i),O=y.sizeFromShape(P),M=P.length,L=y.getArrayFromDType(o.dtype,O);for(let z=0;z=0&&oe=0&&lere&&(re=ve)}}}let ne=y.locToIndex([z,U,q,J],M,y.computeStrides(P));L[ne]=re}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),P,o.dtype),shape:P,dtype:o.dtype}}};var E$={kernelName:zi,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=e,u=t,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=w.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===D.length,()=>`Error in ${zi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ne,re=oe)}}}O[J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var R$={kernelName:Bi,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=e,u=t,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=w.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===D.length,()=>`Error in ${Bi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ee,re=ie)}}}O[L][J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function QY(r){let{inputs:t,backend:e,attrs:o}=r,{image:n}=t,{canvas:s,options:a}=o,{contextOptions:i,imageOptions:p}=a||{},u=(p==null?void 0:p.alpha)||1,c=(i==null?void 0:i.contextType)||"2d";if(c!=="2d")throw new Error(`Context type ${i.contextType} is not supported by the CPU backend.`);let l=s.getContext(c,(i==null?void 0:i.contextAttributes)||{});if(l==null)throw new Error(`Could not get the context with ${c} type.`);let[m,d]=n.shape.slice(0,2),f=n.shape.length===2?1:n.shape[2],h=e.data.get(n.dataId).values,g=n.dtype==="float32"?255:1,x=new Uint8ClampedArray(d*m*4);for(let C=0;C1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=c.strideDepth,m=c.strideHeight,d=c.strideWidth,f=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,C=c.dilationWidth,S=c.effectiveFilterDepth,k=c.effectiveFilterHeight,_=c.effectiveFilterWidth,E=S-1-c.padInfo.front,R=_-1-c.padInfo.left,D=k-1-c.padInfo.top,P=me(s.shape,"float32"),O=1/(f*h*g),M=e.bufferSync(n);for(let L=0;L=c.outDepth||Math.floor(ee)!==ee))for(let oe=0;oe=c.outHeight||Math.floor(ie)!==ie))for(let le=0;le<_;le+=C){let be=(J+le)/d;if(be<0||be>=c.outWidth||Math.floor(be)!==be)continue;let _e=M.get(L,ee,ie,be,B);re+=_e}}}P.set(re*O,L,z,U,j,B)}return e.makeTensorInfo(P.shape,P.dtype,P.values)}var o$={kernelName:Ei,backendName:"cpu",kernelFunc:_Y};function $Y(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;Q([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=c.strideHeight,m=c.strideWidth,d=c.filterHeight,f=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,C=b-1-c.padInfo.left,S=x-1-c.padInfo.top,k=me(a.shape,"float32"),_=1/(d*f),E=e.data.get(n.dataId).values,R=me(n.shape,"float32",E);for(let D=0;D=c.outHeight||Math.floor(j)!==j))for(let q=0;q=c.outWidth||Math.floor(Y)!==Y)continue;let J=R.get(D,j,Y,P);z+=J}}k.set(z*_,D,O,M,P)}return e.makeTensorInfo(k.shape,k.dtype,k.values)}var n$={kernelName:$i,backendName:"cpu",kernelFunc:$Y};function EY(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,scale:s,offset:a,mean:i,variance:p}=t;y.assert(i.shape.length===p.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient 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i=s.reduce((x,b)=>x*b),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=Ve({inputs:{x:n},backend:e,attrs:{shape:p}}),f=St({inputs:{x:d},backend:e,attrs:{perm:u}}),h=Ve({inputs:{x:f},backend:e,attrs:{shape:c}}),g=Ao({inputs:{x:h},backend:e,attrs:{begin:l,size:m}});return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var a$={kernelName:Js,backendName:"cpu",kernelFunc:RY};function DY(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=e.data.get(n.dataId).values,p=e.data.get(s.dataId).values,u=yc(i,p,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,u)}var i$={kernelName:Jo,backendName:"cpu",kernelFunc:DY};function AY(r){let{inputs:t,backend:e}=r,{s0:o,s1:n}=t,s=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values,i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return e.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var u$={kernelName:ea,backendName:"cpu",kernelFunc:AY};var FY=Ie(bo,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,o=new Float32Array(y.sizeFromShape(t.shape)),n=e.data.get(t.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=e.data.get(s.dataId).values,p=e.data.get(a.dataId).values;for(let u=0;uh.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let p=t.filter(h=>y.sizeFromShape(h.shape)>0);if(p.length===1)return lr({inputs:{x:p[0]},backend:e});if(p[0].dtype==="complex64"){let h=p.map(S=>Eo({inputs:{input:S},backend:e})),g=p.map(S=>Oa({inputs:{input:S},backend:e})),x=du({inputs:h,backend:e,attrs:{axis:s}}),b=du({inputs:g,backend:e,attrs:{axis:s}}),C=Ht({inputs:{real:x,imag:b},backend:e});return h.forEach(S=>e.disposeIntermediateTensorInfo(S)),g.forEach(S=>e.disposeIntermediateTensorInfo(S)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),C}let u=p.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Ve({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=u.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));i=w.computeOutShape(u.map(h=>h.shape),1);let l=u[0].shape[0]===1,m=np(c,i,t[0].dtype,l),d=w.computeOutShape(p.map(h=>h.shape),s),f=e.makeTensorInfo(d,t[0].dtype,m);return u.forEach(h=>e.disposeIntermediateTensorInfo(h)),f}var m$={kernelName:ta,backendName:"cpu",kernelFunc:du};function bI(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o;Q([n,s],"conv2d");let l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat==="channelsLast",S=new tt(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),E=k[0],R=C?k[1]:k[2],D=C?k[2]:1,P=C?1:k[1],O=S.strides[0],M=C?S.strides[1]:S.strides[2],L=C?S.strides[2]:1,B=C?1:S.strides[1],z=e.data.get(n.dataId).values,U=e.data.get(s.dataId).values,j=S.values;for(let q=0;q=m.inHeight)continue;let le=oe*_[0],be=Y+ie*R;for(let _e=0;_e=m.inWidth)continue;let ct=le+Pe*_[1],Ge=be+st*D,lt=ct;for(let it=0;it=u.inDepth)continue;let q=U*D[0],Y=O+j*R[1];for(let J=0;J=u.inHeight)continue;let ie=q+ee*D[1],le=Y+oe*R[2];for(let be=0;be=u.inWidth)continue;let st=ie+Fe*D[2],ct=le+Pe*u.inChannels,Ge=st;for(let lt=0;ltMath.cos(r)),b$={kernelName:sn,backendName:"cpu",kernelFunc:VY};var WY=Ie(an,r=>Math.cosh(r)),C$={kernelName:an,backendName:"cpu",kernelFunc:WY};function UY(r){let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],"float32"),b=e.data.get(s.dataId).values,C=e.data.get(a.dataId).values,S=e.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let E=0;E=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-P)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let q=0;q1?P*(m-1)+re*z:.5*(P+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?P*(m-1)+q*z:.5*(P+M)*(m-1);if(Y<0||Y>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=e.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. 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0:return TR();case 1:return bZ(r,t,e);case 2:return TZ(r,t,e);case 3:return wZ(r,t,e);case 4:return IZ(r,t,e);case 5:return vZ(r,t);case 6:return kZ(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function cZ(r){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${r.texture2D}(textureSampler, uv).r; } @@ -260,7 +260,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function NR(){return` +`;function TR(){return` int getOutputCoords() { return 0; } @@ -357,7 +357,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Ip(["r","c","d"],r)} + ${hp(["r","c","d"],r)} return ivec3(r, c, d); } `;let o=Ws(["r","c","d"],r);return` @@ -414,7 +414,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Ip(["r","c","d","d2"],r)} + ${hp(["r","c","d","d2"],r)} return ivec4(r, c, d, d2); } `;let o=Ws(["r","c","d","d2"],r);return` @@ -535,7 +535,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${r[1]}; return ivec2(r, c); } - `}function vp(r){return`offset${r}`}function _Z(r){let t=r.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),o=vt();return` + `}function gp(r){return`offset${r}`}function _Z(r){let t=r.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),o=It();return` vec4 ${e}() { return ${o.texture2D}(${t}, halfCR); } @@ -543,7 +543,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float ${o}() { return sampleTexture(${e}, halfCR); } - `;let a=vp(e);if(t)return` + `;let a=gp(e);if(t)return` float ${o}() { vec2 uv = uvFromFlat(${e}TexShape[0], ${e}TexShape[1], ${a}); return sampleTexture(${e}, uv); @@ -553,7 +553,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${i}, ${p}, ${a}); return sampleTexture(${e}, uv); } - `}function EZ(r,t){let e=r.name,o="get"+e.charAt(0).toUpperCase()+e.slice(1),n=r.shapeInfo.texShape,s=vt();if(t)return` + `}function EZ(r,t){let e=r.name,o="get"+e.charAt(0).toUpperCase()+e.slice(1),n=r.shapeInfo.texShape,s=It();if(t)return` vec4 ${o}(int index) { ivec2 packedTexShape = ivec2(ceil(float(${e}TexShape[0]) / 2.0), ceil(float(${e}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( @@ -568,13 +568,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } `}function RZ(r,t){let e=r.name,o="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return` float ${o}(int index) { - ${Lc(r)} + ${Dc(r)} } `;let n=r.shapeInfo.texShape,s=n[0],a=n[1];if(a===1&&s===1)return` float ${o}(int index) { return sampleTexture(${e}, halfCR); } - `;let i=vp(e);return a===1?t?` + `;let i=gp(e);return a===1?t?` float ${o}(int index) { vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${e}TexShape[0])); return sampleTexture(${e}, uv); @@ -604,7 +604,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${s}, ${a}, index + ${i}); return sampleTexture(${e}, uv); } - `}function DZ(r,t){let e=r.shapeInfo.logicalShape,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=s[0],i=s[1],p=vt();if(s!=null&&y.arraysEqual(e,s))return t?` + `}function DZ(r,t){let e=r.shapeInfo.logicalShape,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=s[0],i=s[1],p=It();if(s!=null&&y.arraysEqual(e,s))return t?` vec4 ${n}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]); @@ -638,17 +638,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${m}.0); return sampleTexture(${o}, uv); } - `}let{newShape:a,keptDims:i}=y.squeezeShape(e),p=a;if(p.length=1?c="coords = 0;":c=i.map(b=>`coords.${l[b+u]} = 0;`).join(` + `}function zZ(r,t){let e=r.name,o=e.charAt(0).toUpperCase()+e.slice(1),n="get"+o+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=t.logicalShape.length,i=vR(r.shapeInfo.logicalShape,t.logicalShape),p=Re(a),u=a-s,c,l=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(b=>`coords.${l[b+u]} = 0;`).join(` `);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,C)=>`coords.${l[C+u]}`).join(", ");let d="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!x)d=` return vec4(outputValue.xy, outputValue.xy); `;else if(h&&!x)a===1?d=` @@ -969,16 +969,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float ${n}() { return sampleTexture(${e}, resultUV); } - `;let u=Re(p),c=IR(r.shapeInfo.logicalShape,t.logicalShape),l=p-i,m,d=["x","y","z","w","u","v"];i===0?m="":p<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${d[h+l]} = 0;`).join(` + `;let u=Re(p),c=vR(r.shapeInfo.logicalShape,t.logicalShape),l=p-i,m,d=["x","y","z","w","u","v"];i===0?m="":p<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${d[h+l]} = 0;`).join(` `);let f="";return p<2&&i>0?f="coords":f=r.shapeInfo.logicalShape.map((h,g)=>`coords.${d[g+l]}`).join(", "),` float ${n}() { ${u} coords = getOutputCoords(); ${m} return get${o}(${f}); } - `}function Re(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function Zf(r,t,e){let{newShape:o,keptDims:n}=y.squeezeShape(t),s=t.length,a=r&&s===3&&t[0]===1,i=a?t.slice(1):o,p=!r&&s>1&&!y.arraysEqual(t,e)&&o.lengthr[e]).join(", ")}function _R(r,t,e,o){let n=e.map((c,l)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[l],shapeInfo:m}}),s=n.map(c=>c.shapeInfo),a={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},i=vR(n,a,t),p=$I(r.gl,i),u=r.createProgram(p);return A().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(u),Object.assign({program:t,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a},qI(r,t,u)))}function qI(r,t,e){let o=[],n=[],s,a,i,p=null,u=null;u=r.getUniformLocation(e,"NAN",!1),A().getNumber("WEBGL_VERSION")===1&&(p=r.getUniformLocation(e,"INFINITY",!1));let c=!1;for(let l of t.variableNames){let m={name:l,uniform:r.getUniformLocation(e,l,c),offset:r.getUniformLocation(e,`offset${l}`,c)};t.enableShapeUniforms&&(m.shape=r.getUniformLocation(e,`${l}Shape`,c),m.texShape=r.getUniformLocation(e,`${l}TexShape`,c)),o.push(m)}if(t.enableShapeUniforms&&(s=r.getUniformLocation(e,"outShape",c),i=r.getUniformLocation(e,"outShapeStrides",c),a=r.getUniformLocation(e,"outTexShape",c)),t.customUniforms)for(let l of t.customUniforms)n.push(r.getUniformLocation(e,l.name,c));return{variablesLocations:o,customUniformLocations:n,infLoc:p,nanLoc:u,outShapeLocation:s,outShapeStridesLocation:i,outTexShapeLocation:a}}function TR(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,o)=>{let n=e.logicalShape,s=t[o],a=s.shape;if(!y.arraysEqual(n,a))throw Error(`Binary was compiled with different shapes than the current args. 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Shape ${i} and ${p} must match`)})}function $R(r,t,e,o,n){t.program.enableShapeUniforms||(TR(t.inShapeInfos,e),TR([t.outShapeInfo],[o]));let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,a[0],a[1]):r.setOutputMatrixTexture(s.texture,a[0],a[1]),r.setProgram(t.webGLProgram),r.bindVertexArray(t.webGLProgram.vao),A().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let p=0;p{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!a.isUniform){let p=a.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:l}=Zf(r.packedInputs,a.shape,p),m="",d="",f="";if(c.length===1&&r.packedInputs){let k=[Math.ceil(p[0]/2),Math.ceil(p[1]/2)];m=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!r.packedInputs)d=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let k=y.computeStrides(c);f=`${k[0]===p[1]}_${k[k.length-1]===p[1]}`}let h=a.shape.length,g=c.length===2&&y.arraysEqual(a.shape,p),x=y.sizeFromShape(a.shape)===1,b=w.getBroadcastDims(a.shape,e.shape),C=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(p,e.texData.texShape),S=r.packedInputs||c.length>2?"":`${p[0]>1}_${p[1]>1}`;o+=`${h}_${C}_${u?l:""}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${S}_${i}`}else{let p=a.isUniform?"uniform":a.texData.texShape;o+=`${a.shape}_${p}_${i}`}});let n=r.userCode,s=r.constructor.name;return s+="_"+o+"_"+n+`${A().getNumber("WEBGL_VERSION")}`,s}function pt(r){return A().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&r<=4}var Jf=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=bu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=vt();this.outputShape=t,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=` + `}function Re(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function Qf(r,t,e){let{newShape:o,keptDims:n}=y.squeezeShape(t),s=t.length,a=r&&s===3&&t[0]===1,i=a?t.slice(1):o,p=!r&&s>1&&!y.arraysEqual(t,e)&&o.lengthr[e]).join(", ")}function $R(r,t,e,o){let n=e.map((c,l)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[l],shapeInfo:m}}),s=n.map(c=>c.shapeInfo),a={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},i=kR(n,a,t),p=EI(r.gl,i),u=r.createProgram(p);return A().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(u),Object.assign({program:t,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a},jI(r,t,u)))}function jI(r,t,e){let o=[],n=[],s,a,i,p=null,u=null;u=r.getUniformLocation(e,"NAN",!1),A().getNumber("WEBGL_VERSION")===1&&(p=r.getUniformLocation(e,"INFINITY",!1));let c=!1;for(let l of t.variableNames){let m={name:l,uniform:r.getUniformLocation(e,l,c),offset:r.getUniformLocation(e,`offset${l}`,c)};t.enableShapeUniforms&&(m.shape=r.getUniformLocation(e,`${l}Shape`,c),m.texShape=r.getUniformLocation(e,`${l}TexShape`,c)),o.push(m)}if(t.enableShapeUniforms&&(s=r.getUniformLocation(e,"outShape",c),i=r.getUniformLocation(e,"outShapeStrides",c),a=r.getUniformLocation(e,"outTexShape",c)),t.customUniforms)for(let l of t.customUniforms)n.push(r.getUniformLocation(e,l.name,c));return{variablesLocations:o,customUniformLocations:n,infLoc:p,nanLoc:u,outShapeLocation:s,outShapeStridesLocation:i,outTexShapeLocation:a}}function _R(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,o)=>{let n=e.logicalShape,s=t[o],a=s.shape;if(!y.arraysEqual(n,a))throw Error(`Binary was compiled with different shapes than the current args. 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Shape ${i} and ${p} must match`)})}function ER(r,t,e,o,n){t.program.enableShapeUniforms||(_R(t.inShapeInfos,e),_R([t.outShapeInfo],[o]));let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,a[0],a[1]):r.setOutputMatrixTexture(s.texture,a[0],a[1]),r.setProgram(t.webGLProgram),r.bindVertexArray(t.webGLProgram.vao),A().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let p=0;p{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!a.isUniform){let p=a.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:l}=Qf(r.packedInputs,a.shape,p),m="",d="",f="";if(c.length===1&&r.packedInputs){let k=[Math.ceil(p[0]/2),Math.ceil(p[1]/2)];m=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!r.packedInputs)d=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let k=y.computeStrides(c);f=`${k[0]===p[1]}_${k[k.length-1]===p[1]}`}let h=a.shape.length,g=c.length===2&&y.arraysEqual(a.shape,p),x=y.sizeFromShape(a.shape)===1,b=w.getBroadcastDims(a.shape,e.shape),C=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(p,e.texData.texShape),S=r.packedInputs||c.length>2?"":`${p[0]>1}_${p[1]>1}`;o+=`${h}_${C}_${u?l:""}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${S}_${i}`}else{let p=a.isUniform?"uniform":a.texData.texShape;o+=`${a.shape}_${p}_${i}`}});let n=r.userCode,s=r.constructor.name;return s+="_"+o+"_"+n+`${A().getNumber("WEBGL_VERSION")}`,s}function ut(r){return A().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&r<=4}var Zf=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=fu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=It();this.outputShape=t,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Ip(["r","c","d"],t):Ws(["r","c","d"],t)} + ${this.enableShapeUniforms?hp(["r","c","d"],t):Ws(["r","c","d"],t)} return ivec3(r, c, d); } @@ -996,9 +996,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${e.output} = result; } - `}};var eh=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=bu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=vt();this.outputShape=t,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=` + `}};var Jf=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=fu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=It();this.outputShape=t,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Ip(["r","c","d"],t):Ws(["r","c","d"],t)} + ${this.enableShapeUniforms?hp(["r","c","d"],t):Ws(["r","c","d"],t)} return ivec3(r, c, d); } @@ -1016,26 +1016,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${e.output} = result; } - `}};var th=class{constructor(t){this.variableNames=["A"],this.outTexUsage=dr.DOWNLOAD;let e=vt();this.outputShape=t,this.userCode=` - ${Qf} + `}};var eh=class{constructor(t){this.variableNames=["A"],this.outTexUsage=mr.DOWNLOAD;let e=It();this.outputShape=t,this.userCode=` + ${Yf} void main() { float x = getAAtOutCoords(); ${e.output} = encode_float(x); } - `}};var rh=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=dr.DOWNLOAD;let e=vt();this.outputShape=t,this.userCode=` - ${Qf} + `}};var th=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=mr.DOWNLOAD;let e=It();this.outputShape=t,this.userCode=` + ${Yf} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${e.output} = encode_float(x); } - `}};var GZ={R:0,G:1,B:2,A:3},Zl=class{constructor(t,e=!1,o="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=vt();this.outputShape=t,this.enableShapeUniforms=pt(this.outputShape.length);let s="result";e&&(s="floor(result * 255. + 0.5)");let a="";for(let i=0;irv,createBufferFromOutputTexture:()=>sv,createFloat16MatrixTexture:()=>ZI,createFloat16PackedMatrixTexture:()=>tv,createFloat32MatrixTexture:()=>QI,createIndexBuffer:()=>YI,createPackedMatrixTexture:()=>ev,createUnsignedBytesMatrixTexture:()=>JI,createVertexBuffer:()=>XI,createVertexShader:()=>jI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>iv,downloadFloat32MatrixFromBuffer:()=>av,downloadMatrixFromPackedOutputTexture:()=>pv,downloadPackedMatrixFromBuffer:()=>uv,getInternalFormatForFloat16MatrixTexture:()=>sh,getInternalFormatForFloat16PackedMatrixTexture:()=>uh,getInternalFormatForFloat32MatrixTexture:()=>nh,getInternalFormatForPackedMatrixTexture:()=>ih,getInternalFormatForUnsignedBytesMatrixTexture:()=>ah,uploadDenseMatrixToTexture:()=>ov,uploadPixelDataToTexture:()=>nv});function jI(r){let t=vt(),e=`${t.version} + `}};var lv={};qe(lv,{bindVertexProgramAttributeStreams:()=>ov,createBufferFromOutputTexture:()=>av,createFloat16MatrixTexture:()=>JI,createFloat16PackedMatrixTexture:()=>rv,createFloat32MatrixTexture:()=>ZI,createIndexBuffer:()=>QI,createPackedMatrixTexture:()=>tv,createUnsignedBytesMatrixTexture:()=>ev,createVertexBuffer:()=>YI,createVertexShader:()=>XI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>uv,downloadFloat32MatrixFromBuffer:()=>iv,downloadMatrixFromPackedOutputTexture:()=>cv,downloadPackedMatrixFromBuffer:()=>pv,getInternalFormatForFloat16MatrixTexture:()=>nh,getInternalFormatForFloat16PackedMatrixTexture:()=>ih,getInternalFormatForFloat32MatrixTexture:()=>oh,getInternalFormatForPackedMatrixTexture:()=>ah,getInternalFormatForUnsignedBytesMatrixTexture:()=>sh,uploadDenseMatrixToTexture:()=>nv,uploadPixelDataToTexture:()=>sv});function XI(r){let t=It(),e=`${t.version} precision highp float; ${t.attribute} vec3 clipSpacePos; ${t.attribute} vec2 uv; @@ -1107,11 +1107,11 @@ vec2 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if(A().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(o="EXT_color_buffer_float",Kr(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(Kr(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=YI(this.gl),this.indexBuffer=QI(this.gl),this.framebuffer=MI(this.gl),this.textureConfig=ql(this.gl,this.textureHalfFloatExtension)}get debug(){return A().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let t=this.gl;ce(t,()=>t.finish()),ce(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ce(t,()=>t.deleteFramebuffer(this.framebuffer)),ce(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ce(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),ZI(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),JI(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),ev(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),sv(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,o,n){this.throwIfDisposed(),nv(this.gl,t,e,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),rv(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),tv(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(jf(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,o){return this.downloadMatrixDriver(t,()=>uv(this.gl,e,o,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,o,n,s,a){return pv(this.gl,t,e,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return iv(this.gl,t,e)}createBufferFromTexture(t,e,o){this.bindTextureToFrameBuffer(t);let n=av(this.gl,e,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,o;if(A().getBool("WEBGL_FENCE_API_ENABLED")){let n=t,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},e=s}else A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(e=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(e,A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):o=()=>!0;return{query:e,isFencePassed:o}}downloadMatrixFromPackedTexture(t,e,o){return this.downloadMatrixDriver(t,()=>cv(this.gl,e,o))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=XI(e));let o=RI(e);ce(e,()=>e.attachShader(o,this.vertexShader)),ce(e,()=>e.attachShader(o,t)),DI(e,o);let n=Object.assign(o,{vao:this.createVertexArray()});return this.debug&&jl(e,n),n}buildVao(t){this.setProgram(t),this.bindVertexArray(t.vao);let e=this.gl;ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),ov(e,t,this.vertexBuffer)}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ce(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&jl(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,o=!0){return this.throwIfDisposed(),o?LI(this.gl,t,e):BI(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,o){this.throwIfDisposed(),this.throwIfNoProgram(),zI(this.gl,t,e,o)}setOutputMatrixTexture(t,e,o){this.setOutputMatrixTextureDriver(t,o,e)}setOutputPackedMatrixTexture(t,e,o){this.throwIfDisposed();let[n,s]=Ma(e,o);this.setOutputMatrixTextureDriver(t,n,s)}setOutputMatrixWriteRegion(t,e,o,n){this.setOutputMatrixWriteRegionDriver(o,t,n,e)}setOutputPackedMatrixWriteRegion(t,e,o,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&jl(this.gl,this.program),kc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}ce(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=vc(this.gl,A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let e=this.gl,o=this.getQueryTimerExtensionWebGL2();e.endQuery(o.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(t,A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let o=this.gl;return o.getQueryParameter(t,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(t,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(t,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(t,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=HZ(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:o}=this.itemsToPoll[e];o()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let o;"setTimeoutCustom"in A().platform&&(o=A().platform.setTimeoutCustom.bind(A().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),Xl(this.gl,t,this.framebuffer),this.debug&&kc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Xl(this.gl,this.outputTexture,this.framebuffer),this.debug&&kc(this.gl)):jf(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let o=e();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(t,e,o){this.throwIfDisposed();let n=this.gl;Xl(n,t,this.framebuffer),this.debug&&kc(n),this.outputTexture=t,ce(n,()=>n.viewport(0,0,e,o)),ce(n,()=>n.scissor(0,0,e,o))}setOutputMatrixWriteRegionDriver(t,e,o,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(t,e,o,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function HZ(r){let t=0;for(;t`${r}.${e}`)}function Rt(r,t){return t===1?[r]:mv(r,t)}function ID(r,t){if(r===1)return"rc";let e="";for(let o=0;o= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}), cEdge ? 0. : getA(${e[1]}), rEdge ? 0. : getA(${e[2]}), - rEdge || cEdge ? 0. : getA(${e[3]})`}};var Vc=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=t,this.enableShapeUniforms=pt(this.outputShape.length);let o="";for(let n=0;n<4;n++){let s="thisRC = rc;";n%2===1&&(s+="thisRC.z += 1;"),n>1&&(s+="thisRC.y += 1;"),o+=` + rEdge || cEdge ? 0. : getA(${e[3]})`}};var Pc=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=t,this.enableShapeUniforms=ut(this.outputShape.length);let o="";for(let n=0;n<4;n++){let s="thisRC = rc;";n%2===1&&(s+="thisRC.z += 1;"),n>1&&(s+="thisRC.y += 1;"),o+=` ${s} ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); @@ -1147,7 +1147,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n>0?"}":""} `}this.userCode=` ${KZ(e,this.enableShapeUniforms)} - ${this.enableShapeUniforms?Pc():Fc(t)} + ${this.enableShapeUniforms?$c():_c(t)} void main() { ivec3 rc = getOutputCoords(); @@ -1164,10 +1164,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } `}};function KZ(r,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?SR(["r","c","d"],"inputShape"):Ws(["r","c","d"],r)} + ${t?IR(["r","c","d"],"inputShape"):Ws(["r","c","d"],r)} return ivec3(r, c, d); } - `}var dh=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,o){let n=vD(e,o),s=kD(t,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=ID(t,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].pop();return this.usedTextures[s].push(p),p}let i;return n===rr.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):n===rr.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):n===rr.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):n===rr.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):n===rr.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(t,e,o,n){if(this.freeTextures==null)return;let s=vD(o,n),a=kD(e,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=ID(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=A().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(t),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(t);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function qZ(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function ID(r,t,e,o,n){let s=jZ(t,o),a;if(n){let[p,u]=La(r[0],r[1]);a=p*u}else{let[p,u]=Sp(r[0],r[1]);a=p*u}let i=qZ(e,s);return a*i}function jZ(r,t){switch(r){case rr.PACKED_2X2_FLOAT32:return ih(t);case rr.PACKED_2X2_FLOAT16:return uh(t);case rr.UNPACKED_FLOAT32:return nh(t);case rr.UNPACKED_FLOAT16:return sh(t);case rr.PACKED_4X1_UNSIGNED_BYTE:return ah(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function XZ(r){return A().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?rr.PACKED_2X2_FLOAT32:rr.UNPACKED_FLOAT32:r?rr.PACKED_2X2_FLOAT16:rr.UNPACKED_FLOAT16}function vD(r,t){if(r===dr.UPLOAD)return rr.PACKED_2X2_FLOAT32;if(r===dr.RENDER||r==null)return XZ(t);if(r===dr.DOWNLOAD||r===dr.PIXELS)return rr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function kD(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var or=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=` + `}var mh=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,o){let n=kD(e,o),s=ND(t,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=vD(t,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].pop();return this.usedTextures[s].push(p),p}let i;return n===er.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):n===er.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):n===er.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):n===er.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):n===er.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(t,e,o,n){if(this.freeTextures==null)return;let s=kD(o,n),a=ND(e,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=vD(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=A().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(t),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(t);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function qZ(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function vD(r,t,e,o,n){let s=jZ(t,o),a;if(n){let[p,u]=Ma(r[0],r[1]);a=p*u}else{let[p,u]=fp(r[0],r[1]);a=p*u}let i=qZ(e,s);return a*i}function jZ(r,t){switch(r){case er.PACKED_2X2_FLOAT32:return ah(t);case er.PACKED_2X2_FLOAT16:return ih(t);case er.UNPACKED_FLOAT32:return oh(t);case er.UNPACKED_FLOAT16:return nh(t);case er.PACKED_4X1_UNSIGNED_BYTE:return sh(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function XZ(r){return A().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?er.PACKED_2X2_FLOAT32:er.UNPACKED_FLOAT32:r?er.PACKED_2X2_FLOAT16:er.UNPACKED_FLOAT16}function kD(r,t){if(r===mr.UPLOAD)return er.PACKED_2X2_FLOAT32;if(r===mr.RENDER||r==null)return XZ(t);if(r===mr.DOWNLOAD||r===mr.PIXELS)return er.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function ND(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var tr=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${e} } @@ -1178,11 +1178,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Ut="if (isnan(x)) return x;",ND="return x;",mv="return abs(x);";var TD="return (x >= 0.0) ? x : (exp(x) - 1.0);",_D=Ut+` + `}},Wt="if (isnan(x)) return x;",TD="return x;",dv="return abs(x);";var _D="return (x >= 0.0) ? x : (exp(x) - 1.0);",$D=Wt+` return (x < 0.0) ? 0.0 : x; -`,$D=Ut+` +`,ED=Wt+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Ba="return x;",ED="return 1.0 / (1.0 + exp(-1.0 * x));";var DD="return x;",AD=` +`,La="return x;",RD="return 1.0 / (1.0 + exp(-1.0 * x));";var AD="return x;",FD=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1191,7 +1191,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,FD=` +`,PD=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1201,7 +1201,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,PD=` +`,OD=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1211,7 +1211,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,OD="return 1.0 / (1.0 + exp(-1.0 * x));",Fr=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=` +`,MD="return 1.0 / (1.0 + exp(-1.0 * x));",Ar=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${e} } @@ -1222,17 +1222,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}};var fh=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=pt(this.outputShape.length);let e=t.length,o=Dt("rc",e),n=Re(e),s=SD(e,o),a=o.slice(-2),i=e<=1?"rc":`vec2(${a.join(",")})`;this.userCode=` + `}};var dh=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=ut(this.outputShape.length);let e=t.length,o=Rt("rc",e),n=Re(e),s=ID(e,o),a=o.slice(-2),i=e<=1?"rc":`vec2(${a.join(",")})`;this.userCode=` void main() { ${n} rc = getOutputCoords(); vec4 packedInput = getA(${s}); setOutput(getChannel(packedInput, ${i})); } - `}};var QZ=Wt.whereImpl,ZZ=1e-7,JZ=1e-4,hh={};function e9(r){return r in hh||(hh[r]={}),hh[r]}var t9=A().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),r9=600;function o9(){return A().global.screen==null?1024:A().global.screen.height*A().global.screen.width*window.devicePixelRatio*r9/1024/1024}var wu=class extends ao{nextDataId(){return wu.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!A().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof kp)e=t;else{let o=Kr(A().getNumber("WEBGL_VERSION"),t);e=new kp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Kr(A().getNumber("WEBGL_VERSION"));e=new kp(o),this.binaryCache=e9(A().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new dh(this.gpgpu),this.numMBBeforeWarning=o9(),this.texData=new zo(this,pr())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,o,n,s,a){let i=this.makeTensorInfo(e,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:t,texShape:[n,s]},p.texShape=[n,s];let u=Dc(e),c=new Zl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=e,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(t,e,o){if((A().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||A().getBool("DEBUG"))&&this.checkNumericalProblems(t),o==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:e,dtype:o,values:t,usage:dr.UPLOAD,refCount:1}),n}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,o,n,s){if(A().getBool("DEBUG")&&this.checkNumericalProblems(e),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:o,dtype:n,values:e,usage:dr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=e;if(a!=null){let m;p?m=new Fr(i,Ba):m=new or(i,Ba);let d=this.runWebGLProgram(m,[{dataId:t,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(t);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n==="complex64"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=w.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,l)}async read(t){if(this.pendingRead.has(t)){let f=this.pendingRead.get(t);return new Promise(h=>f.push(h))}let e=this.texData.get(t),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=e;if(s!=null){let f;p?f=new Fr(n,Ba):f=new or(n,Ba);let h=this.runWebGLProgram(f,[{dataId:t,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(t);if(A().getBool("DEBUG")&&!A().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&A().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&A().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...jl(n))}this.pendingRead.set(t,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let l;if(a==="complex64"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=w.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(t);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(t,l),d=this.pendingRead.get(t);return this.pendingRead.delete(t),d.forEach(f=>f(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&pr().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let o=this.texData.get(t),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;p?d=new Fr(s,Ba):d=new or(s,Ba);let f=this.runWebGLProgram(d,[{dataId:t,shape:s,dtype:i}],i),h=this.readToGPU(f,e);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),l=pr().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let o=e.map(n=>y.decodeString(n));return me(t.shape,t.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return me(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,t();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=e,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:o}=this.texData.get(t);return o!=null&&(this.disposeData(o.real.dataId,e),this.disposeData(o.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(t),p=i&&i.origDataId||t,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),e!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(e,n,s,a)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=t9){return A().getBool("WEBGL_CPU_FORWARD")&&t.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,t,e)}else n=this.write(o,t,e);return this.texData.get(n).usage=null,{dataId:n,shape:t,dtype:e}}makeOutput(t,e,o){return pr().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,o),this)}unpackTensor(t){let e=new fh(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new mh(t.shape),o=!0;return this.runWebGLProgram(e,[t],t.dtype,null,o)}packedReshape(t,e){let o=[yi(t.shape),...bi(t.shape)],n={dtype:t.dtype,shape:o,dataId:t.dataId},s=[yi(e),...bi(e)],a=new Vc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],t.dtype,p,i);return{dataId:u.dataId,shape:e,dtype:u.dtype}}decode(t,e){let o=this.texData.get(t),{isPacked:n,shape:s,dtype:a}=o;if(e!=null){let m=y.sizeFromShape(s),d=e[0]*e[1]*4;y.assert(m<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Dc(s),p;n?p=new eh(i):p=new Jf(i);let u=!0,c=[e!=null?e:jl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:t}],a,c,u,e);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(t,e,o,n,s=!1,a){let i=this.makeTensorInfo(t.outputShape,o),p=this.texData.get(i.dataId);if(t.packedOutput&&(p.isPacked=!0),t.outPackingScheme===bu.DENSE){let x=a!=null?a:jl(t.outputShape);p.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(p.usage=t.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=e.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=A().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Cu(b.shape,x.shape)){let C=x,S=x.shape;x.shape=b.shape,x=this.packedReshape(x,S),u.push(x),b=this.texData.get(x.dataId),C.shape=S}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=ER(t,c,l),d=this.getAndSaveBinary(m,()=>_R(this.gpgpu,t,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),A().get("ENGINE_COMPILE_ONLY")||$R(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=A().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!A().getBool("WEBGL_LAZILY_UNPACK")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(t,e,o,n,s=!1){return o=o||e[0].dtype,this.runWebGLProgram(t,e,o,n,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(A().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!A().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=A().getBool("DEBUG");A().set("DEBUG",!1);let e=this.abs(ke(1e-8)).dataSync()[0];if(A().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?ZZ:JZ}uploadToGPU(t){let e=this.texData.get(t),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=e;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=e.texShape;if(l==null&&(l=zI(o,p),e.texShape=l),s!=null){let m=Dc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=La(l[0],l[1])),p?d=new oh(m,g):d=new Zl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=dr.PIXELS:C.usage=dr.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let S=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,S,k),E=this.texData.get(_.dataId);e.texShape=E.texShape,e.isPacked=E.isPacked,e.usage=E.usage,A().get("ENGINE_COMPILE_ONLY")?this.disposeData(_.dataId):(e.texture=E.texture,e.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);e.texture=m}}convertAndCacheOnCPU(t,e){let o=this.texData.get(t),{dtype:n}=o;return e!=null&&(o.values=n9(e,n)),o.values}acquireTexture(t,e,o,n){if(this.numBytesInGPU+=this.computeBytes(t,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,n)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(e),n(!0)}catch(s){throw s}});t.push(o)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await pS(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(qf(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:o,infLoc:n,nanLoc:s,outShapeLocation:a,outShapeStridesLocation:i,outTexShapeLocation:p}=qI(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=o,t.infLoc=n,t.nanLoc=s,t.outShapeLocation=a,t.outShapeStridesLocation=i,t.outTexShapeLocation=p}}createTensorFromGPUData(t,e,o){t.channels=t.channels||"RGBA";let{texture:n,height:s,width:a,channels:i}=t,p=pr().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=p.writeTexture(n,e,o,s,a,i);return pr().makeTensorFromDataId(u,e,o,p)}};wu.nextDataId=0;function n9(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew wu,2);var Cat={forceHalfFloat:MD};var Wc=` + `}};var QZ=Vt.whereImpl,ZZ=1e-7,JZ=1e-4,fh={};function e9(r){return r in fh||(fh[r]={}),fh[r]}var t9=A().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),r9=600;function o9(){return A().global.screen==null?1024:A().global.screen.height*A().global.screen.width*window.devicePixelRatio*r9/1024/1024}var Oc=class r extends so{nextDataId(){return r.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!A().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof xp)e=t;else{let o=Hr(A().getNumber("WEBGL_VERSION"),t);e=new xp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Hr(A().getNumber("WEBGL_VERSION"));e=new xp(o),this.binaryCache=e9(A().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new mh(this.gpgpu),this.numMBBeforeWarning=o9(),this.texData=new Bo(this,ur())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,o,n,s,a){let i=this.makeTensorInfo(e,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:t,texShape:[n,s]},p.texShape=[n,s];let u=Nc(e),c=new Yl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=e,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(t,e,o){if((A().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||A().getBool("DEBUG"))&&this.checkNumericalProblems(t),o==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:e,dtype:o,values:t,usage:mr.UPLOAD,refCount:1}),n}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,o,n,s){if(A().getBool("DEBUG")&&this.checkNumericalProblems(e),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:o,dtype:n,values:e,usage:mr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=e;if(a!=null){let m;p?m=new Ar(i,La):m=new tr(i,La);let d=this.runWebGLProgram(m,[{dataId:t,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(t);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n==="complex64"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=w.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,l)}async read(t){if(this.pendingRead.has(t)){let f=this.pendingRead.get(t);return new Promise(h=>f.push(h))}let e=this.texData.get(t),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=e;if(s!=null){let f;p?f=new Ar(n,La):f=new tr(n,La);let h=this.runWebGLProgram(f,[{dataId:t,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(t);if(A().getBool("DEBUG")&&!A().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&A().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&A().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Kl(n))}this.pendingRead.set(t,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let l;if(a==="complex64"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=w.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(t);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(t,l),d=this.pendingRead.get(t);return this.pendingRead.delete(t),d.forEach(f=>f(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&ur().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let o=this.texData.get(t),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;p?d=new Ar(s,La):d=new tr(s,La);let f=this.runWebGLProgram(d,[{dataId:t,shape:s,dtype:i}],i),h=this.readToGPU(f,e);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),l=ur().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let o=e.map(n=>y.decodeString(n));return me(t.shape,t.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return me(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,t();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=e,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:o}=this.texData.get(t);return o!=null&&(this.disposeData(o.real.dataId,e),this.disposeData(o.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(t),p=i&&i.origDataId||t,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),e!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(e,n,s,a)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=t9){return A().getBool("WEBGL_CPU_FORWARD")&&t.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,t,e)}else n=this.write(o,t,e);return this.texData.get(n).usage=null,{dataId:n,shape:t,dtype:e}}makeOutput(t,e,o){return ur().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,o),this)}unpackTensor(t){let e=new dh(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new lh(t.shape),o=!0;return this.runWebGLProgram(e,[t],t.dtype,null,o)}packedReshape(t,e){let o=[hi(t.shape),...gi(t.shape)],n={dtype:t.dtype,shape:o,dataId:t.dataId},s=[hi(e),...gi(e)],a=new Pc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],t.dtype,p,i);return{dataId:u.dataId,shape:e,dtype:u.dtype}}decode(t,e){let o=this.texData.get(t),{isPacked:n,shape:s,dtype:a}=o;if(e!=null){let m=y.sizeFromShape(s),d=e[0]*e[1]*4;y.assert(m<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Nc(s),p;n?p=new Jf(i):p=new Zf(i);let u=!0,c=[e!=null?e:Kl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:t}],a,c,u,e);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(t,e,o,n,s=!1,a){let i=this.makeTensorInfo(t.outputShape,o),p=this.texData.get(i.dataId);if(t.packedOutput&&(p.isPacked=!0),t.outPackingScheme===fu.DENSE){let x=a!=null?a:Kl(t.outputShape);p.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(p.usage=t.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=e.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=A().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!hu(b.shape,x.shape)){let C=x,S=x.shape;x.shape=b.shape,x=this.packedReshape(x,S),u.push(x),b=this.texData.get(x.dataId),C.shape=S}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=RR(t,c,l),d=this.getAndSaveBinary(m,()=>$R(this.gpgpu,t,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),A().get("ENGINE_COMPILE_ONLY")||ER(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=A().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!A().getBool("WEBGL_LAZILY_UNPACK")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(t,e,o,n,s=!1){return o=o||e[0].dtype,this.runWebGLProgram(t,e,o,n,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(A().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!A().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=A().getBool("DEBUG");A().set("DEBUG",!1);let e=this.abs(ke(1e-8)).dataSync()[0];if(A().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?ZZ:JZ}uploadToGPU(t){let e=this.texData.get(t),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=e;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=e.texShape;if(l==null&&(l=VI(o,p),e.texShape=l),s!=null){let m=Nc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ma(l[0],l[1])),p?d=new rh(m,g):d=new Yl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=mr.PIXELS:C.usage=mr.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let S=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,S,k),E=this.texData.get(_.dataId);e.texShape=E.texShape,e.isPacked=E.isPacked,e.usage=E.usage,A().get("ENGINE_COMPILE_ONLY")?this.disposeData(_.dataId):(e.texture=E.texture,e.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);e.texture=m}}convertAndCacheOnCPU(t,e){let o=this.texData.get(t),{dtype:n}=o;return e!=null&&(o.values=n9(e,n)),o.values}acquireTexture(t,e,o,n){if(this.numBytesInGPU+=this.computeBytes(t,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,n)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(e),n(!0)}catch(s){throw s}});t.push(o)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await pS(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Kf(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:o,infLoc:n,nanLoc:s,outShapeLocation:a,outShapeStridesLocation:i,outTexShapeLocation:p}=jI(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=o,t.infLoc=n,t.nanLoc=s,t.outShapeLocation=a,t.outShapeStridesLocation=i,t.outTexShapeLocation=p}}createTensorFromGPUData(t,e,o){t.channels=t.channels||"RGBA";let{texture:n,height:s,width:a,channels:i}=t,p=ur().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=p.writeTexture(n,e,o,s,a,i);return ur().makeTensorFromDataId(u,e,o,p)}};Oc.nextDataId=0;function n9(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew Oc,2);var Cat={forceHalfFloat:LD};var Mc=` if (isnan(a)) return a; if (isnan(b)) return b; -`;var Pr=class{constructor(t,e,o){this.variableNames=["A","B"],this.outputShape=w.assertAndGetBroadcastShape(e,o),this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=` +`;var Fr=class{constructor(t,e,o){this.variableNames=["A","B"],this.outputShape=w.assertAndGetBroadcastShape(e,o),this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${t} } @@ -1242,12 +1242,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}};var Xr=` + `}};var jr=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; -`;var jr=class{constructor(t,e,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=w.assertAndGetBroadcastShape(e,o);let s=this.outputShape.length;this.enableShapeUniforms=pt(s);let a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=` +`;var qr=class{constructor(t,e,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=w.assertAndGetBroadcastShape(e,o);let s=this.outputShape.length;this.enableShapeUniforms=ut(s);let a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=` result.y = 0.; result.z = 0.; result.w = 0.; @@ -1261,7 +1261,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; - `;else{let p=Dt("coords",s);this.enableShapeUniforms?a+=` + `;else{let p=Rt("coords",s);this.enableShapeUniforms?a+=` bool nextRowOutOfBounds = (${p[s-2]} + 1) >= outShape[${s} - 2]; bool nextColOutOfBounds = @@ -1291,13 +1291,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function At(r){let{inputs:t,backend:e}=r,{x:o}=t;return e.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var LD={kernelName:wo,backendName:"webgl",kernelFunc:At};function Or(r){let{inputs:t,backend:e}=r,{real:o,imag:n}=t,s=e.makeTensorInfo(o.shape,"complex64"),a=e.texData.get(s.dataId),i=At({inputs:{x:o},backend:e}),p=At({inputs:{x:n},backend:e});return a.complexTensorInfos={real:i,imag:p},s}var BD={kernelName:Fi,backendName:"webgl",kernelFunc:Or};var dv="return (a < 0.) ? b * a : a;",fv=` + `}};function Dt(r){let{inputs:t,backend:e}=r,{x:o}=t;return e.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var BD={kernelName:Co,backendName:"webgl",kernelFunc:Dt};function Pr(r){let{inputs:t,backend:e}=r,{real:o,imag:n}=t,s=e.makeTensorInfo(o.shape,"complex64"),a=e.texData.get(s.dataId),i=Dt({inputs:{x:o},backend:e}),p=Dt({inputs:{x:n},backend:e});return a.complexTensorInfos={real:i,imag:p},s}var zD={kernelName:Ri,backendName:"webgl",kernelFunc:Pr};var fv="return (a < 0.) ? b * a : a;",hv=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function a9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{alpha:s}=o,a=e.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(fv,n.shape,a.shape):new Pr(dv,n.shape,a.shape),p=e.runWebGLProgram(i,[n,a],"float32");return e.disposeIntermediateTensorInfo(a),p}var zD={kernelName:Rn,backendName:"webgl",kernelFunc:a9};var hv="return (a < 0.) ? b * a : a;",gv=` +`;function a9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{alpha:s}=o,a=e.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(hv,n.shape,a.shape):new Fr(fv,n.shape,a.shape),p=e.runWebGLProgram(i,[n,a],"float32");return e.disposeIntermediateTensorInfo(a),p}var VD={kernelName:En,backendName:"webgl",kernelFunc:a9};var gv="return (a < 0.) ? b * a : a;",xv=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function i9(r){let{inputs:t,backend:e}=r,{x:o,alpha:n}=t,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(gv,o.shape,n.shape):new Pr(hv,o.shape,n.shape);return e.runWebGLProgram(s,[o,n],"float32")}var VD={kernelName:os,backendName:"webgl",kernelFunc:i9};var Po="if (isnan(x)) return x;";function xe({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&e!=null){let l=i.texData.get(a.dataId),m=e(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=A().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new Fr(a.shape,t):c=new or(a.shape,r),i.runWebGLProgram(c,[a],p)}}function nt({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype==="complex64"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[S,k]=C,_={dataId:S.dataId,dtype:S.dtype,shape:p.shape},E={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new Pr(r,p.shape,u.shape);return c.runWebGLProgram(R,[_,E],dt(S.dtype,k.dtype))}),b=Or({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype==="string"?w.fromUint8ToStringArray(f):f,x=p.dtype==="string"?w.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),S=c.makeTensorInfo(C,l),k=c.texData.get(S.dataId);return k.values=b,S}let m=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,d;return m?d=new jr(t,p.shape,u.shape,e):d=new Pr(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function Ci(r,t=!1){if(r==="linear")return t?DD:ND;if(r==="relu")return t?FD:_D;if(r==="elu")return t?AD:TD;if(r==="relu6")return t?PD:$D;if(r==="prelu")return t?gv:hv;if(r==="leakyrelu")return t?fv:dv;if(r==="sigmoid")return t?OD:ED;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Uc=class{constructor(t,e,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=pt(this.outputShape.length);let c=n?t[1]:t[2],l=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",d=s?"rc.z, i * 2":"i * 2, rc.z",f=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(p?g=`vec4 activation(vec4 a) { +`;function i9(r){let{inputs:t,backend:e}=r,{x:o,alpha:n}=t,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(xv,o.shape,n.shape):new Fr(gv,o.shape,n.shape);return e.runWebGLProgram(s,[o,n],"float32")}var WD={kernelName:rs,backendName:"webgl",kernelFunc:i9};var Fo="if (isnan(x)) return x;";function xe({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&e!=null){let l=i.texData.get(a.dataId),m=e(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=A().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new Ar(a.shape,t):c=new tr(a.shape,r),i.runWebGLProgram(c,[a],p)}}function nt({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype==="complex64"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[S,k]=C,_={dataId:S.dataId,dtype:S.dtype,shape:p.shape},E={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new Fr(r,p.shape,u.shape);return c.runWebGLProgram(R,[_,E],dt(S.dtype,k.dtype))}),b=Pr({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype==="string"?w.fromUint8ToStringArray(f):f,x=p.dtype==="string"?w.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),S=c.makeTensorInfo(C,l),k=c.texData.get(S.dataId);return k.values=b,S}let m=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,d;return m?d=new qr(t,p.shape,u.shape,e):d=new Fr(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function xi(r,t=!1){if(r==="linear")return t?AD:TD;if(r==="relu")return t?PD:$D;if(r==="elu")return t?FD:_D;if(r==="relu6")return t?OD:ED;if(r==="prelu")return t?xv:gv;if(r==="leakyrelu")return t?hv:fv;if(r==="sigmoid")return t?MD:RD;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Lc=class{constructor(t,e,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=ut(this.outputShape.length);let c=n?t[1]:t[2],l=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",d=s?"rc.z, i * 2":"i * 2, rc.z",f=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(p?g=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:u?g=`vec4 activation(vec4 a) { @@ -1336,7 +1336,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};var xv={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},em=class{constructor(t,e,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=w.assertAndGetBroadcastShape(e,o),this.userCode=` + `}};var yv={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Zl=class{constructor(t,e,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=w.assertAndGetBroadcastShape(e,o),this.userCode=` float binaryOpComplex( float areal, float aimag, float breal, float bimag) { ${t} @@ -1349,7 +1349,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float bimag = getBImagAtOutCoords(); setOutput(binaryOpComplex(areal, aimag, breal, bimag)); } - `}};var WD="return a * b;";function tm(r){let{inputs:t,backend:e}=r,{a:o,b:n}=t,s=w.upcastType(o.dtype,n.dtype);if(o.dtype==="complex64"){let i=e.texData.get(o.dataId),p=e.texData.get(n.dataId),u=new em(xv.REAL,o.shape,n.shape),c=new em(xv.IMAG,o.shape,n.shape),l=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:o.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:n.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:n.shape}],m=e.runWebGLProgram(u,l,"float32"),d=e.runWebGLProgram(c,l,"float32"),f=Or({inputs:{real:m,imag:d},backend:e});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(d),f}if(e.shouldExecuteOnCPU([o,n])){let i=e.texData.get(o.dataId),p=e.texData.get(n.dataId),[u,c]=ZR(o.shape,n.shape,i.values,p.values,s),l=e.makeTensorInfo(c,s),m=e.texData.get(l.dataId);return m.values=u,l}let a;return A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new jr(WD,o.shape,n.shape):a=new Pr(WD,o.shape,n.shape),e.runWebGLProgram(a,[o,n],s)}var UD={kernelName:Yn,backendName:"webgl",kernelFunc:tm};function GD(r,t,e){let o=[yi(r.shape),...bi(r.shape)],n={dtype:r.dtype,shape:o,dataId:r.dataId},s=[yi(t),...bi(t)],a=new Vc(s,o),i=!0,p=[o],u=e.runWebGLProgram(a,[n],r.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function te(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{shape:s}=o,a=e,i=y.sizeFromShape(n.shape),p=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(p);y.assert(i===u,()=>`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!Cu(n.shape,p)&&!(c.texture!==null&&Cu(c.shape,p))?GD(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var HD={kernelName:da,backendName:"webgl",kernelFunc:te};var rm=class{constructor(t,e){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=t;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u="sumValue += dot(values, ones);";if(e!=null){let l=1/e;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c="";s%o>0&&(c=` + `}};var UD="return a * b;";function Jl(r){let{inputs:t,backend:e}=r,{a:o,b:n}=t,s=w.upcastType(o.dtype,n.dtype);if(o.dtype==="complex64"){let i=e.texData.get(o.dataId),p=e.texData.get(n.dataId),u=new Zl(yv.REAL,o.shape,n.shape),c=new Zl(yv.IMAG,o.shape,n.shape),l=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:o.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:n.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:n.shape}],m=e.runWebGLProgram(u,l,"float32"),d=e.runWebGLProgram(c,l,"float32"),f=Pr({inputs:{real:m,imag:d},backend:e});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(d),f}if(e.shouldExecuteOnCPU([o,n])){let i=e.texData.get(o.dataId),p=e.texData.get(n.dataId),[u,c]=JR(o.shape,n.shape,i.values,p.values,s),l=e.makeTensorInfo(c,s),m=e.texData.get(l.dataId);return m.values=u,l}let a;return A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new qr(UD,o.shape,n.shape):a=new Fr(UD,o.shape,n.shape),e.runWebGLProgram(a,[o,n],s)}var GD={kernelName:Xn,backendName:"webgl",kernelFunc:Jl};function HD(r,t,e){let o=[hi(r.shape),...gi(r.shape)],n={dtype:r.dtype,shape:o,dataId:r.dataId},s=[hi(t),...gi(t)],a=new Pc(s,o),i=!0,p=[o],u=e.runWebGLProgram(a,[n],r.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function te(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{shape:s}=o,a=e,i=y.sizeFromShape(n.shape),p=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(p);y.assert(i===u,()=>`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!hu(n.shape,p)&&!(c.texture!==null&&hu(c.shape,p))?HD(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var KD={kernelName:da,backendName:"webgl",kernelFunc:te};var em=class{constructor(t,e){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=t;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u="sumValue += dot(values, ones);";if(e!=null){let l=1/e;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c="";s%o>0&&(c=` if (inIdx < 0 || inIdx >= ${s}) { return 0.0; } @@ -1402,7 +1402,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}};var gh=class{constructor(t,e){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=t;this.outputShape=[n,a];let i="0.0",p="";e==="prod"?i="1.0":e==="min"?(i="1.0 / 1e-20",p="min"):e==="max"&&(i="-1.0 / 1e-20",p="max");let u=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?u="sumValue":e==="prod"?u="prodValue":e==="all"?u="allValue":e==="any"&&(u="anyValue");let c=Math.floor(o/4)*4,l=o%4,m=` + `}};var hh=class{constructor(t,e){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=t;this.outputShape=[n,a];let i="0.0",p="";e==="prod"?i="1.0":e==="min"?(i="1.0 / 1e-20",p="min"):e==="max"&&(i="-1.0 / 1e-20",p="max");let u=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?u="sumValue":e==="prod"?u="prodValue":e==="all"?u="allValue":e==="any"&&(u="anyValue");let c=Math.floor(o/4)*4,l=o%4,m=` if (${e==="sum"}) { sumValue += dot(values, ones); } else if (${e==="prod"}) { @@ -1494,12 +1494,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${u}); } - `}};function p9(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],o=w.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:o,outSize:Math.ceil(e/o)})}return t}function Yr(r,t,e,o){let n=p9(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(t);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Re(this.rank),s=lv("rc",this.rank),a=new Array(this.rank);for(let c=0;c6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(t);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Re(this.rank),s=mv("rc",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${o} must match.`);let k=e?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=te({inputs:{x:r},backend:n,attrs:{shape:k}}),R=te({inputs:{x:t},backend:n,attrs:{shape:_}}),D=[E,R],P=Math.max(x,b),O=e?E.shape[1]:E.shape[2],M=s!=null,L=a!=null,B=p==="leakyrelu",z=p!=null?Ci(p,!0):null,U=M||L||B||z!=null,j;if((d===1||f===1)&&O>yv&&U===!1){let Y=E,J=R;e&&(Y=Ct({inputs:{x:E},backend:n,attrs:{perm:[0,2,1]}}),D.push(Y)),o&&(J=Ct({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),D.push(J));let re=f!==1,ne=f===1,ee=Y;re&&(ee=te({inputs:{x:Y},backend:n,attrs:{shape:[P,O,1]}}),D.push(ee));let oe=f===1?2:1,ie=J;ne&&(ie=te({inputs:{x:J},backend:n,attrs:{shape:[P,1,O]}}),D.push(ie));let le=tm({inputs:{a:ee,b:ie},backend:n});j=Tp({inputs:{x:le},backend:n,attrs:{axis:oe,keepDims:!0}}),D.push(le)}else{let Y=dt(r.dtype,t.dtype),J=new Uc(k,_,[P,d,f],e,o,M,z,L,B),re=[E,R];if(s!=null&&re.push(s),L&&re.push(a),B){let ne=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));re.push(ne),D.push(ne)}j=n.runWebGLProgram(J,re,Y)}let q=te({inputs:{x:j},backend:n,attrs:{shape:S}});D.push(j);for(let Y of D)n.disposeIntermediateTensorInfo(Y);return q}function l9(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return _p({a:n,b:s,transposeA:p,transposeB:u,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var XD={kernelName:Io,backendName:"webgl",kernelFunc:l9};var YD="return abs(x);";function m9(r){let{inputs:t,backend:e}=r,{x:o}=t;if(e.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=e.texData.get(o.dataId),a=ch(s.values);return e.makeTensorInfo(o.shape,o.dtype,a)}let n;return A().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Fr(o.shape,YD):n=new or(o.shape,YD),e.runWebGLProgram(n,[o],o.dtype)}var QD={kernelName:Xs,backendName:"webgl",kernelFunc:m9};var d9=Ut+` + `}};function gu(r,t,e){let o=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xh(r.shape,t):new gh(r.shape,t);return e.runWebGLProgram(o,[r],r.dtype)}function qD(r,t,e,o){let n=t,s=r.shape.length,a=y.parseAxisParam(n,r.shape),i=a,p=w.getAxesPermutation(i,s),u=p!=null,c=r;u&&(c=gu(r,p,o),i=w.getInnerMostAxes(i.length,s)),w.assertAxesAreInnerMostDims("sum",i,s);let[l,m]=w.computeOutAndReduceShapes(c.shape,i),d=l;e&&(d=w.expandShapeToKeepDim(l,a));let f=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/f,x=te({inputs:{x:c},attrs:{shape:[g,f]},backend:o}),b=oi(r.dtype),C=Xr(x,b,"sum",o),S=te({inputs:{x:C},attrs:{shape:d},backend:o});return o.disposeIntermediateTensorInfo(x),o.disposeIntermediateTensorInfo(C),u&&o.disposeIntermediateTensorInfo(c),S}function bp(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return qD(n,s,a,e)}var jD={kernelName:Ss,backendName:"webgl",kernelFunc:bp};function bt(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{perm:s}=o,a=e,i=n.shape.length,p=new Array(i);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and 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ne=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));re.push(ne),D.push(ne)}j=n.runWebGLProgram(J,re,Y)}let q=te({inputs:{x:j},backend:n,attrs:{shape:S}});D.push(j);for(let Y of D)n.disposeIntermediateTensorInfo(Y);return q}function l9(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Cp({a:n,b:s,transposeA:p,transposeB:u,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var YD={kernelName:So,backendName:"webgl",kernelFunc:l9};var QD="return abs(x);";function m9(r){let{inputs:t,backend:e}=r,{x:o}=t;if(e.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=e.texData.get(o.dataId),a=ph(s.values);return e.makeTensorInfo(o.shape,o.dtype,a)}let n;return A().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Ar(o.shape,QD):n=new tr(o.shape,QD),e.runWebGLProgram(n,[o],o.dtype)}var ZD={kernelName:Xs,backendName:"webgl",kernelFunc:m9};var d9=Wt+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,f9=xe({opSnippet:d9}),ZD={kernelName:Wo,backendName:"webgl",kernelFunc:f9};var h9=Ut+` +`,f9=xe({opSnippet:d9}),JD={kernelName:Vo,backendName:"webgl",kernelFunc:f9};var h9=Wt+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,g9=xe({opSnippet:h9}),JD={kernelName:Uo,backendName:"webgl",kernelFunc:g9};var eA="return a + b;",x9=nt({opSnippet:eA,packedOpSnippet:eA,supportsComplex:!0,cpuKernelImpl:RR}),tA={kernelName:uo,backendName:"webgl",kernelFunc:x9};var bh=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`,g9=xe({opSnippet:h9}),eA={kernelName:Wo,backendName:"webgl",kernelFunc:g9};var tA="return a + b;",x9=nt({opSnippet:tA,packedOpSnippet:tA,supportsComplex:!0,cpuKernelImpl:DR}),rA={kernelName:io,backendName:"webgl",kernelFunc:x9};var yh=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${o.join(` `)} @@ -1531,7 +1531,7 @@ return log(x + sqrt(x * x - 1.0));`,g9=xe({opSnippet:h9}),JD={kernelName:Uo,back float result = ${n}; setOutput(result); } - `}};var Ch=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` + `}};var bh=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${o.join(` `)} @@ -1539,7 +1539,7 @@ return log(x + sqrt(x * x - 1.0));`,g9=xe({opSnippet:h9}),JD={kernelName:Uo,back vec4 result = ${n}; setOutput(result); } - `}};function wh(r){let{inputs:t,backend:e}=r,o=t;if(o.length===1)return At({inputs:{x:o[0]},backend:e});if(o.length>A().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let p=Math.floor(o.length/2),u=wh({inputs:o.slice(0,p),backend:e}),c=wh({inputs:o.slice(p),backend:e});return wh({inputs:[u,c],backend:e})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=A().getBool("WEBGL_PACK")?new Ch(o[0].shape,s):new bh(o[0].shape,s);return e.runWebGLProgram(i,o,n)}var rA={kernelName:Go,backendName:"webgl",kernelFunc:wh};function y9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=Ct({inputs:{x:n},backend:e,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims("all",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:e,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,"all",e),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(l),x}var oA={kernelName:Ho,backendName:"webgl",kernelFunc:y9};function b9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=Ct({inputs:{x:n},backend:e,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims("any",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:e,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,"any",e),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(l),x}var nA={kernelName:Ko,backendName:"webgl",kernelFunc:b9};var Sh=class{constructor(t,e,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=t;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=e==="max"?">":"<",p=o?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function Ch(r){let{inputs:t,backend:e}=r,o=t;if(o.length===1)return Dt({inputs:{x:o[0]},backend:e});if(o.length>A().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let p=Math.floor(o.length/2),u=Ch({inputs:o.slice(0,p),backend:e}),c=Ch({inputs:o.slice(p),backend:e});return Ch({inputs:[u,c],backend:e})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=A().getBool("WEBGL_PACK")?new bh(o[0].shape,s):new yh(o[0].shape,s);return e.runWebGLProgram(i,o,n)}var oA={kernelName:Uo,backendName:"webgl",kernelFunc:Ch};function y9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:e,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims("all",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:e,attrs:{shape:[-1,f]}}),g=Xr(h,h.dtype,"all",e),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(l),x}var nA={kernelName:Go,backendName:"webgl",kernelFunc:y9};function b9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:e,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims("any",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:e,attrs:{shape:[-1,f]}}),g=Xr(h,h.dtype,"any",e),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(l),x}var sA={kernelName:Ho,backendName:"webgl",kernelFunc:b9};var wh=class{constructor(t,e,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=t;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=e==="max"?">":"<",p=o?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1559,7 +1559,7 @@ return log(x + sqrt(x * x - 1.0));`,g9=xe({opSnippet:h9}),JD={kernelName:Uo,back } setOutput(float(bestIndex)); } - `}};var Ih=class{constructor(t,e,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],a=Math.ceil(s/e);this.outputShape=t.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,p=i.length,u=Re(p),c=Dt("coords",p),l,m;if(a===1){m=p+1;let R=Re(m);l=` + `}};var Sh=class{constructor(t,e,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],a=Math.ceil(s/e);this.outputShape=t.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,p=i.length,u=Re(p),c=Rt("coords",p),l,m;if(a===1){m=p+1;let R=Re(m);l=` ${R} sourceLocR = ${R}(${c.join()}, 0); ++${c[p-1]}; ${R} sourceLocG = ${R}(${c.join()}, 0); @@ -1575,7 +1575,7 @@ return log(x + sqrt(x * x - 1.0));`,g9=xe({opSnippet:h9}),JD={kernelName:Uo,back ${u} sourceLocA = coords; --${c[p-1]}; ${u} sourceLocB = coords; - --${c[p-2]};`;let d=["x","y","z","w","u","v"].slice(0,m),f="."+d[m-1],h=d.map(R=>"int "+R),g=Dt("sourceLocR",m-1).concat("inIdx.r"),x=Dt("sourceLocG",m-1).concat("inIdx.g"),b=Dt("sourceLocB",m-1).concat("inIdx.b"),C=Dt("sourceLocA",m-1).concat("inIdx.a"),S=o==="max"?"greaterThan":"lessThan",k=n?"":` + --${c[p-2]};`;let d=["x","y","z","w","u","v"].slice(0,m),f="."+d[m-1],h=d.map(R=>"int "+R),g=Rt("sourceLocR",m-1).concat("inIdx.r"),x=Rt("sourceLocG",m-1).concat("inIdx.g"),b=Rt("sourceLocB",m-1).concat("inIdx.b"),C=Rt("sourceLocA",m-1).concat("inIdx.a"),S=o==="max"?"greaterThan":"lessThan",k=n?"":` inIdx = round(vec4(getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${x.join()}), getBestIndicesAChannel(${b.join()}), @@ -1621,25 +1621,25 @@ return log(x + sqrt(x * x - 1.0));`,g9=xe({opSnippet:h9}),JD={kernelName:Uo,back } setOutput(bestIndex); } - `}};function sA(r,t,e,o=null){let n=t.shape[0],s=t.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=w.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new Sh(i,e,o==null),u=[t];o!=null&&u.push(o);let c=r.runWebGLProgram(p,u,"int32");if(c.shape[1]===1)return c;let l=sA(r,t,e,c);return r.disposeIntermediateTensorInfo(c),l}function aA(r,t,e,o=null){let n=o!=null?o.shape:t.shape,s=n[n.length-1],a=w.computeOptimalWindowSize(s),i=new Ih(n,a,e,o==null),p=o==null?[t]:[t,o],u=r.runWebGLProgram(i,p,"int32");if(u.shape.length===t.shape.length){let c=aA(r,t,e,u);return r.disposeIntermediateTensorInfo(u),c}return u}function vh(r,t,e,o){let n=[e];if(w.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,t.shape.length),!A().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],a=r.texData.get(t.dataId),i=a!==null&&a.isPacked,p=t;i&&(p=r.unpackTensor(t),s.push(p));let[u,c]=w.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=sA(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return aA(r,t,o)}function C9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Ct({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let c=vh(e,p,a[0],"max");return u.forEach(l=>e.disposeIntermediateTensorInfo(l)),c}var iA={kernelName:Ys,backendName:"webgl",kernelFunc:C9};function w9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Ct({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=vh(e,p,a[0],"min");return u.forEach(l=>e.disposeIntermediateTensorInfo(l)),c}var uA={kernelName:Qs,backendName:"webgl",kernelFunc:w9};var S9=Ut+` + `}};function aA(r,t,e,o=null){let n=t.shape[0],s=t.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=w.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new wh(i,e,o==null),u=[t];o!=null&&u.push(o);let c=r.runWebGLProgram(p,u,"int32");if(c.shape[1]===1)return c;let l=aA(r,t,e,c);return r.disposeIntermediateTensorInfo(c),l}function iA(r,t,e,o=null){let n=o!=null?o.shape:t.shape,s=n[n.length-1],a=w.computeOptimalWindowSize(s),i=new Sh(n,a,e,o==null),p=o==null?[t]:[t,o],u=r.runWebGLProgram(i,p,"int32");if(u.shape.length===t.shape.length){let c=iA(r,t,e,u);return r.disposeIntermediateTensorInfo(u),c}return u}function Ih(r,t,e,o){let n=[e];if(w.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,t.shape.length),!A().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],a=r.texData.get(t.dataId),i=a!==null&&a.isPacked,p=t;i&&(p=r.unpackTensor(t),s.push(p));let[u,c]=w.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=aA(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return iA(r,t,o)}function C9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let c=Ih(e,p,a[0],"max");return u.forEach(l=>e.disposeIntermediateTensorInfo(l)),c}var uA={kernelName:Ys,backendName:"webgl",kernelFunc:C9};function w9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=Ih(e,p,a[0],"min");return u.forEach(l=>e.disposeIntermediateTensorInfo(l)),c}var pA={kernelName:Qs,backendName:"webgl",kernelFunc:w9};var S9=Wt+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,I9=xe({opSnippet:S9}),pA={kernelName:qo,backendName:"webgl",kernelFunc:I9};var v9=Ut+"return log(x + sqrt(x * x + 1.0));",k9=xe({opSnippet:v9}),cA={kernelName:jo,backendName:"webgl",kernelFunc:k9};var N9=Ut+` +`,I9=xe({opSnippet:S9}),cA={kernelName:Ko,backendName:"webgl",kernelFunc:I9};var v9=Wt+"return log(x + sqrt(x * x + 1.0));",k9=xe({opSnippet:v9}),lA={kernelName:qo,backendName:"webgl",kernelFunc:k9};var N9=Wt+` return atan(x); 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a=t.filterWidth,i=t.strideHeight,p=t.strideWidth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterHeight,m=t.effectiveFilterWidth,d=t.padInfo.top,f=t.padInfo.left;this.outputShape=t.outShape;let h=e==="avg",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let R=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),fA={kernelName:Xo,backendName:"webgl",kernelFunc:D9};var Us=class{constructor(t,e,o,n=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=t.filterWidth,i=t.strideHeight,p=t.strideWidth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterHeight,m=t.effectiveFilterWidth,d=t.padInfo.top,f=t.padInfo.left;this.outputShape=t.outShape;let h=e==="avg",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * 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a=t.filterWidth,i=t.strideDepth,p=t.strideHeight,u=t.strideWidth,c=t.dilationDepth,l=t.dilationHeight,m=t.dilationWidth,d=t.effectiveFilterDepth,f=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let C=e==="avg",S="0.0";if(C||(S="-1.0 / 1e-20"),o){let P=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${p}, ${u}); const ivec3 pads = ivec3(${g}, ${x}, ${b}); @@ -1943,7 +1943,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(${_}); } - `}};function A9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t;Vs(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:e});let l=new Us(c,"avg",!1);return e.runWebGLProgram(l,[n],"float32")}var hA={kernelName:Qo,backendName:"webgl",kernelFunc:A9};function F9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new xu(l,"avg",!1);return e.runWebGLProgram(m,[n],"float32")}var gA={kernelName:Zs,backendName:"webgl",kernelFunc:F9};var vh=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,p=t.effectiveFilterHeight,u=t.effectiveFilterWidth,c=p-1-t.padInfo.top,l=u-1-t.padInfo.left,m=1/(e*o);this.userCode=` const ivec2 pads = ivec2(${c}, ${l}); const float avgMultiplier = float(${m}); @@ -1985,7 +1985,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(dotProd); } - `}},Nh=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,o=t.filterHeight,n=t.filterWidth,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterDepth,m=t.effectiveFilterHeight,d=t.effectiveFilterWidth,f=l-1-t.padInfo.front,h=m-1-t.padInfo.top,g=d-1-t.padInfo.left,x=1/(e*o*n);this.userCode=` + `}},kh=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,o=t.filterHeight,n=t.filterWidth,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterDepth,m=t.effectiveFilterHeight,d=t.effectiveFilterWidth,f=l-1-t.padInfo.front,h=m-1-t.padInfo.top,g=d-1-t.padInfo.left,x=1/(e*o*n);this.userCode=` const ivec3 pads = ivec3(${f}, ${h}, ${g}); const float avgMultiplier = float(${x}); @@ -2041,7 +2041,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(dotProd); } - `}};function P9(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Nh(m);return e.runWebGLProgram(d,[n],a.dtype)}var gA={kernelName:Ai,backendName:"webgl",kernelFunc:P9};function O9(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;Vs([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new kh(c);return e.runWebGLProgram(l,[n],a.dtype)}var xA={kernelName:Di,backendName:"webgl",kernelFunc:O9};function M9(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s}=t,{transposeA:a,transposeB:i}=o;return _p({a:n,b:s,transposeA:a,transposeB:i,backend:e})}var yA={kernelName:Jo,backendName:"webgl",kernelFunc:M9};var Th=class{constructor(t,e,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],w.assertAndGetBroadcastShape(t,e),w.assertAndGetBroadcastShape(t,o);let i="0.0";n!=null&&(w.assertAndGetBroadcastShape(t,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="1.0";s!=null&&(w.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` + `}};function P9(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new kh(m);return e.runWebGLProgram(d,[n],a.dtype)}var xA={kernelName:Ei,backendName:"webgl",kernelFunc:P9};function O9(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;Vs([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new vh(c);return e.runWebGLProgram(l,[n],a.dtype)}var yA={kernelName:$i,backendName:"webgl",kernelFunc:O9};function M9(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s}=t,{transposeA:a,transposeB:i}=o;return Cp({a:n,b:s,transposeA:a,transposeB:i,backend:e})}var bA={kernelName:Zo,backendName:"webgl",kernelFunc:M9};var Nh=class{constructor(t,e,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],w.assertAndGetBroadcastShape(t,e),w.assertAndGetBroadcastShape(t,o);let i="0.0";n!=null&&(w.assertAndGetBroadcastShape(t,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="1.0";s!=null&&(w.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2051,7 +2051,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam float inv = scale * inversesqrt(variance + float(${a})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}};var _h=class{constructor(t,e,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],w.assertAndGetBroadcastShape(t,e),w.assertAndGetBroadcastShape(t,o);let i="vec4(0.0)";n!=null&&(w.assertAndGetBroadcastShape(t,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="vec4(1.0)";s!=null&&(w.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` + `}};var Th=class{constructor(t,e,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],w.assertAndGetBroadcastShape(t,e),w.assertAndGetBroadcastShape(t,o);let i="vec4(0.0)";n!=null&&(w.assertAndGetBroadcastShape(t,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="vec4(1.0)";s!=null&&(w.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${p}; @@ -2064,7 +2064,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam setOutput((x - mean) * inv + offset); } - `}};var L9=({inputs:r,backend:t,attrs:e})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:p}=e;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=A().getBool("WEBGL_PACK_NORMALIZATION")?new _h(o.shape,n.shape,s.shape,c,l,p):new Th(o.shape,n.shape,s.shape,c,l,p);return t.runWebGLProgram(m,u,u[0].dtype)},bA={kernelName:vn,backendName:"webgl",kernelFunc:L9};var $h=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=Re(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let o=B9(this.rank),n,s=t.map((a,i)=>`sourceLoc.${bv[i]} = start[${i}] + coords.${bv[i]};`);n=` + `}};var L9=({inputs:r,backend:t,attrs:e})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:p}=e;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=A().getBool("WEBGL_PACK_NORMALIZATION")?new Th(o.shape,n.shape,s.shape,c,l,p):new Nh(o.shape,n.shape,s.shape,c,l,p);return t.runWebGLProgram(m,u,u[0].dtype)},CA={kernelName:In,backendName:"webgl",kernelFunc:L9};var _h=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=Re(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let o=B9(this.rank),n,s=t.map((a,i)=>`sourceLoc.${Cv[i]} = start[${i}] + coords.${Cv[i]};`);n=` ${e} sourceLoc; ${e} coords = getOutputCoords(); ${s.join(` @@ -2074,7 +2074,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam ${n} setOutput(getSource(${o})); } - `}},bv=["x","y","z","w","u","v"];function B9(r){if(r===1)return"sourceLoc";if(r<=6)return bv.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var Eh=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=Re(this.rank),o=Dt("coords",this.rank),n=Dt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=` + `}},Cv=["x","y","z","w","u","v"];function B9(r){if(r===1)return"sourceLoc";if(r<=6)return Cv.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var $h=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=Re(this.rank),o=Rt("coords",this.rank),n=Rt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=` result.x = ${a}; if (++${o[this.rank-1]} < ${t[this.rank-1]}) { ++${n[this.rank-1]}; @@ -2103,7 +2103,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam ${p} setOutput(result); } - `}};function z9(r,t,e,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(e,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=e,a.dtype=r.dtype;let i=ct.computeFlatOffset(t,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function Gs(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,size:a}=o,[i,p]=ct.parseSliceParams(n,s,a);if(ct.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return e.makeTensorInfo(p,n.dtype,[]);if(e.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=e.texData.get(n.dataId),m=pD(l.values,i,p,n.shape,n.dtype);return e.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=e.texData.get(n.dataId),c=ct.isSliceContinous(n.shape,i,p);if(u||!c){let l=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Eh(p):new $h(p),m=[i];return e.runWebGLProgram(l,[n],n.dtype,m)}return e.uploadToGPU(n.dataId),z9(n,i,p,e)}var CA={kernelName:ha,backendName:"webgl",kernelFunc:Gs};var V9=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:e,attrs:{shape:p}}),h=Ct({inputs:{x:f},backend:e,attrs:{perm:u}}),g=te({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Gs({inputs:{x:g},backend:e,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},wA={kernelName:Js,backendName:"webgl",kernelFunc:V9};function W9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=e.readSync(n.dataId),p=e.readSync(s.dataId),u=ph(i,p,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,u)}var SA={kernelName:en,backendName:"webgl",kernelFunc:W9};var U9=` + `}};function z9(r,t,e,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(e,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=e,a.dtype=r.dtype;let i=pt.computeFlatOffset(t,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function Gs(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,size:a}=o,[i,p]=pt.parseSliceParams(n,s,a);if(pt.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return e.makeTensorInfo(p,n.dtype,[]);if(e.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=e.texData.get(n.dataId),m=cD(l.values,i,p,n.shape,n.dtype);return e.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=e.texData.get(n.dataId),c=pt.isSliceContinous(n.shape,i,p);if(u||!c){let l=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $h(p):new _h(p),m=[i];return e.runWebGLProgram(l,[n],n.dtype,m)}return e.uploadToGPU(n.dataId),z9(n,i,p,e)}var wA={kernelName:ha,backendName:"webgl",kernelFunc:Gs};var V9=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:e,attrs:{shape:p}}),h=bt({inputs:{x:f},backend:e,attrs:{perm:u}}),g=te({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Gs({inputs:{x:g},backend:e,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},SA={kernelName:Js,backendName:"webgl",kernelFunc:V9};function W9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=e.readSync(n.dataId),p=e.readSync(s.dataId),u=uh(i,p,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,u)}var IA={kernelName:Jo,backendName:"webgl",kernelFunc:W9};var U9=` int r = int(a.r) & int(b.r); int g = int(a.g) & int(b.g); int rb = int(a.b) & int(b.b); @@ -2111,7 +2111,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam return vec4(r, g, rb, ra); `,G9=` return float(int(a.r) & int(b.r)); -`;function H9(r){let{inputs:t,backend:e}=r,{a:o,b:n}=t,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS"),a=A().getNumber("WEBGL_VERSION");if(e.shouldExecuteOnCPU([o,n])||a===1){let p=e.texData.get(o.dataId).values,u=e.texData.get(n.dataId).values,[c,l]=AR(o.shape,n.shape,p,u,o.dtype),m=e.makeTensorInfo(l,o.dtype),d=e.texData.get(m.dataId);return d.values=c,m}let i;return s?i=new jr(U9,o.shape,n.shape,!1):i=new Pr(G9,o.shape,n.shape),e.runWebGLProgram(i,[o,n],o.dtype)}var IA={kernelName:ja,backendName:"webgl",kernelFunc:H9};function K9(r){let{inputs:t,backend:e}=r,{s0:o,s1:n}=t,s=e.readSync(o.dataId),a=e.readSync(n.dataId),i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return e.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var vA={kernelName:ea,backendName:"webgl",kernelFunc:K9};var q9="return float(a != b);",Cv=nt({opSnippet:q9,cpuKernelImpl:eD,dtype:"bool"}),kA={kernelName:Qn,backendName:"webgl",kernelFunc:Cv};function wi(r){let{inputs:t,backend:e}=r,{input:o}=t,n=e.texData.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.real},backend:e})}var NA={kernelName:qi,backendName:"webgl",kernelFunc:wi};var j9="return float(int(x));";function TA(r,t){let e=new or(r.shape,j9),o=t.runWebGLProgram(e,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function wv(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return At({inputs:{x:n},backend:e});let a=Gr(n.shape),i=wv({inputs:{x:n},backend:e,attrs:{dtype:"float32"}}),p=Or({inputs:{real:i,imag:a},backend:e});return a.dispose(),e.disposeIntermediateTensorInfo(i),p}if(n.dtype==="complex64"){let a=wi({inputs:{input:n},backend:e}),i=wv({inputs:{x:a},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:e});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(e.shouldExecuteOnCPU([n])){let a=e.texData.get(n.dataId).values,[i,p,u]=FR(a,n.shape,n.dtype,s);return e.makeTensorInfo(i,p,u)}if(s==="int32")return TA(n,e);if(s==="bool"){let a=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=Cv({inputs:{a:n,b:a},backend:e});return e.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var _A={kernelName:bo,backendName:"webgl",kernelFunc:wv};var $A="return ceil(x);",X9=xe({opSnippet:$A,packedOpSnippet:$A,cpuKernelImpl:PR}),EA={kernelName:tn,backendName:"webgl",kernelFunc:X9};var Rh=class{constructor(t){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` +`;function H9(r){let{inputs:t,backend:e}=r,{a:o,b:n}=t,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS"),a=A().getNumber("WEBGL_VERSION");if(e.shouldExecuteOnCPU([o,n])||a===1){let p=e.texData.get(o.dataId).values,u=e.texData.get(n.dataId).values,[c,l]=FR(o.shape,n.shape,p,u,o.dtype),m=e.makeTensorInfo(l,o.dtype),d=e.texData.get(m.dataId);return d.values=c,m}let i;return s?i=new qr(U9,o.shape,n.shape,!1):i=new Fr(G9,o.shape,n.shape),e.runWebGLProgram(i,[o,n],o.dtype)}var vA={kernelName:qa,backendName:"webgl",kernelFunc:H9};function K9(r){let{inputs:t,backend:e}=r,{s0:o,s1:n}=t,s=e.readSync(o.dataId),a=e.readSync(n.dataId),i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return e.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var kA={kernelName:ea,backendName:"webgl",kernelFunc:K9};var q9="return float(a != b);",wv=nt({opSnippet:q9,cpuKernelImpl:tD,dtype:"bool"}),NA={kernelName:Yn,backendName:"webgl",kernelFunc:wv};function yi(r){let{inputs:t,backend:e}=r,{input:o}=t,n=e.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.real},backend:e})}var TA={kernelName:Gi,backendName:"webgl",kernelFunc:yi};var j9="return float(int(x));";function _A(r,t){let e=new tr(r.shape,j9),o=t.runWebGLProgram(e,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Sv(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return Dt({inputs:{x:n},backend:e});let a=Ur(n.shape),i=Sv({inputs:{x:n},backend:e,attrs:{dtype:"float32"}}),p=Pr({inputs:{real:i,imag:a},backend:e});return a.dispose(),e.disposeIntermediateTensorInfo(i),p}if(n.dtype==="complex64"){let a=yi({inputs:{input:n},backend:e}),i=Sv({inputs:{x:a},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Dt({inputs:{x:n},backend:e});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(e.shouldExecuteOnCPU([n])){let a=e.texData.get(n.dataId).values,[i,p,u]=PR(a,n.shape,n.dtype,s);return e.makeTensorInfo(i,p,u)}if(s==="int32")return _A(n,e);if(s==="bool"){let a=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=wv({inputs:{a:n,b:a},backend:e});return e.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var $A={kernelName:yo,backendName:"webgl",kernelFunc:Sv};var EA="return ceil(x);",X9=xe({opSnippet:EA,packedOpSnippet:EA,cpuKernelImpl:OR}),RA={kernelName:en,backendName:"webgl",kernelFunc:X9};var Eh=class{constructor(t){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2122,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam setOutput(clamp(value, minVal, maxVal)); } - `}};var Dh=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` + `}};var Rh=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` void main() { vec4 value = getAAtOutCoords(); @@ -2133,7 +2133,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function Y9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{clipValueMin:s,clipValueMax:a}=o,i;A().getBool("WEBGL_PACK_CLIP")?i=new Dh(n.shape):i=new Rh(n.shape);let p=[[s],[a]];return e.runWebGLProgram(i,[n],n.dtype,p)}var RA={kernelName:Co,backendName:"webgl",kernelFunc:Y9};var Ah=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=` + `}};function Y9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{clipValueMin:s,clipValueMax:a}=o,i;A().getBool("WEBGL_PACK_CLIP")?i=new Rh(n.shape):i=new Eh(n.shape);let p=[[s],[a]];return e.runWebGLProgram(i,[n],n.dtype,p)}var DA={kernelName:bo,backendName:"webgl",kernelFunc:Y9};var Dh=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2146,7 +2146,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function DA(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function Q9(r){let{inputs:t,backend:e}=r,{x:o}=t,n=e.texData.get(o.dataId),s=new Ah(o.shape),a=[DA(o,n.complexTensorInfos.real),DA(o,n.complexTensorInfos.imag)];return e.runWebGLProgram(s,a,a[0].dtype)}var AA={kernelName:Pi,backendName:"webgl",kernelFunc:Q9};var Fh=class{constructor(t){this.outputShape=[],this.outputShape=w.computeOutShape(t,1),this.variableNames=t.map((a,i)=>`T${i}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let a=1;a`T${i}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let a=1;a`T${g}`);let p=new Array(t.length-1);p[0]=t[0][e];for(let h=1;h`T${g}`);let p=new Array(t.length-1);p[0]=t[0][e];for(let h=1;h= ${p[h-1]}) { return getChannel( - getT${h}(${Ph(i,u,g)}), - vec2(${Ph(c,u,g)})); + getT${h}(${Fh(i,u,g)}), + vec2(${Fh(c,u,g)})); }`}let d=p.length,f=p[p.length-1];m+=` return getChannel( - getT${d}(${Ph(i,u,f)}), - vec2(${Ph(c,u,f)}));`,this.userCode=` + getT${d}(${Fh(i,u,f)}), + vec2(${Fh(c,u,f)}));`,this.userCode=` float getValue(${i.map(h=>"int "+h)}) { ${m} } @@ -2192,7 +2192,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(result); } - `}};function Ph(r,t,e){let o=r.indexOf(t);return r.map((s,a)=>a===o?`${s} - ${e}`:s).join()}function $p(r){let{inputs:t,backend:e}=r,{input:o}=t,n=e.texData.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.imag},backend:e})}var FA={kernelName:Gi,backendName:"webgl",kernelFunc:$p};function Gc(r,t,e){let o=r[0].dtype;if(o==="complex64"){let d=r.map(b=>wi({inputs:{input:b},backend:e})),f=r.map(b=>$p({inputs:{input:b},backend:e})),h=Gc(d,t,e),g=Gc(f,t,e),x=Or({inputs:{real:h,imag:g},backend:e});return d.forEach(b=>e.disposeIntermediateTensorInfo(b)),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let n=e.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let d=r.map(S=>{let _=[-1,y.sizeFromShape(S.shape.slice(t))];return te({inputs:{x:S},backend:e,attrs:{shape:_}})}),f=d.map(S=>({vals:e.readSync(S.dataId),shape:S.shape})),h=w.computeOutShape(d.map(S=>S.shape),1),g=d[0].shape[0]===1,x=OR(f,h,o,g),b=w.computeOutShape(r.map(S=>S.shape),t),C=e.makeTensorInfo(b,o,x);return d.forEach(S=>e.disposeIntermediateTensorInfo(S)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let d=a?new or(r[0].shape,Ba):new Fr(r[0].shape,Ba);return e.runWebGLProgram(d,r,o)}let i=A().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>i){let d=[];for(let h=0;hf.shape),t);return e.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=Z9(s,t,e),c=new Fh(p.map(d=>d.shape)),l=e.runWebGLProgram(c,p,o);p.forEach(d=>e.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:l},attrs:{shape:u},backend:e});return e.disposeIntermediateTensorInfo(l),m}function Z9(r,t,e){let o=w.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:o}}function Sv(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,t[0].shape)[0],a=t.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(t.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let p=t.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:e}):Gc(p,s,e)}var PA={kernelName:ta,backendName:"webgl",kernelFunc:Sv};var Hc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let a=t.padInfo.top,i=t.padInfo.left,p=t.strideHeight,u=t.strideWidth,c=t.dilationHeight,l=t.dilationWidth,m=t.filterHeight,d=t.filterWidth,f=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,C=g?3:1,S="",k="";o&&(n?S=`float activation(float a) { + `}};function Fh(r,t,e){let o=r.indexOf(t);return r.map((s,a)=>a===o?`${s} - ${e}`:s).join()}function wp(r){let{inputs:t,backend:e}=r,{input:o}=t,n=e.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.imag},backend:e})}var PA={kernelName:Vi,backendName:"webgl",kernelFunc:wp};function Bc(r,t,e){let o=r[0].dtype;if(o==="complex64"){let d=r.map(b=>yi({inputs:{input:b},backend:e})),f=r.map(b=>wp({inputs:{input:b},backend:e})),h=Bc(d,t,e),g=Bc(f,t,e),x=Pr({inputs:{real:h,imag:g},backend:e});return d.forEach(b=>e.disposeIntermediateTensorInfo(b)),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let n=e.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let d=r.map(S=>{let _=[-1,y.sizeFromShape(S.shape.slice(t))];return te({inputs:{x:S},backend:e,attrs:{shape:_}})}),f=d.map(S=>({vals:e.readSync(S.dataId),shape:S.shape})),h=w.computeOutShape(d.map(S=>S.shape),1),g=d[0].shape[0]===1,x=MR(f,h,o,g),b=w.computeOutShape(r.map(S=>S.shape),t),C=e.makeTensorInfo(b,o,x);return d.forEach(S=>e.disposeIntermediateTensorInfo(S)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let d=a?new tr(r[0].shape,La):new Ar(r[0].shape,La);return e.runWebGLProgram(d,r,o)}let i=A().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>i){let d=[];for(let h=0;hf.shape),t);return e.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=Z9(s,t,e),c=new Ah(p.map(d=>d.shape)),l=e.runWebGLProgram(c,p,o);p.forEach(d=>e.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:l},attrs:{shape:u},backend:e});return e.disposeIntermediateTensorInfo(l),m}function Z9(r,t,e){let o=w.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:o}}function Iv(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,t[0].shape)[0],a=t.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(t.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let p=t.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Dt({inputs:{x:p[0]},backend:e}):Bc(p,s,e)}var OA={kernelName:ta,backendName:"webgl",kernelFunc:Iv};var zc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let a=t.padInfo.top,i=t.padInfo.left,p=t.strideHeight,u=t.strideWidth,c=t.dilationHeight,l=t.dilationWidth,m=t.filterHeight,d=t.filterWidth,f=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,C=g?3:1,S="",k="";o&&(n?S=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${o} }`:s?S=`float activation(float a) { @@ -2326,7 +2326,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam ${k} setOutput(result); } - `}},Mh=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,o=t.padInfo.top,n=t.padInfo.left,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.filterDepth,m=t.filterHeight,d=t.filterWidth,f=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=` + `}},Oh=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,o=t.padInfo.top,n=t.padInfo.left,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.filterDepth,m=t.filterHeight,d=t.filterWidth,f=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${s}, ${a}, ${i}); const ivec3 pads = ivec3(${e}, ${o}, ${n}); @@ -2414,7 +2414,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(dotProd); } - `}};var Kc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=t.padInfo.left,i=t.strideWidth,p=t.dilationWidth,u=t.filterHeight,c=t.filterWidth,l=c,m=` + `}};var Vc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=t.padInfo.left,i=t.strideWidth,p=t.dilationWidth,u=t.filterHeight,c=t.filterWidth,l=c,m=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function zh({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=e.inChannels,l=p[0]*p[1]*p[2],m=e.outChannels,d=e.dataFormat==="channelsLast",f=!1,h=!1,g,x=[];if(s!=null){let S=Bh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Bh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((l===1||m===1)&&c>yv)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let S=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,S,e.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(Cu(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let E=te({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(E);let R=_p({a:k,b:E,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),D=o.texData.get(R.dataId);y.assert(D.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=_,D.shape=e.outShape,g=At({inputs:{x:R},backend:o}),g.shape=e.outShape,x.push(R)}else{let S=e.outHeight*e.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[e.batchSize,S,e.inChannels]:[e.batchSize,e.inChannels,S]}}),_=te({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}}),E=_p({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:E},backend:o,attrs:{shape:e.outShape}}),x.push(k),x.push(_),x.push(E)}for(let S of x)o.disposeIntermediateTensorInfo(S);return g}function Vh({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=e,f=d==="channelsLast",h=p*u*c,g=m*l,x=[e.batchSize,h,g],b=!0,C=!1,S=[];if(s!=null){let q=Bh(s.shape,f);q!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:q}}),S.push(s))}if(n!=null){let q=Bh(n.shape,f);q!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:q}}),S.push(n))}let k=te({inputs:{x:t},backend:o,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});S.push(k);let _=new Lh(x,e),E=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],R=o.runWebGLProgram(_,[r],"float32",E),D=te({inputs:{x:R},backend:o,attrs:{shape:x}});S.push(R),S.push(D);let P=n!=null,O=s!=null,M=i==="leakyrelu",L=i?Ci(i,!0):null,B=new Uc(f?D.shape:k.shape,f?k.shape:D.shape,f?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,C,P,L,O,M),z=f?[D,k]:[k,D];if(n&&z.push(n),O&&z.push(s),M){let q=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));z.push(q),S.push(q)}let U=o.runWebGLProgram(B,z,"float32"),j=te({inputs:{x:U},backend:o,attrs:{shape:e.outShape}});S.push(U);for(let q of S)o.disposeIntermediateTensorInfo(q);return j}function J9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))d=zh({x:n,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&l==="channelsLast"&&A().getBool("WEBGL_EXP_CONV")){let h=new Kc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=e.runWebGLProgram(h,[n,s],"float32",g)}else if(A().getBool("WEBGL_CONV_IM2COL"))d=Vh({x:n,filter:s,convInfo:m,backend:e});else{let h=new Hc(m);d=e.runWebGLProgram(h,[n,s],"float32")}let f=te({inputs:{x:d},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(d),f}var OA={kernelName:rn,backendName:"webgl",kernelFunc:J9};var Wh=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,o=t.strideWidth,n=t.padInfo.top,s=t.padInfo.left,a=t.dataFormat==="channelsLast";this.userCode=` + `}};function Lh(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function Bh({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=e.inChannels,l=p[0]*p[1]*p[2],m=e.outChannels,d=e.dataFormat==="channelsLast",f=!1,h=!1,g,x=[];if(s!=null){let S=Lh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Lh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((l===1||m===1)&&c>bv)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let S=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,S,e.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(hu(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let E=te({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(E);let R=Cp({a:k,b:E,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),D=o.texData.get(R.dataId);y.assert(D.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=_,D.shape=e.outShape,g=Dt({inputs:{x:R},backend:o}),g.shape=e.outShape,x.push(R)}else{let S=e.outHeight*e.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[e.batchSize,S,e.inChannels]:[e.batchSize,e.inChannels,S]}}),_=te({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}}),E=Cp({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:E},backend:o,attrs:{shape:e.outShape}}),x.push(k),x.push(_),x.push(E)}for(let S of x)o.disposeIntermediateTensorInfo(S);return g}function zh({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=e,f=d==="channelsLast",h=p*u*c,g=m*l,x=[e.batchSize,h,g],b=!0,C=!1,S=[];if(s!=null){let q=Lh(s.shape,f);q!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:q}}),S.push(s))}if(n!=null){let q=Lh(n.shape,f);q!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:q}}),S.push(n))}let k=te({inputs:{x:t},backend:o,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});S.push(k);let _=new Mh(x,e),E=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],R=o.runWebGLProgram(_,[r],"float32",E),D=te({inputs:{x:R},backend:o,attrs:{shape:x}});S.push(R),S.push(D);let P=n!=null,O=s!=null,M=i==="leakyrelu",L=i?xi(i,!0):null,B=new Lc(f?D.shape:k.shape,f?k.shape:D.shape,f?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,C,P,L,O,M),z=f?[D,k]:[k,D];if(n&&z.push(n),O&&z.push(s),M){let q=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));z.push(q),S.push(q)}let U=o.runWebGLProgram(B,z,"float32"),j=te({inputs:{x:U},backend:o,attrs:{shape:e.outShape}});S.push(U);for(let q of S)o.disposeIntermediateTensorInfo(q);return j}function J9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))d=Bh({x:n,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&l==="channelsLast"&&A().getBool("WEBGL_EXP_CONV")){let h=new Vc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=e.runWebGLProgram(h,[n,s],"float32",g)}else if(A().getBool("WEBGL_CONV_IM2COL"))d=zh({x:n,filter:s,convInfo:m,backend:e});else{let h=new zc(m);d=e.runWebGLProgram(h,[n,s],"float32")}let f=te({inputs:{x:d},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(d),f}var MA={kernelName:tn,backendName:"webgl",kernelFunc:J9};var Vh=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,o=t.strideWidth,n=t.padInfo.top,s=t.padInfo.left,a=t.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2694,7 +2694,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(dotProd); } - `}},Uh=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=t.dataFormat==="channelsLast",i=e-1-t.padInfo.top,p=o-1-t.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=` + `}},Wh=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=t.dataFormat==="channelsLast",i=e-1-t.padInfo.top,p=o-1-t.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${p}); void main() { @@ -2747,7 +2747,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(dotProd); } - `}},Gh=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,o=t.strideHeight,n=t.strideWidth,s=t.padInfo.front,a=t.padInfo.top,i=t.padInfo.left;this.userCode=` + `}},Uh=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,o=t.strideHeight,n=t.strideWidth,s=t.padInfo.front,a=t.padInfo.top,i=t.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2789,7 +2789,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(dotProd); } - `}},Hh=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,o=t.filterHeight,n=t.filterWidth,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=e-1-t.padInfo.front,u=o-1-t.padInfo.top,c=n-1-t.padInfo.left;this.userCode=` + `}},Gh=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,o=t.filterHeight,n=t.filterWidth,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=e-1-t.padInfo.front,u=o-1-t.padInfo.top,c=n-1-t.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${p}, ${u}, ${c}); void main() { @@ -2846,7 +2846,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(dotProd); } - `}};function eJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Wh(m);return e.runWebGLProgram(d,[n,s],"float32")}var MA={kernelName:Oi,backendName:"webgl",kernelFunc:eJ};var Kh=class{constructor(t){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=t.inShape,this.enableShapeUniforms=pt(this.outputShape.length);let e=t.filterHeight,o=t.filterWidth,n=e-1-t.padInfo.top,s=o-1-t.padInfo.left;this.userCode=` + `}};function eJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Vh(m);return e.runWebGLProgram(d,[n,s],"float32")}var LA={kernelName:Ai,backendName:"webgl",kernelFunc:eJ};var Hh=class{constructor(t){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=t.inShape,this.enableShapeUniforms=ut(this.outputShape.length);let e=t.filterHeight,o=t.filterWidth,n=e-1-t.padInfo.top,s=o-1-t.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${n}, ${s}); void main() { @@ -2920,17 +2920,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(result); } - `}};function tJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l);if(A().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&l==="channelsLast"){let d=[[m.strideHeight,m.strideWidth]],f=new Kh(m);return e.runWebGLProgram(f,[n,s],"float32",d)}else{let d=new Uh(m);return e.runWebGLProgram(d,[n,s],"float32")}}var LA={kernelName:on,backendName:"webgl",kernelFunc:tJ};function rJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Mh(u);return e.runWebGLProgram(c,[n,s],"float32")}var BA={kernelName:nn,backendName:"webgl",kernelFunc:rJ};function oJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Gh(u);return e.runWebGLProgram(c,[n,s],"float32")}var zA={kernelName:Xa,backendName:"webgl",kernelFunc:oJ};function nJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{pad:a,strides:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,i,1,a),c=new Hh(u);return e.runWebGLProgram(c,[n,s],"float32")}var VA={kernelName:sn,backendName:"webgl",kernelFunc:nJ};var sJ=Po+` + `}};function tJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l);if(A().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&l==="channelsLast"){let d=[[m.strideHeight,m.strideWidth]],f=new Hh(m);return e.runWebGLProgram(f,[n,s],"float32",d)}else{let d=new Wh(m);return e.runWebGLProgram(d,[n,s],"float32")}}var BA={kernelName:rn,backendName:"webgl",kernelFunc:tJ};function rJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Oh(u);return e.runWebGLProgram(c,[n,s],"float32")}var zA={kernelName:on,backendName:"webgl",kernelFunc:rJ};function oJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Uh(u);return e.runWebGLProgram(c,[n,s],"float32")}var VA={kernelName:ja,backendName:"webgl",kernelFunc:oJ};function nJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{pad:a,strides:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,i,1,a),c=new Gh(u);return e.runWebGLProgram(c,[n,s],"float32")}var WA={kernelName:nn,backendName:"webgl",kernelFunc:nJ};var sJ=Fo+` return cos(x); `,aJ=` vec4 result = cos(x); bvec4 isNaN = isnan(x); - ${Xr} + ${jr} return result; -`,iJ=xe({opSnippet:sJ,packedOpSnippet:aJ}),WA={kernelName:an,backendName:"webgl",kernelFunc:iJ};var uJ=` +`,iJ=xe({opSnippet:sJ,packedOpSnippet:aJ}),UA={kernelName:sn,backendName:"webgl",kernelFunc:iJ};var uJ=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,pJ=xe({opSnippet:uJ}),UA={kernelName:un,backendName:"webgl",kernelFunc:pJ};var qh=class{constructor(t,e,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,p,u]=t,[c]=e,[l,m]=o;this.outputShape=[c,l,m,u];let d=n==="bilinear"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,"(y2-y1) * height_ratio",`y1*${f} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${f}`],[C,S,k]=m>1?[`${(p-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` +`,pJ=xe({opSnippet:uJ}),GA={kernelName:an,backendName:"webgl",kernelFunc:pJ};var Kh=class{constructor(t,e,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,p,u]=t,[c]=e,[l,m]=o;this.outputShape=[c,l,m,u];let d=n==="bilinear"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,"(y2-y1) * height_ratio",`y1*${f} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${f}`],[C,S,k]=m>1?[`${(p-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` const float height_ratio = float(${g}); const float width_ratio = float(${C}); void main() { @@ -2991,20 +2991,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam setOutput(newValue); } } - `}};var cJ=r=>{let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,c=new qh(n.shape,s.shape,i,p,u);return e.runWebGLProgram(c,[n,s,a],"float32")},GA={kernelName:ln,backendName:"webgl",kernelFunc:cJ};var Ep;(function(r){r.Prod="*",r.Sum="+"})(Ep||(Ep={}));var om=class{constructor(t,e,o,n){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,a=this.op===Ep.Prod?"1.0":"0.0",i=o?a:`getX(${HA(s,"coords",this.op)})`,p=this.outputShape[this.outputShape.length-1],u="",c="";o?(u=n?`end != ${p-1}`:"end != 0",c=n?"end + 1":"end - 1"):(u=n?`end + pow2 < ${p}`:"end >= pow2",c=n?"end + pow2":"end - pow2"),this.userCode=` + `}};var cJ=r=>{let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Kh(n.shape,s.shape,i,p,u);return e.runWebGLProgram(c,[n,s,a],"float32")},HA={kernelName:cn,backendName:"webgl",kernelFunc:cJ};var Sp;(function(r){r.Prod="*",r.Sum="+"})(Sp||(Sp={}));var tm=class{constructor(t,e,o,n){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,a=this.op===Sp.Prod?"1.0":"0.0",i=o?a:`getX(${KA(s,"coords",this.op)})`,p=this.outputShape[this.outputShape.length-1],u="",c="";o?(u=n?`end != ${p-1}`:"end != 0",c=n?"end + 1":"end - 1"):(u=n?`end + pow2 < ${p}`:"end >= pow2",c=n?"end + pow2":"end - pow2"),this.userCode=` void main() { ${Re(s)} coords = getOutputCoords(); - int end = ${KA(s,"coords",this.op)}; + int end = ${qA(s,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${u}) { int idx = ${c}; - ${KA(s,"coords",this.op)} = idx; - val ${this.op}= getX(${HA(s,"coords",this.op)}); + ${qA(s,"coords",this.op)} = idx; + val ${this.op}= getX(${KA(s,"coords",this.op)}); } setOutput(val); } - `}};function HA(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function KA(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function jh(r,t,e,o,n,s){let a=t.shape.length,i=w.getAxesPermutation([o],a),p=t;i!=null&&(p=Ct({inputs:{x:t},backend:e,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new om(r,p.shape,!1,s),f=[[m]],h=l;l=e.runWebGLProgram(d,[l],l.dtype,f),e.disposeIntermediateTensorInfo(h)}if(n){let m=new om(r,p.shape,n,s),d=l;l=e.runWebGLProgram(m,[l],l.dtype),e.disposeIntermediateTensorInfo(d)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=Ct({inputs:{x:l},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(p),d}return l}function lJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return jh(Ep.Prod,n,e,s,a,i)}var qA={kernelName:pn,backendName:"webgl",kernelFunc:lJ};function mJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return jh(Ep.Sum,n,e,s,a,i)}var jA={kernelName:cn,backendName:"webgl",kernelFunc:mJ};function dJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=e.readSync(n.dataId),u=e.readSync(s.dataId),c=ph(p,u,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=e.bufferSync(n),u=e.bufferSync(s),c=DR(p,u,a,i);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var XA={kernelName:ra,backendName:"webgl",kernelFunc:dJ};var Xh=class{constructor(t,e,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=o,this.userCode=` + `}};function KA(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function qA(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function qh(r,t,e,o,n,s){let a=t.shape.length,i=w.getAxesPermutation([o],a),p=t;i!=null&&(p=bt({inputs:{x:t},backend:e,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new tm(r,p.shape,!1,s),f=[[m]],h=l;l=e.runWebGLProgram(d,[l],l.dtype,f),e.disposeIntermediateTensorInfo(h)}if(n){let m=new tm(r,p.shape,n,s),d=l;l=e.runWebGLProgram(m,[l],l.dtype),e.disposeIntermediateTensorInfo(d)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=bt({inputs:{x:l},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(p),d}return l}function lJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return qh(Sp.Prod,n,e,s,a,i)}var jA={kernelName:un,backendName:"webgl",kernelFunc:lJ};function mJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return qh(Sp.Sum,n,e,s,a,i)}var XA={kernelName:pn,backendName:"webgl",kernelFunc:mJ};function dJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=e.readSync(n.dataId),u=e.readSync(s.dataId),c=uh(p,u,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=e.bufferSync(n),u=e.bufferSync(s),c=AR(p,u,a,i);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var YA={kernelName:ra,backendName:"webgl",kernelFunc:dJ};var jh=class{constructor(t,e,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=o,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3023,7 +3023,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function fJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=new Xh(f,s,a);return e.runWebGLProgram(h,[n],n.dtype)}var YA={kernelName:mn,backendName:"webgl",kernelFunc:fJ};var qc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=t.filterHeight,i=t.filterWidth,p=t.outChannels/t.inChannels,u="",c="";o&&(n?u=`float activation(float a) { + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function fJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=new jh(f,s,a);return e.runWebGLProgram(h,[n],n.dtype)}var QA={kernelName:ln,backendName:"webgl",kernelFunc:fJ};var Wc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=t.filterHeight,i=t.filterWidth,p=t.outChannels/t.inChannels,u="",c="";o&&(n?u=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${o} }`:s?u=`float activation(float a) { @@ -3076,7 +3076,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam ${c} setOutput(result); } - `}};var jc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=t.outChannels/t.inChannels,i=t.padInfo.left,p=t.strideWidth,u=t.dilationWidth,c=t.filterHeight,l=t.filterWidth,m=l,d=` + `}};var Uc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=t.outChannels/t.inChannels,i=t.padInfo.left,p=t.strideWidth,u=t.dilationWidth,c=t.filterHeight,l=t.filterWidth,m=l,d=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=w.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;A().getBool("WEBGL_PACK_DEPTHWISECONV")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new jc(l):m=new qc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return e.runWebGLProgram(m,[n,s],"float32",d)}var QA={kernelName:dn,backendName:"webgl",kernelFunc:hJ};var Yh=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,o=t.strideWidth,n=t.padInfo.top,s=t.padInfo.left,a=t.outChannels/t.inChannels;this.userCode=` + `}};function hJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p,dimRoundingMode:u}=o,c=p;c==null&&(c=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=w.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;A().getBool("WEBGL_PACK_DEPTHWISECONV")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new Uc(l):m=new Wc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return e.runWebGLProgram(m,[n,s],"float32",d)}var ZA={kernelName:mn,backendName:"webgl",kernelFunc:hJ};var Xh=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,o=t.strideWidth,n=t.padInfo.top,s=t.padInfo.left,a=t.outChannels/t.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -3300,7 +3300,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(dotProd); } - `}},Qh=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=e-1-t.padInfo.top,i=o-1-t.padInfo.left,p=t.outChannels/t.inChannels;this.userCode=` + `}},Yh=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=e-1-t.padInfo.top,i=o-1-t.padInfo.left,p=t.outChannels/t.inChannels;this.userCode=` const ivec2 pads = ivec2(${a}, ${i}); void main() { @@ -3345,13 +3345,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(dotProd); } - `}};function gJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Yh(l);return e.runWebGLProgram(m,[n,s],"float32")}var ZA={kernelName:Mi,backendName:"webgl",kernelFunc:gJ};function xJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Qh(l);return e.runWebGLProgram(m,[n,s],"float32")}var JA={kernelName:Li,backendName:"webgl",kernelFunc:xJ};var Zh=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=` + `}};function gJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Xh(l);return e.runWebGLProgram(m,[n,s],"float32")}var JA={kernelName:Fi,backendName:"webgl",kernelFunc:gJ};function xJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Yh(l);return e.runWebGLProgram(m,[n,s],"float32")}var eF={kernelName:Pi,backendName:"webgl",kernelFunc:xJ};var Qh=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function yJ(r){let{inputs:t,backend:e}=r,{x:o}=t,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:e,attrs:{shape:[s]}}),i=new Zh(s),p=e.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:e,attrs:{shape:n}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(p),u}var eF={kernelName:oa,backendName:"webgl",kernelFunc:yJ};var Jh=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=t,{top:l,left:m}=n;this.userCode=` + `}};function yJ(r){let{inputs:t,backend:e}=r,{x:o}=t,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:e,attrs:{shape:[s]}}),i=new Qh(s),p=e.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:e,attrs:{shape:n}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(p),u}var tF={kernelName:oa,backendName:"webgl",kernelFunc:yJ};var Zh=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=t,{top:l,left:m}=n;this.userCode=` const ivec2 strides = ivec2(${s}, ${a}); const ivec2 pads = ivec2(${l}, ${m}); const float neg_infinity = -3.4e38; @@ -3389,7 +3389,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam float result = curVal; setOutput(result); } - `}};function bJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c,l=new Jh(u);c=e.runWebGLProgram(l,[n,s],"float32");let m=te({inputs:{x:c},backend:e,attrs:{shape:u.outShape}});return e.disposeIntermediateTensorInfo(c),m}var tF={kernelName:fn,backendName:"webgl",kernelFunc:bJ};function CJ(r){let{inputs:t,backend:e,attrs:o}=r,{equation:n}=o,s=t,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Tp({inputs:{x:m},backend:e,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var rF={kernelName:Vi,backendName:"webgl",kernelFunc:CJ};var wJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",SJ=` + `}};function bJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c,l=new Zh(u);c=e.runWebGLProgram(l,[n,s],"float32");let m=te({inputs:{x:c},backend:e,attrs:{shape:u.outShape}});return e.disposeIntermediateTensorInfo(c),m}var rF={kernelName:dn,backendName:"webgl",kernelFunc:bJ};function CJ(r){let{inputs:t,backend:e,attrs:o}=r,{equation:n}=o,s=t,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=bp({inputs:{x:m},backend:e,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var oF={kernelName:Li,backendName:"webgl",kernelFunc:CJ};var wJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",SJ=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3398,12 +3398,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,IJ=xe({opSnippet:wJ,packedOpSnippet:SJ}),oF={kernelName:gn,backendName:"webgl",kernelFunc:IJ};var vJ="return (b >= 0.0) ? a : a * (b + 1.0);",kJ=` +`,IJ=xe({opSnippet:wJ,packedOpSnippet:SJ}),nF={kernelName:hn,backendName:"webgl",kernelFunc:IJ};var vJ="return (b >= 0.0) ? a : a * (b + 1.0);",kJ=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,NJ=r=>{let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(kJ,o.shape,n.shape):new Pr(vJ,o.shape,n.shape);return e.runWebGLProgram(s,[o,n],o.dtype)},nF={kernelName:Ya,backendName:"webgl",kernelFunc:NJ};var TJ=` +`,NJ=r=>{let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(kJ,o.shape,n.shape):new Fr(vJ,o.shape,n.shape);return e.runWebGLProgram(s,[o,n],o.dtype)},sF={kernelName:Xa,backendName:"webgl",kernelFunc:NJ};var TJ=` return vec4(equal(a, b)); -`,_J="return float(a == b);",$J=nt({opSnippet:_J,packedOpSnippet:TJ,dtype:"bool",cpuKernelImpl:MR}),sF={kernelName:yn,backendName:"webgl",kernelFunc:$J};var EJ=` +`,_J="return float(a == b);",$J=nt({opSnippet:_J,packedOpSnippet:TJ,dtype:"bool",cpuKernelImpl:LR}),aF={kernelName:xn,backendName:"webgl",kernelFunc:$J};var EJ=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -3418,7 +3418,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,RJ=xe({opSnippet:EJ}),aF={kernelName:xn,backendName:"webgl",kernelFunc:RJ};var DJ=Po+` +`,RJ=xe({opSnippet:EJ}),iF={kernelName:gn,backendName:"webgl",kernelFunc:RJ};var DJ=Fo+` return exp(x); `,AJ=` vec4 result = exp(x); @@ -3429,7 +3429,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam result.a = isNaN.a ? x.a : result.a; return result; -`,Iv=xe({opSnippet:DJ,packedOpSnippet:AJ,cpuKernelImpl:LR,dtype:"float32"}),iF={kernelName:bn,backendName:"webgl",kernelFunc:Iv};function eg(r){let{inputs:t,attrs:e,backend:o}=r,{dim:n}=e,{input:s}=t,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var uF={kernelName:na,backendName:"webgl",kernelFunc:eg};var pF="return exp(x) - 1.0;",FJ=xe({opSnippet:pF,packedOpSnippet:pF,cpuKernelImpl:BR}),cF={kernelName:Cn,backendName:"webgl",kernelFunc:FJ};var nm=class{constructor(t,e,o){this.variableNames=["real","imag"];let n=e[1];this.outputShape=e;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(t==="real")i="return real * expR - imag * expI;";else if(t==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=` +`,vv=xe({opSnippet:DJ,packedOpSnippet:AJ,cpuKernelImpl:BR,dtype:"float32"}),uF={kernelName:yn,backendName:"webgl",kernelFunc:vv};function Jh(r){let{inputs:t,attrs:e,backend:o}=r,{dim:n}=e,{input:s}=t,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var pF={kernelName:na,backendName:"webgl",kernelFunc:Jh};var cF="return exp(x) - 1.0;",FJ=xe({opSnippet:cF,packedOpSnippet:cF,cpuKernelImpl:zR}),lF={kernelName:bn,backendName:"webgl",kernelFunc:FJ};var rm=class{constructor(t,e,o){this.variableNames=["real","imag"];let n=e[1];this.outputShape=e;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(t==="real")i="return real * expR - imag * expI;";else if(t==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=` const float exponentMultiplier = ${s}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -3462,12 +3462,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function tg(r,t,e){let o=e.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:e,attrs:{shape:[a,s]}}),p=i.shape,u=new nm("real",p,t),c=new nm("imag",p,t),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=e.runWebGLProgram(u,l,"float32"),d=e.runWebGLProgram(c,l,"float32"),f=Or({inputs:{real:m,imag:d},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(f),h}function PJ(r){let{inputs:t,backend:e}=r,{input:o}=t;return tg(o,!1,e)}var lF={kernelName:Wi,backendName:"webgl",kernelFunc:PJ};var rg=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=` + `}};function eg(r,t,e){let o=e.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:e,attrs:{shape:[a,s]}}),p=i.shape,u=new rm("real",p,t),c=new rm("imag",p,t),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=e.runWebGLProgram(u,l,"float32"),d=e.runWebGLProgram(c,l,"float32"),f=Pr({inputs:{real:m,imag:d},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(f),h}function PJ(r){let{inputs:t,backend:e}=r,{input:o}=t;return eg(o,!1,e)}var mF={kernelName:Bi,backendName:"webgl",kernelFunc:PJ};var tg=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function Si(r){let{backend:t,attrs:e}=r,{shape:o,value:n}=e,{dtype:s}=e;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),t.makeTensorInfo(o,s,a)}else{let a=new rg(o,n),i=[[n]];return t.runWebGLProgram(a,[],s,i)}}var mF={kernelName:sa,backendName:"webgl",kernelFunc:Si};var og=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=` + `}};function bi(r){let{backend:t,attrs:e}=r,{shape:o,value:n}=e,{dtype:s}=e;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),t.makeTensorInfo(o,s,a)}else{let a=new tg(o,n),i=[[n]];return t.runWebGLProgram(a,[],s,i)}}var dF={kernelName:sa,backendName:"webgl",kernelFunc:bi};var rg=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -3481,7 +3481,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(outputValue); } - `}};var dF={kernelName:wn,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,o=t,n=new og(e.shape);return o.runWebGLProgram(n,[e],e.dtype)}};var fF="return floor(x);",OJ=xe({opSnippet:fF,packedOpSnippet:fF,cpuKernelImpl:zR}),hF={kernelName:Sn,backendName:"webgl",kernelFunc:OJ};var MJ=` + `}};var fF={kernelName:Cn,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,o=t,n=new rg(e.shape);return o.runWebGLProgram(n,[e],e.dtype)}};var hF="return floor(x);",OJ=xe({opSnippet:hF,packedOpSnippet:hF,cpuKernelImpl:VR}),gF={kernelName:wn,backendName:"webgl",kernelFunc:OJ};var MJ=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3512,7 +3512,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,BJ=nt({opSnippet:MJ,packedOpSnippet:LJ,dtype:"int32"}),gF={kernelName:In,backendName:"webgl",kernelFunc:BJ};var ng=class{constructor(t){this.variableNames=["A"];let e=vt(),[o,n]=t;this.outputShape=t,this.userCode=` +`,BJ=nt({opSnippet:MJ,packedOpSnippet:LJ,dtype:"int32"}),xF={kernelName:Sn,backendName:"webgl",kernelFunc:BJ};var og=class{constructor(t){this.variableNames=["A"];let e=It(),[o,n]=t;this.outputShape=t,this.userCode=` void main() { ivec3 coords = getOutputCoords(); 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`,mee=` vec4 result = log(x); @@ -3608,18 +3608,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; -`,dee=xe({opSnippet:lee,packedOpSnippet:mee,cpuKernelImpl:jR}),RF={kernelName:Pn,backendName:"webgl",kernelFunc:dee};var fee=Po+` +`,dee=xe({opSnippet:lee,packedOpSnippet:mee,cpuKernelImpl:XR}),DF={kernelName:Fn,backendName:"webgl",kernelFunc:dee};var fee=Fo+` return log(1.0 + x); -`,hee=xe({opSnippet:fee}),DF={kernelName:On,backendName:"webgl",kernelFunc:hee};var gee="return float(a >= 1.0 && b >= 1.0);",xee=` +`,hee=xe({opSnippet:fee}),AF={kernelName:Pn,backendName:"webgl",kernelFunc:hee};var gee="return float(a >= 1.0 && b >= 1.0);",xee=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,yee=nt({opSnippet:gee,packedOpSnippet:xee,dtype:"bool"}),AF={kernelName:Mn,backendName:"webgl",kernelFunc:yee};var bee="return float(!(x >= 1.0));",Cee=xe({opSnippet:bee}),FF={kernelName:Ln,backendName:"webgl",kernelFunc:Cee};var wee="return float(a >= 1.0 || b >= 1.0);",See=` +`,yee=nt({opSnippet:gee,packedOpSnippet:xee,dtype:"bool"}),FF={kernelName:On,backendName:"webgl",kernelFunc:yee};var bee="return float(!(x >= 1.0));",Cee=xe({opSnippet:bee}),PF={kernelName:Mn,backendName:"webgl",kernelFunc:Cee};var wee="return float(a >= 1.0 || b >= 1.0);",See=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,Iee=nt({opSnippet:wee,packedOpSnippet:See,dtype:"bool"}),PF={kernelName:Bn,backendName:"webgl",kernelFunc:Iee};var ug=class{constructor(t,e,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=e,i=t[3]-1;this.outputShape=t;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=` +`,Iee=nt({opSnippet:wee,packedOpSnippet:See,dtype:"bool"}),OF={kernelName:Ln,backendName:"webgl",kernelFunc:Iee};var ig=class{constructor(t,e,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=e,i=t[3]-1;this.outputShape=t;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3638,7 +3638,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam float val = x * ${p}; setOutput(val); } - `}};var pg=class{constructor(t,e,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=e,i=t[3]-1;this.outputShape=t;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=` + `}};var ug=class{constructor(t,e,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=e,i=t[3]-1;this.outputShape=t;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3700,7 +3700,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam vec4 result = xAtOutputCoords * ${p}; setOutput(result); } - `}};var vee=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=A().getBool("WEBGL_PACK_NORMALIZATION")?new pg(n.shape,s,a,i,p):new ug(n.shape,s,a,i,p);return e.runWebGLProgram(u,[n],n.dtype)},OF={kernelName:zn,backendName:"webgl",kernelFunc:vee};var cg=class{constructor(t,e,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=o,this.alpha=n,this.beta=s,this.userCode=` + `}};var vee=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=A().getBool("WEBGL_PACK_NORMALIZATION")?new ug(n.shape,s,a,i,p):new ig(n.shape,s,a,i,p);return e.runWebGLProgram(u,[n],n.dtype)},MF={kernelName:Bn,backendName:"webgl",kernelFunc:vee};var pg=class{constructor(t,e,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=o,this.alpha=n,this.beta=s,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3755,16 +3755,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(result); } - `}};var kee=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new cg(n.shape,i,p,u,c);return e.runWebGLProgram(l,[n,s,a],n.dtype)},MF={kernelName:Qa,backendName:"webgl",kernelFunc:kee};function LF(r,t,e,o){let n=y.sizeFromShape(t),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Yr(i,r.dtype,"max",o),u=te({inputs:{x:p},attrs:{shape:e},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function Nv(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=e.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=e.texData.get(d.dataId).values,S=new Array(i);for(let E=0;E{let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new pg(n.shape,i,p,u,c);return e.runWebGLProgram(l,[n,s,a],n.dtype)},LF={kernelName:Ya,backendName:"webgl",kernelFunc:kee};function BF(r,t,e,o){let n=y.sizeFromShape(t),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Xr(i,r.dtype,"max",o),u=te({inputs:{x:p},attrs:{shape:e},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function Tv(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=e.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=e.texData.get(d.dataId).values,S=new Array(i);for(let E=0;E`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:e});let l=new Us(c,"max",!1);return e.runWebGLProgram(l,[n],n.dtype)}var VF={kernelName:Un,backendName:"webgl",kernelFunc:$ee};function Eee(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Iu(l,"max",!1);return e.runWebGLProgram(m,[n],n.dtype)}var WF={kernelName:ia,backendName:"webgl",kernelFunc:Eee};var lg=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,o=t.strideWidth,n=t.dilationHeight,s=t.effectiveFilterHeight,a=t.effectiveFilterWidth,i=s-1-t.padInfo.top,p=a-1-t.padInfo.left,u=s*a-1;this.userCode=` +`,_ee=nt({opSnippet:Nee,packedOpSnippet:Tee,cpuKernelImpl:QR}),VF={kernelName:Vn,backendName:"webgl",kernelFunc:_ee};function $ee(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t;Vs(n,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:e});let l=new Us(c,"max",!1);return e.runWebGLProgram(l,[n],n.dtype)}var WF={kernelName:Wn,backendName:"webgl",kernelFunc:$ee};function Eee(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new xu(l,"max",!1);return e.runWebGLProgram(m,[n],n.dtype)}var UF={kernelName:ia,backendName:"webgl",kernelFunc:Eee};var cg=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,o=t.strideWidth,n=t.dilationHeight,s=t.effectiveFilterHeight,a=t.effectiveFilterWidth,i=s-1-t.padInfo.top,p=a-1-t.padInfo.left,u=s*a-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${p}); void main() { @@ -3810,7 +3810,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(dotProd); } - `}},mg=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,o=t.strideHeight,n=t.strideWidth,s=t.dilationDepth,a=t.dilationHeight,i=t.dilationWidth,p=t.effectiveFilterDepth,u=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=p-1-t.padInfo.front,m=u-1-t.padInfo.top,d=c-1-t.padInfo.left,f=p*u*c-1;this.userCode=` + `}},lg=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,o=t.strideHeight,n=t.strideWidth,s=t.dilationDepth,a=t.dilationHeight,i=t.dilationWidth,p=t.effectiveFilterDepth,u=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=p-1-t.padInfo.front,m=u-1-t.padInfo.top,d=c-1-t.padInfo.left,f=p*u*c-1;this.userCode=` const ivec3 pads = ivec3(${l}, ${m}, ${d}); void main() { @@ -3874,16 +3874,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam } setOutput(dotProd); } - `}};function Ree(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Iu(m,"max",!0),f=e.runWebGLProgram(d,[a],a.dtype),h=new mg(m),g=e.runWebGLProgram(h,[n,f],a.dtype);return e.disposeIntermediateTensorInfo(f),g}var UF={kernelName:Ki,backendName:"webgl",kernelFunc:Ree};function Dee(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s,output:a}=t,i=s;Vs([s,a],"maxPoolGrad");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new Us(m,"max",d),h=e.runWebGLProgram(f,[i],i.dtype),g=new lg(m),x=e.runWebGLProgram(g,[n,h],i.dtype);return e.disposeIntermediateTensorInfo(h),x}var GF={kernelName:Hi,backendName:"webgl",kernelFunc:Dee};function HF(r,t,e,o){let n=new Us(e,"max",!1),s=o.runWebGLProgram(n,[r],"float32");n=new Us(e,"max",!0,!0,t);let a=o.runWebGLProgram(n,[r],"float32");return[s,a]}var KF={kernelName:ua,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=t,p=e;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(o.shape,n,s,u,a),[l,m]=HF(o,i,c,p);return[l,m]}};function qF(r,t,e,o){let n=y.sizeFromShape(t),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Yr(i,"float32","mean",o),u=te({inputs:{x:p},attrs:{shape:e},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}var jF={kernelName:Gn,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o}=r,{keepDims:n,axis:s}=t,a=e,i=o.shape.length,p=y.parseAxisParam(s,o.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=a.shouldExecuteOnCPU([o]),d=[],f=o;if(l){if(m){let S=a.texData.get(f.dataId).values,k=new Array(i);for(let R=0;R{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=t,p=e;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(o.shape,n,s,u,a),[l,m]=KF(o,i,c,p);return[l,m]}};function jF(r,t,e,o){let n=y.sizeFromShape(t),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Xr(i,"float32","mean",o),u=te({inputs:{x:p},attrs:{shape:e},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}var XF={kernelName:Un,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o}=r,{keepDims:n,axis:s}=t,a=e,i=o.shape.length,p=y.parseAxisParam(s,o.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=a.shouldExecuteOnCPU([o]),d=[],f=o;if(l){if(m){let S=a.texData.get(f.dataId).values,k=new Array(i);for(let R=0;Rc[0]+t[l]+c[1]);let n=t.length,s=Re(n),a=e.map(c=>c[0]).join(","),i=e.map((c,l)=>c[0]+t[l]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),u=o==="reflect"?0:1;if(n===1){this.userCode=` +`,Oee=nt({opSnippet:Fee,packedOpSnippet:Pee,cpuKernelImpl:ZR}),QF={kernelName:Hn,backendName:"webgl",kernelFunc:Oee};var mg=class{constructor(t,e,o){this.variableNames=["x"],this.outputShape=e.map((c,l)=>c[0]+t[l]+c[1]);let n=t.length,s=Re(n),a=e.map(c=>c[0]).join(","),i=e.map((c,l)=>c[0]+t[l]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),u=o==="reflect"?0:1;if(n===1){this.userCode=` int start = ${a}; int end = ${i}; @@ -3912,7 +3912,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam ${s} coords = outC - start; setOutput(getX(${p})); } - `}};var fg=class{constructor(t,e,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((f,h)=>f[0]+t[h]+f[1]);let n=t.length,s=Re(n),a=e.map(f=>f[0]).join(","),i=e.map((f,h)=>f[0]+t[h]).join(","),p=Dt("rc",n),u=Dt("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,d="";if(n===1){let f=` + `}};var dg=class{constructor(t,e,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((f,h)=>f[0]+t[h]+f[1]);let n=t.length,s=Re(n),a=e.map(f=>f[0]).join(","),i=e.map((f,h)=>f[0]+t[h]).join(","),p=Rt("rc",n),u=Rt("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,d="";if(n===1){let f=` ${s} source = rc; if (source < start) { source = start * 2 - source - ${m}; @@ -3968,13 +3968,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelNam ${d} setOutput(result); } - `}};var Mee=({inputs:r,backend:t,attrs:e})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fg(o.shape,n,s):new dg(o.shape,n,s);return t.runWebGLProgram(a,[o],o.dtype)},QF={kernelName:qn,backendName:"webgl",kernelFunc:Mee};var Lee=`if (b == 0.0) return NAN; + `}};var Mee=({inputs:r,backend:t,attrs:e})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dg(o.shape,n,s):new mg(o.shape,n,s);return t.runWebGLProgram(a,[o],o.dtype)},ZF={kernelName:Kn,backendName:"webgl",kernelFunc:Mee};var Lee=`if (b == 0.0) return NAN; return mod(a, b);`,Bee=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+Xr+` + `+jr+` return result; -`,zee=nt({opSnippet:Lee,packedOpSnippet:Bee}),ZF={kernelName:jn,backendName:"webgl",kernelFunc:zee};var hg=class{constructor(t,e,o){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,o],this.userCode=` +`,zee=nt({opSnippet:Lee,packedOpSnippet:Bee}),JF={kernelName:qn,backendName:"webgl",kernelFunc:zee};var fg=class{constructor(t,e,o){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,o],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4016,7 +4016,7 @@ return a / b;`,Wee=` } return result; -`,Tv=nt({opSnippet:Vee,packedOpSnippet:Wee,checkOutOfBounds:!0}),JF={kernelName:hn,backendName:"webgl",kernelFunc:Tv};var e3="return a - b;",_v=nt({opSnippet:e3,packedOpSnippet:e3,supportsComplex:!0,cpuKernelImpl:yD}),t3={kernelName:_s,backendName:"webgl",kernelFunc:_v};function $v(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Nv({inputs:{x:n},backend:e,attrs:{reductionIndices:a,keepDims:!1}}),p=w.expandShapeToKeepDim(i.shape,a),u=te({inputs:{x:i},backend:e,attrs:{shape:p}}),c=_v({inputs:{a:n,b:u},backend:e}),l=Iv({inputs:{x:c},backend:e}),m=Tp({inputs:{x:l},backend:e,attrs:{axis:a,keepDims:!1}}),d=te({inputs:{x:m},backend:e,attrs:{shape:p}}),f=Tv({inputs:{a:l,b:d},backend:e});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(d),f}var r3={kernelName:vs,backendName:"webgl",kernelFunc:$v};function Uee(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{numSamples:s,seed:a,normalized:i}=o,p=i?n:$v({inputs:{logits:n},backend:e,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new hg(u,c,s),m=[[a]],d=e.runWebGLProgram(l,[p],"int32",m);return i||e.disposeIntermediateTensorInfo(p),d}var o3={kernelName:Xn,backendName:"webgl",kernelFunc:Uee};var Gee=Ut+` +`,_v=nt({opSnippet:Vee,packedOpSnippet:Wee,checkOutOfBounds:!0}),e3={kernelName:fn,backendName:"webgl",kernelFunc:_v};var t3="return a - b;",$v=nt({opSnippet:t3,packedOpSnippet:t3,supportsComplex:!0,cpuKernelImpl:bD}),r3={kernelName:Ts,backendName:"webgl",kernelFunc:$v};function Ev(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Tv({inputs:{x:n},backend:e,attrs:{reductionIndices:a,keepDims:!1}}),p=w.expandShapeToKeepDim(i.shape,a),u=te({inputs:{x:i},backend:e,attrs:{shape:p}}),c=$v({inputs:{a:n,b:u},backend:e}),l=vv({inputs:{x:c},backend:e}),m=bp({inputs:{x:l},backend:e,attrs:{axis:a,keepDims:!1}}),d=te({inputs:{x:m},backend:e,attrs:{shape:p}}),f=_v({inputs:{a:l,b:d},backend:e});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(d),f}var o3={kernelName:Is,backendName:"webgl",kernelFunc:Ev};function Uee(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{numSamples:s,seed:a,normalized:i}=o,p=i?n:Ev({inputs:{logits:n},backend:e,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new fg(u,c,s),m=[[a]],d=e.runWebGLProgram(l,[p],"int32",m);return i||e.disposeIntermediateTensorInfo(p),d}var n3={kernelName:jn,backendName:"webgl",kernelFunc:Uee};var Gee=Wt+` return -x; `,Hee=` vec4 result = -x; @@ -4028,14 +4028,14 @@ return a / b;`,Wee=` result.a = isNaN.a ? x.a : result.a; return result; -`;function Kee(r){let{inputs:t,backend:e}=r,{x:o}=t;if(e.shouldExecuteOnCPU([o])){let s=e.texData.get(o.dataId),[a,i]=JR(s.values,o.shape,o.dtype);return e.makeTensorInfo(i,o.dtype,a)}let n;return A().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Fr(o.shape,Hee):n=new or(o.shape,Gee),e.runWebGLProgram(n,[o],o.dtype)}var n3={kernelName:pa,backendName:"webgl",kernelFunc:Kee};var qee=Wt.nonMaxSuppressionV3Impl;function jee(r){w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=e.readSync(n.dataId),c=e.readSync(s.dataId),{selectedIndices:l}=qee(u,c,a,i,p);return e.makeTensorInfo([l.length],"int32",new Int32Array(l))}var s3={kernelName:Zn,backendName:"webgl",kernelFunc:jee};var Xee=Wt.nonMaxSuppressionV4Impl;function Yee(r){w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,padToMaxOutputSize:u}=o,c=e.readSync(n.dataId),l=e.readSync(s.dataId),{selectedIndices:m,validOutputs:d}=Xee(c,l,a,i,p,u);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([d]))]}var a3={kernelName:Za,backendName:"webgl",kernelFunc:Yee};var Qee=Wt.nonMaxSuppressionV5Impl;function Zee(r){w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=e.readSync(n.dataId),l=e.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Qee(c,l,m,d,f,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var i3={kernelName:Jn,backendName:"webgl",kernelFunc:Zee};var gg=class{constructor(t,e,o,n){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=` +`;function Kee(r){let{inputs:t,backend:e}=r,{x:o}=t;if(e.shouldExecuteOnCPU([o])){let s=e.texData.get(o.dataId),[a,i]=eD(s.values,o.shape,o.dtype);return e.makeTensorInfo(i,o.dtype,a)}let n;return A().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Ar(o.shape,Hee):n=new tr(o.shape,Gee),e.runWebGLProgram(n,[o],o.dtype)}var s3={kernelName:pa,backendName:"webgl",kernelFunc:Kee};var qee=Vt.nonMaxSuppressionV3Impl;function jee(r){w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=e.readSync(n.dataId),c=e.readSync(s.dataId),{selectedIndices:l}=qee(u,c,a,i,p);return e.makeTensorInfo([l.length],"int32",new Int32Array(l))}var a3={kernelName:Qn,backendName:"webgl",kernelFunc:jee};var Xee=Vt.nonMaxSuppressionV4Impl;function Yee(r){w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,padToMaxOutputSize:u}=o,c=e.readSync(n.dataId),l=e.readSync(s.dataId),{selectedIndices:m,validOutputs:d}=Xee(c,l,a,i,p,u);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([d]))]}var i3={kernelName:Qa,backendName:"webgl",kernelFunc:Yee};var Qee=Vt.nonMaxSuppressionV5Impl;function Zee(r){w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=e.readSync(n.dataId),l=e.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Qee(c,l,m,d,f,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var u3={kernelName:Zn,backendName:"webgl",kernelFunc:Zee};var hg=class{constructor(t,e,o,n){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${n}), float(${o}), float(index == coords.y))); } - `}};var Jee=r=>{let{inputs:t,backend:e,attrs:o}=r,{indices:n}=t,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new gg(u,a,i,p),l=te({inputs:{x:n},backend:e,attrs:{shape:[u]}}),m=e.runWebGLProgram(c,[l],s);e.disposeIntermediateTensorInfo(l);let d=[...n.shape,a],f=te({inputs:{x:m},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(m),f},u3={kernelName:es,backendName:"webgl",kernelFunc:Jee};function sm(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype==="complex64"){let n=wi({inputs:{input:o},backend:e}),s=sm({inputs:{x:n},backend:e}),a=$p({inputs:{input:o},backend:e}),i=sm({inputs:{x:a},backend:e}),p=Or({inputs:{real:s,imag:i},backend:e});return e.disposeIntermediateTensorInfo(n),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(i),p}else return Si({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:e})}var p3={kernelName:Sa,backendName:"webgl",kernelFunc:sm};function c3(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=wi({inputs:{input:o},backend:e}),s=c3({inputs:{x:n},backend:e}),a=$p({inputs:{input:o},backend:e}),i=sm({inputs:{x:a},backend:e}),p=Or({inputs:{real:s,imag:i},backend:e});return e.disposeIntermediateTensorInfo(n),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(i),p}else return Si({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:e})}var l3={kernelName:ca,backendName:"webgl",kernelFunc:c3};function ete(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o;if(t.length===1)return eg({inputs:{input:t[0]},backend:e,attrs:{dim:n}});let s=t[0].shape,a=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=t.map(c=>{let l=eg({inputs:{input:c},backend:e,attrs:{dim:n}});return i.push(l),l}),u=Sv({inputs:p,backend:e,attrs:{axis:n}});return i.forEach(c=>e.disposeIntermediateTensorInfo(c)),u}var m3={kernelName:la,backendName:"webgl",kernelFunc:ete};var xg=class{constructor(t,e,o){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((u,c)=>u[0]+t[c]+u[1]);let n=t.length,s=Re(n),a=e.map(u=>u[0]).join(","),i=e.map((u,c)=>u[0]+t[c]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` + `}};var Jee=r=>{let{inputs:t,backend:e,attrs:o}=r,{indices:n}=t,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new hg(u,a,i,p),l=te({inputs:{x:n},backend:e,attrs:{shape:[u]}}),m=e.runWebGLProgram(c,[l],s);e.disposeIntermediateTensorInfo(l);let d=[...n.shape,a],f=te({inputs:{x:m},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(m),f},p3={kernelName:Jn,backendName:"webgl",kernelFunc:Jee};function om(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype==="complex64"){let n=yi({inputs:{input:o},backend:e}),s=om({inputs:{x:n},backend:e}),a=wp({inputs:{input:o},backend:e}),i=om({inputs:{x:a},backend:e}),p=Pr({inputs:{real:s,imag:i},backend:e});return e.disposeIntermediateTensorInfo(n),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(i),p}else return bi({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:e})}var c3={kernelName:Sa,backendName:"webgl",kernelFunc:om};function l3(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=yi({inputs:{input:o},backend:e}),s=l3({inputs:{x:n},backend:e}),a=wp({inputs:{input:o},backend:e}),i=om({inputs:{x:a},backend:e}),p=Pr({inputs:{real:s,imag:i},backend:e});return e.disposeIntermediateTensorInfo(n),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(i),p}else return bi({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:e})}var m3={kernelName:ca,backendName:"webgl",kernelFunc:l3};function ete(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o;if(t.length===1)return Jh({inputs:{input:t[0]},backend:e,attrs:{dim:n}});let s=t[0].shape,a=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=t.map(c=>{let l=Jh({inputs:{input:c},backend:e,attrs:{dim:n}});return i.push(l),l}),u=Iv({inputs:p,backend:e,attrs:{axis:n}});return i.forEach(c=>e.disposeIntermediateTensorInfo(c)),u}var d3={kernelName:la,backendName:"webgl",kernelFunc:ete};var gg=class{constructor(t,e,o){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((u,c)=>u[0]+t[c]+u[1]);let n=t.length,s=Re(n),a=e.map(u=>u[0]).join(","),i=e.map((u,c)=>u[0]+t[c]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` int start = ${a}; int end = ${i}; @@ -4060,7 +4060,7 @@ return a / b;`,Wee=` setOutput(getX(${p})); } } - `}};var yg=class{constructor(t,e,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let n=t.length,s=Re(n),a=e.map(h=>h[0]).join(","),i=e.map((h,g)=>h[0]+t[g]).join(","),p=Dt("rc",n),u=Dt("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1; + `}};var xg=class{constructor(t,e,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let n=t.length,s=Re(n),a=e.map(h=>h[0]).join(","),i=e.map((h,g)=>h[0]+t[g]).join(","),p=Rt("rc",n),u=Rt("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1; if(${c}) { `,n===1?"":`} rc = outputLoc; @@ -4084,7 +4084,7 @@ return a / b;`,Wee=` ${f} setOutput(result); } - `}};var Ev=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Si({backend:e,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yg(n.shape,s,a):new xg(n.shape,s,a),p=[[a]];return e.runWebGLProgram(i,[n],n.dtype,p)},d3={kernelName:ts,backendName:"webgl",kernelFunc:Ev};var tte=` + `}};var Rv=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return bi({backend:e,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xg(n.shape,s,a):new gg(n.shape,s,a),p=[[a]];return e.runWebGLProgram(i,[n],n.dtype,p)},f3={kernelName:es,backendName:"webgl",kernelFunc:Rv};var tte=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4109,9 +4109,9 @@ return a / b;`,Wee=` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - `+Xr+` + `+jr+` return result; -`,ote=nt({opSnippet:tte,packedOpSnippet:rte}),f3={kernelName:rs,backendName:"webgl",kernelFunc:ote};function nte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o,i=n.shape.length,p=[],u=y.parseAxisParam(s,n.shape),c=u,l=w.getAxesPermutation(c,i),m=n;l!=null&&(m=Ct({inputs:{x:n},backend:e,attrs:{perm:l}}),c=w.getInnerMostAxes(c.length,i),p.push(m)),w.assertAxesAreInnerMostDims("prod",c,i);let d;if(e.shouldExecuteOnCPU([m])){let f=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=tD(m.shape,m.dtype,f,c);d=e.makeTensorInfo(g,x,h)}else{let[f,h]=w.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=te({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=ni(n.dtype),C=Yr(x,b,"prod",e);d=te({inputs:{x:C},backend:e,attrs:{shape:f}}),p.push(x),p.push(C)}if(a){p.push(d);let f=w.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:e,attrs:{shape:f}})}return p.forEach(f=>e.disposeIntermediateTensorInfo(f)),d}var h3={kernelName:ns,backendName:"webgl",kernelFunc:nte};function ste(r){let{inputs:t,backend:e,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=t,{outputRaggedRank:i}=o,p=n.map(x=>e.readSync(x.dataId)),u=n.map(x=>x.shape),c=e.readSync(s.dataId),l=e.readSync(a.dataId),[m,d,f]=rD(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var g3={kernelName:Qp,backendName:"webgl",kernelFunc:ste};function ate(r){let{inputs:t,backend:e}=r,{starts:o,limits:n,deltas:s}=t,a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=oD(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=e.makeTensorInfo([u.length],"int32",u),m=e.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var x3={kernelName:Zp,backendName:"webgl",kernelFunc:ate};function ite(r){let{inputs:t,backend:e,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=t,{rowPartitionTypes:p}=o,u=e.readSync(n.dataId),c=e.readSync(s.dataId),l=e.readSync(a.dataId),m=i.map(g=>e.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=nD(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return e.makeTensorInfo(f,s.dtype,h)}var y3={kernelName:Jp,backendName:"webgl",kernelFunc:ite};var Rv=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=sD(o,n,s,a);return t.makeTensorInfo([i.length],a,i)},b3={kernelName:ma,backendName:"webgl",kernelFunc:Rv};var ute="return 1.0 / x;",pte=xe({opSnippet:ute}),C3={kernelName:ss,backendName:"webgl",kernelFunc:pte};var cte=Ut+` +`,ote=nt({opSnippet:tte,packedOpSnippet:rte}),h3={kernelName:ts,backendName:"webgl",kernelFunc:ote};function nte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o,i=n.shape.length,p=[],u=y.parseAxisParam(s,n.shape),c=u,l=w.getAxesPermutation(c,i),m=n;l!=null&&(m=bt({inputs:{x:n},backend:e,attrs:{perm:l}}),c=w.getInnerMostAxes(c.length,i),p.push(m)),w.assertAxesAreInnerMostDims("prod",c,i);let d;if(e.shouldExecuteOnCPU([m])){let f=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=rD(m.shape,m.dtype,f,c);d=e.makeTensorInfo(g,x,h)}else{let[f,h]=w.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=te({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=oi(n.dtype),C=Xr(x,b,"prod",e);d=te({inputs:{x:C},backend:e,attrs:{shape:f}}),p.push(x),p.push(C)}if(a){p.push(d);let f=w.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:e,attrs:{shape:f}})}return p.forEach(f=>e.disposeIntermediateTensorInfo(f)),d}var g3={kernelName:os,backendName:"webgl",kernelFunc:nte};function ste(r){let{inputs:t,backend:e,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=t,{outputRaggedRank:i}=o,p=n.map(x=>e.readSync(x.dataId)),u=n.map(x=>x.shape),c=e.readSync(s.dataId),l=e.readSync(a.dataId),[m,d,f]=oD(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var x3={kernelName:Up,backendName:"webgl",kernelFunc:ste};function ate(r){let{inputs:t,backend:e}=r,{starts:o,limits:n,deltas:s}=t,a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=nD(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=e.makeTensorInfo([u.length],"int32",u),m=e.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var y3={kernelName:Gp,backendName:"webgl",kernelFunc:ate};function ite(r){let{inputs:t,backend:e,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=t,{rowPartitionTypes:p}=o,u=e.readSync(n.dataId),c=e.readSync(s.dataId),l=e.readSync(a.dataId),m=i.map(g=>e.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=sD(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return e.makeTensorInfo(f,s.dtype,h)}var b3={kernelName:Hp,backendName:"webgl",kernelFunc:ite};var Dv=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=aD(o,n,s,a);return t.makeTensorInfo([i.length],a,i)},C3={kernelName:ma,backendName:"webgl",kernelFunc:Dv};var ute="return 1.0 / x;",pte=xe({opSnippet:ute}),w3={kernelName:ns,backendName:"webgl",kernelFunc:pte};var cte=Wt+` return (x < 0.0) ? 0.0 : x; `,lte=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); @@ -4123,7 +4123,7 @@ return a / b;`,Wee=` result.a = isNaN.a ? x.a : result.a; return result; -`,mte=xe({opSnippet:cte,packedOpSnippet:lte}),w3={kernelName:as,backendName:"webgl",kernelFunc:mte};var dte=Ut+` +`,mte=xe({opSnippet:cte,packedOpSnippet:lte}),S3={kernelName:ss,backendName:"webgl",kernelFunc:mte};var dte=Wt+` return (x < 0.0) ? 0.0 : min(6.0, x); `,fte=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); @@ -4135,7 +4135,7 @@ return a / b;`,Wee=` result.a = isNaN.a ? x.a : result.a; return result; -`,hte=xe({opSnippet:dte,packedOpSnippet:fte}),S3={kernelName:ps,backendName:"webgl",kernelFunc:hte};var bg=class{constructor(t,e,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,hte=xe({opSnippet:dte,packedOpSnippet:fte}),I3={kernelName:us,backendName:"webgl",kernelFunc:hte};var yg=class{constructor(t,e,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/l[0]}, ${c[1]/l[1]}); @@ -4168,7 +4168,7 @@ return a / b;`,Wee=` setOutput(newValue); } - `}};var Cg=class{constructor(t,e,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};var bg=class{constructor(t,e,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/l[0]}, ${c[1]/l[1]}, @@ -4245,7 +4245,7 @@ return a / b;`,Wee=` setOutput(newValue); } - `}};function gte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Cg(n.shape,p,u,s,a):new bg(n.shape,p,u,s,a);return e.runWebGLProgram(c,[n],"float32")}var I3={kernelName:us,backendName:"webgl",kernelFunc:gte};var wg=class{constructor(t,e,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,n,s]=e,[,a,i]=t,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` + `}};function gte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new bg(n.shape,p,u,s,a):new yg(n.shape,p,u,s,a);return e.runWebGLProgram(c,[n],"float32")}var v3={kernelName:is,backendName:"webgl",kernelFunc:gte};var Cg=class{constructor(t,e,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,n,s]=e,[,a,i]=t,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4326,7 +4326,7 @@ return a / b;`,Wee=` setOutput(accumulator); } - `}};function xte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=new wg(s.shape,n.shape,a);return e.runWebGLProgram(i,[s],s.dtype)}var v3={kernelName:ei,backendName:"webgl",kernelFunc:xte};var Sg=class{constructor(t,e,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m=n?"0.5":"0.0",d;s?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function xte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=new Cg(s.shape,n.shape,a);return e.runWebGLProgram(i,[s],s.dtype)}var k3={kernelName:Ja,backendName:"webgl",kernelFunc:xte};var wg=class{constructor(t,e,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m=n?"0.5":"0.0",d;s?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/l[0]}, ${c[1]/l[1]}); @@ -4348,7 +4348,7 @@ return a / b;`,Wee=` setOutput(newValue); } - `}};var Ig=class{constructor(t,e,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m=n?"0.5":"0.0",d;s?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};var Sg=class{constructor(t,e,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m=n?"0.5":"0.0",d;s?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/l[0]}, ${c[1]/l[1]}, @@ -4389,7 +4389,7 @@ return a / b;`,Wee=` setOutput(newValue); } - `}};function yte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Ig(n.shape,p,u,s,a):new Sg(n.shape,p,u,s,a);return e.runWebGLProgram(c,[n],n.dtype)}var k3={kernelName:is,backendName:"webgl",kernelFunc:yte};var vg=class{constructor(t,e,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,n,s]=e,[,a,i]=t,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` + `}};function yte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Sg(n.shape,p,u,s,a):new wg(n.shape,p,u,s,a);return e.runWebGLProgram(c,[n],n.dtype)}var N3={kernelName:as,backendName:"webgl",kernelFunc:yte};var Ig=class{constructor(t,e,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,n,s]=e,[,a,i]=t,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4459,7 +4459,7 @@ return a / b;`,Wee=` setOutput(accumulator); } - `}};function bte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=new vg(s.shape,n.shape,a);return e.runWebGLProgram(i,[s],s.dtype)}var N3={kernelName:Ja,backendName:"webgl",kernelFunc:bte};var kg=class{constructor(t,e){this.variableNames=["x"];let o=t.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=t,o===1){this.userCode=` + `}};function bte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=new Ig(s.shape,n.shape,a);return e.runWebGLProgram(i,[s],s.dtype)}var T3={kernelName:Za,backendName:"webgl",kernelFunc:bte};var vg=class{constructor(t,e){this.variableNames=["x"];let o=t.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=t,o===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${t[0]} - coord - 1)); @@ -4469,7 +4469,7 @@ return a / b;`,Wee=` ${a} coords = getOutputCoords(); setOutput(getX(${s})); } - `}};var Ng=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=t.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=t;let n=Dt("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Re(o);o===1?this.userCode=` + `}};var kg=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=t.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=t;let n=Rt("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Re(o);o===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -4497,7 +4497,7 @@ return a / b;`,Wee=` } setOutput(result); } - `;function p(f){return m(f)}function u(f){return f[o-1]="("+f[o-1]+" + 1)",m(f)}function c(f){return f[o-2]="("+f[o-2]+" + 1)",m(f)}function l(f){return f[o-1]="("+f[o-1]+" + 1)",f[o-2]="("+f[o-2]+" + 1)",m(f)}function m(f){let h=t.map((b,C)=>d(C,f)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return e.indexOf(f)!==-1&&t[f]!==1?`${t[f]} - ${h[f]} - 1`:`${h[f]}`}}};function Cte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return At({inputs:{x:n},backend:e});let p=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ng(n.shape,i):new kg(n.shape,i);return e.runWebGLProgram(p,[n],n.dtype)}var T3={kernelName:cs,backendName:"webgl",kernelFunc:Cte};var Tg=class{constructor(t,e){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let o=t[1],n=t[2];this.outputShape=t;let s="";typeof e=="number"?s=`float outputValue = ${e.toFixed(2)};`:s=` + `;function p(f){return m(f)}function u(f){return f[o-1]="("+f[o-1]+" + 1)",m(f)}function c(f){return f[o-2]="("+f[o-2]+" + 1)",m(f)}function l(f){return f[o-1]="("+f[o-1]+" + 1)",f[o-2]="("+f[o-2]+" + 1)",m(f)}function m(f){let h=t.map((b,C)=>d(C,f)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return e.indexOf(f)!==-1&&t[f]!==1?`${t[f]} - ${h[f]} - 1`:`${h[f]}`}}};function Cte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return Dt({inputs:{x:n},backend:e});let p=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new kg(n.shape,i):new vg(n.shape,i);return e.runWebGLProgram(p,[n],n.dtype)}var _3={kernelName:ps,backendName:"webgl",kernelFunc:Cte};var Ng=class{constructor(t,e){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let o=t[1],n=t[2];this.outputShape=t;let s="";typeof e=="number"?s=`float outputValue = ${e.toFixed(2)};`:s=` vec3 fill = vec3(${e.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -4516,7 +4516,7 @@ return a / b;`,Wee=` } setOutput(outputValue); } - `}};var _3={kernelName:As,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=t,i=e,p=new Tg(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var wte=` + `}};var $3={kernelName:Ds,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=t,i=e,p=new Ng(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var wte=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4531,7 +4531,7 @@ return a / b;`,Wee=` return base + 1.0; } } -`,Ste=xe({opSnippet:wte}),$3={kernelName:ls,backendName:"webgl",kernelFunc:Ste};var Ite="return inversesqrt(x);",vte=xe({opSnippet:Ite,cpuKernelImpl:aD}),E3={kernelName:ms,backendName:"webgl",kernelFunc:vte};var vu=class{constructor(t,e,o,n,s,a,i=!0,p=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let u=Re(s.length),c=Re(a.length),l="";o===1?l="i":o===2&&(l="i, j");let m=`getIndices(${l})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let f=`getUpdates(${d})`,h="";p&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides";this.userCode=` +`,Ste=xe({opSnippet:wte}),E3={kernelName:cs,backendName:"webgl",kernelFunc:Ste};var Ite="return inversesqrt(x);",vte=xe({opSnippet:Ite,cpuKernelImpl:iD}),R3={kernelName:ls,backendName:"webgl",kernelFunc:vte};var yu=class{constructor(t,e,o,n,s,a,i=!0,p=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let u=Re(s.length),c=Re(a.length),l="";o===1?l="i":o===2&&(l="i, j");let m=`getIndices(${l})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let f=`getUpdates(${d})`,h="";p&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides";this.userCode=` ${u} strides = ${u}(${s}); void main() { @@ -4551,7 +4551,7 @@ return a / b;`,Wee=` } setOutput(mix(${g}, sum, float(found))); } - `}};var _g=class{constructor(t,e,o,n,s,a,i=!0,p=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=Re(s.length),c=Re(a.length),l="";o===1?l="i":o===2&&(l="i, j");let m=`getIndices(${l})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let f=`getUpdates(${d})`,h="";p&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides",b=e>1?"strides[j + 1]":"strides";this.userCode=` + `}};var Tg=class{constructor(t,e,o,n,s,a,i=!0,p=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=Re(s.length),c=Re(a.length),l="";o===1?l="i":o===2&&(l="i, j");let m=`getIndices(${l})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let f=`getUpdates(${d})`,h="";p&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides",b=e>1?"strides[j + 1]":"strides";this.userCode=` ${u} strides = ${u}(${s}); void main() { @@ -4588,7 +4588,7 @@ return a / b;`,Wee=` } setOutput(mix(${g}, sum, found)); } - `}};function kte(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n,updates:s}=t,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return e.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:e,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:e,attrs:{shape:[p,u]}}),h=e.makeTensorInfo([],"float32",new Float32Array([0])),g;A().getBool("WEBGL_PACK")?g=new _g(p,i,d.shape.length,f.shape.length,c,m):g=new vu(p,i,d.shape.length,f.shape.length,c,m);let x=e.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var R3={kernelName:ds,backendName:"webgl",kernelFunc:kte};var $g=class{constructor(t,e,o,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[t,o];let s="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,i=A().getNumber("WEBGL_VERSION")===2?s:a,p=n==="left"?"<":"<=";this.userCode=` + `}};function kte(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n,updates:s}=t,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return e.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:e,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:e,attrs:{shape:[p,u]}}),h=e.makeTensorInfo([],"float32",new Float32Array([0])),g;A().getBool("WEBGL_PACK")?g=new Tg(p,i,d.shape.length,f.shape.length,c,m):g=new yu(p,i,d.shape.length,f.shape.length,c,m);let x=e.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var D3={kernelName:ms,backendName:"webgl",kernelFunc:kte};var _g=class{constructor(t,e,o,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[t,o];let s="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,i=A().getNumber("WEBGL_VERSION")===2?s:a,p=n==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; @@ -4613,7 +4613,7 @@ return a / b;`,Wee=` setOutput(float(findBound(batch, value))); } - `}};function Nte(r){let{inputs:t,backend:e,attrs:o}=r,{sortedSequence:n,values:s}=t,{side:a}=o,i=new $g(n.shape[0],n.shape[1],s.shape[1],a),p=[[n.shape[1]]];return e.runWebGLProgram(i,[n,s],"int32",p)}var D3={kernelName:hs,backendName:"webgl",kernelFunc:Nte};var Eg=class{constructor(t,e,o){this.variableNames=["c","a","b"],this.outputShape=e;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],p=[],u=[];for(let c=0;c4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],p=[],u=[];for(let c=0;c= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,$te=xe({opSnippet:_te}),F3={kernelName:gs,backendName:"webgl",kernelFunc:$te};var Ete=Po+` +`,$te=xe({opSnippet:_te}),P3={kernelName:hs,backendName:"webgl",kernelFunc:$te};var Ete=Fo+` return 1.0 / (1.0 + exp(-1.0 * x)); `,Rte=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); @@ -4641,20 +4641,20 @@ return a / b;`,Wee=` result.a = isNaN.a ? x.a : result.a; return result; -`,Dte=xe({opSnippet:Ete,packedOpSnippet:Rte,cpuKernelImpl:uD}),P3={kernelName:Cs,backendName:"webgl",kernelFunc:Dte};var Ate=` +`,Dte=xe({opSnippet:Ete,packedOpSnippet:Rte,cpuKernelImpl:pD}),O3={kernelName:bs,backendName:"webgl",kernelFunc:Dte};var Ate=` if (isnan(x)) { return 0.0; } return sign(x); -`,Fte=xe({opSnippet:Ate}),O3={kernelName:bs,backendName:"webgl",kernelFunc:Fte};var Pte=Po+` +`,Fte=xe({opSnippet:Ate}),M3={kernelName:ys,backendName:"webgl",kernelFunc:Fte};var Pte=Fo+` return sin(x); `,Ote=` vec4 result = sin(x); bvec4 isNaN = isnan(x); - ${Xr} + ${jr} return result; -`,Mte=xe({opSnippet:Pte,packedOpSnippet:Ote}),M3={kernelName:xs,backendName:"webgl",kernelFunc:Mte};var Lte=` +`,Mte=xe({opSnippet:Pte,packedOpSnippet:Ote}),L3={kernelName:gs,backendName:"webgl",kernelFunc:Mte};var Lte=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,Bte=xe({opSnippet:Lte}),L3={kernelName:ys,backendName:"webgl",kernelFunc:Bte};var zte=` +`,Bte=xe({opSnippet:Lte}),B3={kernelName:xs,backendName:"webgl",kernelFunc:Bte};var zte=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -4674,17 +4674,17 @@ return a / b;`,Wee=` result = log(exp_x + 1.0); } return result; -`,Vte=xe({opSnippet:zte}),B3={kernelName:ws,backendName:"webgl",kernelFunc:Vte};var Wte=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xe.disposeIntermediateTensorInfo(x)),g},z3={kernelName:ga,backendName:"webgl",kernelFunc:Wte};function Ute(r){let{inputs:t,backend:e}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: +`,Vte=xe({opSnippet:zte}),z3={kernelName:Cs,backendName:"webgl",kernelFunc:Vte};var Wte=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xe.disposeIntermediateTensorInfo(x)),g},V3={kernelName:ga,backendName:"webgl",kernelFunc:Wte};function Ute(r){let{inputs:t,backend:e}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: ${s.shape}`);if(o.shape.length!==2)throw new Error(`Indices must be a matrix, saw: ${o.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw: ${n.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw: - ${a.shape}`);let i=e.readSync(o.dataId),p=e.readSync(n.dataId),u=e.readSync(s.dataId),c=e.readSync(a.dataId)[0],[l,m,d,f,h]=cD(i,o.shape,o.dtype,p,n.dtype,u,c);return[e.makeTensorInfo(m,o.dtype,l),e.makeTensorInfo([m[0]],n.dtype,d),e.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),e.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var V3={kernelName:ji,backendName:"webgl",kernelFunc:Ute};function Gte(r){let{inputs:t,backend:e}=r,{inputIndices:o,inputShape:n,newShape:s}=t;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(e.readSync(n.dataId)),i=e.readSync(o.dataId),p=Array.from(e.readSync(s.dataId)),[u,c,l]=lD(i,o.shape,o.dtype,a,p);return[e.makeTensorInfo(c,o.dtype,u),e.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var W3={kernelName:ti,backendName:"webgl",kernelFunc:Gte};function Hte(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;if(o.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${a.shape}`);let i=e.readSync(o.dataId),p=e.readSync(n.dataId),u=e.readSync(s.dataId),c=e.readSync(a.dataId)[0],[l,m,d,f,h]=lD(i,o.shape,o.dtype,p,n.dtype,u,c);return[e.makeTensorInfo(m,o.dtype,l),e.makeTensorInfo([m[0]],n.dtype,d),e.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),e.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var W3={kernelName:Hi,backendName:"webgl",kernelFunc:Ute};function Gte(r){let{inputs:t,backend:e}=r,{inputIndices:o,inputShape:n,newShape:s}=t;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(e.readSync(n.dataId)),i=e.readSync(o.dataId),p=Array.from(e.readSync(s.dataId)),[u,c,l]=mD(i,o.shape,o.dtype,a,p);return[e.makeTensorInfo(c,o.dtype,u),e.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var U3={kernelName:ei,backendName:"webgl",kernelFunc:Gte};function Hte(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;if(o.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${s.shape}`);let a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=lh(a,o.shape,o.dtype,i,p,!0);return e.makeTensorInfo(c,o.dtype,u)}var U3={kernelName:ya,backendName:"webgl",kernelFunc:Hte};function Kte(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;if(o.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${s.shape}`);let a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=ch(a,o.shape,o.dtype,i,p,!0);return e.makeTensorInfo(c,o.dtype,u)}var G3={kernelName:ya,backendName:"webgl",kernelFunc:Hte};function Kte(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;if(o.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${s.shape}`);let a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=lh(a,o.shape,o.dtype,i,p);return e.makeTensorInfo(c,o.dtype,u)}var G3={kernelName:ba,backendName:"webgl",kernelFunc:Kte};function qte(r){let{inputs:t,backend:e,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=t,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype==="string"){let x=e.bufferSync(n),b=e.bufferSync(s),C=y.decodeString(e.readSync(a.dataId)[0]),S=iD(x,b,i,m,c,u,p,l,C,d);return e.makeTensorInfo(i,S.dtype,S.values)}let f=new vu(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=e.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(h),g}var H3={kernelName:ks,backendName:"webgl",kernelFunc:qte};function jte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Gs({inputs:{x:n},backend:e,attrs:{begin:c,size:d}});return c[i]+=m,f})}var K3={kernelName:xa,backendName:"webgl",kernelFunc:jte};var q3="return sqrt(x);",Xte=xe({opSnippet:q3,packedOpSnippet:q3,cpuKernelImpl:mD}),j3={kernelName:Ss,backendName:"webgl",kernelFunc:Xte};var Yte="return x * x;",Qte=xe({opSnippet:Yte}),X3={kernelName:Xi,backendName:"webgl",kernelFunc:Qte};var Y3="return (a - b) * (a - b);",Zte=nt({opSnippet:Y3,packedOpSnippet:Y3}),Q3={kernelName:Ns,backendName:"webgl",kernelFunc:Zte};function Jte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t;if(n.dtype!=="string")throw new Error("Input must be of datatype string");let s=e.readSync(n.dataId),a=w.fromUint8ToStringArray(s),i=dD(a,"string",o);return e.makeTensorInfo(n.shape,"string",i)}var Z3={kernelName:Ou,backendName:"webgl",kernelFunc:Jte};function ere({inputs:r,attrs:t,backend:e}){let{x:o}=r,n=Ut+` + ${s.shape}`);let a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=ch(a,o.shape,o.dtype,i,p);return e.makeTensorInfo(c,o.dtype,u)}var H3={kernelName:ba,backendName:"webgl",kernelFunc:Kte};function qte(r){let{inputs:t,backend:e,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=t,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype==="string"){let x=e.bufferSync(n),b=e.bufferSync(s),C=y.decodeString(e.readSync(a.dataId)[0]),S=uD(x,b,i,m,c,u,p,l,C,d);return e.makeTensorInfo(i,S.dtype,S.values)}let f=new yu(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=e.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(h),g}var K3={kernelName:vs,backendName:"webgl",kernelFunc:qte};function jte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Gs({inputs:{x:n},backend:e,attrs:{begin:c,size:d}});return c[i]+=m,f})}var q3={kernelName:xa,backendName:"webgl",kernelFunc:jte};var j3="return sqrt(x);",Xte=xe({opSnippet:j3,packedOpSnippet:j3,cpuKernelImpl:dD}),X3={kernelName:ws,backendName:"webgl",kernelFunc:Xte};var Yte="return x * x;",Qte=xe({opSnippet:Yte}),Y3={kernelName:Ki,backendName:"webgl",kernelFunc:Qte};var Q3="return (a - b) * (a - b);",Zte=nt({opSnippet:Q3,packedOpSnippet:Q3}),Z3={kernelName:ks,backendName:"webgl",kernelFunc:Zte};function Jte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t;if(n.dtype!=="string")throw new Error("Input must be of datatype string");let s=e.readSync(n.dataId),a=w.fromUint8ToStringArray(s),i=fD(a,"string",o);return e.makeTensorInfo(n.shape,"string",i)}var J3={kernelName:$u,backendName:"webgl",kernelFunc:Jte};function ere({inputs:r,attrs:t,backend:e}){let{x:o}=r,n=Wt+` return x > 0.0 ? 1.0 : float(${t.alpha}); - `,s=new or(o.shape,n);return e.runWebGLProgram(s,[o],o.dtype)}var J3={kernelName:So,backendName:"webgl",kernelFunc:ere};var Rg=class{constructor(t,e,o){this.variableNames=["x"],this.outputShape=o;let n=o.length,s=Re(o.length),a=Re(o.length),i="";if(n===1)i="coords * strides + begin";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=` + `,s=new tr(o.shape,n);return e.runWebGLProgram(s,[o],o.dtype)}var eP={kernelName:wo,backendName:"webgl",kernelFunc:ere};var Eg=class{constructor(t,e,o){this.variableNames=["x"],this.outputShape=o;let n=o.length,s=Re(o.length),a=Re(o.length),i="";if(n===1)i="coords * strides + begin";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=` ${s} begin = ${s}(${t}); ${s} strides = ${s}(${e}); @@ -4692,15 +4692,15 @@ return a / b;`,Wee=` ${a} coords = getOutputCoords(); setOutput(getX(${i})); } - `}};function 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rre(r){let{inputs:t,backend:e,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=t,m=e.readSync(c.dataId),d=e.readSync(l.dataId),[f,h]=hD(m,d,n,s,a,i,p,u);return[e.makeTensorInfo([f.length],"string",f),e.makeTensorInfo(l.shape,"int32",h)]}var tP={kernelName:Ca,backendName:"webgl",kernelFunc:rre};function ore(r){let{inputs:t,backend:e,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=e.readSync(s.dataId),p=e.readSync(a.dataId)[0],[u,c,l]=gD(i,p,n),m=c.length;return[e.makeTensorInfo([m,2],"int32",u),e.makeTensorInfo([m],"string",c),e.makeTensorInfo([2],"int32",new Int32Array(l))]}var rP={kernelName:Yi,backendName:"webgl",kernelFunc:ore};function nre(r){let{inputs:t,backend:e,attrs:o}=r,{numBuckets:n}=o,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(n<=0)throw new Error("Number of buckets must be at least 1");let a=e.readSync(s.dataId),i=xD(a,n);return e.makeTensorInfo(s.shape,"int32",i)}var oP={kernelName:Qi,backendName:"webgl",kernelFunc:nre};var sre="return tan(x);",are=xe({opSnippet:sre}),nP={kernelName:$s,backendName:"webgl",kernelFunc:are};var ire=` + `}};function tre(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=pt.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=te({inputs:{x:n},backend:e,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let 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e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],o=[];for(let n=0;n5){let p=e.readSync(n.dataId),u=n.dtype==="string"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=CD(c,s);return e.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new Rg(n.shape,s);return e.runWebGLProgram(a,[n],n.dtype)}var uP={kernelName:uo,backendName:"webgl",kernelFunc:Av};var Dg=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=t,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4740,7 +4740,7 @@ return a / b;`,Wee=` setOutput(float(i1)); } } - `}},Fg=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=t,this.userCode=` + `}},Ag=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=t,this.userCode=` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); @@ -4774,7 +4774,7 @@ return a / b;`,Wee=` setOutput(x0 >= x1 ? float(i0) : float(i1)); } - `}};function Rp(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function uP(r){let t=1;for(;tp){let P=e.readSync(n.dataId),[O,M]=CD(P,u,n.dtype,s,a);return[e.makeTensorInfo(O.shape,O.dtype,O.values),e.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[e.makeTensorInfo(u,n.dtype,[]),e.makeTensorInfo(u,"int32",[])];if(c===1)return[n,Si({attrs:{shape:u,dtype:"int32",value:0},backend:e})];let l=e.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?e.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=te({inputs:{x:d},attrs:{shape:[h,c]},backend:e});m&&Rp(e,d);let x=uP(s),b=uP(c),C=null,S=()=>C===null?[g,g]:[g,C],k=(P,O,M)=>{let L=S(),B=new Ag(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[P],[O]],j=C;C=e.runWebGLProgram(B,L,"int32",U),Rp(e,j)};for(let P=1;P=1;M/=2)k(O,M,[h,b])}for(let P=b;P>x;P/=2){let O=S(),M=new Fg([h,P/2]),B=[[c],[C===null?1:0],[x]],z=C;C=e.runWebGLProgram(M,O,"int32",B),Rp(e,z);let U=x/2,j=U*2;for(let q=U;q>=1;q/=2)k(j,q,C.shape)}let _=C;C=Gs({inputs:{x:C},backend:e,attrs:{begin:0,size:[h,s]}}),Rp(e,_);let E=kv({inputs:{x:g,indices:C},backend:e,attrs:{axis:1,batchDims:1}});Rp(e,g);let R=u.slice(0,-1);R.push(s),_=C,C=te({inputs:{x:C},attrs:{shape:R},backend:e}),Rp(e,_);let D=E;return E=te({inputs:{x:E},attrs:{shape:R},backend:e}),Rp(e,D),[E,C]}var pP={kernelName:Rs,backendName:"webgl",kernelFunc:lre};var Pg=class{constructor(t,e,o,n,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=o==="nearest"?1:2,p;switch(n){case"constant":p=1;break;case"reflect":p=2;break;case"wrap":p=3;break;case"nearest":p=4;break;default:p=1;break}this.userCode=` + `}};function Ip(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function pP(r){let t=1;for(;tp){let P=e.readSync(n.dataId),[O,M]=wD(P,u,n.dtype,s,a);return[e.makeTensorInfo(O.shape,O.dtype,O.values),e.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[e.makeTensorInfo(u,n.dtype,[]),e.makeTensorInfo(u,"int32",[])];if(c===1)return[n,bi({attrs:{shape:u,dtype:"int32",value:0},backend:e})];let l=e.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?e.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=te({inputs:{x:d},attrs:{shape:[h,c]},backend:e});m&&Ip(e,d);let x=pP(s),b=pP(c),C=null,S=()=>C===null?[g,g]:[g,C],k=(P,O,M)=>{let L=S(),B=new Dg(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[P],[O]],j=C;C=e.runWebGLProgram(B,L,"int32",U),Ip(e,j)};for(let P=1;P=1;M/=2)k(O,M,[h,b])}for(let P=b;P>x;P/=2){let O=S(),M=new Ag([h,P/2]),B=[[c],[C===null?1:0],[x]],z=C;C=e.runWebGLProgram(M,O,"int32",B),Ip(e,z);let U=x/2,j=U*2;for(let q=U;q>=1;q/=2)k(j,q,C.shape)}let _=C;C=Gs({inputs:{x:C},backend:e,attrs:{begin:0,size:[h,s]}}),Ip(e,_);let E=Nv({inputs:{x:g,indices:C},backend:e,attrs:{axis:1,batchDims:1}});Ip(e,g);let R=u.slice(0,-1);R.push(s),_=C,C=te({inputs:{x:C},attrs:{shape:R},backend:e}),Ip(e,_);let D=E;return E=te({inputs:{x:E},attrs:{shape:R},backend:e}),Ip(e,D),[E,C]}var cP={kernelName:Es,backendName:"webgl",kernelFunc:lre};var Fg=class{constructor(t,e,o,n,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=o==="nearest"?1:2,p;switch(n){case"constant":p=1;break;case"reflect":p=2;break;case"wrap":p=3;break;case"nearest":p=4;break;default:p=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${p} == 2) { @@ -4886,7 +4886,7 @@ return a / b;`,Wee=` } setOutput(outputValue); } - `}};function 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bO={kernelName:pn,backendName:"wasm",setupFunc:ioe,kernelFunc:uoe};var CO;function poe(r){CO=r.wasm.cwrap(cn,null,["number","number","number","number","number","number"])}function coe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o,p=n.shape.length;y.assert(n.dtype==="float32"||n.dtype==="int32",()=>`cumsum does not support ${n.dtype} tensors in the WASM backend`);let u=w.getAxesPermutation([s],p),c=n;u!==null&&(c=go({inputs:{x:n},attrs:{perm:u},backend:e}));let l=w.getInnerMostAxes(1,p)[0];w.assertAxesAreInnerMostDims("cumsum",[l],p);let m=e.makeOutput(c.shape,c.dtype),d=c.shape[l],f=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;CO(f,a?1:0,i?1:0,d,h,we[n.dtype]);let g=m;if(u!==null){let x=w.getUndoAxesPermutation(u);g=go({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var wO={kernelName:cn,backendName:"wasm",setupFunc:poe,kernelFunc:coe};var SO;function 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foe(r){let{backend:t,inputs:e,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=t.makeOutput(f,"float32"),x=t.dataIdMap.get(n.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer),C=new Uint8Array(new Int32Array(f).buffer),S=new Uint8Array(new Int32Array(y.computeStrides(f)).buffer),k=t.dataIdMap.get(h.dataId).id;return vO(x,s,a==="NHWC"?1:0,b,n.shape.length-1,C,S,f.length,k),h}var kO={kernelName:mn,backendName:"wasm",setupFunc:doe,kernelFunc:foe};var NO;function hoe(r){NO=r.wasm.cwrap(dn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function 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Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=e.makeOutput(s.shape,s.dtype);return DO(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var AO={kernelName:zi,backendName:"wasm",setupFunc:woe,kernelFunc:Soe};var FO;function Ioe(r){FO=r.wasm.cwrap(Bi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function voe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=e.makeOutput(n.shape,n.dtype);return FO(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var PO={kernelName:Bi,backendName:"wasm",setupFunc:Ioe,kernelFunc:voe};var OO=he(gn);var MO;function koe(r){MO=r.wasm.cwrap(Ya,null,["number","number","number"])}function Noe(r){let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=e.makeOutput(n.shape,"float32"),a=i=>e.dataIdMap.get(i.dataId).id;return MO(a(n),a(o),a(s)),s}var LO={kernelName:Ya,backendName:"wasm",setupFunc:koe,kernelFunc:Noe};var Toe=!1,BO=Ue(yn,Toe,"bool");var zO=he(xn);var VO=he(bn,"float32");function Lg(r){let{inputs:t,attrs:e,backend:o}=r,{input:n}=t,{dim:s}=e,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),Vt({inputs:{x:n},backend:o,attrs:{shape:i}})}var WO={kernelName:na,backendName:"wasm",kernelFunc:Lg};var UO=he(Cn,"float32");function Pv(r){let{attrs:{shape:t,value:e,dtype:o},backend:n}=r,s=n.makeOutput(t,o);return n.typedArrayFromHeap(s).fill(e),s}var GO={kernelName:sa,backendName:"wasm",kernelFunc:Pv};var HO;function _oe(r){HO=r.wasm.cwrap(wn,null,["number","number","number","number","number","number"])}function $oe(r){let{inputs:t,backend:e}=r,{image:o}=t,n=e.makeOutput(o.shape,o.dtype),s=e.dataIdMap.get(o.dataId).id,a=e.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return HO(s,i,p,u,c,a),n}var KO={kernelName:wn,backendName:"wasm",kernelFunc:$oe,setupFunc:_oe};var qO=he(Sn);var Eoe=!1,jO=Ue(In,Eoe);var XO;function Roe(r){XO=r.wasm.cwrap(vn,null,["number","number","number","number","number","number","number"])}function Doe(r){let{backend:t,inputs:e,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=e,c=t.dataIdMap.get(s.dataId).id,l=t.dataIdMap.get(a.dataId).id,m=t.dataIdMap.get(i.dataId).id,d=p!=null?t.dataIdMap.get(p.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return XO(c,l,m,d,f,n,g),h}var YO={kernelName:vn,backendName:"wasm",setupFunc:Roe,kernelFunc:Doe};var QO;function Aoe(r){QO=r.wasm.cwrap(vo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Foe(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=e,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=ku[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type==="SAME"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. 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zoe(r){let{backend:t,inputs:e,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=t.readSync(s.dataId),c=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let l=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=Vt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:t}),d=y.sizeFromShape(s.shape),f=Vt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:t}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=t.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,C=t.dataIdMap.get(m.dataId).id,k=t.dataIdMap.get(f.dataId).id,_=t.dataIdMap.get(g.dataId).id,E=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return oM(C,we[n.dtype],E,x,k,l.batchSize,R,_),t.disposeData(m.dataId),t.disposeData(f.dataId),g.shape=l.outputShape,g}var nM={kernelName:aa,backendName:"wasm",setupFunc:Boe,kernelFunc:zoe};var Voe=!1,sM=Ue(Nn,Voe,"bool");var Woe=!1,aM=Ue(Tn,Woe,"bool");var iM=he(_n,"bool");var uM=he($n,"bool");var pM=he(En,"bool");var cM;function Uoe(r){cM=r.wasm.cwrap(Rn,null,["number","number","number","number"])}function Goe(r){let{inputs:{x:t},attrs:{alpha:e},backend:o}=r,n=o.dataIdMap.get(t.dataId).id,s=o.makeOutput(t.shape,"float32");if(y.sizeFromShape(t.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;cM(n,we[t.dtype],e,a)}return s}var lM={kernelName:Rn,backendName:"wasm",setupFunc:Uoe,kernelFunc:Goe};var Hoe=!1,mM=Ue(Dn,Hoe,"bool");var Koe=!1,dM=Ue(An,Koe,"bool");var fM;function qoe(r){fM=r.wasm.cwrap(Fn,null,["number","number","number","number"])}function joe(r){let{attrs:t,backend:e}=r,{start:o,stop:n,num:s}=t,a=Math.floor(s),i=e.makeOutput([a],"float32");return fM(e.dataIdMap.get(i.dataId).id,o,n,a),i}var hM={kernelName:Fn,backendName:"wasm",setupFunc:qoe,kernelFunc:joe};var gM=he(Pn);var xM=he(On);var Xoe=!1,yM=Ue(Mn,Xoe,"bool");var bM=he(Ln);var Yoe=!1,CM=Ue(Bn,Yoe,"bool");var Qoe=!1,wM=Ue($0,Qoe,"bool");var SM;function Zoe(r){SM=r.wasm.cwrap(zn,null,["number","number","number","number","number","number","number"])}function Joe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o;if(n.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=e.makeOutput(n.shape,n.dtype);return SM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var IM={kernelName:zn,backendName:"wasm",setupFunc:Zoe,kernelFunc:Joe};var vM;function ene(r){vM=r.wasm.cwrap(Qa,null,["number","number","number","number","number","number","number","number","number"])}function tne(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o;if(n.dtype!=="float32"||s.dtype!=="float32"||a.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let l=e.makeOutput(n.shape,n.dtype);return vM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,a.shape[3],i,p,u,c),l}var kM={kernelName:Qa,backendName:"wasm",setupFunc:ene,kernelFunc:tne};var NM;function rne(r){NM=r.wasm.cwrap(Vn,null,["number","number","number","number"])}function one(r){let{backend:t,inputs:e,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=e,p=t.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,t);if(d){let C=t.dataIdMap.get(c.dataId).id;u=c,p=C}let f=u.shape.length;w.assertAxesAreInnerMostDims("max",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=t.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let C=t.dataIdMap.get(b.dataId).id;NM(p,we[a.dtype],x,C)}if(d&&t.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var TM={kernelName:Vn,backendName:"wasm",setupFunc:rne,kernelFunc:one};var nne=!1,_M=Ue(Wn,nne);var $M;function sne(r){$M=r.wasm.cwrap(Un,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ane(r){let{inputs:t,attrs:e,backend:o}=r,n=t.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. 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Please use 'channelsLast'.`);let E=o.makeOutput(c.outShape,"float32"),R=o.dataIdMap.get(E.dataId).id;return $M(s,n.shape[0],n.shape[1],n.shape[2],l,m,d,f,h,g,x,b,C,S,k,_,R),E}var EM={kernelName:Un,backendName:"wasm",setupFunc:sne,kernelFunc:ane};var RM;function ine(r){RM=r.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function une(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=e.makeOutput(c.outShape,n.dtype);return RM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var DM={kernelName:ia,backendName:"wasm",setupFunc:ine,kernelFunc:une};var AM;function pne(r){AM=r.wasm.cwrap("MaxPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cne(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=e.makeOutput(s.shape,s.dtype);return AM(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var FM={kernelName:Ki,backendName:"wasm",setupFunc:pne,kernelFunc:cne};var PM;function lne(r){PM=r.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function mne(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool2DInfo(s.shape,a,i,1,p,u),l=e.makeOutput(s.shape,s.dtype);return PM(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),l}var OM={kernelName:Hi,backendName:"wasm",setupFunc:lne,kernelFunc:mne};var MM;function dne(r){MM=r.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fne(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,includeBatchInIndex:p}=o;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,[1,1],i),l=e.makeOutput(c.outShape,n.dtype),m=e.makeOutput(c.outShape,"int32");return MM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,e.dataIdMap.get(m.dataId).id,we[n.dtype],p,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[l,m]}var LM={kernelName:ua,backendName:"wasm",setupFunc:dne,kernelFunc:fne};var BM;function hne(r){BM=r.wasm.cwrap(Gn,null,["number, number, number"])}function gne(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,t),f=l;if(d){let S=t.dataIdMap.get(c.dataId).id;S!==i&&(u=c,p=S,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=Mr({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),p=t.dataIdMap.get(b.dataId).id);let C=t.makeOutput(h,"float32");if(y.sizeFromShape(u.shape)!==0){let S=t.dataIdMap.get(C.dataId).id;BM(p,x,S)}if(d&&t.disposeData(c.dataId),s){let S=w.expandShapeToKeepDim(C.shape,m);C.shape=S}return u.dtype!=="float32"&&t.disposeData(b.dataId),C}var zM={kernelName:Gn,backendName:"wasm",setupFunc:hne,kernelFunc:gne};var VM;function xne(r){VM=r.wasm.cwrap(Hn,null,["number","number","number","number"])}function yne(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,t);if(d){let C=t.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C)}let f=u.shape.length;w.assertAxesAreInnerMostDims("min",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=t.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=t.dataIdMap.get(b.dataId).id;VM(p,we[a.dtype],x,C)}if(d&&t.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var WM={kernelName:Hn,backendName:"wasm",setupFunc:xne,kernelFunc:yne};var bne=!1,UM=Ue(Kn,bne);var Ov;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(Ov||(Ov={}));var GM;function Cne(r){GM=r.wasm.cwrap(qn,null,["number","array","number","number","array","array","number","number"])}function wne(r){let{inputs:{x:t},backend:e,attrs:{paddings:o,mode:n}}=r,s=o.map((f,h)=>f[0]+t.shape[h]+f[1]),a=e.dataIdMap.get(t.dataId).id,i=e.makeOutput(s,t.dtype),p=e.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=o.map(f=>f[0]),l=o.map(f=>f[1]),m=new Uint8Array(new Int32Array(c).buffer),d=new Uint8Array(new Int32Array(l).buffer);return GM(a,u,t.shape.length,we[t.dtype],m,d,Ov[n],p),i}var HM={kernelName:qn,backendName:"wasm",kernelFunc:wne,setupFunc:Cne};var KM;function Sne(r){KM=r.wasm.cwrap(vs,null,["number","number","number","number"])}function Mv(r){let{backend:t,inputs:{logits:e},attrs:{dim:o}}=r,n=t.dataIdMap.get(e.dataId).id,s=t.makeOutput(e.shape,e.dtype),a=t.dataIdMap.get(s.dataId).id,i=e.shape[o],p=y.sizeFromShape(e.shape)/i;return y.sizeFromShape(s.shape)===0||KM(n,a,i,p),s}var qM={kernelName:vs,backendName:"wasm",setupFunc:Sne,kernelFunc:Mv};var jM;function Ine(r){jM=r.wasm.cwrap(Xn,null,["number","number","number","number","number","number"])}function vne(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{numSamples:s,seed:a,normalized:i}=o;if(n.dtype!=="float32")throw new Error(`Tensor logits must have dtype float32, got ${n.dtype}`);let p=i?n:Mv({inputs:{logits:n},backend:e,attrs:{dim:n.shape.length-1}}),[u,c]=p.shape,l=e.makeOutput([u,s],"int32");return jM(e.dataIdMap.get(p.dataId).id,u,c,s,a,e.dataIdMap.get(l.dataId).id),i||e.disposeData(p.dataId),l}var XM={kernelName:Xn,backendName:"wasm",setupFunc:Ine,kernelFunc:vne};var YM=Ue(jn,!0);var kne=!0,QM=Ue(Yn,kne);var ZM=he(pa);function Yc(r,t){let e=new Int32Array(r.wasm.HEAPU8.buffer,t,4),o=e[0],n=e[1],s=e[2],a=e[3];return r.wasm._free(t),{pSelectedIndices:o,selectedSize:n,pSelectedScores:s,pValidOutputs:a}}var JM;function Nne(r){JM=r.wasm.cwrap(Zn,"number",["number","number","number","number","number"])}function Tne(r){let{backend:t,inputs:e,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a}=o,{boxes:i,scores:p}=e,u=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(p.dataId).id,l=JM(u,c,s,n,a),{pSelectedIndices:m,selectedSize:d,pSelectedScores:f,pValidOutputs:h}=Yc(t,l);return t.wasm._free(f),t.wasm._free(h),t.makeOutput([d],"int32",m)}var eL={kernelName:Zn,backendName:"wasm",setupFunc:Nne,kernelFunc:Tne};var tL;function _ne(r){tL=r.wasm.cwrap(Za,"number",["number","number","number","number","number","bool"])}function $ne(r){let{backend:t,inputs:e,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,padToMaxOutputSize:i}=o,{boxes:p,scores:u}=e,c=t.dataIdMap.get(p.dataId).id,l=t.dataIdMap.get(u.dataId).id,m=tL(c,l,s,n,a,i),{pSelectedIndices:d,selectedSize:f,pSelectedScores:h,pValidOutputs:g}=Yc(t,m);t.wasm._free(h);let x=t.makeOutput([f],"int32",d),b=t.makeOutput([],"int32",g);return[x,b]}var rL={kernelName:Za,backendName:"wasm",setupFunc:_ne,kernelFunc:$ne};var oL;function Ene(r){oL=r.wasm.cwrap(Jn,"number",["number","number","number","number","number","number"])}function Rne(r){let{backend:t,inputs:e,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,softNmsSigma:i}=o,{boxes:p,scores:u}=e,c=t.dataIdMap.get(p.dataId).id,l=t.dataIdMap.get(u.dataId).id,m=oL(c,l,s,n,a,i),{pSelectedIndices:d,selectedSize:f,pSelectedScores:h,pValidOutputs:g}=Yc(t,m);t.wasm._free(g);let x=t.makeOutput([f],"int32",d),b=t.makeOutput([f],"float32",h);return[x,b]}var nL={kernelName:Jn,backendName:"wasm",setupFunc:Ene,kernelFunc:Rne};var Dne=!1,sL=Ue(Qn,Dne,"bool");var aL;function Ane(r){aL=r.wasm.cwrap(es,null,["number","number","number","number","number"])}function Fne(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n}=t,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=e.makeOutput([...n.shape,a],s),c=e.dataIdMap.get(u.dataId).id,m=e.dataIdMap.get(n.dataId).id;return aL(m,a,i,p,c),u}var iL={kernelName:es,backendName:"wasm",setupFunc:Ane,kernelFunc:Fne};function Pne(r){let{inputs:{x:t},backend:e}=r,o=e.makeOutput(t.shape,t.dtype);return e.typedArrayFromHeap(o).fill(1),o}var uL={kernelName:ca,backendName:"wasm",kernelFunc:Pne};function One(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o;if(t.length===1)return Lg({inputs:{input:t[0]},backend:e,attrs:{dim:n}});let s=t[0].shape,a=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching 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Bg={kernelName:ts,backendName:"wasm",kernelFunc:Lne,setupFunc:Mne};var Bne=!1,lL=Ue(rs,Bne);var mL;function zne(r){mL=r.wasm.cwrap(os,null,["number","number","number"])}function Vne(r){let{inputs:t,backend:e}=r,{x:o,alpha:n}=t,s=e.dataIdMap.get(o.dataId).id,a=e.dataIdMap.get(n.dataId).id,i=s,p=o,u=p;p.dtype!=="float32"&&(u=Mr({backend:e,inputs:{x:o},attrs:{dtype:"float32"}}),i=e.dataIdMap.get(u.dataId).id);let c=e.makeOutput(o.shape,"float32"),l=e.dataIdMap.get(c.dataId).id;return mL(i,a,l),p.dtype!=="float32"&&e.disposeData(u.dataId),c}var dL={kernelName:os,backendName:"wasm",setupFunc:zne,kernelFunc:Vne};var fL;function Wne(r){fL=r.wasm.cwrap(ns,null,["number","number","number","number"])}function Une(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,t),f=l;if(d){let C=t.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=t.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=t.dataIdMap.get(b.dataId).id;fL(p,x,we[b.dtype],C)}if(d&&t.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var hL={kernelName:ns,backendName:"wasm",setupFunc:Wne,kernelFunc:Une};var Gne=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=fp(o,n,s,a),p=t.makeOutput([i.length],a);return t.typedArrayFromHeap(p).set(i),p},gL={kernelName:ma,backendName:"wasm",kernelFunc:Gne};var Hne=!0,xL=Ue(hn,Hne);var yL=he(ss);var bL=he(as);var CL=he(ps);var wL;function Kne(r){wL=r.wasm.cwrap(us,null,["number","number","number","number","number","number","number","number","number","number"])}function qne(r){let{backend:t,inputs:e,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,[c,l,m,d]=n.shape,f=[c,p,u,d],h=t.dataIdMap.get(n.dataId),g;h.dtype!=="float32"&&(g=Mr({backend:t,inputs:{x:n},attrs:{dtype:"float32"}}),h=t.dataIdMap.get(g.dataId));let x=h.id,b=t.makeOutput(f,"float32");if(y.sizeFromShape(n.shape)===0)return b;let C=t.dataIdMap.get(b.dataId).id;return wL(x,c,l,m,d,p,u,s?1:0,a?1:0,C),g!=null&&t.disposeData(g.dataId),b}var SL={kernelName:us,backendName:"wasm",setupFunc:Kne,kernelFunc:qne};var IL;function jne(r){IL=r.wasm.cwrap(ei,null,["number","number","number","array","array","boolean"])}function Xne(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=e.makeOutput(n.shape,"float32"),p=e.dataIdMap.get(n.dataId),u;return p.dtype!=="float32"&&(u=Mr({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),p=e.dataIdMap.get(u.dataId)),IL(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,new Uint8Array(new Int32Array(n.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),a),u!=null&&e.disposeData(u.dataId),i}var vL={kernelName:ei,backendName:"wasm",setupFunc:jne,kernelFunc:Xne};var kL;function Yne(r){kL=r.wasm.cwrap(is,null,["number","number","number","number","number","number","number","number","number","number"])}function Qne(r){let{backend:t,inputs:e,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,[c,l,m,d]=n.shape,f=[c,p,u,d],h=t.makeOutput(f,"float32");if(y.sizeFromShape(n.shape)===0)return h;let g=t.dataIdMap.get(n.dataId),x;g.dtype!=="float32"&&(x=Mr({backend:t,inputs:{x:n},attrs:{dtype:"float32"}}),g=t.dataIdMap.get(x.dataId));let b=g.id,C=t.dataIdMap.get(h.dataId).id;return kL(b,c,l,m,d,p,u,s?1:0,a?1:0,C),x!=null&&t.disposeData(x.dataId),h}var NL={kernelName:is,backendName:"wasm",setupFunc:Yne,kernelFunc:Qne};var TL;function Zne(r){TL=r.wasm.cwrap(Ja,null,["number","number","number","array","array","boolean"])}function Jne(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=e.makeOutput(n.shape,"float32"),p=e.dataIdMap.get(n.dataId),u;return p.dtype!=="float32"&&(u=Mr({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),p=e.dataIdMap.get(u.dataId)),TL(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,new Uint8Array(new Int32Array(n.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),a),u!=null&&e.disposeData(u.dataId),i}var _L={kernelName:Ja,backendName:"wasm",setupFunc:Zne,kernelFunc:Jne};var $L;function ese(r){$L=r.wasm.cwrap(cs,null,["number","array","number","array","number","number"])}function tse(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return Dp({inputs:{x:n},backend:e});let i=e.makeOutput(n.shape,n.dtype),p=e.dataIdMap.get(n.dataId).id,u=e.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),l=new Uint8Array(new Int32Array(n.shape).buffer);$L(p,c,a.length,l,n.shape.length,u);let m=Vt({inputs:{x:i},attrs:{shape:n.shape},backend:e});return e.disposeData(i.dataId),m}var EL={kernelName:cs,backendName:"wasm",kernelFunc:tse,setupFunc:ese};var RL;function rse(r){RL=r.wasm.cwrap(As,null,["number","number","number","number","number","number","number","number","array","number","number"])}function ose(r){let{inputs:t,backend:e,attrs:o}=r,{image:n}=t,{radians:s,fillValue:a,center:i}=o,p=e.makeOutput(n.shape,n.dtype),u=e.dataIdMap.get(n.dataId).id,c=e.dataIdMap.get(p.dataId).id,[l,m,d,f]=n.shape,[h,g]=w.getImageCenter(i,m,d),x=a===0,b=255,C=typeof a=="number"?[a,a,a,x?0:b]:[...a,b],S=new Uint8Array(new Int32Array(C).buffer);return RL(u,l,m,d,f,s,h,g,S,C.length,c),p}var DL={kernelName:As,backendName:"wasm",kernelFunc:ose,setupFunc:rse};var AL=he(ls);var FL=he(ms);var PL;function nse(r){PL=r.wasm.cwrap(ds,null,["number","number","number","number","number","number","array","number","number"])}function 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bu;(function(r){r[r.linear=0]="linear",r[r.relu=1]="relu",r[r.relu6=2]="relu6",r[r.prelu=3]="prelu",r[r.leakyrelu=4]="leakyrelu",r[r.sigmoid=5]="sigmoid",r[r.elu=6]="elu"})(bu||(bu={}));var hP;function xre(r){hP=r.wasm.cwrap(So,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function yre(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t;if(n.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o,m=e.dataIdMap.get(n.dataId).id,d=e.dataIdMap.get(s.dataId).id,f=0;if(a!=null){let R=e.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let h=i==null?0:e.dataIdMap.get(i.dataId).id,g=bu[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the 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vp(r){let{inputs:{x:t},backend:e}=r;if(t.dtype==="string")return ar(e.readSync(t.dataId),t.shape,t.dtype);let o=e.makeOutput(t.shape,t.dtype),n=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(o).set(n),o}var IP={kernelName:Co,backendName:"wasm",kernelFunc:vp};var vP;function Sre(r){vP=r.wasm.cwrap(po,null,["number","array","number","number","number","array","number"])}function ho(r){let{inputs:t,backend:e,attrs:o}=r,[n,s]=vre(t.x.shape,o.perm),a=!0;for(let f=0;f=n&&(s===-1||o[s]>o[a])&&(s=a);o[s]=n}return[e,o]}var kP={kernelName:po,backendName:"wasm",kernelFunc:ho,setupFunc:Sre};function Tr(r,t,e){let o=r.shape,n=r.shape.length,s=y.parseAxisParam(t,o),a=s,i=w.getAxesPermutation(a,n),p=null,u=!1;if(i!=null){let c=new Array(n);for(let d=0;d`new shape: ${a}, old shape: ${o.shape}. 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CO={kernelName:un,backendName:"wasm",setupFunc:ioe,kernelFunc:uoe};var wO;function poe(r){wO=r.wasm.cwrap(pn,null,["number","number","number","number","number","number"])}function coe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o,p=n.shape.length;y.assert(n.dtype==="float32"||n.dtype==="int32",()=>`cumsum does not support ${n.dtype} tensors in the WASM backend`);let u=w.getAxesPermutation([s],p),c=n;u!==null&&(c=ho({inputs:{x:n},attrs:{perm:u},backend:e}));let l=w.getInnerMostAxes(1,p)[0];w.assertAxesAreInnerMostDims("cumsum",[l],p);let m=e.makeOutput(c.shape,c.dtype),d=c.shape[l],f=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;wO(f,a?1:0,i?1:0,d,h,we[n.dtype]);let g=m;if(u!==null){let x=w.getUndoAxesPermutation(u);g=ho({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var SO={kernelName:pn,backendName:"wasm",setupFunc:poe,kernelFunc:coe};var IO;function 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Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=e.makeOutput(s.shape,s.dtype);return AO(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var FO={kernelName:Mi,backendName:"wasm",setupFunc:woe,kernelFunc:Soe};var PO;function Ioe(r){PO=r.wasm.cwrap(Oi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function voe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=e.makeOutput(n.shape,n.dtype);return PO(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var OO={kernelName:Oi,backendName:"wasm",setupFunc:Ioe,kernelFunc:voe};var MO=he(hn);var LO;function koe(r){LO=r.wasm.cwrap(Xa,null,["number","number","number"])}function Noe(r){let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=e.makeOutput(n.shape,"float32"),a=i=>e.dataIdMap.get(i.dataId).id;return LO(a(n),a(o),a(s)),s}var BO={kernelName:Xa,backendName:"wasm",setupFunc:koe,kernelFunc:Noe};var Toe=!1,zO=Ue(xn,Toe,"bool");var VO=he(gn);var WO=he(yn,"float32");function Mg(r){let{inputs:t,attrs:e,backend:o}=r,{input:n}=t,{dim:s}=e,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),zt({inputs:{x:n},backend:o,attrs:{shape:i}})}var UO={kernelName:na,backendName:"wasm",kernelFunc:Mg};var GO=he(bn,"float32");function Ov(r){let{attrs:{shape:t,value:e},backend:o}=r,{attrs:{dtype:n}}=r;n=n||y.inferDtype(e);let s=o.makeOutput(t,n);return o.typedArrayFromHeap(s).fill(e),s}var HO={kernelName:sa,backendName:"wasm",kernelFunc:Ov};var KO;function _oe(r){KO=r.wasm.cwrap(Cn,null,["number","number","number","number","number","number"])}function $oe(r){let{inputs:t,backend:e}=r,{image:o}=t,n=e.makeOutput(o.shape,o.dtype),s=e.dataIdMap.get(o.dataId).id,a=e.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return KO(s,i,p,u,c,a),n}var qO={kernelName:Cn,backendName:"wasm",kernelFunc:$oe,setupFunc:_oe};var jO=he(wn);var Eoe=!1,XO=Ue(Sn,Eoe);var YO;function Roe(r){YO=r.wasm.cwrap(In,null,["number","number","number","number","number","number","number"])}function Doe(r){let{backend:t,inputs:e,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=e,c=t.dataIdMap.get(s.dataId).id,l=t.dataIdMap.get(a.dataId).id,m=t.dataIdMap.get(i.dataId).id,d=p!=null?t.dataIdMap.get(p.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return YO(c,l,m,d,f,n,g),h}var QO={kernelName:In,backendName:"wasm",setupFunc:Roe,kernelFunc:Doe};var ZO;function Aoe(r){ZO=r.wasm.cwrap(Io,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Foe(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=e,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=bu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type==="SAME"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. 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sM={kernelName:aa,backendName:"wasm",setupFunc:Boe,kernelFunc:zoe};var Voe=!1,aM=Ue(kn,Voe,"bool");var Woe=!1,iM=Ue(Nn,Woe,"bool");var uM=he(Tn,"bool");var pM=he(_n,"bool");var cM=he($n,"bool");var lM;function Uoe(r){lM=r.wasm.cwrap(En,null,["number","number","number","number"])}function Goe(r){let{inputs:{x:t},attrs:{alpha:e},backend:o}=r,n=o.dataIdMap.get(t.dataId).id,s=o.makeOutput(t.shape,"float32");if(y.sizeFromShape(t.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;lM(n,we[t.dtype],e,a)}return s}var mM={kernelName:En,backendName:"wasm",setupFunc:Uoe,kernelFunc:Goe};var Hoe=!1,dM=Ue(Rn,Hoe,"bool");var Koe=!1,fM=Ue(Dn,Koe,"bool");var hM;function qoe(r){hM=r.wasm.cwrap(An,null,["number","number","number","number"])}function joe(r){let{attrs:t,backend:e}=r,{start:o,stop:n,num:s}=t,a=Math.floor(s),i=e.makeOutput([a],"float32");return hM(e.dataIdMap.get(i.dataId).id,o,n,a),i}var gM={kernelName:An,backendName:"wasm",setupFunc:qoe,kernelFunc:joe};var xM=he(Fn);var yM=he(Pn);var Xoe=!1,bM=Ue(On,Xoe,"bool");var CM=he(Mn);var Yoe=!1,wM=Ue(Ln,Yoe,"bool");var Qoe=!1,SM=Ue(E0,Qoe,"bool");var IM;function Zoe(r){IM=r.wasm.cwrap(Bn,null,["number","number","number","number","number","number","number"])}function Joe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o;if(n.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=e.makeOutput(n.shape,n.dtype);return IM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var vM={kernelName:Bn,backendName:"wasm",setupFunc:Zoe,kernelFunc:Joe};var kM;function ene(r){kM=r.wasm.cwrap(Ya,null,["number","number","number","number","number","number","number","number","number"])}function tne(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o;if(n.dtype!=="float32"||s.dtype!=="float32"||a.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let 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nne=!1,$M=Ue(Vn,nne);var EM;function sne(r){EM=r.wasm.cwrap(Wn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ane(r){let{inputs:t,attrs:e,backend:o}=r,n=t.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=e,c=w.computePool2DInfo(n.shape,a,i,1,p,u),l=c.filterHeight,m=c.filterWidth,d=c.padInfo.top,f=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,C=c.strideHeight,S=c.strideWidth,k=c.inChannels,_=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let E=o.makeOutput(c.outShape,"float32"),R=o.dataIdMap.get(E.dataId).id;return EM(s,n.shape[0],n.shape[1],n.shape[2],l,m,d,f,h,g,x,b,C,S,k,_,R),E}var RM={kernelName:Wn,backendName:"wasm",setupFunc:sne,kernelFunc:ane};var DM;function ine(r){DM=r.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function une(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=e.makeOutput(c.outShape,n.dtype);return DM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var AM={kernelName:ia,backendName:"wasm",setupFunc:ine,kernelFunc:une};var FM;function pne(r){FM=r.wasm.cwrap("MaxPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cne(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=e.makeOutput(s.shape,s.dtype);return FM(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var PM={kernelName:Ui,backendName:"wasm",setupFunc:pne,kernelFunc:cne};var OM;function lne(r){OM=r.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function mne(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool2DInfo(s.shape,a,i,1,p,u),l=e.makeOutput(s.shape,s.dtype);return OM(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),l}var MM={kernelName:Wi,backendName:"wasm",setupFunc:lne,kernelFunc:mne};var LM;function dne(r){LM=r.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fne(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,includeBatchInIndex:p}=o;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,[1,1],i),l=e.makeOutput(c.outShape,n.dtype),m=e.makeOutput(c.outShape,"int32");return LM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,e.dataIdMap.get(m.dataId).id,we[n.dtype],p,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[l,m]}var BM={kernelName:ua,backendName:"wasm",setupFunc:dne,kernelFunc:fne};var zM;function hne(r){zM=r.wasm.cwrap(Un,null,["number, number, number"])}function gne(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,t),f=l;if(d){let S=t.dataIdMap.get(c.dataId).id;S!==i&&(u=c,p=S,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=Or({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),p=t.dataIdMap.get(b.dataId).id);let C=t.makeOutput(h,"float32");if(y.sizeFromShape(u.shape)!==0){let S=t.dataIdMap.get(C.dataId).id;zM(p,x,S)}if(d&&t.disposeData(c.dataId),s){let S=w.expandShapeToKeepDim(C.shape,m);C.shape=S}return u.dtype!=="float32"&&t.disposeData(b.dataId),C}var VM={kernelName:Un,backendName:"wasm",setupFunc:hne,kernelFunc:gne};var WM;function xne(r){WM=r.wasm.cwrap(Gn,null,["number","number","number","number"])}function yne(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,t);if(d){let C=t.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C)}let f=u.shape.length;w.assertAxesAreInnerMostDims("min",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=t.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=t.dataIdMap.get(b.dataId).id;WM(p,we[a.dtype],x,C)}if(d&&t.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var UM={kernelName:Gn,backendName:"wasm",setupFunc:xne,kernelFunc:yne};var bne=!1,GM=Ue(Hn,bne);var Mv;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(Mv||(Mv={}));var HM;function Cne(r){HM=r.wasm.cwrap(Kn,null,["number","array","number","number","array","array","number","number"])}function wne(r){let{inputs:{x:t},backend:e,attrs:{paddings:o,mode:n}}=r,s=o.map((f,h)=>f[0]+t.shape[h]+f[1]),a=e.dataIdMap.get(t.dataId).id,i=e.makeOutput(s,t.dtype),p=e.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=o.map(f=>f[0]),l=o.map(f=>f[1]),m=new Uint8Array(new Int32Array(c).buffer),d=new Uint8Array(new Int32Array(l).buffer);return HM(a,u,t.shape.length,we[t.dtype],m,d,Mv[n],p),i}var KM={kernelName:Kn,backendName:"wasm",kernelFunc:wne,setupFunc:Cne};var qM;function Sne(r){qM=r.wasm.cwrap(Is,null,["number","number","number","number"])}function Lv(r){let{backend:t,inputs:{logits:e},attrs:{dim:o}}=r,n=t.dataIdMap.get(e.dataId).id,s=t.makeOutput(e.shape,e.dtype),a=t.dataIdMap.get(s.dataId).id,i=e.shape[o],p=y.sizeFromShape(e.shape)/i;return y.sizeFromShape(s.shape)===0||qM(n,a,i,p),s}var jM={kernelName:Is,backendName:"wasm",setupFunc:Sne,kernelFunc:Lv};var XM;function Ine(r){XM=r.wasm.cwrap(jn,null,["number","number","number","number","number","number"])}function vne(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{numSamples:s,seed:a,normalized:i}=o;if(n.dtype!=="float32")throw new Error(`Tensor logits must have dtype float32, got ${n.dtype}`);let p=i?n:Lv({inputs:{logits:n},backend:e,attrs:{dim:n.shape.length-1}}),[u,c]=p.shape,l=e.makeOutput([u,s],"int32");return 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_ne(r){rL=r.wasm.cwrap(Qa,"number",["number","number","number","number","number","bool"])}function $ne(r){let{backend:t,inputs:e,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,padToMaxOutputSize:i}=o,{boxes:p,scores:u}=e,c=t.dataIdMap.get(p.dataId).id,l=t.dataIdMap.get(u.dataId).id,m=rL(c,l,s,n,a,i),{pSelectedIndices:d,selectedSize:f,pSelectedScores:h,pValidOutputs:g}=Hc(t,m);t.wasm._free(h);let x=t.makeOutput([f],"int32",d),b=t.makeOutput([],"int32",g);return[x,b]}var oL={kernelName:Qa,backendName:"wasm",setupFunc:_ne,kernelFunc:$ne};var nL;function Ene(r){nL=r.wasm.cwrap(Zn,"number",["number","number","number","number","number","number"])}function Rne(r){let{backend:t,inputs:e,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,softNmsSigma:i}=o,{boxes:p,scores:u}=e,c=t.dataIdMap.get(p.dataId).id,l=t.dataIdMap.get(u.dataId).id,m=nL(c,l,s,n,a,i),{pSelectedIndices:d,selectedSize:f,pSelectedScores:h,pValidOutputs:g}=Hc(t,m);t.wasm._free(g);let x=t.makeOutput([f],"int32",d),b=t.makeOutput([f],"float32",h);return[x,b]}var sL={kernelName:Zn,backendName:"wasm",setupFunc:Ene,kernelFunc:Rne};var Dne=!1,aL=Ue(Yn,Dne,"bool");var iL;function Ane(r){iL=r.wasm.cwrap(Jn,null,["number","number","number","number","number"])}function Fne(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n}=t,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=e.makeOutput([...n.shape,a],s),c=e.dataIdMap.get(u.dataId).id,m=e.dataIdMap.get(n.dataId).id;return iL(m,a,i,p,c),u}var uL={kernelName:Jn,backendName:"wasm",setupFunc:Ane,kernelFunc:Fne};function Pne(r){let{inputs:{x:t},backend:e}=r,o=e.makeOutput(t.shape,t.dtype);return e.typedArrayFromHeap(o).fill(1),o}var pL={kernelName:ca,backendName:"wasm",kernelFunc:Pne};function One(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o;if(t.length===1)return Mg({inputs:{input:t[0]},backend:e,attrs:{dim:n}});let s=t[0].shape,a=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching 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Lg={kernelName:es,backendName:"wasm",kernelFunc:Lne,setupFunc:Mne};var Bne=!1,mL=Ue(ts,Bne);var dL;function zne(r){dL=r.wasm.cwrap(rs,null,["number","number","number"])}function Vne(r){let{inputs:t,backend:e}=r,{x:o,alpha:n}=t,s=e.dataIdMap.get(o.dataId).id,a=e.dataIdMap.get(n.dataId).id,i=s,p=o,u=p;p.dtype!=="float32"&&(u=Or({backend:e,inputs:{x:o},attrs:{dtype:"float32"}}),i=e.dataIdMap.get(u.dataId).id);let c=e.makeOutput(o.shape,"float32"),l=e.dataIdMap.get(c.dataId).id;return dL(i,a,l),p.dtype!=="float32"&&e.disposeData(u.dataId),c}var fL={kernelName:rs,backendName:"wasm",setupFunc:zne,kernelFunc:Vne};var hL;function Wne(r){hL=r.wasm.cwrap(os,null,["number","number","number","number"])}function Une(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,t),f=l;if(d){let C=t.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=t.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=t.dataIdMap.get(b.dataId).id;hL(p,x,we[b.dtype],C)}if(d&&t.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var gL={kernelName:os,backendName:"wasm",setupFunc:Wne,kernelFunc:Une};var Gne=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=ap(o,n,s,a),p=t.makeOutput([i.length],a);return t.typedArrayFromHeap(p).set(i),p},xL={kernelName:ma,backendName:"wasm",kernelFunc:Gne};var Hne=!0,yL=Ue(fn,Hne);var bL=he(ns);var CL=he(ss);var wL=he(us);var SL;function Kne(r){SL=r.wasm.cwrap(is,null,["number","number","number","number","number","number","number","number","number","number"])}function qne(r){let{backend:t,inputs:e,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,[c,l,m,d]=n.shape,f=[c,p,u,d],h=t.dataIdMap.get(n.dataId),g;h.dtype!=="float32"&&(g=Or({backend:t,inputs:{x:n},attrs:{dtype:"float32"}}),h=t.dataIdMap.get(g.dataId));let x=h.id,b=t.makeOutput(f,"float32");if(y.sizeFromShape(n.shape)===0)return b;let C=t.dataIdMap.get(b.dataId).id;return SL(x,c,l,m,d,p,u,s?1:0,a?1:0,C),g!=null&&t.disposeData(g.dataId),b}var IL={kernelName:is,backendName:"wasm",setupFunc:Kne,kernelFunc:qne};var vL;function jne(r){vL=r.wasm.cwrap(Ja,null,["number","number","number","array","array","boolean"])}function Xne(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=e.makeOutput(n.shape,"float32"),p=e.dataIdMap.get(n.dataId),u;return p.dtype!=="float32"&&(u=Or({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),p=e.dataIdMap.get(u.dataId)),vL(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,new Uint8Array(new Int32Array(n.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),a),u!=null&&e.disposeData(u.dataId),i}var kL={kernelName:Ja,backendName:"wasm",setupFunc:jne,kernelFunc:Xne};var NL;function Yne(r){NL=r.wasm.cwrap(as,null,["number","number","number","number","number","number","number","number","number","number"])}function Qne(r){let{backend:t,inputs:e,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,[c,l,m,d]=n.shape,f=[c,p,u,d],h=t.makeOutput(f,"float32");if(y.sizeFromShape(n.shape)===0)return h;let g=t.dataIdMap.get(n.dataId),x;g.dtype!=="float32"&&(x=Or({backend:t,inputs:{x:n},attrs:{dtype:"float32"}}),g=t.dataIdMap.get(x.dataId));let b=g.id,C=t.dataIdMap.get(h.dataId).id;return NL(b,c,l,m,d,p,u,s?1:0,a?1:0,C),x!=null&&t.disposeData(x.dataId),h}var TL={kernelName:as,backendName:"wasm",setupFunc:Yne,kernelFunc:Qne};var _L;function Zne(r){_L=r.wasm.cwrap(Za,null,["number","number","number","array","array","boolean"])}function Jne(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=e.makeOutput(n.shape,"float32"),p=e.dataIdMap.get(n.dataId),u;return p.dtype!=="float32"&&(u=Or({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),p=e.dataIdMap.get(u.dataId)),_L(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,new Uint8Array(new Int32Array(n.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),a),u!=null&&e.disposeData(u.dataId),i}var $L={kernelName:Za,backendName:"wasm",setupFunc:Zne,kernelFunc:Jne};var EL;function ese(r){EL=r.wasm.cwrap(ps,null,["number","array","number","array","number","number"])}function tse(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return vp({inputs:{x:n},backend:e});let i=e.makeOutput(n.shape,n.dtype),p=e.dataIdMap.get(n.dataId).id,u=e.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),l=new Uint8Array(new Int32Array(n.shape).buffer);EL(p,c,a.length,l,n.shape.length,u);let m=zt({inputs:{x:i},attrs:{shape:n.shape},backend:e});return e.disposeData(i.dataId),m}var RL={kernelName:ps,backendName:"wasm",kernelFunc:tse,setupFunc:ese};var DL;function rse(r){DL=r.wasm.cwrap(Ds,null,["number","number","number","number","number","number","number","number","array","number","number"])}function ose(r){let{inputs:t,backend:e,attrs:o}=r,{image:n}=t,{radians:s,fillValue:a,center:i}=o,p=e.makeOutput(n.shape,n.dtype),u=e.dataIdMap.get(n.dataId).id,c=e.dataIdMap.get(p.dataId).id,[l,m,d,f]=n.shape,[h,g]=w.getImageCenter(i,m,d),x=a===0,b=255,C=typeof a=="number"?[a,a,a,x?0:b]:[...a,b],S=new Uint8Array(new Int32Array(C).buffer);return DL(u,l,m,d,f,s,h,g,S,C.length,c),p}var AL={kernelName:Ds,backendName:"wasm",kernelFunc:ose,setupFunc:rse};var FL=he(cs);var PL=he(ls);var OL;function nse(r){OL=r.wasm.cwrap(ms,null,["number","number","number","number","number","number","array","number","number"])}function sse(r){let{backend:t,inputs:e,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,i=t.makeOutput(a,s.dtype);if(y.sizeFromShape(a)===0)return i;let{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=lu.calculateShapes(s,n,a),f=t.dataIdMap.get(n.dataId).id,g=t.dataIdMap.get(s.dataId).id,x=new Uint8Array(new Int32Array(l).buffer),b=t.dataIdMap.get(i.dataId).id;return OL(f,g,we[s.dtype],p,u,c,x,m,b),i}var ML={kernelName:ms,backendName:"wasm",setupFunc:nse,kernelFunc:sse};var LL;function ase(r){LL=r.wasm.cwrap(fs,null,["number","number","number","number","number","number","bool","number"])}function ise(r){let{inputs:t,backend:e,attrs:o}=r,{sortedSequence:n,values:s}=t,{side:a}=o;if(n.dtype!==s.dtype)throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. 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p;let{sliceRank:u,numUpdates:c,sliceSize:l,strides:m,outputSize:d}=w.calculateShapes(s,n,i),f=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(s.dataId).id,g=t.dataIdMap.get(a.dataId).id,x=new Uint8Array(new Int32Array(m).buffer),b=t.dataIdMap.get(p.dataId).id;return rB(f,h,s.shape.length,g,we[a.dtype],u,c,l,x,d,b),p}var oB={kernelName:ks,backendName:"wasm",setupFunc:bse,kernelFunc:Cse};function wse(r){let{inputs:t,attrs:e,backend:o}=r,{x:n}=t,{numOrSizeSplits:s,axis:a}=e,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=new Array(n.shape.length).fill(0),c=n.shape.slice();return p.map(l=>{let m=[...c];m[i]=l;let d=Oo({inputs:{x:n},attrs:{begin:u,size:m},backend:o});return u[i]+=l,d})}var nB={kernelName:xa,backendName:"wasm",kernelFunc:wse};var sB=he(Ss);var aB=he(Xi);var Sse=!0,iB=Ue(Ns,Sse);var uB;function Ise(r){uB=r.wasm.cwrap(So,null,["number","number","number","number"])}function 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Tse(r){let{backend:t,inputs:e,attrs:o}=r,{data:n,dataSplits:s}=e,{separator:a,nGramWidths:i,leftPad:p,rightPad:u,padWidth:c,preserveShortSequences:l}=o,m=t.readSync(n.dataId),d=t.readSync(s.dataId),[f,h]=gp(m,d,a,i,p,u,c,l),g=t.makeOutput([f.length],"string"),x=t.dataIdMap.get(g.dataId);x.stringBytes=f;let b=t.makeOutput(s.shape,"int32");return t.typedArrayFromHeap(b).set(h),[g,b]}var mB={kernelName:Ca,backendName:"wasm",kernelFunc:Tse};function _se(r){let{backend:t,inputs:e,attrs:o}=r,{input:n,delimiter:s}=e,{skipEmpty:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c,l]=xp(i,p[0],a),m=c.length,d=t.makeOutput([m,2],"int32");t.typedArrayFromHeap(d).set(u);let h=t.makeOutput([m],"string"),g=t.dataIdMap.get(h.dataId);g.stringBytes=c;let x=t.makeOutput([2],"int32");return t.typedArrayFromHeap(x).set(l),[d,h,x]}var dB={kernelName:Yi,backendName:"wasm",kernelFunc:_se};function $se(r){let{backend:t,inputs:e,attrs:o}=r,{input:n}=e,{numBuckets:s}=o,a=t.readSync(n.dataId),i=yp(a,s),p=t.makeOutput(n.shape,"int32");return t.typedArrayFromHeap(p).set(i),p}var fB={kernelName:Qi,backendName:"wasm",kernelFunc:$se};var Ese=!0,hB=Ue(_s,Ese);var gB;function Rse(r){gB=r.wasm.cwrap(Is,null,["number","number","number","number"])}function Dse(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,t),f=l;if(d){let C=t.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims("sum",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=t.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=t.dataIdMap.get(b.dataId).id;gB(p,x,we[b.dtype],C)}if(d&&t.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var xB={kernelName:Is,backendName:"wasm",setupFunc:Rse,kernelFunc:Dse};var yB=he($s);var bB=he(Es);var CB;function Ase(r){CB=r.wasm.cwrap(fs,null,["number","number","number","number","number","number","array","number","number","number"])}function Fse(r){let{backend:t,inputs:e,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,i=t.makeOutput(n.shape,n.dtype);if(y.sizeFromShape(n.shape)===0)return i;let{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=hu.calculateShapes(a,s,n.shape),f=t.dataIdMap.get(s.dataId).id,g=t.dataIdMap.get(a.dataId).id,b=t.dataIdMap.get(n.dataId).id,C=new Uint8Array(new Int32Array(l).buffer),S=t.dataIdMap.get(i.dataId).id;return CB(f,g,we[a.dtype],p,u,c,C,m,S,b),i}var wB={kernelName:fs,backendName:"wasm",setupFunc:Ase,kernelFunc:Fse};var SB;function Pse(r){SB=r.wasm.cwrap(po,null,["number","array","number","array","number","number"])}function Ose(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,s=e.dataIdMap.get(n.dataId).id,{reps:a}=o,i=new Array(n.shape.length);for(let m=0;m{let{x:o}=r,{k:n,sorted:s}=e,a=t.dataIdMap.get(o.dataId).id,i=new Uint8Array(new Int32Array(o.shape).buffer),p=o.shape.slice();p[p.length-1]=n;let u=t.makeOutput(p,o.dtype),c=t.dataIdMap.get(u.dataId).id,l=t.makeOutput(p,"int32"),m=t.dataIdMap.get(l.dataId).id;return vB(a,i,o.shape.length,we[o.dtype],n,s,c,m),[u,l]},kB={kernelName:Rs,backendName:"wasm",setupFunc:Mse,kernelFunc:Lse};var NB;function Bse(r){NB=r.wasm.cwrap(Ds,null,["number","number","bool","number","number","number","number","number","number","array","number","array","number","number","number","number","number"])}function zse(r){let{backend:t,inputs:e,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer),b=new Uint8Array(new 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_se(r){let{backend:t,inputs:e,attrs:o}=r,{input:n,delimiter:s}=e,{skipEmpty:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c,l]=pp(i,p[0],a),m=c.length,d=t.makeOutput([m,2],"int32");t.typedArrayFromHeap(d).set(u);let h=t.makeOutput([m],"string"),g=t.dataIdMap.get(h.dataId);g.stringBytes=c;let x=t.makeOutput([2],"int32");return t.typedArrayFromHeap(x).set(l),[d,h,x]}var fB={kernelName:qi,backendName:"wasm",kernelFunc:_se};function $se(r){let{backend:t,inputs:e,attrs:o}=r,{input:n}=e,{numBuckets:s}=o,a=t.readSync(n.dataId),i=cp(a,s),p=t.makeOutput(n.shape,"int32");return t.typedArrayFromHeap(p).set(i),p}var hB={kernelName:ji,backendName:"wasm",kernelFunc:$se};var Ese=!0,gB=Ue(Ts,Ese);var xB;function Rse(r){xB=r.wasm.cwrap(Ss,null,["number","number","number","number"])}function Dse(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,t),f=l;if(d){let 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kB(a,i,o.shape.length,we[o.dtype],n,s,c,m),[u,l]},NB={kernelName:Es,backendName:"wasm",setupFunc:Mse,kernelFunc:Lse};var TB;function Bse(r){TB=r.wasm.cwrap(Rs,null,["number","number","bool","number","number","number","number","number","number","array","number","array","number","number","number","number","number"])}function zse(r){let{backend:t,inputs:e,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer),b=new Uint8Array(new Int32Array(y.computeStrides(g)).buffer),C=t.makeOutput(g,n.dtype),S=t.dataIdMap.get(C.dataId).id,_=t.dataIdMap.get(n.dataId).id,R=t.dataIdMap.get(s.dataId).id,D=a==="nearest"?1:2,P;switch(i){case"constant":P=1;break;case"reflect":P=2;break;case"wrap":P=3;break;case"nearest":P=4;break;default:P=1;break}return TB(_,R,s.shape[0]>1,c,f,h,d,m,l,x,n.shape.length-1,b,g.length-1,D,P,p,S),C}var _B={kernelName:Rs,backendName:"wasm",setupFunc:Bse,kernelFunc:zse};function Vse(r){let{inputs:t,attrs:e,backend:o}=r,{axis:n}=e,{x:s}=t,{outputValues:a,outputShape:i,indices:p}=lp(o.readSync(s.dataId),n,s.shape,s.dtype);return[o.makeOutput(i,s.dtype,void 0,a),o.makeOutput([p.length],"int32",void 0,p)]}var $B={kernelName:Xi,backendName:"wasm",kernelFunc:Vse};function Wse(r){let{inputs:t,backend:e,attrs:o}=r,{value:n}=t,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n.shape[s],i=n.shape.length,p=new Array(i-1),u=0;for(let d=0;d({dataId:d,dtype:f,shape:p}))}var EB={kernelName:wa,backendName:"wasm",kernelFunc:Wse};function Use(r){let{inputs:{x:t},backend:e}=r,o=e.makeOutput(t.shape,t.dtype);return e.typedArrayFromHeap(o).fill(0),o}var RB={kernelName:Sa,backendName:"wasm",kernelFunc:Use};var Gse=[gP,xP,yP,bP,CP,SP,TP,$P,EP,RP,DP,AP,FP,PP,OP,LP,GP,zP,WP,qP,XP,QP,ZP,JP,eO,tO,oO,nO,aO,uO,cO,mO,fO,hO,gO,yO,CO,SO,vO,NO,_O,EO,DO,FO,OO,MO,BO,zO,VO,WO,UO,GO,HO,qO,jO,XO,QO,JO,tM,oM,sM,aM,iM,IP,uM,pM,cM,mM,dM,fM,gM,yM,xM,bM,CM,wM,SM,vM,NM,_M,$M,RM,AM,PM,MM,BM,VM,UM,GM,KM,YM,QM,ZM,JM,tL,oL,sL,aL,uL,pL,cL,Lg,mL,fL,gL,xL,yL,bL,CL,wL,HP,IL,kL,TL,$L,RL,AL,FL,PL,ML,BL,VL,WL,GL,HL,KL,qL,jP,jM,jL,XL,QL,JL,tB,rB,nB,sB,aB,iB,uB,cB,mB,dB,fB,hB,gB,yB,bB,CB,SB,vB,NB,_B,kP,$B,EB,RB];for(let r of Gse)ti(r);var Bv=A();Bv.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>{try{return WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11]))}catch(r){return!1}});Bv.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Bv.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(r){return!1}});var qv=Lp(PB()),VB=Lp(MB()),jv=Lp(LB());var BB=qv.default||qv,Hse=jv.default||jv,im=class extends so{constructor(t){super(),this.wasm=t,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(UB),Kv=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new Bo(this,ur())}write(t,e,o){let n={id:this.dataIdNextNumber++};return this.move(n,t,e,o,1),n}numDataIds(){return this.dataIdMap.numDataIds()}async time(t){let e=y.now();return t(),{kernelMs:y.now()-e}}move(t,e,o,n,s){let a=this.dataIdNextNumber++;if(n==="string"){let c=e;this.dataIdMap.set(t,{id:a,stringBytes:c,shape:o,dtype:n,memoryOffset:null,refCount:s});return}let i=y.sizeFromShape(o),p=i*y.bytesPerElement(n),u=this.wasm._malloc(p)>>>0;this.dataIdMap.set(t,{id:a,memoryOffset:u,shape:o,dtype:n,refCount:s}),this.wasm.tfjs.registerTensor(a,i,u),e!=null&&this.wasm.HEAPU8.set(new Uint8Array(e.buffer,e.byteOffset,p),u)}async read(t){return this.readSync(t)}readSync(t,e,o){let{memoryOffset:n,dtype:s,shape:a,stringBytes:i}=this.dataIdMap.get(t);if(s==="string")return(e==null||e===0)&&(o==null||o>=i.length)?i:i.slice(e,o);e=e||0,o=o||y.sizeFromShape(a);let p=y.bytesPerElement(s),u=this.wasm.HEAPU8.slice(n+e*p,n+o*p);return qse(u.buffer,s)}disposeData(t,e=!1){if(this.dataIdMap.has(t)){let o=this.dataIdMap.get(t);if(o.refCount--,!e&&o.refCount>0)return!1;this.wasm._free(o.memoryOffset),this.wasm.tfjs.disposeData(o.id),this.dataIdMap.delete(t)}return!0}refCount(t){return this.dataIdMap.has(t)?this.dataIdMap.get(t).refCount:0}incRef(t){let e=this.dataIdMap.get(t);e!=null&&e.refCount++}floatPrecision(){return 32}getMemoryOffset(t){return this.dataIdMap.get(t).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(t,e,o,n){let s;if(o==null)s=this.write(n!=null?n:null,t,e);else{let a=this.dataIdNextNumber++;s={id:a},this.dataIdMap.set(s,{id:a,memoryOffset:o,shape:t,dtype:e,refCount:1});let i=y.sizeFromShape(t);this.wasm.tfjs.registerTensor(a,i,o)}return{dataId:s,shape:t,dtype:e}}typedArrayFromHeap({shape:t,dtype:e,dataId:o}){let n=this.wasm.HEAPU8.buffer,{memoryOffset:s}=this.dataIdMap.get(o),a=y.sizeFromShape(t);switch(e){case"float32":return new Float32Array(n,s,a);case"int32":return new Int32Array(n,s,a);case"bool":return new Uint8Array(n,s,a);default:throw new Error(`Unknown dtype ${e}`)}}};function Kse(r){return(t,e)=>(y.fetch(r,{credentials:"same-origin"}).then(o=>{o.ok||t.env.a(`failed to load wasm binary file at '${r}'`),o.arrayBuffer().then(n=>{WebAssembly.instantiate(n,t).then(s=>{e(s.instance,s.module)})})}),{})}function zB(r,t,e){if(Ug!=null)return Ug;let o="tfjs-backend-wasm.wasm";return r&&t?o="tfjs-backend-wasm-threaded-simd.wasm":r&&(o="tfjs-backend-wasm-simd.wasm"),sm!=null&&sm[o]!=null?sm[o]:e+o}async function WB(){let[r,t]=await Promise.all([A().getAsync("WASM_HAS_SIMD_SUPPORT"),A().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((e,o)=>{let n={};n.locateFile=(i,p)=>{if(i.endsWith(".worker.js")){let u=VB.wasmWorkerContents.replace(/\n/g,"\\n"),c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return i.endsWith(".wasm")?zB(r,t,nm!=null?nm:p):p+i},Xv&&(n.instantiateWasm=Kse(zB(r,t,nm!=null?nm:"")));let s=!1;n.onAbort=()=>{if(s||am)return;am=!0,o({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"})};let a;t&&r&&Ug==null?(n.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+BB.toString()],{type:"text/javascript"}),a=BB(n)):a=Hse(n),a.then(i=>{s=!0,am=!1;let p=null;i.tfjs={init:i.cwrap("init",null,[]),initWithThreadsCount:i.cwrap("init_with_threads_count",null,["number"]),getThreadsCount:i.cwrap("get_threads_count","number",[]),registerTensor:i.cwrap("register_tensor",null,["number","number","number"]),disposeData:i.cwrap("dispose_data",p,["number"]),dispose:i.cwrap("dispose",p,[])},e({wasm:i})}).catch(o)})}function qse(r,t){switch(t){case"float32":return new Float32Array(r);case"int32":return new Int32Array(r);case"bool":return new Uint8Array(r);default:throw new Error(`Unknown dtype ${t}`)}}var jse=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Ug=null,nm=null,sm={},am=!1,Xv=!1;function Xse(r,t=!1){if(Pw("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),am)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Ug=r,Xv=t}function Yse(r,t=!1){if(am)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof r=="string")nm=r;else{sm=r;let e=jse.filter(o=>sm[o]==null);if(e.length>0)throw new Error(`There were no entries found for the following binaries: ${e.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}Xv=t}var UB=-1,Kv=-1;function Qse(r){UB=r}function Zse(){if(Kv===-1)throw new Error("WASM backend not initialized.");return Kv}var Jse="4.14.0";var eae=2;tu("wasm",async()=>{let{wasm:r}=await WB();return new im(r)},eae);var go=A();go.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);go.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);go.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE",()=>-1);go.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!0);go.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);go.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);go.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);go.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE",()=>!0);go.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG",()=>!1);go.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL",()=>-1);go.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);go.registerFlag("WEBGPU_PRINT_SHADER",()=>"");go.registerFlag("WEBGPU_ENGINE_COMPILE_ONLY",()=>!1);var Gg=class{constructor(t){t&&(this.vendor=t.vendor,this.architecture=t.architecture,this.intelGPUGeneration=this.getIntelGPUGeneration())}getIntelGPUGeneration(){if(this.isIntel()){if(this.architecture.startsWith("gen"))return Number(this.architecture.match(/\d+/));if(this.architecture.startsWith("xe"))return 12}return 0}isIntel(){return this.vendor==="intel"}};var Hg=class{constructor(t){this.device=t,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(t,e,o=!1,n=!0){let s,a=GB(t,e);return n?(this.freeBuffers.has(a)||this.freeBuffers.set(a,[]),this.freeBuffers.get(a).length>0?(s=this.freeBuffers.get(a).pop(),this.numFreeBuffers--):(s=this.device.createBuffer({size:t,usage:e,mappedAtCreation:o}),this.numBytesAllocated+=t)):(s=this.device.createBuffer({size:t,usage:e,mappedAtCreation:o}),this.numBytesAllocated+=t),this.usedBuffers.has(a)||this.usedBuffers.set(a,[]),this.usedBuffers.get(a).push(s),this.numUsedBuffers++,this.numBytesUsed+=t,s}releaseBuffer(t,e=!0){if(this.freeBuffers.size===0)return;let o=t.size,n=t.usage,s=GB(o,n),a=this.usedBuffers.get(s),i=a.indexOf(t);if(i<0)throw new Error("Cannot find the 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s=KB(o),a=t*e*s,i=HB(t,e,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[t,e],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(t){if(this.freeTextures.size===0)return;let e=t.width,o=t.height,n=t.format,s=t.usage,a=HB(e,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(t),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(t);if(p<0)throw new Error("Cannot release a texture that was never provided by this texture manager");i.splice(p,1);let u=KB(n),c=e*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((t,e)=>{t.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((t,e)=>{t.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function HB(r,t,e,o){return`${r}_${t}_${e}_${o}`}function KB(r){if(r==="rgba8unorm")return 16;throw new Error(`${r} is not supported!`)}function qB(r,t){if(Math.max(...r)>5)throw new Error("Cannot symbolically compute strides for rank > 6 tensor.");let e=r.length,o="xyzwuv",n=r.map(a=>`${t}.${o[a]}`),s=new Array(e-1);s[e-2]=n[e-1];for(let a=e-3;a>=0;--a)s[a]=`(${s[a+1]} * ${n[a+1]})`;return s}var Yr=(r,t,e)=>e==="int32"?`atomicAdd(${r}, bitcast(${t}));`:` { var oldValue = 0; loop { @@ -5006,11 +5006,11 @@ return a / b;`,Wee=` } oldValue = res.old_value; } - }`;var Ii;(function(r){r[r.FROM_PIXELS=0]="FROM_PIXELS",r[r.DRAW=1]="DRAW"})(Ii||(Ii={}));var YB=(r,t,e,o,n)=>{let s={dtype:o.dtype,shape:o.shape},a=rae(e,s,t),i=r.createShaderModule({code:a,label:t.constructor.name}),p=A().get("WEBGPU_PRINT_SHADER");if(p!==""){p=p.toLowerCase();let u=p.split(",");(p==="all"||u.some(c=>t.shaderKey.toLowerCase().includes(c)))&&(console.group(t.shaderKey),console.debug(a),console.groupEnd())}return n?r.createComputePipelineAsync({compute:{module:i,entryPoint:"_start"},label:t.constructor.name,layout:"auto"}):r.createComputePipeline({compute:{module:i,entryPoint:"_start"},label:t.constructor.name,layout:"auto"})},Ae=(r,t="f32")=>{switch(r){case 1:return`${t}`;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${r}-component ${t} is not supported.`)}};function ht(r){if(r<=1)return"i32";if(r===2)return"vec2";if(r===3)return"vec3";if(r===4)return"vec4";if(r===5)return"vec5";if(r===6)return"vec6";throw Error(`GPU for rank ${r} is not yet supported`)}function 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4:return`vec4<${t}>`;default:throw new Error(`${r}-component ${t} is not supported.`)}};function ft(r){if(r<=1)return"i32";if(r===2)return"vec2";if(r===3)return"vec3";if(r===4)return"vec4";if(r===5)return"vec5";if(r===6)return"vec6";throw Error(`GPU for rank ${r} is not yet supported`)}function Oo(r){if(r===0)return"x";if(r===1)return"y";if(r===2)return"z";if(r===3)return"w";if(r===4)return"u";if(r===5)return"v";throw Error(`Index ${r} is not yet supported`)}function G(...r){let t;switch(r.length){case 0:t=` fn main() `;break;case 1:t=` fn main(${r[0]} : i32) - `;break;default:throw Error("Unreachable")}return t}function qB(r,t){let e;return e=` + `;break;default:throw Error("Unreachable")}return t}function jB(r,t){let e;return e=` ${tae(t)} fn _start(@builtin(local_invocation_id) LocalId : vec3, @builtin(global_invocation_id) GlobalId : vec3, @@ -5036,12 +5036,12 @@ return a / b;`,Wee=` // Only used when the y/z dimension of workgroup size is 1. fn getGlobalIndex() -> i32 { - ${ZB(e)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + + ${JB(e)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u + localIndex); `} } - `),e.pixelsOpType!=null){let f=e.pixelsOpType===Ii.FROM_PIXELS?`@group(0) @binding(0) var result: array<${Nu(t.dtype,e.outputComponent)}>;`:`@group(0) @binding(1) var inBuf : array<${Nu(r[0].dtype,e.outputComponent)}>;`,h=t.shape.length===3?"vec2":"i32";o.push(` + `),e.pixelsOpType!=null){let f=e.pixelsOpType===Ci.FROM_PIXELS?`@group(0) @binding(0) var result: array<${Cu(t.dtype,e.outputComponent)}>;`:`@group(0) @binding(1) var inBuf : array<${Cu(r[0].dtype,e.outputComponent)}>;`,h=t.shape.length===3?"vec2":"i32";o.push(` struct Uniform { outShapeStrides : ${h}, size : i32, @@ -5051,21 +5051,21 @@ return a / b;`,Wee=` ${f} @group(0) @binding(2) var uniforms: Uniform; - `);let g=XB(e);return[jB,o.join(` -`),cm(t.shape),e.getUserCode(),qB(g,e)].join(` -`)}let s,a,i="struct Uniforms { NAN : f32, INFINITY : f32, ";e.variableNames.forEach((f,h)=>{let g=ht(r[h].shape.length);i+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `,s=r[h].shape.length-1,a=ht(s),i+=`${f.charAt(0).toLowerCase()+f.slice(1)}ShapeStrides: ${a}, `});let p=ht(t.shape.length);i+=`outShape : ${p}, `,s=t.shape.length-1,a=ht(s),i+=` + `);let g=YB(e);return[XB,o.join(` +`),um(t.shape),e.getUserCode(),jB(g,e)].join(` +`)}let s,a,i="struct Uniforms { NAN : f32, INFINITY : f32, ";e.variableNames.forEach((f,h)=>{let g=ft(r[h].shape.length);i+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `,s=r[h].shape.length-1,a=ft(s),i+=`${f.charAt(0).toLowerCase()+f.slice(1)}ShapeStrides: ${a}, `});let p=ft(t.shape.length);i+=`outShape : ${p}, `,s=t.shape.length-1,a=ft(s),i+=` outShapeStrides: ${a}, `,e.size&&(i+="size : i32, "),e.uniforms&&(i+=e.uniforms),i+="};",i=cae(i),o.push(i),e.atomic?o.push(` @group(0) @binding(0) var result: array>; `):o.push(` - @group(0) @binding(0) var result: array<${Nu(t.dtype,e.outputComponent)}>; + @group(0) @binding(0) var result: array<${Cu(t.dtype,e.outputComponent)}>; `),e.variableNames.forEach((f,h)=>{o.push(` - @group(0) @binding(${1+h}) var ${f}: array<${e.variableComponents?Nu(r[h].dtype,e.variableComponents[h]):Nu(r[h].dtype,e.outputComponent)}>; + @group(0) @binding(${1+h}) var ${f}: array<${e.variableComponents?Cu(r[h].dtype,e.variableComponents[h]):Cu(r[h].dtype,e.outputComponent)}>; `)}),i!==""&&o.push(` @group(0) @binding(${1+e.variableNames.length}) var uniforms: Uniforms; - `);let u=iae(t.shape,e.dispatchLayout),c=[jB,o.join(` -`)+oae,cm(t.shape),u,uae(t.shape.length)];e.atomic||c.push(pae(t.shape,t.dtype,e.outputComponent)),e.variableNames.forEach((f,h)=>{c.push(`${cm(r[h].shape,f)}`)});let l=r.map((f,h)=>aae(f,t.shape,e.variableComponents?e.variableComponents[h]:e.outputComponent,e.dispatchLayout.x.length===t.shape.length)).join(` 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c.push(jB(m,e)),c.join(` +`)}function ZB(r,t,e){let o=r.shaderKey;if(r.pixelsOpType!=null)return o;let n=[],s=[];t.forEach(c=>{n.push(c.shape),s.push(c.dtype)}),n.push(e.shape),s.push(e.dtype);let a=t.map(c=>w.getBroadcastDims(c.shape,e.shape)),i=t.map(c=>y.arraysEqual(c.shape,e.shape)).join("_"),p=a.map(c=>c.join("_")).join(";"),u=JB(r)?"flatDispatch":"";return o+="_"+(r.workgroupSize?r.workgroupSize.join(","):"")+n.map(c=>c.length).join(",")+s.join(",")+r.variableNames.join(",")+p+i+u,o}var XB=` struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32}; struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32}; @@ -5119,15 +5119,15 @@ return a / b;`,Wee=` fn isinf(val: f32) -> bool { return abs(val) == uniforms.INFINITY; } -`;function cm(r,t=""){let e=r.length,o=t!==""?`get${t.charAt(0).toUpperCase()+t.slice(1)}CoordsFromIndex`:"getCoordsFromIndex",n=t!==""?`${t.charAt(0).toLowerCase()+t.slice(1)}ShapeStrides`:"outShapeStrides";if(e<=1)return`fn ${o}(index : i32) -> i32 { return index; }`;let s=y.computeStrides(r),a=ht(e),i=[];for(let u=0;u vec2 { +`;function um(r,t=""){let e=r.length,o=t!==""?`get${t.charAt(0).toUpperCase()+t.slice(1)}CoordsFromIndex`:"getCoordsFromIndex",n=t!==""?`${t.charAt(0).toLowerCase()+t.slice(1)}ShapeStrides`:"outShapeStrides";if(e<=1)return`fn ${o}(index : i32) -> i32 { return index; }`;let s=y.computeStrides(r),a=ft(e),i=[];for(let u=0;u vec2 { let d0 = index / uniforms.${n}; let d1 = index - d0 * uniforms.${n}; return vec2(d0, d1); - }`;let p;return p="var index2 = index;"+s.map((u,c)=>{let l=`let ${i[c]} = index2 / uniforms.${n}.${Mo(c)}`,m=c===s.length-1?`let ${i[c+1]} = index2 - ${i[c]} * uniforms.${n}.${Mo(c)}`:`index2 = index2 - ${i[c]} * uniforms.${n}.${Mo(c)}`;return`${l}; ${m};`}).join(""),` + }`;let p;return p="var index2 = index;"+s.map((u,c)=>{let l=`let ${i[c]} = index2 / uniforms.${n}.${Oo(c)}`,m=c===s.length-1?`let ${i[c+1]} = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`:`index2 = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`;return`${l}; ${m};`}).join(""),` fn ${o}(index : i32) -> ${a} { ${p} return ${a}(${i.join(",")}); } - `}function nae(r,t){let e=r.name,o=r.shape.length,n=ht(o),s="get"+e.charAt(0).toUpperCase()+e.slice(1),a=["d0","d1","d2","d3","d4","d5"].slice(0,o),i=a.map(c=>`${c} : i32`).join(", ");if(o<1)return` + `}function nae(r,t){let e=r.name,o=r.shape.length,n=ft(o),s="get"+e.charAt(0).toUpperCase()+e.slice(1),a=["d0","d1","d2","d3","d4","d5"].slice(0,o),i=a.map(c=>`${c} : i32`).join(", ");if(o<1)return` fn ${s}() -> ${Ae(t)} { return ${Ae(t)}(${e}[0]); } @@ -5136,7 +5136,7 @@ return a / b;`,Wee=` return ${Ae(t)}(${e}[getIndexFromCoords${u}(${n}(${a.join(",")}), ${p})${t===1?"":` / ${t}`}]); } - `}function sae(r,t,e,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a="get"+s+"ByOutput",i=r.shape.length,p=t.length,u=ht(p);if(y.arraysEqual(r.shape,t)&&o)return` + `}function sae(r,t,e,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a="get"+s+"ByOutput",i=r.shape.length,p=t.length,u=ft(p);if(y.arraysEqual(r.shape,t)&&o)return` fn ${a}Index(globalIndex : i32) -> ${Ae(e)} { return ${Ae(e)}(${n}[globalIndex]); } @@ -5152,8 +5152,8 @@ return a / b;`,Wee=` fn ${a}Coords(coords : ${u}) -> ${Ae(e)}{ return get${s}(); } - `;p<2&&c.length>=1?m="coords = 0;":m=c.map(g=>`coords.${Mo(g+l)} = 0;`).join(` -`);let d="";if(p<2&&i>0)d="coords";else if(p>1){let g=ht(i),x=r.shape.map((b,C)=>`coords.${Mo(C+l)}`).join(", ");d=`${g}(${x})`}else d="coords";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return` + `;p<2&&c.length>=1?m="coords = 0;":m=c.map(g=>`coords.${Oo(g+l)} = 0;`).join(` +`);let d="";if(p<2&&i>0)d="coords";else if(p>1){let g=ft(i),x=r.shape.map((b,C)=>`coords.${Oo(C+l)}`).join(", ");d=`${g}(${x})`}else d="coords";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return` fn ${a}Index(globalIndex : i32) -> ${Ae(e)} { var coords = getCoordsFromIndex(globalIndex); ${m} @@ -5165,11 +5165,11 @@ return a / b;`,Wee=` ${m} return ${Ae(e)}(${n}[getIndexFromCoords${h}(${d}, ${f})${e===1?"":` / ${e}`}]); } -`}function aae(r,t,e,o){let n=nae(r,e);return r.shape.length<=t.length&&(n+=sae(r,t,e,o)),n}function iae(r,t){let{x:e,y:o=[],z:n=[]}=t,s=r.length,a=e.length+o.length+n.length;if(a!==s)return"";if(e.length===s)return`fn getOutputCoords() -> ${ht(s)}{ +`}function aae(r,t,e,o){let n=nae(r,e);return r.shape.length<=t.length&&(n+=sae(r,t,e,o)),n}function iae(r,t){let{x:e,y:o=[],z:n=[]}=t,s=r.length,a=e.length+o.length+n.length;if(a!==s)return"";if(e.length===s)return`fn getOutputCoords() -> ${ft(s)}{ let globalIndex = getGlobalIndex(); return getCoordsFromIndex(globalIndex); } - `;let i="",p=[e,o,n];for(let m=0;m ${c} { + `;let i="",p=[e,o,n];for(let m=0;m ${c} { ${i} `;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(",")}); }`,l}function uae(r){let t="";switch(r){case 0:case 1:t+=` fn 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Yv={};qe(Yv,{GPUBytesPerElement:()=>jg,MatMulProgramType:()=>Lo,assertNotComplex:()=>fm,computeDispatch:()=>H,computeWorkPerThreadForConv2d:()=>mm,computeWorkgroupInfoForMatMul:()=>Xv,computeWorkgroupSizeForConv2d:()=>lm,flatDispatchLayout:()=>X,isWebGPUSupported:()=>dm,tilesFitEvenlyIntoShape:()=>mae});var Ap=r=>{let t=1;for(let e=0;ee%r[o]===0)}function H(r,t,e=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Ap(r.x.map(i=>t[i]))/(e[0]*o[0])),r.y?Math.ceil(Ap(r.y.map(i=>t[i]))/(e[1]*o[1])):1,r.z?Math.ceil(Ap(r.z.map(i=>t[i]))/(e[2]*o[2])):1];return[n,s,a]}function Xv(r,t,e,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),t<=16&&e<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function lm(r,t,e=!1){if(e)return[8,8,1];let o=Ap(r.x.map(s=>t[s])),n=Ap(r.y.map(s=>t[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function mm(r,t,e=!1){if(e)return[4,4,1];let o=Ap(r.x.map(s=>t[s])),n=Ap(r.y.map(s=>t[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function X(r){return{x:r.map((t,e)=>e)}}function jg(r){if(r==="float32"||r==="int32"||r==="bool"||r==="string")return 4;if(r==="complex64")return 8;throw new Error(`Unknown dtype ${r}`)}function dm(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function fm(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var Lo;(function(r){r[r.MatMulReduceProgram=0]="MatMulReduceProgram",r[r.MatMulSplitKProgram=1]="MatMulSplitKProgram",r[r.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",r[r.MatMulPackedProgram=3]="MatMulPackedProgram",r[r.MatMulMax=4]="MatMulMax"})(Lo||(Lo={}));var dae=A().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),fae=(r,t)=>{let e=r.limits.maxComputeWorkgroupsPerDimension,o=t.dispatchLayout,n=t.dispatch;if(n.every(a=>a<=e))return n;y.assert(n[0]>e&&o.y===void 0&&o.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y 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e=this.tensorMap.get(t);if(!(!e||!e.resource)){if(e.external){e.resource=null;return}e.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(e.resource):e.resource instanceof GPUTexture&&this.textureManager.releaseTexture(e.resource),e.resource=null}}refCount(t){return this.tensorMap.has(t)?this.tensorMap.get(t).refCount:0}incRef(t){let e=this.tensorMap.get(t);e.refCount++}decRef(t){if(this.tensorMap.has(t)){let e=this.tensorMap.get(t);e.refCount--}}write(t,e,o){if(o==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:e,values:t,refCount:1}),n}move(t,e,o,n,s){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(t,{dtype:n,shape:o,values:e,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(t=>{this.releaseResource(t),this.tensorMap.delete(t)}),this.uniformPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t)),this.stagingPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let t;try{t=await Promise.all(Object.values(this.pipelineCache))}catch(e){throw new Error(e.message)}Object.keys(this.pipelineCache).map((e,o)=>{this.pipelineCache[e]=t[o]})}async getBufferData(t){if(A().getBool("WEBGPU_ENGINE_COMPILE_ONLY"))return console.warn("The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false"),null;let e=t.size,o=this.bufferManager.acquireBuffer(e,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,o,0,e),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o),A().getBool("WEBGPU_USE_PROFILE_TOOL")&&(y.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(t,e){let o=this.tensorMap.get(t);return o.values=e,o.values}readSync(t){let e=this.tensorMap.get(t),{values:o,complexTensorInfos:n}=e;if(o!=null||e.dtype==="string")return o;if(e.dtype==="complex64"){let h=this.readSync(n.real.dataId),g=this.readSync(n.imag.dataId),x=y.convertBackendValuesAndArrayBuffer(w.mergeRealAndImagArrays(h,g).buffer,"float32");return this.convertAndCacheOnCPU(t,x),x}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn("The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead."));let s=["opaque","premultiplied"],a=e.resource,i=a.size;y.assert(i%4===0,()=>"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.");let p=i/4,u=new ArrayBuffer(i),c=256,l=256,m=s.map(h=>new OffscreenCanvas(c,l)),d=new OffscreenCanvas(c,l);this.endComputePassEncoder(),m.map((h,g)=>{let x=h.getContext("webgpu");return x.configure({device:this.device,format:"bgra8unorm",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),x.getCurrentTexture()}).map((h,g)=>{let x=c*4,b=(R,D,P)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:a,bytesPerRow:x,offset:P},{texture:h},{width:R,height:D}),this.submitQueue();let O=d.getContext("2d",{willReadFrequently:!0});O.clearRect(0,0,R,D),O.drawImage(m[g],0,0);let M=O.getImageData(0,0,R,D).data,L=s[g],B=new Uint8ClampedArray(u,P,R*D*4);for(let z=0;z0&&(b(S,k,_),_+=k*(c*4)),S=E%c,S>0&&b(S,1,_)});let f=y.convertBackendValuesAndArrayBuffer(u,e.dtype);return this.convertAndCacheOnCPU(t,f),f}async read(t){if(!this.tensorMap.has(t))throw new Error(`Tensor ${t} was not registered!`);let e=this.tensorMap.get(t),{values:o}=e;if(o!=null)return o;let n;if(e.dtype==="complex64"){let s=await Promise.all([this.read(e.complexTensorInfos.real.dataId),this.read(e.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=w.mergeRealAndImagArrays(a,i)}else{let s=await this.getBufferData(e.resource);n=y.convertBackendValuesAndArrayBuffer(s,e.dtype)}return this.convertAndCacheOnCPU(t,n),n}copyBuffer(t){let e=t.size,o=t.usage,n=this.bufferManager.acquireBuffer(e,o);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,n,0,e),this.submitQueue(),n}createTensorFromGPUData(t,e,o){let n=t.buffer;if(o==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:e,values:null,refCount:1,external:t.zeroCopy});let a=this.tensorMap.get(s),i=jg(a.dtype)*y.sizeFromShape(a.shape);if(t.buffer.sizey.decodeString(n));return me(t.shape,t.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return me(t.shape,t.dtype,e)}async time(t){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled."),this.hasTimestampQueryWarned=!0);let e=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(t,e,o){return e==="string"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,t,e),shape:t,dtype:e}}tensorToBinding(t){if(!t)return null;let o=this.tensorMap.get(t.dataId).resource;return o instanceof GPUBuffer?{buffer:o}:o instanceof GPUTexture?o.createView():o}uploadToGPU(t){let e=this.tensorMap.get(t);if(e.resource!=null)return;let o=jg(e.dtype)*y.sizeFromShape(e.shape),n,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(e.values){if(n=this.bufferManager.acquireBuffer(o,s,!0),n.mapState==="unmapped"){let a=this.bufferManager.acquireBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),i=a.getMappedRange();e.dtype==="int32"||e.dtype==="bool"?new Int32Array(i).set(e.values):new Float32Array(i).set(e.values),a.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(a,0,n,0,o),this.stagingPendingDisposal.push(a)}else{let a=n.getMappedRange();e.dtype==="int32"||e.dtype==="bool"?new Int32Array(a).set(e.values):new Float32Array(a).set(e.values),n.unmap()}e.values=null}else n=this.bufferManager.acquireBuffer(o,s);e.resource=n}makeUniforms(t){let e=0,o=0,n=[],s=1;t.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),u>s&&(s=u),e=Math.ceil(e/u)*u,o=p.data.length,n.push(e),e+=p.data.length*4}),e=Math.ceil(e/s)*s;let a=new ArrayBuffer(e);t.forEach((p,u)=>{let c=n[u];p.type==="int32"?new Int32Array(a,c,p.data.length).set(p.data):p.type==="uint32"?new Uint32Array(a,c,p.data.length).set(p.data):new Float32Array(a,c,p.data.length).set(p.data)});let i=this.bufferManager.acquireBuffer(e,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(i,0,a,0,e),this.uniformPendingDisposal.push(i),{offset:0,size:e,buffer:i}}runWebGPUProgram(t,e,o,n,s){if(s||(s=this.makeTensorInfo(t.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),t.dispatch=fae(this.device,t);let a=e.map((p,u)=>{if(p.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(p.dataId),{dtype:this.tensorMap.get(p.dataId).dtype,shape:p.shape,name:t.variableNames[u]}});t.shaderKey=QB(t,a,s);let i=A().getBool("WEBGPU_ENGINE_COMPILE_ONLY");return t.shaderKey in this.pipelineCache||(this.pipelineCache[t.shaderKey]=YB(this.device,t,a,s,i)),t.pipeline=this.pipelineCache[t.shaderKey],i||this.recordAndSubmit(t,s,e,n),s}recordAndSubmit(t,e,o,n){if(t.pipeline instanceof Promise)throw new Error("Please call checkCompileCompletionAsync to ensure parallel compilation is done!");let s=[],a=[],i="int32";if(t.pixelsOpType==null){s.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),a=o.concat(e).map(d=>d.shape);let m="int32";a.map(d=>{s.push({type:m,data:d});let f=y.computeStrides(d);s.push({type:m,data:f})})}else{let m=y.computeStrides(e.shape);s.push({type:i,data:m})}if(t.size){let m=y.sizeFromShape(t.outputShape);s.push({type:i,data:[t.outputComponent?m/t.outputComponent:m]})}n&&(s=[...s,...n]);let p=[this.tensorToBinding(e),...o.map(m=>this.tensorToBinding(m)),this.makeUniforms(s)];o.forEach(m=>{this.commandQueueOwnedIds.add(m.dataId)}),this.commandQueueOwnedIds.add(e.dataId);let u=this.device.createBindGroup({layout:t.pipeline.getBindGroupLayout(0),entries:p.map((m,d)=>({binding:d,resource:m}))}),c=this.activeTimers!=null;this.ensureCommandEncoderReady();let l={};c&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.querySetCount})),l.timestampWrites=[{querySet:this.querySet,queryIndex:0,location:"beginning"},{querySet:this.querySet,queryIndex:1,location:"end"}],this.computePassEncoder=this.commandEncoder.beginComputePass(l)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(l)),this.computePassEncoder.setPipeline(t.pipeline),this.computePassEncoder.setBindGroup(0,u),this.computePassEncoder.dispatchWorkgroups(t.dispatch[0],t.dispatch[1],t.dispatch[2]),this.dispatchCountInPass++,(c||A().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchCountInPass||t.pixelsOpType===Ii.DRAW)&&(this.endComputePassEncoder(),c?this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let t=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.querySetCount*8),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let e=new BigUint64Array(t.getMappedRange()),o=Number(e[1]-e[0])/1e6;return t.unmap(),this.bufferManager.releaseBuffer(t),o}shouldExecuteOnCPU(t,e=dae){return A().getBool("WEBGPU_CPU_FORWARD")&&t.every(o=>this.tensorMap.get(o.dataId).resource==null&&y.sizeFromShape(o.shape){let r={powerPreference:A().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(r),e={},o=[];t.features.has("timestamp-query")&&o.push("timestamp-query"),t.features.has("bgra8unorm-storage")&&o.push(["bgra8unorm-storage"]),e.requiredFeatures=o;let n=t.limits;e.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize,maxBufferSize:n.maxBufferSize,maxComputeWorkgroupSizeX:n.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:n.maxComputeInvocationsPerWorkgroup};let s=await t.requestDevice(e),a=await t.requestAdapterInfo();return new Tu(s,a)},3);var fe;(function(r){r[r.ADD=0]="ADD",r[r.ATAN2=1]="ATAN2",r[r.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",r[r.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",r[r.DIV=4]="DIV",r[r.ELU_DER=5]="ELU_DER",r[r.EQUAL=6]="EQUAL",r[r.FLOOR_DIV=7]="FLOOR_DIV",r[r.GREATER=8]="GREATER",r[r.GREATER_EQUAL=9]="GREATER_EQUAL",r[r.LESS=10]="LESS",r[r.LESS_EQUAL=11]="LESS_EQUAL",r[r.LOGICAL_AND=12]="LOGICAL_AND",r[r.LOGICAL_OR=13]="LOGICAL_OR",r[r.MAX=14]="MAX",r[r.MIN=15]="MIN",r[r.MOD=16]="MOD",r[r.MUL=17]="MUL",r[r.NOT_EQUAL=18]="NOT_EQUAL",r[r.POW=19]="POW",r[r.PRELU=20]="PRELU",r[r.SQUARED_DIFFERENCE=21]="SQUARED_DIFFERENCE",r[r.SUB=22]="SUB"})(fe||(fe={}));var hae="let resultTemp = a + b;",gae="let resultTemp = atan2(a, b);",xae="let resultTemp = areal * breal - aimag * bimag;",yae="let resultTemp = areal * bimag + aimag * breal;",bae="let resultTemp = a / b;",Cae="let resultTemp = select(a * (b + 1.0), a, b >= b - b);",wae=` + `}return s}function cae(r){let t=/(\w+)\s*:\s*vec(5|6)/g;r=r.replace(t,o=>"@align(16) "+o);let e=/vec(5|6)\s*,\s*(\w+)/g;return r=r.replace(e,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function YB(r){return!(r.dispatchLayout.hasOwnProperty("y")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty("z")&&r.dispatchLayout.z.length!==0)}var Qv={};qe(Qv,{GPUBytesPerElement:()=>qg,MatMulProgramType:()=>Mo,assertNotComplex:()=>mm,computeDispatch:()=>H,computeWorkPerThreadForConv2d:()=>cm,computeWorkgroupInfoForMatMul:()=>Yv,computeWorkgroupSizeForConv2d:()=>pm,flatDispatchLayout:()=>X,isWebGPUSupported:()=>lm,tilesFitEvenlyIntoShape:()=>mae});var kp=r=>{let t=1;for(let e=0;ee%r[o]===0)}function H(r,t,e=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(kp(r.x.map(i=>t[i]))/(e[0]*o[0])),r.y?Math.ceil(kp(r.y.map(i=>t[i]))/(e[1]*o[1])):1,r.z?Math.ceil(kp(r.z.map(i=>t[i]))/(e[2]*o[2])):1];return[n,s,a]}function Yv(r,t,e,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),t<=16&&e<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function pm(r,t,e=!1){if(e)return[8,8,1];let o=kp(r.x.map(s=>t[s])),n=kp(r.y.map(s=>t[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function cm(r,t,e=!1){if(e)return[4,4,1];let o=kp(r.x.map(s=>t[s])),n=kp(r.y.map(s=>t[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function X(r){return{x:r.map((t,e)=>e)}}function qg(r){if(r==="float32"||r==="int32"||r==="bool"||r==="string")return 4;if(r==="complex64")return 8;throw new Error(`Unknown dtype ${r}`)}function lm(){return!!(globalThis&&globalThis.navigator&&globalThis.navigator.gpu)}function mm(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var Mo;(function(r){r[r.MatMulReduceProgram=0]="MatMulReduceProgram",r[r.MatMulSplitKProgram=1]="MatMulSplitKProgram",r[r.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",r[r.MatMulPackedProgram=3]="MatMulPackedProgram",r[r.MatMulMax=4]="MatMulMax"})(Mo||(Mo={}));var dae=A().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),fae=(r,t)=>{let e=r.limits.maxComputeWorkgroupsPerDimension,o=t.dispatchLayout,n=t.dispatch;if(n.every(a=>a<=e))return n;y.assert(n[0]>e&&o.y===void 0&&o.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(n[0]));return s>e?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=e,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},Kc=class r extends so{nextDataId(){return r.nextDataId++}constructor(t,e){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchCountInPass=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.queryResolveBuffer=null,this.querySet=null,this.querySetCount=2,this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,this.hasReadSyncWarned=!1,this.hasTimestampQueryWarned=!1,!lm())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=t,this.queue=t.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new Gg(e),this.supportTimestampQuery=this.device.features.has("timestamp-query"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Hg(this.device),this.textureManager=new Kg(this.device),this.tensorMap=new Bo(this,ur()),A().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:t,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(t,e=!1){if(!this.tensorMap.has(t))return!0;let o=this.tensorMap.get(t);return e?o.refCount=0:o.refCount--,o.refCount>0?!1:(o.complexTensorInfos!=null&&(this.disposeData(o.complexTensorInfos.real.dataId),this.disposeData(o.complexTensorInfos.imag.dataId)),this.commandQueueOwnedIds.has(t)?(this.tensorDataPendingDisposal.push(t),!0):(this.releaseResource(t),this.tensorMap.delete(t),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(t){let e=this.tensorMap.get(t);if(!(!e||!e.resource)){if(e.external){e.resource=null;return}e.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(e.resource):e.resource instanceof GPUTexture&&this.textureManager.releaseTexture(e.resource),e.resource=null}}refCount(t){return this.tensorMap.has(t)?this.tensorMap.get(t).refCount:0}incRef(t){let e=this.tensorMap.get(t);e.refCount++}decRef(t){if(this.tensorMap.has(t)){let e=this.tensorMap.get(t);e.refCount--}}write(t,e,o){if(o==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:e,values:t,refCount:1}),n}move(t,e,o,n,s){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(t,{dtype:n,shape:o,values:e,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(t=>{this.releaseResource(t),this.tensorMap.delete(t)}),this.uniformPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t)),this.stagingPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let t;try{t=await Promise.all(Object.values(this.pipelineCache))}catch(e){throw new Error(e.message)}Object.keys(this.pipelineCache).map((e,o)=>{this.pipelineCache[e]=t[o]})}async getBufferData(t){if(A().getBool("WEBGPU_ENGINE_COMPILE_ONLY"))return console.warn("The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false"),null;let e=t.size,o=this.bufferManager.acquireBuffer(e,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,o,0,e),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o),A().getBool("WEBGPU_USE_PROFILE_TOOL")&&(y.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(t,e){let o=this.tensorMap.get(t);return o.values=e,o.values}readSync(t){let e=this.tensorMap.get(t),{values:o,complexTensorInfos:n}=e;if(o!=null||e.dtype==="string")return o;if(e.dtype==="complex64"){let h=this.readSync(n.real.dataId),g=this.readSync(n.imag.dataId),x=y.convertBackendValuesAndArrayBuffer(w.mergeRealAndImagArrays(h,g).buffer,"float32");return this.convertAndCacheOnCPU(t,x),x}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn("The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead."));let s=["opaque","premultiplied"],a=e.resource,i=a.size;y.assert(i%4===0,()=>"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.");let p=i/4,u=new ArrayBuffer(i),c=256,l=256,m=s.map(h=>new OffscreenCanvas(c,l)),d=new OffscreenCanvas(c,l);this.endComputePassEncoder(),m.map((h,g)=>{let x=h.getContext("webgpu");return x.configure({device:this.device,format:"bgra8unorm",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),x.getCurrentTexture()}).map((h,g)=>{let x=c*4,b=(R,D,P)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:a,bytesPerRow:x,offset:P},{texture:h},{width:R,height:D}),this.submitQueue();let O=d.getContext("2d",{willReadFrequently:!0});O.clearRect(0,0,R,D),O.drawImage(m[g],0,0);let M=O.getImageData(0,0,R,D).data,L=s[g],B=new Uint8ClampedArray(u,P,R*D*4);for(let z=0;z0&&(b(S,k,_),_+=k*(c*4)),S=E%c,S>0&&b(S,1,_)});let f=y.convertBackendValuesAndArrayBuffer(u,e.dtype);return this.convertAndCacheOnCPU(t,f),f}async read(t){if(!this.tensorMap.has(t))throw new Error(`Tensor ${t} was not registered!`);let e=this.tensorMap.get(t),{values:o}=e;if(o!=null)return o;let n;if(e.dtype==="complex64"){let s=await Promise.all([this.read(e.complexTensorInfos.real.dataId),this.read(e.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=w.mergeRealAndImagArrays(a,i)}else{let s=await this.getBufferData(e.resource);n=y.convertBackendValuesAndArrayBuffer(s,e.dtype)}return this.convertAndCacheOnCPU(t,n),n}copyBuffer(t){let e=t.size,o=t.usage,n=this.bufferManager.acquireBuffer(e,o);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,n,0,e),this.submitQueue(),n}createTensorFromGPUData(t,e,o){let n=t.buffer;if(o==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:e,values:null,refCount:1,external:t.zeroCopy});let a=this.tensorMap.get(s),i=qg(a.dtype)*y.sizeFromShape(a.shape);if(t.buffer.sizey.decodeString(n));return me(t.shape,t.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return me(t.shape,t.dtype,e)}async time(t){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --enable-dawn-features=allow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled."),this.hasTimestampQueryWarned=!0);let e=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(t,e,o){return e==="string"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,t,e),shape:t,dtype:e}}tensorToBinding(t){if(!t)return null;let o=this.tensorMap.get(t.dataId).resource;return o instanceof GPUBuffer?{buffer:o}:o instanceof GPUTexture?o.createView():o}uploadToGPU(t){let e=this.tensorMap.get(t);if(e.resource!=null)return;let o=qg(e.dtype)*y.sizeFromShape(e.shape),n,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(e.values){if(n=this.bufferManager.acquireBuffer(o,s,!0),n.mapState==="unmapped"){let a=this.bufferManager.acquireBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),i=a.getMappedRange();e.dtype==="int32"||e.dtype==="bool"?new Int32Array(i).set(e.values):new Float32Array(i).set(e.values),a.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(a,0,n,0,o),this.stagingPendingDisposal.push(a)}else{let a=n.getMappedRange();e.dtype==="int32"||e.dtype==="bool"?new Int32Array(a).set(e.values):new Float32Array(a).set(e.values),n.unmap()}e.values=null}else n=this.bufferManager.acquireBuffer(o,s);e.resource=n}makeUniforms(t){let e=0,o=0,n=[],s=1;t.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),u>s&&(s=u),e=Math.ceil(e/u)*u,o=p.data.length,n.push(e),e+=p.data.length*4}),e=Math.ceil(e/s)*s;let a=new ArrayBuffer(e);t.forEach((p,u)=>{let c=n[u];p.type==="int32"?new Int32Array(a,c,p.data.length).set(p.data):p.type==="uint32"?new Uint32Array(a,c,p.data.length).set(p.data):new Float32Array(a,c,p.data.length).set(p.data)});let i=this.bufferManager.acquireBuffer(e,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(i,0,a,0,e),this.uniformPendingDisposal.push(i),{offset:0,size:e,buffer:i}}runWebGPUProgram(t,e,o,n,s){if(s||(s=this.makeTensorInfo(t.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),t.dispatch=fae(this.device,t);let a=e.map((p,u)=>{if(p.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(p.dataId),{dtype:this.tensorMap.get(p.dataId).dtype,shape:p.shape,name:t.variableNames[u]}});t.shaderKey=ZB(t,a,s);let i=A().getBool("WEBGPU_ENGINE_COMPILE_ONLY");return t.shaderKey in this.pipelineCache||(this.pipelineCache[t.shaderKey]=QB(this.device,t,a,s,i)),t.pipeline=this.pipelineCache[t.shaderKey],i||this.recordAndSubmit(t,s,e,n),s}recordAndSubmit(t,e,o,n){if(t.pipeline instanceof Promise)throw new Error("Please call checkCompileCompletionAsync to ensure parallel compilation is done!");let s=[],a=[],i="int32";if(t.pixelsOpType==null){s.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),a=o.concat(e).map(d=>d.shape);let m="int32";a.map(d=>{s.push({type:m,data:d});let f=y.computeStrides(d);s.push({type:m,data:f})})}else{let m=y.computeStrides(e.shape);s.push({type:i,data:m})}if(t.size){let m=y.sizeFromShape(t.outputShape);s.push({type:i,data:[t.outputComponent?m/t.outputComponent:m]})}n&&(s=[...s,...n]);let p=[this.tensorToBinding(e),...o.map(m=>this.tensorToBinding(m)),this.makeUniforms(s)];o.forEach(m=>{this.commandQueueOwnedIds.add(m.dataId)}),this.commandQueueOwnedIds.add(e.dataId);let u=this.device.createBindGroup({layout:t.pipeline.getBindGroupLayout(0),entries:p.map((m,d)=>({binding:d,resource:m}))}),c=this.activeTimers!=null;this.ensureCommandEncoderReady();let l={};c&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.querySetCount})),l.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:0,endOfPassWriteIndex:1},this.computePassEncoder=this.commandEncoder.beginComputePass(l)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(l)),this.computePassEncoder.setPipeline(t.pipeline),this.computePassEncoder.setBindGroup(0,u),this.computePassEncoder.dispatchWorkgroups(t.dispatch[0],t.dispatch[1],t.dispatch[2]),this.dispatchCountInPass++,(c||A().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchCountInPass||t.pixelsOpType===Ci.DRAW)&&(this.endComputePassEncoder(),c?this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let t=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.querySetCount*8),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let e=new BigUint64Array(t.getMappedRange()),o=Number(e[1]-e[0])/1e6;return t.unmap(),this.bufferManager.releaseBuffer(t),o}shouldExecuteOnCPU(t,e=dae){return A().getBool("WEBGPU_CPU_FORWARD")&&t.every(o=>this.tensorMap.get(o.dataId).resource==null&&y.sizeFromShape(o.shape){let r={powerPreference:A().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(r),e={},o=[];t.features.has("timestamp-query")&&o.push("timestamp-query"),t.features.has("bgra8unorm-storage")&&o.push(["bgra8unorm-storage"]),e.requiredFeatures=o;let n=t.limits;e.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize,maxBufferSize:n.maxBufferSize,maxComputeWorkgroupSizeX:n.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:n.maxComputeInvocationsPerWorkgroup};let s=await t.requestDevice(e),a=await t.requestAdapterInfo();return new Kc(s,a)},3);var fe;(function(r){r[r.ADD=0]="ADD",r[r.ATAN2=1]="ATAN2",r[r.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",r[r.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",r[r.DIV=4]="DIV",r[r.ELU_DER=5]="ELU_DER",r[r.EQUAL=6]="EQUAL",r[r.FLOOR_DIV=7]="FLOOR_DIV",r[r.GREATER=8]="GREATER",r[r.GREATER_EQUAL=9]="GREATER_EQUAL",r[r.LESS=10]="LESS",r[r.LESS_EQUAL=11]="LESS_EQUAL",r[r.LOGICAL_AND=12]="LOGICAL_AND",r[r.LOGICAL_OR=13]="LOGICAL_OR",r[r.MAX=14]="MAX",r[r.MIN=15]="MIN",r[r.MOD=16]="MOD",r[r.MUL=17]="MUL",r[r.NOT_EQUAL=18]="NOT_EQUAL",r[r.POW=19]="POW",r[r.PRELU=20]="PRELU",r[r.SQUARED_DIFFERENCE=21]="SQUARED_DIFFERENCE",r[r.SUB=22]="SUB"})(fe||(fe={}));var hae="let resultTemp = a + b;",gae="let resultTemp = atan2(a, b);",xae="let resultTemp = areal * breal - aimag * bimag;",yae="let resultTemp = areal * bimag + aimag * breal;",bae="let resultTemp = a / b;",Cae="let resultTemp = select(a * (b + 1.0), a, b >= b - b);",wae=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a == b); @@ -5306,7 +5306,7 @@ return a / b;`,Wee=` `,zae="if (a < 0.0) { return b * a; } return a;",Vae=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`,Wae="let resultTemp = (a - b) * (a - b);",Uae="let resultTemp = a - b;";function Qc(r,t){let e;do{switch(r){case fe.ATAN2:e=gae;break;case fe.MAX:e=Rae;break;case fe.MIN:e=Dae;break;case fe.MOD:e=t?Fae:Aae;break;case fe.NOT_EQUAL:e=t?Mae:Oae;break;case fe.POW:e=t?Bae:Lae;break;default:continue}let o,n,s;return t?(o="isnanVec4",n="vec4",s="vec4"):(o="isnan",n="f32",s="bool"),` +`,Wae="let resultTemp = (a - b) * (a - b);",Uae="let resultTemp = a - b;";function qc(r,t){let e;do{switch(r){case fe.ATAN2:e=gae;break;case fe.MAX:e=Rae;break;case fe.MIN:e=Dae;break;case fe.MOD:e=t?Fae:Aae;break;case fe.NOT_EQUAL:e=t?Mae:Oae;break;case fe.POW:e=t?Bae:Lae;break;default:continue}let o,n,s;return t?(o="isnanVec4",n="vec4",s="vec4"):(o="isnan",n="f32",s="bool"),` let aIsNaN = ${o}(a); let aPostLegalization = select(a, ${n}(42), aIsNaN); let bIsNaN = ${o}(b); @@ -5430,17 +5430,17 @@ return a / b;`,Wee=` `,Die="return tan(a);",Aie=` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); -`,Fie="return f32(i32((a)));";function vi(r,t){switch(r){case Z.ABS:return Gae;case Z.ACOS:return Hae;case Z.ACOSH:return Kae;case Z.ASIN:return qae;case Z.ASINH:return jae;case Z.ATAN:return Xae;case Z.ATANH:return Yae;case Z.COS:return Zae;case Z.COSH:return Jae;case Z.CEIL:return Qae;case Z.ELU:return t?rie:tie;case Z.ERF:return oie;case Z.EXP:return nie;case Z.EXPM1:return eie;case Z.FLOOR:return sie;case Z.IS_FINITE:return aie;case Z.IS_INF:return iie;case Z.IS_NAN:return uie;case Z.LINEAR:return pie;case Z.LOG:return cie;case Z.LOG1P:return lie;case Z.LOGICAL_NOT:return mie;case Z.NEG:return die;case Z.LEAKYRELU:return t?hie:fie;case Z.RECIPROCAL:return gie;case Z.RELU:return t?Cie:xie;case Z.RELU6:return t?bie:yie;case Z.ROUND:return wie;case Z.RSQRT:return Sie;case Z.SELU:return Iie;case Z.SIGMOID:return vie;case Z.SIGN:return kie;case Z.SIN:return Nie;case Z.SINH:return Tie;case Z.SOFTPLUS:return _ie;case Z.SQRT:return $ie;case Z.SQUARE:return Eie;case Z.STEP:return Rie;case Z.TAN:return Die;case Z.TANH:return Aie;case Z.TO_INT:return Fie;default:throw new Error(`BinaryType ${r} is not implemented!`)}}function fr(r,t=!1,e=!1,o=3){if(r===null)return"";let n="";if(r==="linear")n=vi(Z.LINEAR);else if(r==="relu")n=vi(Z.RELU,e);else if(r==="elu")n=vi(Z.ELU,e);else if(r==="relu6")n=vi(Z.RELU6,e);else if(r==="prelu")n=Qc(fe.PRELU,e);else if(r==="sigmoid")n=vi(Z.SIGMOID,e);else if(r==="leakyrelu")n=vi(Z.LEAKYRELU,e);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=Ae(e?4:1),i="";return t?i=` +`,Fie="return f32(i32((a)));";function wi(r,t){switch(r){case Z.ABS:return Gae;case Z.ACOS:return Hae;case Z.ACOSH:return Kae;case Z.ASIN:return qae;case Z.ASINH:return jae;case Z.ATAN:return Xae;case Z.ATANH:return Yae;case Z.COS:return Zae;case Z.COSH:return Jae;case Z.CEIL:return Qae;case Z.ELU:return t?rie:tie;case Z.ERF:return oie;case Z.EXP:return nie;case Z.EXPM1:return eie;case Z.FLOOR:return sie;case Z.IS_FINITE:return aie;case Z.IS_INF:return iie;case Z.IS_NAN:return uie;case Z.LINEAR:return pie;case Z.LOG:return cie;case Z.LOG1P:return lie;case Z.LOGICAL_NOT:return mie;case Z.NEG:return die;case Z.LEAKYRELU:return t?hie:fie;case Z.RECIPROCAL:return gie;case Z.RELU:return t?Cie:xie;case Z.RELU6:return t?bie:yie;case Z.ROUND:return wie;case Z.RSQRT:return Sie;case Z.SELU:return Iie;case Z.SIGMOID:return vie;case Z.SIGN:return kie;case Z.SIN:return Nie;case Z.SINH:return Tie;case Z.SOFTPLUS:return _ie;case Z.SQRT:return $ie;case Z.SQUARE:return Eie;case Z.STEP:return Rie;case Z.TAN:return Die;case Z.TANH:return Aie;case Z.TO_INT:return Fie;default:throw new Error(`BinaryType ${r} is not implemented!`)}}function dr(r,t=!1,e=!1,o=3){if(r===null)return"";let n="";if(r==="linear")n=wi(Z.LINEAR);else if(r==="relu")n=wi(Z.RELU,e);else if(r==="elu")n=wi(Z.ELU,e);else if(r==="relu6")n=wi(Z.RELU6,e);else if(r==="prelu")n=qc(fe.PRELU,e);else if(r==="sigmoid")n=wi(Z.SIGMOID,e);else if(r==="leakyrelu")n=wi(Z.LEAKYRELU,e);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=Ae(e?4:1),i="";return t?i=` fn activation(a : ${a}, coords : vec${o}) -> ${a} { let b = getPreluActivationWeightsByOutputCoords(coords); ${n} }`:i=` fn activation(a : ${a}, coords : vec${o}) -> ${a} { ${n} - }`,i}function Zr(r,t){return` + }`,i}function Qr(r,t){return` ${r?"value = value + getBiasByOutputCoords(coords);":""} ${t?"value = activation(value, coords);":""} - `}function Qv(r,t,e=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=` + `}function Zv(r,t,e=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=` ${r?"value = getA(batch, col, row);":"value = getA(batch, row, col);"} `,i=t?"value = getB(batch, col, row);":"value = getB(batch, row, col);";return` @@ -5460,14 +5460,14 @@ return a / b;`,Wee=` ${i} return value; } - `}function hm(r,t,e,o,n=!1,s=!1,a=!1,i=1){return` - ${Qv(e,o,n,s,a,i)} + `}function dm(r,t,e,o,n=!1,s=!1,a=!1,i=1){return` + ${Zv(e,o,n,s,a,i)} fn mm_write(batch: i32, row: i32, col: i32, valueIn: ${Ae(i)}) { ${n&&s?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} { var value = valueIn; let coords = vec3(batch, row, col); - ${Zr(r,t)} + ${Qr(r,t)} setOutputAtCoords(coords[0], coords[1], coords[2], value); } } @@ -5493,7 +5493,7 @@ return a / b;`,Wee=` let ACached = mm_Asub[tileRow + i][k]; ${s} } - }`}};function Fp(r,t,e=!1,o=32,n=!1,s=32,a=!1){let i=t[1]*r[1],p=t[0]*r[0],u=e?i:o,c=e?o:i,l=u/t[0],m=o/t[1],d=r[1],f=r[0];return y.assert((e&&l===4&&r[1]===4||!e&&(l===3||l===4))&&u%t[0]===0&&o%t[1]===0&&r[0]===4,()=>`If transposeA ${e} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4. + }`}};function Np(r,t,e=!1,o=32,n=!1,s=32,a=!1){let i=t[1]*r[1],p=t[0]*r[0],u=e?i:o,c=e?o:i,l=u/t[0],m=o/t[1],d=r[1],f=r[0];return y.assert((e&&l===4&&r[1]===4||!e&&(l===3||l===4))&&u%t[0]===0&&o%t[1]===0&&r[0]===4,()=>`If transposeA ${e} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4. Otherwise, innerElementSize ${l} must be 3 or 4. tileAWidth ${u} must be divisible by workgroupSize[0]${t[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${r[0]} must be 4.`),` var mm_Asub : array, ${u/l}>, ${c}>; @@ -5543,7 +5543,7 @@ return a / b;`,Wee=` for (var innerRow = 0; innerRow < ${d}; innerRow++) { mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); } - }`}var JB=r=>r?` + }`}var ez=r=>r?` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol); @@ -5551,7 +5551,7 @@ return a / b;`,Wee=` mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRowStart + inputRow, kStart + inputCol); - `,Mie=r=>r?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Pp(r,t,e=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*t[1],u=r[0]*t[0],c=e?p:o,l=e?o:p;y.assert(l%t[1]===0&&c%t[0]===0&&o%t[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${t[0]}, tileInner ${o} must be divisible by workgroupSize[1]${t[1]}`);let m=l/t[1],d=c/t[0],f=o/t[1],h=r[1],g=r[0],x=a?` + `,Mie=r=>r?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Tp(r,t,e=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*t[1],u=r[0]*t[0],c=e?p:o,l=e?o:p;y.assert(l%t[1]===0&&c%t[0]===0&&o%t[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${t[0]}, tileInner ${o} must be divisible by workgroupSize[1]${t[1]}`);let m=l/t[1],d=c/t[0],f=o/t[1],h=r[1],g=r[0],x=a?` let localRow = i32(localId.y); let localCol = i32(localId.x); let globalRowStart = i32(workgroupId.y) * ${p}; @@ -5562,7 +5562,7 @@ return a / b;`,Wee=` // Load one tile of A into local memory. for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${t[1]}) { for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${t[0]}) { - ${JB(e)} + ${ez(e)} } } // Load one tile of B into local memory. @@ -5617,7 +5617,7 @@ return a / b;`,Wee=` for (var innerCol = 0; innerCol < ${d}; innerCol++) { let inputRow = tileRowA + innerRow; let inputCol = tileColA + innerCol; - ${JB(e)} + ${ez(e)} } } @@ -5729,10 +5729,10 @@ return a / b;`,Wee=` mm_write(batch, globalRow, globalCol, acc); } - `}var Xg=class{constructor(t,e,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?t[1]:t[2];if(this.isVec4=(u%4===0&&!o||e[1]%4===0&&o)&&e[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=e[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=Xv(e[1],u,e[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(e[1],e[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(t,e,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=t%n===0,i=e%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return` - ${fr(this.activation,this.hasPreluActivationWeights,this.isVec4)} - ${hm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} - ${this.isVec4?Fp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?Bie(this.workgroupSize,this.transposeA):Pp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)} + `}var jg=class{constructor(t,e,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?t[1]:t[2];if(this.isVec4=(u%4===0&&!o||e[1]%4===0&&o)&&e[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=e[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=Yv(e[1],u,e[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(e[1],e[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(t,e,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=t%n===0,i=e%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return` + ${dr(this.activation,this.hasPreluActivationWeights,this.isVec4)} + ${dm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} + ${this.isVec4?Np(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?Bie(this.workgroupSize,this.transposeA):Tp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)} `}};function zie(r){return` var sumValues : array; ${G()} { @@ -5766,9 +5766,9 @@ return a / b;`,Wee=` mm_write(batch, row, col, sum); } } - `}var Yg=class{constructor(t,e=!1,o=!1,n=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=t,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.transposeA=e,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${e}_${o}`}getUserCode(){return` - ${fr(this.activation,this.hasPreluActivationWeights)} - ${hm(this.addBias,this.activation,this.transposeA,this.transposeB)} + `}var Xg=class{constructor(t,e=!1,o=!1,n=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=t,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.transposeA=e,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${e}_${o}`}getUserCode(){return` + ${dr(this.activation,this.hasPreluActivationWeights)} + ${dm(this.addBias,this.activation,this.transposeA,this.transposeB)} ${zie(this.workgroupSize[0])} `}};function Vie(r){let t=r[1],e=r[0],o=t>e?t:e;return` var mm_Asub : array, ${t}>; @@ -5822,12 +5822,12 @@ return a / b;`,Wee=` mm_write(batch, globalRow, globalCol, acc); } - `}var Qg=class{constructor(t,e,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push("bias");let c=p!=null;c&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return` - ${fr(this.activation,this.hasPreluActivationWeights)} - ${hm(this.addBias,this.activation,this.transposeA,this.transposeB)} + `}var Yg=class{constructor(t,e,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push("bias");let c=p!=null;c&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return` + ${dr(this.activation,this.hasPreluActivationWeights)} + ${dm(this.addBias,this.activation,this.transposeA,this.transposeB)} ${Vie(this.workgroupSize)} - `}};var Zg=class{constructor(t,e,o=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(t[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&e%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=H(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],e],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let t=this.outputComponent;return` - ${Qv(!1,this.transposeB,!1,!1,!1,t)} + `}};var Qg=class{constructor(t,e,o=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(t[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&e%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=H(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],e],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let t=this.outputComponent;return` + ${Zv(!1,this.transposeB,!1,!1,!1,t)} fn mm_write(batch: i32, row : i32, col : i32, value : ${Ae(t)}) { if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { let coords = vec3(batch, row, col); @@ -5835,31 +5835,31 @@ return a / b;`,Wee=` // The problem is that we should initialize output to zero before using. // Otherwise, the original value will be added to the result. for (var i = 0; i < ${t}; i = i + 1) { - ${Qr("&result[flatIndex + i]",`${t>1?"value[i]":"value"}`,"float32")} + ${Yr("&result[flatIndex + i]",`${t>1?"value[i]":"value"}`,"float32")} } } } - ${t===4?Fp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Pp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} - `}},Jg=class{constructor(t,e=null,o=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=e!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return` - ${fr(this.activation,this.hasPreluActivationWeights)} + ${t===4?Np(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Tp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} + `}},Zg=class{constructor(t,e=null,o=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=e!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return` + ${dr(this.activation,this.hasPreluActivationWeights)} ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); - ${Zr(this.addBias,this.activation)} + ${Qr(this.addBias,this.activation)} setOutputAtIndex(index, value); } } - `}};var ex=class{constructor(t){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return` + `}};var Jg=class{constructor(t){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.value); } } - `}};function kt(r){let{backend:t,attrs:e}=r,{shape:o,value:n}=e,{dtype:s}=e;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),t.makeTensorInfo(o,s,a)}else{let a=new ex(o),i=[{type:"float32",data:[n]}];return t.runWebGPUProgram(a,[],s,i)}}var ez={kernelName:sa,backendName:"webgpu",kernelFunc:kt};function pe(r){let{inputs:t,attrs:e}=r,{x:o}=t,{shape:n}=e,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var tz={kernelName:da,backendName:"webgpu",kernelFunc:pe};function Op({a:r,b:t,transposeA:e,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=t.shape.length,l=e?r.shape[u-2]:r.shape[u-1],m=o?t.shape[c-1]:t.shape[c-2],d=e?r.shape[u-1]:r.shape[u-2],f=o?t.shape[c-2]:t.shape[c-1],h=r.shape.slice(0,-2),g=t.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=Ir.assertAndGetBroadcastShape(r.shape.slice(0,-2),t.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${o} must match.`);let k=e?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=pe({inputs:{x:r},backend:n,attrs:{shape:k}}),R=pe({inputs:{x:t},backend:n,attrs:{shape:_}}),D=[E,R],P=Math.max(x,b),O=[E,R],M=[{type:"int32",data:[d]},{type:"int32",data:[f]},{type:"int32",data:[l]}],L,B,z=[P,d,f],U=A().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(U<0){let q=A().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),Y=q>0?q:n.thresholdToIncreaseWorkgroups,J=P*Math.ceil(d/32)*Math.ceil(f/32);J<=Y||d<=8&&J<=Y*2?P*d*f<=128?U=Lo.MatMulReduceProgram:P===1&&m>=2e3?U=Lo.MatMulSplitKProgram:U=Lo.MatMulSmallOutputSizeProgram:U=Lo.MatMulPackedProgram}switch(U){case Lo.MatMulReduceProgram:L=new Yg(z,e,o,s,p,a);break;case Lo.MatMulSplitKProgram:{if(B=kt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Zg(z,m,e,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let Y=new Jg(B.shape,s,p,a),J=null,re=[B];s&&re.push(s),a&&re.push(a),p==="leakyrelu"&&(J=[{type:"float32",data:[i]}],Y.uniforms+=" alpha : f32,");let ne=n.runWebGPUProgram(Y,re,B.dtype,J);D.push(B);let ee=pe({inputs:{x:ne},backend:n,attrs:{shape:S}});D.push(ne);for(let oe of D)n.disposeData(oe.dataId);return ee}break}case Lo.MatMulSmallOutputSizeProgram:L=new Qg(k,_,z,e,o,s,p,a);break;case Lo.MatMulPackedProgram:let q=n.adapterInfo.isIntel();L=new Xg(k,z,e,o,s,p,a,q);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p==="leakyrelu"&&(M.push({type:"float32",data:[i]}),L.uniforms+=" alpha : f32,"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=pe({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let q of D)n.disposeData(q.dataId);return j}function Wie(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Op({a:n,b:s,transposeA:p,transposeB:u,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var rz={kernelName:Io,backendName:"webgpu",kernelFunc:Wie};var gm=class{constructor(t,e,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=w.assertAndGetBroadcastShape(e,o),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${t}`,this.op=t}getUserCode(){return` + `}};function vt(r){let{backend:t,attrs:e}=r,{shape:o,value:n}=e,{dtype:s}=e;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),t.makeTensorInfo(o,s,a)}else{let a=new Jg(o),i=[{type:"float32",data:[n]}];return t.runWebGPUProgram(a,[],s,i)}}var tz={kernelName:sa,backendName:"webgpu",kernelFunc:vt};function pe(r){let{inputs:t,attrs:e}=r,{x:o}=t,{shape:n}=e,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var rz={kernelName:da,backendName:"webgpu",kernelFunc:pe};function _p({a:r,b:t,transposeA:e,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=t.shape.length,l=e?r.shape[u-2]:r.shape[u-1],m=o?t.shape[c-1]:t.shape[c-2],d=e?r.shape[u-1]:r.shape[u-2],f=o?t.shape[c-2]:t.shape[c-1],h=r.shape.slice(0,-2),g=t.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=Sr.assertAndGetBroadcastShape(r.shape.slice(0,-2),t.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${o} must match.`);let k=e?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=pe({inputs:{x:r},backend:n,attrs:{shape:k}}),R=pe({inputs:{x:t},backend:n,attrs:{shape:_}}),D=[E,R],P=Math.max(x,b),O=[E,R],M=[{type:"int32",data:[d]},{type:"int32",data:[f]},{type:"int32",data:[l]}],L,B,z=[P,d,f],U=A().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(U<0){let q=A().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),Y=q>0?q:n.thresholdToIncreaseWorkgroups,J=P*Math.ceil(d/32)*Math.ceil(f/32);J<=Y||d<=8&&J<=Y*2?P*d*f<=128?U=Mo.MatMulReduceProgram:P===1&&m>=2e3?U=Mo.MatMulSplitKProgram:U=Mo.MatMulSmallOutputSizeProgram:U=Mo.MatMulPackedProgram}switch(U){case Mo.MatMulReduceProgram:L=new Xg(z,e,o,s,p,a);break;case Mo.MatMulSplitKProgram:{if(B=vt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Qg(z,m,e,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let Y=new Zg(B.shape,s,p,a),J=null,re=[B];s&&re.push(s),a&&re.push(a),p==="leakyrelu"&&(J=[{type:"float32",data:[i]}],Y.uniforms+=" alpha : f32,");let ne=n.runWebGPUProgram(Y,re,B.dtype,J);D.push(B);let ee=pe({inputs:{x:ne},backend:n,attrs:{shape:S}});D.push(ne);for(let oe of D)n.disposeData(oe.dataId);return ee}break}case Mo.MatMulSmallOutputSizeProgram:L=new Yg(k,_,z,e,o,s,p,a);break;case Mo.MatMulPackedProgram:let q=n.adapterInfo.isIntel();L=new jg(k,z,e,o,s,p,a,q);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p==="leakyrelu"&&(M.push({type:"float32",data:[i]}),L.uniforms+=" alpha : f32,"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=pe({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let q of D)n.disposeData(q.dataId);return j}function Wie(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return _p({a:n,b:s,transposeA:p,transposeB:u,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var oz={kernelName:So,backendName:"webgpu",kernelFunc:Wie};var fm=class{constructor(t,e,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=w.assertAndGetBroadcastShape(e,o),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${t}`,this.op=t}getUserCode(){return` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { - ${Qc(this.op,!1)} + ${qc(this.op,!1)} } ${G("index")} { @@ -5871,9 +5871,9 @@ return a / b;`,Wee=` setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag)); } } - `}};var ki=class{constructor(t,e,o){if(this.size=!0,this.variableNames=["A","B"],this.outputShape=w.assertAndGetBroadcastShape(e,o),this.dispatchLayout=X(this.outputShape),this.op=t,this.useSharedMemoryWithA=e.length<=1&&o.length>1&&e[0]<128,this.useSharedMemoryWithB=o.length<=1&&e.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:e[0],this.shaderKey=`binary_${t}_${this.lastDimensionSize}`,this.type="shared",this.workgroupSize=[256,1,1];else{let n=e.length>0&&e[e.length-1]%4===0,s=o.length>0&&o[o.length-1]%4===0;n&&s?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(y.isScalarShape(o)||o[o.length-1]===1)||s&&(y.isScalarShape(e)||e[e.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type="nonshared",this.shaderKey=`binary_${t}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let t,e=this.outputComponent===4?"vec4":"f32",o=` + `}};var Si=class{constructor(t,e,o){if(this.size=!0,this.variableNames=["A","B"],this.outputShape=w.assertAndGetBroadcastShape(e,o),this.dispatchLayout=X(this.outputShape),this.op=t,this.useSharedMemoryWithA=e.length<=1&&o.length>1&&e[0]<128,this.useSharedMemoryWithB=o.length<=1&&e.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:e[0],this.shaderKey=`binary_${t}_${this.lastDimensionSize}`,this.type="shared",this.workgroupSize=[256,1,1];else{let n=e.length>0&&e[e.length-1]%4===0,s=o.length>0&&o[o.length-1]%4===0;n&&s?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(y.isScalarShape(o)||o[o.length-1]===1)||s&&(y.isScalarShape(e)||e[e.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type="nonshared",this.shaderKey=`binary_${t}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let t,e=this.outputComponent===4?"vec4":"f32",o=` fn binaryOperation(a : ${e}, b : ${e}) -> ${e} { - ${Qc(this.op,this.outputComponent===4)} + ${qc(this.op,this.outputComponent===4)} }; `;if(this.type==="shared"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index); let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}]; @@ -5904,9 +5904,9 @@ return a / b;`,Wee=` setOutputAtIndex(index, binaryOperation(a, b)); } } - `;return t}};function Ft(r){let{inputs:t}=r,{x:e}=t;return r.backend.incRef(e.dataId),{dataId:e.dataId,shape:e.shape,dtype:e.dtype}}var oz={kernelName:wo,backendName:"webgpu",kernelFunc:Ft};function yo(r){let{inputs:t,backend:e}=r,{real:o,imag:n}=t,s=e.makeTensorInfo(o.shape,"complex64"),a=e.tensorMap.get(s.dataId),i=Ft({inputs:{x:o},backend:e}),p=Ft({inputs:{x:n},backend:e});return a.complexTensorInfos={real:i,imag:p},s}var nz={kernelName:Fi,backendName:"webgpu",kernelFunc:yo};var Jr=class{constructor(t,e,o=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=e,o!==""&&(this.uniforms=o),this.shaderKey=`unary_${e}`}getUserCode(){return` + `;return t}};function At(r){let{inputs:t}=r,{x:e}=t;return r.backend.incRef(e.dataId),{dataId:e.dataId,shape:e.shape,dtype:e.dtype}}var nz={kernelName:Co,backendName:"webgpu",kernelFunc:At};function xo(r){let{inputs:t,backend:e}=r,{real:o,imag:n}=t,s=e.makeTensorInfo(o.shape,"complex64"),a=e.tensorMap.get(s.dataId),i=At({inputs:{x:o},backend:e}),p=At({inputs:{x:n},backend:e});return a.complexTensorInfos={real:i,imag:p},s}var sz={kernelName:Ri,backendName:"webgpu",kernelFunc:xo};var Zr=class{constructor(t,e,o=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=e,o!==""&&(this.uniforms=o),this.shaderKey=`unary_${e}`}getUserCode(){return` fn unaryOperation(a : f32) -> f32 { - ${vi(this.op,!1)} + ${wi(this.op,!1)} } ${G("index")} { if (index < uniforms.size) { @@ -5914,7 +5914,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, unaryOperation(a)); } } - `}};function ye({opType:r,cpuKernelImpl:t,dtype:e}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=e||s.dtype;if(a.shouldExecuteOnCPU([s])&&t!=null){let u=a.tensorMap.get(s.dataId),c=t(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Jr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function et({opType:r,cpuKernelImpl:t,supportsComplex:e=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(e&&a.dtype==="complex64"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new ki(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,S],dt(x.dtype,b.dtype))});else{let g=new gm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new gm(fe.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,"float32"),f=p.runWebGPUProgram(x,b,"float32")}let h=yo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype==="string"||i.dtype==="string"||p.shouldExecuteOnCPU([a,i]))&&t!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype==="string"?w.fromUint8ToStringArray(l):l,f=a.dtype==="string"?w.fromUint8ToStringArray(m):m,[h,g]=t(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new ki(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:sz,castImpl:az,ceilImpl:iz,concatImpl:uz,equalImpl:pz,expImpl:cz,expm1Impl:lz,floorImpl:mz,floorDivImpl:dz,gatherNdImpl:fz,gatherV2Impl:hz,greaterEqualImpl:gz,greaterImpl:xz,lessEqualImpl:yz,lessImpl:bz,logImpl:Cz,maxImpl:wz,maximumImpl:Sz,minimumImpl:Iz,multiplyImpl:vz,negImpl:kz,notEqualImpl:Nz,prodImpl:Tz,rangeImpl:_z,rsqrtImpl:$z,scatterImpl:Ez,simpleAbsImpl:Rz,sliceImpl:Dz,stridedSliceImpl:Az,stringNGramsImpl:Fz,subImpl:Pz,tileImpl:Oz,topKImpl:Mz,transposeImpl:Lz,uniqueImpl:HPt}=Tc;var Uie=ye({opType:Z.ABS,cpuKernelImpl:Rz}),Bz={kernelName:Xs,backendName:"webgpu",kernelFunc:Uie};var Gie=ye({opType:Z.ACOS}),zz={kernelName:Wo,backendName:"webgpu",kernelFunc:Gie};var Hie=ye({opType:Z.ACOSH}),Vz={kernelName:Uo,backendName:"webgpu",kernelFunc:Hie};var Kie=et({opType:fe.ADD,cpuKernelImpl:sz,supportsComplex:!0}),Wz={kernelName:uo,backendName:"webgpu",kernelFunc:Kie};var tx=class{constructor(t){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t[0],this.variableNames=t.map((e,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let t=[];this.variableNames.forEach(n=>{t.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let e=this.variableNames.map(n=>`v${n}`).join(" + ");return` + `}};function ye({opType:r,cpuKernelImpl:t,dtype:e}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=e||s.dtype;if(a.shouldExecuteOnCPU([s])&&t!=null){let u=a.tensorMap.get(s.dataId),c=t(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Zr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function et({opType:r,cpuKernelImpl:t,supportsComplex:e=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(e&&a.dtype==="complex64"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Si(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,S],dt(x.dtype,b.dtype))});else{let g=new fm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new fm(fe.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,"float32"),f=p.runWebGPUProgram(x,b,"float32")}let h=xo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype==="string"||i.dtype==="string"||p.shouldExecuteOnCPU([a,i]))&&t!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype==="string"?w.fromUint8ToStringArray(l):l,f=a.dtype==="string"?w.fromUint8ToStringArray(m):m,[h,g]=t(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Si(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:az,castImpl:iz,ceilImpl:uz,concatImpl:pz,equalImpl:cz,expImpl:lz,expm1Impl:mz,floorImpl:dz,floorDivImpl:fz,gatherNdImpl:hz,gatherV2Impl:gz,greaterEqualImpl:xz,greaterImpl:yz,lessEqualImpl:bz,lessImpl:Cz,logImpl:wz,maxImpl:Sz,maximumImpl:Iz,minimumImpl:vz,multiplyImpl:kz,negImpl:Nz,notEqualImpl:Tz,prodImpl:_z,rangeImpl:$z,rsqrtImpl:Ez,scatterImpl:Rz,simpleAbsImpl:Dz,sliceImpl:Az,stridedSliceImpl:Fz,stringNGramsImpl:Pz,subImpl:Oz,tileImpl:Mz,topKImpl:Lz,transposeImpl:Bz,uniqueImpl:KPt}=wc;var Uie=ye({opType:Z.ABS,cpuKernelImpl:Dz}),zz={kernelName:Xs,backendName:"webgpu",kernelFunc:Uie};var Gie=ye({opType:Z.ACOS}),Vz={kernelName:Vo,backendName:"webgpu",kernelFunc:Gie};var Hie=ye({opType:Z.ACOSH}),Wz={kernelName:Wo,backendName:"webgpu",kernelFunc:Hie};var Kie=et({opType:fe.ADD,cpuKernelImpl:az,supportsComplex:!0}),Uz={kernelName:io,backendName:"webgpu",kernelFunc:Kie};var ex=class{constructor(t){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t[0],this.variableNames=t.map((e,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let t=[];this.variableNames.forEach(n=>{t.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let e=this.variableNames.map(n=>`v${n}`).join(" + ");return` ${G("index")} { for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; @@ -5926,7 +5926,7 @@ return a / b;`,Wee=` } } } - `}};function qie(r){let{inputs:t,backend:e}=r,o=t;if(o.length===1)return Ft({inputs:{x:o[0]},backend:e});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new tx(s);return e.runWebGPUProgram(a,o,n)}var Uz={kernelName:Go,backendName:"webgpu",kernelFunc:qie};var rx=class{constructor(t,e){this.variableNames=["A"],this.workgroupSize=[16,16,1];let o=new Array(t.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let t=this.workgroupSize[0];return` + `}};function qie(r){let{inputs:t,backend:e}=r,o=t;if(o.length===1)return At({inputs:{x:o[0]},backend:e});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new ex(s);return e.runWebGPUProgram(a,o,n)}var Gz={kernelName:Uo,backendName:"webgpu",kernelFunc:qie};var tx=class{constructor(t,e){this.variableNames=["A"],this.workgroupSize=[16,16,1];let o=new Array(t.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let t=this.workgroupSize[0];return` var tile : array, ${this.workgroupSize[0]}>; ${G()} { var x = i32(workgroupId.x) * ${t} + i32(localId.x); @@ -5945,7 +5945,7 @@ return a / b;`,Wee=` [localId.y]); } } - `}};var ox=class{constructor(t,e){this.variableNames=["A"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(t.length);for(let n=0;n6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=new Array(t);for(let o=0;o=32768&&o>=512?this.workgroupSize=[512,1,1]:t.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=e,this.shaderKey=`reduce_${e}`}getUserCode(){let t="",e="0.0",o=this.workgroupSize[0];this.reduceType==="min"||this.reduceType==="max"?(t=` + `}};function Jv(r){let t=r.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=new Array(t);for(let o=0;o=32768&&o>=512?this.workgroupSize=[512,1,1]:t.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=e,this.shaderKey=`reduce_${e}`}getUserCode(){let t="",e="0.0",o=this.workgroupSize[0];this.reduceType==="min"||this.reduceType==="max"?(t=` if (isnan(candidate)) { bestValue = uniforms.NAN; } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue) @@ -6004,7 +6004,7 @@ return a / b;`,Wee=` ${n} } } - `}};var jie={mean:"float32",all:"bool",any:"bool"};function eo(r,t,e,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(t,r.shape),p=i,u=w.getAxesPermutation(p,s),c=r;u!=null&&(c=yr({inputs:{x:r},attrs:{perm:u},backend:n}),p=w.getInnerMostAxes(p.length,s),a.push(c)),w.assertAxesAreInnerMostDims(o,p,s);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=l;e&&(d=w.expandShapeToKeepDim(l,i));let f;if((o==="max"||o==="prod")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case"max":let g=wz(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case"prod":let{outVals:x,outShape:b,outDtype:C}=Tz(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=jie[o]||ni(r.dtype),S=[{type:"int32",data:[h]}],k=new nx(b,o,n.device.limits.maxComputeWorkgroupSizeX),_=n.runWebGPUProgram(k,[c],C,S);a.push(_),f=pe({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function Xie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return eo(n,a,s,"all",e)}var Hz={kernelName:Ho,backendName:"webgpu",kernelFunc:Xie};function Yie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return eo(n,a,s,"any",e)}var Kz={kernelName:Ko,backendName:"webgpu",kernelFunc:Yie};var Zc=class{constructor(t,e,o){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[e];this.op=o==="min"?"<":">";let[s,a]=w.computeOutAndReduceShapes(t,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=X(this.outputShape),y.sizeFromShape(a)<32?(this.type="plain",this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=t,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let t=this.workgroupSize[0],e=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Mo(this.inputShape.length-1)}`,o=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let s=0;sn.disposeData(h.dataId)),f}function Xie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return Jr(n,a,s,"all",e)}var Kz={kernelName:Go,backendName:"webgpu",kernelFunc:Xie};function Yie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return Jr(n,a,s,"any",e)}var qz={kernelName:Ho,backendName:"webgpu",kernelFunc:Yie};var jc=class{constructor(t,e,o){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[e];this.op=o==="min"?"<":">";let[s,a]=w.computeOutAndReduceShapes(t,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=X(this.outputShape),y.sizeFromShape(a)<32?(this.type="plain",this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=t,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let t=this.workgroupSize[0],e=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Oo(this.inputShape.length-1)}`,o=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let s=0;s u32 { return ((a - 1u) / b + 1u); } @@ -6070,7 +6070,7 @@ return a / b;`,Wee=` setOutputAtIndexI32(index, bestIndex); } } - `}};function Qie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yr({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let c=new Zc(p.shape,a[0],"max"),l=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],m=e.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>e.disposeData(d.dataId)),m}var qz={kernelName:Ys,backendName:"webgpu",kernelFunc:Qie};function Zie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yr({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=new Zc(p.shape,a[0],"min"),l=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],m=e.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>e.disposeData(d.dataId)),m}var jz={kernelName:Qs,backendName:"webgpu",kernelFunc:Zie};var Jie=ye({opType:Z.ASIN}),Xz={kernelName:qo,backendName:"webgpu",kernelFunc:Jie};var eue=ye({opType:Z.ASINH}),Yz={kernelName:jo,backendName:"webgpu",kernelFunc:eue};var tue=ye({opType:Z.ATAN}),Qz={kernelName:Xo,backendName:"webgpu",kernelFunc:tue};var rue=et({opType:fe.ATAN2}),Zz={kernelName:Qo,backendName:"webgpu",kernelFunc:rue};var oue=ye({opType:Z.ATANH}),Jz={kernelName:Yo,backendName:"webgpu",kernelFunc:oue};var sx=class{constructor(t){this.variableNames=["x"],this.uniforms="strides : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` + `}};function Qie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xr({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let c=new jc(p.shape,a[0],"max"),l=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],m=e.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>e.disposeData(d.dataId)),m}var jz={kernelName:Ys,backendName:"webgpu",kernelFunc:Qie};function Zie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xr({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=new jc(p.shape,a[0],"min"),l=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],m=e.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>e.disposeData(d.dataId)),m}var Xz={kernelName:Qs,backendName:"webgpu",kernelFunc:Zie};var Jie=ye({opType:Z.ASIN}),Yz={kernelName:Ko,backendName:"webgpu",kernelFunc:Jie};var eue=ye({opType:Z.ASINH}),Qz={kernelName:qo,backendName:"webgpu",kernelFunc:eue};var tue=ye({opType:Z.ATAN}),Zz={kernelName:jo,backendName:"webgpu",kernelFunc:tue};var rue=et({opType:fe.ATAN2}),Jz={kernelName:Yo,backendName:"webgpu",kernelFunc:rue};var oue=ye({opType:Z.ATANH}),eV={kernelName:Xo,backendName:"webgpu",kernelFunc:oue};var nx=class{constructor(t){this.variableNames=["x"],this.uniforms="strides : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -6085,7 +6085,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, value); } } - `}};var za=class{constructor(t,e,o=!1,n=!1,s=!1){if(this.variableNames=["x"],this.uniforms="strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,e==="avg"&&o)throw new Error("Cannot compute positions for average pool.");this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=e,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${e}_${o}_${n}_${s}`}getUserCode(){let t;this.poolType==="avg"?t="resultValue = resultValue + value; count = count + 1.0;":this.computePositions?t=`let currMaxValue = mix(value, maxValue, maxValueFound); + `}};var Ba=class{constructor(t,e,o=!1,n=!1,s=!1){if(this.variableNames=["x"],this.uniforms="strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,e==="avg"&&o)throw new Error("Cannot compute positions for average pool.");this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=e,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${e}_${o}_${n}_${s}`}getUserCode(){let t;this.poolType==="avg"?t="resultValue = resultValue + value; count = count + 1.0;":this.computePositions?t=`let currMaxValue = mix(value, maxValue, maxValueFound); if (value >= currMaxValue) { maxValue = value; maxValueFound = 1.0; @@ -6126,7 +6126,7 @@ return a / b;`,Wee=` ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${e});`} } } - `}},_u=class{constructor(t,e,o=!1,n=!1,s=!1){if(this.variableNames=["x"],this.uniforms="strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,",this.workgroupSize=[128,1,1],this.size=!0,e==="avg"&&o)throw new Error("Cannot compute positions for average pool.");this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=e,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${e}_${o}_${n}_${s}`}getUserCode(){let t;this.poolType==="avg"?t="resultValue += value; count += 1.0;":this.computePositions?t=`let currMaxValue = mix(value, maxValue, maxValueFound); + `}},wu=class{constructor(t,e,o=!1,n=!1,s=!1){if(this.variableNames=["x"],this.uniforms="strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,",this.workgroupSize=[128,1,1],this.size=!0,e==="avg"&&o)throw new Error("Cannot compute positions for average pool.");this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=e,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${e}_${o}_${n}_${s}`}getUserCode(){let t;this.poolType==="avg"?t="resultValue += value; count += 1.0;":this.computePositions?t=`let currMaxValue = mix(value, maxValue, maxValueFound); if (value >= currMaxValue) { maxValue = value; maxValueFound = 1.0; @@ -6175,7 +6175,7 @@ return a / b;`,Wee=` ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${e});`} } } - `}};function Jv(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{reductionIndices:s,keepDims:a}=o;return eo(n,s,a,"max",e)}var eV={kernelName:Vn,backendName:"webgpu",kernelFunc:Jv};function e0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return eo(n,a,s,"mean",e)}var tV={kernelName:Gn,backendName:"webgpu",kernelFunc:e0};function ax(r,t,e,o){if(t.filterWidth===1&&t.filterHeight===1&&y.arraysEqual(t.inShape,t.outShape))return Ft({inputs:{x:r},backend:o});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;e==="avg"?p=e0({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(e==="max",()=>`Invalid pool type ${e}`),p=Jv({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:t.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?n=new sx(t):(e==="avg"?n=new za(t,"avg"):(y.assert(e==="max",()=>`Invalid pool type ${e}`),n=new za(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function nue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=w.computePool2DInfo(n.shape,s,a,u,i,p);return ax(n,c,"avg",e)}var rV={kernelName:Zo,backendName:"webgpu",kernelFunc:nue};function sue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new _u(l,"avg"),d=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.inDepth,l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return e.runWebGPUProgram(m,[n],n.dtype,d)}var oV={kernelName:Zs,backendName:"webgpu",kernelFunc:sue};var ix=class{constructor(t){this.variableNames=["dy"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, + `}};function e0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{reductionIndices:s,keepDims:a}=o;return Jr(n,s,a,"max",e)}var tV={kernelName:zn,backendName:"webgpu",kernelFunc:e0};function t0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return Jr(n,a,s,"mean",e)}var rV={kernelName:Un,backendName:"webgpu",kernelFunc:t0};function sx(r,t,e,o){if(t.filterWidth===1&&t.filterHeight===1&&y.arraysEqual(t.inShape,t.outShape))return At({inputs:{x:r},backend:o});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;e==="avg"?p=t0({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(e==="max",()=>`Invalid pool type ${e}`),p=e0({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:t.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?n=new nx(t):(e==="avg"?n=new Ba(t,"avg"):(y.assert(e==="max",()=>`Invalid pool type ${e}`),n=new Ba(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function nue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=w.computePool2DInfo(n.shape,s,a,u,i,p);return sx(n,c,"avg",e)}var oV={kernelName:Qo,backendName:"webgpu",kernelFunc:nue};function sue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new wu(l,"avg"),d=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.inDepth,l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return e.runWebGPUProgram(m,[n],n.dtype,d)}var nV={kernelName:Zs,backendName:"webgpu",kernelFunc:sue};var ax=class{constructor(t){this.variableNames=["dy"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool2DBackprop"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { @@ -6214,7 +6214,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, dotProd); } } - `}},ux=class{constructor(t){this.variableNames=["dy"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, + `}},ix=class{constructor(t){this.variableNames=["dy"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool3DBackprop"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { @@ -6263,7 +6263,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, dotProd); } } - `}};function aue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=w.computePool3DInfo(a.shape,i,p,1,u,c),m=new ux(l),d=1/(l.filterDepth*l.filterHeight*l.filterWidth),f=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.effectiveFilterDepth-1-l.padInfo.front,l.effectiveFilterHeight-1-l.padInfo.top,l.effectiveFilterWidth-1-l.padInfo.left]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]},{type:"int32",data:[l.outDepth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"float32",data:[d]}];return e.runWebGPUProgram(m,[n],a.dtype,f)}var nV={kernelName:Ai,backendName:"webgpu",kernelFunc:aue};function iue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;fm([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new ix(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"float32",data:[m]}];return e.runWebGPUProgram(l,[n],a.dtype,d)}var sV={kernelName:Di,backendName:"webgpu",kernelFunc:iue};function uue(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s}=t,{transposeA:a,transposeB:i}=o;return Op({a:n,b:s,transposeA:a,transposeB:i,backend:e})}var aV={kernelName:Jo,backendName:"webgpu",kernelFunc:uue};var px=class{constructor(t,e){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.rank=e.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=t,this.uniforms=`start : ${ht(t.length)}, `,this.shaderKey="slice"}getUserCode(){let t=ht(this.rank),e=pue(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>"sourceLoc = uniforms.start + coords;"):o=this.outputShape.map((s,a)=>`sourceLoc.${t0[a]} = uniforms.start.${Mo(a)} + coords.${t0[a]};`),` + `}};function aue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=w.computePool3DInfo(a.shape,i,p,1,u,c),m=new ix(l),d=1/(l.filterDepth*l.filterHeight*l.filterWidth),f=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.effectiveFilterDepth-1-l.padInfo.front,l.effectiveFilterHeight-1-l.padInfo.top,l.effectiveFilterWidth-1-l.padInfo.left]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]},{type:"int32",data:[l.outDepth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"float32",data:[d]}];return e.runWebGPUProgram(m,[n],a.dtype,f)}var sV={kernelName:Ei,backendName:"webgpu",kernelFunc:aue};function iue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;mm([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new ax(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"float32",data:[m]}];return e.runWebGPUProgram(l,[n],a.dtype,d)}var aV={kernelName:$i,backendName:"webgpu",kernelFunc:iue};function uue(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s}=t,{transposeA:a,transposeB:i}=o;return _p({a:n,b:s,transposeA:a,transposeB:i,backend:e})}var iV={kernelName:Zo,backendName:"webgpu",kernelFunc:uue};var ux=class{constructor(t,e){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.rank=e.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=t,this.uniforms=`start : ${ft(t.length)}, `,this.shaderKey="slice"}getUserCode(){let t=ft(this.rank),e=pue(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>"sourceLoc = uniforms.start + coords;"):o=this.outputShape.map((s,a)=>`sourceLoc.${r0[a]} = uniforms.start.${Oo(a)} + coords.${r0[a]};`),` ${G("index")} { if (index < uniforms.size) { var sourceLoc : ${t}; @@ -6273,15 +6273,15 @@ return a / b;`,Wee=` setOutputAtIndex(index, getSource(${e})); } } - `}},t0=["x","y","z","w","u","v"];function pue(r){if(r===1)return"sourceLoc";if(r<=6)return t0.slice(0,r).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}function Hs(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,size:a}=o,[i,p]=ct.parseSliceParams(n,s,a);if(ct.assertParamsValid(n,i,p),e.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=e.tensorMap.get(n.dataId),m=Dz(l.values,i,p,n.shape,n.dtype);return e.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return e.makeTensorInfo(p,n.dtype,[]);let u=new px(i,p),c=[{type:"int32",data:i}];return e.runWebGPUProgram(u,[n],n.dtype,c)}var iV={kernelName:ha,backendName:"webgpu",kernelFunc:Hs};var cue=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=pe({inputs:{x:n},backend:e,attrs:{shape:p}}),h=yr({inputs:{x:f},backend:e,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Hs({inputs:{x:g},backend:e,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>e.disposeData(b.dataId)),x},uV={kernelName:Js,backendName:"webgpu",kernelFunc:cue};var lue=` + `}},r0=["x","y","z","w","u","v"];function pue(r){if(r===1)return"sourceLoc";if(r<=6)return r0.slice(0,r).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}function Hs(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,size:a}=o,[i,p]=pt.parseSliceParams(n,s,a);if(pt.assertParamsValid(n,i,p),e.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=e.tensorMap.get(n.dataId),m=Az(l.values,i,p,n.shape,n.dtype);return e.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return e.makeTensorInfo(p,n.dtype,[]);let u=new ux(i,p),c=[{type:"int32",data:i}];return e.runWebGPUProgram(u,[n],n.dtype,c)}var uV={kernelName:ha,backendName:"webgpu",kernelFunc:Hs};var cue=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=pe({inputs:{x:n},backend:e,attrs:{shape:p}}),h=xr({inputs:{x:f},backend:e,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Hs({inputs:{x:g},backend:e,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>e.disposeData(b.dataId)),x},pV={kernelName:Js,backendName:"webgpu",kernelFunc:cue};var lue=` fn bincount_write(index: i32, value: f32) { - ${Qr("&result[index]","value","float32")} + ${Yr("&result[index]","value","float32")} } `,mue=` fn bincount_write(index: i32, value: f32) { atomicStore(&result[index], bitcast(value)); } -`,Jc=class{constructor(t,e,o=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=t,this.rank=t.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=e,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` +`,Xc=class{constructor(t,e,o=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=t,this.rank=t.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=e,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` ${this.binaryOutput?mue:lue} ${G("index")} { ${this.rank===1?`if (index < uniforms.xShape) { @@ -6299,7 +6299,7 @@ return a / b;`,Wee=` } }`} } - `}};function due(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=kt({backend:e,attrs:{shape:c,value:0,dtype:l}}),d=new Jc([i],u),f=[{type:"int32",data:[a]}],h=u?[n,s]:[n];return e.runWebGPUProgram(d,h,l,f,m)}var pV={kernelName:en,backendName:"webgpu",kernelFunc:due};var cx=class{constructor(t){this.outputShape=[],this.variableNames=["s0","s1"],this.uniforms="s0Size : i32, s1Size : i32, ",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="broadcastArgs"}getUserCode(){return` + `}};function due(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=vt({backend:e,attrs:{shape:c,value:0,dtype:l}}),d=new Xc([i],u),f=[{type:"int32",data:[a]}],h=u?[n,s]:[n];return e.runWebGPUProgram(d,h,l,f,m)}var cV={kernelName:Jo,backendName:"webgpu",kernelFunc:due};var px=class{constructor(t){this.outputShape=[],this.variableNames=["s0","s1"],this.uniforms="s0Size : i32, s1Size : i32, 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r0=et({opType:fe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Nz}),lV={kernelName:Qn,backendName:"webgpu",kernelFunc:r0};function Ni(r){let{inputs:t,backend:e}=r,{input:o}=t,n=e.tensorMap.get(o.dataId);return Ft({inputs:{x:n.complexTensorInfos.real},backend:e})}var mV={kernelName:qi,backendName:"webgpu",kernelFunc:Ni};function dV(r,t){let e=new Jr(r.shape,Z.TO_INT),o=t.runWebGPUProgram(e,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function o0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return Ft({inputs:{x:n},backend:e});let a=Gr(n.shape),i=o0({inputs:{x:n},backend:e,attrs:{dtype:"float32"}}),p=yo({inputs:{real:i,imag:a},backend:e});return a.dispose(),e.disposeData(i.dataId),p}if(n.dtype==="complex64"){let a=Ni({inputs:{input:n},backend:e}),i=o0({inputs:{x:a},backend:e,attrs:{dtype:s}});return e.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let 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f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` + `}};function fue(r){let{inputs:t,backend:e}=r,{s0:o,s1:n}=t;if(e.shouldExecuteOnCPU([o,n])){let c=e.tensorMap.get(o.dataId),l=e.tensorMap.get(n.dataId),m=c.values,d=l.values,f=w.assertAndGetBroadcastShape(Array.from(m),Array.from(d));return e.makeTensorInfo([f.length],"int32",Int32Array.from(f))}let s=y.sizeFromShape(o.shape),a=y.sizeFromShape(n.shape),i=Math.max(s,a),p=new px(i),u=[{type:"int32",data:[s]},{type:"int32",data:[a]}];return e.runWebGPUProgram(p,[o,n],"int32",u)}var lV={kernelName:ea,backendName:"webgpu",kernelFunc:fue};var o0=et({opType:fe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Tz}),mV={kernelName:Yn,backendName:"webgpu",kernelFunc:o0};function Ii(r){let{inputs:t,backend:e}=r,{input:o}=t,n=e.tensorMap.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.real},backend:e})}var dV={kernelName:Gi,backendName:"webgpu",kernelFunc:Ii};function fV(r,t){let e=new Zr(r.shape,Z.TO_INT),o=t.runWebGPUProgram(e,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function n0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return At({inputs:{x:n},backend:e});let a=Ur(n.shape),i=n0({inputs:{x:n},backend:e,attrs:{dtype:"float32"}}),p=xo({inputs:{real:i,imag:a},backend:e});return a.dispose(),e.disposeData(i.dataId),p}if(n.dtype==="complex64"){let a=Ii({inputs:{input:n},backend:e}),i=n0({inputs:{x:a},backend:e,attrs:{dtype:s}});return e.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:e});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(e.shouldExecuteOnCPU([n])){let a=e.tensorMap.get(n.dataId).values,[i,p,u]=iz(a,n.shape,n.dtype,s);return e.makeTensorInfo(i,p,u)}if(s==="int32")return fV(n,e);if(s==="bool"){let a=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=o0({inputs:{a:n,b:a},backend:e});return e.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var hV={kernelName:yo,backendName:"webgpu",kernelFunc:n0};var hue=ye({opType:Z.CEIL,cpuKernelImpl:uz}),gV={kernelName:en,backendName:"webgpu",kernelFunc:hue};var cx=class{constructor(t){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` ${G("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); @@ -6334,7 +6334,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, clampedValue); } } - `}};var mx=class{constructor(t){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return` + `}};var lx=class{constructor(t){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return` ${G("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); @@ -6345,7 +6345,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal)); } } - `}};function gue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:"float32",data:[s]},{type:"float32",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new lx(n.shape):i=new mx(n.shape),e.runWebGPUProgram(i,[n],n.dtype,p)}var gV={kernelName:Co,backendName:"webgpu",kernelFunc:gue};var dx=class{constructor(t){this.outputShape=[],this.variableNames=["real","imag"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="complexAbs"}getUserCode(){return` + `}};function gue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:"float32",data:[s]},{type:"float32",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new cx(n.shape):i=new lx(n.shape),e.runWebGPUProgram(i,[n],n.dtype,p)}var xV={kernelName:bo,backendName:"webgpu",kernelFunc:gue};var mx=class{constructor(t){this.outputShape=[],this.variableNames=["real","imag"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="complexAbs"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let re = abs(getRealByOutputIndex(index)); @@ -6357,7 +6357,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0)); } } - `}};function xV(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function xue(r){let{inputs:t,backend:e}=r,{x:o}=t,n=e.tensorMap.get(o.dataId),s=new dx(o.shape),a=[xV(o,n.complexTensorInfos.real),xV(o,n.complexTensorInfos.imag)];return e.runWebGPUProgram(s,a,a[0].dtype)}var yV={kernelName:Pi,backendName:"webgpu",kernelFunc:xue};var fx=class{constructor(t){this.uniforms="",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=w.computeOutShape(t,1),this.variableNames=t.map((e,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=t.length-1;for(let e=0;e0){t.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let s=1;s`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=t.length-1;for(let e=0;e0){t.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let s=1;sNi({inputs:{input:C},backend:e})),h=r.map(C=>Mp({inputs:{input:C},backend:e})),g=el(f,t,e),x=el(h,t,e),b=yo({inputs:{real:g,imag:x},backend:e});return f.forEach(C=>e.disposeData(C.dataId)),h.forEach(C=>e.disposeData(C.dataId)),e.disposeData(g.dataId),e.disposeData(x.dataId),b}let n=e.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let f=r.map(k=>{let E=[-1,y.sizeFromShape(k.shape.slice(t))];return pe({inputs:{x:k},backend:e,attrs:{shape:E}})}),h=f.map(k=>({vals:e.readSync(k.dataId),shape:k.shape})),g=w.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=uz(h,g,o,x),C=w.computeOutShape(r.map(k=>k.shape),t),S=e.makeTensorInfo(C,o,b);return f.forEach(k=>e.disposeData(k.dataId)),S}let s=e.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new fx(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:"int32",data:[l[0]]});for(let f=1;fe.disposeData(f.dataId));let d=pe({inputs:{x:m},backend:e,attrs:{shape:i}});return e.disposeData(m.dataId),d}function yue(r,t,e){let o=w.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>pe({inputs:{x:s},backend:e,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,t)),y.sizeFromShape(s.shape.slice(t))]}})),outShape:o}}function n0(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,t[0].shape)[0],a=t.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(t.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let p=t.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Ft({inputs:{x:p[0]},backend:e}):el(p,s,e)}var CV={kernelName:ta,backendName:"webgpu",kernelFunc:n0};function bue(r,t,e,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=D=>{switch(D){case 1:return"resData = f32(x[xIndex]);";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = vec4(x[xIndex / 4]);";default:throw new Error(`innerElementSize ${D} is not supported.`)}},l=D=>{switch(D){case 1:return"return f32(W[row * uniforms.wShape[3] + col]);";case 4:return"return vec4(W[(row * uniforms.wShape[3] + col) / 4]);";default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=r?` + `}};function $p(r){let{inputs:t,backend:e}=r,{input:o}=t,n=e.tensorMap.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.imag},backend:e})}var CV={kernelName:Vi,backendName:"webgpu",kernelFunc:$p};function Yc(r,t,e){let o=r[0].dtype;if(o==="complex64"){let f=r.map(C=>Ii({inputs:{input:C},backend:e})),h=r.map(C=>$p({inputs:{input:C},backend:e})),g=Yc(f,t,e),x=Yc(h,t,e),b=xo({inputs:{real:g,imag:x},backend:e});return f.forEach(C=>e.disposeData(C.dataId)),h.forEach(C=>e.disposeData(C.dataId)),e.disposeData(g.dataId),e.disposeData(x.dataId),b}let n=e.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let f=r.map(k=>{let E=[-1,y.sizeFromShape(k.shape.slice(t))];return pe({inputs:{x:k},backend:e,attrs:{shape:E}})}),h=f.map(k=>({vals:e.readSync(k.dataId),shape:k.shape})),g=w.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=pz(h,g,o,x),C=w.computeOutShape(r.map(k=>k.shape),t),S=e.makeTensorInfo(C,o,b);return f.forEach(k=>e.disposeData(k.dataId)),S}let s=e.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new dx(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:"int32",data:[l[0]]});for(let f=1;fe.disposeData(f.dataId));let d=pe({inputs:{x:m},backend:e,attrs:{shape:i}});return e.disposeData(m.dataId),d}function yue(r,t,e){let o=w.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>pe({inputs:{x:s},backend:e,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,t)),y.sizeFromShape(s.shape.slice(t))]}})),outShape:o}}function s0(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,t[0].shape)[0],a=t.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(t.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let p=t.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:e}):Yc(p,s,e)}var wV={kernelName:ta,backendName:"webgpu",kernelFunc:s0};function bue(r,t,e,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=D=>{switch(D){case 1:return"resData = f32(x[xIndex]);";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = vec4(x[xIndex / 4]);";default:throw new Error(`innerElementSize ${D} is not supported.`)}},l=D=>{switch(D){case 1:return"return f32(W[row * uniforms.wShape[3] + col]);";case 4:return"return vec4(W[(row * uniforms.wShape[3] + col) / 4]);";default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=r?` let coord = vec4(batch, xRow, xCol, xCh); `:` let coord = vec4(batch, xCh, xRow, xCol); @@ -6417,7 +6417,7 @@ return a / b;`,Wee=` ${b} } return ${Ae(i)}(0.0);`,S=`${l(p)}`,k=Ae(u),_=r?Ae(i):Ae(p),E=r?Ae(p):Ae(i);return` - ${fr(s,a,u===4,4)} + ${dr(s,a,u===4,4)} fn mm_readA(batch: i32, row : i32, col : i32) -> ${_} { ${r?C:S} } @@ -6432,14 +6432,14 @@ return a / b;`,Wee=` var value = valueIn; let outWidth = ${r?"uniforms.outShape[2]":"uniforms.outShape[3]"}; ${d} - ${Zr(n,s)} + ${Qr(n,s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } - }`}var hx=class{constructor(t,e,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t.outShape,this.isChannelsLast=t.dataFormat==="channelsLast",this.isVec4=((t.inChannels%4===0||t.inChannels%3===0)&&this.isChannelsLast||t.outWidth%4===0&&!this.isChannelsLast)&&t.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=lm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=mm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&t.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push("bias"),this.variableComponents.push(4)),i&&(this.variableNames.push("preluActivationWeights"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=e%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let t=this.isVec4?Fp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Pp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),e=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` + }`}var fx=class{constructor(t,e,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t.outShape,this.isChannelsLast=t.dataFormat==="channelsLast",this.isVec4=((t.inChannels%4===0||t.inChannels%3===0)&&this.isChannelsLast||t.outWidth%4===0&&!this.isChannelsLast)&&t.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=pm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=cm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&t.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push("bias"),this.variableComponents.push(4)),i&&(this.variableNames.push("preluActivationWeights"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=e%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let t=this.isVec4?Np(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Tp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),e=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` ${bue(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,e[0],e[1],e[2])} ${t} - `}};var gx=class{constructor(t,e=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,",this.workgroupSize=[4,4,8],this.outputShape=t.outShape,this.isChannelsLast=t.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=e,this.activation=o,this.hasPreluActivationWeights=n,e&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` - ${fr(this.activation,this.hasPreluActivationWeights,!1,4)} + `}};var hx=class{constructor(t,e=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,",this.workgroupSize=[4,4,8],this.outputShape=t.outShape,this.isChannelsLast=t.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=e,this.activation=o,this.hasPreluActivationWeights=n,e&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` + ${dr(this.activation,this.hasPreluActivationWeights,!1,4)} fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{ let coords = vec4(batch, row, col, chan); if (coordsInBounds4D(coords, uniforms.xShape)) { @@ -6460,7 +6460,7 @@ return a / b;`,Wee=` let coords = ${this.isChannelsLast?"vec4(batch, row, col, chan);":"vec4(batch, chan, row, col);"} if (coordsInBounds4D(coords, uniforms.outShape)) { var value = valueIn; - ${Zr(this.addBias,this.activation)} + ${Qr(this.addBias,this.activation)} setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value); } } @@ -6484,7 +6484,7 @@ return a / b;`,Wee=` } writeResult(batch, outRow, outCol, outChannel, acc); } - `}};var xx=class{constructor(t,e){this.variableNames=["x"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32, + `}};var gx=class{constructor(t,e){this.variableNames=["x"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32, inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let t=this.isChannelsLast?1:2,e=this.isChannelsLast?2:3,o=this.isChannelsLast?"coords[1]":"coords[2]",n=this.isChannelsLast?"coords[2]":"coords[1]",s=this.isChannelsLast?"getX(batch, xRow, xCol, ch)":"getX(batch, ch, xRow, xCol)";return` ${G("index")} { let coords = getCoordsFromIndex(index); @@ -6508,7 +6508,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, value); } } - `}};function yx(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function Cue({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=e.dataFormat==="channelsLast",u=!p,c=!1,l=p&&e.filterHeight===e.inHeight&&e.filterWidth===e.inWidth&&e.padInfo.type==="VALID",m=[],d,f;if(l){let x=e.inHeight*e.inWidth*e.inChannels;d=pe({inputs:{x:r},backend:o,attrs:{shape:[1,e.batchSize,x]}}),f=pe({inputs:{x:t},backend:o,attrs:{shape:[1,x,e.outChannels]}})}else d=pe({inputs:{x:r},backend:o,attrs:{shape:p?[e.batchSize,e.inHeight*e.inWidth,e.inChannels]:[e.batchSize,e.inChannels,e.inHeight*e.inWidth]}}),f=pe({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=yx(s.shape,p);x!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=yx(n.shape,p);x!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Op({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=pe({inputs:{x:h},backend:o,attrs:{shape:e.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function wue({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=e,C=b==="channelsLast",S=p*u*c,k=h*f,_=C?[e.batchSize,k,S]:[e.batchSize,S,k],E=new xx(_,C),R=[{type:"int32",data:[d.top,d.left]},{type:"int32",data:[m,l]},{type:"int32",data:[x,g]},{type:"int32",data:[f]},{type:"int32",data:[c*p]},{type:"int32",data:[c]}],D=o.runWebGPUProgram(E,[r],r.dtype,R),P=[];P.push(D);let O=pe({inputs:{x:t},backend:o,attrs:{shape:[1,S,-1]}});if(P.push(O),s!=null){let U=yx(s.shape,C);U!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:U}}),P.push(s))}if(n!=null){let U=yx(n.shape,C);U!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:U}}),P.push(n))}let B=Op({a:C?D:O,b:C?O:D,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=pe({inputs:{x:B},backend:o,attrs:{shape:e.outShape}});P.push(B);for(let U of P)o.disposeData(U.dataId);return z}function bx({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=e.dataFormat==="channelsLast",l=c&&e.filterHeight===e.inHeight&&e.filterWidth===e.inWidth&&e.padInfo.type==="VALID",m=A().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!m&&(l||e.filterHeight===1&&e.filterWidth===1&&e.dilationHeight===1&&e.dilationWidth===1&&e.strideHeight===1&&e.strideWidth===1&&(e.padInfo.type==="SAME"||e.padInfo.type==="VALID")))return Cue({x:r,filter:t,convInfo:e,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=A().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),f=d>-1?d:o.thresholdToIncreaseWorkgroups,h=e.batchSize*Math.ceil(e.outHeight*e.outWidth/32)*Math.ceil(e.outChannels/32);if(A().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||h<=f)return wue({x:r,filter:t,convInfo:e,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[e.padInfo.top,e.padInfo.left],b=[{type:"int32",data:[e.filterHeight,e.filterWidth]},{type:"int32",data:[...x]},{type:"int32",data:[e.strideHeight,e.strideWidth]},{type:"int32",data:[e.dilationHeight,e.dilationWidth]}];if(m)g=new gx(e,p,i,u);else{let _=c?e.outHeight*e.outWidth:e.outChannels,E=c?e.outChannels:e.outHeight*e.outWidth,R=e.filterHeight*e.filterWidth*e.inChannels;b.push({type:"int32",data:[_]},{type:"int32",data:[E]},{type:"int32",data:[R]});let D=o.adapterInfo.isIntel();g=new hx(e,_,E,R,p,i,u,D)}let C=[],S=[r,t];p&&(!c&&n.shape.length===1&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),S.push(n)),u&&(!c&&s.shape.length===1&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),S.push(s)),i==="leakyrelu"&&(b.push({type:"float32",data:[a]}),g.uniforms+=" alpha : f32,");let k=o.runWebGPUProgram(g,S,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function Sue(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=e,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return bx({x:n,filter:s,convInfo:m,backend:o})}var wV={kernelName:rn,backendName:"webgpu",kernelFunc:Sue};var Cx=class{constructor(t){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=t.inShape,this.isChannelsLast=t.dataFormat==="channelsLast",this.isVec4=this.isChannelsLast&&t.outChannels%4===0&&t.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let t=this.isChannelsLast?1:2,e=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1,n=` + `}};function xx(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function Cue({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=e.dataFormat==="channelsLast",u=!p,c=!1,l=p&&e.filterHeight===e.inHeight&&e.filterWidth===e.inWidth&&e.padInfo.type==="VALID",m=[],d,f;if(l){let x=e.inHeight*e.inWidth*e.inChannels;d=pe({inputs:{x:r},backend:o,attrs:{shape:[1,e.batchSize,x]}}),f=pe({inputs:{x:t},backend:o,attrs:{shape:[1,x,e.outChannels]}})}else d=pe({inputs:{x:r},backend:o,attrs:{shape:p?[e.batchSize,e.inHeight*e.inWidth,e.inChannels]:[e.batchSize,e.inChannels,e.inHeight*e.inWidth]}}),f=pe({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=xx(s.shape,p);x!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=xx(n.shape,p);x!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=_p({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=pe({inputs:{x:h},backend:o,attrs:{shape:e.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function wue({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=e,C=b==="channelsLast",S=p*u*c,k=h*f,_=C?[e.batchSize,k,S]:[e.batchSize,S,k],E=new gx(_,C),R=[{type:"int32",data:[d.top,d.left]},{type:"int32",data:[m,l]},{type:"int32",data:[x,g]},{type:"int32",data:[f]},{type:"int32",data:[c*p]},{type:"int32",data:[c]}],D=o.runWebGPUProgram(E,[r],r.dtype,R),P=[];P.push(D);let O=pe({inputs:{x:t},backend:o,attrs:{shape:[1,S,-1]}});if(P.push(O),s!=null){let U=xx(s.shape,C);U!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:U}}),P.push(s))}if(n!=null){let U=xx(n.shape,C);U!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:U}}),P.push(n))}let B=_p({a:C?D:O,b:C?O:D,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=pe({inputs:{x:B},backend:o,attrs:{shape:e.outShape}});P.push(B);for(let U of P)o.disposeData(U.dataId);return z}function yx({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=e.dataFormat==="channelsLast",l=c&&e.filterHeight===e.inHeight&&e.filterWidth===e.inWidth&&e.padInfo.type==="VALID",m=A().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!m&&(l||e.filterHeight===1&&e.filterWidth===1&&e.dilationHeight===1&&e.dilationWidth===1&&e.strideHeight===1&&e.strideWidth===1&&(e.padInfo.type==="SAME"||e.padInfo.type==="VALID")))return Cue({x:r,filter:t,convInfo:e,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=A().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),f=d>-1?d:o.thresholdToIncreaseWorkgroups,h=e.batchSize*Math.ceil(e.outHeight*e.outWidth/32)*Math.ceil(e.outChannels/32);if(A().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||h<=f)return wue({x:r,filter:t,convInfo:e,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[e.padInfo.top,e.padInfo.left],b=[{type:"int32",data:[e.filterHeight,e.filterWidth]},{type:"int32",data:[...x]},{type:"int32",data:[e.strideHeight,e.strideWidth]},{type:"int32",data:[e.dilationHeight,e.dilationWidth]}];if(m)g=new hx(e,p,i,u);else{let _=c?e.outHeight*e.outWidth:e.outChannels,E=c?e.outChannels:e.outHeight*e.outWidth,R=e.filterHeight*e.filterWidth*e.inChannels;b.push({type:"int32",data:[_]},{type:"int32",data:[E]},{type:"int32",data:[R]});let D=o.adapterInfo.isIntel();g=new fx(e,_,E,R,p,i,u,D)}let C=[],S=[r,t];p&&(!c&&n.shape.length===1&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),S.push(n)),u&&(!c&&s.shape.length===1&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),S.push(s)),i==="leakyrelu"&&(b.push({type:"float32",data:[a]}),g.uniforms+=" alpha : f32,");let k=o.runWebGPUProgram(g,S,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function Sue(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=e,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return yx({x:n,filter:s,convInfo:m,backend:o})}var SV={kernelName:tn,backendName:"webgpu",kernelFunc:Sue};var bx=class{constructor(t){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=t.inShape,this.isChannelsLast=t.dataFormat==="channelsLast",this.isVec4=this.isChannelsLast&&t.outChannels%4===0&&t.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let t=this.isChannelsLast?1:2,e=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1,n=` ${G()} { let batch = i32(globalId.z) / uniforms.outShape[1]; let r = i32(globalId.z) % uniforms.outShape[1]; @@ -6651,7 +6651,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, dotProd); } } - `}},wx=class{constructor(t){this.variableNames=["x","dy"],this.uniforms="pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t.dataFormat==="channelsLast",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return` + `}},Cx=class{constructor(t){this.variableNames=["x","dy"],this.uniforms="pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t.dataFormat==="channelsLast",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return` ${G("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -6692,7 +6692,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, dotProd); } } - `}},Sx=class{constructor(t){this.variableNames=["x","dy"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32, + `}},wx=class{constructor(t){this.variableNames=["x","dy"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32, outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3DDerFilter"}getUserCode(){return` ${G("index")} { if(index < uniforms.size) { @@ -6733,7 +6733,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, dotProd); } } - `}},Ix=class{constructor(t){this.variableNames=["dy","W"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3, + `}},Sx=class{constructor(t){this.variableNames=["dy","W"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3, outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3DDerInput"}getUserCode(){return` ${G("index")} { if(index < uniforms.size) { @@ -6787,7 +6787,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, dotProd); } } - `}};function Iue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new wx(m),f=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]},{type:"int32",data:[m.inHeight]},{type:"int32",data:[m.inWidth]}];return e.runWebGPUProgram(d,[n,s],n.dtype,f)}var SV={kernelName:Oi,backendName:"webgpu",kernelFunc:Iue};function vue(r=4){let t=s=>{switch(s){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` + `}};function Iue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Cx(m),f=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]},{type:"int32",data:[m.inHeight]},{type:"int32",data:[m.inWidth]}];return e.runWebGPUProgram(d,[n,s],n.dtype,f)}var IV={kernelName:Ai,backendName:"webgpu",kernelFunc:Iue};function vue(r=4){let t=s=>{switch(s){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` let coord1 = vec4(coordX, coordY, col + 1, rowInner); let coord2 = vec4(coordX, coordY, col + 2, rowInner); let coord3 = vec4(coordX, coordY, col + 3, rowInner); @@ -6847,10 +6847,10 @@ return a / b;`,Wee=` col); result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value; } - }`}var vx=class{constructor(t){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t.inShape,y.assert(t.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=t.inChannels%4===0&&t.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=lm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=mm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let t=this.isVec4?Fp(this.elementsPerThread,this.workgroupSize):Pp(this.elementsPerThread,this.workgroupSize);return` + }`}var Ix=class{constructor(t){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t.inShape,y.assert(t.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=t.inChannels%4===0&&t.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=pm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=cm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let t=this.isVec4?Np(this.elementsPerThread,this.workgroupSize):Tp(this.elementsPerThread,this.workgroupSize);return` ${vue(this.isVec4?4:1)} ${t} - `}};function kue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:"int32",data:[m.filterHeight,m.filterWidth]},{type:"int32",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(A().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||m.dataFormat!=="channelsLast")f=new Cx(m);else{f=new vx(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:"uint32",data:[h]},{type:"uint32",data:[g]},{type:"uint32",data:[x]})}return e.runWebGPUProgram(f,[n,s],"float32",d)}var IV={kernelName:on,backendName:"webgpu",kernelFunc:kue};var kx=class{constructor(t){this.variableNames=["x","W"],this.uniforms="filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3dnaive"}getUserCode(){return` + `}};function kue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:"int32",data:[m.filterHeight,m.filterWidth]},{type:"int32",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(A().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||m.dataFormat!=="channelsLast")f=new bx(m);else{f=new Ix(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:"uint32",data:[h]},{type:"uint32",data:[g]},{type:"uint32",data:[x]})}return e.runWebGPUProgram(f,[n,s],"float32",d)}var vV={kernelName:rn,backendName:"webgpu",kernelFunc:kue};var vx=class{constructor(t){this.variableNames=["x","W"],this.uniforms="filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3dnaive"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); @@ -6932,7 +6932,7 @@ return a / b;`,Wee=` } setOutputAtIndex(index, dotProd); } - }`}};function Nue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new kx(u),d=dt(n.dtype,s.dtype);return e.runWebGPUProgram(m,[n,s],d,l)}var vV={kernelName:nn,backendName:"webgpu",kernelFunc:Nue};function Tue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Sx(u),l=[{type:"int32",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.batchSize]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.inDepth]},{type:"int32",data:[u.inHeight]},{type:"int32",data:[u.inWidth]}];return e.runWebGPUProgram(c,[n,s],s.dtype,l)}var kV={kernelName:Xa,backendName:"webgpu",kernelFunc:Tue};function _ue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,pad:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,a,1,i),c=new Ix(u),l=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.outChannels]}];return e.runWebGPUProgram(c,[n,s],n.dtype,l)}var NV={kernelName:sn,backendName:"webgpu",kernelFunc:_ue};var $ue=ye({opType:Z.COS}),TV={kernelName:an,backendName:"webgpu",kernelFunc:$ue};var Eue=ye({opType:Z.COSH}),_V={kernelName:un,backendName:"webgpu",kernelFunc:Eue};var Nx=class{constructor(t,e,o,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[s]=e;this.outputShape=[s,o[0],o[1],t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[t,e]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[o,n,s]=this.cropHeightBiggerThan1?[`(${t} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${t} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${t}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${e} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${e} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${e}`];return` + }`}};function Nue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new vx(u),d=dt(n.dtype,s.dtype);return e.runWebGPUProgram(m,[n,s],d,l)}var kV={kernelName:on,backendName:"webgpu",kernelFunc:Nue};function Tue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new wx(u),l=[{type:"int32",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.batchSize]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.inDepth]},{type:"int32",data:[u.inHeight]},{type:"int32",data:[u.inWidth]}];return e.runWebGPUProgram(c,[n,s],s.dtype,l)}var NV={kernelName:ja,backendName:"webgpu",kernelFunc:Tue};function _ue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,pad:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,a,1,i),c=new Sx(u),l=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.outChannels]}];return e.runWebGPUProgram(c,[n,s],n.dtype,l)}var TV={kernelName:nn,backendName:"webgpu",kernelFunc:_ue};var $ue=ye({opType:Z.COS}),_V={kernelName:sn,backendName:"webgpu",kernelFunc:$ue};var Eue=ye({opType:Z.COSH}),$V={kernelName:an,backendName:"webgpu",kernelFunc:Eue};var kx=class{constructor(t,e,o,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[s]=e;this.outputShape=[s,o[0],o[1],t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[t,e]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[o,n,s]=this.cropHeightBiggerThan1?[`(${t} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${t} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${t}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${e} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${e} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${e}`];return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -6988,23 +6988,23 @@ return a / b;`,Wee=` } } } - `}};var Rue=r=>{let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Nx(n.shape[3],s.shape,i,p),l=[{type:"float32",data:[u]}];return e.runWebGPUProgram(c,[n,s,a],"float32",l)},$V={kernelName:ln,backendName:"webgpu",kernelFunc:Rue};var Lp;(function(r){r.Prod="*",r.Sum="+"})(Lp||(Lp={}));var xm=class{constructor(t,e,o,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=t,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let t=this.outputShape.length,e=this.op===Lp.Prod?"1.0":"0.0",o=this.exclusive?e:`getX(${EV(t,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],s="",a="";return this.exclusive?(s=this.reverse?`end != ${n-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(s=this.reverse?`end + pow2 < ${n}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),` + `}};var Rue=r=>{let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,c=new kx(n.shape[3],s.shape,i,p),l=[{type:"float32",data:[u]}];return e.runWebGPUProgram(c,[n,s,a],"float32",l)},EV={kernelName:cn,backendName:"webgpu",kernelFunc:Rue};var Ep;(function(r){r.Prod="*",r.Sum="+"})(Ep||(Ep={}));var hm=class{constructor(t,e,o,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=t,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let t=this.outputShape.length,e=this.op===Ep.Prod?"1.0":"0.0",o=this.exclusive?e:`getX(${RV(t,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],s="",a="";return this.exclusive?(s=this.reverse?`end != ${n-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(s=this.reverse?`end + pow2 < ${n}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),` ${G("index")} { if (index < uniforms.size) { var coords = getCoordsFromIndex(index); - let end = ${RV(t,"coords",this.op)}; + let end = ${DV(t,"coords",this.op)}; var val = ${o}; let pow2 = i32(pow(2.0, uniforms.index)); if (${s}) { let idx = ${a}; - ${RV(t,"coords",this.op)} = idx; - val ${this.op}= getX(${EV(t,"coords",this.op)}); + ${DV(t,"coords",this.op)} = idx; + val ${this.op}= getX(${RV(t,"coords",this.op)}); } setOutputAtIndex(index, val); } } - `}};function EV(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function RV(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function Tx(r,t,e,o,n,s){let a=t.shape.length,i=w.getAxesPermutation([o],a),p=t;i!=null&&(p=yr({inputs:{x:t},backend:e,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Ft({inputs:{x:p},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new xm(r,p.shape,!1,s),f=l,h=[{type:"float32",data:[m]}];l=e.runWebGPUProgram(d,[l],l.dtype,h),e.disposeData(f.dataId)}if(n){let m=new xm(r,p.shape,n,s),d=l,f=[{type:"float32",data:[0]}];l=e.runWebGPUProgram(m,[l],l.dtype,f),e.disposeData(d.dataId)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=yr({inputs:{x:l},backend:e,attrs:{perm:m}});return e.disposeData(l.dataId),e.disposeData(p.dataId),d}return l}function Due(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return Tx(Lp.Prod,n,e,s,a,i)}var DV={kernelName:pn,backendName:"webgpu",kernelFunc:Due};function Aue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return Tx(Lp.Sum,n,e,s,a,i)}var AV={kernelName:cn,backendName:"webgpu",kernelFunc:Aue};function Fue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=kt({backend:e,attrs:{shape:d,value:0,dtype:l}}),h=new Jc(m,c,i),g=[{type:"int32",data:[a]}],x=c?[n,s]:[n];return e.runWebGPUProgram(h,x,l,g,f)}var FV={kernelName:ra,backendName:"webgpu",kernelFunc:Fue};var _x=class{constructor(t,e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${e}`,this.dataFormat=e}getUserCode(){return` + `}};function RV(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function DV(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function Nx(r,t,e,o,n,s){let a=t.shape.length,i=w.getAxesPermutation([o],a),p=t;i!=null&&(p=xr({inputs:{x:t},backend:e,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new hm(r,p.shape,!1,s),f=l,h=[{type:"float32",data:[m]}];l=e.runWebGPUProgram(d,[l],l.dtype,h),e.disposeData(f.dataId)}if(n){let m=new hm(r,p.shape,n,s),d=l,f=[{type:"float32",data:[0]}];l=e.runWebGPUProgram(m,[l],l.dtype,f),e.disposeData(d.dataId)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=xr({inputs:{x:l},backend:e,attrs:{perm:m}});return e.disposeData(l.dataId),e.disposeData(p.dataId),d}return l}function Due(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return Nx(Ep.Prod,n,e,s,a,i)}var AV={kernelName:un,backendName:"webgpu",kernelFunc:Due};function Aue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return Nx(Ep.Sum,n,e,s,a,i)}var FV={kernelName:pn,backendName:"webgpu",kernelFunc:Aue};function Fue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=vt({backend:e,attrs:{shape:d,value:0,dtype:l}}),h=new Xc(m,c,i),g=[{type:"int32",data:[a]}],x=c?[n,s]:[n];return e.runWebGPUProgram(h,x,l,g,f)}var PV={kernelName:ra,backendName:"webgpu",kernelFunc:Fue};var Tx=class{constructor(t,e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${e}`,this.dataFormat=e}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7024,8 +7024,8 @@ return a / b;`,Wee=` let rlt = ${this.getInputSamplingString()}; setOutputAtIndex(index, rlt); } - }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Pue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=[{type:"int32",data:[s]}],g=new _x(f,a);return e.runWebGPUProgram(g,[n],n.dtype,h)}var PV={kernelName:mn,backendName:"webgpu",kernelFunc:Pue};var $x=class{constructor(t,e,o,n=!1,s=null,a=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=e,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let t=this.filterWidth*this.filterHeight,e=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` - ${fr(this.activation,this.hasPreluActivation,!1,4)} + }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Pue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=[{type:"int32",data:[s]}],g=new Tx(f,a);return e.runWebGPUProgram(g,[n],n.dtype,h)}var OV={kernelName:ln,backendName:"webgpu",kernelFunc:Pue};var _x=class{constructor(t,e,o,n=!1,s=null,a=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=e,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let t=this.filterWidth*this.filterHeight,e=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` + ${dr(this.activation,this.hasPreluActivation,!1,4)} var mm_Asub : array, ${o}>; var mm_Bsub : array, ${this.filterHeight}>; @@ -7081,13 +7081,13 @@ return a / b;`,Wee=` value = fma(xVal, wVal, value); } } - ${Zr(this.addBias,this.activation)} + ${Qr(this.addBias,this.activation)} if (coordsInBounds4D(coords, uniforms.outShape)) { setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } - `}};var tl=class{constructor(t,e=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2, virtualWidth : i32,",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=t.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let s=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=X(s),this.dispatch=H(this.dispatchLayout,s,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),y.assert(t.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),e&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=t,this.addBias=e,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let t=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,e=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return` - ${fr(this.activation,this.hasPreluActivation,!0,4)} + `}};var Qc=class{constructor(t,e=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2, virtualWidth : i32,",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=t.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let s=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=X(s),this.dispatch=H(this.dispatchLayout,s,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),y.assert(t.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),e&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=t,this.addBias=e,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let t=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,e=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return` + ${dr(this.activation,this.hasPreluActivation,!0,4)} fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 { var value = vec4(0.0); if (col >=0 && col < uniforms.inDims[1]) { @@ -7136,14 +7136,14 @@ return a / b;`,Wee=` let coords = vec4(batch, r, c + i, d1); if (coordsInBounds4D(coords, uniforms.outShape)) { var value = dotProd[i]; - ${Zr(this.addBias,this.activation)} + ${Qr(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } } - `}};var rl=class{constructor(t,e=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32, + `}};var Zc=class{constructor(t,e=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32, filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t.dataFormat==="channelsLast",e&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=t,this.addBias=e,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let t=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` - ${fr(this.activation,this.hasPreluActivation,!1,4)} + ${dr(this.activation,this.hasPreluActivation,!1,4)} ${G("index")} { if (index < uniforms.size) { @@ -7204,11 +7204,11 @@ return a / b;`,Wee=` } } } - ${Zr(this.addBias,this.activation)} + ${Qr(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } - `}};function Oue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.inHeight,d.inWidth]}],h=d.dataFormat==="channelsLast",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new $x(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?(g=new tl(d),f.push({type:"int32",data:[g.virtualWidth]})):(g=new rl(d),f.push({type:"int32",data:[d.filterHeight]},{type:"int32",data:[d.filterWidth]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]})),e.runWebGPUProgram(g,[n,s],n.dtype,f)}var OV={kernelName:dn,backendName:"webgpu",kernelFunc:Oue};var Ex=class{constructor(t){this.variableNames=["x","dy"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, + `}};function Oue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.inHeight,d.inWidth]}],h=d.dataFormat==="channelsLast",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new _x(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?(g=new Qc(d),f.push({type:"int32",data:[g.virtualWidth]})):(g=new Zc(d),f.push({type:"int32",data:[d.filterHeight]},{type:"int32",data:[d.filterWidth]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]})),e.runWebGPUProgram(g,[n,s],n.dtype,f)}var MV={kernelName:mn,backendName:"webgpu",kernelFunc:Oue};var $x=class{constructor(t){this.variableNames=["x","dy"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_filter"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { @@ -7244,7 +7244,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, dotProd); } } - `}},Rx=class{constructor(t){this.variableNames=["dy","W"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, + `}},Ex=class{constructor(t){this.variableNames=["dy","W"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_input"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { @@ -7287,7 +7287,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, dotProd); } } - `}};function Mue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Ex(l),d=[{type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.filterHeight,l.filterWidth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"int32",data:[l.inHeight]},{type:"int32",data:[l.inWidth]},{type:"int32",data:[l.batchSize]},{type:"int32",data:[l.outChannels/l.inChannels]}];return e.runWebGPUProgram(m,[n,s],"float32",d)}var MV={kernelName:Mi,backendName:"webgpu",kernelFunc:Mue};function Lue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Rx(l),d=[{type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.filterHeight-1-l.padInfo.top,l.filterWidth-1-l.padInfo.left]},{type:"int32",data:[l.filterHeight,l.filterWidth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"int32",data:[l.outChannels/l.inChannels]}];return e.runWebGPUProgram(m,[n,s],n.dtype,d)}var LV={kernelName:Li,backendName:"webgpu",kernelFunc:Lue};var Dx=class{constructor(t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return` + `}};function Mue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new $x(l),d=[{type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.filterHeight,l.filterWidth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"int32",data:[l.inHeight]},{type:"int32",data:[l.inWidth]},{type:"int32",data:[l.batchSize]},{type:"int32",data:[l.outChannels/l.inChannels]}];return e.runWebGPUProgram(m,[n,s],"float32",d)}var LV={kernelName:Fi,backendName:"webgpu",kernelFunc:Mue};function Lue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Ex(l),d=[{type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.filterHeight-1-l.padInfo.top,l.filterWidth-1-l.padInfo.left]},{type:"int32",data:[l.filterHeight,l.filterWidth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"int32",data:[l.outChannels/l.inChannels]}];return e.runWebGPUProgram(m,[n,s],n.dtype,d)}var BV={kernelName:Pi,backendName:"webgpu",kernelFunc:Lue};var Rx=class{constructor(t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); @@ -7295,7 +7295,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, value); } } - `}};function Bue(r){let{inputs:t,backend:e}=r,{x:o}=t,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:e,attrs:{shape:[s]}}),i=new Dx(s),p=e.runWebGPUProgram(i,[a],a.dtype),u=pe({inputs:{x:p},backend:e,attrs:{shape:n}});return e.disposeData(a.dataId),e.disposeData(p.dataId),u}var BV={kernelName:oa,backendName:"webgpu",kernelFunc:Bue};var Ax=class{constructor(t){this.variableNames=["x","w"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return` + `}};function Bue(r){let{inputs:t,backend:e}=r,{x:o}=t,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:e,attrs:{shape:[s]}}),i=new Rx(s),p=e.runWebGPUProgram(i,[a],a.dtype),u=pe({inputs:{x:p},backend:e,attrs:{shape:n}});return e.disposeData(a.dataId),e.disposeData(p.dataId),u}var zV={kernelName:oa,backendName:"webgpu",kernelFunc:Bue};var Dx=class{constructor(t){this.variableNames=["x","w"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let neg_infinity = -3.4e38; @@ -7327,7 +7327,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, curVal); } } - `}};function zue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],m=new Ax(u);return e.runWebGPUProgram(m,[n,s],n.dtype,l)}var zV={kernelName:fn,backendName:"webgpu",kernelFunc:zue};var Fx=class{constructor(t,e){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t.inShape,this.dispatchLayout=X(t.outShape),this.dispatch=H(this.dispatchLayout,t.outShape,this.workgroupSize),e!=="float32"&&e!=="int32")throw new Error(`Dilation2DBackpropInput only supports float32 and int32 + `}};function zue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],m=new Dx(u);return e.runWebGPUProgram(m,[n,s],n.dtype,l)}var VV={kernelName:dn,backendName:"webgpu",kernelFunc:zue};var Ax=class{constructor(t,e){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t.inShape,this.dispatchLayout=X(t.outShape),this.dispatch=H(this.dispatchLayout,t.outShape,this.workgroupSize),e!=="float32"&&e!=="int32")throw new Error(`Dilation2DBackpropInput only supports float32 and int32 types, does not support ${e} type.`);this.type=e,this.shaderKey="dilation2DBackpropInput"}getUserCode(){return` ${G("index")} { if (index < uniforms.dySize) { @@ -7368,10 +7368,10 @@ return a / b;`,Wee=` let flatIndexIn = d + uniforms.xShape[3] * (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b)); let value = getDy(b, r, c, d); - ${Qr("&result[flatIndexIn]","value",this.type)} + ${Yr("&result[flatIndexIn]","value",this.type)} } } - `}},Px=class{constructor(t,e,o){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(t.outShape),this.dispatch=H(this.dispatchLayout,t.outShape,this.workgroupSize),o!=="float32"&&o!=="int32")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32 + `}},Fx=class{constructor(t,e,o){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(t.outShape),this.dispatch=H(this.dispatchLayout,t.outShape,this.workgroupSize),o!=="float32"&&o!=="int32")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32 types, does not support ${o} type.`);this.type=o,this.shaderKey="dilation2DBackpropFilter"}getUserCode(){return` ${G("index")} { if (index < uniforms.dySize) { @@ -7411,10 +7411,10 @@ return a / b;`,Wee=` let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]); let value = getDy(b, r, c, d); - ${Qr("&result[flatIndexIn]","value",this.type)} + ${Yr("&result[flatIndexIn]","value",this.type)} } } - `}};function Vue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=s.dtype,m=new Px(c,s.shape,l),d=[{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[y.sizeFromShape(c.outShape)]}],f=kt({backend:e,attrs:{shape:s.shape,value:0,dtype:l}});return e.runWebGPUProgram(m,[n,s,a],l,d,f)}var VV={kernelName:zi,backendName:"webgpu",kernelFunc:Vue};function Wue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=n.dtype,m=new Fx(c,l),d=[{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[y.sizeFromShape(c.outShape)]}],f=kt({backend:e,attrs:{shape:c.inShape,value:0,dtype:l}});return e.runWebGPUProgram(m,[n,s,a],l,d,f)}var WV={kernelName:Bi,backendName:"webgpu",kernelFunc:Wue};var Ox=class{constructor(t,e,o){this.variableNames=["Image"],this.uniforms="alpha: f32,",this.workgroupSize=[64,1,1],this.pixelsOpType=Ii.DRAW,this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=e,this.textureFormat=o,this.shaderKey=`draw_${e}_${o}`}getUserCode(){let t,e=this.type==="float32"?"value":"value / 255.0";return t=` + `}};function Vue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=s.dtype,m=new Fx(c,s.shape,l),d=[{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[y.sizeFromShape(c.outShape)]}],f=vt({backend:e,attrs:{shape:s.shape,value:0,dtype:l}});return e.runWebGPUProgram(m,[n,s,a],l,d,f)}var WV={kernelName:Mi,backendName:"webgpu",kernelFunc:Vue};function Wue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=n.dtype,m=new Ax(c,l),d=[{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[y.sizeFromShape(c.outShape)]}],f=vt({backend:e,attrs:{shape:c.inShape,value:0,dtype:l}});return e.runWebGPUProgram(m,[n,s,a],l,d,f)}var UV={kernelName:Oi,backendName:"webgpu",kernelFunc:Wue};var Px=class{constructor(t,e,o){this.variableNames=["Image"],this.uniforms="alpha: f32,",this.workgroupSize=[64,1,1],this.pixelsOpType=Ci.DRAW,this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=e,this.textureFormat=o,this.shaderKey=`draw_${e}_${o}`}getUserCode(){let t,e=this.type==="float32"?"value":"value / 255.0";return t=` if (uniforms.numChannels == 1) { rgba[0] = ${e}; rgba[1] = ${e}; @@ -7437,7 +7437,7 @@ return a / b;`,Wee=` textureStore(outImage, vec2(coords.yx), rgba); } } - `}};function Uue(r){let{inputs:t,backend:e,attrs:o}=r,{image:n}=t,{canvas:s,options:a}=o,[i,p]=n.shape.slice(0,2),{imageOptions:u}=a||{},c=(u==null?void 0:u.alpha)||1,l=e.device.features.has("bgra8unorm-storage")?"bgra8unorm":"rgba8unorm",m=[i,p],d=new Ox(m,n.dtype,l);s.width=p,s.height=i;let f="webgpu",h=s.getContext(f),g;h||(g=new OffscreenCanvas(p,i),h=g.getContext(f));let x=n.shape.length===3?n.shape[2]:1;h.configure({device:e.device,format:l,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:"premultiplied"});let b="int32",C=e.makeTensorInfo(m,b),S=e.tensorMap.get(C.dataId);S.resource=h.getCurrentTexture(),S.external=!0;let k=[{type:"uint32",data:[x]},{type:"float32",data:[c]}];if(e.runWebGPUProgram(d,[n],b,k,C),g){let _=s.getContext("2d");if(!_)throw new Error("Please make sure this canvas has only been used for 2d or webgpu context!");_.drawImage(g,0,0)}return e.disposeData(C.dataId),n}var UV={kernelName:Pu,backendName:"webgpu",kernelFunc:Uue};var s0=et({opType:fe.MUL,cpuKernelImpl:vz,supportsComplex:!0}),GV={kernelName:Yn,backendName:"webgpu",kernelFunc:s0};function a0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return eo(n,s,a,"sum",e)}var HV={kernelName:Is,backendName:"webgpu",kernelFunc:a0};function Gue(r){let{inputs:t,backend:e,attrs:o}=r,{equation:n}=o,s=t,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=a0({inputs:{x:m},backend:e,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&e.disposeData(h.dataId);return m}var KV={kernelName:Vi,backendName:"webgpu",kernelFunc:Gue};var Hue=ye({opType:Z.ELU}),qV={kernelName:gn,backendName:"webgpu",kernelFunc:Hue};var Kue=r=>{let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=new ki(fe.ELU_DER,o.shape,n.shape);return e.runWebGPUProgram(s,[o,n],o.dtype)},jV={kernelName:Ya,backendName:"webgpu",kernelFunc:Kue};var que=et({opType:fe.EQUAL,dtype:"bool",cpuKernelImpl:pz}),XV={kernelName:yn,backendName:"webgpu",kernelFunc:que};var jue=ye({opType:Z.ERF}),YV={kernelName:xn,backendName:"webgpu",kernelFunc:jue};var Xue=ye({opType:Z.EXP,cpuKernelImpl:cz,dtype:"float32"}),QV={kernelName:bn,backendName:"webgpu",kernelFunc:Xue};function Mx(r){let{inputs:t,attrs:e,backend:o}=r,{dim:n}=e,{input:s}=t,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var ZV={kernelName:na,backendName:"webgpu",kernelFunc:Mx};var Yue=ye({opType:Z.EXPM1,cpuKernelImpl:lz}),JV={kernelName:Cn,backendName:"webgpu",kernelFunc:Yue};var ym=class{constructor(t,e){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=t,this.shaderKey=`fft_${t}`}getUserCode(){return` + `}};function Uue(r){let{inputs:t,backend:e,attrs:o}=r,{image:n}=t,{canvas:s,options:a}=o,[i,p]=n.shape.slice(0,2),{imageOptions:u}=a||{},c=(u==null?void 0:u.alpha)||1,l=e.device.features.has("bgra8unorm-storage")?"bgra8unorm":"rgba8unorm",m=[i,p],d=new Px(m,n.dtype,l);s.width=p,s.height=i;let f="webgpu",h=s.getContext(f),g;h||(g=new OffscreenCanvas(p,i),h=g.getContext(f));let x=n.shape.length===3?n.shape[2]:1;h.configure({device:e.device,format:l,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:"premultiplied"});let b="int32",C=e.makeTensorInfo(m,b),S=e.tensorMap.get(C.dataId);S.resource=h.getCurrentTexture(),S.external=!0;let k=[{type:"uint32",data:[x]},{type:"float32",data:[c]}];if(e.runWebGPUProgram(d,[n],b,k,C),g){let _=s.getContext("2d");if(!_)throw new Error("Please make sure this canvas has only been used for 2d or webgpu context!");_.drawImage(g,0,0)}return e.disposeData(C.dataId),n}var GV={kernelName:_u,backendName:"webgpu",kernelFunc:Uue};var a0=et({opType:fe.MUL,cpuKernelImpl:kz,supportsComplex:!0}),HV={kernelName:Xn,backendName:"webgpu",kernelFunc:a0};function i0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return Jr(n,s,a,"sum",e)}var KV={kernelName:Ss,backendName:"webgpu",kernelFunc:i0};function Gue(r){let{inputs:t,backend:e,attrs:o}=r,{equation:n}=o,s=t,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=i0({inputs:{x:m},backend:e,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&e.disposeData(h.dataId);return m}var qV={kernelName:Li,backendName:"webgpu",kernelFunc:Gue};var Hue=ye({opType:Z.ELU}),jV={kernelName:hn,backendName:"webgpu",kernelFunc:Hue};var Kue=r=>{let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=new Si(fe.ELU_DER,o.shape,n.shape);return e.runWebGPUProgram(s,[o,n],o.dtype)},XV={kernelName:Xa,backendName:"webgpu",kernelFunc:Kue};var que=et({opType:fe.EQUAL,dtype:"bool",cpuKernelImpl:cz}),YV={kernelName:xn,backendName:"webgpu",kernelFunc:que};var jue=ye({opType:Z.ERF}),QV={kernelName:gn,backendName:"webgpu",kernelFunc:jue};var Xue=ye({opType:Z.EXP,cpuKernelImpl:lz,dtype:"float32"}),ZV={kernelName:yn,backendName:"webgpu",kernelFunc:Xue};function Ox(r){let{inputs:t,attrs:e,backend:o}=r,{dim:n}=e,{input:s}=t,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var JV={kernelName:na,backendName:"webgpu",kernelFunc:Ox};var Yue=ye({opType:Z.EXPM1,cpuKernelImpl:mz}),eW={kernelName:bn,backendName:"webgpu",kernelFunc:Yue};var gm=class{constructor(t,e){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=t,this.shaderKey=`fft_${t}`}getUserCode(){return` fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 { ${this.component==="real"?"return real * expR - imag * expI;":"return real * expI + imag * expR;"} } @@ -7470,7 +7470,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, mulMatDFT(coords[0], coords[1])); } } - `}};function Lx(r,t,e){let o=e.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:e,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new ym("real",u),l=new ym("imag",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=t?2*Math.PI:-2*Math.PI,f=t?u[1]:1,h=[{type:"float32",data:[d]},{type:"float32",data:[f]}],g=e.runWebGPUProgram(c,m,"float32",h);i.push(g);let x=e.runWebGPUProgram(l,m,"float32",h);i.push(x);let b=yo({inputs:{real:g,imag:x},backend:e});i.push(b);let C=pe({inputs:{x:b},backend:e,attrs:{shape:r.shape}});return i.forEach(S=>e.disposeData(S.dataId)),C}function Que(r){let{inputs:t,backend:e}=r,{input:o}=t;return Lx(o,!1,e)}var eW={kernelName:Wi,backendName:"webgpu",kernelFunc:Que};var Bx=class{constructor(t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` + `}};function Mx(r,t,e){let o=e.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:e,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new gm("real",u),l=new gm("imag",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=t?2*Math.PI:-2*Math.PI,f=t?u[1]:1,h=[{type:"float32",data:[d]},{type:"float32",data:[f]}],g=e.runWebGPUProgram(c,m,"float32",h);i.push(g);let x=e.runWebGPUProgram(l,m,"float32",h);i.push(x);let b=xo({inputs:{real:g,imag:x},backend:e});i.push(b);let C=pe({inputs:{x:b},backend:e,attrs:{shape:r.shape}});return i.forEach(S=>e.disposeData(S.dataId)),C}function Que(r){let{inputs:t,backend:e}=r,{input:o}=t;return Mx(o,!1,e)}var tW={kernelName:Bi,backendName:"webgpu",kernelFunc:Que};var Lx=class{constructor(t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7479,7 +7479,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, outputValue); } } - `}};var tW={kernelName:wn,backendName:"webgpu",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,o=t,n=new Bx(e.shape);return o.runWebGPUProgram(n,[e],e.dtype)}};var Zue=ye({opType:Z.FLOOR,cpuKernelImpl:mz}),rW={kernelName:Sn,backendName:"webgpu",kernelFunc:Zue};var Jue=et({opType:fe.FLOOR_DIV,cpuKernelImpl:dz,dtype:"int32"}),oW={kernelName:In,backendName:"webgpu",kernelFunc:Jue};var zx=class{constructor(t,e,o=!1){this.pixelsOpType=Ii.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[e,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let t=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` + `}};var rW={kernelName:Cn,backendName:"webgpu",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,o=t,n=new Lx(e.shape);return o.runWebGPUProgram(n,[e],e.dtype)}};var Zue=ye({opType:Z.FLOOR,cpuKernelImpl:dz}),oW={kernelName:wn,backendName:"webgpu",kernelFunc:Zue};var Jue=et({opType:fe.FLOOR_DIV,cpuKernelImpl:fz,dtype:"int32"}),nW={kernelName:Sn,backendName:"webgpu",kernelFunc:Jue};var Bx=class{constructor(t,e,o=!1){this.pixelsOpType=Ci.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[e,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let t=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` @binding(1) @group(0) var src: ${this.importVideo?"texture_external":"texture_2d"}; ${G("index")} { let flatIndex = index * uniforms.numChannels; @@ -7491,7 +7491,7 @@ return a / b;`,Wee=` } } } - `}};var nW={kernelName:Mu,backendName:"webgpu",kernelFunc:epe},ol,i0=A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function epe(r){let{inputs:t,backend:e,attrs:o}=r,{pixels:n}=t,{numChannels:s}=o;if(n==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!="undefined"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=!1,f=a||i;if(u||p||f){let b;if(d)b=e.device.importExternalTexture({source:n});else{if(f){let L=A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(ol==null||L!==i0)&&(i0=L,ol=document.createElement("canvas").getContext("2d",{willReadFrequently:i0})),ol.canvas.width=c,ol.canvas.height=l,ol.drawImage(n,0,0,c,l),n=ol.canvas}let P=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,O="rgba8unorm",M=e.textureManager.acquireTexture(m[1],m[0],O,P);e.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b=M}let C=y.sizeFromShape(m),S=y.computeStrides(m),k=new zx(m,s,d),_=[{type:"uint32",data:[C]},{type:"uint32",data:[s]},{type:"uint32",data:[...S]}],E=e.makeTensorInfo([l,c],"int32"),R=e.tensorMap.get(E.dataId);R.resource=b;let D=e.runWebGPUProgram(k,[E],"int32",_);return e.disposeData(E.dataId),D}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,C=0;for(let S=0;S(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } } - `}};var sW={kernelName:vn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=t,u=e,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new Vx(o.shape,a.shape,i.shape,l,m),f=[{type:"float32",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function tpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return bx({x:n,filter:s,convInfo:g,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var aW={kernelName:vo,backendName:"webgpu",kernelFunc:tpe};function rpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=w.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],S;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(S=new tl(h,x,m,b),C.push({type:"int32",data:[S.virtualWidth]})):(S=new rl(h,x,m,b),C.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),m==="leakyrelu"&&(C.push({type:"float32",data:[d]}),S.uniforms+=" alpha : f32,"),e.runWebGPUProgram(S,g,"float32",C)}var iW={kernelName:ko,backendName:"webgpu",kernelFunc:rpe};var Wx=class{constructor(t,e){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${t}`,this.sliceDim=t,this.uniforms=`sliceDim : i32, strides : ${ht(t)},`}getUserCode(){let t;return this.sliceDim>1?t="uniforms.strides[j]":t="uniforms.strides",` + `}};var aW={kernelName:In,backendName:"webgpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=t,u=e,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new zx(o.shape,a.shape,i.shape,l,m),f=[{type:"float32",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function tpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return yx({x:n,filter:s,convInfo:g,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var iW={kernelName:Io,backendName:"webgpu",kernelFunc:tpe};function rpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=w.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],S;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(S=new Qc(h,x,m,b),C.push({type:"int32",data:[S.virtualWidth]})):(S=new Zc(h,x,m,b),C.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),m==="leakyrelu"&&(C.push({type:"float32",data:[d]}),S.uniforms+=" alpha : f32,"),e.runWebGPUProgram(S,g,"float32",C)}var uW={kernelName:vo,backendName:"webgpu",kernelFunc:rpe};var Vx=class{constructor(t,e){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${t}`,this.sliceDim=t,this.uniforms=`sliceDim : i32, strides : ${ft(t)},`}getUserCode(){let t;return this.sliceDim>1?t="uniforms.strides[j]":t="uniforms.strides",` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7518,7 +7518,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, getA(flattenIndex, coords[1])); } } - `}};function ope(r){let{inputs:t,backend:e}=r,{params:o,indices:n}=t,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=pe({inputs:{x:n},backend:e,attrs:{shape:[u,a]}}),d=pe({inputs:{x:o},backend:e,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(e.shouldExecuteOnCPU([o,n])||o.dtype==="string"){let b=e.readSync(n.dataId),C=e.bufferSync(o),S=fz(b,C,o.dtype,u,a,c,l,o.shape,i);return e.makeTensorInfo(p,o.dtype,S.values)}let f=new Wx(a,[u,c]),h=[{type:"int32",data:[a]},{type:"int32",data:l}],g=e.runWebGPUProgram(f,[d,m],d.dtype,h),x=pe({inputs:{x:g},backend:e,attrs:{shape:p}});return e.disposeData(m.dataId),e.disposeData(d.dataId),e.disposeData(g.dataId),x}var uW={kernelName:kn,backendName:"webgpu",kernelFunc:ope};var Ux=class{constructor(t,e){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.slice(),this.aShape=t,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let t=npe(this.aShape);return` + `}};function ope(r){let{inputs:t,backend:e}=r,{params:o,indices:n}=t,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=pe({inputs:{x:n},backend:e,attrs:{shape:[u,a]}}),d=pe({inputs:{x:o},backend:e,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(e.shouldExecuteOnCPU([o,n])||o.dtype==="string"){let b=e.readSync(n.dataId),C=e.bufferSync(o),S=hz(b,C,o.dtype,u,a,c,l,o.shape,i);return e.makeTensorInfo(p,o.dtype,S.values)}let f=new Vx(a,[u,c]),h=[{type:"int32",data:[a]},{type:"int32",data:l}],g=e.runWebGPUProgram(f,[d,m],d.dtype,h),x=pe({inputs:{x:g},backend:e,attrs:{shape:p}});return e.disposeData(m.dataId),e.disposeData(d.dataId),e.disposeData(g.dataId),x}var pW={kernelName:vn,backendName:"webgpu",kernelFunc:ope};var Wx=class{constructor(t,e){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.slice(),this.aShape=t,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let t=npe(this.aShape);return` ${G("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); @@ -7527,13 +7527,13 @@ return a / b;`,Wee=` setOutputAtIndex(index, inBounds * getA(${t})); } } - `}};function npe(r){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],e=[];for(let o=0;oe.disposeData(D.dataId)),e.makeTensorInfo(u.outputShape,R.dtype,R.values)}let h=new Ux(m.shape,f),g=e.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=pe({inputs:{x:g},backend:e,attrs:{shape:u.outputShape}});return l.forEach(b=>e.disposeData(b.dataId)),x}var pW={kernelName:aa,backendName:"webgpu",kernelFunc:u0};var spe=et({opType:fe.GREATER,cpuKernelImpl:xz,dtype:"bool"}),cW={kernelName:Nn,backendName:"webgpu",kernelFunc:spe};var ape=et({opType:fe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:gz}),lW={kernelName:Tn,backendName:"webgpu",kernelFunc:ape};function ipe(r){let{inputs:t,backend:e}=r,{input:o}=t;return Lx(o,!0,e)}var mW={kernelName:Ui,backendName:"webgpu",kernelFunc:ipe};var upe=ye({opType:Z.IS_FINITE,dtype:"bool"}),dW={kernelName:_n,backendName:"webgpu",kernelFunc:upe};var ppe=ye({opType:Z.IS_INF,dtype:"bool"}),fW={kernelName:$n,backendName:"webgpu",kernelFunc:ppe};var cpe=ye({opType:Z.IS_NAN,dtype:"bool"}),hW={kernelName:En,backendName:"webgpu",kernelFunc:cpe};function lpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{alpha:s}=o,a=[{type:"float32",data:[s]}],i=new Jr(n.shape,Z.LEAKYRELU,"alpha : f32,");return e.runWebGPUProgram(i,[n],"float32",a)}var gW={kernelName:Rn,backendName:"webgpu",kernelFunc:lpe};var mpe=et({opType:fe.LESS,dtype:"bool",cpuKernelImpl:bz}),xW={kernelName:Dn,backendName:"webgpu",kernelFunc:mpe};var dpe=et({opType:fe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:yz}),yW={kernelName:An,backendName:"webgpu",kernelFunc:dpe};var Gx=class{constructor(t){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return` + `}};function npe(r){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],e=[];for(let o=0;oe.disposeData(D.dataId)),e.makeTensorInfo(u.outputShape,R.dtype,R.values)}let h=new Wx(m.shape,f),g=e.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=pe({inputs:{x:g},backend:e,attrs:{shape:u.outputShape}});return l.forEach(b=>e.disposeData(b.dataId)),x}var cW={kernelName:aa,backendName:"webgpu",kernelFunc:p0};var spe=et({opType:fe.GREATER,cpuKernelImpl:yz,dtype:"bool"}),lW={kernelName:kn,backendName:"webgpu",kernelFunc:spe};var ape=et({opType:fe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:xz}),mW={kernelName:Nn,backendName:"webgpu",kernelFunc:ape};function ipe(r){let{inputs:t,backend:e}=r,{input:o}=t;return Mx(o,!0,e)}var dW={kernelName:zi,backendName:"webgpu",kernelFunc:ipe};var upe=ye({opType:Z.IS_FINITE,dtype:"bool"}),fW={kernelName:Tn,backendName:"webgpu",kernelFunc:upe};var ppe=ye({opType:Z.IS_INF,dtype:"bool"}),hW={kernelName:_n,backendName:"webgpu",kernelFunc:ppe};var cpe=ye({opType:Z.IS_NAN,dtype:"bool"}),gW={kernelName:$n,backendName:"webgpu",kernelFunc:cpe};function lpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{alpha:s}=o,a=[{type:"float32",data:[s]}],i=new Zr(n.shape,Z.LEAKYRELU,"alpha : f32,");return e.runWebGPUProgram(i,[n],"float32",a)}var xW={kernelName:En,backendName:"webgpu",kernelFunc:lpe};var mpe=et({opType:fe.LESS,dtype:"bool",cpuKernelImpl:Cz}),yW={kernelName:Rn,backendName:"webgpu",kernelFunc:mpe};var dpe=et({opType:fe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:bz}),bW={kernelName:Dn,backendName:"webgpu",kernelFunc:dpe};var Ux=class{constructor(t){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step); } } - `}};function fpe(r){let{backend:t,attrs:e}=r,{start:o,stop:n,num:s}=e,a=(n-o)/(s-1),i=new Gx(s),p=[{type:"float32",data:[o]},{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],"float32",p)}var bW={kernelName:Fn,backendName:"webgpu",kernelFunc:fpe};var hpe=ye({opType:Z.LOG,cpuKernelImpl:Cz}),CW={kernelName:Pn,backendName:"webgpu",kernelFunc:hpe};var gpe=ye({opType:Z.LOG1P}),wW={kernelName:On,backendName:"webgpu",kernelFunc:gpe};var xpe=et({opType:fe.LOGICAL_AND,dtype:"bool"}),SW={kernelName:Mn,backendName:"webgpu",kernelFunc:xpe};var ype=ye({opType:Z.LOGICAL_NOT}),IW={kernelName:Ln,backendName:"webgpu",kernelFunc:ype};var bpe=et({opType:fe.LOGICAL_OR}),vW={kernelName:Bn,backendName:"webgpu",kernelFunc:bpe};var kW=` + `}};function fpe(r){let{backend:t,attrs:e}=r,{start:o,stop:n,num:s}=e,a=(n-o)/(s-1),i=new Ux(s),p=[{type:"float32",data:[o]},{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],"float32",p)}var CW={kernelName:An,backendName:"webgpu",kernelFunc:fpe};var hpe=ye({opType:Z.LOG,cpuKernelImpl:wz}),wW={kernelName:Fn,backendName:"webgpu",kernelFunc:hpe};var gpe=ye({opType:Z.LOG1P}),SW={kernelName:Pn,backendName:"webgpu",kernelFunc:gpe};var xpe=et({opType:fe.LOGICAL_AND,dtype:"bool"}),IW={kernelName:On,backendName:"webgpu",kernelFunc:xpe};var ype=ye({opType:Z.LOGICAL_NOT}),vW={kernelName:Mn,backendName:"webgpu",kernelFunc:ype};var bpe=et({opType:fe.LOGICAL_OR}),kW={kernelName:Ln,backendName:"webgpu",kernelFunc:bpe};var NW=` var powValue = 0.0; let basis = uniforms.bias + uniforms.alpha * sum; if (uniforms.beta == 0.5) { @@ -7543,7 +7543,7 @@ return a / b;`,Wee=` } else { powValue = exp(log(basis) * (-uniforms.beta)); } -`,Hx=class{constructor(t){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn"}getUserCode(){return` +`,Gx=class{constructor(t){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); @@ -7561,12 +7561,12 @@ return a / b;`,Wee=` sum = sum + z * z; } } - ${kW} + ${NW} setOutputAtIndex(index, x * powValue); } } - `}},Kx=class{constructor(t,e){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(e<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${e}`),this.outputShape=t,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey="lrn_shared"}getUserCode(){return` + `}},Hx=class{constructor(t,e){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(e<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${e}`),this.outputShape=t,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey="lrn_shared"}getUserCode(){return` var lrnSub: array; const elementsPerWorkgroup = ${this.elementsPerWorkgroup}; const maxAllowRadius = ${this.maxAllowRadius}; @@ -7594,11 +7594,11 @@ return a / b;`,Wee=` let z = lrnSub[index + i]; sum = sum + z * z; } - ${kW} + ${NW} setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue); } - } `}};function Cpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Hx(n.shape):u=new Kx(n.shape,s);let c=[{type:"int32",data:[s]},{type:"float32",data:[a]},{type:"float32",data:[i]},{type:"float32",data:[p]}];return e.runWebGPUProgram(u,[n],n.dtype,c)}var NW={kernelName:zn,backendName:"webgpu",kernelFunc:Cpe};var qx=class{constructor(t){this.outputShape=[],this.variableNames=["inputImage","outputImage","dy"],this.uniforms="depthRadius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn_grad"}getUserCode(){return` + } `}};function Cpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Gx(n.shape):u=new Hx(n.shape,s);let c=[{type:"int32",data:[s]},{type:"float32",data:[a]},{type:"float32",data:[i]},{type:"float32",data:[p]}];return e.runWebGPUProgram(u,[n],n.dtype,c)}var TW={kernelName:Bn,backendName:"webgpu",kernelFunc:Cpe};var Kx=class{constructor(t){this.outputShape=[],this.variableNames=["inputImage","outputImage","dy"],this.uniforms="depthRadius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn_grad"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); @@ -7648,7 +7648,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, result); } } - `}};function wpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new qx(n.shape),m=[{type:"int32",data:[i]},{type:"float32",data:[p]},{type:"float32",data:[u]},{type:"float32",data:[c]}];return e.runWebGPUProgram(l,[n,s,a],n.dtype,m)}var TW={kernelName:Qa,backendName:"webgpu",kernelFunc:wpe};var Spe=et({opType:fe.MAX,cpuKernelImpl:Sz}),_W={kernelName:Wn,backendName:"webgpu",kernelFunc:Spe};function Ipe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=w.computePool2DInfo(n.shape,s,a,u,i,p);return ax(n,c,"max",e)}var $W={kernelName:Un,backendName:"webgpu",kernelFunc:Ipe};function vpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new _u(l,"max"),d=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.inDepth,l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return e.runWebGPUProgram(m,[n],n.dtype,d)}var EW={kernelName:ia,backendName:"webgpu",kernelFunc:vpe};var jx=class{constructor(t){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, + `}};function wpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new Kx(n.shape),m=[{type:"int32",data:[i]},{type:"float32",data:[p]},{type:"float32",data:[u]},{type:"float32",data:[c]}];return e.runWebGPUProgram(l,[n,s,a],n.dtype,m)}var _W={kernelName:Ya,backendName:"webgpu",kernelFunc:wpe};var Spe=et({opType:fe.MAX,cpuKernelImpl:Iz}),$W={kernelName:Vn,backendName:"webgpu",kernelFunc:Spe};function Ipe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=w.computePool2DInfo(n.shape,s,a,u,i,p);return sx(n,c,"max",e)}var EW={kernelName:Wn,backendName:"webgpu",kernelFunc:Ipe};function vpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new wu(l,"max"),d=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.inDepth,l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return e.runWebGPUProgram(m,[n],n.dtype,d)}var RW={kernelName:ia,backendName:"webgpu",kernelFunc:vpe};var qx=class{constructor(t){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool2DBackprop"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { @@ -7693,7 +7693,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, dotProd); } } - `}},Xx=class{constructor(t){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, + `}},jx=class{constructor(t){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool3DBackprop"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { @@ -7751,7 +7751,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, dotProd); } } - `}};function kpe(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new _u(m,"max",!0),f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inDepth,m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=e.runWebGPUProgram(d,[a],"int32",f),g=new Xx(m);f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:"int32",data:[m.outDepth]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]}];let x=e.runWebGPUProgram(g,[n,h],a.dtype,f);return e.disposeData(h.dataId),x}var RW={kernelName:Ki,backendName:"webgpu",kernelFunc:kpe};function Npe(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s,output:a}=t,i=s;fm([s,a],"maxPoolGrad");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=new za(m,"max",!0),f=[{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=e.runWebGPUProgram(d,[i],"int32",f),g=new jx(m);f=[{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]}];let x=e.runWebGPUProgram(g,[n,h],i.dtype,f);return e.disposeData(h.dataId),x}var DW={kernelName:Hi,backendName:"webgpu",kernelFunc:Npe};function Tpe(r){let{inputs:t,backend:e,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=t;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(p.shape,n,s,u,a),l=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new za(c,"max",!1),d=e.runWebGPUProgram(m,[p],p.dtype,l);m=new za(c,"max",!0,!0,i);let f=e.runWebGPUProgram(m,[p],"int32",l);return[d,f]}var AW={kernelName:ua,backendName:"webgpu",kernelFunc:Tpe};function _pe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return eo(n,s,a,"min",e)}var FW={kernelName:Hn,backendName:"webgpu",kernelFunc:_pe};var $pe=et({opType:fe.MIN,cpuKernelImpl:Iz}),PW={kernelName:Kn,backendName:"webgpu",kernelFunc:$pe};var Yx=class{constructor(t,e,o){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.map((n,s)=>n[0]+t[s]+n[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=t,e.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o==="reflect"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let t=this.xShape.length,e=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(","),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${t>1?`[${c}]`:""}`).join(","),n=t===1?"start":"start[i]",s=t===1?"end":"end[i]",a=t===1?"outC":"outC[i]",i=ht(t),p=t>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,t):"coords";return` + `}};function kpe(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new wu(m,"max",!0),f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inDepth,m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=e.runWebGPUProgram(d,[a],"int32",f),g=new jx(m);f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:"int32",data:[m.outDepth]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]}];let x=e.runWebGPUProgram(g,[n,h],a.dtype,f);return e.disposeData(h.dataId),x}var DW={kernelName:Ui,backendName:"webgpu",kernelFunc:kpe};function Npe(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s,output:a}=t,i=s;mm([s,a],"maxPoolGrad");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=new Ba(m,"max",!0),f=[{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=e.runWebGPUProgram(d,[i],"int32",f),g=new qx(m);f=[{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]}];let x=e.runWebGPUProgram(g,[n,h],i.dtype,f);return e.disposeData(h.dataId),x}var AW={kernelName:Wi,backendName:"webgpu",kernelFunc:Npe};function Tpe(r){let{inputs:t,backend:e,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=t;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(p.shape,n,s,u,a),l=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new Ba(c,"max",!1),d=e.runWebGPUProgram(m,[p],p.dtype,l);m=new Ba(c,"max",!0,!0,i);let f=e.runWebGPUProgram(m,[p],"int32",l);return[d,f]}var FW={kernelName:ua,backendName:"webgpu",kernelFunc:Tpe};function _pe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return Jr(n,s,a,"min",e)}var PW={kernelName:Gn,backendName:"webgpu",kernelFunc:_pe};var $pe=et({opType:fe.MIN,cpuKernelImpl:vz}),OW={kernelName:Hn,backendName:"webgpu",kernelFunc:$pe};var Xx=class{constructor(t,e,o){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.map((n,s)=>n[0]+t[s]+n[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=t,e.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o==="reflect"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let t=this.xShape.length,e=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(","),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${t>1?`[${c}]`:""}`).join(","),n=t===1?"start":"start[i]",s=t===1?"end":"end[i]",a=t===1?"outC":"outC[i]",i=ft(t),p=t>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,t):"coords";return` ${G("index")} { if (index < uniforms.size) { let start = ${i}(${e}); @@ -7768,7 +7768,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, getX(${p})); } } - `}};var OW={kernelName:qn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=e,i=n.map(c=>({type:"int32",data:[c[0],c[1]]})),p=new Yx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var Epe=et({opType:fe.MOD}),MW={kernelName:jn,backendName:"webgpu",kernelFunc:Epe};var Qx=class{constructor(t,e){this.variableNames=["probs"],this.outputShape=[],this.uniforms="seed : f32, numOutcomes: i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="multinomial"}getUserCode(){return` + `}};var MW={kernelName:Kn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=e,i=n.map(c=>({type:"int32",data:[c[0],c[1]]})),p=new Xx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var Epe=et({opType:fe.MOD}),LW={kernelName:qn,backendName:"webgpu",kernelFunc:Epe};var Yx=class{constructor(t,e){this.variableNames=["probs"],this.outputShape=[],this.uniforms="seed : f32, numOutcomes: i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="multinomial"}getUserCode(){return` //Based on the work of Dave Hoskins //https://www.shadertoy.com/view/4djSRW fn random (seed : f32, resultUV : vec2) -> f32 { @@ -7801,7 +7801,7 @@ return a / b;`,Wee=` setOutputAtIndexI32(index, uniforms.numOutcomes - 1); } } - `}};var Zx=class{constructor(t){this.variableNames=["logits"],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey="softmax"}getUserCode(){return` + `}};var Qx=class{constructor(t){this.variableNames=["logits"],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey="softmax"}getUserCode(){return` var buf : array; var rowMaxShared : f32; var rowSumShared : f32; @@ -7860,7 +7860,7 @@ return a / b;`,Wee=` setOutputAtCoords(row, col, value); } } - `}};function p0(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{dim:s}=o,a=pe({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new Zx(a.shape),p=e.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:e,attrs:{shape:n.shape}});return e.disposeData(a.dataId),e.disposeData(p.dataId),u}var LW={kernelName:vs,backendName:"webgpu",kernelFunc:p0};function Rpe(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{numSamples:s,seed:a,normalized:i}=o,p=i?n:p0({inputs:{logits:n},backend:e,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Qx(u,s),m=[{type:"float32",data:[a]},{type:"int32",data:[c]}],d=e.runWebGPUProgram(l,[p],"int32",m);return i||e.disposeData(p.dataId),d}var BW={kernelName:Xn,backendName:"webgpu",kernelFunc:Rpe};function Dpe(r){let{inputs:t,backend:e}=r,{x:o}=t;if(e.shouldExecuteOnCPU([o])){let s=e.tensorMap.get(o.dataId),[a,i]=kz(s.values,o.shape,o.dtype);return e.makeTensorInfo(i,o.dtype,a)}let n=new Jr(o.shape,Z.NEG);return e.runWebGPUProgram(n,[o],o.dtype)}var zW={kernelName:pa,backendName:"webgpu",kernelFunc:Dpe};function Ape(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=e.readSync(n.dataId),c=e.readSync(s.dataId),{selectedIndices:l}=Wt.nonMaxSuppressionV3Impl(u,c,a,i,p);return e.makeTensorInfo([l.length],"int32",new Int32Array(l))}var VW={kernelName:Zn,backendName:"webgpu",kernelFunc:Ape};function Fpe(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=e.readSync(n.dataId),l=e.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Wt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var WW={kernelName:Jn,backendName:"webgpu",kernelFunc:Fpe};var Jx=class{constructor(t,e){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` + `}};function c0(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{dim:s}=o,a=pe({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new Qx(a.shape),p=e.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:e,attrs:{shape:n.shape}});return e.disposeData(a.dataId),e.disposeData(p.dataId),u}var BW={kernelName:Is,backendName:"webgpu",kernelFunc:c0};function Rpe(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{numSamples:s,seed:a,normalized:i}=o,p=i?n:c0({inputs:{logits:n},backend:e,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Yx(u,s),m=[{type:"float32",data:[a]},{type:"int32",data:[c]}],d=e.runWebGPUProgram(l,[p],"int32",m);return i||e.disposeData(p.dataId),d}var zW={kernelName:jn,backendName:"webgpu",kernelFunc:Rpe};function Dpe(r){let{inputs:t,backend:e}=r,{x:o}=t;if(e.shouldExecuteOnCPU([o])){let s=e.tensorMap.get(o.dataId),[a,i]=Nz(s.values,o.shape,o.dtype);return e.makeTensorInfo(i,o.dtype,a)}let n=new Zr(o.shape,Z.NEG);return e.runWebGPUProgram(n,[o],o.dtype)}var VW={kernelName:pa,backendName:"webgpu",kernelFunc:Dpe};function Ape(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=e.readSync(n.dataId),c=e.readSync(s.dataId),{selectedIndices:l}=Vt.nonMaxSuppressionV3Impl(u,c,a,i,p);return e.makeTensorInfo([l.length],"int32",new Int32Array(l))}var WW={kernelName:Qn,backendName:"webgpu",kernelFunc:Ape};function Fpe(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=e.readSync(n.dataId),l=e.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Vt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var UW={kernelName:Zn,backendName:"webgpu",kernelFunc:Fpe};var Zx=class{constructor(t,e){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` ${G("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7868,7 +7868,7 @@ return a / b;`,Wee=` f32(i32(round(getX(coords.x))) == coords.y))); } } - `}};function Ppe(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n}=t,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Jx(u,a),l=pe({inputs:{x:n},backend:e,attrs:{shape:[u]}}),m=[{type:"float32",data:[i]},{type:"float32",data:[p]}],d=e.runWebGPUProgram(c,[l],s,m);e.disposeData(l.dataId);let f=[...n.shape,a],h=pe({inputs:{x:d},backend:e,attrs:{shape:f}});return e.disposeData(d.dataId),h}var UW={kernelName:es,backendName:"webgpu",kernelFunc:Ppe};function bm(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype==="complex64"){let n=Ni({inputs:{input:o},backend:e}),s=bm({inputs:{x:n},backend:e}),a=Mp({inputs:{input:o},backend:e}),i=bm({inputs:{x:a},backend:e}),p=yo({inputs:{real:s,imag:i},backend:e});return e.disposeData(n.dataId),e.disposeData(s.dataId),e.disposeData(a.dataId),e.disposeData(i.dataId),p}else return kt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:e})}var GW={kernelName:Sa,backendName:"webgpu",kernelFunc:bm};function HW(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=Ni({inputs:{input:o},backend:e}),s=HW({inputs:{x:n},backend:e}),a=Mp({inputs:{input:o},backend:e}),i=bm({inputs:{x:a},backend:e}),p=yo({inputs:{real:s,imag:i},backend:e});return e.disposeData(n.dataId),e.disposeData(s.dataId),e.disposeData(a.dataId),e.disposeData(i.dataId),p}else return kt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:e})}var KW={kernelName:ca,backendName:"webgpu",kernelFunc:HW};function Ope(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o;if(t.length===1)return Mx({inputs:{input:t[0]},backend:e,attrs:{dim:n}});let s=t[0].shape,a=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=t.map(c=>{let l=Mx({inputs:{input:c},backend:e,attrs:{dim:n}});return i.push(l),l}),u=n0({inputs:p,backend:e,attrs:{axis:n}});return i.forEach(c=>e.disposeData(c.dataId)),u}var qW={kernelName:la,backendName:"webgpu",kernelFunc:Ope};function c0(r,t=!1){let e=r.length,o=ht(e),n=r.map((l,m)=>`uniforms.pad${m}[0]`).join(","),s=r.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:""}`).join(","),a=e>1?`${o}(${n})`:`${n}`,i=e>1?`${o}(${s})`:`${s}`,p=e>1?"any(paddedCoords < start)":"paddedCoords < start",u=e>1?"any(paddedCoords >= end)":"paddedCoords >= end",c=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` + `}};function Ppe(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n}=t,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Zx(u,a),l=pe({inputs:{x:n},backend:e,attrs:{shape:[u]}}),m=[{type:"float32",data:[i]},{type:"float32",data:[p]}],d=e.runWebGPUProgram(c,[l],s,m);e.disposeData(l.dataId);let f=[...n.shape,a],h=pe({inputs:{x:d},backend:e,attrs:{shape:f}});return e.disposeData(d.dataId),h}var GW={kernelName:Jn,backendName:"webgpu",kernelFunc:Ppe};function xm(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype==="complex64"){let n=Ii({inputs:{input:o},backend:e}),s=xm({inputs:{x:n},backend:e}),a=$p({inputs:{input:o},backend:e}),i=xm({inputs:{x:a},backend:e}),p=xo({inputs:{real:s,imag:i},backend:e});return e.disposeData(n.dataId),e.disposeData(s.dataId),e.disposeData(a.dataId),e.disposeData(i.dataId),p}else return vt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:e})}var HW={kernelName:Sa,backendName:"webgpu",kernelFunc:xm};function KW(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=Ii({inputs:{input:o},backend:e}),s=KW({inputs:{x:n},backend:e}),a=$p({inputs:{input:o},backend:e}),i=xm({inputs:{x:a},backend:e}),p=xo({inputs:{real:s,imag:i},backend:e});return e.disposeData(n.dataId),e.disposeData(s.dataId),e.disposeData(a.dataId),e.disposeData(i.dataId),p}else return vt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:e})}var qW={kernelName:ca,backendName:"webgpu",kernelFunc:KW};function Ope(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o;if(t.length===1)return Ox({inputs:{input:t[0]},backend:e,attrs:{dim:n}});let s=t[0].shape,a=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=t.map(c=>{let l=Ox({inputs:{input:c},backend:e,attrs:{dim:n}});return i.push(l),l}),u=s0({inputs:p,backend:e,attrs:{axis:n}});return i.forEach(c=>e.disposeData(c.dataId)),u}var jW={kernelName:la,backendName:"webgpu",kernelFunc:Ope};function l0(r,t=!1){let e=r.length,o=ft(e),n=r.map((l,m)=>`uniforms.pad${m}[0]`).join(","),s=r.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:""}`).join(","),a=e>1?`${o}(${n})`:`${n}`,i=e>1?`${o}(${s})`:`${s}`,p=e>1?"any(paddedCoords < start)":"paddedCoords < start",u=e>1?"any(paddedCoords >= end)":"paddedCoords >= end",c=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` let start = ${a}; let end = ${i}; if (${p} || ${u}) { @@ -7877,14 +7877,14 @@ return a / b;`,Wee=` let coords = paddedCoords - start; setOutputAtIndex(index, getX(${c})); } - `}var ey=class{constructor(t,e){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.map((o,n)=>o[0]+t[n]+o[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),e.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=t,this.shaderKey="pad"}getUserCode(){return` + `}var Jx=class{constructor(t,e){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.map((o,n)=>o[0]+t[n]+o[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),e.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=t,this.shaderKey="pad"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let paddedCoords = getCoordsFromIndex(index); - ${c0(this.xShape)} + ${l0(this.xShape)} } } - `}};var Mpe=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return Ft({inputs:{x:n},backend:e});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return kt({backend:e,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:"float32",data:[a]}];s.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let p=new ey(n.shape,s);return e.runWebGPUProgram(p,[n],n.dtype,i)},jW={kernelName:ts,backendName:"webgpu",kernelFunc:Mpe};var Lpe=et({opType:fe.POW}),XW={kernelName:rs,backendName:"webgpu",kernelFunc:Lpe};function Bpe(r){let{inputs:t,backend:e}=r,{x:o,alpha:n}=t,s=new ki(fe.PRELU,o.shape,n.shape);return e.runWebGPUProgram(s,[o,n],"float32")}var YW={kernelName:os,backendName:"webgpu",kernelFunc:Bpe};function zpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return eo(n,s,a,"prod",e)}var QW={kernelName:ns,backendName:"webgpu",kernelFunc:zpe};var Vpe=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=_z(o,n,s,a);return t.makeTensorInfo([i.length],a,i)},ZW={kernelName:ma,backendName:"webgpu",kernelFunc:Vpe};var Wpe=et({opType:fe.DIV}),JW={kernelName:hn,backendName:"webgpu",kernelFunc:Wpe};var Upe=ye({opType:Z.RECIPROCAL}),eU={kernelName:ss,backendName:"webgpu",kernelFunc:Upe};var Gpe=ye({opType:Z.RELU}),tU={kernelName:as,backendName:"webgpu",kernelFunc:Gpe};var Hpe=ye({opType:Z.RELU6}),rU={kernelName:ps,backendName:"webgpu",kernelFunc:Hpe};var ty=class{constructor(t,e,o){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t[0],e,o,t[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` + `}};var Mpe=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return At({inputs:{x:n},backend:e});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return vt({backend:e,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:"float32",data:[a]}];s.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let p=new Jx(n.shape,s);return e.runWebGPUProgram(p,[n],n.dtype,i)},XW={kernelName:es,backendName:"webgpu",kernelFunc:Mpe};var Lpe=et({opType:fe.POW}),YW={kernelName:ts,backendName:"webgpu",kernelFunc:Lpe};function Bpe(r){let{inputs:t,backend:e}=r,{x:o,alpha:n}=t,s=new Si(fe.PRELU,o.shape,n.shape);return e.runWebGPUProgram(s,[o,n],"float32")}var QW={kernelName:rs,backendName:"webgpu",kernelFunc:Bpe};function zpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return Jr(n,s,a,"prod",e)}var ZW={kernelName:os,backendName:"webgpu",kernelFunc:zpe};var Vpe=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=$z(o,n,s,a);return t.makeTensorInfo([i.length],a,i)},JW={kernelName:ma,backendName:"webgpu",kernelFunc:Vpe};var Wpe=et({opType:fe.DIV}),eU={kernelName:fn,backendName:"webgpu",kernelFunc:Wpe};var Upe=ye({opType:Z.RECIPROCAL}),tU={kernelName:ns,backendName:"webgpu",kernelFunc:Upe};var Gpe=ye({opType:Z.RELU}),rU={kernelName:ss,backendName:"webgpu",kernelFunc:Gpe};var Hpe=ye({opType:Z.RELU6}),oU={kernelName:us,backendName:"webgpu",kernelFunc:Hpe};var ey=class{constructor(t,e,o){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t[0],e,o,t[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7927,7 +7927,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, newValue); } } - `}};function Kpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[i?.5:0]}],f=new ty(n.shape,p,u);return e.runWebGPUProgram(f,[n],"float32",d)}var oU={kernelName:us,backendName:"webgpu",kernelFunc:Kpe};var ry=class{constructor(t,e){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32, + `}};function Kpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[i?.5:0]}],f=new ey(n.shape,p,u);return e.runWebGPUProgram(f,[n],"float32",d)}var nU={kernelName:is,backendName:"webgpu",kernelFunc:Kpe};var ty=class{constructor(t,e){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32, invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=e,this.shaderKey=`resizeBilinearBackprop_${e}`}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { @@ -8002,7 +8002,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, accumulator); } } - `}};function qpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ry(n.shape,a),S=[{type:"int32",data:l},{type:"int32",data:m},{type:"float32",data:[d]},{type:"float32",data:[f]},{type:"float32",data:[h]},{type:"float32",data:[g]},{type:"int32",data:[x]},{type:"int32",data:[b]}];return e.runWebGPUProgram(C,[s],s.dtype,S)}var nU={kernelName:ei,backendName:"webgpu",kernelFunc:qpe};var oy=class{constructor(t,e,o,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t[0],e,o,t[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let t;return this.halfPixelCenters?t="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":t="vec2(rc) * effectiveInputOverOutputRatioRC",` + `}};function qpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ty(n.shape,a),S=[{type:"int32",data:l},{type:"int32",data:m},{type:"float32",data:[d]},{type:"float32",data:[f]},{type:"float32",data:[h]},{type:"float32",data:[g]},{type:"int32",data:[x]},{type:"int32",data:[b]}];return e.runWebGPUProgram(C,[s],s.dtype,S)}var sU={kernelName:Ja,backendName:"webgpu",kernelFunc:qpe};var ry=class{constructor(t,e,o,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t[0],e,o,t[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let t;return this.halfPixelCenters?t="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":t="vec2(rc) * effectiveInputOverOutputRatioRC",` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -8033,7 +8033,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, newValue); } } - `}};function jpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[s?.5:0]}],f=new oy(n.shape,p,u,a);return e.runWebGPUProgram(f,[n],n.dtype,d)}var sU={kernelName:is,backendName:"webgpu",kernelFunc:jpe};var ny=class{constructor(t,e){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, + `}};function jpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[s?.5:0]}],f=new ry(n.shape,p,u,a);return e.runWebGPUProgram(f,[n],n.dtype,d)}var aU={kernelName:as,backendName:"webgpu",kernelFunc:jpe};var oy=class{constructor(t,e){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=e,this.shaderKey=`resizeNearestNeigborBackprop_${e}`}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { @@ -8093,7 +8093,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, accumulator); } } - `}};function Xpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ny(n.shape,a),S=[{type:"int32",data:l},{type:"int32",data:m},{type:"float32",data:[h]},{type:"float32",data:[g]},{type:"int32",data:[x]},{type:"int32",data:[b]}];return e.runWebGPUProgram(C,[s],s.dtype,S)}var aU={kernelName:Ja,backendName:"webgpu",kernelFunc:Xpe};var sy=class{constructor(t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` + `}};function Xpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new oy(n.shape,a),S=[{type:"int32",data:l},{type:"int32",data:m},{type:"float32",data:[h]},{type:"float32",data:[g]},{type:"int32",data:[x]},{type:"int32",data:[b]}];return e.runWebGPUProgram(C,[s],s.dtype,S)}var iU={kernelName:Za,backendName:"webgpu",kernelFunc:Xpe};var ny=class{constructor(t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` // Using uniform variables as judging conditions, so the function has // coherent execution within all threads. @@ -8123,7 +8123,7 @@ return a / b;`,Wee=` reverseCoords[1], reverseCoords[2], reverseCoords[3])); } } - `}};function Ype(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=n.shape.length;if(a===0)return Ft({inputs:{x:n},backend:e});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:"int32",data:c}],m=pe({inputs:{x:n},backend:e,attrs:{shape:p}}),d=new sy(p),f=e.runWebGPUProgram(d,[m],m.dtype,l);e.disposeData(m.dataId);let h=pe({inputs:{x:f},backend:e,attrs:{shape:i}});return e.disposeData(f.dataId),h}var iU={kernelName:cs,backendName:"webgpu",kernelFunc:Ype};var ay=class{constructor(t,e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, + `}};function Ype(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=n.shape.length;if(a===0)return At({inputs:{x:n},backend:e});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:"int32",data:c}],m=pe({inputs:{x:n},backend:e,attrs:{shape:p}}),d=new ny(p),f=e.runWebGPUProgram(d,[m],m.dtype,l);e.disposeData(m.dataId);let h=pe({inputs:{x:f},backend:e,attrs:{shape:i}});return e.disposeData(f.dataId),h}var uU={kernelName:ps,backendName:"webgpu",kernelFunc:Ype};var sy=class{constructor(t,e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=t,typeof e=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { @@ -8144,7 +8144,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, outputValue); } } - `}};var uU={kernelName:As,backendName:"webgpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=t,i=e,p=new ay(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:"float32",data:[u]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(n)]},{type:"float32",data:[Math.cos(n)]}];return typeof s=="number"?l.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:"float32",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Qpe=ye({opType:Z.ROUND}),pU={kernelName:ls,backendName:"webgpu",kernelFunc:Qpe};var Zpe=ye({opType:Z.RSQRT,cpuKernelImpl:$z}),cU={kernelName:ms,backendName:"webgpu",kernelFunc:Zpe};var Va=class{constructor(t,e,o,n,s,a,i,p=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.sliceDimGreaterThanOne=e>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=ht(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let t="";this.indicesRank===1?t="coords[0]":this.indicesRank===2&&(t="coords[0], j");let e=`getIndices(${t})`,o=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",s="";this.dispatchLayout.x.length===1?(n="flattenedIndex",s=` + `}};var pU={kernelName:Ds,backendName:"webgpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=t,i=e,p=new sy(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:"float32",data:[u]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(n)]},{type:"float32",data:[Math.cos(n)]}];return typeof s=="number"?l.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:"float32",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Qpe=ye({opType:Z.ROUND}),cU={kernelName:cs,backendName:"webgpu",kernelFunc:Qpe};var Zpe=ye({opType:Z.RSQRT,cpuKernelImpl:Ez}),lU={kernelName:ls,backendName:"webgpu",kernelFunc:Zpe};var za=class{constructor(t,e,o,n,s,a,i,p=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.sliceDimGreaterThanOne=e>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}_${s.length}`;let u=ft(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let t="";this.indicesRank===1?t="coords[0]":this.indicesRank===2&&(t="coords[0], j");let e=`getIndices(${t})`,o=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",s="";this.dispatchLayout.x.length===1?(n="flattenedIndex",s=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 { return index; } @@ -8170,12 +8170,12 @@ return a / b;`,Wee=` flattenedIndex = flattenedIndex + indexInside * ${o}; } let updateValue = - ${Nu(this.type)}(${i}); + ${Cu(this.type)}(${i}); let flatIndex = getOutputIndexFromCoords(${n}); - ${this.sumDupeIndices?Qr("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} + ${this.sumDupeIndices?Yr("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} } - }`}};function Jpe(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n,updates:s}=t,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return e.makeTensorInfo(a,n.dtype);let d=pe({inputs:{x:n},backend:e,attrs:{shape:[p,i]}}),f=pe({inputs:{x:s},backend:e,attrs:{shape:[p,u]}}),h=f.dtype,g=kt({backend:e,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[x]}],C=new Va(f.shape,i,d.shape.length,f.shape.length,c,m,h),S=e.runWebGPUProgram(C,[f,d],h,b,g),k=pe({inputs:{x:S},backend:e,attrs:{shape:a}});return e.disposeData(d.dataId),e.disposeData(f.dataId),e.disposeData(S.dataId),k}var lU={kernelName:ds,backendName:"webgpu",kernelFunc:Jpe};var iy=class{constructor(t,e){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=e,this.shaderKey=`search_sorted_${e}`}getUserCode(){return` + }`}};function Jpe(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n,updates:s}=t,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return e.makeTensorInfo(a,n.dtype);let d=pe({inputs:{x:n},backend:e,attrs:{shape:[p,i]}}),f=pe({inputs:{x:s},backend:e,attrs:{shape:[p,u]}}),h=f.dtype,g=vt({backend:e,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[x]}],C=new za(f.shape,i,d.shape.length,f.shape.length,c,m,h),S=e.runWebGPUProgram(C,[f,d],h,b,g),k=pe({inputs:{x:S},backend:e,attrs:{shape:a}});return e.disposeData(d.dataId),e.disposeData(f.dataId),e.disposeData(S.dataId),k}var mU={kernelName:ms,backendName:"webgpu",kernelFunc:Jpe};var ay=class{constructor(t,e){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=e,this.shaderKey=`search_sorted_${e}`}getUserCode(){return` fn findBound(batch: i32, value: f32) -> i32 { var left = i32(0); var right = uniforms.numInputs; @@ -8197,7 +8197,7 @@ return a / b;`,Wee=` setOutputAtIndexI32(index, findBound(coords[0], value)); } } - `}};function ece(r){let{inputs:t,backend:e,attrs:o}=r,{sortedSequence:n,values:s}=t,{side:a}=o,i=new iy([s.shape[0],s.shape[1]],a),p=[{type:"int32",data:[n.shape[1]]}];return e.runWebGPUProgram(i,[n,s],"int32",p)}var mU={kernelName:hs,backendName:"webgpu",kernelFunc:ece};var uy=class{constructor(t,e,o){this.variableNames=["c","a","b"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=t,this.rank=o,this.shaderKey="select"}getUserCode(){let t,e;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)e="resRC",t="resRC";else{let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[],a=[];for(let i=0;i4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)e="resRC",t="resRC";else{let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[],a=[];for(let i=0;i{this.uniforms+=` pad${u} : vec2,`}),this.shaderKey=`spaceToBatchND_${s}`}getUserCode(){let t=ht(this.outputShape.length),e=Zv(this.newDim);return` - ${cm(this.paddedXShape,"PaddedX")} + `}};function tce(r){let{inputs:t,backend:e}=r,{condition:o,t:n,e:s}=t,a=new iy(o.shape.length,n.shape,n.shape.length);return e.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var fU={kernelName:fa,backendName:"webgpu",kernelFunc:tce};var rce=ye({opType:Z.SELU}),hU={kernelName:hs,backendName:"webgpu",kernelFunc:rce};var oce=ye({opType:Z.SIGMOID}),gU={kernelName:bs,backendName:"webgpu",kernelFunc:oce};var nce=ye({opType:Z.SIGN}),xU={kernelName:ys,backendName:"webgpu",kernelFunc:nce};var sce=ye({opType:Z.SIN}),yU={kernelName:gs,backendName:"webgpu",kernelFunc:sce};var ace=ye({opType:Z.SINH}),bU={kernelName:xs,backendName:"webgpu",kernelFunc:ace};var ice=ye({opType:Z.SOFTPLUS}),CU={kernelName:Cs,backendName:"webgpu",kernelFunc:ice};var uy=class{constructor(t,e,o,n,s,a){this.variableNames=["x"],this.outputShape=[],this.uniforms="",this.workgroupSize=[64,1,1],this.size=!0;let i=new Array(n.length);for(let p=0;p{this.uniforms+=` pad${u} : vec2,`}),this.shaderKey=`spaceToBatchND_${s}`}getUserCode(){let t=ft(this.outputShape.length),e=Jv(this.newDim);return` + ${um(this.paddedXShape,"PaddedX")} ${G("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${t}(${e}), uniforms.reshapedPaddedXShape); let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex); - ${c0(this.xShape,!0)} + ${l0(this.xShape,!0)} } } - `}};var uce=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=[[0,0]];p.push(...a);for(let b=1+s.length;bb[0]+n.shape[C]+b[1]),c=w.getReshaped(u,s,i,!1),l=w.getPermuted(c.length,s.length,!1),m=w.getReshapedPermuted(u,s,i,!1),d=y.computeStrides(u),f=new py(n.shape,u,p,c,l,d.length),h=[{type:"int32",data:c},{type:"int32",data:d}];p.map(b=>h.push({type:"int32",data:[b[0],b[1]]}));let g=e.runWebGPUProgram(f,[n],n.dtype,h),x=pe({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeData(g.dataId),x},CU={kernelName:ga,backendName:"webgpu",kernelFunc:uce};var cy=class{constructor(t,e,o){this.variableNames=["input","indices","segmentIds"],this.outputShape=[],this.uniforms="segmentSize : i32, sparseSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.type=o,this.dispatchLayout=X([e]),this.dispatch=H(this.dispatchLayout,[e],this.workgroupSize),this.shaderKey="sparseSegmentSum"}getUserCode(){return` + `}};var uce=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=[[0,0]];p.push(...a);for(let b=1+s.length;bb[0]+n.shape[C]+b[1]),c=w.getReshaped(u,s,i,!1),l=w.getPermuted(c.length,s.length,!1),m=w.getReshapedPermuted(u,s,i,!1),d=y.computeStrides(u),f=new uy(n.shape,u,p,c,l,d.length),h=[{type:"int32",data:c},{type:"int32",data:d}];p.map(b=>h.push({type:"int32",data:[b[0],b[1]]}));let g=e.runWebGPUProgram(f,[n],n.dtype,h),x=pe({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeData(g.dataId),x},wU={kernelName:ga,backendName:"webgpu",kernelFunc:uce};var py=class{constructor(t,e,o){this.variableNames=["input","indices","segmentIds"],this.outputShape=[],this.uniforms="segmentSize : i32, sparseSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.type=o,this.dispatchLayout=X([e]),this.dispatch=H(this.dispatchLayout,[e],this.workgroupSize),this.shaderKey="sparseSegmentSum"}getUserCode(){return` ${G("index")} { if (index < uniforms.sparseSize) { let indexInSegmentIds = index / uniforms.segmentSize; @@ -8229,17 +8229,17 @@ return a / b;`,Wee=` let value = input[indexInInput * uniforms.segmentSize + indexInSegment]; let outIndex = segmentId * uniforms.segmentSize + indexInSegment; - ${Qr("&result[outIndex]","value",this.type)} + ${Yr("&result[outIndex]","value",this.type)} } } - `}},ly=class{constructor(t,e){this.variableNames=["segmentIds"],this.outputShape=[],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=[t],this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),this.shaderKey="sparseSegmentIdCountProgram"}getUserCode(){return` + `}},cy=class{constructor(t,e){this.variableNames=["segmentIds"],this.outputShape=[],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=[t],this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),this.shaderKey="sparseSegmentIdCountProgram"}getUserCode(){return` ${G("index")} { if (index < uniforms.segmentIdsShape) { let segmentId = segmentIds[index]; - ${Qr("&result[segmentId]","1","int32")} + ${Yr("&result[segmentId]","1","int32")} } } - `}},my=class{constructor(t,e){this.variableNames=["segmentSum","sameSegmentIdCount"],this.outputShape=[],this.uniforms="segmentSize : i32",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.type=e,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.shaderKey="sparseSegmentMean"}getUserCode(){return` + `}},ly=class{constructor(t,e){this.variableNames=["segmentSum","sameSegmentIdCount"],this.outputShape=[],this.uniforms="segmentSize : i32",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.type=e,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.shaderKey="sparseSegmentMean"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let segmentId = index / uniforms.segmentSize; @@ -8249,21 +8249,21 @@ return a / b;`,Wee=` } } } - `}};function dy(r,t,e,o=!1,n){let a=y.sizeFromShape(r.shape)/r.shape[0],i=r.dtype,p=y.sizeFromShape(t.shape),u=n.readSync(e.dataId),l=p>0?u[p-1]+1:0,m,d=r.shape.slice();d[0]=l;let f=p*a,h=kt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new cy(d,f,i);let g=[{type:"int32",data:[a]},{type:"int32",data:[f]}],x=n.runWebGPUProgram(m,[r,t,e],i,g,h);if(o)return x;let b=kt({backend:n,attrs:{shape:[l],value:0,dtype:"int32"}});m=new ly(l,e.shape);let C=n.runWebGPUProgram(m,[e],"int32",null,b),S=kt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new my(d,i),g=[{type:"int32",data:[a]}];let k=n.runWebGPUProgram(m,[x,C],i,g,S);return n.disposeData(x.dataId),n.disposeData(C.dataId),k}function pce(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;return dy(o,n,s,!1,e)}var wU={kernelName:ya,backendName:"webgpu",kernelFunc:pce};function cce(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;return dy(o,n,s,!0,e)}var SU={kernelName:ba,backendName:"webgpu",kernelFunc:cce};var fy=class{constructor(t,e){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(t.length);for(let n=0;n0?u[p-1]+1:0,m,d=r.shape.slice();d[0]=l;let f=p*a,h=vt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new py(d,f,i);let g=[{type:"int32",data:[a]},{type:"int32",data:[f]}],x=n.runWebGPUProgram(m,[r,t,e],i,g,h);if(o)return x;let b=vt({backend:n,attrs:{shape:[l],value:0,dtype:"int32"}});m=new cy(l,e.shape);let C=n.runWebGPUProgram(m,[e],"int32",null,b),S=vt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new ly(d,i),g=[{type:"int32",data:[a]}];let k=n.runWebGPUProgram(m,[x,C],i,g,S);return n.disposeData(x.dataId),n.disposeData(C.dataId),k}function pce(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;return my(o,n,s,!1,e)}var SU={kernelName:ya,backendName:"webgpu",kernelFunc:pce};function cce(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;return my(o,n,s,!0,e)}var IU={kernelName:ba,backendName:"webgpu",kernelFunc:cce};var dy=class{constructor(t,e){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(t.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${t}aShape)`;let e=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n=5){let p=e.readSync(n.dataId),u=n.dtype==="string"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=Oz(c,s);return e.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new fy(n.shape,s);return e.runWebGPUProgram(a,[n],n.dtype)}var IU={kernelName:po,backendName:"webgpu",kernelFunc:Cm};function mce(r){let{inputs:t,backend:e,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=t,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype==="string"){let R=e.bufferSync(n),D=e.bufferSync(s),P=y.decodeString(e.readSync(a.dataId)[0]),O=Ez(R,D,i,m,c,u,p,l,P,d);return e.makeTensorInfo(i,O.dtype,O.values)}let f=[m/c,c],h=pe({inputs:{x:n},backend:e,attrs:{shape:[u,p]}}),g=s.shape.length?pe({inputs:{x:s},backend:e,attrs:{shape:[u,c]}}):Ft({inputs:{x:s},backend:e}),x=g.dtype,b=e.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=pe({inputs:{x:a},backend:e,attrs:{shape:Array(f.length).fill(1)}}),S=Cm({inputs:{x:C},backend:e,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:"int32",data:[p]},{type:"int32",data:l},{type:"int32",data:[k]}];switch(u){case 0:break;case 1:{let R=new Va([u,c],p,h.shape.length,g.shape.length,l,f,x,d);e.runWebGPUProgram(R,[g,h],x,_,S)}break;default:{let R=new 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f=[m/c,c],h=pe({inputs:{x:n},backend:e,attrs:{shape:[u,p]}}),g=s.shape.length?pe({inputs:{x:s},backend:e,attrs:{shape:[u,c]}}):At({inputs:{x:s},backend:e}),x=g.dtype,b=e.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=pe({inputs:{x:a},backend:e,attrs:{shape:Array(f.length).fill(1)}}),S=ym({inputs:{x:C},backend:e,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:"int32",data:[p]},{type:"int32",data:l},{type:"int32",data:[k]}];switch(u){case 0:break;case 1:{let R=new za([u,c],p,h.shape.length,g.shape.length,l,f,x,d);e.runWebGPUProgram(R,[g,h],x,_,S)}break;default:{let R=new za([u,c],p,h.shape.length,b.shape.length,l,f,x,d);e.runWebGPUProgram(R,[b,h],x,_,S)}{let R=new za([u,c],p,h.shape.length,g.shape.length,l,f,x);e.runWebGPUProgram(R,[g,h],x,_,S)}}let E=pe({inputs:{x:S},backend:e,attrs:{shape:i}});return e.disposeData(h.dataId),e.disposeData(g.dataId),e.disposeData(C.dataId),e.disposeData(b.dataId),e.disposeData(S.dataId),E}var kU={kernelName:vs,backendName:"webgpu",kernelFunc:mce};function dce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Hs({inputs:{x:n},backend:e,attrs:{begin:c,size:d}});return c[i]+=m,f})}var NU={kernelName:xa,backendName:"webgpu",kernelFunc:dce};var fce=ye({opType:Z.SQRT}),TU={kernelName:ws,backendName:"webgpu",kernelFunc:fce};var _U={kernelName:Ki,backendName:"webgpu",kernelFunc:({inputs:r,backend:t})=>{let{x:e}=r,o=t,n=new Zr(e.shape,Z.SQUARE);return o.runWebGPUProgram(n,[e],e.dtype)}};var hce=et({opType:fe.SQUARED_DIFFERENCE}),$U={kernelName:ks,backendName:"webgpu",kernelFunc:hce};function gce({inputs:r,attrs:t,backend:e}){let{x:o}=r,n=new Zr(o.shape,Z.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[t.alpha]}];return e.runWebGPUProgram(n,[o],o.dtype,s)}var EU={kernelName:wo,backendName:"webgpu",kernelFunc:gce};var fy=class{constructor(t){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let e=ft(this.outputShape.length);this.uniforms=`begin : ${e}, strides : ${e}, `,this.shaderKey="stridedSlice"}getUserCode(){let t=this.outputShape.length,e="";if(t===1)e="coords * uniforms.strides + uniforms.begin";else{let n=0;e=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, getX(${e})); } } - `}};function xce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=ct.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=pe({inputs:{x:n},backend:e,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ct.computeOutShape(b,C,S),E=Hs({inputs:{x:n},backend:e,attrs:{begin:b,size:_}});k=pe({inputs:{x:E},backend:e,attrs:{shape:f}}),e.disposeData(E.dataId)}else if(e.shouldExecuteOnCPU([n])){let E=e.readSync(n.dataId),R=me(n.shape,n.dtype,E),D=Az(d,R,S,b);k=e.makeTensorInfo(f,n.dtype,D.values)}else{let E=new hy(d),R=[{type:"int32",data:b},{type:"int32",data:S}],D=e.runWebGPUProgram(E,[n],n.dtype,R);k=pe({inputs:{x:D},backend:e,attrs:{shape:f}}),e.disposeData(D.dataId)}return k}var EU={kernelName:Ts,backendName:"webgpu",kernelFunc:xce};function yce(r){let{inputs:t,backend:e,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=t,m=e.readSync(c.dataId),d=e.readSync(l.dataId),[f,h]=Fz(m,d,n,s,a,i,p,u);return[e.makeTensorInfo([f.length],"string",f),e.makeTensorInfo(l.shape,"int32",h)]}var RU={kernelName:Ca,backendName:"webgpu",kernelFunc:yce};var bce=et({opType:fe.SUB,cpuKernelImpl:Pz,supportsComplex:!0}),DU={kernelName:_s,backendName:"webgpu",kernelFunc:bce};var Cce=ye({opType:Z.TAN}),AU={kernelName:$s,backendName:"webgpu",kernelFunc:Cce};var wce=ye({opType:Z.TANH}),FU={kernelName:Es,backendName:"webgpu",kernelFunc:wce};function Sce(r){let{inputs:t,backend:e,attrs:o}=r,{tensor:n,indices:s,updates:a}=t,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return e.makeTensorInfo(n.shape,s.dtype);let d=[],f=pe({inputs:{x:s},backend:e,attrs:{shape:[p,i]}});d.push(f);let h=pe({inputs:{x:a},backend:e,attrs:{shape:[p,u]}});d.push(h);let g=pe({inputs:{x:n},backend:e,attrs:{shape:m}});d.push(g);let x=Cm({inputs:{x:g},backend:e,attrs:{reps:Array(m.length).fill(1)}}),b=new Va([p,u],i,f.shape.length,h.shape.length,c,m,n.dtype,!1),C=y.sizeFromShape([p,u]),S=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[C]}],k=e.runWebGPUProgram(b,[h,f],g.dtype,S,x);d.push(k);let _=pe({inputs:{x:k},backend:e,attrs:{shape:n.shape}});return d.forEach(E=>e.disposeData(E.dataId)),_}var PU={kernelName:fs,backendName:"webgpu",kernelFunc:Sce};var gy=class{constructor(t){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, + `}};function xce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=pt.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=pe({inputs:{x:n},backend:e,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=pt.computeOutShape(b,C,S),E=Hs({inputs:{x:n},backend:e,attrs:{begin:b,size:_}});k=pe({inputs:{x:E},backend:e,attrs:{shape:f}}),e.disposeData(E.dataId)}else if(e.shouldExecuteOnCPU([n])){let E=e.readSync(n.dataId),R=me(n.shape,n.dtype,E),D=Fz(d,R,S,b);k=e.makeTensorInfo(f,n.dtype,D.values)}else{let E=new fy(d),R=[{type:"int32",data:b},{type:"int32",data:S}],D=e.runWebGPUProgram(E,[n],n.dtype,R);k=pe({inputs:{x:D},backend:e,attrs:{shape:f}}),e.disposeData(D.dataId)}return k}var RU={kernelName:Ns,backendName:"webgpu",kernelFunc:xce};function yce(r){let{inputs:t,backend:e,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=t,m=e.readSync(c.dataId),d=e.readSync(l.dataId),[f,h]=Pz(m,d,n,s,a,i,p,u);return[e.makeTensorInfo([f.length],"string",f),e.makeTensorInfo(l.shape,"int32",h)]}var DU={kernelName:Ca,backendName:"webgpu",kernelFunc:yce};var bce=et({opType:fe.SUB,cpuKernelImpl:Oz,supportsComplex:!0}),AU={kernelName:Ts,backendName:"webgpu",kernelFunc:bce};var Cce=ye({opType:Z.TAN}),FU={kernelName:_s,backendName:"webgpu",kernelFunc:Cce};var wce=ye({opType:Z.TANH}),PU={kernelName:$s,backendName:"webgpu",kernelFunc:wce};function Sce(r){let{inputs:t,backend:e,attrs:o}=r,{tensor:n,indices:s,updates:a}=t,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return e.makeTensorInfo(n.shape,s.dtype);let d=[],f=pe({inputs:{x:s},backend:e,attrs:{shape:[p,i]}});d.push(f);let h=pe({inputs:{x:a},backend:e,attrs:{shape:[p,u]}});d.push(h);let g=pe({inputs:{x:n},backend:e,attrs:{shape:m}});d.push(g);let x=ym({inputs:{x:g},backend:e,attrs:{reps:Array(m.length).fill(1)}}),b=new za([p,u],i,f.shape.length,h.shape.length,c,m,n.dtype,!1),C=y.sizeFromShape([p,u]),S=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[C]}],k=e.runWebGPUProgram(b,[h,f],g.dtype,S,x);d.push(k);let _=pe({inputs:{x:k},backend:e,attrs:{shape:n.shape}});return d.forEach(E=>e.disposeData(E.dataId)),_}var OU={kernelName:ds,backendName:"webgpu",kernelFunc:Sce};var hy=class{constructor(t){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { @@ -8331,7 +8331,7 @@ return a / b;`,Wee=` } } } - `}},xy=class{constructor(t){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return` + `}},gy=class{constructor(t){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); @@ -8390,7 +8390,7 @@ return a / b;`,Wee=` } } } - `}};function nl(r,t){t!==null&&r.disposeData(t.dataId)}function OU(r){let t=1;for(;tf===null?[l,l]:[l,f],g=(k,_,E)=>{let R=h(),D=new gy(E),O=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[_]}],M=f;f=e.runWebGPUProgram(D,R,"int32",O),nl(e,M)};for(let k=1;k=1;E/=2)g(_,E,[c,d])}for(let k=d;k>m;k/=2){let _=h(),E=new xy([c,k/2]),D=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[m]}],P=f;f=e.runWebGPUProgram(E,_,"int32",D),nl(e,P);let O=m/2,M=O*2;for(let L=O;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=Hs({inputs:{x:f},backend:e,attrs:{begin:0,size:[c,s]}}),nl(e,x);let b=u0({inputs:{x:l,indices:f},backend:e,attrs:{axis:1,batchDims:1}});nl(e,l);let C=i.slice(0,-1);C.push(s),x=f,f=pe({inputs:{x:f},attrs:{shape:C},backend:e}),nl(e,x);let S=b;return b=pe({inputs:{x:b},attrs:{shape:C},backend:e}),nl(e,S),[b,f]}var MU={kernelName:Rs,backendName:"webgpu",kernelFunc:Ice};var yy=class{constructor(t){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="transform"}getUserCode(){return` + `}};function el(r,t){t!==null&&r.disposeData(t.dataId)}function MU(r){let t=1;for(;tf===null?[l,l]:[l,f],g=(k,_,E)=>{let R=h(),D=new hy(E),O=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[_]}],M=f;f=e.runWebGPUProgram(D,R,"int32",O),el(e,M)};for(let k=1;k=1;E/=2)g(_,E,[c,d])}for(let k=d;k>m;k/=2){let _=h(),E=new gy([c,k/2]),D=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[m]}],P=f;f=e.runWebGPUProgram(E,_,"int32",D),el(e,P);let O=m/2,M=O*2;for(let L=O;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=Hs({inputs:{x:f},backend:e,attrs:{begin:0,size:[c,s]}}),el(e,x);let b=p0({inputs:{x:l,indices:f},backend:e,attrs:{axis:1,batchDims:1}});el(e,l);let C=i.slice(0,-1);C.push(s),x=f,f=pe({inputs:{x:f},attrs:{shape:C},backend:e}),el(e,x);let S=b;return b=pe({inputs:{x:b},attrs:{shape:C},backend:e}),el(e,S),[b,f]}var LU={kernelName:Es,backendName:"webgpu",kernelFunc:Ice};var xy=class{constructor(t){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="transform"}getUserCode(){return` fn mapCoord(outCoord : f32, len : f32) -> f32{ var inCoord = outCoord; if(uniforms.fillModeId == 2) { @@ -8506,7 +8506,7 @@ return a / b;`,Wee=` setOutputAtIndex(index, outputValue); } } - `}};function vce(r){let{inputs:t,backend:e,attrs:o}=r,{image:n,transforms:s}=t,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new yy(g),b=a==="nearest"?1:2,C;switch(i){case"constant":C=1;break;case"reflect":C=2;break;case"wrap":C=3;break;case"nearest":C=4;break;default:C=1;break}let S=[{type:"int32",data:[b]},{type:"int32",data:[C]},{type:"float32",data:[p]}];return e.runWebGPUProgram(x,[n,s],"float32",S)}var LU={kernelName:Ds,backendName:"webgpu",kernelFunc:vce};function kce(r){let{inputs:t,backend:e,attrs:o}=r,{value:n}=t,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;he.disposeData(h.dataId)),f}var BU={kernelName:wa,backendName:"webgpu",kernelFunc:kce};var by=class{constructor(t,e,o){if(this.outputShape=[],this.variableNames=["x","segmentIds"],this.uniforms="numSegments : i32, xSize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),o!=="float32"&&o!=="int32")throw new Error(`UnsortedSegmentSum only supports float32 and int32 + `}};function vce(r){let{inputs:t,backend:e,attrs:o}=r,{image:n,transforms:s}=t,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new xy(g),b=a==="nearest"?1:2,C;switch(i){case"constant":C=1;break;case"reflect":C=2;break;case"wrap":C=3;break;case"nearest":C=4;break;default:C=1;break}let S=[{type:"int32",data:[b]},{type:"int32",data:[C]},{type:"float32",data:[p]}];return e.runWebGPUProgram(x,[n,s],"float32",S)}var BU={kernelName:Rs,backendName:"webgpu",kernelFunc:vce};function kce(r){let{inputs:t,backend:e,attrs:o}=r,{value:n}=t,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;he.disposeData(h.dataId)),f}var zU={kernelName:wa,backendName:"webgpu",kernelFunc:kce};var yy=class{constructor(t,e,o){if(this.outputShape=[],this.variableNames=["x","segmentIds"],this.uniforms="numSegments : i32, xSize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),o!=="float32"&&o!=="int32")throw new Error(`UnsortedSegmentSum only supports float32 and int32 types, does not support ${o} type.`);this.type=o,this.shaderKey="unsortedSegmentSum"}getUserCode(){return` ${G("index")} { if (index < uniforms.xSize) { @@ -8519,8 +8519,8 @@ return a / b;`,Wee=` let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments; let value = getX(b, inCol); - ${Qr("&result[flatIndex]","value",this.type)} + ${Yr("&result[flatIndex]","value",this.type)} } } } - `}};function Nce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,segmentIds:s}=t,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=yr({inputs:{x:n},backend:e,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=pe({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=kt({backend:e,attrs:{shape:g,value:0,dtype:h}}),b=new by(f.shape,g,h),C=[{type:"int32",data:[a]},{type:"int32",data:[y.sizeFromShape(f.shape)]}],S=e.runWebGPUProgram(b,[f,s],h,C,x),k=pe({inputs:{x:S},backend:e,attrs:{shape:m}});p.push(S);let _=k;if(c!=null){p.push(k);let E=w.getUndoAxesPermutation(c);_=yr({inputs:{x:_},backend:e,attrs:{perm:E}})}return p.forEach(E=>e.disposeData(E.dataId)),_}var zU={kernelName:Ji,backendName:"webgpu",kernelFunc:Nce};var Tce=[rz,Bz,zz,Vz,Wz,Uz,Hz,Kz,qz,jz,Xz,Yz,Qz,Zz,Jz,rV,oV,nV,sV,aV,uV,pV,cV,fV,hV,gV,nz,yV,CV,wV,SV,IV,vV,kV,NV,TV,_V,$V,DV,AV,FV,PV,MV,LV,OV,BV,zV,VV,WV,UV,KV,qV,jV,XV,YV,QV,ZV,JV,eW,ez,tW,nW,rW,oW,sW,aW,iW,uW,pW,cW,lW,oz,mW,bV,dW,fW,hW,gW,xW,yW,bW,wW,CW,SW,IW,vW,NW,TW,eV,_W,$W,DW,EW,RW,AW,tV,FW,PW,OW,MW,BW,GV,zW,VW,WW,lV,UW,KW,qW,jW,XW,YW,QW,ZW,mV,JW,eU,tU,rU,tz,oU,nU,sU,aU,iU,uU,pU,cU,lU,mU,dU,fU,hU,gU,xU,yU,iV,$U,EU,RU,LW,bU,CU,wU,SU,vU,kU,NU,TU,_U,DU,HV,AU,FU,PU,IU,MU,LU,Gz,BU,zU,GW];for(let r of Tce)ri(r);var VU="4.11.0",_ce="4.11.0",$ce="4.11.0",Ece="4.11.0",Rce="4.11.0",Dce="4.11.0",Ace={tfjs:VU,"tfjs-core":VU,"tfjs-converter":_ce,"tfjs-backend-cpu":$ce,"tfjs-backend-webgl":Ece,"tfjs-backend-wasm":Rce,"tfjs-backend-webgpu":Dce};export{Xs as Abs,Wo as Acos,Uo as Acosh,np as AdadeltaOptimizer,sp as AdagradOptimizer,ap as AdamOptimizer,ip as AdamaxOptimizer,uo as Add,Go as AddN,Ho as All,Ko as Any,Ys as ArgMax,Qs as ArgMin,qo as Asin,jo as Asinh,Xo as Atan,Qo as Atan2,Yo as Atanh,Zo as AvgPool,Zs as AvgPool3D,Ai as AvgPool3DGrad,Di as AvgPoolGrad,pm as BackendWasm,Jo as BatchMatMul,Js as BatchToSpaceND,en as Bincount,ja as BitwiseAnd,ea as BroadcastArgs,Bce as BroadcastTo,bo as Cast,tn as Ceil,Co as ClipByValue,Fi as Complex,Pi as ComplexAbs,ta as Concat,rn as Conv2D,Oi as Conv2DBackpropFilter,on as Conv2DBackpropInput,nn as Conv3D,Xa as Conv3DBackpropFilterV2,sn as Conv3DBackpropInputV2,an as Cos,un as Cosh,ln as CropAndResize,pn as Cumprod,cn as Cumsum,zo as DataStorage,ra as DenseBincount,mn as DepthToSpace,dn as DepthwiseConv2dNative,Mi as DepthwiseConv2dNativeBackpropFilter,Li as DepthwiseConv2dNativeBackpropInput,oa as Diag,fn as Dilation2D,zi as Dilation2DBackpropFilter,Bi as Dilation2DBackpropInput,Pu as Draw,nw as ENV,Vi as Einsum,gn as Elu,Ya as EluGrad,hl as Environment,yn as Equal,xn as Erf,bn as Exp,na as ExpandDims,Cn as Expm1,Wi as FFT,sa as Fill,wn as FlipLeftRight,Sn as Floor,In as FloorDiv,Mu as FromPixels,vn as FusedBatchNorm,vo as FusedConv2D,ko as FusedDepthwiseConv2D,kp as GPGPUContext,kn as GatherNd,aa as GatherV2,Bl as GraphModel,Nn as Greater,Tn as GreaterEqual,Ui as IFFT,wo as Identity,Gi as Imag,_n as IsFinite,$n as IsInf,En as IsNan,ao as KernelBackend,zn as LRN,Qa as LRNGrad,Rn as LeakyRelu,Dn as Less,An as LessEqual,Fn as LinSpace,Pn as Log,On as Log1p,zce as LogSoftmax,Mn as LogicalAnd,Ln as LogicalNot,Bn as LogicalOr,$0 as LogicalXor,Vce as LowerBound,xu as MathBackendCPU,wu as MathBackendWebGL,Wce as MatrixBandPart,Vn as Max,Un as MaxPool,ia as MaxPool3D,Ki as MaxPool3DGrad,Hi as MaxPoolGrad,ua as MaxPoolWithArgmax,Wn as Maximum,Gn as Mean,Hn as Min,Kn as Minimum,qn as MirrorPad,jn as Mod,up as MomentumOptimizer,Xn as Multinomial,Yn as Multiply,pa as Neg,Zn as NonMaxSuppressionV3,Za as NonMaxSuppressionV4,Jn as NonMaxSuppressionV5,Qn as NotEqual,kw as OP_SCOPE_SUFFIX,es as OneHot,ca as OnesLike,Nr as Optimizer,Pl as OptimizerConstructors,la as Pack,ts as PadV2,Uce as Pool,rs as Pow,os as Prelu,ns as Prod,pp as RMSPropOptimizer,Qp as RaggedGather,Zp as RaggedRange,Jp as RaggedTensorToTensor,ma as Range,hw as Rank,qi as Real,hn as RealDiv,ss as Reciprocal,Rt as Reduction,as as Relu,ps as Relu6,da as Reshape,us as ResizeBilinear,ei as ResizeBilinearGrad,is as ResizeNearestNeighbor,Ja as ResizeNearestNeighborGrad,cs as Reverse,As as RotateWithOffset,ls as Round,ms as Rsqrt,mi as SGDOptimizer,ds as ScatterNd,hs as SearchSorted,fa as Select,gs as Selu,Cs as Sigmoid,bs as Sign,xs as Sin,ys as Sinh,ha as Slice,vs as Softmax,ws as Softplus,ga as SpaceToBatchND,ji as SparseFillEmptyRows,ti as SparseReshape,ya as SparseSegmentMean,ba as SparseSegmentSum,ks as SparseToDense,xa as SplitV,Ss as Sqrt,Xi as Square,Ns as SquaredDifference,Ou as StaticRegexReplace,So as Step,Ts as StridedSlice,Ca as StringNGrams,Yi as StringSplit,Qi as StringToHashBucketFast,_s as Sub,Is as Sum,$s as Tan,Es as Tanh,ut as Tensor,tt as TensorBuffer,fs as TensorScatterUpdate,po as Tile,Rs as TopK,Ds as Transform,co as Transpose,Zi as Unique,wa as Unpack,Ji as UnsortedSegmentSum,Gce as UpperBound,oi as Variable,Tu as WebGPUBackend,Sa as ZerosLike,Io as _FusedMatMul,Jt as abs,kk as acos,Nk as acosh,Ce as add,Tk as addN,_k as all,$k as any,Ek as argMax,Rk as argMin,Dk as asin,Ak as asinh,Fk as atan,Pk as atan2,Ok as atanh,fd as avgPool,Bk as avgPool3d,vde as backend,w as backend_util,zk as basicLSTMCell,au as batchNorm,Wk as batchNorm2d,Uk as batchNorm3d,Gk as batchNorm4d,hd as batchToSpaceND,gd as bincount,Hk as bitwiseAnd,E6 as booleanMaskAsync,Kk as broadcastArgs,iu as broadcastTo,Ir as broadcast_util,oT as browser,me as buffer,We as cast,qk as ceil,jk as clipByValue,Ur as clone,Er as complex,bt as concat,Xk as concat1d,Yk as concat2d,Qk as concat3d,Zk as concat4d,Jk as conv1d,uu as conv2d,e2 as conv2dTranspose,t2 as conv3d,o2 as conv3dTranspose,Zce as copyRegisteredKernels,n2 as cos,s2 as cosh,Rl as cosineWindow,a2 as cumprod,i2 as cumsum,vr as customGrad,u2 as denseBincount,Pw as deprecationWarn,p2 as depthToSpace,lc as depthwiseConv2d,A5 as deregisterOp,ou as device_util,c2 as diag,l2 as dilation2d,dde as disableDeprecationWarnings,Mt as dispose,fde as disposeVariables,je as div,d2 as divNoNan,f2 as dot,W6 as dropout,pu as einsum,Cd as elu,mde as enableDebugMode,lde as enableProdMode,Qw as enclosingPowerOfTwo,pr as engine,h2 as ensureShape,A as env,bd as equal,g2 as erf,b2 as euclideanNorm,$o as exp,Ms as expandDims,C2 as expm1,wd as eye,fc as fft,Ea as fill,Sde as findBackend,Ide as findBackendFactory,Sd as floor,dd as floorDiv,MD as forceHalfFloat,Zw as fused,Id as gather,z6 as gatherND,af as gather_util,Cde as getBackend,iw as getGradient,tc as getKernel,Ym as getKernelsForBackend,Zse as getThreadsCount,cv as gpgpu_util,AK as grad,FK as grads,qu as greater,vd as greaterEqual,Ju as ifft,lu as imag,Kj as image,G6 as inTopKAsync,fi as io,Kd as irfft,w2 as isFinite,S2 as isInf,I2 as isNaN,Rr as keep,Wt as kernel_impls,kd as leakyRelu,_l as less,mc as lessEqual,qj as linalg,v2 as linspace,$8 as loadGraphModel,E8 as loadGraphModelSync,k2 as localResponseNormalization,pi as log,Nd as log1p,N2 as logSigmoid,T2 as logSoftmax,$d as logSumExp,ju as logicalAnd,Ed as logicalNot,Rd as logicalOr,_2 as logicalXor,jj as losses,$2 as lowerBound,Ze as matMul,JN as math,Ra as max,Ad as maxPool,E2 as maxPool3d,R2 as maxPoolWithArgmax,Fd as maximum,Xu as mean,hde as memory,D2 as meshgrid,Tl as min,Yu as minimum,A2 as mirrorPad,F2 as mod,P2 as moments,A6 as movingAverage,se as mul,O2 as multiRNNCell,M2 as multinomial,cr as neg,pS as nextFrame,Ku as norm,Pd as notEqual,El as oneHot,Da as ones,L2 as onesLike,N as op,B2 as outerProduct,Aa as pad,z2 as pad1d,V2 as pad2d,W2 as pad3d,U2 as pad4d,G2 as pool,ui as pow,Md as prelu,md as print,H2 as prod,gde as profile,K2 as raggedGather,q2 as raggedRange,j2 as raggedTensorToTensor,X2 as rand,g1 as randomGamma,Ud as randomNormal,x1 as randomStandardNormal,dc as randomUniform,y1 as randomUniformInt,mu as range,bde as ready,ci as real,b1 as reciprocal,su as registerBackend,Xce as registerGradient,ri as registerKernel,D5 as registerOp,du as relu,Gd as relu6,wde as removeBackend,W as reshape,mo as reverse,C1 as reverse1d,w1 as reverse2d,S1 as reverse3d,I1 as reverse4d,hc as rfft,Hd as round,v1 as rsqrt,ke as scalar,P6 as scatterND,hu as scatter_util,$l as searchSorted,k1 as selu,N1 as separableConv2d,WN as serialization,yde as setBackend,kde as setPlatform,Qse as setThreadsCount,Xse as setWasmPath,Yse as setWasmPaths,vI as setWebGLContext,T1 as setdiff1dAsync,Tc as shared,$a as sigmoid,_1 as sign,Hj as signal,$1 as sin,E1 as sinh,Xe as slice,R1 as slice1d,D1 as slice2d,A1 as slice3d,F1 as slice4d,ct as slice_util,P1 as softmax,_d as softplus,Od as spaceToBatchND,Xj as sparse,L6 as sparseToDense,Gj as spectral,li as split,Dr as sqrt,er as square,qd as squaredDifference,gc as squeeze,kr as stack,jd as step,O1 as stridedSlice,Yj as string,Te as sub,ot as sum,ni as sumOutType,M1 as tan,Nl as tanh,ur as tensor,tr as tensor1d,fu as tensor2d,Xd as tensor3d,L1 as tensor4d,B1 as tensor5d,z1 as tensor6d,W1 as tensorScatterUpdate,ek as tensor_util,h1 as test_util,De as tidy,cu as tile,xde as time,U1 as topk,TGe as train,yc as transpose,G1 as truncatedNormal,H1 as unique,Qce as unregisterGradient,Yce as unregisterKernel,K1 as unsortedSegmentSum,fo as unstack,dt as upcastType,q1 as upperBound,y as util,PK as valueAndGrad,OK as valueAndGrads,j1 as variable,zw as variableGrads,Ace as version,D8 as version_converter,_X as version_core,cY as version_cpu,Jse as version_wasm,s9 as version_webgl,Cat as webgl,Ac as webgl_util,Yv as webgpu_util,lo as where,Qd as whereAsync,Gr as zeros,Ht as zerosLike}; + `}};function Nce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,segmentIds:s}=t,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=xr({inputs:{x:n},backend:e,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=pe({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=vt({backend:e,attrs:{shape:g,value:0,dtype:h}}),b=new yy(f.shape,g,h),C=[{type:"int32",data:[a]},{type:"int32",data:[y.sizeFromShape(f.shape)]}],S=e.runWebGPUProgram(b,[f,s],h,C,x),k=pe({inputs:{x:S},backend:e,attrs:{shape:m}});p.push(S);let _=k;if(c!=null){p.push(k);let E=w.getUndoAxesPermutation(c);_=xr({inputs:{x:_},backend:e,attrs:{perm:E}})}return p.forEach(E=>e.disposeData(E.dataId)),_}var VU={kernelName:Yi,backendName:"webgpu",kernelFunc:Nce};var Tce=[oz,zz,Vz,Wz,Uz,Gz,Kz,qz,jz,Xz,Yz,Qz,Zz,Jz,eV,oV,nV,sV,aV,iV,pV,cV,lV,hV,gV,xV,sz,bV,wV,SV,IV,vV,kV,NV,TV,_V,$V,EV,AV,FV,PV,OV,LV,BV,MV,zV,VV,WV,UV,GV,qV,jV,XV,YV,QV,ZV,JV,eW,tW,tz,rW,sW,oW,nW,aW,iW,uW,pW,cW,lW,mW,nz,dW,CV,fW,hW,gW,xW,yW,bW,CW,SW,wW,IW,vW,kW,TW,_W,tV,$W,EW,AW,RW,DW,FW,rV,PW,OW,MW,LW,zW,HV,VW,WW,UW,mV,GW,qW,jW,XW,YW,QW,ZW,JW,dV,eU,tU,rU,oU,rz,nU,sU,aU,iU,uU,pU,cU,lU,mU,dU,fU,hU,gU,xU,yU,bU,uV,EU,RU,DU,BW,CU,wU,SU,IU,kU,NU,TU,_U,$U,AU,KV,FU,PU,OU,vU,LU,BU,Hz,zU,VU,HW];for(let r of Tce)ti(r);var WU="4.14.0",_ce="4.14.0",$ce="4.14.0",Ece="4.14.0",Rce="4.14.0",Dce="4.14.0",Ace={tfjs:WU,"tfjs-core":WU,"tfjs-converter":_ce,"tfjs-backend-cpu":$ce,"tfjs-backend-webgl":Ece,"tfjs-backend-wasm":Rce,"tfjs-backend-webgpu":Dce};var bQt=void 0;export{Xs as Abs,Vo as Acos,Wo as Acosh,Qu as AdadeltaOptimizer,Zu as AdagradOptimizer,Ju as AdamOptimizer,ep as AdamaxOptimizer,io as Add,Uo as AddN,Go as All,Ho as Any,Ys as ArgMax,Qs as ArgMin,Ko as Asin,qo as Asinh,jo as Atan,Yo as Atan2,Xo as Atanh,Qo as AvgPool,Zs as AvgPool3D,Ei as AvgPool3DGrad,$i as AvgPoolGrad,im as BackendWasm,Zo as BatchMatMul,Js as BatchToSpaceND,Jo as Bincount,qa as BitwiseAnd,ea as BroadcastArgs,Bce as BroadcastTo,yo as Cast,en as Ceil,bo as ClipByValue,Ri as Complex,Di as ComplexAbs,ta as Concat,tn as Conv2D,Ai as Conv2DBackpropFilter,rn as Conv2DBackpropInput,on as Conv3D,ja as Conv3DBackpropFilterV2,nn as Conv3DBackpropInputV2,sn as Cos,an as Cosh,cn as CropAndResize,un as Cumprod,pn as Cumsum,Bo as DataStorage,ra as DenseBincount,ln as DepthToSpace,mn as DepthwiseConv2dNative,Fi as DepthwiseConv2dNativeBackpropFilter,Pi as DepthwiseConv2dNativeBackpropInput,oa as Diag,dn as Dilation2D,Mi as Dilation2DBackpropFilter,Oi as Dilation2DBackpropInput,_u as Draw,ow as ENV,Li as Einsum,hn as Elu,Xa as EluGrad,ll as Environment,xn as Equal,gn as Erf,yn as Exp,na as ExpandDims,bn as Expm1,Bi as FFT,sa as Fill,Cn as FlipLeftRight,wn as Floor,Sn as FloorDiv,Eu as FromPixels,In as FusedBatchNorm,Io as FusedConv2D,vo as FusedDepthwiseConv2D,xp as GPGPUContext,vn as GatherNd,aa as GatherV2,Ml as GraphModel,kn as Greater,Nn as GreaterEqual,zi as IFFT,Co as Identity,Vi as Imag,Tn as IsFinite,_n as IsInf,$n as IsNan,so as KernelBackend,Bn as LRN,Ya as LRNGrad,En as LeakyRelu,Rn as Less,Dn as LessEqual,An as LinSpace,Fn as Log,Pn as Log1p,zce as LogSoftmax,On as LogicalAnd,Mn as LogicalNot,Ln as LogicalOr,E0 as LogicalXor,Vce as LowerBound,hc as MathBackendCPU,Oc as MathBackendWebGL,Wce as MatrixBandPart,zn as Max,Wn as MaxPool,ia as MaxPool3D,Ui as MaxPool3DGrad,Wi as MaxPoolGrad,ua as MaxPoolWithArgmax,Vn as Maximum,Un as Mean,Gn as Min,Hn as Minimum,Kn as MirrorPad,qn as Mod,tp as MomentumOptimizer,jn as Multinomial,Xn as Multiply,pa as Neg,Qn as NonMaxSuppressionV3,Qa as NonMaxSuppressionV4,Zn as NonMaxSuppressionV5,Yn as NotEqual,kw as OP_SCOPE_SUFFIX,Jn as OneHot,ca as OnesLike,kr as Optimizer,Dl as OptimizerConstructors,la as Pack,es as PadV2,Uce as Pool,ts as Pow,rs as Prelu,os as Prod,rp as RMSPropOptimizer,Up as RaggedGather,Gp as RaggedRange,Hp as RaggedTensorToTensor,ma as Range,hw as Rank,Gi as Real,fn as RealDiv,ns as Reciprocal,Et as Reduction,ss as Relu,us as Relu6,da as Reshape,is as ResizeBilinear,Ja as ResizeBilinearGrad,as as ResizeNearestNeighbor,Za as ResizeNearestNeighborGrad,ps as Reverse,Ds as RotateWithOffset,cs as Round,ls as Rsqrt,li as SGDOptimizer,ms as ScatterNd,fs as SearchSorted,fa as Select,hs as Selu,bs as Sigmoid,ys as Sign,gs as Sin,xs as Sinh,ha as Slice,Is as Softmax,Cs as Softplus,ga as SpaceToBatchND,Hi as SparseFillEmptyRows,ei as SparseReshape,ya as SparseSegmentMean,ba as SparseSegmentSum,vs as SparseToDense,xa as SplitV,ws as Sqrt,Ki as Square,ks as SquaredDifference,$u as StaticRegexReplace,wo as Step,Ns as StridedSlice,Ca as StringNGrams,qi as StringSplit,ji as StringToHashBucketFast,Ts as Sub,Ss as Sum,_s as Tan,$s as Tanh,mt as Tensor,tt as TensorBuffer,ds as TensorScatterUpdate,uo as Tile,Es as TopK,Rs as Transform,po as Transpose,Xi as Unique,wa as Unpack,Yi as UnsortedSegmentSum,Gce as UpperBound,ri as Variable,Kc as WebGPUBackend,Sa as ZerosLike,So as _FusedMatMul,Qt as abs,Nk as acos,Tk as acosh,Ce as add,_k as addN,$k as all,Ek as any,Rk as argMax,Dk as argMin,Ak as asin,Fk as asinh,Pk as atan,Ok as atan2,Mk as atanh,md as avgPool,zk as avgPool3d,vde as backend,w as backend_util,Vk as basicLSTMCell,ru as batchNorm,Uk as batchNorm2d,Gk as batchNorm3d,Hk as batchNorm4d,dd as batchToSpaceND,fd as bincount,Kk as bitwiseAnd,E6 as booleanMaskAsync,qk as broadcastArgs,ou as broadcastTo,Sr as broadcast_util,nT as browser,me as buffer,We as cast,jk as ceil,Xk as clipByValue,Wr as clone,$r as complex,yt as concat,Yk as concat1d,Qk as concat2d,Zk as concat3d,Jk as concat4d,e2 as conv1d,nu as conv2d,t2 as conv2dTranspose,r2 as conv3d,n2 as conv3dTranspose,Zce as copyRegisteredKernels,s2 as cos,a2 as cosh,_l as cosineWindow,i2 as cumprod,u2 as cumsum,Ir as customGrad,p2 as denseBincount,Pw as deprecationWarn,c2 as depthToSpace,oc as depthwiseConv2d,A5 as deregisterOp,Ji as device_util,l2 as diag,m2 as dilation2d,dde as disableDeprecationWarnings,Ot as dispose,fde as disposeVariables,je as div,f2 as divNoNan,h2 as dot,W6 as dropout,su as einsum,yd as elu,mde as enableDebugMode,lde as enableProdMode,Qw as enclosingPowerOfTwo,ur as engine,g2 as ensureShape,A as env,xd as equal,x2 as erf,C2 as euclideanNorm,_o as exp,Ms as expandDims,w2 as expm1,bd as eye,ac as fft,Ea as fill,Sde as findBackend,Ide as findBackendFactory,Cd as floor,ld as floorDiv,LD as forceHalfFloat,Zw as fused,wd as gather,z6 as gatherND,sf as gather_util,Cde as getBackend,aw as getGradient,qp as getKernel,jm as getKernelsForBackend,Zse as getThreadsCount,lv as gpgpu_util,AK as grad,FK as grads,zu as greater,Sd as greaterEqual,Ku as ifft,iu as imag,Kj as image,G6 as inTopKAsync,mi as io,Gd as irfft,S2 as isFinite,I2 as isInf,v2 as isNaN,Er as keep,Vt as kernel_impls,Id as leakyRelu,kl as less,nc as lessEqual,qj as linalg,k2 as linspace,$8 as loadGraphModel,E8 as loadGraphModelSync,N2 as localResponseNormalization,ui as log,vd as log1p,T2 as logSigmoid,_2 as logSoftmax,Td as logSumExp,Vu as logicalAnd,_d as logicalNot,$d as logicalOr,$2 as logicalXor,jj as losses,E2 as lowerBound,Ze as matMul,eT as math,Ra as max,Rd as maxPool,R2 as maxPool3d,D2 as maxPoolWithArgmax,Dd as maximum,Wu as mean,hde as memory,A2 as meshgrid,vl as min,Uu as minimum,F2 as mirrorPad,P2 as mod,O2 as moments,A6 as movingAverage,se as mul,M2 as multiRNNCell,L2 as multinomial,pr as neg,pS as nextFrame,bQt as node,Bu as norm,Ad as notEqual,Tl as oneHot,Da as ones,B2 as onesLike,N as op,z2 as outerProduct,Aa as pad,V2 as pad1d,W2 as pad2d,U2 as pad3d,G2 as pad4d,H2 as pool,ii as pow,Pd as prelu,cd as print,K2 as prod,gde as profile,q2 as raggedGather,j2 as raggedRange,X2 as raggedTensorToTensor,Y2 as rand,x1 as randomGamma,Vd as randomNormal,y1 as randomStandardNormal,sc as randomUniform,b1 as randomUniformInt,uu as range,bde as ready,pi as real,C1 as reciprocal,tu as registerBackend,Xce as registerGradient,ti as registerKernel,D5 as registerOp,pu as relu,Wd as relu6,wde as removeBackend,W as reshape,lo as reverse,w1 as reverse1d,S1 as reverse2d,I1 as reverse3d,v1 as reverse4d,ic as rfft,Ud as round,k1 as rsqrt,ke as scalar,P6 as scatterND,lu as scatter_util,Nl as searchSorted,N1 as selu,T1 as separableConv2d,UN as serialization,yde as setBackend,kde as setPlatform,Qse as setThreadsCount,Xse as setWasmPath,Yse as setWasmPaths,kI as setWebGLContext,_1 as setdiff1dAsync,wc as shared,$a as sigmoid,$1 as sign,Hj as signal,E1 as sin,R1 as sinh,Xe as slice,D1 as slice1d,A1 as slice2d,F1 as slice3d,P1 as slice4d,pt as slice_util,O1 as softmax,Nd as softplus,Fd as spaceToBatchND,Xj as sparse,L6 as sparseToDense,Gj as spectral,ci as split,Rr as sqrt,Zt as square,Hd as squaredDifference,uc as squeeze,vr as stack,Kd as step,M1 as stridedSlice,Yj as string,Te as sub,ot as sum,oi as sumOutType,L1 as tan,Il as tanh,ar as tensor,Jt as tensor1d,cu as tensor2d,qd as tensor3d,B1 as tensor4d,z1 as tensor5d,V1 as tensor6d,U1 as tensorScatterUpdate,tk as tensor_util,g1 as test_util,De as tidy,au as tile,xde as time,G1 as topk,TGe as train,cc as transpose,H1 as truncatedNormal,K1 as unique,Qce as unregisterGradient,Yce as unregisterKernel,q1 as unsortedSegmentSum,mo as unstack,dt as upcastType,j1 as upperBound,y as util,PK as valueAndGrad,OK as valueAndGrads,X1 as variable,zw as variableGrads,Ace as version,D8 as version_converter,_X as version_core,cY as version_cpu,Jse as version_wasm,s9 as version_webgl,Cat as webgl,Tc as webgl_util,Qv as webgpu_util,co as where,Xd as whereAsync,Ur as zeros,Gt as zerosLike}; diff --git a/dist/tfjs.version.js b/dist/tfjs.version.js index 2f442fdb..15752b1c 100644 --- a/dist/tfjs.version.js +++ b/dist/tfjs.version.js @@ -4,4 +4,4 @@ author: ' */ -var e="4.11.0";var s="4.11.0";var t="4.11.0";var n="4.11.0";var r="4.11.0";var i="4.11.0";var h={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":r,"tfjs-backend-webgpu":i};export{h as version}; +var e="4.14.0";var s="4.14.0";var t="4.14.0";var n="4.14.0";var r="4.14.0";var i="4.14.0";var h={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":r,"tfjs-backend-webgpu":i};export{h as version}; diff --git a/package.json b/package.json index 7dfa91bc..f2057a40 100644 --- a/package.json +++ b/package.json @@ -95,8 +95,8 @@ "@types/offscreencanvas": "^2019.7.3", "@typescript-eslint/eslint-plugin": "^6.13.2", "@typescript-eslint/parser": "^6.13.2", - "@vladmandic/build": "^0.9.2", - "@vladmandic/pilogger": "^0.4.8", + "@vladmandic/build": "^0.9.3", + "@vladmandic/pilogger": "^0.4.9", "@vladmandic/tfjs": "github:vladmandic/tfjs", "canvas": "^2.11.2", "esbuild": "^0.19.8", @@ -111,6 +111,6 @@ "rimraf": "^5.0.5", "tslib": "^2.6.2", "typedoc": "0.25.4", - "typescript": "~5.1.0" + "typescript": "~5.3.0" } } diff --git a/src/util/env.ts b/src/util/env.ts index f28adbee..2a644b91 100644 --- a/src/util/env.ts +++ b/src/util/env.ts @@ -99,7 +99,7 @@ export class Env { set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; } constructor() { - this.browser = typeof navigator !== 'undefined' && Object.keys(navigator).length !== 0; + this.browser = (typeof navigator !== 'undefined') && (typeof navigator.appVersion !== 'undefined'); this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined'); this.tfjs = { version: tf.version['tfjs-core'] }; this.offscreen = typeof OffscreenCanvas !== 'undefined'; diff --git a/test/build.log b/test/build.log index 15ab1ea1..c7d87fcf 100644 --- a/test/build.log +++ b/test/build.log @@ -1,51 +1,51 @@ -2023-09-18 12:49:46 DATA:  Build {"name":"@vladmandic/human","version":"3.1.2"} -2023-09-18 12:49:46 INFO:  Application: {"name":"@vladmandic/human","version":"3.1.2"} -2023-09-18 12:49:46 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} -2023-09-18 12:49:46 INFO:  Toolchain: {"build":"0.9.2","esbuild":"0.17.19","typescript":"5.1.6","typedoc":"0.23.28","eslint":"8.49.0"} -2023-09-18 12:49:46 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} -2023-09-18 12:49:46 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} -2023-09-18 12:49:46 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":358} -2023-09-18 12:49:46 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":566,"outputBytes":957} -2023-09-18 12:49:46 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":675948,"outputBytes":320762} -2023-09-18 12:49:46 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":574,"outputBytes":965} -2023-09-18 12:49:46 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":675956,"outputBytes":320766} -2023-09-18 12:49:46 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":662,"outputBytes":2003} -2023-09-18 12:49:46 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":676994,"outputBytes":320877} -2023-09-18 12:49:46 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1372,"outputBytes":667} -2023-09-18 12:49:46 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":675658,"outputBytes":319325} -2023-09-18 12:49:46 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1372,"outputBytes":1264969} -2023-09-18 12:49:46 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1939960,"outputBytes":1579985} -2023-09-18 12:49:46 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1939960,"outputBytes":2057229} -2023-09-18 12:49:50 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":15} -2023-09-18 12:49:51 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":81,"generated":true} -2023-09-18 12:49:51 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":2970} -2023-09-18 12:49:51 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17503,"outputBytes":9403} -2023-09-18 12:49:51 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":22791} -2023-09-18 12:49:59 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":172,"errors":0,"warnings":0} -2023-09-18 12:49:59 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} -2023-09-18 12:49:59 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} -2023-09-18 12:49:59 INFO:  Done... -2023-09-18 12:49:59 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} -2023-09-18 12:49:59 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} -2023-09-18 12:49:59 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} -2023-09-18 12:49:59 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} -2023-09-18 12:49:59 STATE: Filter: {"input":"types/tfjs-core.d.ts"} -2023-09-18 12:50:00 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":211} -2023-09-18 12:50:00 STATE: Filter: {"input":"types/human.d.ts"} -2023-09-18 12:50:00 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} -2023-09-18 12:50:00 STATE: Write: {"output":"dist/human.esm.d.ts"} -2023-09-18 12:50:00 STATE: Write: {"output":"dist/human.d.ts"} -2023-09-18 12:50:00 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} -2023-09-18 12:50:00 STATE: Write: {"output":"dist/human.node.d.ts"} -2023-09-18 12:50:00 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} -2023-09-18 12:50:00 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} -2023-09-18 12:50:00 STATE: Models {"folder":"./models","models":12} -2023-09-18 12:50:00 STATE: Models {"folder":"../human-models/models","models":44} -2023-09-18 12:50:00 STATE: Models {"folder":"../blazepose/model/","models":4} -2023-09-18 12:50:00 STATE: Models {"folder":"../anti-spoofing/model","models":1} -2023-09-18 12:50:00 STATE: Models {"folder":"../efficientpose/models","models":3} -2023-09-18 12:50:00 STATE: Models {"folder":"../insightface/models","models":5} -2023-09-18 12:50:00 STATE: Models {"folder":"../movenet/models","models":3} -2023-09-18 12:50:00 STATE: Models {"folder":"../nanodet/models","models":4} -2023-09-18 12:50:00 STATE: Models: {"count":58,"totalSize":380063249} -2023-09-18 12:50:00 INFO:  Human Build complete... {"logFile":"test/build.log"} +2023-12-06 14:57:40 DATA:  Build {"name":"@vladmandic/human","version":"3.2.0"} +2023-12-06 14:57:40 INFO:  Application: {"name":"@vladmandic/human","version":"3.2.0"} +2023-12-06 14:57:40 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} +2023-12-06 14:57:40 INFO:  Toolchain: {"build":"0.9.2","esbuild":"0.19.8","typescript":"5.3.3","typedoc":"0.25.4","eslint":"8.55.0"} +2023-12-06 14:57:40 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} +2023-12-06 14:57:40 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} +2023-12-06 14:57:40 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":358} +2023-12-06 14:57:40 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":566,"outputBytes":957} +2023-12-06 14:57:40 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":676002,"outputBytes":320790} +2023-12-06 14:57:40 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":574,"outputBytes":965} +2023-12-06 14:57:40 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":676010,"outputBytes":320794} +2023-12-06 14:57:40 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":662,"outputBytes":2003} +2023-12-06 14:57:40 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":677048,"outputBytes":320905} +2023-12-06 14:57:40 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1403,"outputBytes":690} +2023-12-06 14:57:40 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":675735,"outputBytes":319371} +2023-12-06 14:57:40 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1403,"outputBytes":1265002} +2023-12-06 14:57:40 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1940047,"outputBytes":1580115} +2023-12-06 14:57:40 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1940047,"outputBytes":2069972} +2023-12-06 14:57:42 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":14} +2023-12-06 14:57:44 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":81,"generated":true} +2023-12-06 14:57:44 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":2970} +2023-12-06 14:57:44 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17499,"outputBytes":9399} +2023-12-06 14:57:44 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":22791} +2023-12-06 14:57:51 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":172,"errors":0,"warnings":0} +2023-12-06 14:57:51 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} +2023-12-06 14:57:51 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} +2023-12-06 14:57:51 INFO:  Done... +2023-12-06 14:57:51 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} +2023-12-06 14:57:51 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} +2023-12-06 14:57:51 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} +2023-12-06 14:57:51 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} +2023-12-06 14:57:51 STATE: Filter: {"input":"types/tfjs-core.d.ts"} +2023-12-06 14:57:52 ERROR: API-Extractor: {} +2023-12-06 14:57:52 STATE: Filter: {"input":"types/human.d.ts"} +2023-12-06 14:57:52 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} +2023-12-06 14:57:52 STATE: Write: {"output":"dist/human.esm.d.ts"} +2023-12-06 14:57:52 STATE: Write: {"output":"dist/human.d.ts"} +2023-12-06 14:57:52 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} +2023-12-06 14:57:52 STATE: Write: {"output":"dist/human.node.d.ts"} +2023-12-06 14:57:52 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} +2023-12-06 14:57:52 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} +2023-12-06 14:57:52 STATE: Models {"folder":"./models","models":12} +2023-12-06 14:57:52 STATE: Models {"folder":"../human-models/models","models":44} +2023-12-06 14:57:52 STATE: Models {"folder":"../blazepose/model/","models":4} +2023-12-06 14:57:52 STATE: Models {"folder":"../anti-spoofing/model","models":1} +2023-12-06 14:57:52 STATE: Models {"folder":"../efficientpose/models","models":3} +2023-12-06 14:57:52 STATE: Models {"folder":"../insightface/models","models":5} +2023-12-06 14:57:52 STATE: Models {"folder":"../movenet/models","models":3} +2023-12-06 14:57:52 STATE: Models {"folder":"../nanodet/models","models":4} +2023-12-06 14:57:52 STATE: Models: {"count":58,"totalSize":380063249} +2023-12-06 14:57:52 INFO:  Human Build complete... {"logFile":"test/build.log"} diff --git a/test/test.log b/test/test.log index 29200871..02ae18da 100644 --- a/test/test.log +++ b/test/test.log @@ -1,905 +1,905 @@ -2023-09-18 12:45:27 INFO:  @vladmandic/human version 3.1.2 -2023-09-18 12:45:27 INFO:  User: vlado Platform: linux Arch: x64 Node: v20.6.1 -2023-09-18 12:45:27 INFO:  demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]},{"cmd":"../demo/nodejs/node-bench.js","args":[]},{"cmd":"../test/test-node-emotion.js","args":[]}] -2023-09-18 12:45:27 INFO:  {"cmd":"../demo/nodejs/node.js","args":[]} start -2023-09-18 12:45:28 INFO:  {"cmd":"../demo/nodejs/node-simple.js","args":[]} start -2023-09-18 12:45:29 INFO:  {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start -2023-09-18 12:45:30 INFO:  {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start -2023-09-18 12:45:30 INFO:  {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start -2023-09-18 12:45:31 INFO:  {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start -2023-09-18 12:45:33 INFO:  {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start -2023-09-18 12:45:45 INFO:  {"cmd":"../demo/facematch/node-match.js","args":[]} start -2023-09-18 12:45:46 INFO:  {"cmd":"../demo/nodejs/node-bench.js","args":[]} start -2023-09-18 12:46:01 INFO:  {"cmd":"../test/test-node-emotion.js","args":[]} start -2023-09-18 12:46:07 INFO:  tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"] -2023-09-18 12:46:07 INFO:  -2023-09-18 12:46:07 INFO:  test-node-load.js start -2023-09-18 12:46:07 INFO:  test-node-load.js load start {"human":"3.1.2","tf":"4.11.0","progress":0} -2023-09-18 12:46:07 DATA:  test-node-load.js load interval {"elapsed":0,"progress":0} -2023-09-18 12:46:07 DATA:  test-node-load.js load interval {"elapsed":12,"progress":0} -2023-09-18 12:46:07 DATA:  test-node-load.js load interval {"elapsed":26,"progress":0.12865823126207815} -2023-09-18 12:46:07 DATA:  test-node-load.js load interval {"elapsed":40,"progress":0.33826045252170456} -2023-09-18 12:46:07 DATA:  test-node-load.js load interval {"elapsed":64,"progress":0.4726970548784964} -2023-09-18 12:46:07 DATA:  test-node-load.js load interval {"elapsed":77,"progress":0.68355493315821} -2023-09-18 12:46:07 STATE: test-node-load.js passed {"progress":1} -2023-09-18 12:46:07 INFO:  test-node-load.js load final {"progress":1} -2023-09-18 12:46:07 DATA:  test-node-load.js load interval {"elapsed":435,"progress":1} -2023-09-18 12:46:07 INFO:  -2023-09-18 12:46:07 INFO:  test-node-gear.js start -2023-09-18 12:46:08 DATA:  test-node-gear.js input: ["samples/in/ai-face.jpg"] -2023-09-18 12:46:09 STATE: test-node-gear.js passed: gear faceres samples/in/ai-face.jpg -2023-09-18 12:46:09 DATA:  test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.5,"gender":"female","genderScore":0.92,"emotion":[]} -2023-09-18 12:46:09 STATE: test-node-gear.js passed: gear gear samples/in/ai-face.jpg -2023-09-18 12:46:09 DATA:  test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":23.3,"gender":"female","genderScore":0.51,"race":[{"score":0.93,"race":"white"}],"emotion":[]} -2023-09-18 12:46:09 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg -2023-09-18 12:46:09 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"female","genderScore":0.99,"emotion":[]} -2023-09-18 12:46:10 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg -2023-09-18 12:46:10 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"male","emotion":[]} -2023-09-18 12:46:10 INFO:  -2023-09-18 12:46:10 INFO:  test-backend-node.js start -2023-09-18 12:46:10 INFO:  test-backend-node.js test: configuration validation -2023-09-18 12:46:10 STATE: test-backend-node.js passed: configuration default validation [] -2023-09-18 12:46:10 STATE: test-backend-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2023-09-18 12:46:10 INFO:  test-backend-node.js test: model load -2023-09-18 12:46:10 STATE: test-backend-node.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] -2023-09-18 12:46:10 INFO:  test-backend-node.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2023-09-18 12:46:10 INFO:  test-backend-node.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2023-09-18 12:46:10 INFO:  test-backend-node.js test: warmup -2023-09-18 12:46:10 STATE: test-backend-node.js passed: create human -2023-09-18 12:46:10 INFO:  test-backend-node.js human version: 3.1.2 -2023-09-18 12:46:10 INFO:  test-backend-node.js platform: linux x64 agent: NodeJS v20.6.1 -2023-09-18 12:46:10 INFO:  test-backend-node.js tfjs version: 4.11.0 -2023-09-18 12:46:10 INFO:  test-backend-node.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v20.6.1","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.11.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":172} -2023-09-18 12:46:10 STATE: test-backend-node.js passed: set backend: tensorflow -2023-09-18 12:46:10 STATE: test-backend-node.js tensors 1785 -2023-09-18 12:46:10 STATE: test-backend-node.js result: defined models: 11 loaded models: 11 -2023-09-18 12:46:10 STATE: test-backend-node.js passed: load models 11 -2023-09-18 12:46:10 STATE: test-backend-node.js passed: warmup: none default -2023-09-18 12:46:10 DATA:  test-backend-node.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2023-09-18 12:46:10 DATA:  test-backend-node.js result: performance: load: null total: null -2023-09-18 12:46:10 STATE: test-backend-node.js passed: warmup none result match -2023-09-18 12:46:10 STATE: test-backend-node.js event: image -2023-09-18 12:46:10 STATE: test-backend-node.js event: detect -2023-09-18 12:46:10 STATE: test-backend-node.js event: warmup -2023-09-18 12:46:10 STATE: test-backend-node.js passed: warmup: face default -2023-09-18 12:46:10 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} -2023-09-18 12:46:10 DATA:  test-backend-node.js result: performance: load: null total: 420 -2023-09-18 12:46:10 STATE: test-backend-node.js passed: warmup face result match -2023-09-18 12:46:10 STATE: test-backend-node.js event: image -2023-09-18 12:46:11 STATE: test-backend-node.js event: detect -2023-09-18 12:46:11 STATE: test-backend-node.js event: warmup -2023-09-18 12:46:11 STATE: test-backend-node.js passed: warmup: body default -2023-09-18 12:46:11 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:11 DATA:  test-backend-node.js result: performance: load: null total: 323 -2023-09-18 12:46:11 STATE: test-backend-node.js passed: warmup body result match -2023-09-18 12:46:11 STATE: test-backend-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2023-09-18 12:46:11 INFO:  test-backend-node.js test: details verification -2023-09-18 12:46:11 STATE: test-backend-node.js start default -2023-09-18 12:46:11 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:11 STATE: test-backend-node.js event: image -2023-09-18 12:46:11 STATE: test-backend-node.js event: detect -2023-09-18 12:46:11 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg default -2023-09-18 12:46:11 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:11 DATA:  test-backend-node.js result: performance: load: null total: 311 -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details face length 1 -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details face score 1 0.93 1 -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details face age/gender 23.7 female 0.97 2.34 -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details face arrays 4 478 1024 -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details face anti-spoofing 0.79 -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details face liveness 0.83 -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details body length 1 -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details body 0.92 17 6 -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details hand length 1 -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details hand 0.51 0.73 point -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details hand arrays 21 5 7 -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details gesture length 7 -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details gesture first {"face":0,"gesture":"facing right"} -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details object length 1 -2023-09-18 12:46:11 STATE: test-backend-node.js passed: details object 0.72 person -2023-09-18 12:46:11 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} -2023-09-18 12:46:11 STATE: test-backend-node.js event: image -2023-09-18 12:46:12 STATE: test-backend-node.js event: detect -2023-09-18 12:46:12 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2023-09-18 12:46:12 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} -2023-09-18 12:46:12 STATE: test-backend-node.js event: image -2023-09-18 12:46:12 STATE: test-backend-node.js event: detect -2023-09-18 12:46:12 STATE: test-backend-node.js passed: tensor shape: [1200,1200,4] dtype: float32 -2023-09-18 12:46:12 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:12 STATE: test-backend-node.js event: image -2023-09-18 12:46:13 STATE: test-backend-node.js event: detect -2023-09-18 12:46:13 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2023-09-18 12:46:13 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:13 STATE: test-backend-node.js event: image -2023-09-18 12:46:13 STATE: test-backend-node.js event: detect -2023-09-18 12:46:13 STATE: test-backend-node.js passed: tensor shape: [1200,1200,3] dtype: float32 -2023-09-18 12:46:13 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2023-09-18 12:46:13 STATE: test-backend-node.js event: image -2023-09-18 12:46:13 STATE: test-backend-node.js event: detect -2023-09-18 12:46:13 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2023-09-18 12:46:13 INFO:  test-backend-node.js test default -2023-09-18 12:46:13 STATE: test-backend-node.js start async -2023-09-18 12:46:14 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:14 STATE: test-backend-node.js event: image -2023-09-18 12:46:14 STATE: test-backend-node.js event: detect -2023-09-18 12:46:14 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg async -2023-09-18 12:46:14 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:14 DATA:  test-backend-node.js result: performance: load: null total: 311 -2023-09-18 12:46:14 STATE: test-backend-node.js passed: default result face match 1 female 0.97 -2023-09-18 12:46:14 INFO:  test-backend-node.js test sync -2023-09-18 12:46:14 STATE: test-backend-node.js start sync -2023-09-18 12:46:14 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:14 STATE: test-backend-node.js event: image -2023-09-18 12:46:15 STATE: test-backend-node.js event: detect -2023-09-18 12:46:15 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sync -2023-09-18 12:46:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:15 DATA:  test-backend-node.js result: performance: load: null total: 318 -2023-09-18 12:46:15 STATE: test-backend-node.js passed: default sync 1 female 0.97 -2023-09-18 12:46:15 INFO:  test-backend-node.js test: image process -2023-09-18 12:46:15 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:15 STATE: test-backend-node.js passed: image input null [1,256,256,3] -2023-09-18 12:46:15 INFO:  test-backend-node.js test: image null -2023-09-18 12:46:15 STATE: test-backend-node.js passed: invalid input could not convert input to tensor -2023-09-18 12:46:15 INFO:  test-backend-node.js test face similarity -2023-09-18 12:46:15 STATE: test-backend-node.js start face similarity -2023-09-18 12:46:15 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:15 STATE: test-backend-node.js event: image -2023-09-18 12:46:15 STATE: test-backend-node.js event: detect -2023-09-18 12:46:15 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face similarity -2023-09-18 12:46:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2023-09-18 12:46:15 DATA:  test-backend-node.js result: performance: load: null total: 308 -2023-09-18 12:46:15 STATE: test-backend-node.js start face similarity -2023-09-18 12:46:15 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:15 STATE: test-backend-node.js event: image -2023-09-18 12:46:15 STATE: test-backend-node.js event: detect -2023-09-18 12:46:15 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg face similarity -2023-09-18 12:46:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:15 DATA:  test-backend-node.js result: performance: load: null total: 301 -2023-09-18 12:46:15 STATE: test-backend-node.js start face similarity -2023-09-18 12:46:15 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-09-18 12:46:15 STATE: test-backend-node.js event: image -2023-09-18 12:46:16 STATE: test-backend-node.js event: detect -2023-09-18 12:46:16 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg face similarity -2023-09-18 12:46:16 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2023-09-18 12:46:16 DATA:  test-backend-node.js result: performance: load: null total: 274 -2023-09-18 12:46:16 STATE: test-backend-node.js passed: face descriptor -2023-09-18 12:46:16 STATE: test-backend-node.js passed: face similarity {"similarity":[1,0.44727441595492046,0.556793560189727],"descriptors":[1024,1024,1024]} -2023-09-18 12:46:16 INFO:  test-backend-node.js test object -2023-09-18 12:46:16 STATE: test-backend-node.js start object -2023-09-18 12:46:16 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:16 STATE: test-backend-node.js event: image -2023-09-18 12:46:16 STATE: test-backend-node.js event: detect -2023-09-18 12:46:16 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object -2023-09-18 12:46:16 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:16 DATA:  test-backend-node.js result: performance: load: null total: 299 -2023-09-18 12:46:16 STATE: test-backend-node.js passed: centernet -2023-09-18 12:46:16 STATE: test-backend-node.js start object -2023-09-18 12:46:17 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:17 STATE: test-backend-node.js event: image -2023-09-18 12:46:17 STATE: test-backend-node.js event: detect -2023-09-18 12:46:17 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object -2023-09-18 12:46:17 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:17 DATA:  test-backend-node.js result: performance: load: null total: 294 -2023-09-18 12:46:17 STATE: test-backend-node.js passed: nanodet -2023-09-18 12:46:17 INFO:  test-backend-node.js test sensitive -2023-09-18 12:46:17 STATE: test-backend-node.js start sensitive -2023-09-18 12:46:18 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:18 STATE: test-backend-node.js event: image -2023-09-18 12:46:18 STATE: test-backend-node.js event: detect -2023-09-18 12:46:18 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sensitive -2023-09-18 12:46:18 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2023-09-18 12:46:18 DATA:  test-backend-node.js result: performance: load: null total: 266 -2023-09-18 12:46:18 STATE: test-backend-node.js passed: sensitive result match -2023-09-18 12:46:18 STATE: test-backend-node.js passed: sensitive face result match -2023-09-18 12:46:18 STATE: test-backend-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] -2023-09-18 12:46:18 STATE: test-backend-node.js passed: sensitive body result match -2023-09-18 12:46:18 STATE: test-backend-node.js passed: sensitive hand result match -2023-09-18 12:46:18 INFO:  test-backend-node.js test body -2023-09-18 12:46:18 STATE: test-backend-node.js start blazepose -2023-09-18 12:46:20 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:20 STATE: test-backend-node.js event: image -2023-09-18 12:46:20 STATE: test-backend-node.js event: detect -2023-09-18 12:46:20 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg blazepose -2023-09-18 12:46:20 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} -2023-09-18 12:46:20 DATA:  test-backend-node.js result: performance: load: null total: 370 -2023-09-18 12:46:20 STATE: test-backend-node.js passed: blazepose -2023-09-18 12:46:20 STATE: test-backend-node.js start efficientpose -2023-09-18 12:46:21 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:21 STATE: test-backend-node.js event: image -2023-09-18 12:46:21 STATE: test-backend-node.js event: detect -2023-09-18 12:46:21 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg efficientpose -2023-09-18 12:46:21 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} -2023-09-18 12:46:21 DATA:  test-backend-node.js result: performance: load: null total: 306 -2023-09-18 12:46:21 STATE: test-backend-node.js passed: efficientpose -2023-09-18 12:46:21 STATE: test-backend-node.js start posenet -2023-09-18 12:46:22 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:22 STATE: test-backend-node.js event: image -2023-09-18 12:46:22 STATE: test-backend-node.js event: detect -2023-09-18 12:46:22 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg posenet -2023-09-18 12:46:22 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} -2023-09-18 12:46:22 DATA:  test-backend-node.js result: performance: load: null total: 257 -2023-09-18 12:46:22 STATE: test-backend-node.js passed: posenet -2023-09-18 12:46:22 STATE: test-backend-node.js start movenet -2023-09-18 12:46:23 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:23 STATE: test-backend-node.js event: image -2023-09-18 12:46:23 STATE: test-backend-node.js event: detect -2023-09-18 12:46:23 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg movenet -2023-09-18 12:46:23 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2023-09-18 12:46:23 DATA:  test-backend-node.js result: performance: load: null total: 251 -2023-09-18 12:46:23 STATE: test-backend-node.js passed: movenet -2023-09-18 12:46:23 INFO:  test-backend-node.js test face matching -2023-09-18 12:46:23 STATE: test-backend-node.js passed: face database 40 -2023-09-18 12:46:23 STATE: test-backend-node.js passed: face match {"first":{"index":4,"similarity":0.7827852251220577}} {"second":{"index":4,"similarity":0.5002052057057577}} {"third":{"index":4,"similarity":0.5401588464054732}} -2023-09-18 12:46:23 INFO:  test-backend-node.js test face similarity alternative -2023-09-18 12:46:23 STATE: test-backend-node.js start face embeddings -2023-09-18 12:46:24 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:24 STATE: test-backend-node.js event: image -2023-09-18 12:46:24 STATE: test-backend-node.js event: detect -2023-09-18 12:46:24 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings -2023-09-18 12:46:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-09-18 12:46:24 DATA:  test-backend-node.js result: performance: load: null total: 308 -2023-09-18 12:46:24 STATE: test-backend-node.js passed: mobilefacenet {"embedding":192} -2023-09-18 12:46:24 STATE: test-backend-node.js start face embeddings -2023-09-18 12:46:25 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:25 STATE: test-backend-node.js event: image -2023-09-18 12:46:25 STATE: test-backend-node.js event: detect -2023-09-18 12:46:25 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings -2023-09-18 12:46:25 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-09-18 12:46:25 DATA:  test-backend-node.js result: performance: load: null total: 305 -2023-09-18 12:46:25 STATE: test-backend-node.js passed: insightface {"embedding":512} -2023-09-18 12:46:25 INFO:  test-backend-node.js test face attention -2023-09-18 12:46:25 STATE: test-backend-node.js start face attention -2023-09-18 12:46:26 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:26 STATE: test-backend-node.js event: image -2023-09-18 12:46:26 STATE: test-backend-node.js event: detect -2023-09-18 12:46:26 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face attention -2023-09-18 12:46:26 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-09-18 12:46:26 DATA:  test-backend-node.js result: performance: load: null total: 284 -2023-09-18 12:46:26 STATE: test-backend-node.js passed: face attention -2023-09-18 12:46:26 INFO:  test-backend-node.js test detectors -2023-09-18 12:46:26 STATE: test-backend-node.js start detectors -2023-09-18 12:46:27 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:27 STATE: test-backend-node.js event: image -2023-09-18 12:46:27 STATE: test-backend-node.js event: detect -2023-09-18 12:46:27 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg detectors -2023-09-18 12:46:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-09-18 12:46:27 DATA:  test-backend-node.js result: performance: load: null total: 137 -2023-09-18 12:46:27 STATE: test-backend-node.js passed: detector result face match -2023-09-18 12:46:27 STATE: test-backend-node.js passed: detector result hand match -2023-09-18 12:46:27 INFO:  test-backend-node.js test: multi-instance -2023-09-18 12:46:27 STATE: test-backend-node.js start multi instance -2023-09-18 12:46:27 STATE: test-backend-node.js event: image -2023-09-18 12:46:27 STATE: test-backend-node.js event: detect -2023-09-18 12:46:27 STATE: test-backend-node.js passed: detect: random multi instance -2023-09-18 12:46:27 DATA:  test-backend-node.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} -2023-09-18 12:46:27 DATA:  test-backend-node.js result: performance: load: null total: 123 -2023-09-18 12:46:27 INFO:  test-backend-node.js test: first instance -2023-09-18 12:46:27 STATE: test-backend-node.js start multi instance -2023-09-18 12:46:27 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-09-18 12:46:27 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance -2023-09-18 12:46:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-09-18 12:46:27 DATA:  test-backend-node.js result: performance: load: null total: 139 -2023-09-18 12:46:27 INFO:  test-backend-node.js test: second instance -2023-09-18 12:46:27 STATE: test-backend-node.js start multi instance -2023-09-18 12:46:27 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-09-18 12:46:27 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance -2023-09-18 12:46:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-09-18 12:46:27 DATA:  test-backend-node.js result: performance: load: null total: 122 -2023-09-18 12:46:27 INFO:  test-backend-node.js test: concurrent -2023-09-18 12:46:27 STATE: test-backend-node.js start concurrent -2023-09-18 12:46:27 STATE: test-backend-node.js start concurrent -2023-09-18 12:46:27 STATE: test-backend-node.js start concurrent -2023-09-18 12:46:27 STATE: test-backend-node.js start concurrent -2023-09-18 12:46:27 STATE: test-backend-node.js start concurrent -2023-09-18 12:46:27 STATE: test-backend-node.js start concurrent -2023-09-18 12:46:27 STATE: test-backend-node.js start concurrent -2023-09-18 12:46:27 STATE: test-backend-node.js start concurrent -2023-09-18 12:46:27 STATE: test-backend-node.js start concurrent -2023-09-18 12:46:27 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:27 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:27 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:28 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:28 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:28 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:28 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-09-18 12:46:28 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-09-18 12:46:28 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-09-18 12:46:28 STATE: test-backend-node.js event: image -2023-09-18 12:46:28 STATE: test-backend-node.js event: image -2023-09-18 12:46:28 STATE: test-backend-node.js event: image -2023-09-18 12:46:29 STATE: test-backend-node.js event: detect -2023-09-18 12:46:29 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2023-09-18 12:46:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-09-18 12:46:29 DATA:  test-backend-node.js result: performance: load: null total: 1139 -2023-09-18 12:46:29 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2023-09-18 12:46:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-09-18 12:46:29 DATA:  test-backend-node.js result: performance: load: null total: 1139 -2023-09-18 12:46:29 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2023-09-18 12:46:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-09-18 12:46:29 DATA:  test-backend-node.js result: performance: load: null total: 1139 -2023-09-18 12:46:29 STATE: test-backend-node.js event: detect -2023-09-18 12:46:29 STATE: test-backend-node.js event: detect -2023-09-18 12:46:29 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2023-09-18 12:46:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2023-09-18 12:46:29 DATA:  test-backend-node.js result: performance: load: null total: 1139 -2023-09-18 12:46:29 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2023-09-18 12:46:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2023-09-18 12:46:29 DATA:  test-backend-node.js result: performance: load: null total: 1139 -2023-09-18 12:46:29 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2023-09-18 12:46:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2023-09-18 12:46:29 DATA:  test-backend-node.js result: performance: load: null total: 1139 -2023-09-18 12:46:29 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2023-09-18 12:46:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-09-18 12:46:29 DATA:  test-backend-node.js result: performance: load: null total: 1139 -2023-09-18 12:46:29 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2023-09-18 12:46:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-09-18 12:46:29 DATA:  test-backend-node.js result: performance: load: null total: 1139 -2023-09-18 12:46:29 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2023-09-18 12:46:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-09-18 12:46:29 DATA:  test-backend-node.js result: performance: load: null total: 1139 -2023-09-18 12:46:29 INFO:  test-backend-node.js test: monkey-patch -2023-09-18 12:46:29 STATE: test-backend-node.js event: image -2023-09-18 12:46:29 STATE: test-backend-node.js event: detect -2023-09-18 12:46:29 STATE: test-backend-node.js passed: monkey patch -2023-09-18 12:46:30 STATE: test-backend-node.js passed: segmentation [262144] -2023-09-18 12:46:30 STATE: test-backend-node.js passeed: equal usage -2023-09-18 12:46:30 INFO:  test-backend-node.js test: input compare -2023-09-18 12:46:30 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:30 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:30 STATE: test-backend-node.js passed: image compare 0 23.275441687091504 -2023-09-18 12:46:30 INFO:  test-backend-node.js events: {"image":29,"detect":29,"warmup":2} -2023-09-18 12:46:30 INFO:  test-backend-node.js tensors 14855 -2023-09-18 12:46:30 INFO:  test-backend-node.js test complete: 20515 ms -2023-09-18 12:46:30 INFO:  -2023-09-18 12:46:30 INFO:  test-backend-node-gpu.js start -2023-09-18 12:46:31 INFO:  test-backend-node-gpu.js test: configuration validation -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js passed: configuration default validation [] -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2023-09-18 12:46:31 INFO:  test-backend-node-gpu.js test: model load -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] -2023-09-18 12:46:31 INFO:  test-backend-node-gpu.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2023-09-18 12:46:31 INFO:  test-backend-node-gpu.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2023-09-18 12:46:31 INFO:  test-backend-node-gpu.js test: warmup -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js passed: create human -2023-09-18 12:46:31 INFO:  test-backend-node-gpu.js human version: 3.1.2 -2023-09-18 12:46:31 INFO:  test-backend-node-gpu.js platform: linux x64 agent: NodeJS v20.6.1 -2023-09-18 12:46:31 INFO:  test-backend-node-gpu.js tfjs version: 4.11.0 -2023-09-18 12:46:31 INFO:  test-backend-node-gpu.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v20.6.1","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.11.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":172} -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js passed: set backend: tensorflow -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js tensors 1785 -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js result: defined models: 11 loaded models: 11 -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js passed: load models 11 -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js passed: warmup: none default -2023-09-18 12:46:31 DATA:  test-backend-node-gpu.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2023-09-18 12:46:31 DATA:  test-backend-node-gpu.js result: performance: load: null total: null -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js passed: warmup none result match -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js event: warmup -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js passed: warmup: face default -2023-09-18 12:46:31 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} -2023-09-18 12:46:31 DATA:  test-backend-node-gpu.js result: performance: load: null total: 429 -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js passed: warmup face result match -2023-09-18 12:46:31 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js event: warmup -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: warmup: body default -2023-09-18 12:46:32 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:32 DATA:  test-backend-node-gpu.js result: performance: load: null total: 341 -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: warmup body result match -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2023-09-18 12:46:32 INFO:  test-backend-node-gpu.js test: details verification -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js start default -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg default -2023-09-18 12:46:32 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:32 DATA:  test-backend-node-gpu.js result: performance: load: null total: 327 -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details face length 1 -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details face score 1 0.93 1 -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details face age/gender 23.7 female 0.97 2.34 -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details face arrays 4 478 1024 -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details face anti-spoofing 0.79 -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details face liveness 0.83 -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details body length 1 -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details body 0.92 17 6 -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details hand length 1 -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details hand 0.51 0.73 point -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details hand arrays 21 5 7 -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details gesture length 7 -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details object length 1 -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: details object 0.72 person -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} -2023-09-18 12:46:32 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:33 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:33 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2023-09-18 12:46:33 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} -2023-09-18 12:46:33 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:33 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:33 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 -2023-09-18 12:46:33 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:33 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:34 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:34 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2023-09-18 12:46:34 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:34 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:34 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:34 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 -2023-09-18 12:46:34 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2023-09-18 12:46:34 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:34 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:34 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2023-09-18 12:46:34 INFO:  test-backend-node-gpu.js test default -2023-09-18 12:46:34 STATE: test-backend-node-gpu.js start async -2023-09-18 12:46:35 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:35 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:35 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:35 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg async -2023-09-18 12:46:35 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:35 DATA:  test-backend-node-gpu.js result: performance: load: null total: 317 -2023-09-18 12:46:35 STATE: test-backend-node-gpu.js passed: default result face match 1 female 0.97 -2023-09-18 12:46:35 INFO:  test-backend-node-gpu.js test sync -2023-09-18 12:46:35 STATE: test-backend-node-gpu.js start sync -2023-09-18 12:46:35 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:35 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sync -2023-09-18 12:46:36 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:36 DATA:  test-backend-node-gpu.js result: performance: load: null total: 325 -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js passed: default sync 1 female 0.97 -2023-09-18 12:46:36 INFO:  test-backend-node-gpu.js test: image process -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js passed: image input null [1,256,256,3] -2023-09-18 12:46:36 INFO:  test-backend-node-gpu.js test: image null -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js passed: invalid input could not convert input to tensor -2023-09-18 12:46:36 INFO:  test-backend-node-gpu.js test face similarity -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js start face similarity -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face similarity -2023-09-18 12:46:36 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2023-09-18 12:46:36 DATA:  test-backend-node-gpu.js result: performance: load: null total: 315 -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js start face similarity -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg face similarity -2023-09-18 12:46:36 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:36 DATA:  test-backend-node-gpu.js result: performance: load: null total: 312 -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js start face similarity -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-09-18 12:46:36 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:37 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:37 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg face similarity -2023-09-18 12:46:37 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2023-09-18 12:46:37 DATA:  test-backend-node-gpu.js result: performance: load: null total: 281 -2023-09-18 12:46:37 STATE: test-backend-node-gpu.js passed: face descriptor -2023-09-18 12:46:37 STATE: test-backend-node-gpu.js passed: face similarity {"similarity":[1,0.44727441595492046,0.556793560189727],"descriptors":[1024,1024,1024]} -2023-09-18 12:46:37 INFO:  test-backend-node-gpu.js test object -2023-09-18 12:46:37 STATE: test-backend-node-gpu.js start object -2023-09-18 12:46:37 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:37 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:37 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:37 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object -2023-09-18 12:46:37 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:37 DATA:  test-backend-node-gpu.js result: performance: load: null total: 304 -2023-09-18 12:46:37 STATE: test-backend-node-gpu.js passed: centernet -2023-09-18 12:46:37 STATE: test-backend-node-gpu.js start object -2023-09-18 12:46:38 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:38 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:38 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:38 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object -2023-09-18 12:46:38 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:38 DATA:  test-backend-node-gpu.js result: performance: load: null total: 297 -2023-09-18 12:46:38 STATE: test-backend-node-gpu.js passed: nanodet -2023-09-18 12:46:38 INFO:  test-backend-node-gpu.js test sensitive -2023-09-18 12:46:38 STATE: test-backend-node-gpu.js start sensitive -2023-09-18 12:46:39 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:39 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:39 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:39 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sensitive -2023-09-18 12:46:39 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2023-09-18 12:46:39 DATA:  test-backend-node-gpu.js result: performance: load: null total: 263 -2023-09-18 12:46:39 STATE: test-backend-node-gpu.js passed: sensitive result match -2023-09-18 12:46:39 STATE: test-backend-node-gpu.js passed: sensitive face result match -2023-09-18 12:46:39 STATE: test-backend-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] -2023-09-18 12:46:39 STATE: test-backend-node-gpu.js passed: sensitive body result match -2023-09-18 12:46:39 STATE: test-backend-node-gpu.js passed: sensitive hand result match -2023-09-18 12:46:39 INFO:  test-backend-node-gpu.js test body -2023-09-18 12:46:39 STATE: test-backend-node-gpu.js start blazepose -2023-09-18 12:46:41 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:41 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:41 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:41 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg blazepose -2023-09-18 12:46:41 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} -2023-09-18 12:46:41 DATA:  test-backend-node-gpu.js result: performance: load: null total: 360 -2023-09-18 12:46:41 STATE: test-backend-node-gpu.js passed: blazepose -2023-09-18 12:46:41 STATE: test-backend-node-gpu.js start efficientpose -2023-09-18 12:46:41 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:41 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:42 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:42 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg efficientpose -2023-09-18 12:46:42 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} -2023-09-18 12:46:42 DATA:  test-backend-node-gpu.js result: performance: load: null total: 323 -2023-09-18 12:46:42 STATE: test-backend-node-gpu.js passed: efficientpose -2023-09-18 12:46:42 STATE: test-backend-node-gpu.js start posenet -2023-09-18 12:46:42 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:42 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:43 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:43 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg posenet -2023-09-18 12:46:43 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} -2023-09-18 12:46:43 DATA:  test-backend-node-gpu.js result: performance: load: null total: 278 -2023-09-18 12:46:43 STATE: test-backend-node-gpu.js passed: posenet -2023-09-18 12:46:43 STATE: test-backend-node-gpu.js start movenet -2023-09-18 12:46:43 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:43 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:43 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:43 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg movenet -2023-09-18 12:46:43 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2023-09-18 12:46:43 DATA:  test-backend-node-gpu.js result: performance: load: null total: 256 -2023-09-18 12:46:43 STATE: test-backend-node-gpu.js passed: movenet -2023-09-18 12:46:43 INFO:  test-backend-node-gpu.js test face matching -2023-09-18 12:46:43 STATE: test-backend-node-gpu.js passed: face database 40 -2023-09-18 12:46:43 STATE: test-backend-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7827852251220577}} {"second":{"index":4,"similarity":0.5002052057057577}} {"third":{"index":4,"similarity":0.5401588464054732}} -2023-09-18 12:46:43 INFO:  test-backend-node-gpu.js test face similarity alternative -2023-09-18 12:46:43 STATE: test-backend-node-gpu.js start face embeddings -2023-09-18 12:46:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:44 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:44 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:44 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings -2023-09-18 12:46:44 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-09-18 12:46:44 DATA:  test-backend-node-gpu.js result: performance: load: null total: 298 -2023-09-18 12:46:44 STATE: test-backend-node-gpu.js passed: mobilefacenet {"embedding":192} -2023-09-18 12:46:44 STATE: test-backend-node-gpu.js start face embeddings -2023-09-18 12:46:45 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:45 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:45 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:45 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings -2023-09-18 12:46:45 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-09-18 12:46:45 DATA:  test-backend-node-gpu.js result: performance: load: null total: 315 -2023-09-18 12:46:45 STATE: test-backend-node-gpu.js passed: insightface {"embedding":512} -2023-09-18 12:46:45 INFO:  test-backend-node-gpu.js test face attention -2023-09-18 12:46:45 STATE: test-backend-node-gpu.js start face attention -2023-09-18 12:46:45 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:45 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face attention -2023-09-18 12:46:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-09-18 12:46:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 280 -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js passed: face attention -2023-09-18 12:46:46 INFO:  test-backend-node-gpu.js test detectors -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js start detectors -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg detectors -2023-09-18 12:46:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-09-18 12:46:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 140 -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js passed: detector result face match -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js passed: detector result hand match -2023-09-18 12:46:46 INFO:  test-backend-node-gpu.js test: multi-instance -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js start multi instance -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js passed: detect: random multi instance -2023-09-18 12:46:46 DATA:  test-backend-node-gpu.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} -2023-09-18 12:46:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 128 -2023-09-18 12:46:46 INFO:  test-backend-node-gpu.js test: first instance -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js start multi instance -2023-09-18 12:46:46 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance -2023-09-18 12:46:47 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-09-18 12:46:47 DATA:  test-backend-node-gpu.js result: performance: load: null total: 130 -2023-09-18 12:46:47 INFO:  test-backend-node-gpu.js test: second instance -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js start multi instance -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance -2023-09-18 12:46:47 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-09-18 12:46:47 DATA:  test-backend-node-gpu.js result: performance: load: null total: 132 -2023-09-18 12:46:47 INFO:  test-backend-node-gpu.js test: concurrent -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js start concurrent -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js start concurrent -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js start concurrent -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js start concurrent -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js start concurrent -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js start concurrent -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js start concurrent -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js start concurrent -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js start concurrent -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-09-18 12:46:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2023-09-18 12:46:48 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:48 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:48 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1162 -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1162 -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1162 -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1164 -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1164 -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1164 -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1164 -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1164 -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2023-09-18 12:46:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1164 -2023-09-18 12:46:49 INFO:  test-backend-node-gpu.js test: monkey-patch -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js event: image -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js event: detect -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passed: monkey patch -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passed: segmentation [262144] -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passeed: equal usage -2023-09-18 12:46:49 INFO:  test-backend-node-gpu.js test: input compare -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2023-09-18 12:46:49 STATE: test-backend-node-gpu.js passed: image compare 0 23.275441687091504 -2023-09-18 12:46:49 INFO:  test-backend-node-gpu.js events: {"image":29,"detect":29,"warmup":2} -2023-09-18 12:46:49 INFO:  test-backend-node-gpu.js tensors 14855 -2023-09-18 12:46:49 INFO:  test-backend-node-gpu.js test complete: 18651 ms -2023-09-18 12:46:49 INFO:  -2023-09-18 12:46:49 INFO:  test-backend-node-wasm.js start -2023-09-18 12:46:50 DATA:  test-backend-node-wasm.js stdout: 2023-09-18 12:46:50 INFO:  { supported: true, backend: true, simd: undefined, multithread: undefined } https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@4.11.0/dist/ -2023-09-18 12:46:50 STATE: test-backend-node-wasm.js passed: model server: https://vladmandic.github.io/human-models/models/ -2023-09-18 12:46:50 INFO:  test-backend-node-wasm.js test: configuration validation -2023-09-18 12:46:50 STATE: test-backend-node-wasm.js passed: configuration default validation [] -2023-09-18 12:46:50 STATE: test-backend-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2023-09-18 12:46:50 INFO:  test-backend-node-wasm.js test: model load -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/blazeface.json"},{"name":"liveness","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/liveness.json"},{"name":"emotion","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/emotion.json"},{"name":"antispoof","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/antispoof.json"},{"name":"facemesh","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/handlandmark-lite.json"},{"name":"handtrack","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/handtrack.json"},{"name":"iris","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/iris.json"},{"name":"centernet","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/centernet.json"},{"name":"faceres","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/faceres.json"},{"name":"movenet","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/movenet-lightning.json"}] -2023-09-18 12:46:52 INFO:  test-backend-node-wasm.js memory: {"memory":{"unreliable":false,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2023-09-18 12:46:52 INFO:  test-backend-node-wasm.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2023-09-18 12:46:52 INFO:  test-backend-node-wasm.js test: warmup -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js passed: create human -2023-09-18 12:46:52 INFO:  test-backend-node-wasm.js human version: 3.1.2 -2023-09-18 12:46:52 INFO:  test-backend-node-wasm.js platform: linux x64 agent: NodeJS v20.6.1 -2023-09-18 12:46:52 INFO:  test-backend-node-wasm.js tfjs version: 4.11.0 -2023-09-18 12:46:52 INFO:  test-backend-node-wasm.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v20.6.1","backends":["wasm"],"initial":false,"tfjs":{"version":"4.11.0"},"offscreen":false,"perfadd":false,"tensorflow":{},"wasm":{"supported":true,"backend":true,"simd":true,"multithread":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":174} -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js passed: set backend: wasm -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js tensors 1785 -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js result: defined models: 11 loaded models: 11 -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js passed: load models 11 -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js passed: warmup: none default -2023-09-18 12:46:52 DATA:  test-backend-node-wasm.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2023-09-18 12:46:52 DATA:  test-backend-node-wasm.js result: performance: load: null total: null -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js passed: warmup none result match -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js event: warmup -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js passed: warmup: face default -2023-09-18 12:46:52 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2023-09-18 12:46:52 DATA:  test-backend-node-wasm.js result: performance: load: null total: 472 -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js passed: warmup face result match -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js event: warmup -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js passed: warmup: body default -2023-09-18 12:46:52 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:52 DATA:  test-backend-node-wasm.js result: performance: load: null total: 326 -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js passed: warmup body result match -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 21% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2023-09-18 12:46:52 INFO:  test-backend-node-wasm.js test: details verification -2023-09-18 12:46:52 STATE: test-backend-node-wasm.js start default -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg default -2023-09-18 12:46:53 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:53 DATA:  test-backend-node-wasm.js result: performance: load: null total: 315 -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details face length 1 -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details face score 1 0.93 1 -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details face age/gender 23.7 female 0.97 2.34 -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details face arrays 4 478 1024 -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details face anti-spoofing 0.79 -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details face liveness 0.83 -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details body length 1 -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details body 0.92 17 6 -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details hand length 1 -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details hand 0.51 0.73 point -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details hand arrays 21 5 7 -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details gesture length 7 -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details object length 1 -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: details object 0.72 person -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} -2023-09-18 12:46:53 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:46:54 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:46:54 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2023-09-18 12:46:54 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} -2023-09-18 12:46:54 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:46:54 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:46:54 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 -2023-09-18 12:46:54 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-09-18 12:46:54 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:46:55 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:46:55 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2023-09-18 12:46:55 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} -2023-09-18 12:46:55 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:46:55 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:46:55 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 -2023-09-18 12:46:55 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2023-09-18 12:46:55 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:46:56 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:46:56 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2023-09-18 12:46:56 INFO:  test-backend-node-wasm.js test default -2023-09-18 12:46:56 STATE: test-backend-node-wasm.js start async -2023-09-18 12:46:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-09-18 12:46:58 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:46:58 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:46:58 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg async -2023-09-18 12:46:58 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:46:58 DATA:  test-backend-node-wasm.js result: performance: load: null total: 370 -2023-09-18 12:46:58 STATE: test-backend-node-wasm.js passed: default result face match 1 female 0.97 -2023-09-18 12:46:58 INFO:  test-backend-node-wasm.js test sync -2023-09-18 12:46:58 STATE: test-backend-node-wasm.js start sync -2023-09-18 12:47:00 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-09-18 12:47:00 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:47:00 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:47:00 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sync -2023-09-18 12:47:00 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:47:00 DATA:  test-backend-node-wasm.js result: performance: load: null total: 390 -2023-09-18 12:47:00 STATE: test-backend-node-wasm.js passed: default sync 1 female 0.97 -2023-09-18 12:47:00 INFO:  test-backend-node-wasm.js test: image process -2023-09-18 12:47:00 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2023-09-18 12:47:00 STATE: test-backend-node-wasm.js passed: image input null [1,256,256,3] -2023-09-18 12:47:00 INFO:  test-backend-node-wasm.js test: image null -2023-09-18 12:47:00 STATE: test-backend-node-wasm.js passed: invalid input could not convert input to tensor -2023-09-18 12:47:00 INFO:  test-backend-node-wasm.js test face similarity -2023-09-18 12:47:00 STATE: test-backend-node-wasm.js start face similarity -2023-09-18 12:47:02 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2023-09-18 12:47:02 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:47:02 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:47:02 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face similarity -2023-09-18 12:47:02 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2023-09-18 12:47:02 DATA:  test-backend-node-wasm.js result: performance: load: null total: 380 -2023-09-18 12:47:02 STATE: test-backend-node-wasm.js start face similarity -2023-09-18 12:47:03 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-09-18 12:47:03 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:47:03 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:47:03 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg face similarity -2023-09-18 12:47:03 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:47:03 DATA:  test-backend-node-wasm.js result: performance: load: null total: 297 -2023-09-18 12:47:03 STATE: test-backend-node-wasm.js start face similarity -2023-09-18 12:47:03 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2023-09-18 12:47:03 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:47:03 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:47:03 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg face similarity -2023-09-18 12:47:03 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2023-09-18 12:47:03 DATA:  test-backend-node-wasm.js result: performance: load: null total: 282 -2023-09-18 12:47:03 STATE: test-backend-node-wasm.js passed: face descriptor -2023-09-18 12:47:03 STATE: test-backend-node-wasm.js passed: face similarity {"similarity":[1,0.4472741837136387,0.5567931378245533],"descriptors":[1024,1024,1024]} -2023-09-18 12:47:03 INFO:  test-backend-node-wasm.js test object -2023-09-18 12:47:03 STATE: test-backend-node-wasm.js start object -2023-09-18 12:47:03 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-09-18 12:47:03 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:47:04 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:47:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object -2023-09-18 12:47:04 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:47:04 DATA:  test-backend-node-wasm.js result: performance: load: null total: 302 -2023-09-18 12:47:04 STATE: test-backend-node-wasm.js passed: centernet -2023-09-18 12:47:04 STATE: test-backend-node-wasm.js start object -2023-09-18 12:47:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-09-18 12:47:05 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:47:06 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:47:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object -2023-09-18 12:47:06 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} -2023-09-18 12:47:06 DATA:  test-backend-node-wasm.js result: performance: load: null total: 341 -2023-09-18 12:47:06 STATE: test-backend-node-wasm.js passed: nanodet -2023-09-18 12:47:06 INFO:  test-backend-node-wasm.js test sensitive -2023-09-18 12:47:06 STATE: test-backend-node-wasm.js start sensitive -2023-09-18 12:47:07 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-09-18 12:47:07 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:47:08 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:47:08 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sensitive -2023-09-18 12:47:08 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2023-09-18 12:47:08 DATA:  test-backend-node-wasm.js result: performance: load: null total: 272 -2023-09-18 12:47:08 STATE: test-backend-node-wasm.js passed: sensitive result match -2023-09-18 12:47:08 STATE: test-backend-node-wasm.js passed: sensitive face result match -2023-09-18 12:47:08 STATE: test-backend-node-wasm.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] -2023-09-18 12:47:08 STATE: test-backend-node-wasm.js passed: sensitive body result match -2023-09-18 12:47:08 STATE: test-backend-node-wasm.js passed: sensitive hand result match -2023-09-18 12:47:08 INFO:  test-backend-node-wasm.js test body -2023-09-18 12:47:08 STATE: test-backend-node-wasm.js start blazepose -2023-09-18 12:47:11 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-09-18 12:47:11 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:47:11 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:47:11 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg blazepose -2023-09-18 12:47:11 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} -2023-09-18 12:47:11 DATA:  test-backend-node-wasm.js result: performance: load: null total: 403 -2023-09-18 12:47:11 STATE: test-backend-node-wasm.js passed: blazepose -2023-09-18 12:47:11 STATE: test-backend-node-wasm.js start efficientpose -2023-09-18 12:47:12 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-09-18 12:47:12 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:47:13 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:47:13 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg efficientpose -2023-09-18 12:47:13 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} -2023-09-18 12:47:13 DATA:  test-backend-node-wasm.js result: performance: load: null total: 622 -2023-09-18 12:47:13 STATE: test-backend-node-wasm.js passed: efficientpose -2023-09-18 12:47:13 STATE: test-backend-node-wasm.js start posenet -2023-09-18 12:47:14 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-09-18 12:47:14 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:47:14 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:47:14 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg posenet -2023-09-18 12:47:14 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} -2023-09-18 12:47:14 DATA:  test-backend-node-wasm.js result: performance: load: null total: 244 -2023-09-18 12:47:14 STATE: test-backend-node-wasm.js passed: posenet -2023-09-18 12:47:14 STATE: test-backend-node-wasm.js start movenet -2023-09-18 12:47:14 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2023-09-18 12:47:14 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:47:14 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:47:14 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg movenet -2023-09-18 12:47:14 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2023-09-18 12:47:14 DATA:  test-backend-node-wasm.js result: performance: load: null total: 203 -2023-09-18 12:47:14 STATE: test-backend-node-wasm.js passed: movenet -2023-09-18 12:47:14 INFO:  test-backend-node-wasm.js test face matching -2023-09-18 12:47:14 STATE: test-backend-node-wasm.js passed: face database 40 -2023-09-18 12:47:14 STATE: test-backend-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5002051178899982}} {"third":{"index":4,"similarity":0.5401586736511599}} -2023-09-18 12:47:14 INFO:  test-backend-node-wasm.js test face similarity alternative -2023-09-18 12:47:14 STATE: test-backend-node-wasm.js start face embeddings -2023-09-18 12:47:16 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2023-09-18 12:47:16 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:47:16 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:47:16 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings -2023-09-18 12:47:16 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-09-18 12:47:16 DATA:  test-backend-node-wasm.js result: performance: load: null total: 288 -2023-09-18 12:47:16 STATE: test-backend-node-wasm.js passed: mobilefacenet {"embedding":192} -2023-09-18 12:47:16 STATE: test-backend-node-wasm.js start face embeddings -2023-09-18 12:47:18 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2023-09-18 12:47:18 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:47:18 STATE: test-backend-node-wasm.js event: detect -2023-09-18 12:47:18 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings -2023-09-18 12:47:18 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2023-09-18 12:47:18 DATA:  test-backend-node-wasm.js result: performance: load: null total: 268 -2023-09-18 12:47:18 STATE: test-backend-node-wasm.js passed: insightface {"embedding":512} -2023-09-18 12:47:18 INFO:  test-backend-node-wasm.js test face attention -2023-09-18 12:47:18 STATE: test-backend-node-wasm.js start face attention -2023-09-18 12:47:19 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2023-09-18 12:47:19 STATE: test-backend-node-wasm.js event: image -2023-09-18 12:47:19 ERROR: test-backend-node-wasm.js failed: testDetect face attention -2023-09-18 12:47:19 ERROR: test-backend-node-wasm.js uncaughtException {"name":"Error","message":"BatchMatMul for non non-float32 tensors not yet supported.","stack":["Error: BatchMatMul for non non-float32 tensors not yet supported."," at Object.batchMatMul [as kernelFunc] (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-backend-wasm/dist/tf-backend-wasm.node.js:1280:15)"," at kernelFunc (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4707:32)"," at /home/vlado/dev/human/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4767:27"," at Engine.scopedRun (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4572:23)"," at Engine.runKernelFunc (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4763:14)"," at Engine.runKernel (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4636:21)"," at matMul_ (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:13408:19)"," at Object.matMul__op [as matMul] (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:12217:29)"," at executeOp$9 (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:29592:25)"," at /home/vlado/dev/human/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:30172:50"]} -2023-09-18 12:47:19 STATE: all tests complete -2023-09-18 12:47:19 INFO:  status {"test":"../demo/nodejs/node.js","passed":1,"failed":0} -2023-09-18 12:47:19 INFO:  status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0} -2023-09-18 12:47:19 INFO:  status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0} -2023-09-18 12:47:19 INFO:  status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0} -2023-09-18 12:47:19 INFO:  status {"test":"../demo/nodejs/node-canvas.js","passed":1,"failed":0} -2023-09-18 12:47:19 INFO:  status {"test":"../demo/nodejs/process-folder.js","passed":1,"failed":0} -2023-09-18 12:47:19 INFO:  status {"test":"../demo/multithread/node-multiprocess.js","passed":1,"failed":0} -2023-09-18 12:47:19 INFO:  status {"test":"../demo/facematch/node-match.js","passed":1,"failed":0} -2023-09-18 12:47:19 INFO:  status {"test":"../demo/nodejs/node-bench.js","passed":1,"failed":0} -2023-09-18 12:47:19 INFO:  status {"test":"../test/test-node-emotion.js","passed":1,"failed":0} -2023-09-18 12:47:19 INFO:  status {"test":"test-node-load.js","passed":1,"failed":0} -2023-09-18 12:47:19 INFO:  status {"test":"test-node-gear.js","passed":4,"failed":0} -2023-09-18 12:47:19 INFO:  status {"test":"test-backend-node.js","passed":125,"failed":0} -2023-09-18 12:47:19 INFO:  status {"test":"test-backend-node-gpu.js","passed":125,"failed":0} -2023-09-18 12:47:19 INFO:  status {"test":"test-backend-node-wasm.js","passed":92,"failed":1} -2023-09-18 12:47:19 INFO:  failures {"count":1} -2023-09-18 12:47:19 WARN:  failed {"test":"test-backend-node-wasm.js","message":["error",["failed:","testDetect face attention"]]} +2023-12-06 14:58:19 INFO:  @vladmandic/human version 3.2.0 +2023-12-06 14:58:19 INFO:  User: vlado Platform: linux Arch: x64 Node: v21.4.0 +2023-12-06 14:58:19 INFO:  demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]},{"cmd":"../demo/nodejs/node-bench.js","args":[]},{"cmd":"../test/test-node-emotion.js","args":[]}] +2023-12-06 14:58:19 INFO:  {"cmd":"../demo/nodejs/node.js","args":[]} start +2023-12-06 14:58:20 INFO:  {"cmd":"../demo/nodejs/node-simple.js","args":[]} start +2023-12-06 14:58:21 INFO:  {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start +2023-12-06 14:58:21 INFO:  {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start +2023-12-06 14:58:22 INFO:  {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start +2023-12-06 14:58:23 INFO:  {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start +2023-12-06 14:58:25 INFO:  {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start +2023-12-06 14:58:43 INFO:  {"cmd":"../demo/facematch/node-match.js","args":[]} start +2023-12-06 14:58:44 INFO:  {"cmd":"../demo/nodejs/node-bench.js","args":[]} start +2023-12-06 14:58:59 INFO:  {"cmd":"../test/test-node-emotion.js","args":[]} start +2023-12-06 14:59:05 INFO:  tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"] +2023-12-06 14:59:05 INFO:  +2023-12-06 14:59:05 INFO:  test-node-load.js start +2023-12-06 14:59:06 INFO:  test-node-load.js load start {"human":"3.2.0","tf":"4.14.0","progress":0} +2023-12-06 14:59:06 DATA:  test-node-load.js load interval {"elapsed":1,"progress":0} +2023-12-06 14:59:06 DATA:  test-node-load.js load interval {"elapsed":10,"progress":0} +2023-12-06 14:59:06 DATA:  test-node-load.js load interval {"elapsed":27,"progress":0.12865823126207815} +2023-12-06 14:59:06 DATA:  test-node-load.js load interval {"elapsed":41,"progress":0.33826045252170456} +2023-12-06 14:59:06 DATA:  test-node-load.js load interval {"elapsed":65,"progress":0.4726970548784964} +2023-12-06 14:59:06 DATA:  test-node-load.js load interval {"elapsed":80,"progress":0.68355493315821} +2023-12-06 14:59:06 STATE: test-node-load.js passed {"progress":1} +2023-12-06 14:59:06 INFO:  test-node-load.js load final {"progress":1} +2023-12-06 14:59:06 DATA:  test-node-load.js load interval {"elapsed":482,"progress":1} +2023-12-06 14:59:06 INFO:  +2023-12-06 14:59:06 INFO:  test-node-gear.js start +2023-12-06 14:59:06 DATA:  test-node-gear.js input: ["samples/in/ai-face.jpg"] +2023-12-06 14:59:08 STATE: test-node-gear.js passed: gear faceres samples/in/ai-face.jpg +2023-12-06 14:59:08 DATA:  test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.5,"gender":"female","genderScore":0.92,"emotion":[]} +2023-12-06 14:59:08 STATE: test-node-gear.js passed: gear gear samples/in/ai-face.jpg +2023-12-06 14:59:08 DATA:  test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":23.3,"gender":"female","genderScore":0.51,"race":[{"score":0.93,"race":"white"}],"emotion":[]} +2023-12-06 14:59:08 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg +2023-12-06 14:59:08 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"female","genderScore":0.99,"emotion":[]} +2023-12-06 14:59:09 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg +2023-12-06 14:59:09 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"male","emotion":[]} +2023-12-06 14:59:09 INFO:  +2023-12-06 14:59:09 INFO:  test-backend-node.js start +2023-12-06 14:59:09 INFO:  test-backend-node.js test: configuration validation +2023-12-06 14:59:09 STATE: test-backend-node.js passed: configuration default validation [] +2023-12-06 14:59:09 STATE: test-backend-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] +2023-12-06 14:59:09 INFO:  test-backend-node.js test: model load +2023-12-06 14:59:09 STATE: test-backend-node.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] +2023-12-06 14:59:09 INFO:  test-backend-node.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} +2023-12-06 14:59:09 INFO:  test-backend-node.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} +2023-12-06 14:59:09 INFO:  test-backend-node.js test: warmup +2023-12-06 14:59:09 STATE: test-backend-node.js passed: create human +2023-12-06 14:59:09 INFO:  test-backend-node.js human version: 3.2.0 +2023-12-06 14:59:09 INFO:  test-backend-node.js platform: agent: +2023-12-06 14:59:09 INFO:  test-backend-node.js tfjs version: 4.14.0 +2023-12-06 14:59:09 INFO:  test-backend-node.js env: {"browser":false,"node":true,"platform":"","agent":"","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.14.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":172} +2023-12-06 14:59:09 STATE: test-backend-node.js passed: set backend: tensorflow +2023-12-06 14:59:09 STATE: test-backend-node.js tensors 1785 +2023-12-06 14:59:09 STATE: test-backend-node.js result: defined models: 11 loaded models: 11 +2023-12-06 14:59:09 STATE: test-backend-node.js passed: load models 11 +2023-12-06 14:59:09 STATE: test-backend-node.js passed: warmup: none default +2023-12-06 14:59:09 DATA:  test-backend-node.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} +2023-12-06 14:59:09 DATA:  test-backend-node.js result: performance: load: null total: null +2023-12-06 14:59:09 STATE: test-backend-node.js passed: warmup none result match +2023-12-06 14:59:09 STATE: test-backend-node.js event: image +2023-12-06 14:59:09 STATE: test-backend-node.js event: detect +2023-12-06 14:59:09 STATE: test-backend-node.js event: warmup +2023-12-06 14:59:09 STATE: test-backend-node.js passed: warmup: face default +2023-12-06 14:59:09 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} +2023-12-06 14:59:09 DATA:  test-backend-node.js result: performance: load: null total: 456 +2023-12-06 14:59:09 STATE: test-backend-node.js passed: warmup face result match +2023-12-06 14:59:09 STATE: test-backend-node.js event: image +2023-12-06 14:59:10 STATE: test-backend-node.js event: detect +2023-12-06 14:59:10 STATE: test-backend-node.js event: warmup +2023-12-06 14:59:10 STATE: test-backend-node.js passed: warmup: body default +2023-12-06 14:59:10 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:10 DATA:  test-backend-node.js result: performance: load: null total: 350 +2023-12-06 14:59:10 STATE: test-backend-node.js passed: warmup body result match +2023-12-06 14:59:10 STATE: test-backend-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} +2023-12-06 14:59:10 INFO:  test-backend-node.js test: details verification +2023-12-06 14:59:10 STATE: test-backend-node.js start default +2023-12-06 14:59:10 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:10 STATE: test-backend-node.js event: image +2023-12-06 14:59:10 STATE: test-backend-node.js event: detect +2023-12-06 14:59:10 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg default +2023-12-06 14:59:10 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:10 DATA:  test-backend-node.js result: performance: load: null total: 329 +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details face length 1 +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details face score 1 0.93 1 +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details face age/gender 23.7 female 0.97 2.34 +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details face arrays 4 478 1024 +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details face anti-spoofing 0.79 +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details face liveness 0.83 +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details body length 1 +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details body 0.92 17 6 +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details hand length 1 +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details hand 0.51 0.73 point +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details hand arrays 21 5 7 +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details gesture length 7 +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details gesture first {"face":0,"gesture":"facing right"} +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details object length 1 +2023-12-06 14:59:10 STATE: test-backend-node.js passed: details object 0.72 person +2023-12-06 14:59:10 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} +2023-12-06 14:59:10 STATE: test-backend-node.js event: image +2023-12-06 14:59:11 STATE: test-backend-node.js event: detect +2023-12-06 14:59:11 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 +2023-12-06 14:59:11 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} +2023-12-06 14:59:11 STATE: test-backend-node.js event: image +2023-12-06 14:59:11 STATE: test-backend-node.js event: detect +2023-12-06 14:59:11 STATE: test-backend-node.js passed: tensor shape: [1200,1200,4] dtype: float32 +2023-12-06 14:59:11 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:11 STATE: test-backend-node.js event: image +2023-12-06 14:59:12 STATE: test-backend-node.js event: detect +2023-12-06 14:59:12 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 +2023-12-06 14:59:12 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:12 STATE: test-backend-node.js event: image +2023-12-06 14:59:12 STATE: test-backend-node.js event: detect +2023-12-06 14:59:12 STATE: test-backend-node.js passed: tensor shape: [1200,1200,3] dtype: float32 +2023-12-06 14:59:13 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} +2023-12-06 14:59:13 STATE: test-backend-node.js event: image +2023-12-06 14:59:13 STATE: test-backend-node.js event: detect +2023-12-06 14:59:13 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 +2023-12-06 14:59:13 INFO:  test-backend-node.js test default +2023-12-06 14:59:13 STATE: test-backend-node.js start async +2023-12-06 14:59:13 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:13 STATE: test-backend-node.js event: image +2023-12-06 14:59:13 STATE: test-backend-node.js event: detect +2023-12-06 14:59:14 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg async +2023-12-06 14:59:14 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:14 DATA:  test-backend-node.js result: performance: load: null total: 341 +2023-12-06 14:59:14 STATE: test-backend-node.js passed: default result face match 1 female 0.97 +2023-12-06 14:59:14 INFO:  test-backend-node.js test sync +2023-12-06 14:59:14 STATE: test-backend-node.js start sync +2023-12-06 14:59:14 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:14 STATE: test-backend-node.js event: image +2023-12-06 14:59:14 STATE: test-backend-node.js event: detect +2023-12-06 14:59:14 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sync +2023-12-06 14:59:14 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:14 DATA:  test-backend-node.js result: performance: load: null total: 351 +2023-12-06 14:59:14 STATE: test-backend-node.js passed: default sync 1 female 0.97 +2023-12-06 14:59:14 INFO:  test-backend-node.js test: image process +2023-12-06 14:59:14 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:14 STATE: test-backend-node.js passed: image input null [1,256,256,3] +2023-12-06 14:59:14 INFO:  test-backend-node.js test: image null +2023-12-06 14:59:14 STATE: test-backend-node.js passed: invalid input could not convert input to tensor +2023-12-06 14:59:14 INFO:  test-backend-node.js test face similarity +2023-12-06 14:59:14 STATE: test-backend-node.js start face similarity +2023-12-06 14:59:14 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:14 STATE: test-backend-node.js event: image +2023-12-06 14:59:15 STATE: test-backend-node.js event: detect +2023-12-06 14:59:15 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face similarity +2023-12-06 14:59:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2023-12-06 14:59:15 DATA:  test-backend-node.js result: performance: load: null total: 352 +2023-12-06 14:59:15 STATE: test-backend-node.js start face similarity +2023-12-06 14:59:15 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:15 STATE: test-backend-node.js event: image +2023-12-06 14:59:15 STATE: test-backend-node.js event: detect +2023-12-06 14:59:15 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg face similarity +2023-12-06 14:59:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:15 DATA:  test-backend-node.js result: performance: load: null total: 348 +2023-12-06 14:59:15 STATE: test-backend-node.js start face similarity +2023-12-06 14:59:15 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-12-06 14:59:15 STATE: test-backend-node.js event: image +2023-12-06 14:59:15 STATE: test-backend-node.js event: detect +2023-12-06 14:59:15 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg face similarity +2023-12-06 14:59:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} +2023-12-06 14:59:15 DATA:  test-backend-node.js result: performance: load: null total: 323 +2023-12-06 14:59:15 STATE: test-backend-node.js passed: face descriptor +2023-12-06 14:59:15 STATE: test-backend-node.js passed: face similarity {"similarity":[1,0.44727441595492046,0.556793560189727],"descriptors":[1024,1024,1024]} +2023-12-06 14:59:15 INFO:  test-backend-node.js test object +2023-12-06 14:59:15 STATE: test-backend-node.js start object +2023-12-06 14:59:16 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:16 STATE: test-backend-node.js event: image +2023-12-06 14:59:16 STATE: test-backend-node.js event: detect +2023-12-06 14:59:16 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object +2023-12-06 14:59:16 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:16 DATA:  test-backend-node.js result: performance: load: null total: 337 +2023-12-06 14:59:16 STATE: test-backend-node.js passed: centernet +2023-12-06 14:59:16 STATE: test-backend-node.js start object +2023-12-06 14:59:18 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:18 STATE: test-backend-node.js event: image +2023-12-06 14:59:18 STATE: test-backend-node.js event: detect +2023-12-06 14:59:18 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object +2023-12-06 14:59:18 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:18 DATA:  test-backend-node.js result: performance: load: null total: 324 +2023-12-06 14:59:18 STATE: test-backend-node.js passed: nanodet +2023-12-06 14:59:18 INFO:  test-backend-node.js test sensitive +2023-12-06 14:59:18 STATE: test-backend-node.js start sensitive +2023-12-06 14:59:19 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:19 STATE: test-backend-node.js event: image +2023-12-06 14:59:19 STATE: test-backend-node.js event: detect +2023-12-06 14:59:19 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sensitive +2023-12-06 14:59:19 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-12-06 14:59:19 DATA:  test-backend-node.js result: performance: load: null total: 297 +2023-12-06 14:59:19 STATE: test-backend-node.js passed: sensitive result match +2023-12-06 14:59:19 STATE: test-backend-node.js passed: sensitive face result match +2023-12-06 14:59:19 STATE: test-backend-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] +2023-12-06 14:59:19 STATE: test-backend-node.js passed: sensitive body result match +2023-12-06 14:59:19 STATE: test-backend-node.js passed: sensitive hand result match +2023-12-06 14:59:19 INFO:  test-backend-node.js test body +2023-12-06 14:59:19 STATE: test-backend-node.js start blazepose +2023-12-06 14:59:21 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:21 STATE: test-backend-node.js event: image +2023-12-06 14:59:21 STATE: test-backend-node.js event: detect +2023-12-06 14:59:21 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg blazepose +2023-12-06 14:59:21 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} +2023-12-06 14:59:21 DATA:  test-backend-node.js result: performance: load: null total: 425 +2023-12-06 14:59:21 STATE: test-backend-node.js passed: blazepose +2023-12-06 14:59:21 STATE: test-backend-node.js start efficientpose +2023-12-06 14:59:23 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:23 STATE: test-backend-node.js event: image +2023-12-06 14:59:23 STATE: test-backend-node.js event: detect +2023-12-06 14:59:23 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg efficientpose +2023-12-06 14:59:23 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} +2023-12-06 14:59:23 DATA:  test-backend-node.js result: performance: load: null total: 377 +2023-12-06 14:59:23 STATE: test-backend-node.js passed: efficientpose +2023-12-06 14:59:23 STATE: test-backend-node.js start posenet +2023-12-06 14:59:24 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:24 STATE: test-backend-node.js event: image +2023-12-06 14:59:24 STATE: test-backend-node.js event: detect +2023-12-06 14:59:24 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg posenet +2023-12-06 14:59:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} +2023-12-06 14:59:24 DATA:  test-backend-node.js result: performance: load: null total: 289 +2023-12-06 14:59:24 STATE: test-backend-node.js passed: posenet +2023-12-06 14:59:24 STATE: test-backend-node.js start movenet +2023-12-06 14:59:25 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:25 STATE: test-backend-node.js event: image +2023-12-06 14:59:25 STATE: test-backend-node.js event: detect +2023-12-06 14:59:25 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg movenet +2023-12-06 14:59:25 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-12-06 14:59:25 DATA:  test-backend-node.js result: performance: load: null total: 265 +2023-12-06 14:59:25 STATE: test-backend-node.js passed: movenet +2023-12-06 14:59:25 INFO:  test-backend-node.js test face matching +2023-12-06 14:59:25 STATE: test-backend-node.js passed: face database 40 +2023-12-06 14:59:25 STATE: test-backend-node.js passed: face match {"first":{"index":4,"similarity":0.7827852251220577}} {"second":{"index":4,"similarity":0.5002052057057577}} {"third":{"index":4,"similarity":0.5401588464054732}} +2023-12-06 14:59:25 INFO:  test-backend-node.js test face similarity alternative +2023-12-06 14:59:25 STATE: test-backend-node.js start face embeddings +2023-12-06 14:59:26 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:26 STATE: test-backend-node.js event: image +2023-12-06 14:59:27 STATE: test-backend-node.js event: detect +2023-12-06 14:59:27 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-12-06 14:59:27 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-12-06 14:59:27 DATA:  test-backend-node.js result: performance: load: null total: 348 +2023-12-06 14:59:27 STATE: test-backend-node.js passed: mobilefacenet {"embedding":192} +2023-12-06 14:59:27 STATE: test-backend-node.js start face embeddings +2023-12-06 14:59:28 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:28 STATE: test-backend-node.js event: image +2023-12-06 14:59:28 STATE: test-backend-node.js event: detect +2023-12-06 14:59:28 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-12-06 14:59:28 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-12-06 14:59:28 DATA:  test-backend-node.js result: performance: load: null total: 369 +2023-12-06 14:59:28 STATE: test-backend-node.js passed: insightface {"embedding":512} +2023-12-06 14:59:28 INFO:  test-backend-node.js test face attention +2023-12-06 14:59:28 STATE: test-backend-node.js start face attention +2023-12-06 14:59:28 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:28 STATE: test-backend-node.js event: image +2023-12-06 14:59:29 STATE: test-backend-node.js event: detect +2023-12-06 14:59:29 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face attention +2023-12-06 14:59:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-12-06 14:59:29 DATA:  test-backend-node.js result: performance: load: null total: 308 +2023-12-06 14:59:29 STATE: test-backend-node.js passed: face attention +2023-12-06 14:59:29 INFO:  test-backend-node.js test detectors +2023-12-06 14:59:29 STATE: test-backend-node.js start detectors +2023-12-06 14:59:29 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:29 STATE: test-backend-node.js event: image +2023-12-06 14:59:29 STATE: test-backend-node.js event: detect +2023-12-06 14:59:29 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg detectors +2023-12-06 14:59:29 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-12-06 14:59:29 DATA:  test-backend-node.js result: performance: load: null total: 169 +2023-12-06 14:59:29 STATE: test-backend-node.js passed: detector result face match +2023-12-06 14:59:29 STATE: test-backend-node.js passed: detector result hand match +2023-12-06 14:59:29 INFO:  test-backend-node.js test: multi-instance +2023-12-06 14:59:29 STATE: test-backend-node.js start multi instance +2023-12-06 14:59:29 STATE: test-backend-node.js event: image +2023-12-06 14:59:30 STATE: test-backend-node.js event: detect +2023-12-06 14:59:30 STATE: test-backend-node.js passed: detect: random multi instance +2023-12-06 14:59:30 DATA:  test-backend-node.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} +2023-12-06 14:59:30 DATA:  test-backend-node.js result: performance: load: null total: 139 +2023-12-06 14:59:30 INFO:  test-backend-node.js test: first instance +2023-12-06 14:59:30 STATE: test-backend-node.js start multi instance +2023-12-06 14:59:30 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-12-06 14:59:30 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance +2023-12-06 14:59:30 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-12-06 14:59:30 DATA:  test-backend-node.js result: performance: load: null total: 132 +2023-12-06 14:59:30 INFO:  test-backend-node.js test: second instance +2023-12-06 14:59:30 STATE: test-backend-node.js start multi instance +2023-12-06 14:59:30 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-12-06 14:59:30 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance +2023-12-06 14:59:30 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-12-06 14:59:30 DATA:  test-backend-node.js result: performance: load: null total: 131 +2023-12-06 14:59:30 INFO:  test-backend-node.js test: concurrent +2023-12-06 14:59:30 STATE: test-backend-node.js start concurrent +2023-12-06 14:59:30 STATE: test-backend-node.js start concurrent +2023-12-06 14:59:30 STATE: test-backend-node.js start concurrent +2023-12-06 14:59:30 STATE: test-backend-node.js start concurrent +2023-12-06 14:59:30 STATE: test-backend-node.js start concurrent +2023-12-06 14:59:30 STATE: test-backend-node.js start concurrent +2023-12-06 14:59:30 STATE: test-backend-node.js start concurrent +2023-12-06 14:59:30 STATE: test-backend-node.js start concurrent +2023-12-06 14:59:30 STATE: test-backend-node.js start concurrent +2023-12-06 14:59:30 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:30 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:30 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:30 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:30 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:31 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:31 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-12-06 14:59:31 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-12-06 14:59:31 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-12-06 14:59:31 STATE: test-backend-node.js event: image +2023-12-06 14:59:31 STATE: test-backend-node.js event: image +2023-12-06 14:59:31 STATE: test-backend-node.js event: image +2023-12-06 14:59:32 STATE: test-backend-node.js event: detect +2023-12-06 14:59:32 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-12-06 14:59:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-12-06 14:59:32 DATA:  test-backend-node.js result: performance: load: null total: 1176 +2023-12-06 14:59:32 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-12-06 14:59:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-12-06 14:59:32 DATA:  test-backend-node.js result: performance: load: null total: 1176 +2023-12-06 14:59:32 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-12-06 14:59:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-12-06 14:59:32 DATA:  test-backend-node.js result: performance: load: null total: 1176 +2023-12-06 14:59:32 STATE: test-backend-node.js event: detect +2023-12-06 14:59:32 STATE: test-backend-node.js event: detect +2023-12-06 14:59:32 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent +2023-12-06 14:59:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-12-06 14:59:32 DATA:  test-backend-node.js result: performance: load: null total: 1176 +2023-12-06 14:59:32 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent +2023-12-06 14:59:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-12-06 14:59:32 DATA:  test-backend-node.js result: performance: load: null total: 1176 +2023-12-06 14:59:32 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent +2023-12-06 14:59:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-12-06 14:59:32 DATA:  test-backend-node.js result: performance: load: null total: 1176 +2023-12-06 14:59:32 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent +2023-12-06 14:59:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-12-06 14:59:32 DATA:  test-backend-node.js result: performance: load: null total: 1176 +2023-12-06 14:59:32 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent +2023-12-06 14:59:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-12-06 14:59:32 DATA:  test-backend-node.js result: performance: load: null total: 1176 +2023-12-06 14:59:32 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent +2023-12-06 14:59:32 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-12-06 14:59:32 DATA:  test-backend-node.js result: performance: load: null total: 1176 +2023-12-06 14:59:32 INFO:  test-backend-node.js test: monkey-patch +2023-12-06 14:59:32 STATE: test-backend-node.js event: image +2023-12-06 14:59:32 STATE: test-backend-node.js event: detect +2023-12-06 14:59:32 STATE: test-backend-node.js passed: monkey patch +2023-12-06 14:59:33 STATE: test-backend-node.js passed: segmentation [262144] +2023-12-06 14:59:33 STATE: test-backend-node.js passeed: equal usage +2023-12-06 14:59:33 INFO:  test-backend-node.js test: input compare +2023-12-06 14:59:33 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:33 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:33 STATE: test-backend-node.js passed: image compare 0 23.275441687091504 +2023-12-06 14:59:33 INFO:  test-backend-node.js events: {"image":29,"detect":29,"warmup":2} +2023-12-06 14:59:33 INFO:  test-backend-node.js tensors 14855 +2023-12-06 14:59:33 INFO:  test-backend-node.js test complete: 24052 ms +2023-12-06 14:59:33 INFO:  +2023-12-06 14:59:33 INFO:  test-backend-node-gpu.js start +2023-12-06 14:59:33 INFO:  test-backend-node-gpu.js test: configuration validation +2023-12-06 14:59:33 STATE: test-backend-node-gpu.js passed: configuration default validation [] +2023-12-06 14:59:33 STATE: test-backend-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] +2023-12-06 14:59:33 INFO:  test-backend-node-gpu.js test: model load +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] +2023-12-06 14:59:34 INFO:  test-backend-node-gpu.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} +2023-12-06 14:59:34 INFO:  test-backend-node-gpu.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} +2023-12-06 14:59:34 INFO:  test-backend-node-gpu.js test: warmup +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js passed: create human +2023-12-06 14:59:34 INFO:  test-backend-node-gpu.js human version: 3.2.0 +2023-12-06 14:59:34 INFO:  test-backend-node-gpu.js platform: agent: +2023-12-06 14:59:34 INFO:  test-backend-node-gpu.js tfjs version: 4.14.0 +2023-12-06 14:59:34 INFO:  test-backend-node-gpu.js env: {"browser":false,"node":true,"platform":"","agent":"","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.14.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":172} +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js passed: set backend: tensorflow +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js tensors 1785 +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js result: defined models: 11 loaded models: 11 +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js passed: load models 11 +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js passed: warmup: none default +2023-12-06 14:59:34 DATA:  test-backend-node-gpu.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} +2023-12-06 14:59:34 DATA:  test-backend-node-gpu.js result: performance: load: null total: null +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js passed: warmup none result match +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js event: warmup +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js passed: warmup: face default +2023-12-06 14:59:34 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} +2023-12-06 14:59:34 DATA:  test-backend-node-gpu.js result: performance: load: null total: 460 +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js passed: warmup face result match +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js event: warmup +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js passed: warmup: body default +2023-12-06 14:59:34 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:34 DATA:  test-backend-node-gpu.js result: performance: load: null total: 354 +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js passed: warmup body result match +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} +2023-12-06 14:59:34 INFO:  test-backend-node-gpu.js test: details verification +2023-12-06 14:59:34 STATE: test-backend-node-gpu.js start default +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg default +2023-12-06 14:59:35 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:35 DATA:  test-backend-node-gpu.js result: performance: load: null total: 332 +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details face length 1 +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details face score 1 0.93 1 +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details face age/gender 23.7 female 0.97 2.34 +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details face arrays 4 478 1024 +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details face anti-spoofing 0.79 +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details face liveness 0.83 +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details body length 1 +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details body 0.92 17 6 +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details hand length 1 +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details hand 0.51 0.73 point +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details hand arrays 21 5 7 +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details gesture length 7 +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details object length 1 +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: details object 0.72 person +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:35 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 +2023-12-06 14:59:36 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} +2023-12-06 14:59:36 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:36 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:36 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 +2023-12-06 14:59:36 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:36 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:36 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:36 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 +2023-12-06 14:59:37 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:37 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:37 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:37 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 +2023-12-06 14:59:37 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} +2023-12-06 14:59:37 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:37 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:37 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 +2023-12-06 14:59:37 INFO:  test-backend-node-gpu.js test default +2023-12-06 14:59:37 STATE: test-backend-node-gpu.js start async +2023-12-06 14:59:38 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:38 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:38 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:38 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg async +2023-12-06 14:59:38 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:38 DATA:  test-backend-node-gpu.js result: performance: load: null total: 345 +2023-12-06 14:59:38 STATE: test-backend-node-gpu.js passed: default result face match 1 female 0.97 +2023-12-06 14:59:38 INFO:  test-backend-node-gpu.js test sync +2023-12-06 14:59:38 STATE: test-backend-node-gpu.js start sync +2023-12-06 14:59:38 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:38 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:39 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:39 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sync +2023-12-06 14:59:39 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:39 DATA:  test-backend-node-gpu.js result: performance: load: null total: 372 +2023-12-06 14:59:39 STATE: test-backend-node-gpu.js passed: default sync 1 female 0.97 +2023-12-06 14:59:39 INFO:  test-backend-node-gpu.js test: image process +2023-12-06 14:59:39 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:39 STATE: test-backend-node-gpu.js passed: image input null [1,256,256,3] +2023-12-06 14:59:39 INFO:  test-backend-node-gpu.js test: image null +2023-12-06 14:59:39 STATE: test-backend-node-gpu.js passed: invalid input could not convert input to tensor +2023-12-06 14:59:39 INFO:  test-backend-node-gpu.js test face similarity +2023-12-06 14:59:39 STATE: test-backend-node-gpu.js start face similarity +2023-12-06 14:59:39 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:39 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:39 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:39 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face similarity +2023-12-06 14:59:39 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2023-12-06 14:59:39 DATA:  test-backend-node-gpu.js result: performance: load: null total: 360 +2023-12-06 14:59:39 STATE: test-backend-node-gpu.js start face similarity +2023-12-06 14:59:39 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:39 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:40 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:40 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg face similarity +2023-12-06 14:59:40 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:40 DATA:  test-backend-node-gpu.js result: performance: load: null total: 333 +2023-12-06 14:59:40 STATE: test-backend-node-gpu.js start face similarity +2023-12-06 14:59:40 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-12-06 14:59:40 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:40 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:40 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg face similarity +2023-12-06 14:59:40 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} +2023-12-06 14:59:40 DATA:  test-backend-node-gpu.js result: performance: load: null total: 341 +2023-12-06 14:59:40 STATE: test-backend-node-gpu.js passed: face descriptor +2023-12-06 14:59:40 STATE: test-backend-node-gpu.js passed: face similarity {"similarity":[1,0.44727441595492046,0.556793560189727],"descriptors":[1024,1024,1024]} +2023-12-06 14:59:40 INFO:  test-backend-node-gpu.js test object +2023-12-06 14:59:40 STATE: test-backend-node-gpu.js start object +2023-12-06 14:59:40 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:40 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:41 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:41 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object +2023-12-06 14:59:41 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:41 DATA:  test-backend-node-gpu.js result: performance: load: null total: 389 +2023-12-06 14:59:41 STATE: test-backend-node-gpu.js passed: centernet +2023-12-06 14:59:41 STATE: test-backend-node-gpu.js start object +2023-12-06 14:59:41 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:41 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:42 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:42 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object +2023-12-06 14:59:42 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:42 DATA:  test-backend-node-gpu.js result: performance: load: null total: 313 +2023-12-06 14:59:42 STATE: test-backend-node-gpu.js passed: nanodet +2023-12-06 14:59:42 INFO:  test-backend-node-gpu.js test sensitive +2023-12-06 14:59:42 STATE: test-backend-node-gpu.js start sensitive +2023-12-06 14:59:42 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:42 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:42 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:42 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sensitive +2023-12-06 14:59:42 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-12-06 14:59:42 DATA:  test-backend-node-gpu.js result: performance: load: null total: 283 +2023-12-06 14:59:42 STATE: test-backend-node-gpu.js passed: sensitive result match +2023-12-06 14:59:42 STATE: test-backend-node-gpu.js passed: sensitive face result match +2023-12-06 14:59:42 STATE: test-backend-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] +2023-12-06 14:59:42 STATE: test-backend-node-gpu.js passed: sensitive body result match +2023-12-06 14:59:42 STATE: test-backend-node-gpu.js passed: sensitive hand result match +2023-12-06 14:59:42 INFO:  test-backend-node-gpu.js test body +2023-12-06 14:59:42 STATE: test-backend-node-gpu.js start blazepose +2023-12-06 14:59:43 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:43 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:44 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:44 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg blazepose +2023-12-06 14:59:44 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} +2023-12-06 14:59:44 DATA:  test-backend-node-gpu.js result: performance: load: null total: 403 +2023-12-06 14:59:44 STATE: test-backend-node-gpu.js passed: blazepose +2023-12-06 14:59:44 STATE: test-backend-node-gpu.js start efficientpose +2023-12-06 14:59:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:44 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:45 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:45 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg efficientpose +2023-12-06 14:59:45 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} +2023-12-06 14:59:45 DATA:  test-backend-node-gpu.js result: performance: load: null total: 349 +2023-12-06 14:59:45 STATE: test-backend-node-gpu.js passed: efficientpose +2023-12-06 14:59:45 STATE: test-backend-node-gpu.js start posenet +2023-12-06 14:59:45 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:45 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:45 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:45 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg posenet +2023-12-06 14:59:45 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} +2023-12-06 14:59:45 DATA:  test-backend-node-gpu.js result: performance: load: null total: 279 +2023-12-06 14:59:45 STATE: test-backend-node-gpu.js passed: posenet +2023-12-06 14:59:45 STATE: test-backend-node-gpu.js start movenet +2023-12-06 14:59:45 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:45 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:46 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg movenet +2023-12-06 14:59:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-12-06 14:59:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 275 +2023-12-06 14:59:46 STATE: test-backend-node-gpu.js passed: movenet +2023-12-06 14:59:46 INFO:  test-backend-node-gpu.js test face matching +2023-12-06 14:59:46 STATE: test-backend-node-gpu.js passed: face database 40 +2023-12-06 14:59:46 STATE: test-backend-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7827852251220577}} {"second":{"index":4,"similarity":0.5002052057057577}} {"third":{"index":4,"similarity":0.5401588464054732}} +2023-12-06 14:59:46 INFO:  test-backend-node-gpu.js test face similarity alternative +2023-12-06 14:59:46 STATE: test-backend-node-gpu.js start face embeddings +2023-12-06 14:59:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:47 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:48 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:48 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-12-06 14:59:48 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-12-06 14:59:48 DATA:  test-backend-node-gpu.js result: performance: load: null total: 324 +2023-12-06 14:59:48 STATE: test-backend-node-gpu.js passed: mobilefacenet {"embedding":192} +2023-12-06 14:59:48 STATE: test-backend-node-gpu.js start face embeddings +2023-12-06 14:59:48 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:48 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:48 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:48 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-12-06 14:59:48 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-12-06 14:59:48 DATA:  test-backend-node-gpu.js result: performance: load: null total: 340 +2023-12-06 14:59:48 STATE: test-backend-node-gpu.js passed: insightface {"embedding":512} +2023-12-06 14:59:48 INFO:  test-backend-node-gpu.js test face attention +2023-12-06 14:59:48 STATE: test-backend-node-gpu.js start face attention +2023-12-06 14:59:49 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:49 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:49 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face attention +2023-12-06 14:59:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-12-06 14:59:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 310 +2023-12-06 14:59:49 STATE: test-backend-node-gpu.js passed: face attention +2023-12-06 14:59:49 INFO:  test-backend-node-gpu.js test detectors +2023-12-06 14:59:49 STATE: test-backend-node-gpu.js start detectors +2023-12-06 14:59:49 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:49 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:49 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:49 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg detectors +2023-12-06 14:59:49 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-12-06 14:59:49 DATA:  test-backend-node-gpu.js result: performance: load: null total: 162 +2023-12-06 14:59:49 STATE: test-backend-node-gpu.js passed: detector result face match +2023-12-06 14:59:49 STATE: test-backend-node-gpu.js passed: detector result hand match +2023-12-06 14:59:49 INFO:  test-backend-node-gpu.js test: multi-instance +2023-12-06 14:59:49 STATE: test-backend-node-gpu.js start multi instance +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js passed: detect: random multi instance +2023-12-06 14:59:50 DATA:  test-backend-node-gpu.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} +2023-12-06 14:59:50 DATA:  test-backend-node-gpu.js result: performance: load: null total: 134 +2023-12-06 14:59:50 INFO:  test-backend-node-gpu.js test: first instance +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js start multi instance +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance +2023-12-06 14:59:50 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-12-06 14:59:50 DATA:  test-backend-node-gpu.js result: performance: load: null total: 134 +2023-12-06 14:59:50 INFO:  test-backend-node-gpu.js test: second instance +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js start multi instance +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance +2023-12-06 14:59:50 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-12-06 14:59:50 DATA:  test-backend-node-gpu.js result: performance: load: null total: 137 +2023-12-06 14:59:50 INFO:  test-backend-node-gpu.js test: concurrent +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js start concurrent +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js start concurrent +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js start concurrent +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js start concurrent +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js start concurrent +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js start concurrent +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js start concurrent +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js start concurrent +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js start concurrent +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:50 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:51 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:51 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:51 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-12-06 14:59:51 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-12-06 14:59:51 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2023-12-06 14:59:51 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:51 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:51 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1213 +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1213 +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1213 +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1213 +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1213 +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1213 +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1213 +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1213 +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2023-12-06 14:59:52 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1213 +2023-12-06 14:59:52 INFO:  test-backend-node-gpu.js test: monkey-patch +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js event: image +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js event: detect +2023-12-06 14:59:52 STATE: test-backend-node-gpu.js passed: monkey patch +2023-12-06 14:59:53 STATE: test-backend-node-gpu.js passed: segmentation [262144] +2023-12-06 14:59:53 STATE: test-backend-node-gpu.js passeed: equal usage +2023-12-06 14:59:53 INFO:  test-backend-node-gpu.js test: input compare +2023-12-06 14:59:53 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2023-12-06 14:59:53 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2023-12-06 14:59:53 STATE: test-backend-node-gpu.js passed: image compare 0 23.275441687091504 +2023-12-06 14:59:53 INFO:  test-backend-node-gpu.js events: {"image":29,"detect":29,"warmup":2} +2023-12-06 14:59:53 INFO:  test-backend-node-gpu.js tensors 14855 +2023-12-06 14:59:53 INFO:  test-backend-node-gpu.js test complete: 19331 ms +2023-12-06 14:59:53 INFO:  +2023-12-06 14:59:53 INFO:  test-backend-node-wasm.js start +2023-12-06 14:59:53 DATA:  test-backend-node-wasm.js stdout: 2023-12-06 14:59:53 INFO:  { supported: true, backend: true, simd: undefined, multithread: undefined } https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@4.14.0/dist/ +2023-12-06 14:59:53 STATE: test-backend-node-wasm.js passed: model server: https://vladmandic.github.io/human-models/models/ +2023-12-06 14:59:53 INFO:  test-backend-node-wasm.js test: configuration validation +2023-12-06 14:59:53 STATE: test-backend-node-wasm.js passed: configuration default validation [] +2023-12-06 14:59:53 STATE: test-backend-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] +2023-12-06 14:59:53 INFO:  test-backend-node-wasm.js test: model load +2023-12-06 14:59:55 STATE: test-backend-node-wasm.js passed: models loaded 11 11 [{"name":"movenet","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/movenet-lightning.json"},{"name":"liveness","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/liveness.json"},{"name":"antispoof","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/antispoof.json"},{"name":"facemesh","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/facemesh.json"},{"name":"centernet","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/centernet.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/iris.json"},{"name":"blazeface","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/blazeface.json"},{"name":"emotion","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/emotion.json"},{"name":"faceres","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/faceres.json"},{"name":"handtrack","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/handtrack.json"}] +2023-12-06 14:59:55 INFO:  test-backend-node-wasm.js memory: {"memory":{"unreliable":false,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} +2023-12-06 14:59:55 INFO:  test-backend-node-wasm.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} +2023-12-06 14:59:55 INFO:  test-backend-node-wasm.js test: warmup +2023-12-06 14:59:55 STATE: test-backend-node-wasm.js passed: create human +2023-12-06 14:59:55 INFO:  test-backend-node-wasm.js human version: 3.2.0 +2023-12-06 14:59:55 INFO:  test-backend-node-wasm.js platform: agent: +2023-12-06 14:59:55 INFO:  test-backend-node-wasm.js tfjs version: 4.14.0 +2023-12-06 14:59:55 INFO:  test-backend-node-wasm.js env: {"browser":false,"node":true,"platform":"","agent":"","backends":["wasm"],"initial":false,"tfjs":{"version":"4.14.0"},"offscreen":false,"perfadd":false,"tensorflow":{},"wasm":{"supported":true,"backend":true,"simd":true,"multithread":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":174} +2023-12-06 14:59:55 STATE: test-backend-node-wasm.js passed: set backend: wasm +2023-12-06 14:59:55 STATE: test-backend-node-wasm.js tensors 1785 +2023-12-06 14:59:55 STATE: test-backend-node-wasm.js result: defined models: 11 loaded models: 11 +2023-12-06 14:59:55 STATE: test-backend-node-wasm.js passed: load models 11 +2023-12-06 14:59:55 STATE: test-backend-node-wasm.js passed: warmup: none default +2023-12-06 14:59:55 DATA:  test-backend-node-wasm.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} +2023-12-06 14:59:55 DATA:  test-backend-node-wasm.js result: performance: load: null total: null +2023-12-06 14:59:55 STATE: test-backend-node-wasm.js passed: warmup none result match +2023-12-06 14:59:55 STATE: test-backend-node-wasm.js event: image +2023-12-06 14:59:55 STATE: test-backend-node-wasm.js event: detect +2023-12-06 14:59:55 STATE: test-backend-node-wasm.js event: warmup +2023-12-06 14:59:55 STATE: test-backend-node-wasm.js passed: warmup: face default +2023-12-06 14:59:55 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2023-12-06 14:59:55 DATA:  test-backend-node-wasm.js result: performance: load: null total: 488 +2023-12-06 14:59:55 STATE: test-backend-node-wasm.js passed: warmup face result match +2023-12-06 14:59:55 STATE: test-backend-node-wasm.js event: image +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js event: detect +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js event: warmup +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: warmup: body default +2023-12-06 14:59:56 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:56 DATA:  test-backend-node-wasm.js result: performance: load: null total: 338 +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: warmup body result match +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 21% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} +2023-12-06 14:59:56 INFO:  test-backend-node-wasm.js test: details verification +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js start default +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js event: image +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js event: detect +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg default +2023-12-06 14:59:56 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 14:59:56 DATA:  test-backend-node-wasm.js result: performance: load: null total: 334 +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details face length 1 +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details face score 1 0.93 1 +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details face age/gender 23.7 female 0.97 2.34 +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details face arrays 4 478 1024 +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details face anti-spoofing 0.79 +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details face liveness 0.83 +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details body length 1 +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details body 0.92 17 6 +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details hand length 1 +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details hand 0.51 0.73 point +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details hand arrays 21 5 7 +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details gesture length 7 +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details object length 1 +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: details object 0.72 person +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} +2023-12-06 14:59:56 STATE: test-backend-node-wasm.js event: image +2023-12-06 14:59:57 STATE: test-backend-node-wasm.js event: detect +2023-12-06 14:59:57 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 +2023-12-06 14:59:57 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} +2023-12-06 14:59:57 STATE: test-backend-node-wasm.js event: image +2023-12-06 14:59:57 STATE: test-backend-node-wasm.js event: detect +2023-12-06 14:59:57 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 +2023-12-06 14:59:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-12-06 14:59:58 STATE: test-backend-node-wasm.js event: image +2023-12-06 14:59:58 STATE: test-backend-node-wasm.js event: detect +2023-12-06 14:59:58 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 +2023-12-06 14:59:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} +2023-12-06 14:59:58 STATE: test-backend-node-wasm.js event: image +2023-12-06 14:59:58 STATE: test-backend-node-wasm.js event: detect +2023-12-06 14:59:58 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 +2023-12-06 14:59:59 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} +2023-12-06 14:59:59 STATE: test-backend-node-wasm.js event: image +2023-12-06 14:59:59 STATE: test-backend-node-wasm.js event: detect +2023-12-06 14:59:59 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 +2023-12-06 14:59:59 INFO:  test-backend-node-wasm.js test default +2023-12-06 14:59:59 STATE: test-backend-node-wasm.js start async +2023-12-06 15:00:01 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-12-06 15:00:01 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:01 STATE: test-backend-node-wasm.js event: detect +2023-12-06 15:00:01 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg async +2023-12-06 15:00:01 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 15:00:01 DATA:  test-backend-node-wasm.js result: performance: load: null total: 395 +2023-12-06 15:00:01 STATE: test-backend-node-wasm.js passed: default result face match 1 female 0.97 +2023-12-06 15:00:01 INFO:  test-backend-node-wasm.js test sync +2023-12-06 15:00:01 STATE: test-backend-node-wasm.js start sync +2023-12-06 15:00:02 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-12-06 15:00:02 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:03 STATE: test-backend-node-wasm.js event: detect +2023-12-06 15:00:03 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sync +2023-12-06 15:00:03 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 15:00:03 DATA:  test-backend-node-wasm.js result: performance: load: null total: 399 +2023-12-06 15:00:03 STATE: test-backend-node-wasm.js passed: default sync 1 female 0.97 +2023-12-06 15:00:03 INFO:  test-backend-node-wasm.js test: image process +2023-12-06 15:00:03 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-12-06 15:00:03 STATE: test-backend-node-wasm.js passed: image input null [1,256,256,3] +2023-12-06 15:00:03 INFO:  test-backend-node-wasm.js test: image null +2023-12-06 15:00:03 STATE: test-backend-node-wasm.js passed: invalid input could not convert input to tensor +2023-12-06 15:00:03 INFO:  test-backend-node-wasm.js test face similarity +2023-12-06 15:00:03 STATE: test-backend-node-wasm.js start face similarity +2023-12-06 15:00:04 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-12-06 15:00:04 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:05 STATE: test-backend-node-wasm.js event: detect +2023-12-06 15:00:05 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face similarity +2023-12-06 15:00:05 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2023-12-06 15:00:05 DATA:  test-backend-node-wasm.js result: performance: load: null total: 397 +2023-12-06 15:00:05 STATE: test-backend-node-wasm.js start face similarity +2023-12-06 15:00:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-12-06 15:00:05 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:05 STATE: test-backend-node-wasm.js event: detect +2023-12-06 15:00:05 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg face similarity +2023-12-06 15:00:05 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 15:00:05 DATA:  test-backend-node-wasm.js result: performance: load: null total: 301 +2023-12-06 15:00:05 STATE: test-backend-node-wasm.js start face similarity +2023-12-06 15:00:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} +2023-12-06 15:00:05 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:06 STATE: test-backend-node-wasm.js event: detect +2023-12-06 15:00:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg face similarity +2023-12-06 15:00:06 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} +2023-12-06 15:00:06 DATA:  test-backend-node-wasm.js result: performance: load: null total: 289 +2023-12-06 15:00:06 STATE: test-backend-node-wasm.js passed: face descriptor +2023-12-06 15:00:06 STATE: test-backend-node-wasm.js passed: face similarity {"similarity":[1,0.4472741837136387,0.5567931378245533],"descriptors":[1024,1024,1024]} +2023-12-06 15:00:06 INFO:  test-backend-node-wasm.js test object +2023-12-06 15:00:06 STATE: test-backend-node-wasm.js start object +2023-12-06 15:00:06 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-12-06 15:00:06 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:06 STATE: test-backend-node-wasm.js event: detect +2023-12-06 15:00:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object +2023-12-06 15:00:06 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 15:00:06 DATA:  test-backend-node-wasm.js result: performance: load: null total: 311 +2023-12-06 15:00:06 STATE: test-backend-node-wasm.js passed: centernet +2023-12-06 15:00:06 STATE: test-backend-node-wasm.js start object +2023-12-06 15:00:07 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-12-06 15:00:07 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:07 STATE: test-backend-node-wasm.js event: detect +2023-12-06 15:00:07 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object +2023-12-06 15:00:07 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} +2023-12-06 15:00:07 DATA:  test-backend-node-wasm.js result: performance: load: null total: 353 +2023-12-06 15:00:07 STATE: test-backend-node-wasm.js passed: nanodet +2023-12-06 15:00:07 INFO:  test-backend-node-wasm.js test sensitive +2023-12-06 15:00:07 STATE: test-backend-node-wasm.js start sensitive +2023-12-06 15:00:08 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-12-06 15:00:08 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:09 STATE: test-backend-node-wasm.js event: detect +2023-12-06 15:00:09 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sensitive +2023-12-06 15:00:09 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-12-06 15:00:09 DATA:  test-backend-node-wasm.js result: performance: load: null total: 276 +2023-12-06 15:00:09 STATE: test-backend-node-wasm.js passed: sensitive result match +2023-12-06 15:00:09 STATE: test-backend-node-wasm.js passed: sensitive face result match +2023-12-06 15:00:09 STATE: test-backend-node-wasm.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] +2023-12-06 15:00:09 STATE: test-backend-node-wasm.js passed: sensitive body result match +2023-12-06 15:00:09 STATE: test-backend-node-wasm.js passed: sensitive hand result match +2023-12-06 15:00:09 INFO:  test-backend-node-wasm.js test body +2023-12-06 15:00:09 STATE: test-backend-node-wasm.js start blazepose +2023-12-06 15:00:11 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-12-06 15:00:11 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:11 STATE: test-backend-node-wasm.js event: detect +2023-12-06 15:00:11 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg blazepose +2023-12-06 15:00:11 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} +2023-12-06 15:00:11 DATA:  test-backend-node-wasm.js result: performance: load: null total: 432 +2023-12-06 15:00:11 STATE: test-backend-node-wasm.js passed: blazepose +2023-12-06 15:00:11 STATE: test-backend-node-wasm.js start efficientpose +2023-12-06 15:00:12 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-12-06 15:00:12 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:13 STATE: test-backend-node-wasm.js event: detect +2023-12-06 15:00:13 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg efficientpose +2023-12-06 15:00:13 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} +2023-12-06 15:00:13 DATA:  test-backend-node-wasm.js result: performance: load: null total: 675 +2023-12-06 15:00:13 STATE: test-backend-node-wasm.js passed: efficientpose +2023-12-06 15:00:13 STATE: test-backend-node-wasm.js start posenet +2023-12-06 15:00:13 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-12-06 15:00:13 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:14 STATE: test-backend-node-wasm.js event: detect +2023-12-06 15:00:14 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg posenet +2023-12-06 15:00:14 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} +2023-12-06 15:00:14 DATA:  test-backend-node-wasm.js result: performance: load: null total: 261 +2023-12-06 15:00:14 STATE: test-backend-node-wasm.js passed: posenet +2023-12-06 15:00:14 STATE: test-backend-node-wasm.js start movenet +2023-12-06 15:00:14 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2023-12-06 15:00:14 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:14 STATE: test-backend-node-wasm.js event: detect +2023-12-06 15:00:14 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg movenet +2023-12-06 15:00:14 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2023-12-06 15:00:14 DATA:  test-backend-node-wasm.js result: performance: load: null total: 214 +2023-12-06 15:00:14 STATE: test-backend-node-wasm.js passed: movenet +2023-12-06 15:00:14 INFO:  test-backend-node-wasm.js test face matching +2023-12-06 15:00:14 STATE: test-backend-node-wasm.js passed: face database 40 +2023-12-06 15:00:14 STATE: test-backend-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5002051178899982}} {"third":{"index":4,"similarity":0.5401586736511599}} +2023-12-06 15:00:14 INFO:  test-backend-node-wasm.js test face similarity alternative +2023-12-06 15:00:14 STATE: test-backend-node-wasm.js start face embeddings +2023-12-06 15:00:16 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-12-06 15:00:16 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:16 STATE: test-backend-node-wasm.js event: detect +2023-12-06 15:00:16 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-12-06 15:00:16 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-12-06 15:00:16 DATA:  test-backend-node-wasm.js result: performance: load: null total: 306 +2023-12-06 15:00:16 STATE: test-backend-node-wasm.js passed: mobilefacenet {"embedding":192} +2023-12-06 15:00:16 STATE: test-backend-node-wasm.js start face embeddings +2023-12-06 15:00:17 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-12-06 15:00:17 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:17 STATE: test-backend-node-wasm.js event: detect +2023-12-06 15:00:17 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings +2023-12-06 15:00:17 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2023-12-06 15:00:17 DATA:  test-backend-node-wasm.js result: performance: load: null total: 273 +2023-12-06 15:00:17 STATE: test-backend-node-wasm.js passed: insightface {"embedding":512} +2023-12-06 15:00:17 INFO:  test-backend-node-wasm.js test face attention +2023-12-06 15:00:17 STATE: test-backend-node-wasm.js start face attention +2023-12-06 15:00:17 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2023-12-06 15:00:17 STATE: test-backend-node-wasm.js event: image +2023-12-06 15:00:17 ERROR: test-backend-node-wasm.js failed: testDetect face attention +2023-12-06 15:00:17 ERROR: test-backend-node-wasm.js uncaughtException {"name":"Error","message":"BatchMatMul for non non-float32 tensors not yet supported.","stack":["Error: BatchMatMul for non non-float32 tensors not yet supported."," at Object.batchMatMul [as kernelFunc] (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-backend-wasm/dist/tf-backend-wasm.node.js:1280:15)"," at kernelFunc (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4707:32)"," at /home/vlado/dev/human/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4767:27"," at Engine.scopedRun (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4572:23)"," at Engine.runKernelFunc (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4763:14)"," at Engine.runKernel (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4636:21)"," at matMul_ (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:13408:19)"," at Object.matMul__op [as matMul] (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:12217:29)"," at executeOp$9 (/home/vlado/dev/human/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:29592:25)"," at /home/vlado/dev/human/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:30172:50"]} +2023-12-06 15:00:17 STATE: all tests complete +2023-12-06 15:00:17 INFO:  status {"test":"../demo/nodejs/node.js","passed":1,"failed":0} +2023-12-06 15:00:17 INFO:  status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0} +2023-12-06 15:00:17 INFO:  status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0} +2023-12-06 15:00:17 INFO:  status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0} +2023-12-06 15:00:17 INFO:  status {"test":"../demo/nodejs/node-canvas.js","passed":1,"failed":0} +2023-12-06 15:00:17 INFO:  status {"test":"../demo/nodejs/process-folder.js","passed":1,"failed":0} +2023-12-06 15:00:17 INFO:  status {"test":"../demo/multithread/node-multiprocess.js","passed":1,"failed":0} +2023-12-06 15:00:17 INFO:  status {"test":"../demo/facematch/node-match.js","passed":1,"failed":0} +2023-12-06 15:00:17 INFO:  status {"test":"../demo/nodejs/node-bench.js","passed":1,"failed":0} +2023-12-06 15:00:17 INFO:  status {"test":"../test/test-node-emotion.js","passed":1,"failed":0} +2023-12-06 15:00:17 INFO:  status {"test":"test-node-load.js","passed":1,"failed":0} +2023-12-06 15:00:17 INFO:  status {"test":"test-node-gear.js","passed":4,"failed":0} +2023-12-06 15:00:17 INFO:  status {"test":"test-backend-node.js","passed":125,"failed":0} +2023-12-06 15:00:17 INFO:  status {"test":"test-backend-node-gpu.js","passed":125,"failed":0} +2023-12-06 15:00:17 INFO:  status {"test":"test-backend-node-wasm.js","passed":92,"failed":1} +2023-12-06 15:00:17 INFO:  failures {"count":1} +2023-12-06 15:00:17 WARN:  failed {"test":"test-backend-node-wasm.js","message":["error",["failed:","testDetect face attention"]]} diff --git a/tfjs/tf-browser.ts b/tfjs/tf-browser.ts index c1e9d7e8..edb3bda5 100644 --- a/tfjs/tf-browser.ts +++ b/tfjs/tf-browser.ts @@ -23,3 +23,4 @@ export * from '@tensorflow/tfjs-backend-webgpu/dist/index.js'; // export versions, overrides version object from @tensorflow/tfjs export { version } from 'dist/tfjs.version.js'; +export const node = undefined; diff --git a/typedoc/assets/main.js b/typedoc/assets/main.js index f7c83669..d0aa8d5f 100644 --- a/typedoc/assets/main.js +++ b/typedoc/assets/main.js @@ -1,7 +1,8 @@ "use strict"; -"use strict";(()=>{var 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\ No newline at end of file diff --git a/typedoc/assets/style.css b/typedoc/assets/style.css index 496e66f2..07a385b7 100644 --- a/typedoc/assets/style.css +++ b/typedoc/assets/style.css @@ -6,17 +6,36 @@ --light-color-background-warning: #e6e600; --light-color-icon-background: var(--light-color-background); --light-color-accent: #c5c7c9; + --light-color-active-menu-item: var(--light-color-accent); --light-color-text: #222; - --light-color-text-aside: #707070; - --light-color-link: #4da6ff; - --light-color-ts: #db1373; - --light-color-ts-interface: #139d2c; - --light-color-ts-enum: #9c891a; - --light-color-ts-class: #2484e5; + --light-color-text-aside: #6e6e6e; + --light-color-link: #1f70c2; + + --light-color-ts-keyword: #056bd6; + --light-color-ts-project: #b111c9; + --light-color-ts-module: var(--light-color-ts-project); + --light-color-ts-namespace: var(--light-color-ts-project); + --light-color-ts-enum: #7e6f15; + --light-color-ts-enum-member: var(--light-color-ts-enum); + --light-color-ts-variable: #4760ec; --light-color-ts-function: #572be7; - --light-color-ts-namespace: #b111c9; - --light-color-ts-private: #707070; - --light-color-ts-variable: #4d68ff; + --light-color-ts-class: #1f70c2; + --light-color-ts-interface: #108024; + --light-color-ts-constructor: var(--light-color-ts-class); + --light-color-ts-property: var(--light-color-ts-variable); + --light-color-ts-method: var(--light-color-ts-function); + --light-color-ts-call-signature: var(--light-color-ts-method); + --light-color-ts-index-signature: var(--light-color-ts-property); + --light-color-ts-constructor-signature: var(--light-color-ts-constructor); + --light-color-ts-parameter: var(--light-color-ts-variable); + /* type literal not included as links will never be generated to it */ + --light-color-ts-type-parameter: var(--light-color-ts-type-alias); + --light-color-ts-accessor: var(--light-color-ts-property); + --light-color-ts-get-signature: var(--light-color-ts-accessor); + --light-color-ts-set-signature: var(--light-color-ts-accessor); + --light-color-ts-type-alias: #d51270; + /* reference not included as links will be colored with the kind that it points to */ + --light-external-icon: url("data:image/svg+xml;utf8,"); --light-color-scheme: light; @@ -27,17 +46,36 @@ --dark-color-warning-text: #222; --dark-color-icon-background: var(--dark-color-background-secondary); --dark-color-accent: #9096a2; + --dark-color-active-menu-item: #5d5d6a; --dark-color-text: #f5f5f5; --dark-color-text-aside: #dddddd; --dark-color-link: #00aff4; - --dark-color-ts: #ff6492; - --dark-color-ts-interface: #6cff87; + + --dark-color-ts-keyword: #3399ff; + --dark-color-ts-project: #e358ff; + --dark-color-ts-module: var(--dark-color-ts-project); + --dark-color-ts-namespace: var(--dark-color-ts-project); --dark-color-ts-enum: #f4d93e; - --dark-color-ts-class: #61b0ff; - --dark-color-ts-function: #9772ff; - --dark-color-ts-namespace: #e14dff; - --dark-color-ts-private: #e2e2e2; - --dark-color-ts-variable: #4d68ff; + --dark-color-ts-enum-member: var(--dark-color-ts-enum); + --dark-color-ts-variable: #798dff; + --dark-color-ts-function: #a280ff; + --dark-color-ts-class: #8ac4ff; + --dark-color-ts-interface: #6cff87; + --dark-color-ts-constructor: var(--dark-color-ts-class); + --dark-color-ts-property: var(--dark-color-ts-variable); + --dark-color-ts-method: var(--dark-color-ts-function); + --dark-color-ts-call-signature: var(--dark-color-ts-method); + --dark-color-ts-index-signature: var(--dark-color-ts-property); + --dark-color-ts-constructor-signature: var(--dark-color-ts-constructor); + --dark-color-ts-parameter: var(--dark-color-ts-variable); + /* type literal not included as links will never be generated to it */ + --dark-color-ts-type-parameter: var(--dark-color-ts-type-alias); + --dark-color-ts-accessor: var(--dark-color-ts-property); + --dark-color-ts-get-signature: var(--dark-color-ts-accessor); + --dark-color-ts-set-signature: var(--dark-color-ts-accessor); + --dark-color-ts-type-alias: #ff6492; + /* reference not included as links will be colored with the kind that it points to */ + --dark-external-icon: url("data:image/svg+xml;utf8,"); --dark-color-scheme: dark; } @@ -50,17 +88,35 @@ --color-warning-text: var(--light-color-warning-text); --color-icon-background: var(--light-color-icon-background); --color-accent: var(--light-color-accent); + --color-active-menu-item: var(--light-color-active-menu-item); --color-text: var(--light-color-text); --color-text-aside: var(--light-color-text-aside); --color-link: var(--light-color-link); - --color-ts: var(--light-color-ts); - --color-ts-interface: var(--light-color-ts-interface); - --color-ts-enum: var(--light-color-ts-enum); - --color-ts-class: var(--light-color-ts-class); - --color-ts-function: var(--light-color-ts-function); + + --color-ts-keyword: var(--light-color-ts-keyword); + --color-ts-module: var(--light-color-ts-module); --color-ts-namespace: var(--light-color-ts-namespace); - --color-ts-private: var(--light-color-ts-private); + --color-ts-enum: var(--light-color-ts-enum); + --color-ts-enum-member: var(--light-color-ts-enum-member); --color-ts-variable: var(--light-color-ts-variable); + --color-ts-function: var(--light-color-ts-function); + --color-ts-class: var(--light-color-ts-class); + --color-ts-interface: var(--light-color-ts-interface); + --color-ts-constructor: var(--light-color-ts-constructor); + --color-ts-property: var(--light-color-ts-property); + --color-ts-method: var(--light-color-ts-method); + --color-ts-call-signature: var(--light-color-ts-call-signature); + --color-ts-index-signature: var(--light-color-ts-index-signature); + --color-ts-constructor-signature: var( + --light-color-ts-constructor-signature + ); + --color-ts-parameter: var(--light-color-ts-parameter); + --color-ts-type-parameter: var(--light-color-ts-type-parameter); + --color-ts-accessor: var(--light-color-ts-accessor); + --color-ts-get-signature: var(--light-color-ts-get-signature); + --color-ts-set-signature: var(--light-color-ts-set-signature); + --color-ts-type-alias: var(--light-color-ts-type-alias); + --external-icon: var(--light-external-icon); --color-scheme: var(--light-color-scheme); } @@ -74,17 +130,35 @@ --color-warning-text: var(--dark-color-warning-text); --color-icon-background: var(--dark-color-icon-background); --color-accent: var(--dark-color-accent); + --color-active-menu-item: var(--dark-color-active-menu-item); --color-text: var(--dark-color-text); --color-text-aside: var(--dark-color-text-aside); --color-link: var(--dark-color-link); - --color-ts: var(--dark-color-ts); - --color-ts-interface: var(--dark-color-ts-interface); - --color-ts-enum: var(--dark-color-ts-enum); - --color-ts-class: var(--dark-color-ts-class); - --color-ts-function: var(--dark-color-ts-function); + + --color-ts-keyword: var(--dark-color-ts-keyword); + --color-ts-module: var(--dark-color-ts-module); --color-ts-namespace: var(--dark-color-ts-namespace); - --color-ts-private: var(--dark-color-ts-private); + --color-ts-enum: var(--dark-color-ts-enum); + --color-ts-enum-member: var(--dark-color-ts-enum-member); --color-ts-variable: var(--dark-color-ts-variable); + --color-ts-function: var(--dark-color-ts-function); + --color-ts-class: var(--dark-color-ts-class); + --color-ts-interface: var(--dark-color-ts-interface); + --color-ts-constructor: var(--dark-color-ts-constructor); + --color-ts-property: var(--dark-color-ts-property); + --color-ts-method: var(--dark-color-ts-method); + --color-ts-call-signature: var(--dark-color-ts-call-signature); + --color-ts-index-signature: var(--dark-color-ts-index-signature); + --color-ts-constructor-signature: var( + --dark-color-ts-constructor-signature + ); + --color-ts-parameter: var(--dark-color-ts-parameter); + --color-ts-type-parameter: var(--dark-color-ts-type-parameter); + --color-ts-accessor: var(--dark-color-ts-accessor); + --color-ts-get-signature: var(--dark-color-ts-get-signature); + --color-ts-set-signature: var(--dark-color-ts-set-signature); + --color-ts-type-alias: var(--dark-color-ts-type-alias); + --external-icon: var(--dark-external-icon); --color-scheme: var(--dark-color-scheme); } @@ -105,17 +179,35 @@ body { --color-warning-text: var(--light-color-warning-text); --color-icon-background: var(--light-color-icon-background); --color-accent: var(--light-color-accent); + --color-active-menu-item: var(--light-color-active-menu-item); --color-text: var(--light-color-text); --color-text-aside: var(--light-color-text-aside); --color-link: var(--light-color-link); - --color-ts: var(--light-color-ts); - --color-ts-interface: var(--light-color-ts-interface); - --color-ts-enum: var(--light-color-ts-enum); - --color-ts-class: var(--light-color-ts-class); - --color-ts-function: var(--light-color-ts-function); + + --color-ts-keyword: var(--light-color-ts-keyword); + --color-ts-module: var(--light-color-ts-module); --color-ts-namespace: var(--light-color-ts-namespace); - --color-ts-private: var(--light-color-ts-private); + --color-ts-enum: var(--light-color-ts-enum); + --color-ts-enum-member: var(--light-color-ts-enum-member); --color-ts-variable: var(--light-color-ts-variable); + --color-ts-function: var(--light-color-ts-function); + --color-ts-class: var(--light-color-ts-class); + --color-ts-interface: var(--light-color-ts-interface); + --color-ts-constructor: var(--light-color-ts-constructor); + --color-ts-property: var(--light-color-ts-property); + --color-ts-method: var(--light-color-ts-method); + --color-ts-call-signature: var(--light-color-ts-call-signature); + --color-ts-index-signature: var(--light-color-ts-index-signature); + --color-ts-constructor-signature: var( + --light-color-ts-constructor-signature + ); + --color-ts-parameter: var(--light-color-ts-parameter); + --color-ts-type-parameter: var(--light-color-ts-type-parameter); + --color-ts-accessor: var(--light-color-ts-accessor); + --color-ts-get-signature: var(--light-color-ts-get-signature); + --color-ts-set-signature: var(--light-color-ts-set-signature); + --color-ts-type-alias: var(--light-color-ts-type-alias); + --external-icon: var(--light-external-icon); --color-scheme: var(--light-color-scheme); } @@ -127,17 +219,35 @@ body { --color-warning-text: var(--dark-color-warning-text); --color-icon-background: var(--dark-color-icon-background); --color-accent: var(--dark-color-accent); + --color-active-menu-item: var(--dark-color-active-menu-item); --color-text: var(--dark-color-text); --color-text-aside: var(--dark-color-text-aside); --color-link: var(--dark-color-link); - --color-ts: var(--dark-color-ts); - --color-ts-interface: var(--dark-color-ts-interface); - --color-ts-enum: var(--dark-color-ts-enum); - --color-ts-class: var(--dark-color-ts-class); - --color-ts-function: var(--dark-color-ts-function); + + --color-ts-keyword: var(--dark-color-ts-keyword); + --color-ts-module: var(--dark-color-ts-module); --color-ts-namespace: var(--dark-color-ts-namespace); - --color-ts-private: var(--dark-color-ts-private); + --color-ts-enum: var(--dark-color-ts-enum); + --color-ts-enum-member: var(--dark-color-ts-enum-member); --color-ts-variable: var(--dark-color-ts-variable); + --color-ts-function: var(--dark-color-ts-function); + --color-ts-class: var(--dark-color-ts-class); + --color-ts-interface: var(--dark-color-ts-interface); + --color-ts-constructor: var(--dark-color-ts-constructor); + --color-ts-property: var(--dark-color-ts-property); + --color-ts-method: var(--dark-color-ts-method); + --color-ts-call-signature: var(--dark-color-ts-call-signature); + --color-ts-index-signature: var(--dark-color-ts-index-signature); + --color-ts-constructor-signature: var( + --dark-color-ts-constructor-signature + ); + --color-ts-parameter: var(--dark-color-ts-parameter); + --color-ts-type-parameter: var(--dark-color-ts-type-parameter); + --color-ts-accessor: var(--dark-color-ts-accessor); + --color-ts-get-signature: var(--dark-color-ts-get-signature); + --color-ts-set-signature: var(--dark-color-ts-set-signature); + --color-ts-type-alias: var(--dark-color-ts-type-alias); + --external-icon: var(--dark-external-icon); --color-scheme: var(--dark-color-scheme); } @@ -156,6 +266,16 @@ h6 { line-height: 1.2; } +h1 > a, +h2 > a, +h3 > a, +h4 > a, +h5 > a, +h6 > a { + text-decoration: none; + color: var(--color-text); +} + h1 { font-size: 1.875rem; margin: 0.67rem 0; @@ -190,12 +310,6 @@ h6 { text-transform: uppercase; } -pre { - white-space: pre; - white-space: pre-wrap; - word-wrap: break-word; -} - dl, menu, ol, @@ -208,26 +322,10 @@ dd { } .container { - max-width: 1600px; + max-width: 1700px; padding: 0 2rem; } -@media (min-width: 640px) { - .container { - padding: 0 4rem; - } -} -@media (min-width: 1200px) { - .container { - padding: 0 8rem; - } -} -@media (min-width: 1600px) { - .container { - padding: 0 12rem; - } -} - /* Footer */ .tsd-generator { border-top: 1px solid var(--color-accent); @@ -243,26 +341,9 @@ dd { } .container-main { - display: flex; - justify-content: space-between; - position: relative; margin: 0 auto; -} - -.col-4, -.col-8 { - box-sizing: border-box; - float: left; - padding: 2rem 1rem; -} - -.col-4 { - flex: 0 0 25%; -} -.col-8 { - flex: 1 0; - flex-wrap: wrap; - padding-left: 0; + /* toolbar, footer, margin */ + min-height: calc(100vh - 41px - 56px - 4rem); } @keyframes fade-in { @@ -305,22 +386,6 @@ dd { opacity: 0; } } -@keyframes shift-to-left { - from { - transform: translate(0, 0); - } - to { - transform: translate(-25%, 0); - } -} -@keyframes unshift-to-left { - from { - transform: translate(-25%, 0); - } - to { - transform: translate(0, 0); - } -} @keyframes pop-in-from-right { from { transform: translate(100%, 0); @@ -369,13 +434,29 @@ pre { } pre { + position: relative; + white-space: pre; + white-space: pre-wrap; + word-wrap: break-word; padding: 10px; - border: 0.1em solid var(--color-accent); + border: 1px solid var(--color-accent); } pre code { padding: 0; font-size: 100%; } +pre > button { + position: absolute; + top: 10px; + right: 10px; + opacity: 0; + transition: opacity 0.1s; + box-sizing: border-box; +} +pre:hover > button, +pre > button.visible { + opacity: 1; +} blockquote { margin: 1em 0; @@ -391,13 +472,12 @@ blockquote { padding: 0 0 0 20px; margin: 0; } -.tsd-typography h4, .tsd-typography .tsd-index-panel h3, .tsd-index-panel .tsd-typography h3, +.tsd-typography h4, .tsd-typography h5, .tsd-typography h6 { font-size: 1em; - margin: 0; } .tsd-typography h5, .tsd-typography h6 { @@ -408,90 +488,18 @@ blockquote { .tsd-typography ol { margin: 1em 0; } - -@media (max-width: 1024px) { - html .col-content { - float: none; - max-width: 100%; - width: 100%; - padding-top: 3rem; - } - html .col-menu { - position: fixed !important; - overflow-y: auto; - -webkit-overflow-scrolling: touch; - z-index: 1024; - top: 0 !important; - bottom: 0 !important; - left: auto !important; - right: 0 !important; - padding: 1.5rem 1.5rem 0 0; - max-width: 25rem; - visibility: hidden; - background-color: var(--color-background); - transform: translate(100%, 0); - } - html .col-menu > *:last-child { - padding-bottom: 20px; - } - html .overlay { - content: ""; - display: block; - position: fixed; - z-index: 1023; - top: 0; - left: 0; - right: 0; - bottom: 0; - background-color: rgba(0, 0, 0, 0.75); - visibility: hidden; - } - - .to-has-menu .overlay { - animation: fade-in 0.4s; - } - - .to-has-menu :is(header, footer, .col-content) { - animation: shift-to-left 0.4s; - } - - .to-has-menu .col-menu { - animation: pop-in-from-right 0.4s; - } - - .from-has-menu .overlay { - animation: fade-out 0.4s; - } - - .from-has-menu :is(header, footer, .col-content) { - animation: unshift-to-left 0.4s; - } - - .from-has-menu .col-menu { - animation: pop-out-to-right 0.4s; - } - - .has-menu body { - overflow: hidden; - } - .has-menu .overlay { - visibility: visible; - } - .has-menu :is(header, footer, .col-content) { - transform: translate(-25%, 0); - } - .has-menu .col-menu { - visibility: visible; - transform: translate(0, 0); - display: flex; - flex-direction: column; - gap: 1.5rem; - max-height: 100vh; - padding: 1rem 2rem; - } - .has-menu .tsd-navigation { - max-height: 100%; - } +.tsd-typography table { + border-collapse: collapse; + border: none; +} +.tsd-typography td, +.tsd-typography th { + padding: 6px 13px; + border: 1px solid var(--color-accent); +} +.tsd-typography thead, +.tsd-typography tr:nth-child(even) { + background-color: var(--color-background-secondary); } .tsd-breadcrumb { @@ -672,43 +680,6 @@ input[type="checkbox"]:checked ~ svg .tsd-checkbox-checkmark { -o-page-break-inside: avoid; page-break-inside: avoid; } -.tsd-index-panel a, -.tsd-index-panel a.tsd-parent-kind-module { - color: var(--color-ts); -} -.tsd-index-panel a.tsd-parent-kind-interface { - color: var(--color-ts-interface); -} -.tsd-index-panel a.tsd-parent-kind-enum { - color: var(--color-ts-enum); -} -.tsd-index-panel a.tsd-parent-kind-class { - color: var(--color-ts-class); -} -.tsd-index-panel a.tsd-kind-module { - color: var(--color-ts-namespace); -} -.tsd-index-panel a.tsd-kind-interface { - color: var(--color-ts-interface); -} -.tsd-index-panel a.tsd-kind-enum { - color: var(--color-ts-enum); -} -.tsd-index-panel a.tsd-kind-class { - color: var(--color-ts-class); -} -.tsd-index-panel a.tsd-kind-function { - color: var(--color-ts-function); -} -.tsd-index-panel a.tsd-kind-namespace { - color: var(--color-ts-namespace); -} -.tsd-index-panel a.tsd-kind-variable { - color: var(--color-ts-variable); -} -.tsd-index-panel a.tsd-is-private { - color: var(--color-ts-private); -} .tsd-flag { display: inline-block; @@ -723,7 +694,7 @@ input[type="checkbox"]:checked ~ svg .tsd-checkbox-checkmark { } .tsd-anchor { - position: absolute; + position: relative; top: -100px; } @@ -737,108 +708,64 @@ input[type="checkbox"]:checked ~ svg .tsd-checkbox-checkmark { margin-bottom: 0; border-bottom: none; } -.tsd-member [data-tsd-kind] { - color: var(--color-ts); -} -.tsd-member [data-tsd-kind="Interface"] { - color: var(--color-ts-interface); -} -.tsd-member [data-tsd-kind="Enum"] { - color: var(--color-ts-enum); -} -.tsd-member [data-tsd-kind="Class"] { - color: var(--color-ts-class); -} -.tsd-member [data-tsd-kind="Private"] { - color: var(--color-ts-private); -} -.tsd-navigation a { - display: block; - margin: 0.4rem 0; - border-left: 2px solid transparent; +.tsd-navigation.settings { + margin: 1rem 0; +} +.tsd-navigation > a, +.tsd-navigation .tsd-accordion-summary { + width: calc(100% - 0.5rem); +} +.tsd-navigation a, +.tsd-navigation summary > span, +.tsd-page-navigation a { + display: inline-flex; + align-items: center; + padding: 0.25rem; color: var(--color-text); text-decoration: none; - transition: border-left-color 0.1s; + box-sizing: border-box; } -.tsd-navigation a:hover { +.tsd-navigation a.current, +.tsd-page-navigation a.current { + background: var(--color-active-menu-item); +} +.tsd-navigation a:hover, +.tsd-page-navigation a:hover { text-decoration: underline; } -.tsd-navigation ul { - margin: 0; +.tsd-navigation ul, +.tsd-page-navigation ul { + margin-top: 0; + margin-bottom: 0; padding: 0; list-style: none; } -.tsd-navigation li { +.tsd-navigation li, +.tsd-page-navigation li { padding: 0; + max-width: 100%; } - -.tsd-navigation.primary .tsd-accordion-details > ul { - margin-top: 0.75rem; +.tsd-nested-navigation { + margin-left: 3rem; } -.tsd-navigation.primary a { - padding: 0.75rem 0.5rem; - margin: 0; +.tsd-nested-navigation > li > details { + margin-left: -1.5rem; } -.tsd-navigation.primary ul li a { - margin-left: 0.5rem; -} -.tsd-navigation.primary ul li li a { +.tsd-small-nested-navigation { margin-left: 1.5rem; } -.tsd-navigation.primary ul li li li a { - margin-left: 2.5rem; -} -.tsd-navigation.primary ul li li li li a { - margin-left: 3.5rem; -} -.tsd-navigation.primary ul li li li li li a { - margin-left: 4.5rem; -} -.tsd-navigation.primary ul li li li li li li a { - margin-left: 5.5rem; -} -.tsd-navigation.primary li.current > a { - border-left: 0.15rem var(--color-text) solid; -} -.tsd-navigation.primary li.selected > a { - font-weight: bold; - border-left: 0.2rem var(--color-text) solid; -} -.tsd-navigation.primary ul li a:hover { - border-left: 0.2rem var(--color-text-aside) solid; -} -.tsd-navigation.primary li.globals + li > span, -.tsd-navigation.primary li.globals + li > a { - padding-top: 20px; +.tsd-small-nested-navigation > li > details { + margin-left: -1.5rem; } -.tsd-navigation.secondary.tsd-navigation--toolbar-hide { - max-height: calc(100vh - 1rem); - top: 0.5rem; +.tsd-nested-navigation > li > a, +.tsd-nested-navigation > li > span { + width: calc(100% - 1.75rem - 0.5rem); } -.tsd-navigation.secondary > ul { - display: inline; - padding-right: 0.5rem; - transition: opacity 0.2s; -} -.tsd-navigation.secondary ul li a { - padding-left: 0; -} -.tsd-navigation.secondary ul li li a { - padding-left: 1.1rem; -} -.tsd-navigation.secondary ul li li li a { - padding-left: 2.2rem; -} -.tsd-navigation.secondary ul li li li li a { - padding-left: 3.3rem; -} -.tsd-navigation.secondary ul li li li li li a { - padding-left: 4.4rem; -} -.tsd-navigation.secondary ul li li li li li li a { - padding-left: 5.5rem; + +.tsd-page-navigation ul { + padding-left: 1.75rem; } #tsd-sidebar-links a { @@ -851,41 +778,40 @@ input[type="checkbox"]:checked ~ svg .tsd-checkbox-checkmark { } a.tsd-index-link { - margin: 0.25rem 0; + padding: 0.25rem 0 !important; font-size: 1rem; line-height: 1.25rem; display: inline-flex; align-items: center; + color: var(--color-text); } -.tsd-accordion-summary > h1, -.tsd-accordion-summary > h2, -.tsd-accordion-summary > h3, -.tsd-accordion-summary > h4, -.tsd-accordion-summary > h5 { - display: inline-flex; - align-items: center; - vertical-align: middle; - margin-bottom: 0; +.tsd-accordion-summary { + list-style-type: none; /* hide marker on non-safari */ + outline: none; /* broken on safari, so just hide it */ +} +.tsd-accordion-summary::-webkit-details-marker { + display: none; /* hide marker on safari */ +} +.tsd-accordion-summary, +.tsd-accordion-summary a { user-select: none; -moz-user-select: none; -webkit-user-select: none; -ms-user-select: none; -} -.tsd-accordion-summary { - display: block; + cursor: pointer; } +.tsd-accordion-summary a { + width: calc(100% - 1.5rem); +} .tsd-accordion-summary > * { margin-top: 0; margin-bottom: 0; padding-top: 0; padding-bottom: 0; } -.tsd-accordion-summary::-webkit-details-marker { - display: none; -} -.tsd-index-accordion .tsd-accordion-summary svg { - margin-right: 0.25rem; +.tsd-index-accordion .tsd-accordion-summary > svg { + margin-left: 0.25rem; } .tsd-index-content > :not(:first-child) { margin-top: 0.75rem; @@ -910,34 +836,6 @@ a.tsd-index-link { margin-right: 0.8rem; } -@media (min-width: 1025px) { - .col-content { - margin: 2rem auto; - } - - .menu-sticky-wrap { - position: sticky; - height: calc(100vh - 2rem); - top: 4rem; - right: 0; - padding: 0 1.5rem; - padding-top: 1rem; - margin-top: 3rem; - transition: 0.3s ease-in-out; - transition-property: top, padding-top, padding, height; - overflow-y: auto; - } - .col-menu { - border-left: 1px solid var(--color-accent); - } - .col-menu--hide { - top: 1rem; - } - .col-menu .tsd-navigation:not(:last-child) { - padding-bottom: 1.75rem; - } -} - .tsd-panel { margin-bottom: 2.5rem; } @@ -1018,8 +916,9 @@ a.tsd-index-link { box-shadow: 0 0 4px rgba(0, 0, 0, 0.25); } #tsd-search .results li { - padding: 0 10px; background-color: var(--color-background); + line-height: initial; + padding: 4px; } #tsd-search .results li:nth-child(even) { background-color: var(--color-background-secondary); @@ -1027,12 +926,15 @@ a.tsd-index-link { #tsd-search .results li.state { display: none; } -#tsd-search .results li.current, -#tsd-search .results li:hover { +#tsd-search .results li.current:not(.no-results), +#tsd-search .results li:hover:not(.no-results) { background-color: var(--color-accent); } #tsd-search .results a { - display: block; + display: flex; + align-items: center; + padding: 0.25rem; + box-sizing: border-box; } #tsd-search .results a:before { top: 10px; @@ -1088,6 +990,11 @@ a.tsd-index-link { overflow-x: auto; } +.tsd-signature-keyword { + color: var(--color-ts-keyword); + font-weight: normal; +} + .tsd-signature-symbol { color: var(--color-text-aside); font-weight: normal; @@ -1143,7 +1050,7 @@ ul.tsd-type-parameter-list h5 { } .tsd-page-toolbar { - position: fixed; + position: sticky; z-index: 1; top: 0; left: 0; @@ -1183,16 +1090,14 @@ ul.tsd-type-parameter-list h5 { padding: 12px 0; } -.tsd-page-toolbar--hide { - transform: translateY(-100%); -} - .tsd-widget { display: inline-block; overflow: hidden; opacity: 0.8; height: 40px; - transition: opacity 0.1s, background-color 0.2s; + transition: + opacity 0.1s, + background-color 0.2s; vertical-align: bottom; cursor: pointer; } @@ -1214,12 +1119,6 @@ ul.tsd-type-parameter-list h5 { .tsd-widget.menu { display: none; } -@media (max-width: 1024px) { - .tsd-widget.options, - .tsd-widget.menu { - display: inline-block; - } -} input[type="checkbox"] + .tsd-widget:before { background-position: -120px 0; } @@ -1250,7 +1149,7 @@ img { } .deprecated { - text-decoration: line-through; + text-decoration: line-through !important; } .warning { @@ -1259,6 +1158,78 @@ img { background: var(--color-background-warning); } +.tsd-kind-project { + color: var(--color-ts-project); +} +.tsd-kind-module { + color: var(--color-ts-module); +} +.tsd-kind-namespace { + color: var(--color-ts-namespace); +} +.tsd-kind-enum { + color: var(--color-ts-enum); +} +.tsd-kind-enum-member { + color: var(--color-ts-enum-member); +} +.tsd-kind-variable { + color: var(--color-ts-variable); +} +.tsd-kind-function { + color: var(--color-ts-function); +} +.tsd-kind-class { + color: var(--color-ts-class); +} +.tsd-kind-interface { + color: var(--color-ts-interface); +} +.tsd-kind-constructor { + color: var(--color-ts-constructor); +} +.tsd-kind-property { + color: var(--color-ts-property); +} +.tsd-kind-method { + color: var(--color-ts-method); +} +.tsd-kind-call-signature { + color: var(--color-ts-call-signature); +} +.tsd-kind-index-signature { + color: var(--color-ts-index-signature); +} +.tsd-kind-constructor-signature { + color: var(--color-ts-constructor-signature); +} +.tsd-kind-parameter { + color: var(--color-ts-parameter); +} +.tsd-kind-type-literal { + color: var(--color-ts-type-literal); +} +.tsd-kind-type-parameter { + color: var(--color-ts-type-parameter); +} +.tsd-kind-accessor { + color: var(--color-ts-accessor); +} +.tsd-kind-get-signature { + color: var(--color-ts-get-signature); +} +.tsd-kind-set-signature { + color: var(--color-ts-set-signature); +} +.tsd-kind-type-alias { + color: var(--color-ts-type-alias); +} + +/* if we have a kind icon, don't color the text by kind */ +.tsd-kind-icon ~ span { + color: var(--color-text); +} + * { scrollbar-width: thin; scrollbar-color: var(--color-accent) var(--color-icon-background); @@ -1277,3 +1248,147 @@ img { border-radius: 999rem; border: 0.25rem solid var(--color-icon-background); } + +/* mobile */ +@media (max-width: 769px) { + .tsd-widget.options, + .tsd-widget.menu { + display: inline-block; + } + + .container-main { + display: flex; + } + html .col-content { + float: none; + max-width: 100%; + width: 100%; + } + html .col-sidebar { + position: fixed !important; + overflow-y: auto; + -webkit-overflow-scrolling: touch; + z-index: 1024; + top: 0 !important; + bottom: 0 !important; + left: auto !important; + right: 0 !important; + padding: 1.5rem 1.5rem 0 0; + width: 75vw; + visibility: hidden; + background-color: var(--color-background); + transform: translate(100%, 0); + } + html .col-sidebar > *:last-child { + padding-bottom: 20px; + } + html .overlay { + content: ""; + display: block; + position: fixed; + z-index: 1023; + top: 0; + left: 0; + right: 0; + bottom: 0; + background-color: rgba(0, 0, 0, 0.75); + visibility: hidden; + } + + .to-has-menu .overlay { + animation: fade-in 0.4s; + } + + .to-has-menu .col-sidebar { + animation: pop-in-from-right 0.4s; + } + + .from-has-menu .overlay { + animation: fade-out 0.4s; + } + + .from-has-menu .col-sidebar { + animation: pop-out-to-right 0.4s; + } + + .has-menu body { + overflow: hidden; + } + .has-menu .overlay { + visibility: visible; + } + .has-menu .col-sidebar { + visibility: visible; + transform: translate(0, 0); + display: flex; + flex-direction: column; + gap: 1.5rem; + max-height: 100vh; + padding: 1rem 2rem; + } + .has-menu .tsd-navigation { + max-height: 100%; + } +} + +/* one sidebar */ +@media (min-width: 770px) { + .container-main { + display: grid; + grid-template-columns: minmax(0, 1fr) minmax(0, 2fr); + grid-template-areas: "sidebar content"; + margin: 2rem auto; + } + + .col-sidebar { + grid-area: sidebar; + } + .col-content { + grid-area: content; + padding: 0 1rem; + } +} +@media (min-width: 770px) and (max-width: 1399px) { + .col-sidebar { + max-height: calc(100vh - 2rem - 42px); + overflow: auto; + position: sticky; + top: 42px; + padding-top: 1rem; + } + .site-menu { + margin-top: 1rem; + } +} + +/* two sidebars */ +@media (min-width: 1200px) { + .container-main { + grid-template-columns: minmax(0, 1fr) minmax(0, 2.5fr) minmax(0, 20rem); + grid-template-areas: "sidebar content toc"; + } + + .col-sidebar { + display: contents; + } + + .page-menu { + grid-area: toc; + padding-left: 1rem; + } + .site-menu { + grid-area: sidebar; + } + + .site-menu { + margin-top: 1rem 0; + } + + .page-menu, + .site-menu { + max-height: calc(100vh - 2rem - 42px); + overflow: auto; + position: sticky; + top: 42px; + } +} diff --git a/typedoc/classes/Env.html b/typedoc/classes/Env.html index 375e40fe..10463c21 100644 --- a/typedoc/classes/Env.html +++ b/typedoc/classes/Env.html @@ -1,379 +1,44 @@ -Env | @vladmandic/human - v3.1.2
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Class Env

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Env class that holds detected capabilities

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Hierarchy

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  • Env
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Constructors

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constructor -
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Properties

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agent -backends -browser -cpu -filter -initial -kernels -node -offscreen -perfadd -platform -tensorflow -tfjs -wasm -webgl -webgpu -worker -
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Accessors

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Canvas -Image -ImageData -
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Methods

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updateBackend -updateCPU -
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Constructors

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constructor

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Properties

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agent

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agent: string = ''
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Detected agent

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backends

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backends: string[] = []
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List of supported backends

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browser

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browser: boolean
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Running in Browser

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cpu

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cpu: {
    flags: string[];
    model: undefined | string;
} = ...
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CPU info

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Type declaration

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    flags: string[]
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    model: undefined | string
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filter

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filter: undefined | boolean
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Are image filters supported?

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initial

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initial: boolean
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Has any work been performed so far

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kernels

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kernels: string[] = []
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List of supported kernels for current backend

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node

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node: boolean
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Running in NodeJS

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offscreen

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offscreen: undefined | boolean
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Is offscreenCanvas supported?

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perfadd

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perfadd: boolean = false
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Are performance counter instant values or additive

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platform

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platform: string = ''
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Detected platform

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tensorflow

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tensorflow: {
    gpu: undefined | boolean;
    version: undefined | string;
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If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled

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Type declaration

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    gpu: undefined | boolean
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    version: undefined | string
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tfjs

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tfjs: {
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}
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TFJS instance details

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Type declaration

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    version: undefined | string
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wasm

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wasm: {
    backend: undefined | boolean;
    multithread: undefined | boolean;
    simd: undefined | boolean;
    supported: undefined | boolean;
} = ...
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WASM detected capabilities

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Type declaration

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    backend: undefined | boolean
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    multithread: undefined | boolean
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    supported: undefined | boolean
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webgl

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webgl: {
    backend: undefined | boolean;
    renderer: undefined | string;
    shader: undefined | string;
    supported: undefined | boolean;
    vendor: undefined | string;
    version: undefined | string;
} = ...
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WebGL detected capabilities

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Type declaration

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    backend: undefined | boolean
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    renderer: undefined | string
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    shader: undefined | string
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    supported: undefined | boolean
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    vendor: undefined | string
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    version: undefined | string
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webgpu

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webgpu: {
    adapter: undefined | GPUAdapterInfo;
    backend: undefined | boolean;
    supported: undefined | boolean;
} = ...
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WebGPU detected capabilities

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Type declaration

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    adapter: undefined | GPUAdapterInfo
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    backend: undefined | boolean
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    supported: undefined | boolean
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worker

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worker: boolean
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Running in WebWorker thread

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Accessors

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Canvas

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    Returns undefined

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  • set Canvas(val: undefined): void
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    Parameters

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    Returns void

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Image

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    Returns undefined

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    Returns void

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ImageData

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    Returns undefined

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  • set ImageData(val: undefined): void
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    Parameters

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      val: undefined
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    Returns void

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Methods

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updateBackend

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    update backend information

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    Returns Promise<void>

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updateCPU

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    update cpu information

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    Returns void

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Settings

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Member Visibility

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Theme

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\ No newline at end of file +Env | @vladmandic/human - v3.2.0

Class Env

Env class that holds detected capabilities

+

Constructors

constructor +

Properties

agent +backends +browser +cpu +filter +initial +kernels +node +offscreen +perfadd +platform +tensorflow +tfjs +wasm +webgl +webgpu +worker +

Accessors

Canvas +Image +ImageData +

Methods

updateBackend +updateCPU +

Constructors

constructor

Properties

agent

agent: string = ''

Detected agent

+

backends

backends: string[] = []

List of supported backends

+

browser

browser: boolean

Running in Browser

+

cpu

cpu: {
    flags: string[];
    model: undefined | string;
} = ...

CPU info

+

Type declaration

  • flags: string[]
  • model: undefined | string

filter

filter: undefined | boolean

Are image filters supported?

+

initial

initial: boolean

Has any work been performed so far

+

kernels

kernels: string[] = []

List of supported kernels for current backend

+

node

node: boolean

Running in NodeJS

+

offscreen

offscreen: undefined | boolean

Is offscreenCanvas supported?

+

perfadd

perfadd: boolean = false

Are performance counter instant values or additive

+

platform

platform: string = ''

Detected platform

+

tensorflow

tensorflow: {
    gpu: undefined | boolean;
    version: undefined | string;
} = ...

If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled

+

Type declaration

  • gpu: undefined | boolean
  • version: undefined | string

tfjs

tfjs: {
    version: undefined | string;
}

TFJS instance details

+

Type declaration

  • version: undefined | string

wasm

wasm: {
    backend: undefined | boolean;
    multithread: undefined | boolean;
    simd: undefined | boolean;
    supported: undefined | boolean;
} = ...

WASM detected capabilities

+

Type declaration

  • backend: undefined | boolean
  • multithread: undefined | boolean
  • simd: undefined | boolean
  • supported: undefined | boolean

webgl

webgl: {
    backend: undefined | boolean;
    renderer: undefined | string;
    shader: undefined | string;
    supported: undefined | boolean;
    vendor: undefined | string;
    version: undefined | string;
} = ...

WebGL detected capabilities

+

Type declaration

  • backend: undefined | boolean
  • renderer: undefined | string
  • shader: undefined | string
  • supported: undefined | boolean
  • vendor: undefined | string
  • version: undefined | string

webgpu

webgpu: {
    adapter: undefined | GPUAdapterInfo;
    backend: undefined | boolean;
    supported: undefined | boolean;
} = ...

WebGPU detected capabilities

+

Type declaration

  • adapter: undefined | GPUAdapterInfo
  • backend: undefined | boolean
  • supported: undefined | boolean

worker

worker: boolean

Running in WebWorker thread

+

Accessors

Canvas

Image

ImageData

Methods

updateBackend

  • updateBackend(): Promise<void>

  • update backend information

    +

    Returns Promise<void>

updateCPU

Settings

Member Visibility

Theme

On This Page

\ No newline at end of file diff --git a/typedoc/classes/GraphModel.html b/typedoc/classes/GraphModel.html index c35a155f..4967f6cf 100644 --- a/typedoc/classes/GraphModel.html +++ b/typedoc/classes/GraphModel.html @@ -1,361 +1,69 @@ -GraphModel | @vladmandic/human - v3.1.2
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Class GraphModel<ModelURL>

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A tf.GraphModel is a directed, acyclic graph built from a +GraphModel | @vladmandic/human - v3.2.0

Class GraphModel<ModelURL>

A tf.GraphModel is a directed, acyclic graph built from a SavedModel GraphDef and allows inference execution.

A tf.GraphModel can only be created by loading from a model converted from a TensorFlow SavedModel using the command line converter tool and loaded via tf.loadGraphModel.

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Doc

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Type Parameters

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    ModelURL extends Url = string | io.IOHandler

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Hierarchy

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  • GraphModel
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Implements

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  • InferenceModel
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Constructors

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constructor -
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Accessors

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inputNodes -inputs -metadata -modelSignature -modelStructuredOutputKeys -modelVersion -outputNodes -outputs -weights -
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Methods

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dispose -disposeIntermediateTensors -execute -executeAsync -getIntermediateTensors -load -loadSync -predict -predictAsync -save -
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Constructors

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constructor

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    Type Parameters

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      ModelURL extends Url = string | IOHandler

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    Parameters

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      url for the model, or an io.IOHandler.

      -
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      Optional loadOptions: LoadOptions
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      Optional tfio: __module
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    Returns GraphModel<ModelURL>

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Accessors

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inputNodes

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  • get inputNodes(): string[]
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    Returns string[]

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inputs

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  • get inputs(): TensorInfo[]
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    Returns TensorInfo[]

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metadata

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  • get metadata(): {}
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    Returns {}

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    modelSignature

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    • get modelSignature(): {}
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      Returns {}

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      modelStructuredOutputKeys

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      • get modelStructuredOutputKeys(): {}
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        Returns {}

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        modelVersion

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        • get modelVersion(): string
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          Returns string

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        outputNodes

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        • get outputNodes(): string[]
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          Returns string[]

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        outputs

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        • get outputs(): TensorInfo[]
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          Returns TensorInfo[]

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        weights

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        • get weights(): NamedTensorsMap
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          Returns NamedTensorsMap

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        Methods

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        dispose

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          Releases the memory used by the weight tensors and resourceManager.

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          Doc

          -

          Returns void

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        disposeIntermediateTensors

        -
          - -
        • -

          Dispose intermediate tensors for model debugging mode (flag +

          Doc

        Type Parameters

        • ModelURL extends Url = string | io.IOHandler

        Implements

        • InferenceModel

        Constructors

        constructor +

        Accessors

        inputNodes +inputs +metadata +modelSignature +modelStructuredOutputKeys +modelVersion +outputNodes +outputs +weights +

        Methods

        dispose +disposeIntermediateTensors +execute +executeAsync +getIntermediateTensors +load +loadSync +predict +predictAsync +save +

        Constructors

        constructor

        • new GraphModel<ModelURL>(modelUrl, loadOptions?, tfio?): GraphModel<ModelURL>

        • Type Parameters

          • ModelURL extends Url = string | IOHandler

          Parameters

          • modelUrl: ModelURL

            url for the model, or an io.IOHandler.

            +
          • Optional loadOptions: LoadOptions
          • Optional tfio: __module

          Returns GraphModel<ModelURL>

        Accessors

        inputNodes

        • get inputNodes(): string[]

        • Returns string[]

        inputs

        • get inputs(): TensorInfo[]

        • Returns TensorInfo[]

        metadata

        • get metadata(): {}

        • Returns {}

          modelSignature

          • get modelSignature(): {}

          • Returns {}

            modelStructuredOutputKeys

            • get modelStructuredOutputKeys(): {}

            • Returns {}

              modelVersion

              • get modelVersion(): string

              • Returns string

              outputNodes

              • get outputNodes(): string[]

              • Returns string[]

              outputs

              • get outputs(): TensorInfo[]

              • Returns TensorInfo[]

              weights

              • get weights(): NamedTensorsMap

              • Returns NamedTensorsMap

              Methods

              dispose

              • dispose(): void

              • Releases the memory used by the weight tensors and resourceManager.

                +

                Returns void

                Doc

              disposeIntermediateTensors

              • disposeIntermediateTensors(): void

              • Dispose intermediate tensors for model debugging mode (flag KEEP_INTERMEDIATE_TENSORS is true).

                - -

                Doc

                -

                Returns void

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              execute

              -
                - -
              • -

                Executes inference for the model for given input tensors.

                - -

                Returns

                A single tensor if provided with a single output or no outputs -are provided and there is only one default output, otherwise return a -tensor array. The order of the tensor array is the same as the outputs -if provided, otherwise the order of outputNodes attribute of the model.

                - -

                Doc

                -
                -

                Parameters

                -
                  -
                • -
                  inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap
                  -

                  tensor, tensor array or tensor map of the inputs for the +

                  Returns void

                  Doc

              execute

              • execute(inputs, outputs?): Tensor<Rank> | Tensor<Rank>[]

              • Executes inference for the model for given input tensors.

                +

                Parameters

                • inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap

                  tensor, tensor array or tensor map of the inputs for the model, keyed by the input node names.

                  -
                  • -
                • -
                  Optional outputs: string | string[]
                  -

                  output node name from the TensorFlow model, if no +

                • Optional outputs: string | string[]

                  output node name from the TensorFlow model, if no outputs are specified, the default outputs of the model would be used. You can inspect intermediate nodes of the model by adding them to the outputs array.

                  -
                -

                Returns Tensor<Rank> | Tensor<Rank>[]

              -
              -

              executeAsync

              -
                - -
              • -

                Executes inference for the model for given input tensors in async +

              Returns Tensor<Rank> | Tensor<Rank>[]

              A single tensor if provided with a single output or no outputs +are provided and there is only one default output, otherwise return a +tensor array. The order of the tensor array is the same as the outputs +if provided, otherwise the order of outputNodes attribute of the model.

              +

              Doc

              executeAsync

              • executeAsync(inputs, outputs?): Promise<Tensor<Rank> | Tensor<Rank>[]>

              • Executes inference for the model for given input tensors in async fashion, use this method when your model contains control flow ops.

                - -

                Returns

                A Promise of single tensor if provided with a single output or -no outputs are provided and there is only one default output, otherwise -return a tensor map.

                - -

                Doc

                -
                -

                Parameters

                -
                  -
                • -
                  inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap
                  -

                  tensor, tensor array or tensor map of the inputs for the +

                  Parameters

                  • inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap

                    tensor, tensor array or tensor map of the inputs for the model, keyed by the input node names.

                    -
                    • -
                  • -
                    Optional outputs: string | string[]
                    -

                    output node name from the TensorFlow model, if no outputs +

                  • Optional outputs: string | string[]

                    output node name from the TensorFlow model, if no outputs are specified, the default outputs of the model would be used. You can inspect intermediate nodes of the model by adding them to the outputs array.

                    -
                  -

                  Returns Promise<Tensor<Rank> | Tensor<Rank>[]>

              -
              -

              getIntermediateTensors

              -
                - -
              • -

                Get intermediate tensors for model debugging mode (flag +

              Returns Promise<Tensor<Rank> | Tensor<Rank>[]>

              A Promise of single tensor if provided with a single output or +no outputs are provided and there is only one default output, otherwise +return a tensor map.

              +

              Doc

              getIntermediateTensors

              • getIntermediateTensors(): NamedTensorsMap

              • Get intermediate tensors for model debugging mode (flag KEEP_INTERMEDIATE_TENSORS is true).

                - -

                Doc

                -

                Returns NamedTensorsMap

              -
              -

              load

              -
                - -
              • -

                Loads the model and weight files, construct the in memory weight map and +

                Returns NamedTensorsMap

                Doc

              load

              • load(): UrlIOHandler<ModelURL> extends IOHandlerSync
                    ? boolean
                    : Promise<boolean>

              • Loads the model and weight files, construct the in memory weight map and compile the inference graph.

                -
                -

                Returns UrlIOHandler<ModelURL> extends IOHandlerSync ? boolean : Promise<boolean>

              -
              -

              loadSync

              -
                - -
              • -

                Synchronously construct the in memory weight map and +

                Returns UrlIOHandler<ModelURL> extends IOHandlerSync
                    ? boolean
                    : Promise<boolean>

              loadSync

              • loadSync(artifacts): boolean

              • Synchronously construct the in memory weight map and compile the inference graph.

                - -

                Doc

                -
                -

                Parameters

                -
                  -
                • -
                  artifacts: ModelArtifacts
                -

                Returns boolean

              -
              -

              predict

              -
                - -
              • -

                Execute the inference for the input tensors.

                - -

                See

                inputNodes

                -

                You can also feed any intermediate nodes using the NamedTensorMap as the -input type. For example, given the graph - InputNode => Intermediate => OutputNode, -you can execute the subgraph Intermediate => OutputNode by calling - model.execute('IntermediateNode' : tf.tensor(...));

                -

                This is useful for models that uses tf.dynamic_rnn, where the intermediate -state needs to be fed manually.

                -

                For batch inference execution, the tensors for each input need to be -concatenated together. For example with mobilenet, the required input shape -is [1, 244, 244, 3], which represents the [batch, height, width, channel]. -If we are provide a batched data of 100 images, the input tensor should be -in the shape of [100, 244, 244, 3].

                - -

                Returns

                Inference result tensors. If the model is converted and it +

                Parameters

                • artifacts: ModelArtifacts

                Returns boolean

                Doc

              predict

              • predict(inputs, config?): Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap

              • Execute the inference for the input tensors.

                +

                Parameters

                • inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap
                • Optional config: ModelPredictConfig

                  Prediction configuration for specifying the batch size. +Currently the batch size option is ignored for graph model.

                  +

                Returns Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap

                Inference result tensors. If the model is converted and it originally had structured_outputs in tensorflow, then a NamedTensorMap will be returned matching the structured_outputs. If no structured_outputs are present, the output will be single tf.Tensor if the model has single output node, otherwise Tensor[].

                - -

                Doc

              -
              -

              Parameters

              -
                -
              • -
                inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap
                • -
              • -
                Optional config: ModelPredictConfig
                -

                Prediction configuration for specifying the batch size. -Currently the batch size option is ignored for graph model.

                -
              -

              Returns Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap

              -
              -

              predictAsync

              -
                - -
              • -

                Execute the inference for the input tensors in async fashion, use this -method when your model contains control flow ops.

                - -

                See

                inputNodes

                +

                See

                GraphModel.inputNodes

                You can also feed any intermediate nodes using the NamedTensorMap as the input type. For example, given the graph InputNode => Intermediate => OutputNode, @@ -368,33 +76,29 @@ concatenated together. For example with mobilenet, the required input shape is [1, 244, 244, 3], which represents the [batch, height, width, channel]. If we are provide a batched data of 100 images, the input tensor should be in the shape of [100, 244, 244, 3].

                - -

                Returns

                A Promise of inference result tensors. If the model is converted +

                Doc

              predictAsync

              • predictAsync(inputs, config?): Promise<Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap>

              • Execute the inference for the input tensors in async fashion, use this +method when your model contains control flow ops.

                +

                Parameters

                • inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap
                • Optional config: ModelPredictConfig

                  Prediction configuration for specifying the batch size. +Currently the batch size option is ignored for graph model.

                  +

                Returns Promise<Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap>

                A Promise of inference result tensors. If the model is converted and it originally had structured_outputs in tensorflow, then a NamedTensorMap will be returned matching the structured_outputs. If no structured_outputs are present, the output will be single tf.Tensor if the model has single output node, otherwise Tensor[].

                - -

                Doc

                -
                -

                Parameters

                -
                  -
                • -
                  inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap
                  • -
                • -
                  Optional config: ModelPredictConfig
                  -

                  Prediction configuration for specifying the batch size. -Currently the batch size option is ignored for graph model.

                  -
                -

                Returns Promise<Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap>

              -
              -

              save

              -
                - -
              • -

                Save the configuration and/or weights of the GraphModel.

                +

                See

                GraphModel.inputNodes

                +

                You can also feed any intermediate nodes using the NamedTensorMap as the +input type. For example, given the graph + InputNode => Intermediate => OutputNode, +you can execute the subgraph Intermediate => OutputNode by calling + model.execute('IntermediateNode' : tf.tensor(...));

                +

                This is useful for models that uses tf.dynamic_rnn, where the intermediate +state needs to be fed manually.

                +

                For batch inference execution, the tensors for each input need to be +concatenated together. For example with mobilenet, the required input shape +is [1, 244, 244, 3], which represents the [batch, height, width, channel]. +If we are provide a batched data of 100 images, the input tensor should be +in the shape of [100, 244, 244, 3].

                +

                Doc

              save

              • save(handlerOrURL, config?): Promise<SaveResult>

              • Save the configuration and/or weights of the GraphModel.

                An IOHandler is an object that has a save method of the proper signature defined. The save method manages the storing or transmission of serialized data ("artifacts") that represent the @@ -411,72 +115,11 @@ as URL-like string shortcuts, such as 'localstorage://' and storage; then load it back.

                const modelUrl =
                   'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
                const model = await tf.loadGraphModel(modelUrl);
                const zeros = tf.zeros([1, 224, 224, 3]);
                model.predict(zeros).print();

                const saveResults = await model.save('localstorage://my-model-1');

                const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
                console.log('Prediction from loaded model:');
                model.predict(zeros).print();
-
                - -

                Returns

                A Promise of SaveResult, which summarizes the result of + +

                Parameters

                • handlerOrURL: string | IOHandler

                  An instance of IOHandler or a URL-like, +scheme-based string shortcut for IOHandler.

                  +
                • Optional config: SaveConfig

                  Options for saving the model.

                  +

                Returns Promise<SaveResult>

                A Promise of SaveResult, which summarizes the result of the saving, such as byte sizes of the saved artifacts for the model's topology and weight values.

                - -

                Doc

                -
                -

                Parameters

                -
                  -
                • -
                  handlerOrURL: string | IOHandler
                  -

                  An instance of IOHandler or a URL-like, -scheme-based string shortcut for IOHandler.

                  -
                  • -
                • -
                  Optional config: SaveConfig
                  -

                  Options for saving the model.

                  -
                -

                Returns Promise<SaveResult>

              -
              -
              -
              -

              Settings

               -
              -
              -

              Member Visibility

              -
                -
              -
              -

              Theme

              - -
              -
              \ No newline at end of file +

              Doc

              Settings

              Member Visibility

              Theme

              On This Page

              \ No newline at end of file diff --git a/typedoc/classes/Human.html b/typedoc/classes/Human.html index de7fe207..9e569961 100644 --- a/typedoc/classes/Human.html +++ b/typedoc/classes/Human.html @@ -1,138 +1,57 @@ -Human | @vladmandic/human - v3.1.2
              -
              - -
              -
              -
              -
              - -

              Class Human

              -
              -

              Human* library main class

              +Human | @vladmandic/human - v3.2.0

              Class Human

              Human* library main class

              All methods and properties are available only as members of Human class

                -
              • Configuration object definition: Config
                • -
              • Results object definition: Result
                • -
              • Possible inputs: Input
                • +
              • Configuration object definition: Config
                • +
              • Results object definition: Result
                • +
              • Possible inputs: Input
              - -

              Param

              Config

              - -

              Returns

              instance of Human

              -
              -
              -

              Hierarchy

              -
                -
              • Human
              -
              -
              -
              - -
              -
              -

              Constructors

              -
              constructor -
              -
              -

              Properties

              -
              config -draw -env -events -faceTriangulation -faceUVMap -match -performance -process -result -state -tf -version -webcam -
              -
              -

              Methods

              -
              analyze -compare -detect -emit -image -init -load -next -now -profile -reset -segmentation -sleep -validate -video -warmup -
              -
              -

              Constructors

              -
              -

              constructor

              -
                - -
              • -

                Constructor for Human library that is futher used for all operations

                -
                -
                -

                Parameters

                -
                  -
                • -
                  Optional userConfig: Partial<Config>
                  -

                  user configuration object Config

                  -
                -

                Returns Human

              -
              -

              Properties

              -
              -

              config

              -
              config: Config
              -

              Current configuration

              +

              Param: userConfig

              Config

              +

              Returns

              instance of Human

              +

              Constructors

              constructor +

              Properties

              config +draw +env +events +faceTriangulation +faceUVMap +match +performance +process +result +state +tf +version +webcam +

              Methods

              analyze +compare +detect +emit +image +init +load +next +now +profile +reset +segmentation +sleep +validate +video +warmup +

              Constructors

              constructor

              • new Human(userConfig?): Human

              • Constructor for Human library that is futher used for all operations

                +

                Parameters

                • Optional userConfig: Partial<Config>

                  user configuration object Config

                  +

                Returns Human

              Properties

              config

              config: Config

              Current configuration

              -
              -
              -

              draw

              -
              draw: draw = draw
              -

              Draw helper classes that can draw detected objects on canvas using specified draw

              +

              draw

              draw: draw = draw

              Draw helper classes that can draw detected objects on canvas using specified draw

              • canvas: draws input to canvas
                • -
              • options: are global settings for all draw operations, can be overriden for each draw method DrawOptions
                • +
              • options: are global settings for all draw operations, can be overriden for each draw method DrawOptions
              • face, body, hand, gesture, object, person: draws detected results as overlays on canvas
              -
              -
              -

              env

              -
              env: Env = env
              -

              Object containing environment information used for diagnostics

              -
              -
              -

              events

              -
              events: undefined | EventTarget
              -

              Container for events dispatched by Human +

              env

              env: Env = env

              Object containing environment information used for diagnostics

              +

              events

              events: undefined | EventTarget

              Container for events dispatched by Human Possible events:

              • create: triggered when Human object is instantiated
                • @@ -142,125 +61,34 @@ Possible events:

              • warmup: triggered when warmup is complete
              • error: triggered on some errors
              -
              -
              -

              faceTriangulation

              -
              faceTriangulation: number[]
              -

              Reference face triangualtion array of 468 points, used for triangle references between points

              -
              -
              -

              faceUVMap

              -
              faceUVMap: [number, number][]
              -

              Refernce UV map of 468 values, used for 3D mapping of the face mesh

              -
              -
              -

              match

              -
              match: match = match
              -

              Face Matching

              +

              faceTriangulation

              faceTriangulation: number[]

              Reference face triangualtion array of 468 points, used for triangle references between points

              +

              faceUVMap

              faceUVMap: [number, number][]

              Refernce UV map of 468 values, used for 3D mapping of the face mesh

              +

              match

              match: match = match

              Face Matching

              • similarity: compare two face descriptors and return similarity index
              • distance: compare two face descriptors and return raw calculated differences
              • find: compare face descriptor to array of face descriptors and return best match
              -
              -
              -

              performance

              -
              performance: Record<string, number>
              -

              Performance object that contains values for all recently performed operations

              -
              -
              -

              process

              -
              process: {
                  canvas: null | AnyCanvas;
                  tensor: null | Tensor<Rank>;
              }
              -

              currenty processed image tensor and canvas

              -
              -
              -

              Type declaration

              -
              -
              -

              result

              -
              result: Result
              -

              Last known result of detect run

              +

              performance

              performance: Record<string, number>

              Performance object that contains values for all recently performed operations

              +

              process

              process: {
                  canvas: null | AnyCanvas;
                  tensor: null | Tensor<Rank>;
              }

              currenty processed image tensor and canvas

              +

              Type declaration

              result

              result: Result

              Last known result of detect run

              • Can be accessed anytime after initial detection
              -
              -
              -

              state

              -
              state: string
              -

              Current state of Human library

              +

              state

              state: string

              Current state of Human library

              • Can be polled to determine operations that are currently executed
              • Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'
              -
              -
              -

              tf

              -
              tf: any
              -

              Instance of TensorFlow/JS used by Human

              +

              tf

              tf: any

              Instance of TensorFlow/JS used by Human

              • Can be embedded or externally provided TFJS API
              -
              -
              -

              version

              -
              version: string
              -

              Current version of Human library in semver format

              -
              -
              -

              webcam

              -
              webcam: WebCam = ...
              -

              WebCam helper methods

              -
              -
              -

              Methods

              -
              -

              analyze

              -
                - -
              • -

                internal function to measure tensor leaks

                -
                -
                -

                Parameters

                -
                  -
                • -
                  Rest ...msg: string[]
                -

                Returns void

              -
              -

              compare

              -

              version

              version: string

              Current version of Human library in semver format

              +

              webcam

              webcam: WebCam = ...

              WebCam helper methods

              +

              Methods

              analyze

              • analyze(...msg): void

              • internal function to measure tensor leaks

                +

                Parameters

                • Rest ...msg: string[]

                Returns void

              compare

              • compare(firstImageTensor, secondImageTensor): Promise<number>

              • Compare two input tensors for pixel similarity

                • use human.image to process any valid input and get a tensor that can be used for compare
                • when passing manually generated tensors:
                  • @@ -268,335 +96,59 @@ Possible events:

                • if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor
                • return value is pixel similarity score normalized by input resolution and rgb channels
                -
                -
                -

                Parameters

                -
                -

                Returns Promise<number>

              Parameters

              Returns Promise<number>

              detect

              -
              -

              detect

              -
              -
              -

              emit

              -
                - -
              • -

                emit event

                -
                -
                -

                Parameters

                -
                  -
                • -
                  event: string
                -

                Returns void

              -
              -

              image

              -
                - -
              • -

                Process input as return canvas and tensor

                -
                -
                -

                Parameters

                -

              emit

              • emit(event): void

              • emit event

                +

                Parameters

                • event: string

                Returns void

              image

              • image(input, getTensor?): Promise<{
                    canvas: null | AnyCanvas;
                    tensor: null | Tensor4D;
                }>

              • Process input as return canvas and tensor

                +

                Parameters

                • input: Input

                  any input Input

                  +
                • getTensor: boolean = false

                  should image processing also return tensor or just canvas Returns object with tensor and canvas

                  -
                -

                Returns Promise<{
                    canvas: null | AnyCanvas;
                    tensor: null | Tensor4D;
                }>

              -
              -

              init

              -
                - -
              • -

                Explicit backend initialization

                +

              Returns Promise<{
                  canvas: null | AnyCanvas;
                  tensor: null | Tensor4D;
              }>

              init

              • init(): Promise<void>

              • Explicit backend initialization

                • Normally done implicitly during initial load phase
                • Call to explictly register and initialize TFJS backend without any other operations
                • Use when changing backend during runtime
                -
                -

                Returns Promise<void>

              -
              -

              load

              -
                - -
              • -

                Load method preloads all configured models on-demand

                +

                Returns Promise<void>

              load

              • load(userConfig?): Promise<void>

              • Load method preloads all configured models on-demand

                • Not explicitly required as any required model is load implicitly on it's first run
                -
                -
                -

                Parameters

                -
                -

                Returns Promise<void>

              -
              -

              next

              -
                - -
              • -

                Runs interpolation using last known result and returns smoothened result +

                Parameters

                Returns Promise<void>

              next

              • next(result?): Result

              • Runs interpolation using last known result and returns smoothened result Interpolation is based on time since last known result so can be called independently

                - -

                Returns

                result - Result

                -
                -
                -

                Parameters

                -
                  -
                • -
                  result: Result = ...
                  -

                  Result optional use specific result set to run interpolation on

                  -
                -

                Returns Result

              -
              -

              now

              -
                - -
              • -

                Utility wrapper for performance.now()

                -
                -

                Returns number

              -
              -

              profile

              -
                - -
              • -

                Run detect with tensorflow profiling

                +

                Parameters

                • result: Result = ...

                  Result optional use specific result set to run interpolation on

                  +

                Returns Result

                result - Result

                +

              now

              • now(): number

              • Utility wrapper for performance.now()

                +

                Returns number

              profile

              • profile(input, userConfig?): Promise<{
                    kernel: string;
                    perc: number;
                    time: number;
                }[]>

              • Run detect with tensorflow profiling

                • result object will contain total exeuction time information for top-20 kernels
                • actual detection object can be accessed via human.result
                -
                -
                -

                Parameters

                -
                  -
                • -
                  input: Input
                  • -
                • -
                  Optional userConfig: Partial<Config>
                -

                Returns Promise<{
                    kernel: string;
                    perc: number;
                    time: number;
                }[]>

              -
              -

              reset

              -
                - -
              • -

                Reset configuration to default values

                -
                -

                Returns void

              -
              -

              segmentation

              -
                - -
              • -

                Segmentation method takes any input and returns RGBA tensor +

                Parameters

                Returns Promise<{
                    kernel: string;
                    perc: number;
                    time: number;
                }[]>

              reset

              • reset(): void

              • Reset configuration to default values

                +

                Returns void

              segmentation

              • segmentation(input, userConfig?): Promise<null | Tensor<Rank>>

              • Segmentation method takes any input and returns RGBA tensor Note: Segmentation is not triggered as part of detect process

                -
                -
                -

                Parameters

                -
              -
              -

              sleep

              -
                - -
              • -

                Helper function

                -
                -
                -

                Parameters

                -
                  -
                • -
                  ms: number
                  -

                  sleep time in miliseconds

                  -
                -

                Returns Promise<void>

              -
              -

              validate

              -
                - -
              • -

                Validate current configuration schema

                -
                -
                -

                Parameters

                -
                  -
                • -
                  Optional userConfig: Partial<Config>
                -

                Returns {
                    expected?: string;
                    reason: string;
                    where: string;
                }[]

              -
              -

              video

              -
                - -
              • -

                Continously detect video frames

                -
                -
                -

                Parameters

                -
                  -
                • -
                  element: HTMLVideoElement
                  -

                  HTMLVideoElement input

                  -
                  • -
                • -
                  run: boolean = true
                  -

                  boolean run continously or stop if already running, default true

                  -
                  • -
                • -
                  delay: number = 0
                  -

                  number delay detection between frames for number of miliseconds, default 0

                  -
                -

                Returns Promise<void>

              -
              -

              warmup

              -
                - -
              • -

                Warmup method pre-initializes all configured models for faster inference

                +
              • Optional userConfig: Partial<Config>

              Returns Promise<null | Tensor<Rank>>

              sleep

              • sleep(ms): Promise<void>

              • Helper function

                +

                Parameters

                • ms: number

                  sleep time in miliseconds

                  +

                Returns Promise<void>

              validate

              • validate(userConfig?): {
                    expected?: string;
                    reason: string;
                    where: string;
                }[]

              • Validate current configuration schema

                +

                Parameters

                • Optional userConfig: Partial<Config>

                Returns {
                    expected?: string;
                    reason: string;
                    where: string;
                }[]

              video

              • video(element, run?, delay?): Promise<void>

              • Continously detect video frames

                +

                Parameters

                • element: HTMLVideoElement

                  HTMLVideoElement input

                  +
                • run: boolean = true

                  boolean run continously or stop if already running, default true

                  +
                • delay: number = 0

                  number delay detection between frames for number of miliseconds, default 0

                  +

                Returns Promise<void>

              warmup

              • warmup(userConfig?): Promise<undefined | Result>

              • Warmup method pre-initializes all configured models for faster inference

                • can take significant time on startup
                • only used for webgl and humangl backends
                - -

                Returns

                result - Result

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                Returns Promise<undefined | Result>

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              Parameters

              Returns Promise<undefined | Result>

              result - Result

              +

              Settings

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              \ No newline at end of file diff --git a/typedoc/classes/Tensor-1.html b/typedoc/classes/Tensor-1.html index 01cdaa7e..a9c94d3b 100644 --- a/typedoc/classes/Tensor-1.html +++ b/typedoc/classes/Tensor-1.html @@ -1,22 +1,4 @@ -Tensor | @vladmandic/human - v3.1.2
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              Class Tensor<R>

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              A tf.Tensor object represents an immutable, multidimensional array of +Tensor | @vladmandic/human - v3.2.0

              Class Tensor<R>

              A tf.Tensor object represents an immutable, multidimensional array of numbers that has a shape and a data type.

              For performance reasons, functions that create tensors do not necessarily perform a copy of the data passed to them (e.g. if the data is passed as a @@ -24,1306 +6,202 @@ perform a copy of the data passed to them (e.g. if the data is passed as a a feature and is not supported. To avoid this behavior, use the tensor before changing the input data or create a copy with copy = tf.add(yourTensor, 0).

              See tf.tensor for details on how to create a tf.Tensor.

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              Doc

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              Type Parameters

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              Hierarchy

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              • Tensor
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              Implements

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              • TensorInfo
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              Constructors

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              constructor -
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              Properties

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              dataId -dtype -id -kept -kerasMask? -rankType -scopeId -shape -size -strides -
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              Accessors

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              isDisposed -rank -
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              Methods

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              abs -acos -acosh -add -all -any -argMax -argMin -array -arraySync -as1D -as2D -as3D -as4D -as5D -asScalar -asType -asin -asinh -atan -atan2 -atanh -avgPool -batchNorm -batchToSpaceND -broadcastTo -buffer -bufferSync -bytes -cast -ceil -clipByValue -clone -concat -conv1d -conv2d -conv2dTranspose -cos -cosh -cumprod -cumsum -data -dataSync -dataToGPU -depthToSpace -depthwiseConv2d -dilation2d -dispose -div -divNoNan -dot -elu -equal -erf -euclideanNorm -exp -expandDims -expm1 -fft -flatten -floor -floorDiv -gather -greater -greaterEqual -ifft -irfft -isFinite -isInf -isNaN -leakyRelu -less -lessEqual -localResponseNormalization -log -log1p -logSigmoid -logSoftmax -logSumExp -logicalAnd -logicalNot -logicalOr -logicalXor -matMul -max -maxPool -maximum -mean -min -minimum -mirrorPad -mod -mul -neg -norm -notEqual -oneHot -onesLike -pad -pool -pow -prelu -print -prod -reciprocal -relu -relu6 -reshape -reshapeAs -resizeBilinear -resizeNearestNeighbor -reverse -rfft -round -rsqrt -selu -separableConv2d -sigmoid -sign -sin -sinh -slice -softmax -softplus -spaceToBatchND -split -sqrt -square -squaredDifference -squeeze -stack -step -stridedSlice -sub -sum -tan -tanh -throwIfDisposed -tile -toBool -toFloat -toInt -toString -topk -transpose -unique -unsortedSegmentSum -unstack -variable -where -zerosLike -
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              Constructors

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              constructor

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                Type Parameters

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                Parameters

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                  shape: ShapeMap[R]
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                  dtype: keyof DataTypeMap
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                  dataId: object
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                  id: number
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                Returns Tensor<R>

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              Properties

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              dataId

              -
              dataId: object
              -

              Id of the bucket holding the data for this tensor. Multiple arrays can +

              Doc

              Type Parameters

              Implements

              • TensorInfo

              Constructors

              constructor +

              Properties

              dataId +dtype +id +kept +kerasMask? +rankType +scopeId +shape +size +strides +

              Accessors

              isDisposed +rank +

              Methods

              abs +acos +acosh +add +all +any +argMax +argMin +array +arraySync +as1D +as2D +as3D +as4D +as5D +asScalar +asType +asin +asinh +atan +atan2 +atanh +avgPool +batchNorm +batchToSpaceND +broadcastTo +buffer +bufferSync +bytes +cast +ceil +clipByValue +clone +concat +conv1d +conv2d +conv2dTranspose +cos +cosh +cumprod +cumsum +data +dataSync +dataToGPU +depthToSpace +depthwiseConv2d +dilation2d +dispose +div +divNoNan +dot +elu +equal +erf +euclideanNorm +exp +expandDims +expm1 +fft +flatten +floor +floorDiv +gather +greater +greaterEqual +ifft +irfft +isFinite +isInf +isNaN +leakyRelu +less +lessEqual +localResponseNormalization +log +log1p +logSigmoid +logSoftmax +logSumExp +logicalAnd +logicalNot +logicalOr +logicalXor +matMul +max +maxPool +maximum +mean +min +minimum +mirrorPad +mod +mul +neg +norm +notEqual +oneHot +onesLike +pad +pool +pow +prelu +print +prod +reciprocal +relu +relu6 +reshape +reshapeAs +resizeBilinear +resizeNearestNeighbor +reverse +rfft +round +rsqrt +selu +separableConv2d +sigmoid +sign +sin +sinh +slice +softmax +softplus +spaceToBatchND +split +sqrt +square +squaredDifference +squeeze +stack +step +stridedSlice +sub +sum +tan +tanh +throwIfDisposed +tile +toBool +toFloat +toInt +toString +topk +transpose +unique +unsortedSegmentSum +unstack +variable +where +zerosLike +

              Constructors

              constructor

              • new Tensor<R>(shape, dtype, dataId, id): Tensor<R>

              • Type Parameters

                Parameters

                • shape: ShapeMap[R]
                • dtype: keyof DataTypeMap
                • dataId: object
                • id: number

                Returns Tensor<R>

              Properties

              dataId

              dataId: object

              Id of the bucket holding the data for this tensor. Multiple arrays can point to the same bucket (e.g. when calling array.reshape()).

              -
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              Readonly dtype

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              dtype: keyof DataTypeMap
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              The data type for the array.

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              Readonly id

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              id: number
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              Unique id of this tensor.

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              kept

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              kept: boolean
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              Whether this tensor has been globally kept.

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              Optional kerasMask

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              kerasMask?: Tensor<Rank>
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              The keras mask that some keras layers attach to the tensor

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              Readonly rankType

              -
              rankType: R
              -

              The rank type for the array (see Rank enum).

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              scopeId

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              scopeId: number
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              The id of the scope this tensor is being tracked in.

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              Readonly shape

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              shape: ShapeMap[R]
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              The shape of the tensor.

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              Readonly size

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              size: number
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              Number of elements in the tensor.

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              Readonly strides

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              strides: number[]
              -

              Number of elements to skip in each dimension when indexing. See -https://docs.scipy.org/doc/numpy/reference/generated/\ +

              Readonly dtype

              dtype: keyof DataTypeMap

              The data type for the array.

              +

              Readonly id

              id: number

              Unique id of this tensor.

              +

              kept

              kept: boolean

              Whether this tensor has been globally kept.

              +

              Optional kerasMask

              kerasMask?: Tensor<Rank>

              The keras mask that some keras layers attach to the tensor

              +

              Readonly rankType

              rankType: R

              The rank type for the array (see Rank enum).

              +

              scopeId

              scopeId: number

              The id of the scope this tensor is being tracked in.

              +

              Readonly shape

              shape: ShapeMap[R]

              The shape of the tensor.

              +

              Readonly size

              size: number

              Number of elements in the tensor.

              +

              Readonly strides

              strides: number[]

              Number of elements to skip in each dimension when indexing. See +https://docs.scipy.org/doc/numpy/reference/generated/\ numpy.ndarray.strides.html

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              Accessors

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              isDisposed

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              • get isDisposed(): boolean
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                Returns boolean

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              rank

              -
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              • get rank(): number
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                Returns number

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              Methods

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              abs

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                Type Parameters

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                Parameters

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                  this: T
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                Returns T

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              acos

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                Type Parameters

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                Parameters

                -
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                  this: T
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                Returns T

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              acosh

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                Type Parameters

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                Parameters

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                  this: T
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                Returns T

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              add

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              all

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                Type Parameters

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                Parameters

                -
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                  this: T
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                  Optional axis: number | number[]
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                  Optional keepDims: boolean
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                Returns T

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              any

              -
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                Type Parameters

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                Parameters

                -
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                  this: T
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                  Optional axis: number | number[]
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                  Optional keepDims: boolean
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                Returns T

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              argMax

              -
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                Type Parameters

                -
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                Parameters

                -
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                  Optional axis: number
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                Returns T

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              argMin

              -
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                Type Parameters

                -
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                Parameters

                -
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                  Optional axis: number
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                Returns T

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              array

              -
                - -
              • -

                Returns the tensor data as a nested array. The transfer of data is done +

              Accessors

              isDisposed

              • get isDisposed(): boolean

              • Returns boolean

              rank

              • get rank(): number

              • Returns number

              Methods

              abs

              • abs<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              acos

              • acos<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              acosh

              • acosh<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              add

              • add<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              all

              • all<T>(this, axis?, keepDims?): T

              • Type Parameters

                Parameters

                • this: T
                • Optional axis: number | number[]
                • Optional keepDims: boolean

                Returns T

              any

              • any<T>(this, axis?, keepDims?): T

              • Type Parameters

                Parameters

                • this: T
                • Optional axis: number | number[]
                • Optional keepDims: boolean

                Returns T

              argMax

              • argMax<T>(axis?): T

              • Type Parameters

                Parameters

                • Optional axis: number

                Returns T

              argMin

              • argMin<T>(axis?): T

              • Type Parameters

                Parameters

                • Optional axis: number

                Returns T

              array

              • array(): Promise<ArrayMap[R]>

              • Returns the tensor data as a nested array. The transfer of data is done asynchronously.

                - -

                Doc

                -

                Returns Promise<ArrayMap[R]>

              -
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              arraySync

              -
                - -
              • -

                Returns the tensor data as a nested array. The transfer of data is done +

                Returns Promise<ArrayMap[R]>

                Doc

              arraySync

              • arraySync(): ArrayMap[R]

              • Returns the tensor data as a nested array. The transfer of data is done synchronously.

                - -

                Doc

                -

                Returns ArrayMap[R]

              -
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              as1D

              -
                - -
              • -
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                Type Parameters

                -
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                Returns Tensor1D

              -
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              as2D

              -
                - -
              • -
                -

                Type Parameters

                -
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                Parameters

                -
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                  rows: number
                  • -
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                  columns: number
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                Returns Tensor2D

              -
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              as3D

              -
                - -
              • -
                -

                Type Parameters

                -
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                Parameters

                -
                  -
                • -
                  rows: number
                  • -
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                  columns: number
                  • -
                • -
                  depth: number
                -

                Returns Tensor3D

              -
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              as4D

              -
                - -
              • -
                -

                Type Parameters

                -
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                -

                Parameters

                -
                  -
                • -
                  rows: number
                  • -
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                  columns: number
                  • -
                • -
                  depth: number
                  • -
                • -
                  depth2: number
                -

                Returns Tensor4D

              -
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              as5D

              -
                - -
              • -
                -

                Type Parameters

                -
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                Parameters

                -
                  -
                • -
                  rows: number
                  • -
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                  columns: number
                  • -
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                  depth: number
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                  depth2: number
                  • -
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                  depth3: number
                -

                Returns Tensor5D

              -
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              asScalar

              -
                - -
              • -
                -

                Type Parameters

                -
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                Returns Scalar

              -
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              asType

              -
                - -
              • -
                -

                Type Parameters

                -
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                Parameters

                -
                  -
                • -
                  this: T
                  • -
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                  dtype: keyof DataTypeMap
                -

                Returns T

              -
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              asin

              -
                - -
              • -
                -

                Type Parameters

                -
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                Parameters

                -
                  -
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                  this: T
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                Returns T

              -
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              asinh

              -
                - -
              • -
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                Type Parameters

                -
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                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
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              atan

              -
                - -
              • -
                -

                Type Parameters

                -
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                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
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              atan2

              -
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              -

              atanh

              -
                - -
              • -
                -

                Type Parameters

                -
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                Parameters

                -
                  -
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                  this: T
                -

                Returns T

              -
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              avgPool

              -
                - -
              • -
                -

                Type Parameters

                -
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                -

                Parameters

                -
                  -
                • -
                  filterSize: number | [number, number]
                  • -
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                  strides: number | [number, number]
                  • -
                • -
                  pad: number | "valid" | "same" | ExplicitPadding
                  • -
                • -
                  Optional dimRoundingMode: "round" | "floor" | "ceil"
                -

                Returns T

              -
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              batchNorm

              -
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              -

              batchToSpaceND

              -
                - -
              • -
                -

                Type Parameters

                -
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                -

                Parameters

                -
                  -
                • -
                  blockShape: number[]
                  • -
                • -
                  crops: number[][]
                -

                Returns Tensor<R>

              -
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              broadcastTo

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  shape: ShapeMap[R]
                -

                Returns Tensor<R>

              -
              -

              buffer

              -
                - -
              • -

                Returns a promise of tf.TensorBuffer that holds the underlying data.

                - -

                Doc

                -
                -

                Type Parameters

                -
                  -
                • -

                  D extends keyof DataTypeMap = "float32"

                -

                Returns Promise<TensorBuffer<R, D>>

              -
              -

              bufferSync

              -
                - -
              • -

                Returns a tf.TensorBuffer that holds the underlying data.

                - -

                Doc

                -
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                Type Parameters

                -
                  -
                • -

                  D extends keyof DataTypeMap = "float32"

                -

                Returns TensorBuffer<R, D>

              -
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              bytes

              -
                - -
              • -

                Returns the underlying bytes of the tensor's data.

                -
                -

                Returns Promise<Uint8Array | Uint8Array[]>

              -
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              cast

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  dtype: keyof DataTypeMap
                -

                Returns T

              -
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              ceil

              -
                - -
              • -
                -

                Type Parameters

                -
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                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
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              clipByValue

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  min: number
                  • -
                • -
                  max: number
                -

                Returns Tensor<Rank>

              -
              -

              clone

              -
                - -
              • -

                Returns a copy of the tensor. See tf.clone for details.

                - -

                Doc

                -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              concat

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  tensors: T | (TensorLike | T)[]
                  • -
                • -
                  Optional axis: number
                -

                Returns T

              -
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              conv1d

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  filter: Tensor3D | TensorLike3D
                  • -
                • -
                  stride: number
                  • -
                • -
                  pad: number | "valid" | "same" | ExplicitPadding
                  • -
                • -
                  Optional dataFormat: "NWC" | "NCW"
                  • -
                • -
                  Optional dilation: number
                  • -
                • -
                  Optional dimRoundingMode: "round" | "floor" | "ceil"
                -

                Returns T

              -
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              conv2d

              -
                - -
              • -
                -

                Type Parameters

                -
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                -

                Parameters

                -
                  -
                • -
                  filter: Tensor4D | TensorLike4D
                  • -
                • -
                  strides: number | [number, number]
                  • -
                • -
                  pad: number | "valid" | "same"
                  • -
                • -
                  Optional dataFormat: "NHWC" | "NCHW"
                  • -
                • -
                  Optional dilations: number | [number, number]
                  • -
                • -
                  Optional dimRoundingMode: "round" | "floor" | "ceil"
                -

                Returns T

              -
              -

              conv2dTranspose

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  filter: Tensor4D | TensorLike4D
                  • -
                • -
                  outputShape: [number, number, number] | [number, number, number, number]
                  • -
                • -
                  strides: number | [number, number]
                  • -
                • -
                  pad: number | "valid" | "same"
                  • -
                • -
                  Optional dimRoundingMode: "round" | "floor" | "ceil"
                -

                Returns T

              -
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              cos

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
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              cosh

              -
                - -
              • -
                -

                Type Parameters

                -
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                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
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              cumprod

              -
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              • -
                -

                Type Parameters

                -
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                -

                Parameters

                -
                  -
                • -
                  Optional axis: number
                  • -
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                  Optional exclusive: boolean
                  • -
                • -
                  Optional reverse: boolean
                -

                Returns Tensor<R>

              -
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              cumsum

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  Optional axis: number
                  • -
                • -
                  Optional exclusive: boolean
                  • -
                • -
                  Optional reverse: boolean
                -

                Returns Tensor<R>

              -
              -

              data

              -
                - -
              • -

                Asynchronously downloads the values from the tf.Tensor. Returns a +

                Returns ArrayMap[R]

                Doc

              as1D

              • as1D<T>(): Tensor1D

              • Type Parameters

                Returns Tensor1D

              as2D

              • as2D<T>(rows, columns): Tensor2D

              • Type Parameters

                Parameters

                • rows: number
                • columns: number

                Returns Tensor2D

              as3D

              • as3D<T>(rows, columns, depth): Tensor3D

              • Type Parameters

                Parameters

                • rows: number
                • columns: number
                • depth: number

                Returns Tensor3D

              as4D

              • as4D<T>(rows, columns, depth, depth2): Tensor4D

              • Type Parameters

                Parameters

                • rows: number
                • columns: number
                • depth: number
                • depth2: number

                Returns Tensor4D

              as5D

              • as5D<T>(rows, columns, depth, depth2, depth3): Tensor5D

              • Type Parameters

                Parameters

                • rows: number
                • columns: number
                • depth: number
                • depth2: number
                • depth3: number

                Returns Tensor5D

              asScalar

              • asScalar<T>(): Scalar

              • Type Parameters

                Returns Scalar

              asType

              • asType<T>(this, dtype): T

              • Type Parameters

                Parameters

                • this: T
                • dtype: keyof DataTypeMap

                Returns T

              asin

              • asin<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              asinh

              • asinh<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              atan

              • atan<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              atan2

              • atan2<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              atanh

              • atanh<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              avgPool

              • avgPool<T>(filterSize, strides, pad, dimRoundingMode?): T

              • Type Parameters

                Parameters

                • filterSize: number | [number, number]
                • strides: number | [number, number]
                • pad: number | "valid" | "same" | ExplicitPadding
                • Optional dimRoundingMode: "floor" | "round" | "ceil"

                Returns T

              batchNorm

              batchToSpaceND

              • batchToSpaceND<R>(blockShape, crops): Tensor<R>

              • Type Parameters

                Parameters

                • blockShape: number[]
                • crops: number[][]

                Returns Tensor<R>

              broadcastTo

              • broadcastTo<R>(shape): Tensor<R>

              • Type Parameters

                Parameters

                • shape: ShapeMap[R]

                Returns Tensor<R>

              buffer

              • buffer<D>(): Promise<TensorBuffer<R, D>>

              • Returns a promise of tf.TensorBuffer that holds the underlying data.

                +

                Type Parameters

                • D extends keyof DataTypeMap = "float32"

                Returns Promise<TensorBuffer<R, D>>

                Doc

              bufferSync

              • bufferSync<D>(): TensorBuffer<R, D>

              • Returns a tf.TensorBuffer that holds the underlying data.

                +

                Type Parameters

                • D extends keyof DataTypeMap = "float32"

                Returns TensorBuffer<R, D>

                Doc

              bytes

              • bytes(): Promise<Uint8Array | Uint8Array[]>

              • Returns the underlying bytes of the tensor's data.

                +

                Returns Promise<Uint8Array | Uint8Array[]>

              cast

              • cast<T>(dtype): T

              • Type Parameters

                Parameters

                • dtype: keyof DataTypeMap

                Returns T

              ceil

              • ceil<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              clipByValue

              • clipByValue<T>(min, max): Tensor<Rank>

              • Type Parameters

                Parameters

                • min: number
                • max: number

                Returns Tensor<Rank>

              clone

              • clone<T>(this): T

              • Returns a copy of the tensor. See tf.clone for details.

                +

                Type Parameters

                Parameters

                • this: T

                Returns T

                Doc

              concat

              • concat<T>(tensors, axis?): T

              • Type Parameters

                Parameters

                • tensors: T | (TensorLike | T)[]
                • Optional axis: number

                Returns T

              conv1d

              • conv1d<T>(filter, stride, pad, dataFormat?, dilation?, dimRoundingMode?): T

              • Type Parameters

                Parameters

                • filter: Tensor3D | TensorLike3D
                • stride: number
                • pad: number | "valid" | "same" | ExplicitPadding
                • Optional dataFormat: "NWC" | "NCW"
                • Optional dilation: number
                • Optional dimRoundingMode: "floor" | "round" | "ceil"

                Returns T

              conv2d

              • conv2d<T>(filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

              • Type Parameters

                Parameters

                • filter: Tensor4D | TensorLike4D
                • strides: number | [number, number]
                • pad: number | "valid" | "same"
                • Optional dataFormat: "NHWC" | "NCHW"
                • Optional dilations: number | [number, number]
                • Optional dimRoundingMode: "floor" | "round" | "ceil"

                Returns T

              conv2dTranspose

              • conv2dTranspose<T>(filter, outputShape, strides, pad, dimRoundingMode?): T

              • Type Parameters

                Parameters

                • filter: Tensor4D | TensorLike4D
                • outputShape: [number, number, number] | [number, number, number, number]
                • strides: number | [number, number]
                • pad: number | "valid" | "same"
                • Optional dimRoundingMode: "floor" | "round" | "ceil"

                Returns T

              cos

              • cos<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              cosh

              • cosh<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              cumprod

              • cumprod<R>(axis?, exclusive?, reverse?): Tensor<R>

              • Type Parameters

                Parameters

                • Optional axis: number
                • Optional exclusive: boolean
                • Optional reverse: boolean

                Returns Tensor<R>

              cumsum

              • cumsum<R>(axis?, exclusive?, reverse?): Tensor<R>

              • Type Parameters

                Parameters

                • Optional axis: number
                • Optional exclusive: boolean
                • Optional reverse: boolean

                Returns Tensor<R>

              data

              • data<D>(): Promise<DataTypeMap[D]>

              • Asynchronously downloads the values from the tf.Tensor. Returns a promise of TypedArray that resolves when the computation has finished.

                - -

                Doc

                -
                -

                Type Parameters

                -
                  -
                • -

                  D extends keyof DataTypeMap = NumericDataType

                -

                Returns Promise<DataTypeMap[D]>

              -
              -

              dataSync

              -
                - -
              • -

                Synchronously downloads the values from the tf.Tensor. This blocks the +

                Type Parameters

                • D extends keyof DataTypeMap = NumericDataType

                Returns Promise<DataTypeMap[D]>

                Doc

              dataSync

              • dataSync<D>(): DataTypeMap[D]

              • Synchronously downloads the values from the tf.Tensor. This blocks the UI thread until the values are ready, which can cause performance issues.

                - -

                Doc

                -
                -

                Type Parameters

                -
                  -
                • -

                  D extends keyof DataTypeMap = NumericDataType

                -

                Returns DataTypeMap[D]

              -
              -

              dataToGPU

              -
                - -
              • -

                Copy the tensor's data to a new GPU resource. Comparing to the dataSync() +

                Type Parameters

                • D extends keyof DataTypeMap = NumericDataType

                Returns DataTypeMap[D]

                Doc

              dataToGPU

              • dataToGPU(options?): GPUData

              • Copy the tensor's data to a new GPU resource. Comparing to the dataSync() and data(), this method prevents data from being downloaded to CPU.

                For WebGL backend, the data will be stored on a densely packed texture. This means that the texture will use the RGBA channels to store value.

                For WebGPU backend, the data will be stored on a buffer. There is no parameter, so can not use a user-defined size to create the buffer.

                - -

                Returns

                For WebGL backend, a GPUData contains the new texture and +

                Parameters

                • Optional options: DataToGPUWebGLOption

                Returns GPUData

                For WebGL backend, a GPUData contains the new texture and its information. { tensorRef: The tensor that is associated with this texture, @@ -1331,2235 +209,10 @@ parameter, so can not use a user-defined size to create the buffer.

                texShape: [number, number] // [height, width] }

                For WebGPU backend, a GPUData contains the new buffer.
                {
                   tensorRef: The tensor that is associated with this buffer,
                   buffer: GPUBuffer,
                }

                Remember to dispose the GPUData after it is used by
                `res.tensorRef.dispose()`.
-
                - -

                Doc

              -
              -

              Parameters

              -
                -
              • -
                Optional options: DataToGPUWebGLOption
              -

              Returns GPUData

              -
              -

              depthToSpace

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  blockSize: number
                  • -
                • -
                  dataFormat: "NHWC" | "NCHW"
                -

                Returns T

              -
              -

              depthwiseConv2d

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  filter: Tensor4D | TensorLike4D
                  • -
                • -
                  strides: number | [number, number]
                  • -
                • -
                  pad: number | "valid" | "same"
                  • -
                • -
                  Optional dataFormat: "NHWC" | "NCHW"
                  • -
                • -
                  Optional dilations: number | [number, number]
                  • -
                • -
                  Optional dimRoundingMode: "round" | "floor" | "ceil"
                -

                Returns T

              -
              -

              dilation2d

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  filter: Tensor3D | TensorLike3D
                  • -
                • -
                  strides: number | [number, number]
                  • -
                • -
                  pad: "valid" | "same"
                  • -
                • -
                  Optional dilations: number | [number, number]
                  • -
                • -
                  Optional dataFormat: "NHWC"
                -

                Returns T

              -
              -

              dispose

              -
                - -
              • -

                Disposes tf.Tensor from memory.

                - -

                Doc

                -

                Returns void

              -
              -

              div

              -
              -
              -

              divNoNan

              -
              -
              -

              dot

              -
              -
              -

              elu

              -
                - -
              • -
                -

                Type Parameters

                -
                -

                Returns T

              -
              -

              equal

              -
              -
              -

              erf

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              euclideanNorm

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                  • -
                • -
                  Optional axis: number | number[]
                  • -
                • -
                  Optional keepDims: boolean
                -

                Returns T

              -
              -

              exp

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              expandDims

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  Optional axis: number
                -

                Returns T

              -
              -

              expm1

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              fft

              -
              -
              -

              flatten

              -
                - -
              • -
                -

                Type Parameters

                -
                -

                Returns Tensor1D

              -
              -

              floor

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              floorDiv

              -
              -
              -

              gather

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                  • -
                • -
                  indices: Tensor<Rank> | TensorLike
                  • -
                • -
                  Optional axis: number
                  • -
                • -
                  Optional batchDims: number
                -

                Returns T

              -
              -

              greater

              -
              -
              -

              greaterEqual

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                -

                Returns T

              -
              -

              ifft

              -
              -
              -

              irfft

              -
              -
              -

              isFinite

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              isInf

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              isNaN

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              leakyRelu

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  alpha: number
                -

                Returns T

              -
              -

              less

              -
              -
              -

              lessEqual

              -
              -
              -

              localResponseNormalization

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  Optional depthRadius: number
                  • -
                • -
                  Optional bias: number
                  • -
                • -
                  Optional alpha: number
                  • -
                • -
                  Optional beta: number
                -

                Returns T

              -
              -

              log

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              log1p

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              logSigmoid

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              logSoftmax

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                  • -
                • -
                  Optional axis: number
                -

                Returns T

              -
              -

              logSumExp

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                  • -
                • -
                  Optional axis: number | number[]
                  • -
                • -
                  Optional keepDims: boolean
                -

                Returns T

              -
              -

              logicalAnd

              -
              -
              -

              logicalNot

              -
                - -
              • -
                -

                Type Parameters

                -
                -

                Returns T

              -
              -

              logicalOr

              -
              -
              -

              logicalXor

              -
              -
              -

              matMul

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                -

                Returns Tensor<Rank>

              -
              -

              max

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  Optional axis: number | number[]
                  • -
                • -
                  Optional keepDims: boolean
                -

                Returns T

              -
              -

              maxPool

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  filterSize: number | [number, number]
                  • -
                • -
                  strides: number | [number, number]
                  • -
                • -
                  pad: number | "valid" | "same" | ExplicitPadding
                  • -
                • -
                  Optional dimRoundingMode: "round" | "floor" | "ceil"
                -

                Returns T

              -
              -

              maximum

              -
              -
              -

              mean

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  Optional axis: number | number[]
                  • -
                • -
                  Optional keepDims: boolean
                -

                Returns T

              -
              -

              min

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  Optional axis: number | number[]
                  • -
                • -
                  Optional keepDims: boolean
                -

                Returns T

              -
              -

              minimum

              -
              -
              -

              mirrorPad

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  paddings: [number, number][]
                  • -
                • -
                  mode: "reflect" | "symmetric"
                -

                Returns T

              -
              -

              mod

              -
              -
              -

              mul

              -
              -
              -

              neg

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              norm

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  Optional ord: number | "euclidean" | "fro"
                  • -
                • -
                  Optional axis: number | number[]
                  • -
                • -
                  Optional keepDims: boolean
                -

                Returns Tensor<Rank>

              -
              -

              notEqual

              -
              -
              -

              oneHot

              -
                - -
              • -
                -

                Parameters

                -
                  -
                • -
                  depth: number
                  • -
                • -
                  onValue: number
                  • -
                • -
                  offValue: number
                -

                Returns Tensor<Rank>

              -
              -

              onesLike

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              pad

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  paddings: [number, number][]
                  • -
                • -
                  Optional constantValue: number
                -

                Returns T

              -
              -

              pool

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  windowShape: number | [number, number]
                  • -
                • -
                  poolingType: "avg" | "max"
                  • -
                • -
                  padding: number | "valid" | "same" | ExplicitPadding
                  • -
                • -
                  Optional diationRate: number | [number, number]
                  • -
                • -
                  Optional strides: number | [number, number]
                  • -
                • -
                  Optional dimRoundingMode: "round" | "floor" | "ceil"
                -

                Returns T

              -
              -

              pow

              -
              -
              -

              prelu

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                -

                Returns T

              -
              -

              print

              -
                - -
              • -

                Prints the tf.Tensor. See tf.print for details.

                - -

                Doc

                -
                -

                Parameters

                -
                  -
                • -
                  Optional verbose: boolean
                  -

                  Whether to print verbose information about the tensor, + +

                  Doc

              depthToSpace

              • depthToSpace<T>(blockSize, dataFormat): T

              • Type Parameters

                Parameters

                • blockSize: number
                • dataFormat: "NHWC" | "NCHW"

                Returns T

              depthwiseConv2d

              • depthwiseConv2d<T>(filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

              • Type Parameters

                Parameters

                • filter: Tensor4D | TensorLike4D
                • strides: number | [number, number]
                • pad: number | "valid" | "same"
                • Optional dataFormat: "NHWC" | "NCHW"
                • Optional dilations: number | [number, number]
                • Optional dimRoundingMode: "floor" | "round" | "ceil"

                Returns T

              dilation2d

              • dilation2d<T>(filter, strides, pad, dilations?, dataFormat?): T

              • Type Parameters

                Parameters

                • filter: Tensor3D | TensorLike3D
                • strides: number | [number, number]
                • pad: "valid" | "same"
                • Optional dilations: number | [number, number]
                • Optional dataFormat: "NHWC"

                Returns T

              dispose

              • dispose(): void

              • Disposes tf.Tensor from memory.

                +

                Returns void

                Doc

              div

              • div<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              divNoNan

              • divNoNan<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              dot

              elu

              • elu<T>(): T

              • Type Parameters

                Returns T

              equal

              • equal<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              erf

              • erf<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              euclideanNorm

              • euclideanNorm<T>(this, axis?, keepDims?): T

              • Type Parameters

                Parameters

                • this: T
                • Optional axis: number | number[]
                • Optional keepDims: boolean

                Returns T

              exp

              • exp<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              expandDims

              • expandDims<T>(axis?): T

              • Type Parameters

                Parameters

                • Optional axis: number

                Returns T

              expm1

              • expm1<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              fft

              flatten

              • flatten<T>(): Tensor1D

              • Type Parameters

                Returns Tensor1D

              floor

              • floor<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              floorDiv

              • floorDiv<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              gather

              • gather<T>(this, indices, axis?, batchDims?): T

              • Type Parameters

                Parameters

                Returns T

              greater

              • greater<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              greaterEqual

              • greaterEqual<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              ifft

              irfft

              isFinite

              • isFinite<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              isInf

              • isInf<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              isNaN

              • isNaN<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              leakyRelu

              • leakyRelu<T>(alpha): T

              • Type Parameters

                Parameters

                • alpha: number

                Returns T

              less

              • less<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              lessEqual

              • lessEqual<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              localResponseNormalization

              • localResponseNormalization<T>(depthRadius?, bias?, alpha?, beta?): T

              • Type Parameters

                Parameters

                • Optional depthRadius: number
                • Optional bias: number
                • Optional alpha: number
                • Optional beta: number

                Returns T

              log

              • log<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              log1p

              • log1p<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              logSigmoid

              • logSigmoid<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              logSoftmax

              • logSoftmax<T>(this, axis?): T

              • Type Parameters

                Parameters

                • this: T
                • Optional axis: number

                Returns T

              logSumExp

              • logSumExp<T>(this, axis?, keepDims?): T

              • Type Parameters

                Parameters

                • this: T
                • Optional axis: number | number[]
                • Optional keepDims: boolean

                Returns T

              logicalAnd

              • logicalAnd<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              logicalNot

              • logicalNot<T>(): T

              • Type Parameters

                Returns T

              logicalOr

              • logicalOr<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              logicalXor

              • logicalXor<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              matMul

              • matMul<T>(b, transposeA?, transposeB?): Tensor<Rank>

              • Type Parameters

                Parameters

                Returns Tensor<Rank>

              max

              • max<T>(axis?, keepDims?): T

              • Type Parameters

                Parameters

                • Optional axis: number | number[]
                • Optional keepDims: boolean

                Returns T

              maxPool

              • maxPool<T>(filterSize, strides, pad, dimRoundingMode?): T

              • Type Parameters

                Parameters

                • filterSize: number | [number, number]
                • strides: number | [number, number]
                • pad: number | "valid" | "same" | ExplicitPadding
                • Optional dimRoundingMode: "floor" | "round" | "ceil"

                Returns T

              maximum

              • maximum<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              mean

              • mean<T>(axis?, keepDims?): T

              • Type Parameters

                Parameters

                • Optional axis: number | number[]
                • Optional keepDims: boolean

                Returns T

              min

              • min<T>(axis?, keepDims?): T

              • Type Parameters

                Parameters

                • Optional axis: number | number[]
                • Optional keepDims: boolean

                Returns T

              minimum

              • minimum<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              mirrorPad

              • mirrorPad<T>(paddings, mode): T

              • Type Parameters

                Parameters

                • paddings: [number, number][]
                • mode: "reflect" | "symmetric"

                Returns T

              mod

              • mod<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              mul

              • mul<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              neg

              • neg<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              norm

              • norm<T>(ord?, axis?, keepDims?): Tensor<Rank>

              • Type Parameters

                Parameters

                • Optional ord: number | "euclidean" | "fro"
                • Optional axis: number | number[]
                • Optional keepDims: boolean

                Returns Tensor<Rank>

              notEqual

              • notEqual<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              oneHot

              • oneHot(depth, onValue, offValue): Tensor<Rank>

              • Parameters

                • depth: number
                • onValue: number
                • offValue: number

                Returns Tensor<Rank>

              onesLike

              • onesLike<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              pad

              • pad<T>(paddings, constantValue?): T

              • Type Parameters

                Parameters

                • paddings: [number, number][]
                • Optional constantValue: number

                Returns T

              pool

              • pool<T>(windowShape, poolingType, padding, diationRate?, strides?, dimRoundingMode?): T

              • Type Parameters

                Parameters

                • windowShape: number | [number, number]
                • poolingType: "avg" | "max"
                • padding: number | "valid" | "same" | ExplicitPadding
                • Optional diationRate: number | [number, number]
                • Optional strides: number | [number, number]
                • Optional dimRoundingMode: "floor" | "round" | "ceil"

                Returns T

              pow

              • pow<T>(exp): T

              • Type Parameters

                Parameters

                Returns T

              prelu

              • prelu<T>(alpha): T

              • Type Parameters

                Parameters

                Returns T

              print

              • print(verbose?): void

              • Prints the tf.Tensor. See tf.print for details.

                +

                Parameters

                • Optional verbose: boolean

                  Whether to print verbose information about the tensor, including dtype and size.

                  -
                -

                Returns void

              -
              -

              prod

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                  • -
                • -
                  Optional axis: number | number[]
                  • -
                • -
                  Optional keepDims: boolean
                -

                Returns T

              -
              -

              reciprocal

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              relu

              -
                - -
              • -
                -

                Type Parameters

                -
                -

                Returns T

              -
              -

              relu6

              -
                - -
              • -
                -

                Type Parameters

                -
                -

                Returns T

              -
              -

              reshape

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  shape: number[]
                -

                Returns T

              -
              -

              reshapeAs

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  x: T
                -

                Returns T

              -
              -

              resizeBilinear

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  newShape2D: [number, number]
                  • -
                • -
                  Optional alignCorners: boolean
                  • -
                • -
                  Optional halfPixelCenters: boolean
                -

                Returns T

              -
              -

              resizeNearestNeighbor

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  newShape2D: [number, number]
                  • -
                • -
                  Optional alignCorners: boolean
                  • -
                • -
                  Optional halfFloatCenters: boolean
                -

                Returns T

              -
              -

              reverse

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                  • -
                • -
                  Optional axis: number | number[]
                -

                Returns T

              -
              -

              rfft

              -
              -
              -

              round

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              rsqrt

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              selu

              -
                - -
              • -
                -

                Type Parameters

                -
                -

                Returns T

              -
              -

              separableConv2d

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  depthwiseFilter: Tensor4D | TensorLike4D
                  • -
                • -
                  pointwiseFilter: Tensor4D | TensorLike
                  • -
                • -
                  strides: number | [number, number]
                  • -
                • -
                  pad: "valid" | "same"
                  • -
                • -
                  Optional dilation: number | [number, number]
                  • -
                • -
                  Optional dataFormat: "NHWC" | "NCHW"
                -

                Returns T

              -
              -

              sigmoid

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              sign

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              sin

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              sinh

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              slice

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                  • -
                • -
                  begin: number | number[]
                  • -
                • -
                  Optional size: number | number[]
                -

                Returns T

              -
              -

              softmax

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                  • -
                • -
                  Optional dim: number
                -

                Returns T

              -
              -

              softplus

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              spaceToBatchND

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  blockShape: number[]
                  • -
                • -
                  paddings: number[][]
                -

                Returns Tensor<R>

              -
              -

              split

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  numOrSizeSplits: number | number[]
                  • -
                • -
                  Optional axis: number
                -

                Returns T[]

              -
              -

              sqrt

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              square

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              squaredDifference

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                -

                Returns T

              -
              -

              squeeze

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  Optional axis: number[]
                -

                Returns T

              -
              -

              stack

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                -

                Returns T

              -
              -

              step

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                  • -
                • -
                  Optional alpha: number
                -

                Returns T

              -
              -

              stridedSlice

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: Tensor<Rank>
                  • -
                • -
                  begin: number[]
                  • -
                • -
                  end: number[]
                  • -
                • -
                  strides: number[]
                  • -
                • -
                  Optional beginMask: number
                  • -
                • -
                  Optional endMask: number
                  • -
                • -
                  Optional ellipsisMask: number
                  • -
                • -
                  Optional newAxisMask: number
                  • -
                • -
                  Optional shrinkAxisMask: number
                -

                Returns Tensor<Rank>

              -
              -

              sub

              -
              -
              -

              sum

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  Optional axis: number | number[]
                  • -
                • -
                  Optional keepDims: boolean
                -

                Returns T

              -
              -

              tan

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              tanh

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              throwIfDisposed

              -
                - -
              • -

                Returns void

              -
              -

              tile

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  b: number[]
                -

                Returns T

              -
              -

              toBool

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              toFloat

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              toInt

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -

              toString

              -
                - -
              • -

                Returns a human-readable description of the tensor. Useful for logging.

                - -

                Doc

                -
                -

                Parameters

                -
                  -
                • -
                  Optional verbose: boolean
                -

                Returns string

              -
              -

              topk

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                  • -
                • -
                  Optional k: number
                  • -
                • -
                  Optional sorted: boolean
                -

                Returns {
                    indices: T;
                    values: T;
                }

                -
                  -
                • -
                  indices: T
                  • -
                • -
                  values: T
              -
              -

              transpose

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  Optional perm: number[]
                -

                Returns T

              -
              -

              unique

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                  • -
                • -
                  Optional axis: number
                -

                Returns {
                    indices: T;
                    values: T;
                }

                -
                  -
                • -
                  indices: T
                  • -
                • -
                  values: T
              -
              -

              unsortedSegmentSum

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                  • -
                • -
                  segmentIds: Tensor1D | TensorLike1D
                  • -
                • -
                  numSegments: number
                -

                Returns T

              -
              -

              unstack

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  Optional axis: number
                -

                Returns T[]

              -
              -

              variable

              -
                - -
              • -
                -

                Parameters

                -
                  -
                • -
                  Optional trainable: boolean
                  • -
                • -
                  Optional name: string
                  • -
                • -
                  Optional dtype: keyof DataTypeMap
                -

                Returns Variable<R>

              -
              -

              where

              -
              -
              -

              zerosLike

              -
                - -
              • -
                -

                Type Parameters

                -
                -
                -

                Parameters

                -
                  -
                • -
                  this: T
                -

                Returns T

              -
              -
              -
              -

              Settings

               -
              -
              -

              Member Visibility

              -
                -
              -
              -

              Theme

              - -
              -
              \ No newline at end of file +

              Returns void

              Doc

              prod

              • prod<T>(this, axis?, keepDims?): T

              • Type Parameters

                Parameters

                • this: T
                • Optional axis: number | number[]
                • Optional keepDims: boolean

                Returns T

              reciprocal

              • reciprocal<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              relu

              • relu<T>(): T

              • Type Parameters

                Returns T

              relu6

              • relu6<T>(): T

              • Type Parameters

                Returns T

              reshape

              • reshape<T>(shape): T

              • Type Parameters

                Parameters

                • shape: number[]

                Returns T

              reshapeAs

              • reshapeAs<T>(x): T

              • Type Parameters

                Parameters

                • x: T

                Returns T

              resizeBilinear

              • resizeBilinear<T>(newShape2D, alignCorners?, halfPixelCenters?): T

              • Type Parameters

                Parameters

                • newShape2D: [number, number]
                • Optional alignCorners: boolean
                • Optional halfPixelCenters: boolean

                Returns T

              resizeNearestNeighbor

              • resizeNearestNeighbor<T>(newShape2D, alignCorners?, halfFloatCenters?): T

              • Type Parameters

                Parameters

                • newShape2D: [number, number]
                • Optional alignCorners: boolean
                • Optional halfFloatCenters: boolean

                Returns T

              reverse

              • reverse<T>(this, axis?): T

              • Type Parameters

                Parameters

                • this: T
                • Optional axis: number | number[]

                Returns T

              rfft

              round

              • round<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              rsqrt

              • rsqrt<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              selu

              • selu<T>(): T

              • Type Parameters

                Returns T

              separableConv2d

              • separableConv2d<T>(depthwiseFilter, pointwiseFilter, strides, pad, dilation?, dataFormat?): T

              • Type Parameters

                Parameters

                • depthwiseFilter: Tensor4D | TensorLike4D
                • pointwiseFilter: TensorLike | Tensor4D
                • strides: number | [number, number]
                • pad: "valid" | "same"
                • Optional dilation: number | [number, number]
                • Optional dataFormat: "NHWC" | "NCHW"

                Returns T

              sigmoid

              • sigmoid<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              sign

              • sign<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              sin

              • sin<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              sinh

              • sinh<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              slice

              • slice<T>(this, begin, size?): T

              • Type Parameters

                Parameters

                • this: T
                • begin: number | number[]
                • Optional size: number | number[]

                Returns T

              softmax

              • softmax<T>(this, dim?): T

              • Type Parameters

                Parameters

                • this: T
                • Optional dim: number

                Returns T

              softplus

              • softplus<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              spaceToBatchND

              • spaceToBatchND<R>(blockShape, paddings): Tensor<R>

              • Type Parameters

                Parameters

                • blockShape: number[]
                • paddings: number[][]

                Returns Tensor<R>

              split

              • split<T>(numOrSizeSplits, axis?): T[]

              • Type Parameters

                Parameters

                • numOrSizeSplits: number | number[]
                • Optional axis: number

                Returns T[]

              sqrt

              • sqrt<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              square

              • square<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              squaredDifference

              • squaredDifference<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              squeeze

              • squeeze<T>(axis?): T

              • Type Parameters

                Parameters

                • Optional axis: number[]

                Returns T

              stack

              • stack<T>(x, axis?): T

              • Type Parameters

                Parameters

                Returns T

              step

              • step<T>(this, alpha?): T

              • Type Parameters

                Parameters

                • this: T
                • Optional alpha: number

                Returns T

              stridedSlice

              • stridedSlice<T>(this, begin, end, strides, beginMask?, endMask?, ellipsisMask?, newAxisMask?, shrinkAxisMask?): Tensor<Rank>

              • Type Parameters

                Parameters

                • this: Tensor<Rank>
                • begin: number[]
                • end: number[]
                • strides: number[]
                • Optional beginMask: number
                • Optional endMask: number
                • Optional ellipsisMask: number
                • Optional newAxisMask: number
                • Optional shrinkAxisMask: number

                Returns Tensor<Rank>

              sub

              • sub<T>(b): T

              • Type Parameters

                Parameters

                Returns T

              sum

              • sum<T>(axis?, keepDims?): T

              • Type Parameters

                Parameters

                • Optional axis: number | number[]
                • Optional keepDims: boolean

                Returns T

              tan

              • tan<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              tanh

              • tanh<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              throwIfDisposed

              • throwIfDisposed(): void

              • Returns void

              tile

              • tile<T>(b): T

              • Type Parameters

                Parameters

                • b: number[]

                Returns T

              toBool

              • toBool<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              toFloat

              • toFloat<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              toInt

              • toInt<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              toString

              • toString(verbose?): string

              • Returns a human-readable description of the tensor. Useful for logging.

                +

                Parameters

                • Optional verbose: boolean

                Returns string

                Doc

              topk

              • topk<T>(this, k?, sorted?): {
                    indices: T;
                    values: T;
                }

              • Type Parameters

                Parameters

                • this: T
                • Optional k: number
                • Optional sorted: boolean

                Returns {
                    indices: T;
                    values: T;
                }

                • indices: T
                • values: T

              transpose

              • transpose<T>(perm?): T

              • Type Parameters

                Parameters

                • Optional perm: number[]

                Returns T

              unique

              • unique<T>(this, axis?): {
                    indices: T;
                    values: T;
                }

              • Type Parameters

                Parameters

                • this: T
                • Optional axis: number

                Returns {
                    indices: T;
                    values: T;
                }

                • indices: T
                • values: T

              unsortedSegmentSum

              • unsortedSegmentSum<T>(this, segmentIds, numSegments): T

              • Type Parameters

                Parameters

                • this: T
                • segmentIds: Tensor1D | TensorLike1D
                • numSegments: number

                Returns T

              unstack

              • unstack<T>(axis?): T[]

              • Type Parameters

                Parameters

                • Optional axis: number

                Returns T[]

              variable

              • variable(trainable?, name?, dtype?): Variable<R>

              • Parameters

                • Optional trainable: boolean
                • Optional name: string
                • Optional dtype: keyof DataTypeMap

                Returns Variable<R>

              where

              zerosLike

              • zerosLike<T>(this): T

              • Type Parameters

                Parameters

                • this: T

                Returns T

              Settings

              Member Visibility

              Theme

              On This Page

              \ No newline at end of file diff --git a/typedoc/classes/WebCam.html b/typedoc/classes/WebCam.html index c4371878..0c9a53c0 100644 --- a/typedoc/classes/WebCam.html +++ b/typedoc/classes/WebCam.html @@ -1,280 +1,35 @@ -WebCam | @vladmandic/human - v3.1.2
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              Class WebCam

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              Hierarchy

              -
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              • WebCam
              -
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              - -
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              Constructors

              -
              constructor -
              -
              -

              Properties

              -
              config -devices -element -stream -
              -
              -

              Accessors

              -
              capabilities -constraints -height -label -paused -settings -track -width -
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              Methods

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              enumerate -pause -play -start -stop -
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              Constructors

              -
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              constructor

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              Properties

              -
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              config

              -
              config: WebCamConfig
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              current webcam configuration

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              devices

              -
              devices: MediaDeviceInfo[] = []
              -

              enumerated video devices

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              element

              -
              element: undefined | HTMLVideoElement
              -

              instance of dom element associated with webcam stream

              -
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              stream

              -
              stream: undefined | MediaStream
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              active webcam stream

              -
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              Accessors

              -
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              capabilities

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              • get capabilities(): undefined | MediaTrackCapabilities
                • -
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                get webcam capabilities

                -
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                Returns undefined | MediaTrackCapabilities

              -
              -

              constraints

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                -
              • get constraints(): undefined | MediaTrackConstraints
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              • -

                get webcam constraints

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                Returns undefined | MediaTrackConstraints

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              height

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              label

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              paused

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              settings

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              • get settings(): undefined | MediaTrackSettings
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                get webcam settings

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                Returns undefined | MediaTrackSettings

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              track

              -
                -
              • get track(): undefined | MediaStreamTrack
                • -
              • -

                get active webcam stream track

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                Returns undefined | MediaStreamTrack

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              width

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              Methods

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              enumerate

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                Returns Promise<MediaDeviceInfo[]>

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              pause

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              play

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              • -

                play webcam video method

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                Returns Promise<void>

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              start

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                start method initializizes webcam stream and associates it with a dom video element

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                Parameters

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                Returns Promise<string>

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              stop

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                stop method stops active webcam stream track and disconnects webcam

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                Returns void

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              \ No newline at end of file +WebCam | @vladmandic/human - v3.2.0

              Class WebCam

              Constructors

              constructor +

              Properties

              config +devices +element +stream +

              Accessors

              capabilities +constraints +height +label +paused +settings +track +width +

              Methods

              enumerate +pause +play +start +stop +

              Constructors

              constructor

              Properties

              config

              config: WebCamConfig

              current webcam configuration

              +

              devices

              devices: MediaDeviceInfo[] = []

              enumerated video devices

              +

              element

              element: undefined | HTMLVideoElement

              instance of dom element associated with webcam stream

              +

              stream

              stream: undefined | MediaStream

              active webcam stream

              +

              Accessors

              capabilities

              • get capabilities(): undefined | MediaTrackCapabilities

              • get webcam capabilities

                +

                Returns undefined | MediaTrackCapabilities

              constraints

              • get constraints(): undefined | MediaTrackConstraints

              • get webcam constraints

                +

                Returns undefined | MediaTrackConstraints

              height

              label

              paused

              settings

              • get settings(): undefined | MediaTrackSettings

              • get webcam settings

                +

                Returns undefined | MediaTrackSettings

              track

              • get track(): undefined | MediaStreamTrack

              • get active webcam stream track

                +

                Returns undefined | MediaStreamTrack

              width

              Methods

              enumerate

              • enumerate(): Promise<MediaDeviceInfo[]>

              • Returns Promise<MediaDeviceInfo[]>

              pause

              play

              start

              • start(webcamConfig?): Promise<string>

              • start method initializizes webcam stream and associates it with a dom video element

                +

                Parameters

                Returns Promise<string>

              stop

              • stop(): void

              • stop method stops active webcam stream track and disconnects webcam

                +

                Returns void

              Settings

              Member Visibility

              Theme

              On This Page

              \ No newline at end of file diff --git a/typedoc/classes/models.Models.html b/typedoc/classes/models.Models.html index 1fd6cfd3..9012490e 100644 --- a/typedoc/classes/models.Models.html +++ b/typedoc/classes/models.Models.html @@ -1,23 +1,4 @@ -Models | @vladmandic/human - v3.1.2
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              Class Models

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              Models class used by Human

              +Models | @vladmandic/human - v3.2.0

              Class Models

              Models class used by Human

              • models: record of all GraphModels
              • list: returns list of configured models with their stats
                • @@ -26,144 +7,12 @@
              • validate: checks loaded models for valid kernel ops vs current backend
              • stats: live detailed model stats that can be checked during model load phase
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              Hierarchy

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              models: Record<string, null | GraphModel<string | IOHandler>> = {}
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              list

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                Returns {
                    loaded: boolean;
                    name: string;
                    size: number;
                    url: any;
                }[]

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              load

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                  Optional instance: Human
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                Returns Promise<void>

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              loaded

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              reset

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              stats

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              validate

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                Returns {
                    missing: string[];
                    name: string;
                }[]

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              Constructors

              constructor +

              Properties

              models +

              Methods

              list +load +loaded +reset +stats +validate +

              Constructors

              constructor

              Properties

              models

              models: Record<string, null | GraphModel<string | IOHandler>> = {}

              Methods

              list

              • list(): {
                    loaded: boolean;
                    name: string;
                    size: number;
                    url: any;
                }[]

              • Returns {
                    loaded: boolean;
                    name: string;
                    size: number;
                    url: any;
                }[]

              load

              • load(instance?): Promise<void>

              • Parameters

                Returns Promise<void>

              loaded

              reset

              stats

              validate

              • validate(): {
                    missing: string[];
                    name: string;
                }[]

              • Returns {
                    missing: string[];
                    name: string;
                }[]

              Settings

              Member Visibility

              Theme

              On This Page

              \ No newline at end of file diff --git a/typedoc/enums/Rank.html b/typedoc/enums/Rank.html index 8b34dc9b..97dae6da 100644 --- a/typedoc/enums/Rank.html +++ b/typedoc/enums/Rank.html @@ -1,105 +1,8 @@ -Rank | @vladmandic/human - v3.1.2
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              Enumeration Rank

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              Enumeration Members

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              R0 -R1 -R2 -R3 -R4 -R5 -R6 -
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              Enumeration Members

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              R0

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              R0: "R0"
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              R1

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              R1: "R1"
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              R2

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              R2: "R2"
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              R3

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              R3: "R3"
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              R4

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              R4: "R4"
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              R5

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              R5: "R5"
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              R6

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              R6: "R6"
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              \ No newline at end of file +Rank | @vladmandic/human - v3.2.0

              Enumeration Rank

              Enumeration Members

              R0 +R1 +R2 +R3 +R4 +R5 +R6 +

              Enumeration Members

              R0

              R0: "R0"

              R1

              R1: "R1"

              R2

              R2: "R2"

              R3

              R3: "R3"

              R4

              R4: "R4"

              R5

              R5: "R5"

              R6

              R6: "R6"

              Settings

              Member Visibility

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              On This Page

              \ No newline at end of file diff --git a/typedoc/functions/draw.all.html b/typedoc/functions/draw.all.html index 954dc895..32184279 100644 --- a/typedoc/functions/draw.all.html +++ b/typedoc/functions/draw.all.html @@ -1,71 +1,2 @@ -all | @vladmandic/human - v3.1.2
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              Function all

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                meta-function that performs draw for: canvas, face, body, hand

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                Parameters

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                Returns Promise<null | [void, void, void, void, void]>

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              \ No newline at end of file +all | @vladmandic/human - v3.2.0

              Function all

              • all(inCanvas, result, drawOptions?): Promise<null | [void, void, void, void, void]>

              • meta-function that performs draw for: canvas, face, body, hand

                +

                Parameters

                Returns Promise<null | [void, void, void, void, void]>

              Settings

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              \ No newline at end of file diff --git a/typedoc/functions/draw.body.html b/typedoc/functions/draw.body.html index 2af6eb48..ad200a8f 100644 --- a/typedoc/functions/draw.body.html +++ b/typedoc/functions/draw.body.html @@ -1,71 +1,2 @@ -body | @vladmandic/human - v3.1.2
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              Function body

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              \ No newline at end of file +body | @vladmandic/human - v3.2.0

              Function body

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              \ No newline at end of file diff --git a/typedoc/functions/draw.canvas.html b/typedoc/functions/draw.canvas.html index 8da7cea5..10855f10 100644 --- a/typedoc/functions/draw.canvas.html +++ b/typedoc/functions/draw.canvas.html @@ -1,69 +1,2 @@ -canvas | @vladmandic/human - v3.1.2
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              Function canvas

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              \ No newline at end of file +canvas | @vladmandic/human - v3.2.0

              Function canvas

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              \ No newline at end of file diff --git a/typedoc/functions/draw.face.html b/typedoc/functions/draw.face.html index d2b3be91..7bab6302 100644 --- a/typedoc/functions/draw.face.html +++ b/typedoc/functions/draw.face.html @@ -1,71 +1,2 @@ -face | @vladmandic/human - v3.1.2
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              Function face

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              \ No newline at end of file +face | @vladmandic/human - v3.2.0

              Function face

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              \ No newline at end of file diff --git a/typedoc/functions/draw.gesture.html b/typedoc/functions/draw.gesture.html index d2d59b7c..1f93639e 100644 --- a/typedoc/functions/draw.gesture.html +++ b/typedoc/functions/draw.gesture.html @@ -1,71 +1,2 @@ -gesture | @vladmandic/human - v3.1.2
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              Function gesture

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              \ No newline at end of file +gesture | @vladmandic/human - v3.2.0

              Function gesture

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              \ No newline at end of file diff --git a/typedoc/functions/draw.hand.html b/typedoc/functions/draw.hand.html index 5f11afff..966b238a 100644 --- a/typedoc/functions/draw.hand.html +++ b/typedoc/functions/draw.hand.html @@ -1,71 +1,2 @@ -hand | @vladmandic/human - v3.1.2
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              Function hand

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              \ No newline at end of file +hand | @vladmandic/human - v3.2.0

              Function hand

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              \ No newline at end of file diff --git a/typedoc/functions/draw.init.html b/typedoc/functions/draw.init.html index 3ea7f2d1..332254fa 100644 --- a/typedoc/functions/draw.init.html +++ b/typedoc/functions/draw.init.html @@ -1,62 +1,2 @@ -init | @vladmandic/human - v3.1.2
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              Function init

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                sets default label templates for face/body/hand/object/gestures

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                Returns void

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              \ No newline at end of file +init | @vladmandic/human - v3.2.0

              Function init

              • init(): void

              • sets default label templates for face/body/hand/object/gestures

                +

                Returns void

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              \ No newline at end of file diff --git a/typedoc/functions/draw.object.html b/typedoc/functions/draw.object.html index 2a1c6561..1260a263 100644 --- a/typedoc/functions/draw.object.html +++ b/typedoc/functions/draw.object.html @@ -1,71 +1,2 @@ -object | @vladmandic/human - v3.1.2
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              Function object

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              \ No newline at end of file +object | @vladmandic/human - v3.2.0

              Function object

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              \ No newline at end of file diff --git a/typedoc/functions/draw.person.html b/typedoc/functions/draw.person.html index ada92e49..559cea19 100644 --- a/typedoc/functions/draw.person.html +++ b/typedoc/functions/draw.person.html @@ -1,71 +1,2 @@ -person | @vladmandic/human - v3.1.2
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              Function person

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              \ No newline at end of file +person | @vladmandic/human - v3.2.0

              Function person

              • person(inCanvas, result, drawOptions?): void

              • draw combined person results instead of individual detection result objects

                +

                Parameters

                Returns void

              Settings

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              \ No newline at end of file diff --git a/typedoc/functions/empty.html b/typedoc/functions/empty.html index 957b5008..727d3451 100644 --- a/typedoc/functions/empty.html +++ b/typedoc/functions/empty.html @@ -1,131 +1 @@ -empty | @vladmandic/human - v3.1.2
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              Function empty

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              \ No newline at end of file +empty | @vladmandic/human - v3.2.0

              Function empty

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              \ No newline at end of file diff --git a/typedoc/functions/match.distance.html b/typedoc/functions/match.distance.html index d305daa7..4606b73b 100644 --- a/typedoc/functions/match.distance.html +++ b/typedoc/functions/match.distance.html @@ -1,74 +1,9 @@ -distance | @vladmandic/human - v3.1.2
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              Function distance

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              \ No newline at end of file diff --git a/typedoc/functions/match.find.html b/typedoc/functions/match.find.html index 3d52dc13..076f55a3 100644 --- a/typedoc/functions/match.find.html +++ b/typedoc/functions/match.find.html @@ -1,85 +1,11 @@ -find | @vladmandic/human - v3.1.2
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              Function find

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                Matches given descriptor to a closest entry in array of descriptors

                -
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                Parameters

                -
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                • -
                  descriptor: Descriptor
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                  face descriptor

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                  descriptors: Descriptor[]
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                  array of face descriptors to commpare given descriptor to

                  -
                  • -
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                  options: MatchOptions = ...
                  -

                  see similarity method for options description +find | @vladmandic/human - v3.2.0

                  Function find

                  • find(descriptor, descriptors, options?): {
                        distance: number;
                        index: number;
                        similarity: number;
                    }

                  • Matches given descriptor to a closest entry in array of descriptors

                    +

                    Parameters

                    • descriptor: Descriptor

                      face descriptor

                      +
                    • descriptors: Descriptor[]

                      array of face descriptors to commpare given descriptor to

                      +
                    • options: MatchOptions = ...

                      see similarity method for options description Returns

                      • index index array index where best match was found or -1 if no matches
                      • distance calculated distance of given descriptor to the best match
                      • similarity calculated normalized similarity of given descriptor to the best match
                      -
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                    Returns {
                        distance: number;
                        index: number;
                        similarity: number;
                    }

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                Returns {
                    distance: number;
                    index: number;
                    similarity: number;
                }

                • distance: number
                • index: number
                • similarity: number

              Settings

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              \ No newline at end of file diff --git a/typedoc/functions/match.similarity.html b/typedoc/functions/match.similarity.html index e575ef1c..1cbd094e 100644 --- a/typedoc/functions/match.similarity.html +++ b/typedoc/functions/match.similarity.html @@ -1,37 +1,5 @@ -similarity | @vladmandic/human - v3.1.2
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              Function similarity

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                Calculates normalized similarity between two face descriptors based on their distance

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                Parameters

                -
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                • -
                  descriptor1: Descriptor
                  • -
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                  descriptor2: Descriptor
                  • -
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                  options: MatchOptions = ...
                  -

                  calculation options

                  +similarity | @vladmandic/human - v3.2.0

                  Function similarity

                  • similarity(descriptor1, descriptor2, options?): number

                  • Calculates normalized similarity between two face descriptors based on their distance

                    +

                    Parameters

                    • descriptor1: Descriptor
                    • descriptor2: Descriptor
                    • options: MatchOptions = ...

                      calculation options

                      • order - algorithm to use Euclidean distance if order is 2 (default), Minkowski distance algorithm of nth order if order is higher than 2
                        • @@ -42,37 +10,4 @@ default is 20 which normalizes results to similarity above 0.5 can be considered default is 0.2...0.8 Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity
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                    Returns number

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                Returns number

              Settings

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              \ No newline at end of file diff --git a/typedoc/functions/models.validateModel.html b/typedoc/functions/models.validateModel.html index 91f97804..6a24f05f 100644 --- a/typedoc/functions/models.validateModel.html +++ b/typedoc/functions/models.validateModel.html @@ -1,63 +1 @@ -validateModel | @vladmandic/human - v3.1.2
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              Function validateModel

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              \ No newline at end of file +validateModel | @vladmandic/human - v3.2.0

              Function validateModel

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              \ No newline at end of file diff --git a/typedoc/index.html b/typedoc/index.html index 96caf2ca..893455cd 100644 --- a/typedoc/index.html +++ b/typedoc/index.html @@ -1,230 +1,82 @@ -@vladmandic/human - v3.1.2
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              @vladmandic/human - v3.1.2

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              References

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              Namespaces

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              Tensor -draw -match -models -
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              Enumerations

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              Rank -
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              Classes

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              Env -GraphModel -Human -Tensor -WebCam -
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              Interfaces

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              BodyConfig -BodyKeypoint -BodyResult -Config -DrawOptions -FaceAntiSpoofConfig -FaceAttentionConfig -FaceConfig -FaceDescriptionConfig -FaceDetectorConfig -FaceEmotionConfig -FaceGearConfig -FaceIrisConfig -FaceLivenessConfig -FaceMeshConfig -FaceResult -FilterConfig -GenericConfig -GestureConfig -HandConfig -HandResult -ModelInfo -ObjectConfig -ObjectResult -PersonResult -Result -SegmentationConfig -WebCamConfig -
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              Type Aliases

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              AnyCanvas -AnyImage -AnyVideo -BackendEnum -BodyAnnotation -BodyAnnotationBlazePose -BodyAnnotationEfficientPose -BodyGesture -BodyLandmark -BodyLandmarkBlazePose -BodyLandmarkEfficientNet -BodyLandmarkMoveNet -BodyLandmarkPoseNet -Box -Emotion -Events -ExternalCanvas -FaceGesture -FaceLandmark -Finger -FingerCurl -FingerDirection -Gender -GestureResult -HandGesture -HandType -ImageObjects -Input -IrisGesture -ObjectType -Point -Race -SegmentationEnum -Tensor1D -Tensor2D -Tensor3D -Tensor4D -TensorLike -WarmupEnum -
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              default

              Renames and re-exports Human
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              \ No newline at end of file +@vladmandic/human - v3.2.0

              @vladmandic/human - v3.2.0

              References

              default +

              Namespaces

              Tensor +draw +match +models +

              Enumerations

              Rank +

              Classes

              Env +GraphModel +Human +Tensor +WebCam +

              Interfaces

              BodyConfig +BodyKeypoint +BodyResult +Config +DrawOptions +FaceAntiSpoofConfig +FaceAttentionConfig +FaceConfig +FaceDescriptionConfig +FaceDetectorConfig +FaceEmotionConfig +FaceGearConfig +FaceIrisConfig +FaceLivenessConfig +FaceMeshConfig +FaceResult +FilterConfig +GenericConfig +GestureConfig +HandConfig +HandResult +ModelInfo +ObjectConfig +ObjectResult +PersonResult +Result +SegmentationConfig +WebCamConfig +

              Type Aliases

              AnyCanvas +AnyImage +AnyVideo +BackendEnum +BodyAnnotation +BodyAnnotationBlazePose +BodyAnnotationEfficientPose +BodyGesture +BodyLandmark +BodyLandmarkBlazePose +BodyLandmarkEfficientNet +BodyLandmarkMoveNet +BodyLandmarkPoseNet +Box +Emotion +Events +ExternalCanvas +FaceGesture +FaceLandmark +Finger +FingerCurl +FingerDirection +Gender +GestureResult +HandGesture +HandType +ImageObjects +Input +IrisGesture +ObjectType +Point +Race +SegmentationEnum +Tensor1D +Tensor2D +Tensor3D +Tensor4D +TensorLike +WarmupEnum +

              Variables

              defaults +env +

              Functions

              empty +

              References

              default

              Renames and re-exports Human

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              \ No newline at end of file diff --git a/typedoc/interfaces/BodyConfig.html b/typedoc/interfaces/BodyConfig.html index 365da61b..28439737 100644 --- a/typedoc/interfaces/BodyConfig.html +++ b/typedoc/interfaces/BodyConfig.html @@ -1,125 +1,16 @@ -BodyConfig | @vladmandic/human - v3.1.2
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              Interface BodyConfig

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              Configures all body detection specific options

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              Hierarchy

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              Properties

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              enabled -maxDetected -minConfidence -modelPath -skipFrames -skipTime -
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              Properties

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              enabled

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              enabled: boolean
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              is module enabled?

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              maxDetected

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              maxDetected: number
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              maximum number of detected bodies

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              minConfidence

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              minConfidence: number
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              minimum confidence for a detected body before results are discarded

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              modelPath

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              modelPath: string
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              path to model json file (relative to modelBasePath

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              skipFrames

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              skipFrames: number
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              how many max frames to go without re-running model if cached results are acceptable +BodyConfig | @vladmandic/human - v3.2.0

              Interface BodyConfig

              Configures all body detection specific options

              +
              interface BodyConfig {
                  enabled: boolean;
                  maxDetected: number;
                  minConfidence: number;
                  modelPath: string;
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              enabled +maxDetected +minConfidence +modelPath +skipFrames +skipTime +

              Properties

              enabled

              enabled: boolean

              is module enabled?

              +

              maxDetected

              maxDetected: number

              maximum number of detected bodies

              +

              minConfidence

              minConfidence: number

              minimum confidence for a detected body before results are discarded

              +

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

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              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

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              skipTime

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              skipTime: number
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              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

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              Settings

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              \ No newline at end of file diff --git a/typedoc/interfaces/BodyKeypoint.html b/typedoc/interfaces/BodyKeypoint.html index 676c1167..8651bfbd 100644 --- a/typedoc/interfaces/BodyKeypoint.html +++ b/typedoc/interfaces/BodyKeypoint.html @@ -1,108 +1,12 @@ -BodyKeypoint | @vladmandic/human - v3.1.2
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              Interface BodyKeypoint

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              Body Result keypoints

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              Hierarchy

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              • BodyKeypoint
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              Properties

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              distance? -part -position -positionRaw -score -
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              Properties

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              Optional distance

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              distance?: Point
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              body part position relative to body center in meters

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              part

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              part: BodyLandmark
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              body part name

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              position

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              position: Point
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              body part position

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              positionRaw

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              body part position normalized to 0..1

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              score

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              score: number
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              body part detection score

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              \ No newline at end of file +BodyKeypoint | @vladmandic/human - v3.2.0

              Interface BodyKeypoint

              Body Result keypoints

              +
              interface BodyKeypoint {
                  distance?: Point;
                  part: BodyLandmark;
                  position: Point;
                  positionRaw: Point;
                  score: number;
              }

              Properties

              distance? +part +position +positionRaw +score +

              Properties

              Optional distance

              distance?: Point

              body part position relative to body center in meters

              +

              part

              part: BodyLandmark

              body part name

              +

              position

              position: Point

              body part position

              +

              positionRaw

              positionRaw: Point

              body part position normalized to 0..1

              +

              score

              score: number

              body part detection score

              +

              Settings

              Member Visibility

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              \ No newline at end of file diff --git a/typedoc/interfaces/BodyResult.html b/typedoc/interfaces/BodyResult.html index 8d469ef2..13e865b8 100644 --- a/typedoc/interfaces/BodyResult.html +++ b/typedoc/interfaces/BodyResult.html @@ -1,117 +1,14 @@ -BodyResult | @vladmandic/human - v3.1.2
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              Interface BodyResult

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              Body results

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              Hierarchy

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              Properties

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              annotations -box -boxRaw -id -keypoints -score -
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              Properties

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              annotations

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              annotations: Record<BodyAnnotation, Point[][]>
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              detected body keypoints combined into annotated parts

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              box

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              box: Box
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              detected body box

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              boxRaw

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              boxRaw: Box
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              detected body box normalized to 0..1

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              id

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              id: number
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              body id

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              keypoints

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              keypoints: BodyKeypoint[]
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              detected body keypoints

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              score

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              score: number
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              body detection score

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              \ No newline at end of file +BodyResult | @vladmandic/human - v3.2.0

              Interface BodyResult

              Body results

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              interface BodyResult {
                  annotations: Record<BodyAnnotation, Point[][]>;
                  box: Box;
                  boxRaw: Box;
                  id: number;
                  keypoints: BodyKeypoint[];
                  score: number;
              }

              Properties

              annotations +box +boxRaw +id +keypoints +score +

              Properties

              annotations

              annotations: Record<BodyAnnotation, Point[][]>

              detected body keypoints combined into annotated parts

              +

              box

              box: Box

              detected body box

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              boxRaw

              boxRaw: Box

              detected body box normalized to 0..1

              +

              id

              id: number

              body id

              +

              keypoints

              keypoints: BodyKeypoint[]

              detected body keypoints

              +

              score

              score: number

              body detection score

              +

              Settings

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              \ No newline at end of file diff --git a/typedoc/interfaces/Config.html b/typedoc/interfaces/Config.html index e6587f95..92c85e70 100644 --- a/typedoc/interfaces/Config.html +++ b/typedoc/interfaces/Config.html @@ -1,282 +1,74 @@ -Config | @vladmandic/human - v3.1.2
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              Interface Config

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              Configuration interface definition for Human library +Config | @vladmandic/human - v3.2.0

              Interface Config

              Configuration interface definition for Human library Contains all configurable parameters Defaults: config

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              Hierarchy

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              Properties

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              async -backend -body -cacheModels -cacheSensitivity -deallocate -debug -face -filter -flags -gesture -hand -modelBasePath -object -segmentation -skipAllowed -softwareKernels -validateModels -warmup -wasmPath -wasmPlatformFetch -
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              Properties

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              async

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              async: boolean
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              Perform model loading and inference concurrently or sequentially

              +
              interface Config {
                  async: boolean;
                  backend: BackendEnum;
                  body: Partial<BodyConfig>;
                  cacheModels: boolean;
                  cacheSensitivity: number;
                  deallocate: boolean;
                  debug: boolean;
                  face: Partial<FaceConfig>;
                  filter: Partial<FilterConfig>;
                  flags: Record<string, unknown>;
                  gesture: Partial<GestureConfig>;
                  hand: Partial<HandConfig>;
                  modelBasePath: string;
                  object: Partial<ObjectConfig>;
                  segmentation: Partial<SegmentationConfig>;
                  skipAllowed: boolean;
                  softwareKernels: boolean;
                  validateModels: boolean;
                  warmup: WarmupEnum;
                  wasmPath: string;
                  wasmPlatformFetch: boolean;
              }

              Properties

              async +backend +body +cacheModels +cacheSensitivity +deallocate +debug +face +filter +flags +gesture +hand +modelBasePath +object +segmentation +skipAllowed +softwareKernels +validateModels +warmup +wasmPath +wasmPlatformFetch +

              Properties

              async

              async: boolean

              Perform model loading and inference concurrently or sequentially

              default: true

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              backend

              -
              backend: BackendEnum
              -

              Backend used for TFJS operations +

              backend

              backend: BackendEnum

              Backend used for TFJS operations valid build-in backends are:

              • Browser: cpu, wasm, webgl, humangl, webgpu
              • NodeJS: cpu, wasm, tensorflow default: webgl for browser and tensorflow for nodejs
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              body

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              body: Partial<BodyConfig>
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              Body config BodyConfig

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              cacheModels

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              cacheModels: boolean
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              Cache models in IndexDB on first sucessfull load +

              body

              body: Partial<BodyConfig>

              Body config BodyConfig

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              cacheModels

              cacheModels: boolean

              Cache models in IndexDB on first sucessfull load default: true if indexdb is available (browsers), false if its not (nodejs)

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              cacheSensitivity

              -
              cacheSensitivity: number
              -

              Cache sensitivity

              +

              cacheSensitivity

              cacheSensitivity: number

              Cache sensitivity

              • values 0..1 where 0.01 means reset cache if input changed more than 1%
              • set to 0 to disable caching

              default: 0.7

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              deallocate

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              deallocate: boolean
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              Perform immediate garbage collection on deallocated tensors instead of caching them

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              debug

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              debug: boolean
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              Print debug statements to console

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              deallocate

              deallocate: boolean

              Perform immediate garbage collection on deallocated tensors instead of caching them

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              debug

              debug: boolean

              Print debug statements to console

              default: true

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              face

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              face: Partial<FaceConfig>
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              Face config FaceConfig

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              filter

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              Filter config FilterConfig

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              flags

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              flags: Record<string, unknown>
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              Explicit flags passed to initialize TFJS

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              gesture

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              gesture: Partial<GestureConfig>
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              Gesture config GestureConfig

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              hand

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              hand: Partial<HandConfig>
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              Hand config HandConfig

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              modelBasePath

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              modelBasePath: string
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              Base model path (typically starting with file://, http:// or https://) for all models

              +

              face

              face: Partial<FaceConfig>

              Face config FaceConfig

              +

              filter

              filter: Partial<FilterConfig>

              Filter config FilterConfig

              +

              flags

              flags: Record<string, unknown>

              Explicit flags passed to initialize TFJS

              +

              gesture

              gesture: Partial<GestureConfig>

              Gesture config GestureConfig

              +

              hand

              hand: Partial<HandConfig>

              Hand config HandConfig

              +

              modelBasePath

              modelBasePath: string

              Base model path (typically starting with file://, http:// or https://) for all models

              • individual modelPath values are relative to this path

              default: ../models/ for browsers and file://models/ for nodejs

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              object

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              object: Partial<ObjectConfig>
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              Object config ObjectConfig

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              segmentation

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              segmentation: Partial<SegmentationConfig>
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              Segmentation config SegmentationConfig

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              skipAllowed

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              skipAllowed: boolean
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              Internal Variable

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              softwareKernels

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              softwareKernels: boolean
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              Software Kernels +

              object

              object: Partial<ObjectConfig>

              Object config ObjectConfig

              +

              segmentation

              segmentation: Partial<SegmentationConfig>

              Segmentation config SegmentationConfig

              +

              skipAllowed

              skipAllowed: boolean

              Internal Variable

              +

              softwareKernels

              softwareKernels: boolean

              Software Kernels Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend

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              validateModels

              -
              validateModels: boolean
              -

              Validate kernel ops used in model during model load +

              validateModels

              validateModels: boolean

              Validate kernel ops used in model during model load default: true any errors will be printed on console but will be treated as non-fatal

              -
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              warmup

              -
              warmup: WarmupEnum
              -

              What to use for human.warmup()

              +

              warmup

              warmup: WarmupEnum

              What to use for human.warmup()

              • warmup pre-initializes all models for faster inference but can take significant time on startup
              • used by webgl, humangl and webgpu backends

              default: full

              -
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              wasmPath

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              wasmPath: string
              -

              Path to *.wasm files if backend is set to wasm

              +

              wasmPath

              wasmPath: string

              Path to *.wasm files if backend is set to wasm

              default: auto-detects to link to CDN jsdelivr when running in browser

              -
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              wasmPlatformFetch

              -
              wasmPlatformFetch: boolean
              -

              Force WASM loader to use platform fetch

              +

              wasmPlatformFetch

              wasmPlatformFetch: boolean

              Force WASM loader to use platform fetch

              default: false

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              \ No newline at end of file diff --git a/typedoc/interfaces/DrawOptions.html b/typedoc/interfaces/DrawOptions.html index 72bac76e..6fc64beb 100644 --- a/typedoc/interfaces/DrawOptions.html +++ b/typedoc/interfaces/DrawOptions.html @@ -1,300 +1,57 @@ -DrawOptions | @vladmandic/human - v3.1.2
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              Draw Options

              +DrawOptions | @vladmandic/human - v3.2.0

              Interface DrawOptions

              Draw Options

              • Accessed via human.draw.options or provided per each draw method as the drawOptions optional parameter
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              Hierarchy

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              Properties

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              alpha -bodyLabels -bodyPartLabels -color -drawAttention -drawBoxes -drawGaze -drawGestures -drawLabels -drawPoints -drawPolygons -faceLabels -fillPolygons -fingerLabels -font -gestureLabels -handLabels -labelColor -lineHeight -lineWidth -objectLabels -pointSize -roundRect -shadowColor -useCurves -useDepth -
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              Properties

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              alpha

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              alpha: number
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              alpha value used for lines

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              bodyLabels

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              bodyLabels: string
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              string template for body labels

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              bodyPartLabels

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              bodyPartLabels: string
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              string template for body part labels

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              color

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              color: string
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              draw line color

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              drawAttention

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              drawAttention: boolean
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              should face attention keypoints be highlighted

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              drawBoxes

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              drawBoxes: boolean
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              should draw boxes around detection results?

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              drawGaze

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              drawGaze: boolean
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              should draw gaze arrows?

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              drawGestures

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              drawGestures: boolean
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              should detected gestures be drawn?

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              drawLabels

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              drawLabels: boolean
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              should labels be drawn?

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              drawPoints

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              drawPoints: boolean
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              should points be drawn?

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              drawPolygons

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              drawPolygons: boolean
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              should draw polygons from detection points?

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              faceLabels

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              faceLabels: string
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              string template for face labels

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              fillPolygons

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              fillPolygons: boolean
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              should fill polygons?

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              fingerLabels

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              fingerLabels: string
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              string template for hand labels

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              font

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              font: string
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              label font

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              gestureLabels

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              gestureLabels: string
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              string template for gesture labels

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              handLabels

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              handLabels: string
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              string template for hand labels

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              labelColor

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              labelColor: string
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              label color

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              lineHeight

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              lineHeight: number
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              line spacing between labels

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              lineWidth

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              lineWidth: number
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              line width for drawn lines

              -
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              objectLabels

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              objectLabels: string
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              string template for object labels

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              pointSize

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              pointSize: number
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              size of drawn points

              -
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              roundRect

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              roundRect: number
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              draw rounded boxes by n pixels

              -
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              shadowColor

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              shadowColor: string
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              label shadow color

              -
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              useCurves

              -
              useCurves: boolean
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              should lines be curved?

              -
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              useDepth

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              useDepth: boolean
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              use z-coordinate when available

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              interface DrawOptions {
                  alpha: number;
                  bodyLabels: string;
                  bodyPartLabels: string;
                  color: string;
                  drawAttention: boolean;
                  drawBoxes: boolean;
                  drawGaze: boolean;
                  drawGestures: boolean;
                  drawLabels: boolean;
                  drawPoints: boolean;
                  drawPolygons: boolean;
                  faceLabels: string;
                  fillPolygons: boolean;
                  fingerLabels: string;
                  font: string;
                  gestureLabels: string;
                  handLabels: string;
                  labelColor: string;
                  lineHeight: number;
                  lineWidth: number;
                  objectLabels: string;
                  pointSize: number;
                  roundRect: number;
                  shadowColor: string;
                  useCurves: boolean;
                  useDepth: boolean;
              }

              Properties

              alpha +bodyLabels +bodyPartLabels +color +drawAttention +drawBoxes +drawGaze +drawGestures +drawLabels +drawPoints +drawPolygons +faceLabels +fillPolygons +fingerLabels +font +gestureLabels +handLabels +labelColor +lineHeight +lineWidth +objectLabels +pointSize +roundRect +shadowColor +useCurves +useDepth +

              Properties

              alpha

              alpha: number

              alpha value used for lines

              +

              bodyLabels

              bodyLabels: string

              string template for body labels

              +

              bodyPartLabels

              bodyPartLabels: string

              string template for body part labels

              +

              color

              color: string

              draw line color

              +

              drawAttention

              drawAttention: boolean

              should face attention keypoints be highlighted

              +

              drawBoxes

              drawBoxes: boolean

              should draw boxes around detection results?

              +

              drawGaze

              drawGaze: boolean

              should draw gaze arrows?

              +

              drawGestures

              drawGestures: boolean

              should detected gestures be drawn?

              +

              drawLabels

              drawLabels: boolean

              should labels be drawn?

              +

              drawPoints

              drawPoints: boolean

              should points be drawn?

              +

              drawPolygons

              drawPolygons: boolean

              should draw polygons from detection points?

              +

              faceLabels

              faceLabels: string

              string template for face labels

              +

              fillPolygons

              fillPolygons: boolean

              should fill polygons?

              +

              fingerLabels

              fingerLabels: string

              string template for hand labels

              +

              font

              font: string

              label font

              +

              gestureLabels

              gestureLabels: string

              string template for gesture labels

              +

              handLabels

              handLabels: string

              string template for hand labels

              +

              labelColor

              labelColor: string

              label color

              +

              lineHeight

              lineHeight: number

              line spacing between labels

              +

              lineWidth

              lineWidth: number

              line width for drawn lines

              +

              objectLabels

              objectLabels: string

              string template for object labels

              +

              pointSize

              pointSize: number

              size of drawn points

              +

              roundRect

              roundRect: number

              draw rounded boxes by n pixels

              +

              shadowColor

              shadowColor: string

              label shadow color

              +

              useCurves

              useCurves: boolean

              should lines be curved?

              +

              useDepth

              useDepth: boolean

              use z-coordinate when available

              +

              Settings

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              \ No newline at end of file diff --git a/typedoc/interfaces/FaceAntiSpoofConfig.html b/typedoc/interfaces/FaceAntiSpoofConfig.html index 2590c8a3..47878f32 100644 --- a/typedoc/interfaces/FaceAntiSpoofConfig.html +++ b/typedoc/interfaces/FaceAntiSpoofConfig.html @@ -1,107 +1,12 @@ -FaceAntiSpoofConfig | @vladmandic/human - v3.1.2
              -
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              Interface FaceAntiSpoofConfig

              -
              -

              Anti-spoofing part of face configuration

              -
              -
              -

              Hierarchy

              -
              -
              -
              -
              - -
              -
              -

              Properties

              -
              enabled -modelPath -skipFrames -skipTime -
              -
              -

              Properties

              -
              -

              enabled

              -
              enabled: boolean
              -

              is module enabled?

              -
              -
              -

              modelPath

              -
              modelPath: string
              -

              path to model json file (relative to modelBasePath

              -
              -
              -

              skipFrames

              -
              skipFrames: number
              -

              how many max frames to go without re-running model if cached results are acceptable +FaceAntiSpoofConfig | @vladmandic/human - v3.2.0

              Interface FaceAntiSpoofConfig

              Anti-spoofing part of face configuration

              +
              interface FaceAntiSpoofConfig {
                  enabled: boolean;
                  modelPath: string;
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              enabled +modelPath +skipFrames +skipTime +

              Properties

              enabled

              enabled: boolean

              is module enabled?

              +

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

              +

              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -

              skipTime

              -
              skipTime: number
              -

              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -
              -
              -

              Settings

               -
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              -

              Member Visibility

              -
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              -
              -

              Theme

              - -
              -
              \ No newline at end of file +

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              \ No newline at end of file diff --git a/typedoc/interfaces/FaceAttentionConfig.html b/typedoc/interfaces/FaceAttentionConfig.html index 8485a6f1..53a39f44 100644 --- a/typedoc/interfaces/FaceAttentionConfig.html +++ b/typedoc/interfaces/FaceAttentionConfig.html @@ -1,107 +1,12 @@ -FaceAttentionConfig | @vladmandic/human - v3.1.2
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              Interface FaceAttentionConfig

              -
              -

              Attention part of face configuration

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              Hierarchy

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              - -
              -
              -

              Properties

              -
              enabled -modelPath -skipFrames -skipTime -
              -
              -

              Properties

              -
              -

              enabled

              -
              enabled: boolean
              -

              is module enabled?

              -
              -
              -

              modelPath

              -
              modelPath: string
              -

              path to model json file (relative to modelBasePath

              -
              -
              -

              skipFrames

              -
              skipFrames: number
              -

              how many max frames to go without re-running model if cached results are acceptable +FaceAttentionConfig | @vladmandic/human - v3.2.0

              Interface FaceAttentionConfig

              Attention part of face configuration

              +
              interface FaceAttentionConfig {
                  enabled: boolean;
                  modelPath: string;
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              enabled +modelPath +skipFrames +skipTime +

              Properties

              enabled

              enabled: boolean

              is module enabled?

              +

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

              +

              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -

              skipTime

              -
              skipTime: number
              -

              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
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              -
              -
              -

              Settings

               -
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              -

              Member Visibility

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              Theme

              - -
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              \ No newline at end of file +

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              \ No newline at end of file diff --git a/typedoc/interfaces/FaceConfig.html b/typedoc/interfaces/FaceConfig.html index 90128516..1a970499 100644 --- a/typedoc/interfaces/FaceConfig.html +++ b/typedoc/interfaces/FaceConfig.html @@ -1,170 +1,21 @@ -FaceConfig | @vladmandic/human - v3.1.2
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              Interface FaceConfig

              -
              -

              Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description

              -
              -
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              Hierarchy

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              -
              - -
              -
              -

              Properties

              -
              antispoof -attention -description -detector -emotion -enabled -gear -iris -liveness -mesh -modelPath -skipFrames -skipTime -
              -
              -

              Properties

              -
              -

              antispoof

              -
              antispoof: Partial<FaceAntiSpoofConfig>
              -
              -

              attention

              -
              attention: Partial<FaceAttentionConfig>
              -
              -

              description

              -
              description: Partial<FaceDescriptionConfig>
              -
              -

              detector

              -
              detector: Partial<FaceDetectorConfig>
              -
              -

              emotion

              -
              emotion: Partial<FaceEmotionConfig>
              -
              -

              enabled

              -
              enabled: boolean
              -

              is module enabled?

              -
              -
              -

              gear

              -
              gear: Partial<FaceGearConfig>
              -
              -

              iris

              -
              iris: Partial<FaceIrisConfig>
              -
              -

              liveness

              -
              liveness: Partial<FaceLivenessConfig>
              -
              -

              mesh

              -
              mesh: Partial<FaceMeshConfig>
              -
              -

              modelPath

              -
              modelPath: string
              -

              path to model json file (relative to modelBasePath

              -
              -
              -

              skipFrames

              -
              skipFrames: number
              -

              how many max frames to go without re-running model if cached results are acceptable +FaceConfig | @vladmandic/human - v3.2.0

              Interface FaceConfig

              Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description

              +
              interface FaceConfig {
                  antispoof: Partial<FaceAntiSpoofConfig>;
                  attention: Partial<FaceAttentionConfig>;
                  description: Partial<FaceDescriptionConfig>;
                  detector: Partial<FaceDetectorConfig>;
                  emotion: Partial<FaceEmotionConfig>;
                  enabled: boolean;
                  gear: Partial<FaceGearConfig>;
                  iris: Partial<FaceIrisConfig>;
                  liveness: Partial<FaceLivenessConfig>;
                  mesh: Partial<FaceMeshConfig>;
                  modelPath: string;
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              antispoof +attention +description +detector +emotion +enabled +gear +iris +liveness +mesh +modelPath +skipFrames +skipTime +

              Properties

              antispoof

              antispoof: Partial<FaceAntiSpoofConfig>

              attention

              attention: Partial<FaceAttentionConfig>

              description

              description: Partial<FaceDescriptionConfig>

              detector

              detector: Partial<FaceDetectorConfig>

              emotion

              emotion: Partial<FaceEmotionConfig>

              enabled

              enabled: boolean

              is module enabled?

              +

              gear

              gear: Partial<FaceGearConfig>

              iris

              iris: Partial<FaceIrisConfig>

              liveness

              liveness: Partial<FaceLivenessConfig>

              mesh

              mesh: Partial<FaceMeshConfig>

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

              +

              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -

              skipTime

              -
              skipTime: number
              -

              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
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              -
              -

              Settings

               -
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              Member Visibility

              -
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              -
              -

              Theme

              - -
              -
              \ No newline at end of file +

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              \ No newline at end of file diff --git a/typedoc/interfaces/FaceDescriptionConfig.html b/typedoc/interfaces/FaceDescriptionConfig.html index 217e80dd..dcf38738 100644 --- a/typedoc/interfaces/FaceDescriptionConfig.html +++ b/typedoc/interfaces/FaceDescriptionConfig.html @@ -1,119 +1,17 @@ -FaceDescriptionConfig | @vladmandic/human - v3.1.2
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              Interface FaceDescriptionConfig

              -
              -

              Description or face embedding part of face configuration

              +FaceDescriptionConfig | @vladmandic/human - v3.2.0

              Interface FaceDescriptionConfig

              Description or face embedding part of face configuration

              • also used by age and gender detection
              -
              -
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              Hierarchy

              -
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              -
              -
              - -
              -
              -

              Properties

              -
              enabled -minConfidence -modelPath -skipFrames -skipTime -
              -
              -

              Properties

              -
              -

              enabled

              -
              enabled: boolean
              -

              is module enabled?

              -
              -
              -

              minConfidence

              -
              minConfidence: number
              -

              minimum confidence for a detected face before results are discarded

              -
              -
              -

              modelPath

              -
              modelPath: string
              -

              path to model json file (relative to modelBasePath

              -
              -
              -

              skipFrames

              -
              skipFrames: number
              -

              how many max frames to go without re-running model if cached results are acceptable +

              interface FaceDescriptionConfig {
                  enabled: boolean;
                  minConfidence: number;
                  modelPath: string;
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              enabled +minConfidence +modelPath +skipFrames +skipTime +

              Properties

              enabled

              enabled: boolean

              is module enabled?

              +

              minConfidence

              minConfidence: number

              minimum confidence for a detected face before results are discarded

              +

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

              +

              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -

              skipTime

              -
              skipTime: number
              -

              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
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              Settings

               -
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              -

              Member Visibility

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              Theme

              - -
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              \ No newline at end of file +

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              \ No newline at end of file diff --git a/typedoc/interfaces/FaceDetectorConfig.html b/typedoc/interfaces/FaceDetectorConfig.html index f9ad333a..3976eb8a 100644 --- a/typedoc/interfaces/FaceDetectorConfig.html +++ b/typedoc/interfaces/FaceDetectorConfig.html @@ -1,172 +1,28 @@ -FaceDetectorConfig | @vladmandic/human - v3.1.2
              -
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              Interface FaceDetectorConfig

              -
              -

              Detector part of face configuration

              -
              -
              -

              Hierarchy

              -
              -
              -
              -
              - -
              -
              -

              Properties

              -
              enabled -iouThreshold -mask -maxDetected -minConfidence -minSize -modelPath -return -rotation -skipFrames -skipTime -
              -
              -

              Properties

              -
              -

              enabled

              -
              enabled: boolean
              -

              is module enabled?

              -
              -
              -

              iouThreshold

              -
              iouThreshold: number
              -

              minimum overlap between two detected faces before one is discarded

              -
              -
              -

              mask

              -
              mask: boolean
              -

              should child models perform on masked image of a face

              -
              -
              -

              maxDetected

              -
              maxDetected: number
              -

              maximum number of detected faces

              -
              -
              -

              minConfidence

              -
              minConfidence: number
              -

              minimum confidence for a detected face before results are discarded

              -
              -
              -

              minSize

              -
              minSize: number
              -

              minimum size in pixels of a detected face box before resutls are discared

              -
              -
              -

              modelPath

              -
              modelPath: string
              -

              path to model json file (relative to modelBasePath

              -
              -
              -

              return

              -
              return: boolean
              -

              should face detection return processed and cropped face tensor that can with an external model for addtional processing? +FaceDetectorConfig | @vladmandic/human - v3.2.0

              Interface FaceDetectorConfig

              Detector part of face configuration

              +
              interface FaceDetectorConfig {
                  enabled: boolean;
                  iouThreshold: number;
                  mask: boolean;
                  maxDetected: number;
                  minConfidence: number;
                  minSize: number;
                  modelPath: string;
                  return: boolean;
                  rotation: boolean;
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              enabled +iouThreshold +mask +maxDetected +minConfidence +minSize +modelPath +return +rotation +skipFrames +skipTime +

              Properties

              enabled

              enabled: boolean

              is module enabled?

              +

              iouThreshold

              iouThreshold: number

              minimum overlap between two detected faces before one is discarded

              +

              mask

              mask: boolean

              should child models perform on masked image of a face

              +

              maxDetected

              maxDetected: number

              maximum number of detected faces

              +

              minConfidence

              minConfidence: number

              minimum confidence for a detected face before results are discarded

              +

              minSize

              minSize: number

              minimum size in pixels of a detected face box before resutls are discared

              +

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

              +

              return

              return: boolean

              should face detection return processed and cropped face tensor that can with an external model for addtional processing? if enabled it must be manually deallocated to avoid memory leak

              -
              -
              -

              rotation

              -
              rotation: boolean
              -

              is face rotation correction performed after detecting face? +

              rotation

              rotation: boolean

              is face rotation correction performed after detecting face? used to correctly analyze faces under high angles

              -
              -
              -

              skipFrames

              -
              skipFrames: number
              -

              how many max frames to go without re-running model if cached results are acceptable +

              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -

              skipTime

              -
              skipTime: number
              -

              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
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              Settings

               -
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              -

              Member Visibility

              -
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              Theme

              - -
              -
              \ No newline at end of file +

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              \ No newline at end of file diff --git a/typedoc/interfaces/FaceEmotionConfig.html b/typedoc/interfaces/FaceEmotionConfig.html index 23fda539..a4949780 100644 --- a/typedoc/interfaces/FaceEmotionConfig.html +++ b/typedoc/interfaces/FaceEmotionConfig.html @@ -1,116 +1,14 @@ -FaceEmotionConfig | @vladmandic/human - v3.1.2
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              Interface FaceEmotionConfig

              -
              -

              Emotion part of face configuration

              -
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              -

              Hierarchy

              -
              -
              -
              -
              - -
              -
              -

              Properties

              -
              enabled -minConfidence -modelPath -skipFrames -skipTime -
              -
              -

              Properties

              -
              -

              enabled

              -
              enabled: boolean
              -

              is module enabled?

              -
              -
              -

              minConfidence

              -
              minConfidence: number
              -

              minimum confidence for a detected face before results are discarded

              -
              -
              -

              modelPath

              -
              modelPath: string
              -

              path to model json file (relative to modelBasePath

              -
              -
              -

              skipFrames

              -
              skipFrames: number
              -

              how many max frames to go without re-running model if cached results are acceptable +FaceEmotionConfig | @vladmandic/human - v3.2.0

              Interface FaceEmotionConfig

              Emotion part of face configuration

              +
              interface FaceEmotionConfig {
                  enabled: boolean;
                  minConfidence: number;
                  modelPath: string;
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              enabled +minConfidence +modelPath +skipFrames +skipTime +

              Properties

              enabled

              enabled: boolean

              is module enabled?

              +

              minConfidence

              minConfidence: number

              minimum confidence for a detected face before results are discarded

              +

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

              +

              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -

              skipTime

              -
              skipTime: number
              -

              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
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              -
              -
              -

              Settings

               -
              -
              -

              Member Visibility

              -
                -
              -
              -

              Theme

              - -
              -
              \ No newline at end of file +

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              \ No newline at end of file diff --git a/typedoc/interfaces/FaceGearConfig.html b/typedoc/interfaces/FaceGearConfig.html index 7e0b8a57..5c0f1758 100644 --- a/typedoc/interfaces/FaceGearConfig.html +++ b/typedoc/interfaces/FaceGearConfig.html @@ -1,116 +1,14 @@ -FaceGearConfig | @vladmandic/human - v3.1.2
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              - -

              Interface FaceGearConfig

              -
              -

              Gear part of face configuration

              -
              -
              -

              Hierarchy

              -
              -
              -
              -
              - -
              -
              -

              Properties

              -
              enabled -minConfidence -modelPath -skipFrames -skipTime -
              -
              -

              Properties

              -
              -

              enabled

              -
              enabled: boolean
              -

              is module enabled?

              -
              -
              -

              minConfidence

              -
              minConfidence: number
              -

              minimum confidence for a detected race before results are discarded

              -
              -
              -

              modelPath

              -
              modelPath: string
              -

              path to model json file (relative to modelBasePath

              -
              -
              -

              skipFrames

              -
              skipFrames: number
              -

              how many max frames to go without re-running model if cached results are acceptable +FaceGearConfig | @vladmandic/human - v3.2.0

              Interface FaceGearConfig

              Gear part of face configuration

              +
              interface FaceGearConfig {
                  enabled: boolean;
                  minConfidence: number;
                  modelPath: string;
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              enabled +minConfidence +modelPath +skipFrames +skipTime +

              Properties

              enabled

              enabled: boolean

              is module enabled?

              +

              minConfidence

              minConfidence: number

              minimum confidence for a detected race before results are discarded

              +

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

              +

              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -

              skipTime

              -
              skipTime: number
              -

              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
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              Settings

               -
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              -

              Member Visibility

              -
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              Theme

              - -
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              \ No newline at end of file diff --git a/typedoc/interfaces/FaceIrisConfig.html b/typedoc/interfaces/FaceIrisConfig.html index fc4449b8..b2882d2f 100644 --- a/typedoc/interfaces/FaceIrisConfig.html +++ b/typedoc/interfaces/FaceIrisConfig.html @@ -1,107 +1,12 @@ -FaceIrisConfig | @vladmandic/human - v3.1.2
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              Interface FaceIrisConfig

              -
              -

              Iris part of face configuration

              -
              -
              -

              Hierarchy

              -
              -
              -
              -
              - -
              -
              -

              Properties

              -
              enabled -modelPath -skipFrames -skipTime -
              -
              -

              Properties

              -
              -

              enabled

              -
              enabled: boolean
              -

              is module enabled?

              -
              -
              -

              modelPath

              -
              modelPath: string
              -

              path to model json file (relative to modelBasePath

              -
              -
              -

              skipFrames

              -
              skipFrames: number
              -

              how many max frames to go without re-running model if cached results are acceptable +FaceIrisConfig | @vladmandic/human - v3.2.0

              Interface FaceIrisConfig

              Iris part of face configuration

              +
              interface FaceIrisConfig {
                  enabled: boolean;
                  modelPath: string;
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              enabled +modelPath +skipFrames +skipTime +

              Properties

              enabled

              enabled: boolean

              is module enabled?

              +

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

              +

              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -

              skipTime

              -
              skipTime: number
              -

              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -
              -
              -

              Settings

               -
              -
              -

              Member Visibility

              -
                -
              -
              -

              Theme

              - -
              -
              \ No newline at end of file +

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              Theme

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              \ No newline at end of file diff --git a/typedoc/interfaces/FaceLivenessConfig.html b/typedoc/interfaces/FaceLivenessConfig.html index 06d9e6f6..dc243e37 100644 --- a/typedoc/interfaces/FaceLivenessConfig.html +++ b/typedoc/interfaces/FaceLivenessConfig.html @@ -1,107 +1,12 @@ -FaceLivenessConfig | @vladmandic/human - v3.1.2
              -
              - -
              -
              -
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              - -

              Interface FaceLivenessConfig

              -
              -

              Liveness part of face configuration

              -
              -
              -

              Hierarchy

              -
              -
              -
              -
              - -
              -
              -

              Properties

              -
              enabled -modelPath -skipFrames -skipTime -
              -
              -

              Properties

              -
              -

              enabled

              -
              enabled: boolean
              -

              is module enabled?

              -
              -
              -

              modelPath

              -
              modelPath: string
              -

              path to model json file (relative to modelBasePath

              -
              -
              -

              skipFrames

              -
              skipFrames: number
              -

              how many max frames to go without re-running model if cached results are acceptable +FaceLivenessConfig | @vladmandic/human - v3.2.0

              Interface FaceLivenessConfig

              Liveness part of face configuration

              +
              interface FaceLivenessConfig {
                  enabled: boolean;
                  modelPath: string;
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              enabled +modelPath +skipFrames +skipTime +

              Properties

              enabled

              enabled: boolean

              is module enabled?

              +

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

              +

              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -

              skipTime

              -
              skipTime: number
              -

              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -
              -
              -

              Settings

               -
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              -

              Member Visibility

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              -
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              Theme

              - -
              -
              \ No newline at end of file +

              Settings

              Member Visibility

              Theme

              On This Page

              \ No newline at end of file diff --git a/typedoc/interfaces/FaceMeshConfig.html b/typedoc/interfaces/FaceMeshConfig.html index 9ec23a60..00fbcd9a 100644 --- a/typedoc/interfaces/FaceMeshConfig.html +++ b/typedoc/interfaces/FaceMeshConfig.html @@ -1,116 +1,14 @@ -FaceMeshConfig | @vladmandic/human - v3.1.2
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              Interface FaceMeshConfig

              -
              -

              Mesh part of face configuration

              -
              -
              -

              Hierarchy

              -
              -
              -
              -
              - -
              -
              -

              Properties

              -
              enabled -keepInvalid -modelPath -skipFrames -skipTime -
              -
              -

              Properties

              -
              -

              enabled

              -
              enabled: boolean
              -

              is module enabled?

              -
              -
              -

              keepInvalid

              -
              keepInvalid: boolean
              -

              Keep detected faces that cannot be verified using facemesh

              -
              -
              -

              modelPath

              -
              modelPath: string
              -

              path to model json file (relative to modelBasePath

              -
              -
              -

              skipFrames

              -
              skipFrames: number
              -

              how many max frames to go without re-running model if cached results are acceptable +FaceMeshConfig | @vladmandic/human - v3.2.0

              Interface FaceMeshConfig

              Mesh part of face configuration

              +
              interface FaceMeshConfig {
                  enabled: boolean;
                  keepInvalid: boolean;
                  modelPath: string;
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              enabled +keepInvalid +modelPath +skipFrames +skipTime +

              Properties

              enabled

              enabled: boolean

              is module enabled?

              +

              keepInvalid

              keepInvalid: boolean

              Keep detected faces that cannot be verified using facemesh

              +

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

              +

              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -

              skipTime

              -
              skipTime: number
              -

              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -
              -
              -

              Settings

               -
              -
              -

              Member Visibility

              -
                -
              -
              -

              Theme

              - -
              -
              \ No newline at end of file +

              Settings

              Member Visibility

              Theme

              On This Page

              \ No newline at end of file diff --git a/typedoc/interfaces/FaceResult.html b/typedoc/interfaces/FaceResult.html index 6105d494..963159d6 100644 --- a/typedoc/interfaces/FaceResult.html +++ b/typedoc/interfaces/FaceResult.html @@ -1,256 +1,48 @@ -FaceResult | @vladmandic/human - v3.1.2
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              - -

              Interface FaceResult

              -
              -

              Face results

              +FaceResult | @vladmandic/human - v3.2.0

              Interface FaceResult

              Face results

              • Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models
              • Some values may be null if specific model is not enabled
              -
              -
              -

              Hierarchy

              -
                -
              • FaceResult
              -
              -
              -
              - -
              -
              -

              Properties

              -
              age? -annotations -box -boxRaw -boxScore -distance? -embedding? -emotion? -faceScore -gender? -genderScore? -id -live? -mesh -meshRaw -race? -real? -rotation? -score -size -tensor? -
              -
              -

              Properties

              -
              -

              Optional age

              -
              age?: number
              -

              detected age

              -
              -
              -

              annotations

              -
              annotations: Record<FaceLandmark, Point[]>
              -

              mesh keypoints combined into annotated results

              -
              -
              -

              box

              -
              box: Box
              -

              detected face box

              -
              -
              -

              boxRaw

              -
              boxRaw: Box
              -

              detected face box normalized to 0..1

              -
              -
              -

              boxScore

              -
              boxScore: number
              -

              detection score

              -
              -
              -

              Optional distance

              -
              distance?: number
              -

              face distance from camera

              -
              -
              -

              Optional embedding

              -
              embedding?: number[]
              -

              face descriptor

              -
              -
              -

              Optional emotion

              -
              emotion?: {
                  emotion: Emotion;
                  score: number;
              }[]
              -

              detected emotions

              -
              -
              -

              faceScore

              -
              faceScore: number
              -

              mesh score

              -
              -
              -

              Optional gender

              -
              gender?: Gender
              -

              detected gender

              -
              -
              -

              Optional genderScore

              -
              genderScore?: number
              -

              gender detection score

              -
              -
              -

              id

              -
              id: number
              -

              face id

              -
              -
              -

              Optional live

              -
              live?: number
              -

              face liveness result confidence

              -
              -
              -

              mesh

              -
              mesh: Point[]
              -

              detected face mesh

              -
              -
              -

              meshRaw

              -
              meshRaw: Point[]
              -

              detected face mesh normalized to 0..1

              -
              -
              -

              Optional race

              -
              race?: {
                  race: Race;
                  score: number;
              }[]
              -

              detected race

              -
              -
              -

              Optional real

              -
              real?: number
              -

              face anti-spoofing result confidence

              -
              -
              -

              Optional rotation

              -
              rotation?: null | {
                  angle: {
                      pitch: number;
                      roll: number;
                      yaw: number;
                  };
                  gaze: {
                      bearing: number;
                      strength: number;
                  };
                  matrix: [number, number, number, number, number, number, number, number, number];
              }
              -

              face rotation details

              -
              -
              -

              score

              -
              score: number
              -

              overall face score

              -
              -
              -

              size

              -
              size: [number, number]
              -

              detected face box size

              -
              -
              -

              Optional tensor

              -
              tensor?: Tensor<Rank>
              -

              detected face as tensor that can be used in further pipelines

              -
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              Settings

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              Member Visibility

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              Theme

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              \ No newline at end of file +
              interface FaceResult {
                  age?: number;
                  annotations: Record<FaceLandmark, Point[]>;
                  box: Box;
                  boxRaw: Box;
                  boxScore: number;
                  distance?: number;
                  embedding?: number[];
                  emotion?: {
                      emotion: Emotion;
                      score: number;
                  }[];
                  faceScore: number;
                  gender?: Gender;
                  genderScore?: number;
                  id: number;
                  live?: number;
                  mesh: Point[];
                  meshRaw: Point[];
                  race?: {
                      race: Race;
                      score: number;
                  }[];
                  real?: number;
                  rotation?: null | {
                      angle: {
                          pitch: number;
                          roll: number;
                          yaw: number;
                      };
                      gaze: {
                          bearing: number;
                          strength: number;
                      };
                      matrix: [number, number, number, number, number, number, number, number, number];
                  };
                  score: number;
                  size: [number, number];
                  tensor?: Tensor<Rank>;
              }

              Properties

              age? +annotations +box +boxRaw +boxScore +distance? +embedding? +emotion? +faceScore +gender? +genderScore? +id +live? +mesh +meshRaw +race? +real? +rotation? +score +size +tensor? +

              Properties

              Optional age

              age?: number

              detected age

              +

              annotations

              annotations: Record<FaceLandmark, Point[]>

              mesh keypoints combined into annotated results

              +

              box

              box: Box

              detected face box

              +

              boxRaw

              boxRaw: Box

              detected face box normalized to 0..1

              +

              boxScore

              boxScore: number

              detection score

              +

              Optional distance

              distance?: number

              face distance from camera

              +

              Optional embedding

              embedding?: number[]

              face descriptor

              +

              Optional emotion

              emotion?: {
                  emotion: Emotion;
                  score: number;
              }[]

              detected emotions

              +

              Type declaration

              faceScore

              faceScore: number

              mesh score

              +

              Optional gender

              gender?: Gender

              detected gender

              +

              Optional genderScore

              genderScore?: number

              gender detection score

              +

              id

              id: number

              face id

              +

              Optional live

              live?: number

              face liveness result confidence

              +

              mesh

              mesh: Point[]

              detected face mesh

              +

              meshRaw

              meshRaw: Point[]

              detected face mesh normalized to 0..1

              +

              Optional race

              race?: {
                  race: Race;
                  score: number;
              }[]

              detected race

              +

              Type declaration

              • race: Race
              • score: number

              Optional real

              real?: number

              face anti-spoofing result confidence

              +

              Optional rotation

              rotation?: null | {
                  angle: {
                      pitch: number;
                      roll: number;
                      yaw: number;
                  };
                  gaze: {
                      bearing: number;
                      strength: number;
                  };
                  matrix: [number, number, number, number, number, number, number, number, number];
              }

              face rotation details

              +

              Type declaration

              • angle: {
                    pitch: number;
                    roll: number;
                    yaw: number;
                }
                • pitch: number
                • roll: number
                • yaw: number
              • gaze: {
                    bearing: number;
                    strength: number;
                }
                • bearing: number
                • strength: number
              • matrix: [number, number, number, number, number, number, number, number, number]

              score

              score: number

              overall face score

              +

              size

              size: [number, number]

              detected face box size

              +

              Optional tensor

              tensor?: Tensor<Rank>

              detected face as tensor that can be used in further pipelines

              +

              Settings

              Member Visibility

              Theme

              On This Page

              \ No newline at end of file diff --git a/typedoc/interfaces/FilterConfig.html b/typedoc/interfaces/FilterConfig.html index c656e59a..e3291e14 100644 --- a/typedoc/interfaces/FilterConfig.html +++ b/typedoc/interfaces/FilterConfig.html @@ -1,260 +1,59 @@ -FilterConfig | @vladmandic/human - v3.1.2
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              Interface FilterConfig

              -
              -

              Run input through image filters before inference

              +FilterConfig | @vladmandic/human - v3.2.0

              Interface FilterConfig

              Run input through image filters before inference

              • available only in Browser environments
              • image filters run with near-zero latency as they are executed on the GPU using WebGL
              -
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              Hierarchy

              -
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              • FilterConfig
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              -
              -

              Properties

              -
              autoBrightness -blur -brightness -contrast -enabled -equalization -flip -height -hue -kodachrome -negative -pixelate -polaroid -return -saturation -sepia -sharpness -technicolor -vintage -width -
              -
              -

              Properties

              -
              -

              autoBrightness

              -
              autoBrightness: boolean
              -

              apply auto-brighness

              -
              -
              -

              blur

              -
              blur: number
              -

              range: 0 (no blur) to N (blur radius in pixels)

              -
              -
              -

              brightness

              -
              brightness: number
              -

              range: -1 (darken) to 1 (lighten)

              -
              -
              -

              contrast

              -
              contrast: number
              -

              range: -1 (reduce contrast) to 1 (increase contrast)

              -
              -
              -

              enabled

              -
              enabled: boolean
              -

              are image filters enabled?

              -
              -
              -

              equalization

              -
              equalization: boolean
              -

              perform image histogram equalization

              +
              interface FilterConfig {
                  autoBrightness: boolean;
                  blur: number;
                  brightness: number;
                  contrast: number;
                  enabled: boolean;
                  equalization: boolean;
                  flip: boolean;
                  height: number;
                  hue: number;
                  kodachrome: boolean;
                  negative: boolean;
                  pixelate: number;
                  polaroid: boolean;
                  return: boolean;
                  saturation: number;
                  sepia: boolean;
                  sharpness: number;
                  technicolor: boolean;
                  vintage: boolean;
                  width: number;
              }

              Properties

              autoBrightness +blur +brightness +contrast +enabled +equalization +flip +height +hue +kodachrome +negative +pixelate +polaroid +return +saturation +sepia +sharpness +technicolor +vintage +width +

              Properties

              autoBrightness

              autoBrightness: boolean

              apply auto-brighness

              +

              blur

              blur: number

              range: 0 (no blur) to N (blur radius in pixels)

              +

              brightness

              brightness: number

              range: -1 (darken) to 1 (lighten)

              +

              contrast

              contrast: number

              range: -1 (reduce contrast) to 1 (increase contrast)

              +

              enabled

              enabled: boolean

              are image filters enabled?

              +

              equalization

              equalization: boolean

              perform image histogram equalization

              • equalization is performed on input as a whole and detected face before its passed for further analysis
              -
              -
              -

              flip

              -
              flip: boolean
              -

              flip input as mirror image

              -
              -
              -

              height

              -
              height: number
              -

              resize input height

              +

              flip

              flip: boolean

              flip input as mirror image

              +

              height

              height: number

              resize input height

              • if both width and height are set to 0, there is no resizing
              • if just one is set, second one is scaled automatically
              • if both are set, values are used as-is
              -
              -
              -

              hue

              -
              hue: number
              -

              range: 0 (no change) to 360 (hue rotation in degrees)

              -
              -
              -

              kodachrome

              -
              kodachrome: boolean
              -

              image kodachrome colors

              -
              -
              -

              negative

              -
              negative: boolean
              -

              image negative

              -
              -
              -

              pixelate

              -
              pixelate: number
              -

              range: 0 (no pixelate) to N (number of pixels to pixelate)

              -
              -
              -

              polaroid

              -
              polaroid: boolean
              -

              image polaroid camera effect

              -
              -
              -

              return

              -
              return: boolean
              -

              return processed canvas imagedata in result

              -
              -
              -

              saturation

              -
              saturation: number
              -

              range: -1 (reduce saturation) to 1 (increase saturation)

              -
              -
              -

              sepia

              -
              sepia: boolean
              -

              image sepia colors

              -
              -
              -

              sharpness

              -
              sharpness: number
              -

              range: 0 (no sharpening) to 1 (maximum sharpening)

              -
              -
              -

              technicolor

              -
              technicolor: boolean
              -

              image technicolor colors

              -
              -
              -

              vintage

              -
              vintage: boolean
              -

              image vintage colors

              -
              -
              -

              width

              -
              width: number
              -

              resize input width

              +

              hue

              hue: number

              range: 0 (no change) to 360 (hue rotation in degrees)

              +

              kodachrome

              kodachrome: boolean

              image kodachrome colors

              +

              negative

              negative: boolean

              image negative

              +

              pixelate

              pixelate: number

              range: 0 (no pixelate) to N (number of pixels to pixelate)

              +

              polaroid

              polaroid: boolean

              image polaroid camera effect

              +

              return

              return: boolean

              return processed canvas imagedata in result

              +

              saturation

              saturation: number

              range: -1 (reduce saturation) to 1 (increase saturation)

              +

              sepia

              sepia: boolean

              image sepia colors

              +

              sharpness

              sharpness: number

              range: 0 (no sharpening) to 1 (maximum sharpening)

              +

              technicolor

              technicolor: boolean

              image technicolor colors

              +

              vintage

              vintage: boolean

              image vintage colors

              +

              width

              width: number

              resize input width

              • if both width and height are set to 0, there is no resizing
              • if just one is set, second one is scaled automatically
              • if both are set, values are used as-is
              -
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              -
              -

              Settings

               -
              -
              -

              Member Visibility

              -
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              -
              -

              Theme

              - -
              -
              \ No newline at end of file +

              Settings

              Member Visibility

              Theme

              On This Page

              \ No newline at end of file diff --git a/typedoc/interfaces/GenericConfig.html b/typedoc/interfaces/GenericConfig.html index 691eda4c..195be68d 100644 --- a/typedoc/interfaces/GenericConfig.html +++ b/typedoc/interfaces/GenericConfig.html @@ -1,116 +1,12 @@ -GenericConfig | @vladmandic/human - v3.1.2
              -
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              Interface GenericConfig

              -
              -

              Generic config type inherited by all module types

              -
              -
              -

              Hierarchy

              -
              -
              -
              -
              - -
              -
              -

              Properties

              -
              enabled -modelPath -skipFrames -skipTime -
              -
              -

              Properties

              -
              -

              enabled

              -
              enabled: boolean
              -

              is module enabled?

              -
              -
              -

              modelPath

              -
              modelPath: string
              -

              path to model json file (relative to modelBasePath

              -
              -
              -

              skipFrames

              -
              skipFrames: number
              -

              how many max frames to go without re-running model if cached results are acceptable +GenericConfig | @vladmandic/human - v3.2.0

              Interface GenericConfig

              Generic config type inherited by all module types

              +
              interface GenericConfig {
                  enabled: boolean;
                  modelPath: string;
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              enabled +modelPath +skipFrames +skipTime +

              Properties

              enabled

              enabled: boolean

              is module enabled?

              +

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

              +

              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -

              skipTime

              -
              skipTime: number
              -

              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -
              -
              -

              Settings

               -
              -
              -

              Member Visibility

              -
                -
              -
              -

              Theme

              - -
              -
              \ No newline at end of file +

              Settings

              Member Visibility

              Theme

              On This Page

              \ No newline at end of file diff --git a/typedoc/interfaces/GestureConfig.html b/typedoc/interfaces/GestureConfig.html index 72fc3777..7fda63b9 100644 --- a/typedoc/interfaces/GestureConfig.html +++ b/typedoc/interfaces/GestureConfig.html @@ -1,72 +1,4 @@ -GestureConfig | @vladmandic/human - v3.1.2
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              Interface GestureConfig

              -
              -

              Controlls gesture detection

              -
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              Hierarchy

              -
                -
              • GestureConfig
              -
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              -
              - -
              -
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              Properties

              -
              enabled -
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              -

              Properties

              -
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              enabled

              -
              enabled: boolean
              -

              is gesture detection enabled?

              -
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              Settings

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              Member Visibility

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              Theme

              - -
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              \ No newline at end of file +GestureConfig | @vladmandic/human - v3.2.0

              Interface GestureConfig

              Controlls gesture detection

              +
              interface GestureConfig {
                  enabled: boolean;
              }

              Properties

              enabled +

              Properties

              enabled

              enabled: boolean

              is gesture detection enabled?

              +

              Settings

              Member Visibility

              Theme

              On This Page

              \ No newline at end of file diff --git a/typedoc/interfaces/HandConfig.html b/typedoc/interfaces/HandConfig.html index aa338a65..52bca2ca 100644 --- a/typedoc/interfaces/HandConfig.html +++ b/typedoc/interfaces/HandConfig.html @@ -1,180 +1,26 @@ -HandConfig | @vladmandic/human - v3.1.2
              -
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              -
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              Interface HandConfig

              -
              -

              Configures all hand detection specific options

              -
              -
              -

              Hierarchy

              -
              -
              -
              -
              - -
              -
              -

              Properties

              -
              detector -enabled -iouThreshold -landmarks -maxDetected -minConfidence -modelPath -rotation -skeleton -skipFrames -skipTime -
              -
              -

              Properties

              -
              -

              detector

              -
              detector: {
                  modelPath?: string;
              }
              -
              -

              Type declaration

              -
                -
              • -
                Optional modelPath?: string
                -

                path to hand detector model json

                -
              -
              -

              enabled

              -
              enabled: boolean
              -

              is module enabled?

              -
              -
              -

              iouThreshold

              -
              iouThreshold: number
              -

              minimum overlap between two detected hands before one is discarded

              -
              -
              -

              landmarks

              -
              landmarks: boolean
              -

              should hand landmarks be detected or just return detected hand box

              -
              -
              -

              maxDetected

              -
              maxDetected: number
              -

              maximum number of detected hands

              -
              -
              -

              minConfidence

              -
              minConfidence: number
              -

              minimum confidence for a detected hand before results are discarded

              -
              -
              -

              modelPath

              -
              modelPath: string
              -

              path to model json file (relative to modelBasePath

              -
              -
              -

              rotation

              -
              rotation: boolean
              -

              should hand rotation correction be performed after hand detection?

              -
              -
              -

              skeleton

              -
              skeleton: {
                  modelPath?: string;
              }
              -
              -

              Type declaration

              -
                -
              • -
                Optional modelPath?: string
                -

                path to hand skeleton model json

                -
              -
              -

              skipFrames

              -
              skipFrames: number
              -

              how many max frames to go without re-running model if cached results are acceptable +HandConfig | @vladmandic/human - v3.2.0

              Interface HandConfig

              Configures all hand detection specific options

              +
              interface HandConfig {
                  detector: {
                      modelPath?: string;
                  };
                  enabled: boolean;
                  iouThreshold: number;
                  landmarks: boolean;
                  maxDetected: number;
                  minConfidence: number;
                  modelPath: string;
                  rotation: boolean;
                  skeleton: {
                      modelPath?: string;
                  };
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              detector +enabled +iouThreshold +landmarks +maxDetected +minConfidence +modelPath +rotation +skeleton +skipFrames +skipTime +

              Properties

              detector

              detector: {
                  modelPath?: string;
              }

              Type declaration

              • Optional modelPath?: string

                path to hand detector model json

                +

              enabled

              enabled: boolean

              is module enabled?

              +

              iouThreshold

              iouThreshold: number

              minimum overlap between two detected hands before one is discarded

              +

              landmarks

              landmarks: boolean

              should hand landmarks be detected or just return detected hand box

              +

              maxDetected

              maxDetected: number

              maximum number of detected hands

              +

              minConfidence

              minConfidence: number

              minimum confidence for a detected hand before results are discarded

              +

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

              +

              rotation

              rotation: boolean

              should hand rotation correction be performed after hand detection?

              +

              skeleton

              skeleton: {
                  modelPath?: string;
              }

              Type declaration

              • Optional modelPath?: string

                path to hand skeleton model json

                +

              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -

              skipTime

              -
              skipTime: number
              -

              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -
              -
              -

              Settings

               -
              -
              -

              Member Visibility

              -
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              -
              -

              Theme

              - -
              -
              \ No newline at end of file +

              Settings

              Member Visibility

              Theme

              On This Page

              \ No newline at end of file diff --git a/typedoc/interfaces/HandResult.html b/typedoc/interfaces/HandResult.html index 359003a5..75d95d23 100644 --- a/typedoc/interfaces/HandResult.html +++ b/typedoc/interfaces/HandResult.html @@ -1,153 +1,22 @@ -HandResult | @vladmandic/human - v3.1.2
              -
              - -
              -
              -
              -
              - -

              Interface HandResult

              -
              -

              Hand results

              -
              -
              -

              Hierarchy

              -
                -
              • HandResult
              -
              -
              -
              - -
              -
              -

              Properties

              -
              annotations -box -boxRaw -boxScore -fingerScore -id -keypoints -label -landmarks -score -
              -
              -

              Properties

              -
              -

              annotations

              -
              annotations: Record<Finger, Point[]>
              -

              detected hand keypoints combined into annotated parts

              -
              -
              -

              box

              -
              box: Box
              -

              detected hand box

              -
              -
              -

              boxRaw

              -
              boxRaw: Box
              -

              detected hand box normalized to 0..1

              -
              -
              -

              boxScore

              -
              boxScore: number
              -

              hand detection score

              -
              -
              -

              fingerScore

              -
              fingerScore: number
              -

              hand skelton score

              -
              -
              -

              id

              -
              id: number
              -

              hand id

              -
              -
              -

              keypoints

              -
              keypoints: Point[]
              -

              detected hand keypoints

              -
              -
              -

              label

              -
              label: HandType
              -

              detected hand class

              -
              -
              -

              landmarks

              -
              landmarks: Record<Finger, {
                  curl: FingerCurl;
                  direction: FingerDirection;
              }>
              -

              detected hand parts annotated with part gestures

              -
              -
              -

              score

              -
              score: number
              -

              hand overal score

              -
              -
              -
              -
              -

              Settings

               -
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              Member Visibility

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              \ No newline at end of file +HandResult | @vladmandic/human - v3.2.0

              Interface HandResult

              Hand results

              +
              interface HandResult {
                  annotations: Record<Finger, Point[]>;
                  box: Box;
                  boxRaw: Box;
                  boxScore: number;
                  fingerScore: number;
                  id: number;
                  keypoints: Point[];
                  label: HandType;
                  landmarks: Record<Finger, {
                      curl: FingerCurl;
                      direction: FingerDirection;
                  }>;
                  score: number;
              }

              Properties

              annotations +box +boxRaw +boxScore +fingerScore +id +keypoints +label +landmarks +score +

              Properties

              annotations

              annotations: Record<Finger, Point[]>

              detected hand keypoints combined into annotated parts

              +

              box

              box: Box

              detected hand box

              +

              boxRaw

              boxRaw: Box

              detected hand box normalized to 0..1

              +

              boxScore

              boxScore: number

              hand detection score

              +

              fingerScore

              fingerScore: number

              hand skelton score

              +

              id

              id: number

              hand id

              +

              keypoints

              keypoints: Point[]

              detected hand keypoints

              +

              label

              label: HandType

              detected hand class

              +

              landmarks

              landmarks: Record<Finger, {
                  curl: FingerCurl;
                  direction: FingerDirection;
              }>

              detected hand parts annotated with part gestures

              +

              Type declaration

              score

              score: number

              hand overal score

              +

              Settings

              Member Visibility

              Theme

              On This Page

              \ No newline at end of file diff --git a/typedoc/interfaces/ModelInfo.html b/typedoc/interfaces/ModelInfo.html index 4840aac6..aca9cd20 100644 --- a/typedoc/interfaces/ModelInfo.html +++ b/typedoc/interfaces/ModelInfo.html @@ -1,102 +1,7 @@ -ModelInfo | @vladmandic/human - v3.1.2
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              Interface ModelInfo

              -
              -

              Hierarchy

              -
                -
              • ModelInfo
              -
              -
              -
              - -
              -
              -

              Properties

              -
              inCache -name -sizeDesired -sizeFromManifest -sizeLoadedWeights -url -
              -
              -

              Properties

              -
              -

              inCache

              -
              inCache: boolean
              -
              -

              name

              -
              name: string
              -
              -

              sizeDesired

              -
              sizeDesired: number
              -
              -

              sizeFromManifest

              -
              sizeFromManifest: number
              -
              -

              sizeLoadedWeights

              -
              sizeLoadedWeights: number
              -
              -

              url

              -
              url: string
              -
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              Settings

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              \ No newline at end of file +ModelInfo | @vladmandic/human - v3.2.0

              Interface ModelInfo

              interface ModelInfo {
                  inCache: boolean;
                  name: string;
                  sizeDesired: number;
                  sizeFromManifest: number;
                  sizeLoadedWeights: number;
                  url: string;
              }

              Properties

              inCache +name +sizeDesired +sizeFromManifest +sizeLoadedWeights +url +

              Properties

              inCache

              inCache: boolean

              name

              name: string

              sizeDesired

              sizeDesired: number

              sizeFromManifest

              sizeFromManifest: number

              sizeLoadedWeights

              sizeLoadedWeights: number

              url

              url: string

              Settings

              Member Visibility

              Theme

              On This Page

              \ No newline at end of file diff --git a/typedoc/interfaces/ObjectConfig.html b/typedoc/interfaces/ObjectConfig.html index 0030336f..b14a0ab2 100644 --- a/typedoc/interfaces/ObjectConfig.html +++ b/typedoc/interfaces/ObjectConfig.html @@ -1,134 +1,18 @@ -ObjectConfig | @vladmandic/human - v3.1.2
              -
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              Interface ObjectConfig

              -
              -

              Configures all object detection specific options

              -
              -
              -

              Hierarchy

              -
              -
              -
              -
              - -
              -
              -

              Properties

              -
              enabled -iouThreshold -maxDetected -minConfidence -modelPath -skipFrames -skipTime -
              -
              -

              Properties

              -
              -

              enabled

              -
              enabled: boolean
              -

              is module enabled?

              -
              -
              -

              iouThreshold

              -
              iouThreshold: number
              -

              minimum overlap between two detected objects before one is discarded

              -
              -
              -

              maxDetected

              -
              maxDetected: number
              -

              maximum number of detected objects

              -
              -
              -

              minConfidence

              -
              minConfidence: number
              -

              minimum confidence for a detected objects before results are discarded

              -
              -
              -

              modelPath

              -
              modelPath: string
              -

              path to model json file (relative to modelBasePath

              -
              -
              -

              skipFrames

              -
              skipFrames: number
              -

              how many max frames to go without re-running model if cached results are acceptable +ObjectConfig | @vladmandic/human - v3.2.0

              Interface ObjectConfig

              Configures all object detection specific options

              +
              interface ObjectConfig {
                  enabled: boolean;
                  iouThreshold: number;
                  maxDetected: number;
                  minConfidence: number;
                  modelPath: string;
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              enabled +iouThreshold +maxDetected +minConfidence +modelPath +skipFrames +skipTime +

              Properties

              enabled

              enabled: boolean

              is module enabled?

              +

              iouThreshold

              iouThreshold: number

              minimum overlap between two detected objects before one is discarded

              +

              maxDetected

              maxDetected: number

              maximum number of detected objects

              +

              minConfidence

              minConfidence: number

              minimum confidence for a detected objects before results are discarded

              +

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

              +

              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -

              skipTime

              -
              skipTime: number
              -

              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
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              -
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              Settings

               -
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              -

              Member Visibility

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              -
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              Theme

              - -
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              \ No newline at end of file +

              Settings

              Member Visibility

              Theme

              On This Page

              \ No newline at end of file diff --git a/typedoc/interfaces/ObjectResult.html b/typedoc/interfaces/ObjectResult.html index a814bd79..10837b0b 100644 --- a/typedoc/interfaces/ObjectResult.html +++ b/typedoc/interfaces/ObjectResult.html @@ -1,117 +1,14 @@ -ObjectResult | @vladmandic/human - v3.1.2
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              Interface ObjectResult

              -
              -

              Object results

              -
              -
              -

              Hierarchy

              -
                -
              • ObjectResult
              -
              -
              -
              - -
              -
              -

              Properties

              -
              box -boxRaw -class -id -label -score -
              -
              -

              Properties

              -
              -

              box

              -
              box: Box
              -

              detected object box

              -
              -
              -

              boxRaw

              -
              boxRaw: Box
              -

              detected object box normalized to 0..1

              -
              -
              -

              class

              -
              class: number
              -

              detected object class id

              -
              -
              -

              id

              -
              id: number
              -

              object id

              -
              -
              -

              label

              -
              label: ObjectType
              -

              detected object class name

              -
              -
              -

              score

              -
              score: number
              -

              object detection score

              -
              -
              -
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              -

              Settings

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              \ No newline at end of file +ObjectResult | @vladmandic/human - v3.2.0

              Interface ObjectResult

              Object results

              +
              interface ObjectResult {
                  box: Box;
                  boxRaw: Box;
                  class: number;
                  id: number;
                  label: ObjectType;
                  score: number;
              }

              Properties

              box +boxRaw +class +id +label +score +

              Properties

              box

              box: Box

              detected object box

              +

              boxRaw

              boxRaw: Box

              detected object box normalized to 0..1

              +

              class

              class: number

              detected object class id

              +

              id

              id: number

              object id

              +

              label

              label: ObjectType

              detected object class name

              +

              score

              score: number

              object detection score

              +

              Settings

              Member Visibility

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              On This Page

              \ No newline at end of file diff --git a/typedoc/interfaces/PersonResult.html b/typedoc/interfaces/PersonResult.html index dbb34c70..2fec0a60 100644 --- a/typedoc/interfaces/PersonResult.html +++ b/typedoc/interfaces/PersonResult.html @@ -1,136 +1,19 @@ -PersonResult | @vladmandic/human - v3.1.2
              -
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              Interface PersonResult

              -
              -

              Person getter

              +PersonResult | @vladmandic/human - v3.2.0

              Interface PersonResult

              Person getter

              • Triggers combining all individual results into a virtual person object
              -
              -
              -

              Hierarchy

              -
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              • PersonResult
              -
              -
              -
              - -
              -
              -

              Properties

              -
              body -box -boxRaw? -face -gestures -hands -id -
              -
              -

              Properties

              -
              -

              body

              -
              body: null | BodyResult
              -

              body result that belongs to this person

              -
              -
              -

              box

              -
              box: Box
              -

              box that defines the person

              -
              -
              -

              Optional boxRaw

              -
              boxRaw?: Box
              -

              box that defines the person normalized to 0..1

              -
              -
              -

              face

              -
              face: FaceResult
              -

              face result that belongs to this person

              -
              -
              -

              gestures

              -
              gestures: GestureResult[]
              -

              detected gestures specific to this person

              -
              -
              -

              hands

              -
              hands: {
                  left: null | HandResult;
                  right: null | HandResult;
              }
              -

              left and right hand results that belong to this person

              -
              -
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              Type declaration

              -
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              -

              id

              -
              id: number
              -

              person id

              -
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              \ No newline at end of file +
              interface PersonResult {
                  body: null | BodyResult;
                  box: Box;
                  boxRaw?: Box;
                  face: FaceResult;
                  gestures: GestureResult[];
                  hands: {
                      left: null | HandResult;
                      right: null | HandResult;
                  };
                  id: number;
              }

              Properties

              body +box +boxRaw? +face +gestures +hands +id +

              Properties

              body

              body: null | BodyResult

              body result that belongs to this person

              +

              box

              box: Box

              box that defines the person

              +

              Optional boxRaw

              boxRaw?: Box

              box that defines the person normalized to 0..1

              +

              face

              face: FaceResult

              face result that belongs to this person

              +

              gestures

              gestures: GestureResult[]

              detected gestures specific to this person

              +

              hands

              hands: {
                  left: null | HandResult;
                  right: null | HandResult;
              }

              left and right hand results that belong to this person

              +

              Type declaration

              id

              id: number

              person id

              +

              Settings

              Member Visibility

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              On This Page

              \ No newline at end of file diff --git a/typedoc/interfaces/Result.html b/typedoc/interfaces/Result.html index eb9d6a64..523433ae 100644 --- a/typedoc/interfaces/Result.html +++ b/typedoc/interfaces/Result.html @@ -1,172 +1,27 @@ -Result | @vladmandic/human - v3.1.2
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              Interface Result

              -
              -

              Result interface definition for Human library

              +Result | @vladmandic/human - v3.2.0

              Interface Result

              Result interface definition for Human library

              Contains all possible detection results

              -
              -
              -

              Hierarchy

              -
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              • Result
              -
              -
              -
              - -
              -
              -

              Properties

              -
              body -canvas? -error -face -gesture -hand -height -object -performance -persons -timestamp -width -
              -
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              Properties

              -
              -

              body

              -
              body: BodyResult[]
              -

              BodyResult: detection & analysis results

              -
              -
              -

              Optional canvas

              -
              canvas?: null | AnyCanvas
              -

              optional processed canvas that can be used to draw input on screen

              -
              -
              -

              error

              -
              error: null | string
              -

              Last known error message

              -
              -
              -

              face

              -
              face: FaceResult[]
              -

              FaceResult: detection & analysis results

              -
              -
              -

              gesture

              -
              gesture: GestureResult[]
              -

              GestureResult: detection & analysis results

              -
              -
              -

              hand

              -
              hand: HandResult[]
              -

              HandResult: detection & analysis results

              -
              -
              -

              height

              -
              height: number
              -

              Resolution height

              -
              -
              -

              object

              -
              object: ObjectResult[]
              -

              ObjectResult: detection & analysis results

              -
              -
              -

              performance

              -
              performance: Record<string, number>
              -

              global performance object with timing values for each operation

              -
              -
              -

              persons

              -
              persons: PersonResult[]
              -

              getter property that returns unified persons object

              -
              -
              -

              Readonly timestamp

              -
              timestamp: number
              -

              timestamp of detection representing the milliseconds elapsed since the UNIX epoch

              -
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              -

              width

              -
              width: number
              -

              Resolution width

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              \ No newline at end of file +
              interface Result {
                  body: BodyResult[];
                  canvas?: null | AnyCanvas;
                  error: null | string;
                  face: FaceResult[];
                  gesture: GestureResult[];
                  hand: HandResult[];
                  height: number;
                  object: ObjectResult[];
                  performance: Record<string, number>;
                  persons: PersonResult[];
                  timestamp: number;
                  width: number;
              }

              Properties

              body +canvas? +error +face +gesture +hand +height +object +performance +persons +timestamp +width +

              Properties

              body

              body: BodyResult[]

              BodyResult: detection & analysis results

              +

              Optional canvas

              canvas?: null | AnyCanvas

              optional processed canvas that can be used to draw input on screen

              +

              error

              error: null | string

              Last known error message

              +

              face

              face: FaceResult[]

              FaceResult: detection & analysis results

              +

              gesture

              gesture: GestureResult[]

              GestureResult: detection & analysis results

              +

              hand

              hand: HandResult[]

              HandResult: detection & analysis results

              +

              height

              height: number

              Resolution height

              +

              object

              object: ObjectResult[]

              ObjectResult: detection & analysis results

              +

              performance

              performance: Record<string, number>

              global performance object with timing values for each operation

              +

              persons

              persons: PersonResult[]

              getter property that returns unified persons object

              +

              Readonly timestamp

              timestamp: number

              timestamp of detection representing the milliseconds elapsed since the UNIX epoch

              +

              width

              width: number

              Resolution width

              +

              Settings

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              \ No newline at end of file diff --git a/typedoc/interfaces/SegmentationConfig.html b/typedoc/interfaces/SegmentationConfig.html index ebfdf7b8..489b8167 100644 --- a/typedoc/interfaces/SegmentationConfig.html +++ b/typedoc/interfaces/SegmentationConfig.html @@ -1,129 +1,20 @@ -SegmentationConfig | @vladmandic/human - v3.1.2
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              Interface SegmentationConfig

              -
              -

              Configures all body segmentation module +SegmentationConfig | @vladmandic/human - v3.2.0

              Interface SegmentationConfig

              Configures all body segmentation module removes background from input containing person if segmentation is enabled it will run as preprocessing task before any other model alternatively leave it disabled and use it on-demand using human.segmentation method which can remove background or replace it with user-provided background

              -
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              Hierarchy

              -
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              Properties

              -
              enabled -mode -modelPath -ratio -skipFrames -skipTime -
              -
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              Properties

              -
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              enabled

              -
              enabled: boolean
              -

              is module enabled?

              -
              -
              -

              mode

              -
              mode: SegmentationEnum
              -

              possible rvm segmentation mode

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              modelPath

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              modelPath: string
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              path to model json file (relative to modelBasePath

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              ratio

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              ratio: number
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              downsample ratio, adjust to reflect approximately how much of input is taken by body

              -
              -
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              skipFrames

              -
              skipFrames: number
              -

              how many max frames to go without re-running model if cached results are acceptable +

              interface SegmentationConfig {
                  enabled: boolean;
                  mode: SegmentationEnum;
                  modelPath: string;
                  ratio: number;
                  skipFrames: number;
                  skipTime: number;
              }

              Hierarchy

              Properties

              enabled +mode +modelPath +ratio +skipFrames +skipTime +

              Properties

              enabled

              enabled: boolean

              is module enabled?

              +

              mode

              mode: SegmentationEnum

              possible rvm segmentation mode

              +

              modelPath

              modelPath: string

              path to model json file (relative to modelBasePath

              +

              ratio

              ratio: number

              downsample ratio, adjust to reflect approximately how much of input is taken by body

              +

              skipFrames

              skipFrames: number

              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

              -
              -
              -

              skipTime

              -
              skipTime: number
              -

              how many max milliseconds to go without re-running model if cached results are acceptable +

              skipTime

              skipTime: number

              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

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              Settings

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              On This Page

              \ No newline at end of file diff --git a/typedoc/interfaces/WebCamConfig.html b/typedoc/interfaces/WebCamConfig.html index 47daa409..7eece962 100644 --- a/typedoc/interfaces/WebCamConfig.html +++ b/typedoc/interfaces/WebCamConfig.html @@ -1,131 +1,21 @@ -WebCamConfig | @vladmandic/human - v3.1.2
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              Interface WebCamConfig

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              WebCam configuration

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              Hierarchy

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              Properties

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              crop -debug -element -height -id? -mode -width -
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              Properties

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              crop

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              crop: boolean
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              camera crop mode

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              debug

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              debug: boolean
              -

              print messages on console

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              element

              -
              element: undefined | string | HTMLVideoElement
              -

              element can be:

              +WebCamConfig | @vladmandic/human - v3.2.0

              Interface WebCamConfig

              WebCam configuration

              +
              interface WebCamConfig {
                  crop: boolean;
                  debug: boolean;
                  element: undefined | string | HTMLVideoElement;
                  height: number;
                  id?: string;
                  mode: "front" | "back";
                  width: number;
              }

              Properties

              crop +debug +element +height +id? +mode +width +

              Properties

              crop

              crop: boolean

              camera crop mode

              +

              debug

              debug: boolean

              print messages on console

              +

              element

              element: undefined | string | HTMLVideoElement

              element can be:

              • string which indicates dom element id
              • actual HTMLVideo dom element
              • undefined in which case a new HTMLVideoElement will be created
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              height

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              height: number
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              desired webcam height

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              Optional id

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              id?: string
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              deviceId of the video device to use

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              mode

              -
              mode: "front" | "back"
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              use front or back camera

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              width

              -
              width: number
              -

              desired webcam width

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              \ No newline at end of file +

              height

              height: number

              desired webcam height

              +

              Optional id

              id?: string

              deviceId of the video device to use

              +

              mode

              mode: "front" | "back"

              use front or back camera

              +

              width

              width: number

              desired webcam width

              +

              Settings

              Member Visibility

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              On This Page

              \ No newline at end of file diff --git a/typedoc/interfaces/models.KernelOps.html b/typedoc/interfaces/models.KernelOps.html index 7e84b185..ad6eca06 100644 --- a/typedoc/interfaces/models.KernelOps.html +++ b/typedoc/interfaces/models.KernelOps.html @@ -1,89 +1,5 @@ -KernelOps | @vladmandic/human - v3.1.2
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              Interface KernelOps

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              Hierarchy

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              missing -name -ops -url -
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              missing

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              name

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              ops

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              url

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              \ No newline at end of file +KernelOps | @vladmandic/human - v3.2.0

              Interface KernelOps

              interface KernelOps {
                  missing: string[];
                  name: string;
                  ops: string[];
                  url: string;
              }

              Properties

              missing +name +ops +url +

              Properties

              missing

              missing: string[]

              name

              name: string

              ops

              ops: string[]

              url

              url: string

              Settings

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              \ No newline at end of file diff --git a/typedoc/interfaces/models.ModelStats.html b/typedoc/interfaces/models.ModelStats.html index 99679f69..2c9a6746 100644 --- a/typedoc/interfaces/models.ModelStats.html +++ b/typedoc/interfaces/models.ModelStats.html @@ -1,113 +1,9 @@ -ModelStats | @vladmandic/human - v3.1.2
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              Interface ModelStats

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              structure that holds global stats for currently loaded models

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              Hierarchy

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              • ModelStats
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              Properties

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              modelStats -numDefinedModels -numLoadedModels -percentageLoaded -totalSizeFromManifest -totalSizeLoading -totalSizeWeights -
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              Properties

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              modelStats

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              modelStats: ModelInfo[]
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              numDefinedModels

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              numDefinedModels: number
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              numLoadedModels

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              numLoadedModels: number
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              percentageLoaded

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              percentageLoaded: number
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              totalSizeFromManifest

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              totalSizeFromManifest: number
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              totalSizeLoading

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              totalSizeLoading: number
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              totalSizeWeights

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              \ No newline at end of file +ModelStats | @vladmandic/human - v3.2.0

              Interface ModelStats

              structure that holds global stats for currently loaded models

              +
              interface ModelStats {
                  modelStats: ModelInfo[];
                  numDefinedModels: number;
                  numLoadedModels: number;
                  percentageLoaded: number;
                  totalSizeFromManifest: number;
                  totalSizeLoading: number;
                  totalSizeWeights: number;
              }

              Properties

              modelStats +numDefinedModels +numLoadedModels +percentageLoaded +totalSizeFromManifest +totalSizeLoading +totalSizeWeights +

              Properties

              modelStats

              modelStats: ModelInfo[]

              numDefinedModels

              numDefinedModels: number

              numLoadedModels

              numLoadedModels: number

              percentageLoaded

              percentageLoaded: number

              totalSizeFromManifest

              totalSizeFromManifest: number

              totalSizeLoading

              totalSizeLoading: number

              totalSizeWeights

              totalSizeWeights: number

              Settings

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              \ No newline at end of file diff --git a/typedoc/modules/Tensor.html b/typedoc/modules/Tensor.html index 8763f27d..6c25dbef 100644 --- a/typedoc/modules/Tensor.html +++ b/typedoc/modules/Tensor.html @@ -1,262 +1,2 @@ -Tensor | @vladmandic/human - v3.1.2
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              Namespace Tensor

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              Explict reexport of main @tensorflow/tfjs types

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              \ No newline at end of file +Tensor | @vladmandic/human - v3.2.0

              Namespace Tensor

              Explict reexport of main @tensorflow/tfjs types

              +

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              \ No newline at end of file diff --git a/typedoc/modules/draw.html b/typedoc/modules/draw.html index c61a4425..685a4284 100644 --- a/typedoc/modules/draw.html +++ b/typedoc/modules/draw.html @@ -1,76 +1,12 @@ -draw | @vladmandic/human - v3.1.2
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              Namespace draw

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              Class Human as default export

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              Index

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              all -body -canvas -face -gesture -hand -init -object -person -
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              \ No newline at end of file +draw | @vladmandic/human - v3.2.0

              Namespace draw

              Class Human as default export

              +

              Index

              Variables

              options +

              Functions

              all +body +canvas +face +gesture +hand +init +object +person +

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              \ No newline at end of file diff --git a/typedoc/modules/match.html b/typedoc/modules/match.html index 3f2bf0a9..22f3a84f 100644 --- a/typedoc/modules/match.html +++ b/typedoc/modules/match.html @@ -1,66 +1,7 @@ -match | @vladmandic/human - v3.1.2
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              Namespace match

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              Class Human as default export

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              Descriptor -MatchOptions -
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              Functions

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              distance -find -similarity -
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              \ No newline at end of file +match | @vladmandic/human - v3.2.0

              Namespace match

              Class Human as default export

              +

              Index

              Type Aliases

              Descriptor +MatchOptions +

              Functions

              distance +find +similarity +

              Settings

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              \ No newline at end of file diff --git a/typedoc/modules/models.html b/typedoc/modules/models.html index c18cf150..517fa342 100644 --- a/typedoc/modules/models.html +++ b/typedoc/modules/models.html @@ -1,67 +1,6 @@ -models | @vladmandic/human - v3.1.2
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              Namespace models

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              Class Human as default export

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              Interfaces

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              KernelOps -ModelStats -
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              Functions

              -
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              \ No newline at end of file +models | @vladmandic/human - v3.2.0

              Namespace models

              Class Human as default export

              +

              Index

              Classes

              Models +

              Interfaces

              KernelOps +ModelStats +

              Functions

              validateModel +

              Settings

              Member Visibility

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              \ No newline at end of file diff --git a/typedoc/types/AnyCanvas.html b/typedoc/types/AnyCanvas.html index 778859df..da7deb05 100644 --- a/typedoc/types/AnyCanvas.html +++ b/typedoc/types/AnyCanvas.html @@ -1,124 +1,2 @@ -AnyCanvas | @vladmandic/human - v3.1.2
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              Type alias AnyCanvas

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              AnyCanvas: HTMLCanvasElement | OffscreenCanvas
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              Defines all possible canvas types

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              \ No newline at end of file +AnyCanvas | @vladmandic/human - v3.2.0

              Type alias AnyCanvas

              AnyCanvas: HTMLCanvasElement | OffscreenCanvas

              Defines all possible canvas types

              +

              Settings

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              \ No newline at end of file diff --git a/typedoc/types/AnyImage.html b/typedoc/types/AnyImage.html index 74d38e66..405699f4 100644 --- a/typedoc/types/AnyImage.html +++ b/typedoc/types/AnyImage.html @@ -1,124 +1,2 @@ -AnyImage | @vladmandic/human - v3.1.2
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              Type alias AnyImage

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              AnyImage: HTMLImageElement | typeof Image
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              Defines all possible image types

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              \ No newline at end of file +AnyImage | @vladmandic/human - v3.2.0

              Type alias AnyImage

              AnyImage: HTMLImageElement | typeof Image

              Defines all possible image types

              +

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              \ No newline at end of file diff --git a/typedoc/types/AnyVideo.html b/typedoc/types/AnyVideo.html index ae0408c5..13047668 100644 --- a/typedoc/types/AnyVideo.html +++ b/typedoc/types/AnyVideo.html @@ -1,124 +1,2 @@ -AnyVideo | @vladmandic/human - v3.1.2
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              Type alias AnyVideo

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              AnyVideo: HTMLMediaElement | HTMLVideoElement
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              Defines all possible video types

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              \ No newline at end of file +AnyVideo | @vladmandic/human - v3.2.0

              Type alias AnyVideo

              AnyVideo: HTMLMediaElement | HTMLVideoElement

              Defines all possible video types

              +

              Settings

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              \ No newline at end of file diff --git a/typedoc/types/BackendEnum.html b/typedoc/types/BackendEnum.html index 2eaf0c9a..5f14bd02 100644 --- a/typedoc/types/BackendEnum.html +++ b/typedoc/types/BackendEnum.html @@ -1,124 +1,2 @@ -BackendEnum | @vladmandic/human - v3.1.2
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              Type alias BackendEnum

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              BackendEnum: "" | "cpu" | "wasm" | "webgl" | "humangl" | "tensorflow" | "webgpu"
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              Possible TensorFlow backends

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              \ No newline at end of file +BackendEnum | @vladmandic/human - v3.2.0

              Type alias BackendEnum

              BackendEnum: "" | "cpu" | "wasm" | "webgl" | "humangl" | "tensorflow" | "webgpu"

              Possible TensorFlow backends

              +

              Settings

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              \ No newline at end of file diff --git a/typedoc/types/BodyAnnotation.html b/typedoc/types/BodyAnnotation.html index f5be9afe..6c32152d 100644 --- a/typedoc/types/BodyAnnotation.html +++ b/typedoc/types/BodyAnnotation.html @@ -1,122 +1 @@ -BodyAnnotation | @vladmandic/human - v3.1.2
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              Type alias BodyAnnotation

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              BodyAnnotation: BodyAnnotationBlazePose | BodyAnnotationEfficientPose
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              \ No newline at end of file +BodyAnnotation | @vladmandic/human - v3.2.0

              Type alias BodyAnnotation

              BodyAnnotation: BodyAnnotationBlazePose | BodyAnnotationEfficientPose

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              \ No newline at end of file diff --git a/typedoc/types/BodyAnnotationBlazePose.html b/typedoc/types/BodyAnnotationBlazePose.html index e1c7d5ed..646dc006 100644 --- a/typedoc/types/BodyAnnotationBlazePose.html +++ b/typedoc/types/BodyAnnotationBlazePose.html @@ -1,122 +1 @@ -BodyAnnotationBlazePose | @vladmandic/human - v3.1.2
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              Type alias BodyAnnotationBlazePose

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              BodyAnnotationBlazePose: "leftLeg" | "rightLeg" | "torso" | "leftArm" | "rightArm" | "leftEye" | "rightEye" | "mouth"
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              \ No newline at end of file +BodyAnnotationBlazePose | @vladmandic/human - v3.2.0

              Type alias BodyAnnotationBlazePose

              BodyAnnotationBlazePose: "leftLeg" | "rightLeg" | "torso" | "leftArm" | "rightArm" | "leftEye" | "rightEye" | "mouth"

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              Member Visibility

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              \ No newline at end of file diff --git a/typedoc/types/BodyAnnotationEfficientPose.html b/typedoc/types/BodyAnnotationEfficientPose.html index 4debc26c..5e861fb5 100644 --- a/typedoc/types/BodyAnnotationEfficientPose.html +++ b/typedoc/types/BodyAnnotationEfficientPose.html @@ -1,122 +1 @@ -BodyAnnotationEfficientPose | @vladmandic/human - v3.1.2
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              Type alias BodyAnnotationEfficientPose

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              BodyAnnotationEfficientPose: "leftLeg" | "rightLeg" | "torso" | "leftArm" | "rightArm" | "head"
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              \ No newline at end of file +BodyAnnotationEfficientPose | @vladmandic/human - v3.2.0

              Type alias BodyAnnotationEfficientPose

              BodyAnnotationEfficientPose: "leftLeg" | "rightLeg" | "torso" | "leftArm" | "rightArm" | "head"

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              \ No newline at end of file diff --git a/typedoc/types/BodyGesture.html b/typedoc/types/BodyGesture.html index f6458e76..c59918b5 100644 --- a/typedoc/types/BodyGesture.html +++ b/typedoc/types/BodyGesture.html @@ -1,124 +1,2 @@ -BodyGesture | @vladmandic/human - v3.1.2
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              Type alias BodyGesture

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              BodyGesture: `leaning ${"left" | "right"}` | `raise ${"left" | "right"} hand` | "i give up"
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              body gesture type

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              \ No newline at end of file +BodyGesture | @vladmandic/human - v3.2.0

              Type alias BodyGesture

              BodyGesture: `leaning ${"left" | "right"}` | `raise ${"left" | "right"} hand` | "i give up"

              body gesture type

              +

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              \ No newline at end of file diff --git a/typedoc/types/BodyLandmark.html b/typedoc/types/BodyLandmark.html index 3d1715e1..1883c907 100644 --- a/typedoc/types/BodyLandmark.html +++ b/typedoc/types/BodyLandmark.html @@ -1,122 +1 @@ -BodyLandmark | @vladmandic/human - v3.1.2
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              Type alias BodyLandmark

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              BodyLandmark: BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose
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              \ No newline at end of file +BodyLandmark | @vladmandic/human - v3.2.0

              Type alias BodyLandmark

              BodyLandmark: BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose

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              \ No newline at end of file diff --git a/typedoc/types/BodyLandmarkBlazePose.html b/typedoc/types/BodyLandmarkBlazePose.html index 0ad8bdad..94bffb55 100644 --- a/typedoc/types/BodyLandmarkBlazePose.html +++ b/typedoc/types/BodyLandmarkBlazePose.html @@ -1,122 +1 @@ -BodyLandmarkBlazePose | @vladmandic/human - v3.1.2
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              Type alias BodyLandmarkBlazePose

              -
              BodyLandmarkBlazePose: "nose" | "leftEyeInside" | "leftEye" | "leftEyeOutside" | "rightEyeInside" | "rightEye" | "rightEyeOutside" | "leftEar" | "rightEar" | "leftMouth" | "rightMouth" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftPinky" | "rightPinky" | "leftIndex" | "rightIndex" | "leftThumb" | "rightThumb" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle" | "leftHeel" | "rightHeel" | "leftFoot" | "rightFoot" | "bodyCenter" | "bodyTop" | "leftPalm" | "leftHand" | "rightPalm" | "rightHand"
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              \ No newline at end of file +BodyLandmarkBlazePose | @vladmandic/human - v3.2.0

              Type alias BodyLandmarkBlazePose

              BodyLandmarkBlazePose: "nose" | "leftEyeInside" | "leftEye" | "leftEyeOutside" | "rightEyeInside" | "rightEye" | "rightEyeOutside" | "leftEar" | "rightEar" | "leftMouth" | "rightMouth" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftPinky" | "rightPinky" | "leftIndex" | "rightIndex" | "leftThumb" | "rightThumb" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle" | "leftHeel" | "rightHeel" | "leftFoot" | "rightFoot" | "bodyCenter" | "bodyTop" | "leftPalm" | "leftHand" | "rightPalm" | "rightHand"

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              \ No newline at end of file diff --git a/typedoc/types/BodyLandmarkEfficientNet.html b/typedoc/types/BodyLandmarkEfficientNet.html index dbcceb41..e64f0371 100644 --- a/typedoc/types/BodyLandmarkEfficientNet.html +++ b/typedoc/types/BodyLandmarkEfficientNet.html @@ -1,122 +1 @@ -BodyLandmarkEfficientNet | @vladmandic/human - v3.1.2
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              Type alias BodyLandmarkEfficientNet

              BodyLandmarkEfficientNet: "head" | "neck" | "rightShoulder" | "rightElbow" | "rightWrist" | "chest" | "leftShoulder" | "leftElbow" | "leftWrist" | "bodyCenter" | "rightHip" | "rightKnee" | "rightAnkle" | "leftHip" | "leftKnee" | "leftAnkle"

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              \ No newline at end of file diff --git a/typedoc/types/BodyLandmarkMoveNet.html b/typedoc/types/BodyLandmarkMoveNet.html index 93a9702a..b4945c89 100644 --- a/typedoc/types/BodyLandmarkMoveNet.html +++ b/typedoc/types/BodyLandmarkMoveNet.html @@ -1,122 +1 @@ -BodyLandmarkMoveNet | @vladmandic/human - v3.1.2
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              \ No newline at end of file +BodyLandmarkMoveNet | @vladmandic/human - v3.2.0

              Type alias BodyLandmarkMoveNet

              BodyLandmarkMoveNet: "nose" | "leftEye" | "rightEye" | "leftEar" | "rightEar" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle"

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              \ No newline at end of file diff --git a/typedoc/types/BodyLandmarkPoseNet.html b/typedoc/types/BodyLandmarkPoseNet.html index 2eb1441a..7079dda8 100644 --- a/typedoc/types/BodyLandmarkPoseNet.html +++ b/typedoc/types/BodyLandmarkPoseNet.html @@ -1,122 +1 @@ -BodyLandmarkPoseNet | @vladmandic/human - v3.1.2
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              \ No newline at end of file +BodyLandmarkPoseNet | @vladmandic/human - v3.2.0

              Type alias BodyLandmarkPoseNet

              BodyLandmarkPoseNet: "nose" | "leftEye" | "rightEye" | "leftEar" | "rightEar" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle"

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              \ No newline at end of file diff --git a/typedoc/types/Box.html b/typedoc/types/Box.html index c7b178b9..c589e569 100644 --- a/typedoc/types/Box.html +++ b/typedoc/types/Box.html @@ -1,124 +1,2 @@ -Box | @vladmandic/human - v3.1.2
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              generic box as [x, y, width, height]

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              Type alias Box

              Box: [number, number, number, number]

              generic box as [x, y, width, height]

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              \ No newline at end of file diff --git a/typedoc/types/Emotion.html b/typedoc/types/Emotion.html index 15f9fe87..2c49fff6 100644 --- a/typedoc/types/Emotion.html +++ b/typedoc/types/Emotion.html @@ -1,122 +1 @@ -Emotion | @vladmandic/human - v3.1.2
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              \ No newline at end of file +Emotion | @vladmandic/human - v3.2.0

              Type alias Emotion

              Emotion: "angry" | "disgust" | "fear" | "happy" | "sad" | "surprise" | "neutral"

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              \ No newline at end of file diff --git a/typedoc/types/Events.html b/typedoc/types/Events.html index 3b629741..c20368d7 100644 --- a/typedoc/types/Events.html +++ b/typedoc/types/Events.html @@ -1,22 +1,4 @@ -Events | @vladmandic/human - v3.1.2
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              Type alias Events

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              Events dispatched by human.events

              +Events | @vladmandic/human - v3.2.0

              Type alias Events

              Events: "create" | "load" | "image" | "result" | "warmup" | "error"

              Events dispatched by human.events

              • create: triggered when Human object is instantiated
              • load: triggered when models are loaded (explicitly or on-demand)
                • @@ -24,108 +6,4 @@
              • result: triggered when detection is complete
              • warmup: triggered when warmup is complete
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              \ No newline at end of file diff --git a/typedoc/types/ExternalCanvas.html b/typedoc/types/ExternalCanvas.html index cca5bfe7..80a49e6d 100644 --- a/typedoc/types/ExternalCanvas.html +++ b/typedoc/types/ExternalCanvas.html @@ -1,124 +1,2 @@ -ExternalCanvas | @vladmandic/human - v3.1.2
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              Type alias ExternalCanvas

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              Defines possible externally defined canvas

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              Type alias ExternalCanvas

              ExternalCanvas: typeof Canvas

              Defines possible externally defined canvas

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              \ No newline at end of file diff --git a/typedoc/types/FaceGesture.html b/typedoc/types/FaceGesture.html index 1ca5749c..79ef01e8 100644 --- a/typedoc/types/FaceGesture.html +++ b/typedoc/types/FaceGesture.html @@ -1,124 +1,2 @@ -FaceGesture | @vladmandic/human - v3.1.2
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              Type alias FaceGesture

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              face gesture type

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              \ No newline at end of file +FaceGesture | @vladmandic/human - v3.2.0

              Type alias FaceGesture

              FaceGesture: `facing ${"left" | "center" | "right"}` | `blink ${"left" | "right"} eye` | `mouth ${number}% open` | `head ${"up" | "down"}`

              face gesture type

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              \ No newline at end of file diff --git a/typedoc/types/FaceLandmark.html b/typedoc/types/FaceLandmark.html index de608147..b54b5e14 100644 --- a/typedoc/types/FaceLandmark.html +++ b/typedoc/types/FaceLandmark.html @@ -1,122 +1 @@ -FaceLandmark | @vladmandic/human - v3.1.2
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              Type alias FaceLandmark

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              FaceLandmark: "leftEye" | "rightEye" | "nose" | "mouth" | "leftEar" | "rightEar" | "symmetryLine" | "silhouette" | "lipsUpperOuter" | "lipsLowerOuter" | "lipsUpperInner" | "lipsLowerInner" | "rightEyeUpper0" | "rightEyeLower0" | "rightEyeUpper1" | "rightEyeLower1" | "rightEyeUpper2" | "rightEyeLower2" | "rightEyeLower3" | "rightEyebrowUpper" | "rightEyebrowLower" | "rightEyeIris" | "leftEyeUpper0" | "leftEyeLower0" | "leftEyeUpper1" | "leftEyeLower1" | "leftEyeUpper2" | "leftEyeLower2" | "leftEyeLower3" | "leftEyebrowUpper" | "leftEyebrowLower" | "leftEyeIris" | "midwayBetweenEyes" | "noseTip" | "noseBottom" | "noseRightCorner" | "noseLeftCorner" | "rightCheek" | "leftCheek"
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              \ No newline at end of file +FaceLandmark | @vladmandic/human - v3.2.0

              Type alias FaceLandmark

              FaceLandmark: "leftEye" | "rightEye" | "nose" | "mouth" | "leftEar" | "rightEar" | "symmetryLine" | "silhouette" | "lipsUpperOuter" | "lipsLowerOuter" | "lipsUpperInner" | "lipsLowerInner" | "rightEyeUpper0" | "rightEyeLower0" | "rightEyeUpper1" | "rightEyeLower1" | "rightEyeUpper2" | "rightEyeLower2" | "rightEyeLower3" | "rightEyebrowUpper" | "rightEyebrowLower" | "rightEyeIris" | "leftEyeUpper0" | "leftEyeLower0" | "leftEyeUpper1" | "leftEyeLower1" | "leftEyeUpper2" | "leftEyeLower2" | "leftEyeLower3" | "leftEyebrowUpper" | "leftEyebrowLower" | "leftEyeIris" | "midwayBetweenEyes" | "noseTip" | "noseBottom" | "noseRightCorner" | "noseLeftCorner" | "rightCheek" | "leftCheek"

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              \ No newline at end of file diff --git a/typedoc/types/Finger.html b/typedoc/types/Finger.html index 5ea670a0..751cb01d 100644 --- a/typedoc/types/Finger.html +++ b/typedoc/types/Finger.html @@ -1,122 +1 @@ -Finger | @vladmandic/human - v3.1.2
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              Type alias Finger

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              \ No newline at end of file +Finger | @vladmandic/human - v3.2.0

              Type alias Finger

              Finger: "index" | "middle" | "pinky" | "ring" | "thumb" | "palm"

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              \ No newline at end of file diff --git a/typedoc/types/FingerCurl.html b/typedoc/types/FingerCurl.html index 7adc8ec9..0992ed7c 100644 --- a/typedoc/types/FingerCurl.html +++ b/typedoc/types/FingerCurl.html @@ -1,122 +1 @@ -FingerCurl | @vladmandic/human - v3.1.2
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              Type alias FingerCurl

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              \ No newline at end of file +FingerCurl | @vladmandic/human - v3.2.0

              Type alias FingerCurl

              FingerCurl: "none" | "half" | "full"

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              \ No newline at end of file diff --git a/typedoc/types/FingerDirection.html b/typedoc/types/FingerDirection.html index 079b5b3f..34fefa67 100644 --- a/typedoc/types/FingerDirection.html +++ b/typedoc/types/FingerDirection.html @@ -1,122 +1 @@ -FingerDirection | @vladmandic/human - v3.1.2
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              Type alias FingerDirection

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              Type alias FingerDirection

              FingerDirection: "verticalUp" | "verticalDown" | "horizontalLeft" | "horizontalRight" | "diagonalUpRight" | "diagonalUpLeft" | "diagonalDownRight" | "diagonalDownLeft"

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              \ No newline at end of file diff --git a/typedoc/types/Gender.html b/typedoc/types/Gender.html index 16037317..b90a075d 100644 --- a/typedoc/types/Gender.html +++ b/typedoc/types/Gender.html @@ -1,122 +1 @@ -Gender | @vladmandic/human - v3.1.2
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              Type alias Gender

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              \ No newline at end of file +Gender | @vladmandic/human - v3.2.0

              Type alias Gender

              Gender: "male" | "female" | "unknown"

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              \ No newline at end of file diff --git a/typedoc/types/GestureResult.html b/typedoc/types/GestureResult.html index 50e479d7..10b96fd2 100644 --- a/typedoc/types/GestureResult.html +++ b/typedoc/types/GestureResult.html @@ -1,129 +1,7 @@ -GestureResult | @vladmandic/human - v3.1.2
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              Type alias GestureResult

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                  face: number;
                  gesture: FaceGesture;
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                  iris: number;
              } | {
                  body: number;
                  gesture: BodyGesture;
              } | {
                  gesture: HandGesture;
                  hand: number;
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              Gesture combined results +GestureResult | @vladmandic/human - v3.2.0

              Type alias GestureResult

              GestureResult: {
                  face: number;
                  gesture: FaceGesture;
              } | {
                  gesture: IrisGesture;
                  iris: number;
              } | {
                  body: number;
                  gesture: BodyGesture;
              } | {
                  gesture: HandGesture;
                  hand: number;
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              Gesture combined results Each result has:

              • part: part name and number where gesture was detected: face, iris, body, hand
              • gesture: gesture detected
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              Type declaration

              Type declaration

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              \ No newline at end of file diff --git a/typedoc/types/HandGesture.html b/typedoc/types/HandGesture.html index 27fc9912..0ac705a0 100644 --- a/typedoc/types/HandGesture.html +++ b/typedoc/types/HandGesture.html @@ -1,124 +1,2 @@ -HandGesture | @vladmandic/human - v3.1.2
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              Type alias HandGesture

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              hand gesture type

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              \ No newline at end of file +HandGesture | @vladmandic/human - v3.2.0

              Type alias HandGesture

              HandGesture: `${"thumb" | "index" | "middle" | "ring" | "pinky"} forward` | `${"thumb" | "index" | "middle" | "ring" | "pinky"} up` | "victory" | "thumbs up"

              hand gesture type

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              \ No newline at end of file diff --git a/typedoc/types/HandType.html b/typedoc/types/HandType.html index 44c2aee0..403dee35 100644 --- a/typedoc/types/HandType.html +++ b/typedoc/types/HandType.html @@ -1,122 +1 @@ -HandType | @vladmandic/human - v3.1.2
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              Type alias HandType

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              \ No newline at end of file +HandType | @vladmandic/human - v3.2.0

              Type alias HandType

              HandType: "hand" | "fist" | "pinch" | "point" | "face" | "tip" | "pinchtip"

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              \ No newline at end of file diff --git a/typedoc/types/ImageObjects.html b/typedoc/types/ImageObjects.html index afa21348..08487788 100644 --- a/typedoc/types/ImageObjects.html +++ b/typedoc/types/ImageObjects.html @@ -1,124 +1,2 @@ -ImageObjects | @vladmandic/human - v3.1.2
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              Type alias ImageObjects

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              Defines all possible image objects

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              Type alias ImageObjects

              ImageObjects: ImageData | ImageBitmap

              Defines all possible image objects

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              \ No newline at end of file diff --git a/typedoc/types/Input.html b/typedoc/types/Input.html index 9ffa2a39..7a631741 100644 --- a/typedoc/types/Input.html +++ b/typedoc/types/Input.html @@ -1,124 +1,2 @@ -Input | @vladmandic/human - v3.1.2
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              Defines all possible input types for Human detection

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              \ No newline at end of file +Input | @vladmandic/human - v3.2.0

              Type alias Input

              Input: Tensor | AnyCanvas | AnyImage | AnyVideo | ImageObjects | ExternalCanvas

              Defines all possible input types for Human detection

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              \ No newline at end of file diff --git a/typedoc/types/IrisGesture.html b/typedoc/types/IrisGesture.html index 50a68205..e02e5692 100644 --- a/typedoc/types/IrisGesture.html +++ b/typedoc/types/IrisGesture.html @@ -1,124 +1,2 @@ -IrisGesture | @vladmandic/human - v3.1.2
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              Type alias IrisGesture

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              iris gesture type

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              \ No newline at end of file +IrisGesture | @vladmandic/human - v3.2.0

              Type alias IrisGesture

              IrisGesture: "facing center" | `looking ${"left" | "right" | "up" | "down"}` | "looking center"

              iris gesture type

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              \ No newline at end of file diff --git a/typedoc/types/ObjectType.html b/typedoc/types/ObjectType.html index ba1bb1c5..834dc66c 100644 --- a/typedoc/types/ObjectType.html +++ b/typedoc/types/ObjectType.html @@ -1,122 +1 @@ -ObjectType | @vladmandic/human - v3.1.2
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              Type alias ObjectType

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              Type alias ObjectType

              ObjectType: "person" | "bicycle" | "car" | "motorcycle" | "airplane" | "bus" | "train" | "truck" | "boat" | "traffic light" | "fire hydrant" | "stop sign" | "parking meter" | "bench" | "bird" | "cat" | "dog" | "horse" | "sheep" | "cow" | "elephant" | "bear" | "zebra" | "giraffe" | "backpack" | "umbrella" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "kite" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "bottle" | "wine glass" | "cup" | "fork" | "knife" | "spoon" | "bowl" | "banana" | "apple" | "sandwich" | "orange" | "broccoli" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "chair" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "toaster" | "sink" | "refrigerator" | "book" | "clock" | "vase" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"

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              \ No newline at end of file diff --git a/typedoc/types/Point.html b/typedoc/types/Point.html index 692718a0..c754385b 100644 --- a/typedoc/types/Point.html +++ b/typedoc/types/Point.html @@ -1,124 +1,2 @@ -Point | @vladmandic/human - v3.1.2
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              Type alias Point

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              generic point as [x, y, z?]

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              \ No newline at end of file +Point | @vladmandic/human - v3.2.0

              Type alias Point

              Point: [number, number, number?]

              generic point as [x, y, z?]

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              \ No newline at end of file diff --git a/typedoc/types/Race.html b/typedoc/types/Race.html index 0fcd75f2..20194777 100644 --- a/typedoc/types/Race.html +++ b/typedoc/types/Race.html @@ -1,122 +1 @@ -Race | @vladmandic/human - v3.1.2
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              \ No newline at end of file +Race | @vladmandic/human - v3.2.0

              Type alias Race

              Race: "white" | "black" | "asian" | "indian" | "other"

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              \ No newline at end of file diff --git a/typedoc/types/SegmentationEnum.html b/typedoc/types/SegmentationEnum.html index 84cc53f2..c37a7629 100644 --- a/typedoc/types/SegmentationEnum.html +++ b/typedoc/types/SegmentationEnum.html @@ -1,124 +1,2 @@ -SegmentationEnum | @vladmandic/human - v3.1.2
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              Possible segmentation model behavior

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              Type alias SegmentationEnum

              SegmentationEnum: "default" | "alpha" | "foreground" | "state"

              Possible segmentation model behavior

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              \ No newline at end of file diff --git a/typedoc/types/Tensor1D.html b/typedoc/types/Tensor1D.html index 4b59b63e..0cdce2cd 100644 --- a/typedoc/types/Tensor1D.html +++ b/typedoc/types/Tensor1D.html @@ -1,125 +1,2 @@ -Tensor1D | @vladmandic/human - v3.1.2
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              \ No newline at end of file +Tensor1D | @vladmandic/human - v3.2.0

              Type alias Tensor1D

              Tensor1D: Tensor<R1>

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              Tensor

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              \ No newline at end of file diff --git a/typedoc/types/Tensor2D.html b/typedoc/types/Tensor2D.html index f1071ca9..ad27d0bc 100644 --- a/typedoc/types/Tensor2D.html +++ b/typedoc/types/Tensor2D.html @@ -1,125 +1,2 @@ -Tensor2D | @vladmandic/human - v3.1.2
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              Type alias Tensor2D

              Tensor2D: Tensor<R2>

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              Tensor

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              \ No newline at end of file diff --git a/typedoc/types/Tensor3D.html b/typedoc/types/Tensor3D.html index 41b7ea71..6d025b47 100644 --- a/typedoc/types/Tensor3D.html +++ b/typedoc/types/Tensor3D.html @@ -1,125 +1,2 @@ -Tensor3D | @vladmandic/human - v3.1.2
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              Type alias Tensor3D

              Tensor3D: Tensor<R3>

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              Tensor

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              \ No newline at end of file diff --git a/typedoc/types/Tensor4D.html b/typedoc/types/Tensor4D.html index 101bd470..2e45db82 100644 --- a/typedoc/types/Tensor4D.html +++ b/typedoc/types/Tensor4D.html @@ -1,125 +1,2 @@ -Tensor4D | @vladmandic/human - v3.1.2
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              Type alias Tensor4D

              Tensor4D: Tensor<R4>

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              Tensor

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              \ No newline at end of file diff --git a/typedoc/types/TensorLike.html b/typedoc/types/TensorLike.html index 54486000..0b63725e 100644 --- a/typedoc/types/TensorLike.html +++ b/typedoc/types/TensorLike.html @@ -1,125 +1,2 @@ -TensorLike | @vladmandic/human - v3.1.2
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              TypedArray|Array

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              Type alias TensorLike

              TensorLike: TypedArray | number | boolean | string | RecursiveArray<number | number[] | TypedArray> | RecursiveArray<boolean> | RecursiveArray<string> | Uint8Array[]

              Docalias

              TypedArray|Array

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              \ No newline at end of file diff --git a/typedoc/types/WarmupEnum.html b/typedoc/types/WarmupEnum.html index 76676904..23b40888 100644 --- a/typedoc/types/WarmupEnum.html +++ b/typedoc/types/WarmupEnum.html @@ -1,124 +1,2 @@ -WarmupEnum | @vladmandic/human - v3.1.2
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              Possible values for human.warmup

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              \ No newline at end of file +WarmupEnum | @vladmandic/human - v3.2.0

              Type alias WarmupEnum

              WarmupEnum: "" | "none" | "face" | "full" | "body"

              Possible values for human.warmup

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              \ No newline at end of file diff --git a/typedoc/types/match.Descriptor.html b/typedoc/types/match.Descriptor.html index 33f68bd4..2dd7f544 100644 --- a/typedoc/types/match.Descriptor.html +++ b/typedoc/types/match.Descriptor.html @@ -1,53 +1,2 @@ -Descriptor | @vladmandic/human - v3.1.2
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              Type alias Descriptor

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              Descriptor: number[]
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              Face descriptor type as number array

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              Type alias Descriptor

              Descriptor: number[]

              Face descriptor type as number array

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              \ No newline at end of file diff --git a/typedoc/types/match.MatchOptions.html b/typedoc/types/match.MatchOptions.html index 034f6192..8cc745f4 100644 --- a/typedoc/types/match.MatchOptions.html +++ b/typedoc/types/match.MatchOptions.html @@ -1,51 +1 @@ -MatchOptions | @vladmandic/human - v3.1.2
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              Type alias MatchOptions

              MatchOptions: {
                  max?: number;
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                  threshold?: number;
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              Type declaration

              • Optional max?: number
              • Optional min?: number
              • Optional multiplier?: number
              • Optional order?: number
              • Optional threshold?: number

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              \ No newline at end of file diff --git a/typedoc/variables/defaults.html b/typedoc/variables/defaults.html index 8c8a4bcf..b6b89511 100644 --- a/typedoc/variables/defaults.html +++ b/typedoc/variables/defaults.html @@ -1,126 +1,4 @@ -defaults | @vladmandic/human - v3.1.2
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              \ No newline at end of file diff --git a/typedoc/variables/draw.options.html b/typedoc/variables/draw.options.html index b867b386..355e4b8c 100644 --- a/typedoc/variables/draw.options.html +++ b/typedoc/variables/draw.options.html @@ -1,58 +1,2 @@ -options | @vladmandic/human - v3.1.2
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              currently set draw options DrawOptions

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              Variable optionsConst

              options: DrawOptions = ...

              currently set draw options DrawOptions

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              \ No newline at end of file diff --git a/typedoc/variables/env-1.html b/typedoc/variables/env-1.html index 563e9bb3..689778f1 100644 --- a/typedoc/variables/env-1.html +++ b/typedoc/variables/env-1.html @@ -1,122 +1 @@ -env | @vladmandic/human - v3.1.2
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              Variable envConst

              env: Env = ...

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